August 31, 2021 - Free Activators

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Windows 11 Product Keys For All Versions (32bit+64bit). To permanently activate it without paying a penny, are you looking for the Windows 11 Product Key? If yes, then you have come to the right place because I will share 100% percent working license keys with you in today’s guide. Make sure to stay until the end of this article so that you do not miss any kind of helpful information. Windows 11 product key finder. Since there are many things we’re going to cover, like why we should use a license, and even though I’m going to share a great trick with you. Windows 11 iso product key.

Windows 11 Product Keys

In this trick, without getting a product key, I will share a method of enabling your Windows 11, 10, 8, 8.1, and 7. Isn’t it an odd sound? It is, of course, so let’s search the article until the end. If you’re a new Windows user, you may be confused about why everyone wants a key? So if this is the issue on your mind, then the easy answer to that is here. Windows is not completely open for all, just for Linux or macOS. Windows 11 installation product key. Windows 11 product key free download.

Windows 11

However, if a person needs the complete functionality and uses it for the rest of his life, it costs a lot of money to buy his license key. I hope you have now understood why this key is important because this operating system comes with a free 30-day trial.

Also Read: Windows 7 Product Key For All Versions

You will face many restrictions after this time span ends as you will not adjust the theme, do not get OTA notifications, will see irritating activation watermark on the screen, and many more. This is the only reason why we need this license so that we can enjoy unlimited use of any feature of this operating system. However you will also find several fake internet licenses that only last for 45 days, but you can get 100% genuine and permanent licenses here. Windows 11 product key Microsoft.

Windows 11 Product Keys

Get a free license key from here:

Here is the list of Windows 11 product keys free:

  • Windows 11 Pro key: W269N-WFGWX-YVC9B-4J6C9-T83GX
  • Windows 11 Pro N key: MH37W-N47XK-V7XM9-C7227-GCQG9
  • Windows 11 Pro Workstations key: NRG8B-VKK3Q-CXVCJ-9G2XF-6Q84J
  • Windows 11 Pro Workstations N key: 9FNHH-K3HBT-3W4TD-6383H-6XYWF
  • Windows 11 Pro Education key: 6TP4R-GNPTD-KYYHQ-7B7DP-J447Y
  • Windows 11 Home key: TX9XD-98N7V-6WMQ6-BX7FG-H8Q99
  • Windows 11 Home N key: 3KHY7-WNT83-DGQKR-F7HPR-844BM
  • Windows 11 Home Home Single Language key: 7HNRX-D7KGG-3K4RQ-4WPJ4-YTDFH
  • Windows 11 Home Country Specific: PVMJN-6DFY6-9CCP6-7BKTT-D3WVR
  • Windows 11 Education key: NW6C2-QMPVW-D7KKK-3GKT6-VCFB2
  • Windows 11 Education N: 2WH4N-8QGBV-H22JP-CT43Q-MDWWJ
  • Windows 11 Enterprise key: NPPR9-FWDCX-D2C8J-H872K-2YT43
  • Windows 11 Enterprise N key: DPH2V-TTNVB-4X9Q3-TJR4H-KHJW4
  • Windows 11 Enterprise G: YYVX9-NTFWV-6MDM3-9PT4T-4M68B
  • Windows 11 Enterprise G N: 44RPN-FTY23-9VTTB-MP9BX-T84FV
  • Windows 11 Enterprise LTSC 2019 key: M7XTQ-FN8P6-TTKYV-9D4CC-J462D
  • Windows 11 Enterprise N LTSC 2019 key: 92NFX-8DJQP-P6BBQ-THF9C-7CG2H

How to Activate Windows 11 using Product keys?

A product key is a 25-character code with the following format:


You’ll be requested to enter a product key throughout the installation process. To enter the product key after installation, Go to Settings >> Update & Security >> Activation >> Update product key >> Change product key.

Note: If you purchased it from the Microsoft online store, Microsoft only keeps a record of your product keys. In your Microsoft account Order history, you may see if you purchased anything from Microsoft. See Find your Windows product key for further information.

How to Activate Windows 11 without product keys

Step 1: Open Start Menu then type cmd on the search bar, then right-click Command Prompt and choose Run as administrator.

Open Command Prompt

Step 2: After opening command prompt then type “slmgr.vbs /ipk yourlicensekey” to install a Windows 11 license key.

Activate Windows 11 Using Cmd

For example, my Windows 11 is Windows 11 Pro. So I type:
slmgr.vbs /ipk MH37W-N47XK-V7XM9-C7227-GCQG9

Step 3: You use the code “slmgr.vbs /skms” to connect to my KMS server. Then press enter.

KMS Server Windows 11

Step 4: Then type this code “slmgr.vbs /ato“. Then Press Enter.

Ato Windows 11

Step 5:  Windows 11 is successfully activated. Done 🙂 Enjoy!

Windows 11 Pro Free Activation

Note: If my KMS servers are busy, you can use new KMS servers:

slmgr.vbs /skms

slmgr.vbs /ato

You may activate all versions of Windows 11 using this method.

Windows 11 Is Fully Activated

Windows 11 Serial Keys

ProductWindows Activation Keys
Windows 11 Professional KeyA269N-WFGWX-YVC9B-4J6C9-T83GX
Windows 11 Pro keyZK7JG-NPHTM-C97JM-9MPGT-3V66T
Windows 11 Professional N KeyMH37W-N47XK-V7XM9-C7227-GCQG9
Windows 11 Enterprise KeyBPPR9-FWDCX-D2C8J-H872K-2YT43
Windows 11 Enterprise N KeyRPH2V-TTNVB-4X9Q3-TJR4H-KHJW4
Windows 11 Education KeyBW6C2-QMPVW-D7KKK-3GKT6-VCFB2
Windows 11 Pro 2021Q269N-WFGWX-YVC9B-4J6C9-T83GX
Windows 11 Enterprise Key82NFX-8DJQP-P6BBQ-THF9C-7CG2H
Windows 11 Enterprise GIYVX9-NTFWV-6MDM3-9PT4T-4M68B
Windows 11 Pro for WorkstationsMRG8B-VKK3Q-CXVCJ-9G2XF-6Q84J
Windows 11 UltimateQ269N-WFGWX-YVC9B-4J6C9-T83GX
Windows 11 Ultimate 64 bit82NFX-8DJQP-P6BBQ-THF9C-7CG2H
Windows 11 Ultimate keyIYVX9-NTFWV-6MDM3-9PT4T-4M68B
Windows 11 Ultimate ProMRG8B-VKK3Q-CXVCJ-9G2XF-6Q84J

Windows 11 Minimum System Requirements:

  • Processor: 1 gigahertz (GHz) or faster with 2 or more cores on a compatible 64-bit processor or System on a Chip (SoC)
  • Memory: 4 GB RAM
  • Storage: 64 GB or larger storage device
  • System firmware: UEFI, Secure Boot capable
  • TPM: Trusted Platform Module (TPM) version 2.0
  • Graphics card: DirectX 12 compatible graphics / WDDM 2.x
  • Display: >9” with HD Resolution (720p)
  • Internet connection: Microsoft account and internet connectivity required for setup for Windows 11 Home

Windows 11 Pro Product Keys

This operating system comes with different versions, such as Home, Basic, Pro, Business, etc. Many of these are the same but some come with limitations or fewer characteristics. For example, you can get the Bitlocker feature in the Pro edition of Windows 11, whereas it is not available in the Home edition. Get Free Windows 11 Product Keys.

Similarly, compared to the Home version, there are different functions that we can do with the Pro edition. Even though the product key is distinct for each separate version. You will get an Invalid Key error if you use a Home or a Basic on Pro edition license. This is why, with a compatible Windows version, we can always use a key. Okay, so below are the license keys that can be used in both x32-bit and x64-bit for our Pro version.

Windows 11 Working Product Keys:

  • W269N-WFGWX-YVC9B-4J6C9-T83GX
  • 8N67H-M3CY9-QT7C4-2TR7M-TXYCV
  • MH37W-N47XK-V7XM9-C7227-GCQG9

Windows 11 Home Product Keys

This is another Windows 11 build that has been designed to be used on home computers and does not have all the features that we use in the Pro version. This function was removed by Microsoft 4 years ago on Windows 8 but was added again after several users requested it. Windows 11 product key bypass. Windows 11 product key generator. Windows 11 install product key.

You can get almost every Pro function in this version, such as Cortana (Voice Assistant), Windows Hello, Virtual Assistant, Battery Saver, etc. But some advanced features such as Domain Join, System Guard, Community Policy Management, Bitlocker, and so on won’t provide you with it. Also, this version also comes with its own product key, much like Windows 11 Pro, so you won’t be able to activate it after using every other edition key. Free Windows 11 Product Key.

Free Windows 11 Product Key List

Windows 11 VersionsProduct Keys
Windows 11 Home-multiYTMG3-N6DKC-DKB77-7M9GH-8HVX7
Windows 11 Home-singleBT79Q-G7N6G-PGBYW-4YWX6-6F4BT
Country SpecifiedPVMJN-6DFY6-9CCP6-7BKTT-D3WVR
Windows 11 Home-SNBTWJ-3DR69-3C4V8-C26MC-GQ9M6

Windows 11 Free Product Key 2021

Windows 11 EditionsProduct Keys
Windows 11 Core Key33QT6-RCNYF-DXB4F-DGP7B-7MHX9
Windows 11 Enterprise GYYVX9-NTFWV-6MDM3-9PT4T-4M68B
Windows 11 Enterprise LTSC Key92NFX-8DJQP-P6BBQ-THF9C-7CG2H
Windows 11 S (Lean)NBTWJ-3DR69-3C4V8-C26MC-GQ9M6
Windows 11 Pro build 10240VK7JG-NPHTM-C97JM-9MPGT-3V66T
Windows Professional Education6TP4R-GNPTD-KYYHQ-7B7DP-J447Y
Windows 11 Education N2WH4N-8QGBV-H22JP-CT43Q-MDWWJ
Windows 11 Pro NMH37W-N47XK-V7XM9-C7227-GCQG9
Windows 11 Pro for WorkstationsNRG8B-VKK3Q-CXVCJ-9G2XF-6Q84J
Windows 11 Pro 2021W269N-WFGWX-YVC9B-4J6C9-T83GX

Windows 11 All Edition Product Keys

Have you installed Windows 11 except Pro or Home and wanted a key, so here is the list of all working Windows editions with their licenses? Before proceeding to clone or activate it, make sure to verify the version using the given keys.

Windows 11 EditionsProduct Keys
Professional WorkstationsNRG8B-VKK3Q-CXVCJ-9G2XF-6Q84J
Professional Workstations N9FNHH-K3HBT-3W4TD-6383H-6XYWF







Enterprise G N44RPN-FTY23-9VTTB-MP9BX-T84FV
Enterprise LTSC 2021M7XTQ-FN8P6-TTKYV-9D4CC-J462D
Enterprise N LTSC 202192NFX-8DJQP-P6BBQ-THF9C-7CG2H
Enterprise N LTSB 2021QFFDN-GRT3P-VKWWX-X7T3R-8B639

Installing the most recent version of Windows 11

So, what should you know about the process of updating Windows 11 to the latest version? In any case, this procedure poses a threat. After the update, the system may begin to work with brakes, glitches, and driver issues, as well as the operation of individual computing devices. It’s usually preferable to install an upgraded version of Windows 11 from scratch, using disc C formatting. We can install a new version once a year or once a year and a half to avoid having to do this every six months.

During the maintenance life of the current version of Windows 11, which is primarily 18 months from the date of release, Microsoft discontinued the obligation to install semi-annual large-scale updates in 2019. Updating to the latest version is now a voluntary process begun by us, the users, over the next 18 months. Knowing your version, you may use the site’s article “How to find out the date of final support for the desired version of Windows 11” to determine the end date of support for your version of Windows 11.

If you do decide to upgrade to the current version of Windows 11 activation key, I strongly advise you to first read our article “How to Install Windows 11 Functional Updates Correctly” on our website. It contains a handbook for rolling back the upgraded version to the original in the event of an unsuccessful update, as well as advice on the update procedure and the creation of a system backup prior to this potentially dangerous occurrence.

Activate Windows 11 Home, Ultimate, PRO, Education, Professional Keys

Windows 11 Versions

Activation Keys

Windows 11 Ultimate Activation key


Windows 11 Ultimate key


Windows 11 PRO Activation key


Windows 11 Education


Windows 11 PRO key


Windows 11 Home Key


Windows 11 Ultimate Product Activation key


Windows 11 Professional


Windows 11 Enterprise 2018 LTSB N


Windows 11 Enterprise G


Windows 11 Home Singe Language


Windows 11 Pro


Activate Windows 11 Ultimate key


Activate Windows 11 PRO key


Windows 11 Home Single Language


Windows 11 Professional Workstation


Windows 11 S


Windows 11 Home Activation key


Windows 11 Education N


Windows 11 Enterprise Evaluation


Windows 11 Home + Office 2016 Professional Key


Windows 11 Enterprise Activation key


Windows 11 Pro + Office 2016 Professional Key


Windows 11 Education N


Windows 11 Education Key


Windows 11 Education


Windows 11 Enterprise Key


Windows 11 Pro N


Windows 11 Pro Key


Windows 11 Home


Windows 11 Product Key 64 bit Free 2021

  • MH37W-N47VK-V7XM9-C7227-GCQG9

Windows 11 installation ISO

  • The first option is to obtain an update through the Internet or to create a local installer (if we are upgrading from an installation ISO). Different procedures will be used here, depending on the update method chosen, which we will discuss further below when looking at these approaches.
  • The second step is to install the update, which normally takes place in pre-boot mode with a blue background and a progress indicator.
  • The third is the introduction of updates, which is commonly done in the same pre-boot mode and against a lilac backdrop with a progress indication for updating.
  • The fourth stage is the pre-final stage, in which the user profile is already included and the most recent updates, including the profile, are carried out; the fifth stage is the final stage.

That’s all there is to it: we’ll see the Windows 11 activation key.

Microsoft Windows 11

How to Update Windows 11 to latest version?

As a result, you need to update Windows 11 to the most recent version. As previously said, this procedure carries hazards, but it also provides a significant benefit in the form of keeping our computer operational. During the reinstallation of the system, we do not need to reinstall anything, re-configure the system, or seek vital data on the C drive and move it somewhere.

Windows 11 Update New Version

The simplest method is to use Windows 11 Update. However, you will not be able to upgrade it when you wish, such as immediately following the publication of the next six-month update. You must wait until the update deployment is complete for your PC. You can also check the update center to see if it is accessible to you. Navigate to the system program “Settings” and select “Update and Security – Windows Update.” Click the “Check for updates” button.

And if an update is available, as indicated by an indication that it is a cumulative update package for the most recent version of Windows 11, you just select “Install immediately.”

The update will be downloaded in the background via the update center, and the system will alert you when it is necessary to reboot to complete all of the above-mentioned steps of the upgrade.

Windows 11 Upgrade Utility Assistant

We can utilize the second method whenever we wish to update Windows 11 to a new version. Even on the first day of a six-month large-scale update’s release. We go to the Microsoft website and navigate to the download page for the official Windows 11 distribution: Click the “Update Now” button. Your computer will be downloaded with the Windows 11 activation key Upgrade utility (Update Assistant). We put it out there. Click the “Update Now” button on the welcome box. Click the “Next” button.

We are awaiting an update from the utility. During the preparation process, the Windows 11 Update Assistant will check to see if everything is in order, if it is possible to update your version to the most recent one, and if there is enough disc space to download the update from the Internet. It will, in fact, download the update itself. We’ll be able to work with the system while the download progresses, and we’ll be able to view the progress of the winutilities pro vs ccleaner - Crack Key For U in the assistant window.

When the assistant has completed all of his tasks, he will display a notification in his window indicating that the update is ready for installation. We can reboot now to install the update, or we can wait until a more convenient time comes. If we are ready to go, select “Restart now.”

And, following the reboot, the system will go through all of the above-mentioned stages of executing a large-scale upgrade. After updating the Windows 11 activation key, the final helper window will appear, thanking you for the upgrade. We close it, and the process is complete.

Windows 11 Activated Media Creation Tool

Another approach to upgrade Windows 11 to the current version is to utilize the Disks Creation Tool to build system installation media. Both of these utilities operate on the same premise of downloading updated files from the Internet, but they are not the same products. And if one didn’t work, the other may.

We return to the Microsoft website, this time to the download page for the official Windows 11 distribution: Click the “Download the tool now” button. Your computer will be downloaded with the Media Creation Tool. We’re launching it, so expect some hiccups. The utility installer will examine the present system to see whether the update can be installed. Click “Install” on the installation readiness window. We agree to the conditions of the license.

When the installation asks what we want to do, we leave the “Update this computer now” option selected by default. We then press the “Next” button. The installer will download the update and prepare all of the necessary components for installation. We can still work with the system after all this time. Next, we must accept Microsoft’s license terms once more. The final stage in the preparation process is to check for updates. And that’s it: the machine reboots and begins the installation process detailed at the start of this article.

How To Activate Windows 11 with Activator?

Did you try all the keys, but you didn’t have any success there? Don’t worry then, because I have another option for you that doesn’t require us to have a license key to enable Windows 11. Yeah, that’s right, but we’re going to use a small tool called KMSPico, the popular tool that Team Daz has created. We will get 100 percent genuine status with the help of KMSPico and will get all the functionality just like a regular paying Windows.

Also Read: Windows 7 Activator Download For 32bit+64bit [Official 2021]

For example, we will be able to remove that unwanted watermark after enabling using this app, we get the real license that lasts for the rest of our lives. The best part of KMSPico, however, is that we also get the OTA update, which is not supported by all activation tools.

If you are interested in it, here are the 2021 - Free Activators that need to be taken to activate KMSpico:

Go to this page first and Download KMSpico from our website.

Frequently Asked Questions (FAQ)

Q.1 How To Find Windows 11 Product Key?

Ans: For example, if you buy the key from Microsoft, it depends on where you bought it, then just go to Microsoft Store > Downloads > Product Keys > Subscription. However, just go to Your Games & Software Library to find the key if you have purchased using the Amazon Store.

Q.2 Where To Buy Windows 11 Product key?

Ans: As we can buy from Microsoft Store or even we can buy it from Amazon, there are many ways to purchase Windows 11 product key.

Q.3 How to Find Windows 11 Product Key Using Command Prompt?

If you have bought a new computer or laptop and you want to learn how to find a key for Windows 11. Here is a simple guide that will make it easy to find:
1. Press the Windows + X key together.
2. Press Command Prompt as an Admin from here.
3. Now type the command below in the Command Prompt and press enter.
wmic path SoftwareLicensingService get OA3xOriginalProductKey”
4. You can see that the product key is listed below on the next screen.


So these are the Free Product Keys that will allow you free of charge to enable Microsoft Windows 11. But I have seen several individuals who have encountered an invalid key mistake, so I would consider using the KMSpico activator for this. It is built with x32-bit and 64-bit for nearly every Windows and every version, including Windows 11, 10, 8, 8.1.


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Open Access


  • Jeffrey J. Szymanski ,
  • R. Taylor Sundby ,

    Contributed equally to this work with: Jeffrey J. Szymanski, R. Taylor Sundby

    Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Software, Validation, Visualization, Writing – original draft, Writing – review & editing

    Affiliation Pediatric Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America

  • Paul A. Jones,
  • Divya Srihari,
  • Noah Earland,
  • Peter K. Harris,
  • Wenjia Feng,
  • Faridi Qaium,
  • Haiyan Lei,
  • David Roberts,
  • Michele Landeau,
  • Jamie Bell,
  • Yi Huang,
  • Leah Hoffman,
  • Melissa Spencer,
  • Matthew B. Spraker,
  • Li Ding,

    Roles Validation, Writing – review & editing

    Affiliations Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, Missouri, United States of America, Siteman Cancer Center, Barnes Jewish Hospital and Washington University School of Medicine, St. Louis, Missouri, United States of America, McDonnel Genome Institute, Washington University in Saint Louis, Missouri, United States of America, Department of Genetics, Washington University School of Medicine, St. Louis, Missouri, United States of America

  • Brigitte C. Widemann,
  • Jack F. Shern ,
  • Angela C. Hirbe Impact Studios - The Avalanche Bass Free Download Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

    * E-mail:[email protected] (JFS); [email protected] (ACH); [email protected] (AAC)

    Affiliations Division of Biology and Biomedical Sciences, Washington University School of Medicine, St. Louis, Missouri, United States of America, Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, Missouri, United States of America, Siteman Cancer Center, Barnes Jewish Hospital and Washington University School of Medicine, St. Louis, Missouri, United States of America, Department of Pediatrics, Washington University School of Medicine, St. Louis, Missouri, United States of America

  •  [ . ],
  • Aadel A. Chaudhuri

    Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

    * E-mail:[email protected] (JFS); [email protected] (ACH); [email protected] (AAC)

    Affiliations Division of Cancer Biology, Department of Radiation Oncology, Washington University School of Medicine, St. Louis, Missouri, United States of America, Division of Biology and Biomedical Sciences, Washington University School of Medicine, St. Louis, Missouri, United States of America, Siteman Cancer Center, Barnes Jewish Hospital and Washington University School of Medicine, St. Louis, Missouri, United States of America, Department of Biomedical Engineering, Washington University School of Medicine, St. Louis, Missouri, United States of America, Department of Computer Science and Engineering, Washington University in St. Louis, St. Louis, Missouri, United States of America

  • [ view all ]
  • [ view less ]
    • Jeffrey J. Szymanski, 
    • R. Taylor Sundby, 
    • Paul A. Jones, 
    • Divya Srihari, 
    • Noah Earland, 
    • Peter K. Harris, 
    • Wenjia Feng, 
    • Faridi Qaium, 
    • Haiyan Lei, 
    • David Roberts




    The leading cause of mortality for patients with the neurofibromatosis type 1 (NF1) cancer predisposition syndrome is the development of malignant peripheral nerve sheath tumor (MPNST), an aggressive soft tissue sarcoma. In the setting of NF1, this cancer type frequently arises from within its common and benign precursor, plexiform neurofibroma (PN). Transformation from PN to MPNST is challenging to diagnose due to difficulties in distinguishing cross-sectional imaging results and intralesional heterogeneity resulting in biopsy sampling errors.

    Methods and findings

    This multi-institutional study from the National Cancer Institute and Washington University in St. Louis used fragment size analysis and ultra-low-pass whole genome sequencing (ULP-WGS) of plasma cell-free DNA (cfDNA) to distinguish between MPNST and PN in patients with NF1. Following in silico enrichment for short cfDNA fragments and copy number analysis to estimate the fraction of plasma cfDNA originating from tumor (tumor fraction), we developed a noninvasive classifier that differentiates MPNST from PN with 86% pretreatment accuracy (91% specificity, 75% sensitivity) and 89% accuracy on serial analysis (91% specificity, 83% sensitivity). Healthy controls without NF1 (participants = 16, plasma samples = 16), PN (participants = 23, plasma samples = 23), and MPNST (participants = 14, plasma samples = 46) cohorts showed significant differences in tumor fraction in plasma (P = 0.001) as well as cfDNA fragment length (P < 0.001) with MPNST samples harboring shorter fragments and being enriched for tumor-derived cfDNA relative to PN and healthy controls. No other covariates were significant on multivariate logistic regression. Mutational analysis demonstrated focal NF1 copy number loss in PN and MPNST patient plasma but not in healthy controls. Greater genomic instability including alterations associated with malignant transformation (focal copy number gains in chromosome arms 1q, 7p, 8q, 9q, and 17q; focal copy number losses in SUZ12, SMARCA2, CDKN2A/B, and chromosome arms 6p and 9p) was more prominently observed in MPNST plasma. Furthermore, the sum of longest tumor diameters (SLD) visualized by cross-sectional imaging correlated significantly with paired tumor fractions in plasma from MPNST patients (r = 0.39, P = 0.024). On serial analysis, tumor fraction levels in plasma dynamically correlated with treatment response to therapy and minimal residual disease (MRD) detection before relapse. Study limitations include a modest MPNST sample size despite accrual from 2 major referral centers for this rare malignancy, and lack of uniform treatment and imaging protocols representing a real-world cohort.


    Tumor fraction levels derived from cfDNA fragment size and copy number alteration analysis of plasma cfDNA using ULP-WGS significantly correlated with MPNST tumor burden, accurately distinguished MPNST from its benign PN precursor, and dynamically correlated with treatment response. In the future, our findings could form the basis for improved early cancer detection and monitoring in high-risk cancer-predisposed populations.

    Author summary

    Why was this study done?

    • Neurofibromatosis type 1 (NF1) is the most common inherited cancer predisposition syndrome.
    • The leading cause of mortality in NF1 is malignant peripheral nerve sheath tumor (MPNST), an aggressive soft tissue sarcoma that arises from a benign plexiform neurofibroma (PN) precursor lesion.
    • Transformation from PN to MPNST is challenging to detect by imaging (due to difficulty in distinguishing PN from MPNST radiologically) or by biopsy (due to intralesional heterogeneity), which often delays the diagnosis of MPNST and results in a worsened prognosis.

    What did the researchers do and find?

    • We conducted a multi-institutional study involving 2 large NF1 referral centers, the National Cancer Institute and Washington University in St. Louis, involving 53 patients from whom plasma cell-free DNA (cfDNA) was analyzed using ultra-low-pass whole genome sequencing (ULP-WGS).
    • We found that cfDNA from patients with MPNST harbors a shorter fragmentation profile compared to patients with PN or healthy donors. Using sequencing reads from this fragmentation profile, we quantified genome-wide copy number alterations (CNAs) in cfDNA and used CNAs to estimate the fraction of plasma cfDNA originating from tumor.
    • Tumor fraction in plasma cfDNA distinguished pretreatment MPSNT from PN with 86% accuracy. Plasma cfDNA from MPNST and PN patients harbored focal copy number loss of NF1 not found in healthy donors. Strikingly, MPNST patient cfDNA also had significantly greater tumor genomic instability compared to PN, with CNAs in key genomic loci previously observed in MPNST tissue (i.e., gain of chromosome arm 8q and loss of 9p), which enabled sensitive and specific liquid biopsy discrimination of MPNST from PN.
    • Plasma-derived tumor fraction correlated with tumor size from imaging in MPNST patients, and serial cfDNA analysis demonstrated the potential for noninvasive detection of minimal residual disease, treatment response assessment, and the potential for even greater assay sensitivity.

    What do these findings mean?

    • Our 2021 - Free Activators suggest that cfDNA fragment analysis followed by ULP-WGS has the potential to be developed as a biomarker for treatment response and as a screening assay for early detection of MPNST.
    • This study provides, to our knowledge, the first evidence for the ability of liquid biopsy to distinguish between benign and malignant tumors in a heritable cancer predisposition syndrome.

    Citation: Szymanski JJ, Sundby RT, Jones PA, Srihari D, Earland N, Harris PK, et al. (2021) Cell-free DNA ultra-low-pass whole genome sequencing to distinguish malignant peripheral nerve sheath tumor (MPNST) from its benign precursor lesion: A cross-sectional study. PLoS Med 18(8): e1003734.

    Academic Editor: Chris Abbosh, University College London, UNITED KINGDOM

    Received: January 16, 2021; Accepted: July 14, 2021; Published: August 31, 2021

    This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

    Data Availability: All sequencing data files are available from the Synapse database (

    Funding: This work was supported by grants from the Children’s Cancer Foundation NextGen Award (R.T.S.), the National Institute of General Medical Sciences (5T32GM007067 supporting P.A.J.), the NCI Center for Cancer Research Intramural Research Program (1ZIABC011722-04 supporting R.T.S, J.F.S., and 1ZIABC010801-13 supporting B.C.W.), the Francis S. Collins Scholars Program in Neurofibromatosis Clinical and Translational Research (A.C.H.), the St. Louis Men’s Group Against Cancer (A.C.H.), the Washington University Alvin J. Siteman Cancer Research Fund (A.A.C.), the National Cancer Institute (1K08CA238711-01 to A.A.C.), the Cancer Research Young Investigator Award (A.A.C.), and the V Foundation for Cancer Research (A.A.C.). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

    Competing interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: A.A.C. has patent filings related to cancer biomarkers, and has served as a consultant/advisor to Roche, Tempus, Geneoscopy, NuProbe, Daiichi Sankyo, AstraZeneca, Fenix Group International and Guidepoint. A.A.C. has stock options in Geneoscopy, research support from Roche, and ownership interests in Droplet Biosciences. A.C.H. has served on advisory boards for AstraZenica and Springworks Therapeutics. F.Q. has stocks: Centene. No potential conflicts of interest were disclosed by the other authors.

    Abbreviations: AUC, area under the curve; cfDNA, cell-free DNA; CNA, copy number alteration; ctDNA, VScodePrint Free Download circulating tumor DNA; FFPE, formalin-fixed paraffin-embedding; IQR, interquartile range; MPNST, malignant peripheral nerve sheath tumor; MRD, minimal residual disease; NCI, National Cancer Institute; NF1, neurofibromatosis type 1; PBMC, peripheral blood mononuclear cell; PN, plexiform neurofibroma; ROC, receiver operating characteristic; SLD, sum of longest tumor diameters; ULP-WGS, ultra-low-pass whole genome sequencing; WES, whole exome sequencing; WGS, whole genome sequencing; WUSTL, Washington University in St. Louis


    Neurofibromatosis type 1 (NF1) is an autosomal dominant disorder affecting one in 3,000 individuals worldwide and is caused by a heterozygous inactivating mutation in the tumor suppressor gene, NF1, located on chromosome 17q11.2 [1–3]. NF1 encodes for the protein, hma pro vpn license key 2020 android - Activators Patch 1, a negative regulator of the RAS signaling pathway. Thus, NF1 loss-of-function mutations lead to hyperactivated RAS, whose downstream effects contribute to the elevated cancer risk in NF1 patients [4–6].

    Approximately 50% of patients with NF1 develop histologically benign plexiform neurofibroma (PN) [1,7], in which Schwann cells acquire biallelic inactivation of the NF1 gene [3,8]. Histologically, PNs are heterogeneous, consisting of primarily S100-positive Schwann cells (60% to 80%), as well as fibroblasts, endothelial cells, perineural cells, smooth muscle cells, mast cells, interspersed axons, and pericytes [2]. Imaging studies of PN mirror this heterogeneity, complicating the radiographic diagnosis of transformation to malignant peripheral nerve sheath tumor (MPNST), which occurs in 8% to 15% of patients with NF1 [1,9,10], as well as the accuracy of diagnostic tissue biopsy.

    MPNST are aggressive cancers with a poor prognosis that frequently arise from within their benign PN precursors [9,11–13]. Due to rapid development of metastasis and resistance to both chemotherapy and radiotherapy, MPNST account for the majority of NF1-associated mortality [1,9] with a 5-year survival rate of only 20% [14]. Despite the high incidence and mortality of MPNST in the NF1 population, screening for malignant transformation and monitoring of MPNST is challenging. Clinical exam has poor sensitivity and may only signify MPNST when a PN lesion is showing sudden growth or causing severe pain [12,15]. Serial PN biopsies are impractical as 9% to 21% of NF1 patients will have multiple PN, with varying levels of malignant potential requiring surveillance [16–18]. Moreover, biopsies can yield false negative results due to geographic tumor heterogeneity resulting from MPNST arising from within heterogeneous PN precursor lesions [19]. Furthermore, standard cross-sectional imaging cannot distinguish MPNST from PN with adequate specificity [20,21]. Given the high prevalence of deadly MPNST in the context of a very common benign precursor lesion in a cancer-predisposed population, it is imperative that more reliable screening modalities be explored.

    We and others have shown that other cancer types can be monitored through plasma cell-free DNA (cfDNA) analysis [22–25] and that malignancy can be associated with distinct cfDNA fragmentation profiles, typically characterized by shorter size [26–30]. We have also shown that sequenced MPNST tissue harbors broad chromosomal copy number alterations (CNAs) characteristic of increased genomic instability compared to PN, including in cases of MPNST transformation arising from within PN lesions [31,32]. Here, we hypothesize that this MPNST-intrinsic genomic instability is also detectable within plasma cfDNA and can be used to noninvasively distinguish MPNST from its benign precursor lesion.

    In the current multi-institutional cross-sectional study involving 2 large referral centers for NF1 patients, the Washington University School of Medicine and the National Cancer Institute (NCI), we aimed to develop a noninvasive liquid biopsy method for distinguishing MPNST from its benign PN precursor using cfDNA fragmentomics and ultra-low-pass whole genome sequencing (ULP-WGS).


    Study design

    This study used blood samples prospectively collected from NF1 patients with MPNST and PN tumors with the aim of distinguishing these different tumor types by plasma cfDNA analysis (Fig 1). Patients from the NCI and Washington University in St. Louis (WUSTL) with clinically and radiographically diagnosed PN or biopsy-proven MPNST were enrolled onto this multi-institutional cross-sectional study with written informed consent (NCI protocol NCT01109394, NIH Intramural IRB identifier 10C0086; NCI protocol NCT00924196, NIH Intramural IRB identifier 08C0079; WUSTL protocol NCT04354064, Washington University in St. Louis School of Medicine Human Research Protection Office IRB identifiers 201903142 and 201203042) between 2016 and 2020. NF1 status was determined clinically by consensus criteria [33]. Five participants with MPNST who did not meet NF1 consensus criteria were also included in the analysis. A total of 14 MPNST and 23 Glary utilities pro 5 crack patients were enrolled with peripheral blood collected at the time of enrollment (S1–S3 Tables). MPNST patients had serial plasma samples collected for a total of 46 MPNST plasma samples (average 3, maximum 6 per participant, S1 Table). When available, tissue was also collected at a single time point (n = 4 participants). When peripheral blood mononuclear cells (PBMCs) were isolated from whole blood, these were sequenced as germline DNA (n = 16 participants). All patients underwent power iso crack management and follow-up by board-certified physicians per the standard-of-care. All samples were collected with informed consent for research and institutional review board approval in accordance with the Declaration of Helsinki. Protocols are available on A STROBE checklist was completed to ensure accurate and complete reporting of the study (see Supplement) [34].


    Fig 1. Study schema.

    Patients with imaging- and biopsy-proven MPNST and established PN along with healthy donors were enrolled onto this multi-institutional prospective cohort, with plasma collected for tumor fraction analysis at the time of study enrollment. Tumor fraction was assessed in all collected plasma samples by ULP-WGS followed by in silico size selection for short cfDNA fragments, which was used to train a noninvasive MPNST vs. PN classifier. During subsequent treatment and follow-up, MPNST patients underwent further serial imaging (analyzed by RECIST) and plasma sample collection (analyzed by ULP-WGS and in silico fragment size selection), with results correlated with each other and with clinical outcomes. cfDNA, cell-free DNA; MPNST, malignant peripheral nerve sheath tumor; 2021 - Free Activators, plexiform neurofibroma; RECIST, response evaluation criteria in solid tumors, version 1.1; SLD, sum of longest tumor diameters; ULP-WGS, ultra-low-pass whole genome sequencing.

    Healthy controls

    After obtaining written consent, healthy donor blood samples were obtained at a single time point from appropriately consented donors at the NIH Department of Transfusion medicine (NIH protocol NCT00001846, NIH Intramural IRB identifier 99-CC-0168) and WUSTL Clinical Translational Research Unit (WUSTL protocol NCT04354064, Washington University in St. Louis School of Medicine Human Research Protection Office IRB identifiers 201903142 and 201203042) (S4 Table). Eligibility for healthy controls included age greater than 18 years old and no known history of neoplastic or hematological disorders. Protocols are available on

    Clinical specimens

    After obtaining written informed consent for genomic analysis, serial peripheral blood samples were collected throughout the clinical course for consenting MPNST patients or at a single time point for PN patients and healthy controls. Treatment regimen for MPNST was determined by the primary treating clinicians and included radiotherapy, surgery, and cytotoxic chemotherapy (S2 Table).

    Venous blood samples (10 to 30 mL) were collected in EDTA (BD Biosciences, San Jose, California) or Cell-Free DNA BCT (Streck Laboratories, La Vista, Nebraska) tubes. EDTA tubes were processed within 4 hours of collection, while Cell-Free DNA BCT tubes were processed within 7 days of collection. Whole blood samples were centrifuged at room temperature (NCI: 1,900 × g for 10 minutes, WUSTL: 1,200 × g for 10 minutes). Isolated plasma was centrifuged a second time at room temperature (NCI: 15,000 × g for 10 minutes, WUSTL: 1,800 × g for 5 minutes) in low-bind Eppendorf tubes to remove residual cells. Purified plasma was frozen at −80°C until cfDNA isolation.

    Plasma cell-free DNA isolation

    Purified plasma was thawed at room temperature, and cfDNA was extracted from 2 to 8 mL of plasma using the QIAamp Circulating Nucleic Acid kit (Qiagen, Hilden, Germany). Extracted DNA concentration was measured using the Qubit dsDNA High-Sensitivity assay (ThermoFisher, Waltham, Massachusetts), and cfDNA concentration and quality were assessed using a Bioanalyzer (Agilent Technologies, Santa Clara, California) or Tapestation (Agilent Technologies, Santa Clara, California). Isolated cfDNA was stored at −20°C until library preparation.

    Germline DNA isolation and processing

    After centrifuging clinical venous blood samples and removing plasma supernatant per above, the red blood cells and buffy coat were resuspended in PBS for germline DNA extraction using the DNeasy Blood and Tissue kit (Qiagen, Hilden, Germany). For a subset of samples, germline DNA from PBMCs was collected in and extracted using PAXgene Blood DNA tubes and kit (PreAnalytix, Germantown, Maryland). DNA was stored at −20°C until further processing. Germline DNA was then fragmented using a LE220 focused ultrasonicator (Covaris, Woburn, Massachusetts) or a Q800R3 sonicator (Qsonica LLC, Newtown, Connecticut) according to the manufacturer’s instructions and previously published methods [35] to a target length of 200 bp. DNA lengths were assessed using a Bioanalyzer (Agilent Technologies, Santa Clara, California).

    Tumor DNA isolation and processing

    Tumor tissue was not procured for research unless clinically indicated and available following the standard clinical pathology workflow. When available, tumor tissue was snap-frozen and stored at −80°C or stored in formalin-fixed paraffin-embedding (FFPE). Nucleic acids were isolated from tumor FFPE samples using the manufacturer’s protocol with the AllPrep DNA/RNA FFPE kit (Qiagen, Hilden, Germany). DNA was extracted from snap-frozen tumor tissue samples using the DNeasy Blood and Tissue kit (Qiagen, Hilden, Germany). Extracted DNA was stored at −20°C until further processing. Tissue DNA was subsequently fragmented using a LE220 focused ultrasonicator (Covaris, Woburn, Massachusetts) or Q800R3 sonicator (Qsonica LLC, Newtown, Connecticut) and analyzed using a Bioanalyzer (Agilent Technologies, Santa Clara, California) as described above.

    DNA library construction and sequencing

    Sequencing libraries were constructed from cfDNA (NCI 5 to 15 ng, WUSTL 10 to 60 ng) or germline/tumor DNA (NCI 100 ng, WUSTL 32 ng) using commercial kits per the manufacturers’ instructions: TruSeq Nano (Illumina, San Diego, California) for NCI samples and Kapa HyperPrep (Roche, Basel, Switzerland) for WUSTL samples. Constructed libraries were balanced, pooled, and sequenced using 150 bp paired-end reads on a NovaSeq (Illumina, San Diego, California) or HiSeq 4000 (Illumina, San Diego, California). Data were then quality filtered and pooled for analysis.

    Copy number alteration and tumor fraction analysis

    Sequencing data were demultiplexed, and raw reads were quality filtered using fastp v.0.2. Quality-filtered reads were then aligned to the hg19 human genome assembly using BWA v.0.7.17. Aligned reads were deduplicated with Samtools v.1.7, then downsampled to 10 million read pairs (WGS coverage approximately 0.6×), or separately for comparison purposes to 5 million read pairs (WGS coverage approximately 0.3×). Genomic coverage was estimated using MosDepth [36]. To enrich for circulating tumor DNA (ctDNA) fragments, in silico size selection was applied to all cfDNA samples [28]. Only quality-filtered reads between fragment lengths of 90 and 150 bp were considered for copy number and tumor fraction analysis for cfDNA samples, while such size selection was not performed for tumor and germline samples. GC content and mappability bias correction, depth-based local copy number estimates, and copy number–based estimation of tumor fraction were then performed using the ichorCNA tool (Broad v.0.2.0) [37]. Briefly, reads were summed in nonoverlapping windows of 106 bases; local read depth was corrected for GC bias and known regions of low mappability, and artifacts were removed by comparison to ichorCNA’s built-in healthy control reference. CNAs were predicted using recommended low tumor fraction parameters for cfDNA samples and default parameters for tumor and germline samples. X and Y chromosomes were not considered in copy number ratios. ichorCNA then used these binned, bias-corrected copy number values to model a two-component mixture of tumor-derived and nontumor-derived fragments, from which it inferred the fraction of reads in each sample originating from tumor (tumor fraction) [37]. Visualization of genome-wide CNAs at specific loci (Fig 3) was generated from compiled log2 ratios of copy number for all study plasma specimens (n = 85 samples). Reads were classified as copy number gain if log2 of the copy number ratio was >0.58 (log2 (3/2)) and loss if log2 of the copy number ratio was <−1.0 (log2 (1/2)). Bin CNA plots (S1 Fig) reflect copy Wondershare TunesGo Crack With Registration Code/Key changes from baseline in the tumor-only fraction of each sample. Both copy number state and tumor fraction were determined by ichorCNA [37].

    Fragment size analysis

    Following the sequencing quality control, deduplication, alignment, and downsampling steps described above, read-pair fragment sizes for cfDNA samples were calculated using deepTools bamPEFragmentSize [38]. The distribution of each sample’s fragment sizes was estimated by kernel density. cfDNA fragment size distributions were compared between the 3 clinical states (healthy control, PN, and MPNST) and between high and low tumor fraction samples by two-sided Kolmogorov–Smirnov testing.

    Comparisons of cfDNA tumor fraction to imaging

    Patients with MPNST and PN were monitored by CT, MRI, and/or FDG-PET imaging at the treating institution at the managing clinicians’ discretion. For patients with MPNST, radiographic tumor burden was quantified by sum of the longest tumor diameters (SLD) per RECIST 1.1 criteria [39]. For comparison to serial time point cfDNA tumor fractions, each plasma sample was matched to the nearest SLD value at the primary institution within 30 days and without any interceding change of therapy. SLDs and plasma tumor fraction levels were then assessed using Pearson correlation coefficient. For comparisons of plasma tumor fraction to clinical status by RECIST, tumor fraction values were first normalized per patient to the lowest value detected on serial analysis, and then log2 transformed to generate the final plotted values in Fig 5B. Changes in clinical status were assessed and categorized as complete response, partial response, stable disease, or progressive disease per RECIST 1.1 criteria [39]. RECIST 1.1 scoring was performed on serial imaging studies relative to a patient’s baseline scan.

    Power and statistical analyses

    Previous tissue-based studies have shown that PN harbor few genome-wide CNAs [40,41] but acquire significant genomic instability during malignant transformation to MPNST [32,41,42]. Based on these known significant CNA differences between MPNST and PN tumors, we assumed a large effect size PhoneRescue 3.9.0 Crack With Serial Code Free Download 2020 also be evident comparing MPNST plasma tumor fraction to plasma from PN patients or healthy controls. Using Cohen’s f = 0.6 with an α = 0.05 and power = 0.80, we projected that the sample size needed to detect differences between these 3 categories would be n = 10 per group. Our category group sizes met or exceeded this estimate for all comparisons (S1–S4 Tables).

    When testing associations between plasma tumor fraction and clinical status (Fig 4), we limited MPNST plasma samples to those collected either prior to all treatments or after a washout period of at least 21 days after completion of chemotherapy or radiotherapy (designated as pretreatment or baseline MPNST samples below). The distributions of plasma tumor fraction for each clinical status were compared by Kruskal–Wallis H test with pairwise comparisons by Dunn test. To further compare pretreatment MPNST to benign PN patients, we generated a receiver operating characteristic (ROC) curve of plasma tumor fraction. Tumor fraction values derived from ctDNA-enriched 90 to 150 bp fragments were compared to tumor fractions derived from all cfDNA fragment lengths. For ctDNA-enriched tumor fraction, an optimized cutpoint was determined by Youden’s index (the point on the ROC curve that maximizes sensitivity + specificity– 1), and high and low plasma tumor fraction groups by cutpoint were compared to clinical status by Fisher exact test. A logistic regression was also performed for the MPNST versus PN groups using the glm function in R, evaluating the effects of age, sex, and institution in addition to pretreatment plasma tumor fraction. Leave-one-out cross-validation was performed in R using the caret package. The reverse Kaplan–Meier method was used to estimate follow-up times. Statistical August 31 were performed using R v.3.6.1 or Prism 9 (GraphPad Software).


    Overview and patient characteristics

    The primary objective of this study was to noninvasively differentiate MPNST from benign PN by analyzing and quantifying genomic CNAs in blood plasma cfDNA (Fig 1). To quantify CNAs, we profiled 105 biospecimens including 85 plasma samples from 53 participants by ULP-WGS downsampled to 10 million paired reads (approximately 0.6× genome-wide coverage) (Fig 1, S1 Table). Participant groups compared were MPNST and PN patients as well as healthy donor controls. Specimen types included blood plasma cfDNA, blood leukocyte germline DNA, and frozen tumor specimens (S1 Table). The median age was 36, 27, and 32.5 for MPNST patients, PN patients, and healthy donors, respectively (S2–S4 Tables). Exclusion criteria included diagnosis of a non-MPNST malignancy. No PN patients developed any clinical or radiographic evidence of MPNST transformation during a median study follow-up time of nearly 2 years (median 690 days; interquartile range (IQR) of 531 to 1,140 days; S3 Table). For 2 participants, sar015 and sar037, large PN resection was performed for lesion-related morbidity with final pathology confirming the PN diagnosis. Among patients with MPNST, 86.7% received chemotherapy, 35.7% received radiotherapy, and 42.9% underwent surgical resection (S2 Table).

    Genome-wide CNAs from tumor are detected in plasma

    Approximately 86% of MPNST and PN patients enrolled onto our study met the NIH criteria for NF1 diagnosis. There was no difference in tumor fraction between the MPNST patients who met NIH criteria and those who did not (P = 0.93 by Wilcoxon rank-sum test). Genomic copy number analysis of plasma cfDNA revealed that focal somatic CNAs that have previously been associated with PN tumor progression in NF1 patients [32] were prominently observed in MPNST patients and were occasionally found in PN patients, but absent in healthy controls (Figs 2 and 3). For example, focal loss of CDKN2A/B, MTAP, SMARCA2, and SUZ12, alterations shown to be associated with malignant transformation of PN [32,43,44], was observed in plasma from MPNST patients. CDKN2B and SUZ12 losses were also found in 2 PN patients, while SMARCA2 appeared to be copy number neutral in plasma across the full PN cohort. Loss of SUZ12 correlated with NF1 loss, consistent with both genes’ location in the 17.q11.2 genomic locus [32,45]. Additionally, we observed broader copy number gains in chromosome arms 1q, 7p, 8q, 9q, and 17q as well as broad losses in arms 6p and 9p only in MPNST patient plasma, again consistent with previous findings from NF1 MPNST tumors [32,42,46] (Fig 3). Finally, while many types of NF1 gene activation can underlie the NF1 disease process, we observed evidence of one of these, NF1 copy number loss, only within MPNST and PN patients, but not in healthy donor controls.


    Fig 2. Participant characteristics and CNAs.

    Heatmap includes all 53 participants in this study, categorized by diagnosis. Each column represents one study participant with ID labels shown below. Highest tumor fraction in plasma and important tumor and participant characteristics are displayed in the top panel. The lower panel shows CNAs in genes relevant to NF1 and MPNST pathogenesis, depicted as log2 of copy number ratio. CNA, copy number alteration; MPNST, malignant peripheral nerve sheath tumor; NF1, neurofibromatosis type 1; SLD, sum of longest tumor diameters as determined by RECIST 1.1 criteria.


    Fig 3. Aggregate CNAs measured across the genome.

    Plots represent plasma cfDNA data compiled from all blood plasma specimens from (A) MPNST (n = 46), (B) PN (n = 23), or (C) healthy donors (n = 16) in this study. Copy number ratios across the genome are shown on a log2 scale with significant gains in red, significant losses in blue, and regions without significant gain or loss depicted in gray (Methods). Loci highlighted in green have been previously associated with MPNST or NF1, with associated genes also labeled and depicted by green diamonds. cfDNA, cell-free DNA; CNA, copy number alteration; MPNST, malignant peripheral nerve sheath tumor; PN, plexiform neurofibroma.

    Given the observed copy number changes in patient plasma, we next compared genome-wide CNAs and associated tumor fractions across specimen types. For MPNST cases where tumor, leukocyte, and plasma were all available, the observed copy number aberrations were most prominent in the tumor samples, but also detected in plasma cfDNA prior to treatment, with a pattern reflective of the original tumor (S1 Fig). The magnitude of these CNAs decreased in posttreatment cfDNA compared to pretreatment cfDNA, and germline samples harbored August 31 least detectable CNAs. This trend also held for estimated tumor fractions, representing a sample’s aggregate genome-wide copy number changes. As expected, there was no such increase in tumor fraction observed in PN lesions or in cfDNA derived from PN or healthy adults.

    Plasma tumor fraction distinguishes MPNST from plexiform neurofibroma

    Given that tumor-derived CNAs were detected in plasma cfDNA from MPNST patients, we next investigated the ability of plasma tumor fraction, inferred from the genome-wide copy number data following in silico size selection of 90 to 150 bp fragments, to noninvasively differentiate MPNST from PN. Plasma tumor fraction was compared between healthy controls, PN, and all pretreatment MPNST samples. Strikingly, baseline cfDNA tumor fraction differentiated MPNST from both healthy (P = 0.0026) and PN (P = 0.001) participants. PN and healthy donors did not differ in cfDNA tumor fraction (P = 1) (Fig 4A). Median tumor fraction levels in healthy (0.026) and PN (0.026) groups were lower than in MPNST (0.058). Comparing plasma tumor fractions between pretreatment MPNST patients and PN patients, ROC analysis further demonstrated an area under the curve (AUC) of 0.83 (Fig 4B), which was higher with approximately 0.6× ULP-WGS than approximately 0.3× (Methods; S4 Fig). Notably, AUC decreased to 0.60 when considering all cfDNA fragment sizes, highlighting the importance of our in silico size selection step. This signified the ability to accurately discriminate between MPNST and PN using only plasma tumor fraction levels derived from ULP-WGS followed by in silico size selection.


    Fig 4. Tumor fraction in plasma stratifies patients by diagnosis.

    (A) Tumor fraction in participants with available pretreatment plasma cfDNA (n = 53), stratified by clinical diagnosis, with significance assessed by the Dunn test after Kruskal–Wallis analysis of variance. (B) ROC curve of plasma cfDNA tumor fraction comparing pretreatment MPNST to PN patients. Solid line represents tumor fraction data derived only from 90–150 bp fragments, while dotted line represents tumor fractions derived from all fragment lengths. Confusion matrix is reported separately (S5 Table). (C) Fragment length density for cfDNA in MPNST and PN patients (n = 69 samples) with high (>0.0413) versus low (<0.0413) tumor fractions in plasma as determined by the Youden’s index-optimized cutpoint from the ROC curve. The dashed line highlights an inflection in the curves with high tumor fraction samples enriched for shorter cfDNA fragment sizes (<138 bp) and low tumor fraction samples enriched for longer cfDNA fragment sizes (D = 0.078, P < 0.001 by two-sample Kolmogorov–Smirnov test). Data are shown for sequencing reads within the 90 to 150 bp in silico size-selection range (Methods). AUC, area under the curve; bp, base pairs; cfDNA, cell-free DNA; MPNST, malignant peripheral nerve sheath tumor; PN, plexiform neurofibroma; ROC, receiver operating characteristic; Sn, sensitivity; Sp, specificity.

    Thus, utilizing a Youden’s index-optimized cutpoint of 0.041, pretreatment plasma tumor fraction differentiated MPNST from PN with an area under the ROC curve of 0.83, and sensitivity of 75% and specificity of 91%, with 21 of 23 PN cases successfully classified based on pretreatment plasma tumor fraction alone (P = 0.001) (S5 Table). This compared favorably to reports of other diagnostic modalities including MRI features and image-guided core-needle biopsy (S6 Table). Model performance was retained in leave-one-out cross-validation using a penalized regression model where overall accuracy was 75% (95% CI 66% to 83%) and improved to 89% with AUC of 0.89, Youden’s index-optimized sensitivity of 83%, and specificity of 91% when considering the highest plasma tumor fraction measured per participant on serial time point analysis (S7 Table). In a multivariate binary logistic regression model including age, sex, and institution, baseline plasma tumor fraction remained significantly associated with clinical status (P = 0.04), while the other covariates were not (S8 Table).

    Validating our original fragment size selection strategy, we observed fragment size differences between clinical states as defined by the tumor fraction ROC cutpoint. Using high-tumor fraction versus low-tumor fraction groups determined by the optimal cutpoint of 0.041, there was a significant difference in fragment length distributions (D = 0.078, P < 0.001 by two-sample Kolmogorov–Smirnov test) with high-plasma tumor fraction cases enriched for shorter cfDNA fragments and low-plasma tumor fraction cfDNA enriched for longer fragments (Fig 4C). Similarly, clinically classified MPNST patients harbored significantly shorter cfDNA fragments compared to PN patients (D = 0.032, P < 0.001) and healthy donors (S2 Fig). Thus, fragmentation profiles appear unique in patients with MPNST compared to those with benign PN.

    MPNST plasma tumor fraction correlates with disease burden by imaging

    Having established plasma cfDNA fragment size and tumor fraction as a specific means to classify MPNST cases noninvasively, we next investigated the relationship between plasma tumor fraction derived using our assay and radiologically measured tumor burden. Radiographic tumor burden was quantified by the sum of longest tumor diameters (SLD) by RECIST 1.1 criteria [39] and compared to matched plasma cfDNA tumor fraction levels (Methods). A significantly positive correlation was observed between SLD and plasma tumor fraction (Pearson r = 0.387, P = 0.024) (Fig 5A). Because RECIST SLD measurements are restricted to 5 total lesions, 2 lesions per organ and do not include bony disease, SLD may underestimate tumor burden in metastatic MPNST patients [39]. Conversely, SLD may overestimate the size of malignant tissue in primary MPNST lesions, which often arise from within PN, with the relative contribution of PN versus MPNST tissue to the overall lesion size difficult to accurately assess radiographically [47]. These challenges limit the ability of SLD to accurately define MPNST disease burden and may explain why the correlation of SLD to tumor fraction was not stronger.


    Fig 5. Tumor fraction in plasma correlates with imaging.

    (A) SLD of target lesions for all MPNST patients are plotted against each SLD’s temporally closest plasma tumor fraction value (Methods). Pearson correlation coefficient is significant at P = 0.024. (B) Timelines of RECIST classification for MPNST patients that underwent serial monitoring (n = 8; S3 Fig). Overlaid are cfDNA tumor fraction levels, normalized per patient to the lowest value detected on serial analysis, and then log2 transformed (Methods). cfDNA, cell-free DNA; MPNST, malignant peripheral nerve sheath tumor; RECIST, response evaluation criteria in solid tumors, version 1.1; SLD, sum of longest tumor diameters.

    We furthermore dynamically tracked both SLD and plasma tumor fractions over time in patients with serial time point data (S1 Table). We applied RECIST 1.1 criteria [39] in these patients to classify radiographic response to therapy disk drill professional 2.0 0.334 full crack - Crack Key For U. Dynamic changes in plasma tumor fraction typically correlated with but preceded imaging changes (Fig 5B, S3 Fig). We thus studied timelines of disease progression versus response for MPNST patients, comparing imaging SLD to plasma tumor fraction in the context of the specific treatments received (Figs 6–8, S3 Fig). For example, sar085 presented with a small recurrent lung MPNST that rapidly progressed into multiple thoracic metastases despite 2 lines of cytotoxic chemotherapy, but then partially responded to third-line chemotherapy (Fig 6). Interestingly, plasma tumor fraction levels closely tracked with SLD from CT imaging throughout this treatment course. These data suggest that plasma tumor fraction could thus be utilized to monitor treatment response.


    Fig 6. Monitoring tumor burden vignette (sar085).

    This patient had a high-grade thoracic MPNST recurrence that progressed rapidly through first- and second-line chemotherapy but responded to third-line chemotherapy. Tumor fraction in plasma was initially undetectable, then rapidly increased during first- and second-line chemotherapy, followed by a rapid decrease during third-line chemotherapy. This dynamic tumor fraction in plasma correlated well with the SLD measured radiographically by RECIST 1.1 criteria. Chemo, chemotherapy; cm, centimeters; MPNST, malignant peripheral nerve sheath tumor; SLD, sum of longest tumor diameters; Tx, treatment.


    Fig 7. Early detection of relapse vignette (sar080).

    This patient previously had a high-grade pelvic MPNST that was completely resected with no evidence of residual disease after surgery. Tumor fraction in plasma was undetectable following resection but was detected 150 days later (tumor fraction = 0.021); this preceded metastatic recurrence identified on surveillance imaging by 89 days. cm, centimeters; MPNST, malignant peripheral nerve sheath tumor; SLD, sum of longest tumor diameters as determined by RECIST 1.1 criteria.

    August 31


    Fig 8. Clinical decision-making vignette (sar102).

    This patient had a high-grade MPNST in the right foot that was stable by imaging and therefore had chemotherapeutic agents held in order to undergo an elective lower limb amputation for improved quality of life. Tumor fraction in plasma, however, increased during this presurgical time period, consistent with progressive metastatic disease, which became apparent on imaging shortly after surgery. The patient would not have had chemotherapy held to undergo lower limb amputation had there been earlier evidence of progressive metastatic disease. Chemo, chemotherapy; cm, centimeters; MPNST, malignant peripheral nerve sheath tumor; SLD, sum of longest tumor diameters as determined by RECIST 1.1 criteria.

    There were also several instances where we observed cfDNA tumor fraction elevations anticipating and preceding corresponding SLD increases. sar080 is an illustrative example in which the patient had complete resection of a right pelvic MPNST prior to plasma cfDNA analysis (Fig 7). Initial plasma tumor fraction was not detected, which was consistent with our finding of no evidence of disease by imaging. Follow-up imaging again showed no evidence of disease 2.5 months later. Plasma tumor fraction rose, however, just 14 days later and preceded radiographic recurrence by 89 days, suggesting that the measurement of plasma tumor fraction could be utilized as a sensitive surveillance tool for minimal residual disease (MRD) detection following the completion of therapy.

    sar102 illustrates another example where cfDNA tumor fraction elevation preceded radiographic progression. This patient had metastatic MPNST with a right foot primary tumor (Fig 8). During and following over 4 months of cytotoxic chemotherapy, imaging showed a partial response with persistently decreasing SLD. Given radiographic evidence of disease control, the medical team then decided to hold cytotoxic chemotherapy for an elective lower limb amputation with the goal of improving quality of life (pain reduction and ability to use a prosthetic limb). In contrast to SLD, however, plasma tumor fraction increased 14-fold during this same interval (S1 Table). Following cessation of cytotoxic agents and amputation, the patient was found to have significant widespread tumor progression with the development of multiple distant metastases including brain metastases. Here, plasma tumor fraction was more consistent than serial imaging with this patient’s ultimate clinical status and could have influenced the decision to hold chemotherapy in order to perform an elective, palliative amputation. This illustrates the potential utility of plasma cfDNA tumor fraction analysis for dynamically monitoring disease, anticipating progression, and influencing clinical decision-making.


    In this multi-institutional cohort study, we performed fragment size analysis and ULP-WGS of cfDNA to noninvasively detect genome-wide CNAs and derive tumor fraction in plasma, which we used to differentiate between MPNST and PN patients. To our knowledge, this is the first demonstration of liquid biopsy to distinguish between malignant and premalignant solid tumor in the setting of a cancer predisposition syndrome. Specifically, we observed that patients with MPNST harbor a unique cfDNA fragmentation profile and have significantly greater tumor genomic instability evident in plasma compared to PN patients. We also demonstrated that cfDNA analysis can be used to dynamically track treatment response in MPNST patients, potentially with greater precision than standard cross-sectional imaging.

    MPNST are aggressive soft tissue sarcomas that can be difficult to distinguish from their benign precursors, illustrating the need for new testing modalities for better disease detection and surveillance. Our data illustrate several important points. First, we accurately identified copy number–altered genomic loci characteristic of malignant transformation from PN using ULP-WGS of cfDNA (Figs 2 and 3) [32,43]. Specifically, loss of CDKN2A/B, SMARCA2, and SUZ12 were commonly found only in MPNST plasma samples, while loss of NF1 was observed in both MPNST and PN plasma, but not healthy controls. When paired tumor was available, plasma cfDNA-detected CNAs recapitulated tissue patterns of genomic instability (S1 Fig). Together, these data suggest that even at low sequencing coverage, the genomic features of both NF1 and of progression from PN to MPNST are detectable in affected patients’ plasma.

    We strikingly also show that cfDNA tumor fraction derived from genome-wide CNAs after selecting for shorter fragment lengths, without applying prior screenshot recorder of patient-specific mutational profiles, differentiated MPNST from PN with high specificity (91%) and moderate sensitivity (75%) pretreatment (Fig 4B) and both high specificity (91%) and sensitivity (83%) when measured serially (S7 Table). This strongly suggests that cfDNA tumor fraction could be a valuable adjunct to aid in monitoring patients with PN with the goal of early cancer detection. Currently, malignant transformation in NF1 patients is difficult to screen for due to overlapping clinical symptoms and radiographic findings that are also associated with benign PN [48,49]. Reflective of this, current standard practice for PN surveillance is to obtain imaging only when clinically indicated. Moreover, clinical surveillance for symptoms such as lesion-associated pain have a low specificity for identifying MPNST on subsequent workup [48,50,51]. Furthermore, the lack of reliable radiographic characteristics using standard sequences that can be replicated across institutions has contributed to overall limited sensitivity of MRI (62.5% to 84%) and specificity of FDG-PET (52.2% to 83%) for MPNST detection [20,21,52].

    Still, it will be important to fully consider state-of-the-art imaging results, such as anatomic MRI features [53], and integrate them with plasma cfDNA results in the future in order to maximize combined modality performance, which we suggest may be possible by coupling advanced MRI features with our highly specific liquid biopsy assay (S6 Table). Indeed, in current practice, confirmation of MPNST identified by clinical/imaging suspicion is usually attempted by solid tumor biopsy, which can be exquisitely painful given the peripheral nerve site, is often technically difficult due to lesions’ propensity for localizing to viscera and the retroperitoneum, and is associated with serious complications including nerve palsy and dissemination of malignant tumor cells [53]. Additionally, biopsy is imagenomic portraiture license code without diagnostic caveat, as the development of MPNST from within PN lesions causes sampling bias with image-guided biopsies shown to result in low negative predictive value (NPV), with 50% of NF1 patients diagnosed with PN on image-guided core-needle biopsy being subsequently reclassified as MPNST following surgical resection in one retrospective study [19]. Unlike image-guided tumor biopsy, our liquid biopsy approach to measure tumor fraction reflects chromosomal instability throughout the body, thus limiting the potential for sampling bias.

    Previous studies have demonstrated the ability of liquid biopsy testing to distinguish patients with cancer from healthy individuals [26,54–57]. Here, we significantly extend this body of work to show that a liquid biopsy test can detect malignant transformation in the context of hereditary cancer predisposition, distinguishing patients with a malignant solid tumor from those with its benign precursor lesion. We also build upon prior literature showing that fragmentation profiles and lengths of cfDNA from cancer patients are distinct from those in healthy donors [26,28–30], showing for the first time that malignancy-associated cfDNA is significantly shorter than its benign-associated counterpart (Fig 4C, S2 Fig). We additionally demonstrate that increasing sequencing coverage to approximately 0.6× improves the sensitivity of our ULP-WGS-based assay (S4 Fig).

    We further note that the moderate pretreatment sensitivity of our cfDNA technology for detecting MPNST versus PN, which we showed was 75% with 91% specificity, compares favorably to landmark early cancer detection studies such as the ones from Grail [56], Thrive [55], and Stanford [54], which reported sensitivities of approximately 20% to 50% with approximately 95% to 99% specificity for common stage 1 to 2 solid tumor malignancies. Indeed, when we set the specificity of our assay to 100%, we still achieved a comparable sensitivity of 50% pretreatment and 58% when considering the highest tumor fraction on serial time point analysis (S7 Table). Overall, our plasma cfDNA assay was able to robustly distinguish MPNST from its PN precursor and should be tested in the setting of other hereditary cancer predisposition syndromes in the future. We nonetheless plan to enhance the technology we present here in order to boost sensitivity, for example, through deep learning following future generation of much larger cfDNA datasets, and by pairing our fragmentation- and WGS-based method with targeted deep sequencing and methylation analysis.

    We also show that cfDNA tumor fraction dynamics appear to anticipate and track with disease burden in MPNST patients. CNAs in plasma cfDNA have been previously shown to correlate with radiographic burden of disease in established cancer patients [58]. Our study, similarly, showed significant correlation between plasma tumor fraction and radiographic tumor burden (Fig 5A). Additionally, for individual patients with serial plasma samples and serial imaging studies, dynamic changes in cfDNA tumor fraction predicted changes in tumor burden and disease state (Figs 5B–8, S3 Fig). These findings highlight the potential for fragment size-selected ULP-WGS surveillance in NF1, not only to distinguish between premalignant and malignant tumors, but also to serve as a real-time biomarker to track treatment response and to improve detection of MRD following local disease control. Multi-institutional prospective validation and evaluation in cfDNA-guided interventional trials is warranted.

    Limitations of our study include a modest MPNST cohort size. Indeed, landmark publications on MPNST genomics comprise of whole genome sequencing (WGS)/whole exome sequencing (WES) cohorts ranging from 7 to 15 patients [43,45], comparable to our MPNST cohort size obtained from prospectively enrolling from 2 major NF1 referral centers. Still, due to our modest study size, we were unable to power a held-out validation cohort (see Methods). To address this, we validated our data using a leave-one-out cross-validation framework, which we show retains an overall accuracy of 75%. Ultimately, a much larger multi-institutional collaboration will be required to validate our avg internet security serial key 2018 and the MPNST versus PN tumor fraction cutpoint. A second important limitation of this study was inconsistent testing and treatment protocols across cohort participants. Imaging data was at clinician discretion using a variety of modalities and time points. Germline NF1 testing was not conducted in all patients, and not all patients met NIH NF1 criteria. While lack of uniform clinical care likely reduced our ability to differentiate disease states, it also reflects real-world diversity in treatment expected in any large MPNST cohort outside of a dedicated prospective trial.

    In conclusion, our findings suggest that cfDNA fragment analysis followed by ULP-WGS can noninvasively detect MPNST and distinguish it from its benign precursor lesion in NF1 patients. To our knowledge, these results represent the first evidence of a liquid biopsy test to capably differentiate between malignant and premalignant tumors in a heritable cancer predisposition syndrome. Application of this liquid biopsy technology has the potential to adjudicate equivocal imaging, serve as an MRD and treatment response biomarker, and, most importantly, facilitate the early detection of MPNST. These advances are critical for improving the substantial morbidity and mortality associated with these aggressive tumors in patients with this common cancer predisposition syndrome.

    Supporting information

    S1 Fig. Genome-wide CNAs by specimen type.

    Genome-wide CNAs assessed in 4 specimen types from a single MPNST patient (sar081): tumor tissue DNA, pretreatment blood plasma cfDNA, posttreatment cfDNA, and germline DNA from pretreatment PBMCs. Log2 of copy number ratio is shown across the genome. Color scale depicts estimated copy number within the tumor fraction as determined by ichorCNA (Methods). cfDNA, cell-free DNA; CNA, copy number alteration; MPNST, malignant peripheral nerve sheath tumor; PBMC, peripheral blood mononuclear cell.


    S2 Fig. Plasma cfDNA fragment sizes in MPNST patients are shorter than in PN patients or healthy controls.

    (A) Fragment size distributions of cfDNA from healthy donors, PN, and MPNST patients (Methods). cfDNA fragment sizes in MPNST patients were significantly shorter than from PN patients (D = 0.032, P < 0.001) or healthy donors (D = 0.062, P < 0.001) by two-sample Kolmogorov–Smirnov testing. (B) Log2 ratio of the differences in cfDNA fragment sizes from patients with MPNST versus PN with the dashed line indicating the upper boundary used for in silico size selection (150 bp). For panel A, all plasma samples in the study were analyzed (16 healthy, 23 PN, 46 MPNST), and in panel B, all PN (n = 23) and MPNST (n = 46) plasma samples were analyzed. bp, base pairs; cfDNA, cell-free DNA; MPNST, malignant peripheral nerve sheath tumor; PN, plexiform neurofibroma.


    S3 Fig. Plasma tumor fraction changes dynamically with imaging in MPNST patients.

    Overlaid plots of cfDNA tumor fraction (red) and SLD (blue) for MPNST patients tracked with serial plasma analysis (see also Fig 5B). cfDNA, cell-free DNA; MPNST, malignant peripheral nerve sheath tumor; SLD, sum of longest tumor diameters as determined by RECIST 1.1 criteria.


    S4 Fig. Higher plasma ULP-WGS sequencing depth improves the ability to differentiate MPNST from PN.

    Comparison of ROC summary statistics from 5 million paired reads (approximately 0.3× coverage) versus 10 million paired reads (approximately 0.6× coverage) followed by size selection of 90–150 bp cfDNA fragments. AUC, area under the curve; bp, base pairs; cfDNA, cell-free DNA; MPNST, malignant peripheral nerve sheath tumor; PN, plexiform neurofibroma; ROC, receiver operating characteristic; ULP-WGS, ultra-low-pass whole genome sequencing.


    S7 Table. Assay performance with alternative ROC cutpoints and time points.

    Comparison of different cutpoints and conditions for the receiver operating characteristic (ROC) curve predicting MPNST vs. plexiform neurofibroma clinical status. Pretreatment represents the baseline cfDNA data, while serial analysis represents the highest plasma-derived tumor fraction detected on serial analysis.



    We are grateful to the patients and families involved in this study, to the clinical research team for collection of samples and clinical data, to the Washington University Neurofibromatosis (NF) Center, and to the NCI Center for Cancer Research Intramural Research Program. We also thank D. Gutmann, T. Ley, and A. Newman for providing critical feedback on the manuscript. This study utilized the computational resources of the McDonnell Genome Institute at Washington University, and the High Performance Computing Biowulf cluster at the National Institutes of Health. Images from BioRender were used to create Fig 1.


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    "We have been cooperating with Utimaco for many years. In a joint project for the customer Bank-Verlag, for example, we implemented an eIDAS-compliant signature activation module (SAM) to carry out remote signatures in banking securely and quickly. Our objective with the new partnership is to establish a broader base for our successful cooperation and focus on three key sectors. Although achelos will continue to operate as development partner to Utimaco, we are also keen to implement our own, customer-specific development projects for HSM firmware, develop solutions based on this and act as a value added reseller for Utimaco's HSM. In this context we will provide 1st and 2nd level support, run training courses and take care of commissioning. Supplemented to include the PKI products from other partners and our services from PKI System Consulting, we thereby offer customers a very interesting added value chain," comments Carola Schwarzenberg from Management and Strategic Sales at achelos, explaining the cooperation with Utimaco.

    "We are delighted to expand our sales network with such a professional partner as achelos. IT security is becoming ever more important - not only in the classic security-critical sectors such as at banks, financial services providers or public authorities, but also in the automotive industry or at organisations that suddenly see themselves exposed to new attack scenarios and thereby also security requirements as a result of the rapid increase in digitisation observed last year. Customers consider sound advice just as important as selecting the right security solution. Combining the strengths of achelos and Utimaco will allow us to offer customers the best of both worlds," comments Mario Brand, VP EMEA at Utimaco.


    Free Fire keeps on releasing the advanced servers from time to time. The Latest variant i.e. OB29 was released on 4th August 2021. After OB27, This was the much-waited version by the players. As the new Free Fire server is live now, We are providing you with all the latest updates related to its VPN, APK file, Download process, Login & registration process with other details like Free Fire Redeem Codes.

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    Mechanical activation of spike fosters SARS-CoV-2 viral infection


    The outbreak of SARS-CoV-2 (SARS2) has caused a global COVID-19 pandemic. The spike protein of SARS2 (SARS2-S) recognizes host receptors, including ACE2, to initiate viral entry in a complex biomechanical environment. Here, we reveal that tensile force, generated by bending of the host cell membrane, strengthens spike recognition of ACE2 and accelerates the detachment of spike’s S1 subunit from the S2 subunit to rapidly prime the viral fusion machinery. Mechanistically, such mechano-activation is fulfilled by force-induced opening and rotation of spike’s receptor-binding domain to prolong the bond lifetime of spike/ACE2 binding, up to 4 times longer than that of SARS-S binding with ACE2 under 10 pN force application, and subsequently by force-accelerated S1/S2 detachment which is up to ~103 times faster than that in the no-force condition. Interestingly, the SARS2-S D614G mutant, a more infectious variant, shows 3-time stronger force-dependent ACE2 binding and 35-time faster force-induced S1/S2 detachment. We also reveal that an anti-S1/S2 non-RBD-blocking antibody that was derived from convalescent COVID-19 patients with potent neutralizing capability can reduce S1/S2 detachment by 3 × 106 times under force. Our study sheds light on the mechano-chemistry of spike activation and on developing a non-RBD-blocking but S1/S2-locking therapeutic strategy to prevent SARS2 invasion.


    A novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, referred to as SARS2 thereafter) causes the pandemic of the coronavirus diseases 2019 (COVID-19), posing a serious threat to public health worldwide.1,2 Although SARS2 and SARS share ~80% nucleotide identity in the whole genome sequences, SARS2 is more infectious and has infected a tremendously larger population worldwide ( However, the underlying molecular mechanism, especially the viral invasion into host cells, still remains elusive.

    SARS2, as well as SARS, belongs to the beta coronavirus family and utilizes its spike protein to recognize host receptors (e.g., angiotensin-converting enzyme II receptor, ACE2)3,4,5,6 to invade host cells. The initial entry of SARS2 or SARS into the host cell occurs in two vital steps, receptor recognition by the spike protein and subsequent conformational changes of the spike to form fusion machinery.7,8,9 Both steps are respectively governed by two subunits of the spike, S1 and S2. Receptor-binding domain (RBD) in the S1 subunit is mainly responsible for ACE2 recognition, and the S2 subunit forms fusion machinery to target host-cell plasma membrane (PM) after S1/S2 detachment (Supplementary information, Fig. S1a).7,8,9,10,11,12 The sequences of SARS2- and SARS-RBDs are similar (Supplementary information, Fig. S1b), with highly conserved ACE2 contact residues.13,14,15 Minimal structural changes of SARS2 spike upon ACE2 binding seem not significant enough to trigger the detachment of tightly associated S1 and S2 subunits, for which additional factors might be required.

    SARS2 and SARS primarily target the respiratory tract associated with complex mechanical cues.16,17,18,19 For instance, tensile force induced by membrane bending has been reported to be involved in cell–cell contact as well as in endocytosis.20,21,22,23 These two physiological processes are reminiscent of viral attachment onto and entry into host cells, leaving the role of membrane bending in viral invasion enigmatic. Similar to endocytosis, once a virion attaches to the epithelium layers of the lung airway, the bent epithelial cell membrane might exert tensile force on the single spike/ACE2 binding complex, which inevitably impacts spike/ACE2 binding and resultant viral host recognition, attachment, and invasion. Several recent studies have reported the structures of the SARS2-RBD with human ACE2 in the static force-free condition, merely demonstrating a similar contact interface to that of the SARS-RBD/ACE2 complex (Supplementary information, Fig. S1c). It has also been reported that SARS2 and SARS spikes or RBDs bind to ACE2 with similar binding affinities,24,25,26 which hardly explains SARS2’s higher contagiousness than SARS. Moreover, S1/S2 tight contact observed from spike structures26,27,28,29 and the observation that the majority of spikes on pre-fused SARS2 viruses are in pre-fusion state30,31 screenshot recorder support the spontaneous S1/S2 dissociation model that is proposed based on the recent observation of post-fusion S2 protein in purified full-length wild-type spikes.27 All of these raise questions whether and how tensile force regulates spike’s dissociation from ACE2 during viral invasion into host cells, whether the mechano-dependent binding differentiates SARS2 and SARS, and whether follow-up S1/S2 detachment also requires or is accelerated by tensile force.

    Herein, by integrating multiple biophysical approaches, we demonstrate that SARS2 exploits mechanical force to enhance its spike recognition of ACE2 and subsequently accelerate S1/S2 detachment for effective invasion into host cells. SARS2 shows greater force-enhanced spike recognition of ACE2 than SARS, in good agreement with its higher infectivity. Such mechanical enhancement is very likely to be a universal regulatory mechanism for the invasion of other beta-coronaviruses. A D614G variation of SARS2 spike enhances force-dependent spike recognition of ACE2 and speeds up the follow-up S1/S2 detachment simultaneously. Moreover, we also identify an S1/S2-binding, non-RBD-blocking, and neutralizing antibody derived from convalescent COVID-19 patients that can unexpectedly restrain force-accelerated S1/S2 detachment.


    Theoretical estimation of the mechanical force exerted on single spike/ACE2 bond

    Once a virion attaches to the host-cell PM through spike/ACE2 interaction, the contact zone gradually grows and enlarges, which is an energy-favored process. Upon spike/ACE2 binding, the potential energy of the virion/host-cell interaction system is reduced,32 and the released portion of the potential energy transfers to bend host-cell PM and deform spike/ACE2 bonds, thereby elevating the bending energy Avira Antivirus Pro License key host-cell PM and the elastic energy of deformed spike/ACE2 complexes.23,33,34 Driven by this energy conversion and owing to the softer host-cell PM compared to that of the virion,35,36 the host-cell PM inevitably bends to wrap the virion (Fig. 1a). Considering the mechanical equilibrium of both the virion and the host-cell PM, for a given contact zone (Fig. 1a, right), forces on the spike/ACE2 bonds within the contact zone between the virion and the host-cell can be calculated numerically (see Materials and Methods).

    a Schematic diagram showing the mechano-environment of the SARS2 virus invading into human body through respiratory system. Host cell membrane is forced to bend by the spike/ACE2 interaction. b Theoretical estimation of the force exerted on single spike/ACE2 bond. Variation of the pulling force at the edge of the contact zone along with the φC change is shown (black curve). The distribution patterns of pulling and compressing forces when the contact zone grows to φC = 20°, 40°, 60° and 80° are shown in the insets. c Schematic diagram of biomembrane force probe setup and its functionalization strategy (zoomed-in panel). d Representative force vs time trace of the dissociation of SARS2-RBDWT (upper panel) or SARS-RBDWT (bottom panel) from ACE2 under force. Different phases are color-coded and indicated respectively. e Force-dependent bond lifetimes of SARS2-RBDWT/ACE2 or SARS-RBDWT/ACE2 binding. Error bars represent SEM.

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    The force varies upon the contact zone growing (Supplementary information, Video S1), and the spike/ACE2 bonds in the center zone and the contact edge respectively bear compressing and pulling force (Fig. 1b, inset; Supplementary information, Video S1). The pulling force together with the compressing force maintains the bending of the PM, and the pulling force relies on and regulates the survival of the spike/ACE2 bonds. From our biomechanical analysis, the pulling force at the edge increases from 0 to 30 pN when the contact zone grows, and it reaches ~27 pN when φC is at 90° (Fig. 1b). In short, spike/ACE2 bonds should be subjected to tensile force during the SARS2 invasion, and the pulling force at the contact zone edge roughly ranges from 0 to 30 pN according to our theoretical analysis.

    Mechanical force prolongs SARS2-RBD/ACE2 bond lifetime to impede their dissociation

    To test whether tensile force regulates spike/ACE2 binding and stiffness, we first carried out single-molecule biomechanical experiments with biomembrane force probe (BFP) to quantify the molecular stiffness of the spike/ACE2 complex (Supplementary information, Fig. S2) and the force-dependent RBD dissociation from ACE2 on live cells (Fig. 1c, d; Supplementary information, Fig. S3). We found that the molecular stiffness of the spike/ACE2 complex is about 1.8 pN/nm (Supplementary information, Fig. S2c). Increasing mechanical force at a low-force regime (< 10 pN) prolongs bond lifetimes of both SARS2-RBDWT and SARS-RBDWT binding with ACE2 (Fig. 1e; Supplementary information, Fig. S3c, d). Optimal force (~10 pN) results in maximum bond lifetimes (3.3 s and 0.7 s respectively for SARS2-RBDWT and SARS-RBDWT binding with ACE2), whereas further increasing force beyond 10 pN shortens their bond lifetimes. This optimal force falls in the theoretical range that each spike/ACE2 bond bears (Fig. 1b). This force-strengthened RBD/ACE2 binding suggests that mechanical cues can be exploited by both SARS2 and SARS to enhance its recognition of host ACE2 and attachment to host cells. Furthermore, the longer force-dependent bond lifetime of SARS2-RBDWT/ACE2 (Fig. 1e) is consistent with and might explain the higher infectivity of SARS2,37 despite similar contact areas in SARS2-RBD/ACE2 and SARS-RBD/ACE2 complex structures (Supplementary information, Fig. S1b, c) and comparable in-solution24,25,26 and in-situ binding affinities of SARS2-RBD or SARS-RBD to ACE2 (Supplementary information, Fig. S4). Together, these results suggest that the force-dependent dissociation rate of RBD/ACE2 binding is a key factor for regulating both SARS2 and SARS viral infection.

    Two force-induced intermediate binding states govern mechanical enhancement of SARS2-RBD/ACE2 interaction

    To dissect the dynamical and structural mechanisms of the mechano-enhanced RBD/ACE2 binding, we next performed steered molecular dynamics (SMD) simulations on SARS2-RBDWT/ACE2 and SARS-RBDWT/ACE2 complexes, and examined their force-induced conformational change and dissociation pathway at atomic resolution. For SARS2-RBDWT/ACE2 dissociation, we found that the tensile force drove the SARS2-RBDWT rotation on the binding interface and gradually increased the inter-domain angle (α) from ~125° at force-free initial state (I0) to ~140° (Intermediate state 1, I1) and then to ~170° (Intermediate state 2, I2) followed by RBD/ACE2 dissociation, and the inter-domain area (nm2) gradually decreased under the tensile force (Fig. 2a, c; Supplementary information, Fig. S5a, b, Video S2). In the I1 state, the binding conformation only changes very little compared with the force-free I0 state. In the I2 state, only RBD’s receptor-binding motif (RBM) interacts with ACE2 while the other regions dissociate (Supplementary information, Fig. S5c, e, f). Similar force-induced conformational changes on SARS2-RBDWT/ACE2 binding interface were observed along this dissociation pathway (denoted as P1) in all 9 independent simulations (Fig. 2e). Interestingly, other than the P1 pathway (Supplementary information, Video S3), another pathway (denoted as P2; Supplementary information, Video S4) was also identified (Fig. 2b, d; Supplementary information, Fig. S5a, b, d) in SARS-RBDWT/ACE2 dissociation. In the P2 pathway, the intermediate state I2 is absent, causing direct dissociation of SARS-2 RBDWT/ACE2 from the state I1. The P1 and P2 pathways occurred 4 and 5 times respectively in 9 independent simulations of SARS-RBDWT/ACE2 dissociation (Fig. 2e). Thus, the incidence of the P2 pathway in SARS-RBDWT/ACE2 forced dissociation is 55%, which is much higher than that in SARS2-RBDWT/ACE2 forced dissociation (0%, i.e., no occurrence). The ability of SARS-RBD to resist forced dissociation from ACE2 is weaker in the P2 than in the P1 pathway, as no force-induced drastic rotational conformational changes solely sustained by RBM/ACE2 binding occurred in the P2 pathway. Together, these findings provide biophysical evidence to support the force-strengthened bond lifetime of SARS2-RBDWT/ACE2 binding. We thus postulated that force-induced intermediate states governed dissociation pathway selection and force-enhanced SARS2-RBDWT/ACE2 binding.

    a, b Sequential SMD snapshots of force-dependent SARS2-RBDWT (a) and SARS-RBDWT (b) dissociation from ACE2. SARS2-RBDWT/ACE2 dissociation adopts a sole pathway P1 with two intermediate states (I1 and I2), but SARS-RBDWT/ACE2 dissociation adopts two different dissociation pathways (P1 and P2), including two (I1 and I2) or one (only I1) intermediate state, respectively. I0 refers to the no-force state. Inter-domain angle (α) between RBD and ACE2, anchoring and force pulling residues (gray balls) and force directions (gray arrows) are indicated. c, d Representative time-courses of the inter-domain angle (α) between SARS2-RBDWT (c) or SARS-RBDWT (d) and ACE2 in the presence (purple in c; green (for P1) and black (for P2) in d) or absence (gray) of force. Horizontal dashed lines indicate the inter-domain angles in states of I0, I1, and I2. e The occurrence probabilities of P1 and P2 pathways in force-dependent dissociation of SARS2-RBDWT or SARS-RBDWT from ACE2.

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    To further test this hypothesis, we then aimed to identify key residues for regulating the stability of two intermediate states and for selecting the dissociation pathways in ACE2 interacting with SARS2-RBDWT or SARS-RBDWT. We examined the residues located at RBD/ACE2 binding interface that were essential for forming either I1 or I2 states. For SARS2-RBDWT, force promotes hydrogen bond (H-bond) formation of its Q493 with ACE2-K31 when switching from the I0 to I1 state (Fig. 3a, b, d; Supplementary information, Fig. S6a); for SARS-RBDWT, corresponding residue N479 either forms H-bond or not with ACE2-K31 in the I0 state of P1 or P2 pathway respectively, and force does not change N479 binding state with ACE2-K31 when switching from the I0 to I1 state in both pathways (Fig. 3c, d; Supplementary information, Fig. S6b, c). For the I2 state, it is defined by the interaction between SARS2-RBDWT RBM and ACE2. The interaction, mainly formed by the hydrophobic packing of SARS2-RBDWT-F486 with the hydrophobic center formed by L79, M82, and Y83 of ACE2, solely maintains SARS2-RBDWT/ACE2 binding after the force-induced RBD rotation (Fig. 3e, g; Supplementary information, Fig. S6d). In contrast, for SARS-RBDWT, these interactions are unstable even in the absence of force (Supplementary information, Fig. S6e, f), as they frequently switch between bound (55% of times) and unbound (45% of times) states (Fig. 3f). The bound state favors the P1 pathway (75% of occurrence frequency), whereas the unbound state more likely (80% of occurrence frequency) leads to the P2 pathway for faster RBD/ACE2 dissociation (Fig. 3f). Simulations on F486L mutant (SARS2-RBDF486L) further reveal unstable SARS2-RBDF486L/ACE2 associations (Supplementary information, Fig. S7) in the absence of force, confirming the importance of F486 in maintaining RBD/ACE2 mechanical stability. Moreover, both SARS2-RBD Q493N and F486L mutations reduce the force-dependent bond lifetime of SARS2-RBD/ACE2 interaction, shortening the maximum bond lifetime almost by two to three folds (Fig. 3h, i; Supplementary information, Fig. S3e, f). Together, these results collectively suggest a model of mechano-enhanced viral infection in which tensile mechanical force can enhance spike binding with ACE2 to foster viral infection.

    a Structure of the RBD/ACE2 complex with orange and gray dashed boxes highlighting residues involved in the force-regulated RBD/ACE2 interaction identified in SMD simulations. bd Representative snapshots showing force-enhanced interaction network (zoomed-in view of the gray dashed box region in a) in the vicinity of indicated residues of SARS2-RBDWT (b) or SARS-RBDWT (c) in force-free state I0 or force-induced intermediate state I1. Their respective probabilities of H-bond formation are compared in d. eg Representative snapshots showing interaction network (zoomed-in view of the orange dashed box region in a) in the vicinity of indicated residues of SARS2-RBDWT (e) or SARS-RBDWT (f) and ACE2 hydrophobic center in force-free state I0 or force-induced intermediate state I2. The distances between F486 (SARS2) or L472 (SARS) and ACE2 hydrophobic center in different states are shown in g. h, i Lifetimes of force-dependent bonds between ACE2 and SARS2-RBD variants with mutations that abolish force-induced I1 (h) or I2 (i) intermediate state (solid plots) in comparison with that of SARS2-RBDWT (purple dashed plots). All error bars represent SEM. 0.01 < *P < 0.05 and 0.0001 < ***P < 0.001 by Student’s t-test.

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    Mechano-enhanced SARS2-S/ACE2 binding fosters viral infection

    To further test this model, we next focused on examining how a spike mutant with higher viral infectivity (SARS2 spike D614G variant, SARS2-SD614G) is impacted by mechano-regulation. SARS2 with spike D614G variation was more epidemic with enhanced replication and transmission than that without this variation.38,39,40,41 D614G mutation was reported to decrease S1/S2 cleavage42 and increase incorporation of the spike into the pseudo-virion.42,43 However, the authentic virion does not demonstrate these phenotypes,44,45 suggesting that the changed level of S1/S2 cleavage or spike incorporation into the virion is not a convincing explanation for the higher infectivity of the D614G mutant. Moreover, SARS2-SWT and SARS2-SD614G have comparable binding affinities to ACE2 as both slightly increased and decreased affinities of the mutant to ACE2 were reported.39,42,46,47,48 Considering that D614G variation makes RBD more flexible and more readily to adopt an up conformation,39 we hypothesized that D614G mutation might affect force-dependent regulation of the SARS2-S/ACE2 bond lifetime. Indeed, we found that SARS2-SD614G bound ACE2 much more strongly than SARS2-SWT under force with an almost four-time longer optimal lifetime (11.2 s for SARS2-SD614G vs 3.2 s for SARS2-SWT) under the 10 pN optimal force (Fig. 4a; Supplementary information, Fig. S3g–i). In contrast, SARS2-SWT, SARS2-S1WT, SARS2-S1D614G, and SARS2-RBDWT bind ACE2 with almost the same force-dependent bond lifetimes (Fig. 4a; Supplementary information, Fig. S8). These results demonstrate that D614G variation enhances force-dependent SARS2-S recognition of ACE2.

    a Force-dependent bond lifetimes of SARS2-SWT (black solid plots) or SARS2-SD614G (red solid plots) binding with ACE2, in comparison with SARS2-RBDWT (purple dashed plots). Error bars represent SEM. b, c Pseudovirus infection of SARS2 wild-type (WT) or mutants (Q493N, F486L and D614G). Representative flow cytometry analysis of GFP in ACE2-expressing 293T cells infected with SARS2 pseudovirus (b). Comparisons of the efficiencies of SARS2 pseudotyped viruses (WT, Q493N, and F486L) infecting ACE2-expressing cells (c). All error bars represent SEM. 0.001 < **P < 0.01 and ****P < 0.0001 by Student’s t-test. dg The affinity determination of SARS2-RBD mutants binding with ACE2 by adhesion frequency assay. Adhesion frequency curves of ACE2 binding with SARS2-RBD mutants (Q493N and F486L) (d) and SARS2-S (SARS2-SWT and SARS2-SD614G) are shown (f). Molecular surface densities (number/μm2) of ACE2 and SARS2-RBD or SARS2-S are indicated. The corresponding effective binding affinities (AcKa) (e and g) are plotted and compared, respectively. hk The affinity determination of SARS2-RBD mutants binding with ACE2 by biolayer interferometry. The representative set of curves of ACE2 binding with SARS2-RBD mutants (WT (h), Q493N (i), and F486L (j)) are shown. The corresponding equilibrium dissociation constant (KD) (k) is calculated by 1:1 binding model. lo The correlation analysis of the infection efficiencies of SARS2 pseudoviruses with SARS-S/ACE2 in-situ affinity (l), in-solution affinity (Ka = 1/KD) (m) and bond lifetime at 10 pN (n). Their respective correlations with the corresponding pseudovirus infectivity are compared (o). All error bars represent SEM.

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    Functionally, the D614G variant exhibits higher pseudovirus (the HIV-based lentivirus pseudotyped with SARS2-S) infectivity to ACE2-expressing cells than WT (Fig. 4b, c), consistent with previous reports.38,39,40 This increased infectivity of the D614G variant can be explained by a longer force-dependent bond lifetime of SARS2-SD614G than WT in binding with ACE2, despite their similar in-solution or in-situ binding affinities to ACE239 (Fig. 4f, g).

    Similarly, SARS2-RBD Q493N and F486L mutants, which show similar binding affinities but shorter force-dependent bond lifetimes than WT in the interaction with ACE2 (Figs. 3h, i; 4d, e, h–k; Supplementary information, Fig. S3e, f; Table S1), significantly attenuate the pseudovirus infection (Fig. 4b, c), further suggesting that force-dependent bond lifetime of SARS2-RBD/ACE2 is a better predictor for SARS2’s infectivity.

    Taken together, our data demonstrate that the mechano-regulated dissociation kinetics (e.g., optimal lifetime at 10 pN) of SARS2-S with ACE2 best correlates with viral infection efficiency (Fig. 4n, o), in contrast to the in-situ or in-solution binding affinity (Fig. 4l, m), suggesting the essential role of force-strengthened spike/ACE2 binding in regulating SARS2 viral infection.

    Mechanical force dramatically accelerates SARS2-S S1/S2 detachment

    As S1/S2 detachment is an essential step preceding SARS2-S S2 structural rearrangement and fusion machinery formation, we further explored whether mechanical force transmitted by RBD/ACE2 interaction could drive and accelerate the detachment process. Using SMD simulations, we pulled a spike trimer on its RBDs (Fig. 5a; Supplementary information, Video S5) and observed that mechanical force indeed decreased the contact area between S1 and S2 from ~450 nm2 to ~0 nm2, leading to rapid S1/S2 detachment (Supplementary information, Fig. S9a). Consequently, SARS2-SWT extended ~23 nm in the direction of force application (Supplementary information, Fig. S9b), which was further validated and confirmed with our single-molecule magnetic tweezers (MT) pulling experiments (Fig. 5b, c). Single SARS2-SWT presents a pronounced conformational extension (~26.6 nm) mostly under ~11.3 pN pulling force (Fig. 5f, g). Based on Bell model,49 the S1/S2 detachment rate (or unfolding rate, ku) of SARS2-SWT is 2.9 × 10–4 s−1 in the absence of force, suggesting that S1/S2 detachment is unlikely to occur spontaneously. Instead, we found that only 10 pN tensile force could drastically increase the detachment rate to 0.2 s−1 (Fig. 5h), almost 1000 times faster than that in no-force condition, further demonstrating the essential role of mechanical force on activating and accelerating S1/S2 rapid detachment.

    a Sequential SMD snapshots of SARS2-SWT S1/S2 detachment under pulling force. The anchoring and force pulling residues (gray ball), force direction (black dashed arrow), and timestamps for all snapshots are indicated. b Schematic diagram of the design of SARS2-SWT with a C-terminal biotin tag (up panel) for single-molecule MT pulling experiments (bottom panel). ch Representative force stretching curves from single-molecule MT pulling experiment to demonstrate force-induced S1/S2 detachment of SARS2-SWT in the absence (c) or presence of neutralizing (3H3) (d) or non-neutralizing S2-binding antibody (4A10) (e). Their respective histograms of S1/S2 detaching distances (f) and forces (g) are compared. The mean values (matched colors) obtained by Gaussian fitting are indicated respectively. Their force-dependent detachment rates derived from Bell model are compared in h.

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    An S1/S2-binding non-RBD-blocking antibody significantly impedes mechano-accelerated SARS2-S S1/S2 detachment

    Unexpectedly, we identified a non-RBD-blocking monoclonal antibody (mAb, clone 3H3, derived from convalescent COVID-19 patients), which was reported to bind both S1 and S2 subdomains of the spike protein and have high neutralization activity against SARS2 infection through ACE2 with no clear mechanisms.50 Single-molecule MT pulling experiments showed that this antibody significantly impedes S1/S2 detachment. Moreover, in the presence of 3H3 mAb, a larger force (~24.3 pN) on average was required to induce a shorter extension distance (~20.2 nm) needed for SARS2-SWT S1/S2 detachment (Fig. 5d, f), dramatically decreasing S1/S2 detachment rate by ~3 × 106 folds from 0.2 s−1 to 6.2 × 10–8 s−1 under ~10 pN force (Fig. 3h). This clearly suggests that 3H3 mAb locks S1/S2 subunits together to stabilize SARS2-S even under force loading, potentially preventing follow-up fusion machinery formation and viral invasion. In contrast, another non-RBD-blocking, S1/S2-binding, and non-neutralizing mAb (clone 4A10, also derived from convalescent COVID-19 patient),50 hardly affects S1/S2 detachment (Fig. 5e–h). Collectively, our SMD simulation analysis and single-molecule measurements demonstrate that mechanical force dramatically accelerates S1/S2 detachment, which can be prevented by a neutralizing mAb targeting S1/S2.

    D614G variation accelerates force-induced S1/S2 detachment

    For SARS2-SWT, residue D614 forms hydrogen bonds with residues on the S2 subunit of neighboring protomer to keep S1/S2 tight assembly. The D614G variation reduces the number of interdomain hydrogen bonds (Fig. 6a, b), specifically abolishing two hydrogen bonds between D614 and K854 or Q836 (Fig. 6b, c). This led us to hypothesize that D614G variation might weaken S1/S2 association. We next performed single-molecule pulling experiments with MT to characterize S1/S2 mechanical stability in the presence of the D614G variation. Interestingly, a shorter S1/S2 detaching distance on average (~19.1 nm) was observed for a single SARS2-SD614G (Fig. 6d, e), suggesting that the D614G variation partially impairs S1/S2 assembly. Compared with 11.3 pN force to detach S1/S2 in SARS2-SWT, a much smaller tensile force (~8.2 pN) is required to detach S1 and S2 in SARS2-SD614G (Fig. 6d, f), drastically increasing the detachment rate by 35 times from 0.2 s−1 (for SARS2-SWT) to 7.2 s−1 under ~10 pN force (Fig. 6g). These results suggest that S1/S2 subunits in SARS2-SD614G are less mechanically stable than those in SARS2-SWT. Integrating force-dependent spike/ACE2 disassociation and S1/S2 detachment kinetics, we built up a kinetic model and revealed that the D614G variant with stronger force-dependent ACE2 binding not only accelerated S1/S2 detachment but also had an 8-time higher probability than WT to make this detachment occur (0.92 for D614G vs 0.1 for WT at 8.4 pN) (Fig. 6h, i), providing an unprecedented quantitative kinetic evidence and molecular mechanism to explain higher infectivity of the D614G variant (Fig. 4b, c).

    ac H-bond network analysis of G614 interacting with residues in its vicinity (marked with a black dashed box in a and zoomed-in views in b) in the structure of SARS2-SD614G. The probabilities of H-bond formation in WT and D614G mutant are compared (c). All error bars represent SEM. d Representative force stretching curves from single-molecule MT demonstrating force-induced S1/S2 detachment of SARS2-SD614G. eg S1/S2 detaching distance (e), force (f), and detachment rate (g) of SARS2-SD614G (red solid plots) are respectively compared with those of SARS2-SWT (black dashed plots). h Schematic diagram of the force-dependent SARS2-S activation model. i Comparison of S1/S2 detaching probability of SARS2-SWT with that of SARS2-SD614G under force.

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    Utilizing single-molecule biophysical approaches, molecular dynamics (MD) simulation, and pseudovirus infection assay, we demonstrate mechanical activation of SARS2-S upon binding to ACE2 and the subsequent S1/S2 detachment for priming S2 fusion machinery. Our findings indicate that SARS2 exploits the mechanical cues to enhance their invasion into host cells by mechanically strengthening its spike binding with host ACE2 receptors and by accelerating S1/S2 detachment to destabilize the pre-fusion spike trimer. Our findings suggest that mechano-activation of SARS2-S is essential to trigger structural rearrangement of SARS2-S and to promote its transition Renee PassNow Pro 2021.10.03.141 Crack With Key [Activation Code] the post-fusion state to facilitate successful viral fusion. Impairment of inter-protomer interactions by the D614G variation not only strengthens force-dependent SARS2-S/ACE2 binding, but more importantly, induces S1/S2 detachment much faster than in the WT protein under force, providing a new molecular explanation for the high infectivity of the SARS2 D614G variant. A non-RBD-blocking but S1/S2-binding neutralizing mAb derived from convalescent COVID-19 patients dramatically impedes S1/S2 forced-detachment, revealing an unprecedented virus-neutralizing strategy for therapeutic antibody development.

    It is the first time to demonstrate that mechanical force prolongs bond lifetime of viral spike binding with host receptors. Our finding of force-strengthened spike/ACE2 binding provides molecular evidence to support the notion that the attachment of SARS2 virion on the host-cell PM survives longer than SARS in a biomechanical environment. This is reminiscent of the previous observations that mechanical force favors membrane fusion and endocytosis,22,51,52 which are two common routes utilized by the coronavirus to enter host cells.10,11,53,54

    As receptor binding and the follow-up S1/S2 detachment are both essential to trigger the S2 structural rearrangement and fusion machinery formation for the effective viral infection,10,11,12 SARS2-S-binding mAbs with neutralization potency are applied for therapeutic interventions of COVID-19. While most of the studies focused on searching for neutralizing mAbs that block SARS2-S-RBD/ACE2 binding,55,56,57 our findings identify an alternative neutralizing strategy that exploits non-RBD-blocking but S1/S2-locking antibodies to stabilize SARS2-S structure by preventing S1/S2 detachment and follow-up S2 fusion machinery formation. Such strategy potentially can compensate or complement receptor-blocking strategy no matter what other novel spike receptor is found.6,58 This finding also suggests that mAbs targeting S1/S2 epitopes and restraining S1/S2 detachment may provide high neutralization potency against SARS2 infection by inhibiting pre-fusion-to-post-fusion transition of SARS2-S.

    Interestingly, SARS2-SD614G exhibits much stronger force-dependent binding than SARS2-SWT with ACE2. As the D614G variation disrupts stable contact between inter-protomers to allosterically favor more SARS2-RBD up conformation,39 this enhancement of force-dependent recognition might be due to the synergistic effect of two or three up RBDs in a single SARS2-S trimer. Although the detailed molecular mechanism for such a synergetic effect is still unclear, there are several possible explanations. One plausible explanation is that the S1 subunits of all three protomers in the D614G variant are more flexible such that it may release the spatial restriction to allow more than one RBD binding with ACE2 dimer simultaneously. Another alternative explanation is that ACE2 sequentially binds with each up RBD of SARS2-SD614G via sliding-rebinding mechanism.59 Also, we can hardly rule out the possibility that D614G variation may cause a larger extent of force-induced rotational conformational changes of RBD, thereby resulting in a longer force-dependent bond lifetime of SARS2-S/ACE2 binding.

    To conclude, we demonstrate that mechanical force counter-intuitively impedes SARS2-S/ACE2 dissociation and induces subsequent S1/S2 rapid detachment for effective viral infection, and that D614G variation further enhances this mechano-regulation to increase SARS2 infectivity. Our results also reveal an unexpected virus-neutralizing mechanism of a non-RBD-blocking antibody from COVID-19 patients via preventing force-regulated S1/S2 detachment (Fig. 7). Thus, our findings not only answer key questions on whether and how mechanical cues impact SARS2 viral entry and infection, but also provide valuable insights into the force-dependent dynamic spike/ACE2 interaction and the follow-up S1/S2 detachment. All of these would shed lights on the development of better therapeutics targeting the mechano-sensitive motifs for COVID-19 treatment.

    Mechanical force strengthens SARS2 spike binding with host ACE2 receptors and accelerates its S1/S2 detachment to facilitate viral invasion. Impairment of inter-protomer interactions by the D614G variation not only strengthens force-dependent SARS2-S/ACE2 binding, but also accelerates force-induced S1/S2 detachment. S1/S2-locking antibodies stabilize SARS2-S structure and dramatically impede S1/S2 force-induced detachment, neutralizing SARS2.

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    We would like to point out that our present study contains a few potential limitations. First, all single-molecule assays performed here were with a widely used soluble recombinant SARS2-S that differs from the native protein in three aspects: ‘GSAS’ and ‘PP’ substitutions at residues 682–685 and 986–987, and synthetic trimerization helix substitution at the transmembrane domain, were introduced to stabilize the trimer. Thus, the structures of the native spike might differ from what we observe in the context of the ectodomain. Second, we propose that the D614G variation of SARS2-S could allosterically strengthen its binding with ACE2 under force and simultaneously enhance the force-accelerated S1/S2 detachment, which might favor effective viral infection. However, our experiments do not rule out other possibilities as potential mechanisms.42,43,44,45 Third, confirmative experimental evidence that mechanical force directly affects SARS2 infection in a setting of authentic SARS2 and live cells is still absent, although we demonstrate that SARS2 exploits mechanical force to impede spike/ACE2 dissociation and accelerate subsequent S1/S2 detachment for effective pseudovirus infection. Fourth, all the SARS2-S mutant infection experiments were performed using pseudovirus infection models in established cell lines, and therefore the results obtained need to be confirmed by authentic SARS2 virus infection experiments in the future. Finally, we propose an alternative neutralizing strategy, the S1/S2-locking neutralization antibody, which also needs to be further investigated.

    Materials and methods

    Plasmid construction

    The plasmids for recombinant protein purification were constructed by inserting the cDNA sequences of SARS2-RBD (residues: 333–527), SARS-RBD (residues: 320–513), SARS2-S1 (residues: 1–685) and SARS2-S (residues:1–1208) into the pHAGE vector using the ClonExpress Ultra One Step Cloning Kit (Cat. C115, Vazyme, China). SARS2-RBD and SARS-RBD recombinant protein plasmids each contains an N-terminal Igκ leader signal peptide plus Flag tag and a C-terminal AviTag plus 6× His tag. SARS2-S recombinant protein plasmid contains ‘GSAS’ and ‘PP’ substitutions at residues 682–685 and 986–987, a C-terminal T4 fibritin trimerization motif and AviTag plus 6× His tag. F486L, Q493N and D614G mutations were introduced by PCR-mediated mutagenesis by Phanta Master Mix (Cat. #P511, Vazyme, China). The plasmid for cell line construction was constructed by cloning full-length ACE2 cDNA sequence (inserting HA tag after signal peptide) into the pHAGE vector.

    Protein expression and purification

    Expi293F cells (Cat. #A14527, Thermo Fisher) were used for recombinant protein expression. pHAGE plasmids containing recombinant protein coding sequences were transiently transfected into the cultured cells Smart Defrag Crack + Serial Key Full Free Download 2021 polyethylenimines (PEI) (Cat. #23966, Polysciences). After 5 days of expression, the supernatants were collected, centrifuged, and concentrated through VivaFlow 200 flipflow filtration MWCO 30 kDa (Sartorius). Soluble recombinant proteins in the concentrated mixture were purified through HisTrap HP (GE Healthcare) and HiTrap Q HP column (GE Healthcare) affinity chromatography column. Then AviTag peptide of the recombinant protein was covalently labeled with biotin through BirA enzymatic biotinylation reaction. Finally, the recombinant protein was further purified via Superdex 75 10/300 GL or Superdex 200 Increase (GE Healthcare) gel filtration chromatography with phosphate buffer saline (pH = 7.4) composed of 2 mM KH2PO4, 8 mM Na2HPO4, 136 mM NaCl, 2.6 mM KCl.

    Cell line construction

    ACE2 was expressed in U937 cells by lentivirus infection. The lentivirus was produced through co-transfection of pHAGE plasmid, psPAX2 and pMD2.G into HEK 293T cells. The U937 cells with similar expression levels of ACE2 were selectively sorted and collected through flow cytometry sorting (Beckman).

    Recombinant protein-coated microspheres/RBC preparation

    The recombinant protein-coated microspheres and red blood cells (RBCs) were prepared according to previously published methods.60,61,62,63,64 Briefly, for force-clamp assay, borosilicate glass microspheres (Cat. #9002, Thermo Fisher) were first chemically modified with -SH group through 3-mercapto-propyl-trimethoxysilane (Cat. #175617, Sigma-Aldrich), then incubated with streptavidin-maleimide (Cat. #S9415, Sigma-Aldrich) overnight in 200 mM phosphate buffer (pH = 6.7) at room temperature (RT). For the adhesion frequency assay, human RBCs were directly reacted with biotin-PEG3500-NHS (Cat. #62717, JenKem, China) at RT for 30 min in 10 mM HEPES buffer (containing with 145 mM NaCl, roughly 300 mOsm osmotic pressure, pH = 7.4) and then coated by streptavidin in HEPES buffer containing 1% BSA at RT for 30 min. Finally, suitable biotin-labeled recombinant protein was coated on microspheres or RBCs through biotin-streptavidin reaction in HEPES buffer containing 1% BSA at RT for 30 min.

    Force-clamp assay

    The detailed experimental procedure was previously described.61,62,64 In brief, human RBCs attached with recombinant protein-coated microsphere, as a pN-level force sensor, were held by a micropipette on the left. An ACE2-expressing U937 cell was aspirated by the other micropipette, whose movement was controlled through a linear piezoelectric actuator (Physic Instrument) with sub-nanometer precision. For the bond lifetime measurement, an ACE2-expressing U937 cell was driven to approach to and contact recombinant protein-coated microsphere. After 0.1 s of contact, the cell was retracted at 1000 pN/s and clamped at a preset force until the bond broke. The bond lifetime was defined as the duration of the clamped phase. To ensure that ~90% bond lifetimes were generated from a single bond, the adhesion frequency was kept < 20% by adjusting recombinant protein densities on the microspheres. All above experiments were conducted in a chamber filled with ~500 μL DMEM medium containing 0.5% BSA.

    Molecular stiffness determination

    The molecular stiffness was measured by the single-molecule force spectroscopy on BFP, which has been described in details previously.65,66 In brief, the stiffness of the spike/ACE2 bond (km) could be calculated from the slope of the force versus displacement curve, which was obtained during the retraction step of each BFP event (Supplementary information, Figs. S2a, S3a). When F < 0 pN, the slope of the curve represents the stiffness of the cell (kc, assuming that spike/ACE2 is incompressible). When F > 0 pN, the slope of the curve represents the stiffness (ks) of the serially connected system, containing a single spike/ACE2 bond and the cell. According to Hooke’s law, we can calculate km = 1/(1/ks − 1/kc) for each force vs displacement curve.

    Adhesion frequency assay

    The force-free in-situ binding kinetics between recombinant proteins and ACE2 was measured by adhesion frequency assay, as previously described.60,63 An ACE2-expressing U937 cell was driven through a linear piezoelectric actuator to contact recombinant protein-coated RBC for a preset contact duration (tc = 0.1, 0.5, 1, 5, 10, 50 and 100 s), and retracted to judge adhesion event occurrence by RBC membrane deformation. After 50 contact–retract cycles, adhesion frequency (Pa) was calculated. Then the obtained Pa and tc curve was fitted with the following kinetic equation:

    $$P_{{{{{{{\mathrm{a}}}}}}}} = 1 - exp\left\{ { - m_{{{{{{{{\mathrm{ACE}}}}}}}}2}m_{{{{{{{{\mathrm{spike}}}}}}}}}A_{{{{{{{\mathrm{c}}}}}}}}K_{{{{{{{\mathrm{a}}}}}}}}\left( {1 - exp\left( {k_{{{{{{{{\mathrm{off}}}}}}}}}t_{{{{{{{\mathrm{c}}}}}}}}} \right)} \right)} \right\}$$

    mACE2 and mspike represent the molecular density of ACE2 and SARS2-RBD, SARS-RBD or SARS2-S recombinant protein respectively, which were calculated through flow cytometry and Quantum™ MESF beads (Bangs Laboratories, Inc.). AcKa and koff were denoted as effective in-situ affinity and in-situ off-rate in a force-free condition, respectively. The effective in-situ on-rate Ackon was calculated using the following kinetic equation:

    $$A_{{{{{{{\mathrm{c}}}}}}}}k_{{{{{{{{\mathrm{on}}}}}}}}} = A_{{{{{{{\mathrm{c}}}}}}}}K_{{{{{{{\mathrm{a}}}}}}}} \times k_{{{{{{{{\mathrm{off}}}}}}}}}$$

    Biolayer interferometry (BLI) binding assay

    BLI binding assay was performed through Octet RE96E instrument (ForteBio), which was supported by Sky-bio Co., Ltd. in Hangzhou. In brief, 25 μg/mL Fc-tagged ACE2 recombinant protein (Cat. #ACE-HM501, Kactus Biosystems Co., Ltd., China) was loaded onto Protein A (ProA) Biosensors (Cat. #18–5010, ForteBio) for 1500 s. Free ACE2 was washed out by a 180-s wash with kinetics buffer (PBS, 0.05% Tween-20, 0.1% BSA, pH = 7.4). Then the SARS2-RBD recombinant protein with different concentrations was loaded to associate with immobilized ACE2 for another 180 s. Finally, kinetics buffer was used to dissociate SARS2-RBD from ProA Biosensors for 300 s. The corresponding binding affinity (KD) was calculated by a 1:1 binding model.

    Magnetic tweezers setup and chamber preparation

    The details about the home-made MT setup, force calibration and experimental design were recently published.21 Briefly, a piece of coverslip (Cat. #12–545-B, Thermo Fisher) was sequentially cleaned with sonication in Decon90 detergent, acetone, isopropanol and deionized water. The coverslip was then thoroughly dried in a 120 °C oven and further cleaned in O2 plasma cleaner for 5 min. Next, the coverslip surface was modified with NH2 group by 1% (3-aminopropyl) triethoxysilane (APTES, Cat. #A107147, Aladdin, China) in methanol for 1 h. The coverslip was then sequentially washed twice with methanol and deionized water before thoroughly dried in a 120 °C oven. The coverslip was packed with another clean coverslip without NH2-modification in a hamburger pattern with two strips of parafilm to form the experimental chamber.

    Single-molecule pulling of SARS2-S with MT

    The single-molecule pulling experiment of SARS2-S was performed to probe S1/S2 detachment through MT. First, the chamber was functionalized with CHO group by 0.5% glutaraldehyde solution for 1 h and then washed twice by PBS buffer. The functionalized chamber was then incubated with 50 μg/mL SARS2-RBD mAb (Cat. #AHA001, Sanyou Biopharmaceuticals, China) for 15 min, and 50 μL polystyrene bead (Cat. #17145-5, polysciences) solution (5 × 107/mL) was added into the chamber for incubation overnight. The potential non-specific interaction in the chamber was then blocked by 2% BSA for 4 h. Approximately 1 pg/mL SARS2-S in 1% BSA solution was vortically incubated with streptavidin-coated magnetic beads (Cat. #65305, Thermo Fisher) for 30 min. Finally, SARS2-S-coated magnetic beads were injected into the chamber and kept for 20 min to allow the beads to be K-Lite Mega Codec Pack Download - Crack Key For U by the SARS2-RBD mAb on coverslip surface before single-molecule MT pulling experiments.

    For single-molecule pulling experiments, the successfully linked tether (SARS2-SWT or SARS2-SD614G) was pulled from 0 to 30 pN with a constant force loading rate of 1 pN/s, then released to 10 pN with –5 pN/s and finally to 0 pN with –0.5 pN/s. These sequential steps together are defined as a force cycle. Between two adjacent force cycles, there was a 60-s waiting time for SARS2-S to thoroughly refold. The S1/S2 detachment forces and distances were collected from hundreds of pulling events of dozens of independent tethers.

    For single-molecule pulling of SARS2-SWT in the presence of neutralizing mAb, after a successfully linked tether was determined by testing the S1/S2 detachment force and distance, 270 nM neutralizing mAb (clone: 3H3 or 4A10) was gently and very slowly injected into the experimental chamber for 15-min incubation and the same tether continued to be measured for the S1/S2 detachment force and distance in the presence of neutralizing mAb. All the above single-molecule pulling experiments were performed in PBS with 1% BSA.

    Force-dependent S1/S2 detachment rate calculation

    The force-dependent S1/S2 detachment rate of SARS2-S could be described and calculated based on Bell’s model.49 Briefly, the S1/S2 detachment rate of SARS2-S was calculated by fitting detachment force histogram to the following equation:67

    $$P^{(F)} = \frac{{k_0}}{r}exp\left\{ {\frac{{{\Delta} xF}}{{k_{{{{{{{\mathrm{B}}}}}}}}T}} + \frac{{k_{{{{{{{\mathrm{B}}}}}}}}Tk_0}}{{{\Delta} xr}}\left[ {1 - exp\left( {\frac{{{\Delta} xF}}{{k_{{{{{{{\mathrm{B}}}}}}}}T}}} \right)} \right]} \right\}$$

    Where P(F) is the probability of detachment force from histogram, k0 is the detachment rate at zero force, r is the force loading rate, Δx is the transition distance of spike between original and transition states, F is the detachment force, kB is Boltzmann’s constant, T is the absolute temperature, kBT is approximately 4.1 pN·nm. With all above known data, Δx and k0 were calculated by fitting with the above equation.

    Once Δx and k0 were obtained, the S1/S2 detachment rate of SRAS2-S at any force can be predicted by the following equation:49,67

    $$k_u = k_0exp\left( {\frac{{{\Delta} xF}}{{k_{{{{{{{\mathrm{B}}}}}}}}T}}} \right)$$

    Where ku is the force-dependent detachment rate.

    Pseudovirus preparation and infection

    SARS2-GFP pseudo-viruses were generated by Auslogics BoostSpeed Activation Key envelop plasmid (pCAG-SARS2-S∆C19), package plasmid (PLP1 and PLP2) and transfer plasmid (pCDH-CMV-CopGFP) into HEK 293 T cells using PEI, and were harvested at 50 h post-transfection. For pseudovirus infection, 2 × 105 cells were seeded into a 24-well plate. After 12 h culture, the crude virus was used to infect the ACE2-expressing HEK 293 T cells. The culture medium was changed 12 h later and cells were incubated for an additional 36 h before analysis by FACS to check GFP expression level.

    MD simulations and SMD simulations on RBD/ACE2 complex

    The crystal structures of ACE2-PD (ACE2) in complex with SARS2-RBDWT (PDB codes: 6LZG,25 6M0J32) or SARS-RBDWT (PDB codes: 2AJF,68 3SCI69) were used as the starting models in MD simulations. The complex models of ACE2 and SARS2-RBDF486L were generated based on SARS2-RBDWT/ACE2 structures with the MUTATE plugin in VMD. These initial models were rotated to make their long axis (the line linking C-terminal of ACE2 and C-terminal of RBD) parallel to the x-axis, and then processed with VMD PSFGEN plugin to add hydrogen atoms and other missing atoms. The resulted systems were solvated in rectangular water boxes with TIP3P water model. Na+ and Cl ions were then added to these solvated systems to neutralize the systems and maintain salt concentration at ~150 mM.

    All systems were first equilibrated with four steps: (1) 10,000 steps energy minimization with the heavy atoms of proteins fixed, followed by 2-ns equilibration simulations under 1-fs timestep with these atoms constrained by 5.0 kcal/mol/Å2 spring; (2) 10,000 steps energy minimization with the heavy atoms of proteins fixed, followed by 2-ns equilibration simulations under 1-fs timestep with these atoms constrained by 1.0 kcal/mol/Å2 spring; (3) 2-ns equilibration simulation under 1-fs timestep with the heavy atoms of proteins constrained by 0.2 kcal/mol/Å2 spring; (4) 10-ns equilibration simulation under 1-fs timestep without constrains. Subsequently, ~400-ns production simulations were carried out with 2-fs time steps under rigid bond algorithms, and the snapshots were saved every 40 ps for further analysis. During the simulations, the temperature of each system was maintained at 310 K with Langevin dynamics and the pressure was controlled at 1 atm with the Nosé-Hoover Langevin piston method.70 Particle Ewald Mesh summation was used for electrostatic calculation Driver Talent Pro Crack v8.0.2.10 + Activation Key Download (Latest) a 12 Å cutoff with 10 to 12 Å smooth switching was used for short-range non-bounded interactions.

    Representative snapshots of the production runs of each system were chosen, and extra water molecules were appended to extend the box dimension along with x-direction to enable complex extension in force-loaded SMD simulations. Before applying forces, these models were first equilibrated with the similar strategy as described above. The final configurations were used Infix Pdf Editor Pro Activation Key the force-loaded SMD simulations. In each SMD simulation, the C-terminal Cα atom of ACE2 was constrained at its initial position with a dummy spring (spring constant is 2.0 kcal/mol/Å2, ~1400 pN/nm) and the C-terminal Cα atom of RBD was pulled with another dummy spring (spring constant is 0.1 kcal/mol/Å2, ~70 pN/nm) which moves at ~0.1 nm/ns velocity. The SMD simulations were performed with 1-fs timestep without Langevin temperature and pressure coupling and lasted till the ACE2 and RBD molecules were completely separated, and the snapshots were saved every 20 ps. For SARS2-RBDWT and SARS-RBDWT systems, 18 SMD trajectories were generated in total for the statistical analyses, 9 simulations for each system.

    The inter-domain angle (α) was used to describe the relative orientation of RBD and ACE2, which was defined as the angle among three centers of mass of heavy atoms of protein: RBD, ACE2/RBD interface (E23–Q42 in ACE2, L492–Q498 in SARS2-RBDWT and W478–I484 in SARS-RBDWT) and ACE2. The contact areas between RBD and ACE2, between RBM (residues Q474–C488 for SARS2-RBDWT and F460–C474 for SARS-RBDWT) of RBD and ACE2, and between RBD except RBM and ACE2 were calculated. The H-bond networks between ACE2 and RBD were analyzed, the distance threshold of H-bond was set to 3.5 Å between the donor and acceptor atoms, and the angle cutoff was set to 50°. All simulations were performed with NAMD271 software using CHARMM36m force field with the CMAP correction.72 The system preparations and trajectory analyses were conducted with VMD.73 Illustrations of the representative frames shown in the Figures and the Supplementary Figures were rendered by UCSF Chimera.74

    MD simulations on SARS2-S S1/S2 detachment

    The crystal structures of SARS2-SWT (PDB codes: 6XR827 and 6VYB26) were used as the starting models in MD simulations on force-driven S1/S2 detachment. SARS2-SWT model with full-open conformations (three up RBDs) was generated by reassigned down RBDs to up conformation. The missing regions in structures were modeled by using the homology of SARS-S structures (PDB codes: 6ACC15 and 5XLR75) or modeled by ModLoop webserver.76 The SARS2-SD614G models were generated with the MUTATE plugin in VMD, which was also based on SARS2-SWT (PDB codes: 6XR827 and 6VYB.26) After processed with VMD PSFGEN plugin to add hydrogen atoms and other missing atoms, the resulted systems were solvated in the rectangular water boxes with TIP3P water model. Na+ and Cl ions were then added to these solvated systems to neutralize the systems (~150 mM).

    All systems were first equilibrated with the similar strategy as described above. Besides, two extra steps were appended before atom constraints were removed, in which 2-ns equilibration simulations were performed under 1-fs timestep with the heavy atoms of protein except for the sidechain atoms of added peptide regions constrained by 0.2 kcal/mol/Å2 spring, and followed by 2-ns equilibration with heavy atoms of protein except all atoms of added missing regions constrained by 0.2 kcal/mol/Å2 spring. Subsequently, more than 100-ns production simulations were carried out ProPresenter 7.2.0 (117571592) Crack + License Key Free Download 2020 2-fs time steps under rigid bond algorithms, and the snapshots were saved every 40 ps for further analyses. Representative snapshots of the production runs of each system were chosen and treated with the similar strategy as described above for SMD simulations of force-driven S1/S2 detachment. In each SMD simulation, Cα atoms of the M900 and A1078 were constrained at their initial positions with a dummy spring (spring constant 2.0 kcal/mol/Å2, ~1400 pN/nm) and the V512 Cα atoms of RBD were pulled with another dummy spring (spring constant ~70 pN/nm) which moved at ~0.5 nm/ns velocity.

    The contact area between S1 (N-terminal–S680) and S2 (S686–C-terminal) was calculated to demarcate their interaction in the presence and absence of force. The extension of the spike was defined as the distance between constrained atoms and pulling atoms, and the S1/S2 detaching distance, which was set to zero in crystal structure, was used to represent the length changes of spike during the simulations. The number of H-bonds was averaged on three monomers in the spike trimer. During the simulations and trajectories analysis, the key simulation parameters, force field and software were used the same as that in RBD/ACE2 simulations.

    Theoretical estimation of the applying force on the spike/ACE2 bond

    Once a virion attaches to the host-cell PM through spike/ACE2 interaction, the interaction potential energy of the virion/host-cell system is reduced. Along with the gradual growth of virion/host-cell contact zone, more spike/ACE2 bonds form, accompanied by a reduction in the interaction potential energy. The reduced interaction potential energy transfers to the bending energy in the bent host-cell PM and the elastic energy in the deformed spike/ACE2 complexes,23,33,34 elevating the bending energy of host-cell PM and the elastic energy of deformed spike/ACE2 bonds.

    To estimate the forces exerted on the spike/ACE2 bonds, we first considered the force equilibrium of the virion and then the force equilibrium of the virion and host-cell PM system. As the virion bears forces from the spike/ACE2 bonds, the force equilibrium of the virion requires the resultant force from the spike/ACE2 bonds to be zero. For the force equilibrium of virion and host-cell PM system, the total energy includes the energy stored in the bent host-cell PM and that stored in the deformed spike/ACE2 bonds. After the spike/ACE2 bond forms, the host-cell PM bending and the spike/ACE2 bond deformation must satisfy the compatibility condition. According to the theorem of minimum potential energy, the host-cell PM bending and spike/ACE2 bond deformation should reach the lowest total elastic potential energy.

    Different types of virus have stiffness ranging from 0.04 to 1 GPa,35 while different cells have PM stiffness ranging from 0.1 to a few tens of kPa.36 As a result of the significant difference in these stiffnesses, the virus shape change is negligible, and the host-cell PM adopts the shape of the virion during the virus entry. Since the virion contacts the host-cell PM in a rotationally symmetric manner, we used a spherical coordinate to describe the space with the origin at the center of the virion (O in Fig. 1a). Any location on the virion shell can be specified by the polar angle (φ) which is defined as an angle between the z-axis (the nadir direction) and the vector from the origin (O) to this location. The angle between the z-axis and the vector from the O’ to the same location is denoted by φ\(\prime\). The contact zone can be quantified by the polar angle (φC) which is defined as the angle between the z-axis and the vector from the origin (O) to the very end of the contact edge (Fig. 1a). For simplicity, we assumed the bent host-cell PM roughly lies on a sphere with the center at O’ and the radius of R. The spike/ACE2 bond supports the virion attached to the host-cell PM, leading to a gap between the virion envelope and the host-cell PM. At a certain location φ in the contact zone, the gap is equal to the spike/ACE2 bond length (lφ). The gap at the apex of the virion is denoted by lapex, which is the same as the length of spike/ACE2 bond at the apex. The radius R and gap lapex could change when the contact zone grows. With R and lapex, the length of spike/ACE2 bond at φ can be described as \(l_\varphi = R - r\frac{{\sin \varphi }}{{\sin \varphi^{\prime} }}\) (Fig. 1a), where r (45 nm)30 is the radius of the virion and:

    $$\left\{{\begin{array}{c}{\sin \varphi^{\prime} = \frac{{r \cdot \sin \varphi }}{{\sqrt {\left( {r \cdot \sin \varphi } \right)^2 + \left( {R - r - l_{{{{{{{{\mathrm{apex}}}}}}}}} + r \cdot \cos \varphi } \right)^2} }}} \\ {\cos \varphi^{\prime} = \frac{{R - r - l_{{{{{{{{\mathrm{apex}}}}}}}}} + r \cdot \cos \varphi }}{{\sqrt {\left( {r \cdot \sin \varphi } \right)^2 + \left( {R - r - l_{{{{{{{{\mathrm{apex}}}}}}}}} + r \cdot \cos \varphi } \right)^2} }}} \end{array}} \right.$$

    As the virion contacts the host-cell PM in a rotationally symmetric manner, the force on virion is naturally balanced in the xy-plane. Thus, we just needed to consider the equilibrium in the z-direction. The virion only bears forces through the binding of spike with ACE2. The force magnitude is determined by the deformation of the bond, \(\begin{array}{l}f_\varphi = k_{{{{{{{{\mathrm{mol}}}}}}}}} \cdot {\Delta} l = k_{{{{{{{{\mathrm{mol}}}}}}}}} \cdot \left( {l_\varphi - l_0} \right)\\ \end{array}\), where kmol is the stiffness of the spike/ACE2 bond (~2 pN/nm, see the Molecular stiffness determination section in Materials and Methods; Supplementary information, Fig. S2) and l0 (23 nm)77 is the spike/ACE2 bond length at relaxation, and the direction is along with the bond (Fig. 1a). The component in z-direction is the projection of the force \(f_{\varphi {{{{{{{\mathrm{Z}}}}}}}}} = f_\varphi \cdot \cos \varphi^{\prime}\). It was previously reported that the average distance between spike molecules is ~15 nm.30 One can estimate the density of spike (n) on the virion envelope is around ~0.0014 nm−2. Thus, the resultant force in z-direction can be obtained by integrating all the z-direction projection of forces on spike/ACE2 in the contact zone, \(F_Z = \int_{A} n \cdot f_{\varphi {{{{{{{\mathrm{Z}}}}}}}}}{{{{{{{\mathrm{d}}}}}}}}A\), which should be 0 for the equilibrium of the virion. Apparently, for any given φC, the force in z-direction FZ is a function of R and lapex. Therefore, R and lapex are not independent. The selection of R and lapex should lead to FZ=0 for the virion equilibrium.

    The elastic potential energy of the virion and host-cell PM system consists of the PM bending energy and the spike/ACE2 bond elastic energy. During the virus entry, the host-cell PM can be divided into three parts: (1) the PM in contact with virus bending to a spherical surface; (2) the PM far away from the contact zone keeping in flat without bending; and (3) the PM in the transition zone joining the flat and spherical contact zone that bends to a surface with negative Gaussian curvature. The bending energy is zero in the flat membrane. To minimize the bending energy, the Hot Virtual Keyboard Activation key PM with negative Gaussian curvature must Spotify Crack Activation Key - Activators Patch a minimal surface for two reasons: the surface area is minimal, and the tension is minimal; the mean curvature is zero, and the bending energy is zero. This minimal curvature membrane has also been found in virus–cell fusion.78 Therefore, we assume the host-cell PM in the transition zone approximately adopts a minimal surface, and the bending energy is dominantly stored in the spherical contact zone. It can be written as \(E_{{{{{{{{\mathrm{mem}}}}}}}}} = \frac{1}{2} \cdot B\kappa ^2A = 4\pi B\left( {1 - \cos \varphi^{\prime} } \right)\), where κ is the mean curvature of the bent host-cell PM, 2/R, A is the contact zone area, and B (= 1.8 × 10–19 J)79 is bending modulus of the cell membrane. The elastic energy stored in each bond can be calculated by \(E_\varphi = 1/2 \cdot k_{{{{{{{{\mathrm{mol}}}}}}}}} \cdot {\Delta} l^2\). The total elastic energy of all bonds is the integration of each individual one, \(E_{{{{{{{{\mathrm{bond}}}}}}}}} = \int_{A} E_\varphi {{{{{{{\mathrm{d}}}}}}}}A = \int_{0}^{\varphi _{{{{{{{\mathrm{C}}}}}}}}} n \cdot \frac{1}{2}k_{{{{{{{{\mathrm{mol}}}}}}}}} \cdot \left( {l_\varphi - l_0} \right)^2 \cdot 2\pi r^2\sin \varphi d\varphi\). The total elastic potential energy is the sum of the aforementioned two parts: \(E_{{{{{{{{\mathrm{tot}}}}}}}}} = E_{{{{{{{{\mathrm{mem}}}}}}}}} + E_{{{{{{{{\mathrm{bond}}}}}}}}}\). According to the theorem of minimum potential energy, the host-cell PM bending and spike/ACE2 bond deformation should reach the lowest total elastic potential energy. All this leads to a mathematic problem, that is, finding a displacement field of the host-cell PM for the minimum value of Etot under the constrain of FZ = 0. By using the sequential least squares programming algorithm, we could solve this problem at any given contact zone size φC (Fig. 1a, b).

    Force-dependent disassociation and S1/S2 detachment model of SARS2-S

    The SARS2-S/ACE2 dissociation rate (koff) from ACE2 is the reciprocal of the average lifetime. By fitting the lifetime data with a logarithm and an exponential function for the catch (ascending) and slip (descending) phase respectively, an approximation relation between the force and dissociation rates was obtained. The SARS2-S can either unfold or dissociate from ACE2 first; if ku > koff, S1/S2 is more likely to be detached before dissociation from ACE2. The probability of S1/S2 detachment first can be calculated by ku/(ku + koff).

    Change history


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      00:1a:79:50:9e:6a;November 26, 2022, 12:41  

      00:1a:79:90:42:53;October 13, 2022, 2:05  

      00:1a:79:17:9c:1b;March 10, 2022, 10:11  

      00:1a:79:5d:ac:86;November 3, 2021, 4:35  

      00:1a:79:50:c2:58;November 18, 2021, 7:20


       MAC: 00:1a:79:70:4c:10 

       Vence:November 17, 2021, 6:45 pm 

       MAC: 00:1a:79:29:7f:af 

       Vence:November 1, 2021, 8:33 am 

       MAC: 00:1a:79:5a:9d:9e 

       Vence:November 21, 2021, 10:25 am 

       MAC: 00:1a:79:31:0f:ee 

       Vence:November 26, 2021, 8:13 pm 

       MAC: 00:1a:79:58:a5:29 

       Vence:November 12, 2021, 8:42 pm

      MAC: 00:1a:79:1f:1f:fa 

       Vence:November 28, 2021, 8:40 pm 

       MAC: 00:1a:79:10:4f:25 

       Vence:November 24, 2021, 3:47 pm 

       MAC: 00:1a:79:56:d7:c1 

       Vence:November 23, 2021, 10:08 am 

       MAC: 00:1a:79:26:6f:f5 

       Vence:November 25, 2021, 6:42 pm 

       MAC: 00:1a:79:76:fb:1f 

       Vence:November 11, 2021, 1:35 pm 

       MAC: 00:1a:79:7c:81:a6 

       Vence:November 29, 2021, 9:26 am 

       MAC: 00:1a:79:3c:0f:e3 

       Vence:November 18, 2021, 7:40 pm 

       MAC: 00:1a:79:73:1c:72 

       Vence:November 25, 2021, 7:53 pm 

       MAC: 00:1a:79:2f:c7:bc 

       Vence:October 29, 2021, 8:13 pm 

       MAC: 00:1a:79:49:4b:7b 

       Vence:December 22, 2021, 2:15 pm 

       MAC: 00:1a:79:5b:bd:c3 

       Vence:December 2, 2021, 3:52 pm 

       MAC: 00:1a:79:5e:5c:bb 

       Vence:November 30, 2021, 9:26 pm 

       MAC: 00:1a:79:96:2a:39 

       Vence:November 15, 2021, 6:01 pm 

       MAC: 00:1a:79:29:7f:ba 

       Vence:November 19, 2021, 2:39 pm 

       MAC: 00:1a:79:56:91:af 

       Vence:November 7, 2021, 12:10 am

      00:1a:79:43:8d:b6;July 14, 2022, 10:26  

      00:1a:79:85:e6:12;October 28, 2022, 9:41  

      00:1a:79:81:a4:bb;January 15, 2022, 5:42  

      00:1a:79:21:45:e1;December 11, 2021, 1:21  

      00:1a:79:6c:0a:e3;December 1, 2021, 10:45  

      00:1a:79:16:9d:5f;November 22, 2021, 8:55  

      00:1a:79:56:af:6d;January 15, 2022, 1:30  

      0:1a:79:32:66:33;November 17, 2021, 12:11  

      00:1a:79:31:ef:de;February 12, 2022, 10:49  

      00:1a:79:5e:2a:8a;January 3, 2022, 11:49  

      00:1a:79:50:a1:63;December 14, 2021, 10:25  

      00:1a:79:99:46:50;August 26, 2022, 10:25  

      00:1a:79:4b:b9:80;November 25, 2021, 11:16  

      00:1a:79:49:62:46;November 12, 2021, 3:24  

      00:1a:79:3b:12:c5;December 11, 2021, 4:02  

      00:1a:79:51:70:7a;January 10, 2022, 4:36  

      00:1a:79:52:9a:03;August 30, 2022, 7:42  

      00:1a:79:12:d8:6e;May 15, 2022, 6:06  

      00:1a:79:13:33:02;December 19, 2021, 6:11  

      00:1a:79:36:97:6b;February 27, 2022, 6:26  

      00:1a:79:4f:b2:9d;March 15, 2022, 11:39  

      00:1a:79:12:cb:7b;December 24, 2021, 9:11 

      00:1a:79:17:49:5d;November 6, 2021, 6:01  

      00:1a:79:57:98:99;November 21, 2021, 8:42  

      00:1a:79:5d:9c:74;December 19, 2022, 12:35 

      00:1a:79:4c:45:52;May 4, 2022, 1:18  

      00:1a:79:37:22:82;October 29, 2022, 6:42  

      00:1a:79:40:e0:54;December 28, 2021, 6:01  

      00:1a:79:29:12:e4;December 28, 2021, 12:15  

      00:1a:79:47:b5:e2;February 28, 2022, 4:51  

      00:1a:79:3a:15:4b;September 28, 2022, 2:57  

      00:1a:79:33:78:09;June 13, 2022, 10:43  

      00:1a:79:6a:6c:f3;May 15, 2022, 6:24  

      00:1a:79:62:cb:26;April 7, 2022, 8:13  

      00:1a:79:3b:4b:58;May 19, 2022, 3:00  

      00:1a:79:36:f5:6b;March 29, 2022, 4:31  

      00:1a:79:99:8d:61;December 16, 2021, 8:00  

      00:1a:79:51:6d:0d;November 10, 2021, 11:35  

      00:1a:79:72:00:8b;December 25, 2021, 11:59  

      00:1a:79:23:47:42;January 29, 2022, 12:30  

      00:1a:79:5b:ca:77;April 17, 2022, 2:54  

      00:1a:79:2d:72:1e;January 5, 2022, 7:45  

      00:1a:79:47:a6:45;June 24, 2022, 3:01  

      00:1a:79:32:2f:77;December 12, 2021, 9:57  

      00:1a:79:64:dd:26;November 7, 2021, 9:29  

      00:1a:79:61:7f:e5;June 28, 2022, 5:50  

      00:1a:79:56:07:d0;November 5, 2021, 6:55  

      00:1a:79:6f:a4:5b;September 2, 2022, 6:38  

      00:1a:79:73:88:5c;September 14, 2022, 12:50  

      00:1a:79:2c:8f:58;August 28, 2022, 2:37  

      00:1a:79:59:7c:dc;January 21, 2022, 6:47  

      00:1a:79:2a:d3:0a;October 4, 2022, 8:08  

      00:1a:79:43:76:1c;September 8, 2022, 8:56  

      00:1a:79:54:23:c9;January 9, 2022, 12:07  

      00:1a:79:4e:81:6c;December 2, 2021, 8:21  

      00:1a:79:53:9f:ce;November 16, 2021, 8:53  

      00:1a:79:1d:13:64;January 13, 2022, 1:56  

      00:1a:79:2d:83:3a;April 23, 2022, 10:00  

      00:1a:79:5f:62:6c;December 13, 2021, 6:02  

      00:1a:79:53:a7:2d;December 8, 2021, 2:50  

      00:1a:79:1c:e3:e6;March 13, 2022, 6:19  

      00:1a:79:3a:af:e5;January 15, 2022, 12:57  

      00:1a:79:8d:da:92;March 18, 2022, 9:00  

      00:1a:79:28:9b:33;October 30, 2021, 3:45  

      00:1a:79:2c:9e:27;January 6, 2022, 6:01  

      00:1a:79:37:11:3e;January 11, 2022, 12:09 

      00:1a:79:40:34:f8;December 19, 2021, 6:31  

      00:1a:79:43:b5:3e;January 16, 2022, 6:49  

      00:1a:79:4c:78:72;December 28, 2021, 11:52


       MAC: 00:1a:79:80:be:e3 

       Vence:January 30, 2022, 3:04 pm 

       MAC: 00:1a:79:58:8f:23 

       Vence:January 28, 2022, 4:01 pm 

       MAC: 00:1a:79:43:3b:eb 

       Vence:April 21, 2022, 7:09 pm 

       MAC: 00:1a:79:3b:12:50 

       Vence:February 2, 2022, 7:08 pm 

       MAC: 00:1a:79:42:28:87 

       Vence:December 29, 2021, 9:33 pm 

       MAC: 00:1a:79:67:f3:c1 

       Vence:December 24, 2021, 12:00 am 

       MAC: 00:1a:79:6b:cc:20 

       Vence:June 18, 2022, 12:00 am 

       MAC: 00:1a:79:6a:85:32 

       Vence:August 12, 2022, 8:50 pm 

       MAC: 00:1a:79:43:3b:e7 

       Vence:March 29, 2022, 8:54 pm 

       MAC: 00:1a:79:6b:ca:bb 

       Vence:July 12, 2022, 12:00 am 

       MAC: 00:1a:79:6c:21:58 

       Vence:February 2, 2022, 12:00 am 

       MAC: 00:1a:79:67:f5:f5 

       Vence:February 2, 2022, 8:06 pm 

       MAC: 00:1a:79:51:51:a2 

       Vence:February 17, 2022, 7:45 pm 

       MAC: 00:1a:79:66:d9:11 

       Vence:December 23, 2021, 12:00 am 

       MAC: 00:1a:79:6c:24:f5 

       Vence:December 22, 2021, 12:00 am 

       MAC: 00:1a:79:4f:e8:48 

       Vence:December 29, 2021, 6:54 pm 

       MAC: 00:1a:79:44:72:cc 

       Vence:February 13, 2022, 2:53 pm 

       MAC: 00:1a:79:68:6f:0f 

       MAC: 00:1a:79:5d:cd:8a

      00:1A:79:3A:F8:CC February 21, 2022, 5:18 pm

      00:1A:79:43:92:29 July 21, 2022, 6:17 pm

      00:1a:79:53:FE:46 July 13, 2022, 3:02 pm

      00:1A:79:54:53:CB February 2, 2022, 8:24 pm

      00:1A:79:83:91:D0 March 17, 2022, 4:15 pm

      00:1a:79:73:73:1d April 3, 2022, 1:40 pm

      00:1a:79:A1:C6:F6 September 22, 2022, 12:38 pm

      00:1A:79:D8:13:3C January 8, 2022, 9:26 pm

      00:1A:79:F1:B8:05 April 12, 2022, 6:49 pm

      00:1A:79:00:28:49 January 30, 2022, 6:42 pm

      00:1A:79:00:70:6C July 12, 2022, 7:59 am

      00:1A:79:02:1B:24 January 20, 2022, 3:24 pm

      00:1A:79:0B:8D:E6 April 18, 2022, 5:26 pm

      00:1A:79:17:13:1A October 20, 2022, 3:32 pm

      00:1A:79:22:33:44 April 23, 2022, 1:29 pm

      00:1A:79:25:3F:09 December 5, 2021, 12:10 pm

      00:1A:79:27:9F:B9 December 15, 2021, 12:38 pm

      00:1a:79:28:9d:a8 June 23, 2022, 11:48 am

      00:1A:79:2E:9A:4D September 24, 2022, 4:28 pm

      00:1A:79:3A:F8:CC February 21, 2022, 5:18 pm

      00:1a:79:40:47:5d September 12, 2022, 4:00 pm

      00:1A:79:42:CF:46 November 19, 2021, 11:46 am

      00:1a:79:42:eb:e1 April 3, 2022, 1:35 pm

      ╠•●۞🛰 𝐏𝐎𝐑𝐓𝐀𝐋:

      ╠•●۞🎖  𝐌𝐀𝐂: 00:1A:79:53:DB:77

      ╠•●۞🔚 𝐄𝐗𝐏𝐈𝐑𝐄𝐒:  January 21, 2022, 6:05 pm ( Days)

      ╠•●۞🛰 𝐏𝐎𝐑𝐓𝐀𝐋:

      ╠•●۞🎖  𝐌𝐀𝐂: 00:1A:79:55:36:23

      ╠•●۞🔚 𝐄𝐗𝐏𝐈𝐑𝐄𝐒:  December 15, 2021, 9:06 pm ( Days)

      ╠•●۞🛰 𝐏𝐎𝐑𝐓𝐀𝐋:

      ╠•●۞🎖  𝐌𝐀𝐂: 00:1A:79:5A:13:D3

      ╠•●۞🔚 𝐄𝐗𝐏𝐈𝐑𝐄𝐒:  February 18, 2022, 2:20 pm ( Days)

      ╠•●۞🛰 𝐏𝐎𝐑𝐓𝐀𝐋:

      ╠•●۞🎖  𝐌𝐀𝐂: 00:1A:79:5A:13:D3

      ╠•●۞🔚 𝐄𝐗𝐏𝐈𝐑𝐄𝐒:  January 1, 2022, 2:20 pm ( Days)

      ╠•●۞🛰 𝐏𝐎𝐑𝐓𝐀𝐋:

      ╠•●۞🎖  𝐌𝐀𝐂: 00:1A:79:6E:DF:3D

      ╠•●۞🔚 𝐄𝐗𝐏𝐈𝐑𝐄𝐒:  February 18, 2022, 2:20 pm ( Days)

      ╠•●۞🛰 𝐏𝐎𝐑𝐓𝐀𝐋:

      ╠•●۞🎖  𝐌𝐀𝐂: 00:1A:79:49:9F:69

      ╠•●۞🔚 𝐄𝐗𝐏𝐈𝐑𝐄𝐒:  April 17, 2022, 10:10 pm ( Days)

      ╠•●۞🛰 𝐏𝐎𝐑𝐓𝐀𝐋:

      ╠•●۞🎖  𝐌𝐀𝐂: 00:1A:79:27:65:E9

      ╠•●۞🔚 𝐄𝐗𝐏𝐈𝐑𝐄𝐒:  April 17, 2022, 10:10 pm ( Days)

      ╠•●۞🛰 𝐏𝐎𝐑𝐓𝐀𝐋:

      ╠•●۞🎖  𝐌𝐀𝐂: 00:1A:79:63:D1:80

      ╠•●۞🔚 𝐄𝐗𝐏𝐈𝐑𝐄𝐒:  April 17, 2022, 10:10 pm ( Days)


      ⏰  November 18, 2021, 11:42 am

      🏴‍☠️  00:1A:79:6E:38:4A

      👽  h2To2wUae9

      🔐  NcRVp83wtq


                                          📡  Country  (LIVE)  📺

      🛰Portal :

      💦Real URL : 

      Ⓜ️MAC : 00:1A:79:3F:63:F6 

      👤Username : 0MIkWGHicz

      🗝Password : OPxR9mJnrM

      ✅Active Connections : 0

      👉Max. Connections : 1

      🔚Expires : December 20, 2021, 6:17 pm

      📽m3u List : 

      💦FFmpeg URL : ffmpeg 

      Jackson🎬 Scary Movie Collection🎬 Sylvester Stallone🎬 Sean Connery Collection🎬 Little Rascals - Our Gang🎬 Lord Of The Rings Trilogy🎬 Sharks Movies Collection🎬 Superman Collection🎬 Shaw Brothers Collection🎬 Spider Man Collection🎬 Star Wars Collection🎬 Steven Seagal Collection🎬 The Bourne Collection🎬 The Twilight Saga Collection🎬 Tom Hanks Collection🎬 Transformers Collection🎬 War Movies🎬 Italian Mafia🎬 X-Men Collection🎬 3D Movies🎬 Errol Flynn Movies🎬 Ida Lupino Movies🎬 Jet Li Collection🎬 Musicals🎬 Kids Movies🎬 🎬 Festivals🎬 Concerts🎬 Italian Movies🎬 Paola Cortellesi Collection🎬 PPV Wwe Boxing Ufc🎬 Documentaries🎬 Triathlon🎬 Classic Football Matches🎬 Stand-up Comedy🎬 For Adults Tinto Brass🎬 For Adults !🎬 For Adults🎬 Maltese Movies🎬



      ╠•❖•🅜🅐🅒•🢂 00:1A:79:57:B9:45

      ╠•❖•🅔🅧🅟•🢂 February 10, 2022, 7:56 pm













      ╠•❖•𝗣𝗼𝗿𝘁•🢂 25461

      ╠•❖•𝗨𝘀𝗲𝗿•🢂 vja7tX6jPZ

      ╠•❖•𝗣𝗮𝘀𝘀•🢂 5uu84C3fNq

      ╠•❖•𝗘𝘅𝗽𝘁•🢂 2022-02-11 03:56:47 

      ╠•❖•𝐀𝐜𝐭𝐢𝐨𝐧𝐂𝐨𝐧•🢂 0

      ╠•❖•𝗠𝗮𝗸𝘀𝗶𝗺𝘂𝗺𝗖𝗼𝗻🢂 1 

      ╠•❖•𝗦𝘁𝗮𝘁𝘂𝘀•🢂 Active

      ╠•❖•𝗧𝗶𝗺𝗲𝗭𝗼𝗻𝗲•🢂 UTC








      ╠•❖•𝖒3𝖚_𝖀𝖗𝖑 •🢂



      ╠•❖•🅜🅐🅒•🢂 00:1A:79:57:A3:94

      ╠•❖•🅔🅧🅟•🢂 June 14, 2022, 10:22 photodirector bundle version cracked - Crack Key For U


      ╠•❖•𝗣𝗼𝗿𝘁•🢂 25461

      ╠•❖•𝗨𝘀𝗲𝗿•🢂 qLQJXzeaqu

      ╠•❖•𝗣𝗮𝘀𝘀•🢂 DmhnNfNtMt

      ╠•❖•𝗘𝘅𝗽𝘁•🢂 2022-06-15 06:22:34 

      ╠•❖•𝐀𝐜𝐭𝐢𝐨𝐧𝐂𝐨𝐧•🢂 0

      ╠•❖•𝗠𝗮𝗸𝘀𝗶𝗺𝘂𝗺𝗖𝗼𝗻🢂 1 

      ╠•❖•𝗦𝘁𝗮𝘁𝘂𝘀•🢂 Active

      ╠•❖•𝗧𝗶𝗺𝗲𝗭𝗼𝗻𝗲•🢂 UTC







      ╠ «❖» SERVER URL:

      ╠ «❖» PORTAL:

      ╠ «❖» MAC ADD: 00:1A:79:40:4B:80

      ╠ «❖» CREAT DATE: 1608563129

      ╠ «❖» EXP DATE: December 22, 2021, 4:05 pm

      ╠ «❖» USER: mag00:1A:79:40:4B:80

      ╠ «❖» PASS: elyWq3C36D

      ╠ «❖» STATUS: Active

      ╠ «❖» TIME ZONE: Europe/Amsterdam

      ╠ «❖» ACTIVE CONN: 0

      ╠ «❖» MAX CONN: 1

      ╠ «❖» M3U LINK: 


      ║ ❖ LIST. (LIVE) ❖ ║ TOTAL 140



      ║ ❖ VOD. (LIVE) ❖ ║ TOTAL 65


      ╔═❖»» SCAN TIME: 30-Oct-2021

      ╠ «❖» SERVER URL:

      ╠ «❖» PORTAL:

      ╠ «❖» MAC ADD: 00:1A:79:46:B8:19

      ╠ «❖» CREAT DATE: 1592050264

      ╠ «❖» EXP DATE: June 13, 2022, 5:15 pm

      ╠ «❖» USER: mag00:1A:79:46:B8:19

      ╠ «❖» PASS: 9NfeE5Xr9e

      ╠ «❖» STATUS: Active

      ╠ «❖» TIME ZONE: Europe/Amsterdam

      ╠ «❖» ACTIVE CONN: 0

      ╠ «❖» MAX CONN: 1

      ╠ «❖» M3U LINK: 


      ║ ❖ LIST. (LIVE) ❖ ║ TOTAL 55



      ║ ❖ VOD. (LIVE) ❖ ║ TOTAL 28


      ╚═❖» *🎬 MOVIES ARABIC ( 2020 + 2021 ) أفلام عربية🎬 MOVIES ARABIC / أفلام عربية🎬 MOVIES ARABIC / أفلام الزمن الجميل 🎬 MOVIES KIDS / أفلام أطفال🎬 ARABIC THEATER / مسرحيات عربية🎬 ALKHALIJ THEATER / مسرحيات خليجية🎬 MOVIES ISLAMIC / أفلام اسلامية🎬 MOVIES DOCUMENT / أفلام وثائقية     🎬 HOLLYWOOD STAR / نجوم هوليوود🎬 MOVIES AMAZON / أفلام أمازون🎬 MOVIES NETFLIX / أفلام نيتفلكس🎬 MOVIES EN ( 4K ) أفلام أجنبية🎬 MOVIES EN ( 2021 ) أفلام أجنبية🎬 MOVIES EN ( 2020 ) أفلام أجنبية🎬 MOVIES EN ( ACTION ) أفلام أجنبية🎬 MOVIES EN ( CLASSIC ) أفلام أجنبية🎬 MOVIES EN ( HORROR ) أفلام أجنبية🎬 MOVIES ASIA / أفلام أسيوية🎬 MOVIES JET LI / أفلام جيت لي🎬 MOVIES RAMBO / أفلام رامبو🎬 MOVIES ARNOLD / أفلام ارنولد🎬 MOVIES JACKIE / أفلام جاكي شان🎬 MOVIES VAN DAMME / أفلام فان دام🎬 MOVIES TOM CRUISE / أفلام توم كروز🎬 MOVIES SHAHRUKH KHAN / أفلام شاروخان🎬 MOVIES AMAZIGH / أفلام أمازيغية🎬 MOVIES TURKEY ( 2021 ) 

      ┣❰❈❱➧𝐏𝐎𝐑𝐓𝐀𝐋 𝐔𝐑𝐋:

      ┣❰❈❱➧𝐑𝐄𝐀𝐋 𝐔𝐑𝐋:

      ┣❰❈❱➧𝐌𝐀𝐂 𝐀𝐃𝐑𝐄𝐒𝐒: 00:1A:79:3C:49:06

      ┣❰❈❱➧𝐄𝐗𝐏𝐈𝐑𝐄 𝐃𝐀𝐓𝐄: October 30, 2022, 9:17 am



      ┣❰❈❱➧𝐂𝐎𝐔𝐍𝐓𝐑𝐘: United States

      ┣❰❈❱➧𝐌𝟑𝐔 𝐋𝐈𝐒𝐓:


      ┣°☆•°       𝑳𝒊𝒔𝒕 (𝑳𝑰𝑽𝑬)    °☆



      ┣❰❈❱➧𝐏𝐎𝐑𝐓𝐀𝐋 𝐔𝐑𝐋:

      ┣❰❈❱➧𝐑𝐄𝐀𝐋 𝐔𝐑𝐋:

      ┣❰❈❱➧𝐌𝐀𝐂 𝐀𝐃𝐑𝐄𝐒𝐒: 00:1A:79:3D:E5:10

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      ┣❰❈❱➧𝐂𝐎𝐔𝐍𝐓𝐑𝐘: Sweden

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      ┣°☆•°       𝑳𝒊𝒔𝒕 (𝑳𝑰𝑽𝑬)    °☆



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          🔷📡 VOD🔷 : TRAND YouTube🌟 Arabic New🌟 Arabic Premier🌟 Classic Arabic🌟 English New🌟 English Movies🌟 English Horror🌟 Netflix Film🌟 Action Movies🌟 Fantasy Movies🌟 Drama Movies🌟 Romantic Movies🌟 English Primer🌟 English Family🌟 English ON Demand🌟 English Movies OLD🌟 Documentary Movies🌟 A N I M A T I O N🌟 A S I A MOVIES🌟 I N D I A N MOVIES🌟 ARABIC SHAM Khaliji🌟 Kids Film🌟 افلام اسماعيل ياسين🌟 مسرحيات🌟 (WWE) مصارعة حره🌟 قصص الانبياء للاطفال🌟 TOM&Jeer

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      📆 EXPIRE : November 20, 2021, 6:59 pm

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