Systems Biology

Control-theoretic immune tradeoffs explain SARS-CoV-2 virulence and transmission variation

1. Anish A. Sarma
2. Aartik Sarma
3. Marie Csete
4. Peter P. Lee
5. John C. Doyle

Reviewed by ScreenIT

Genome-scale metabolic modeling reveals SARS-CoV-2-induced metabolic changes and antiviral targets

1. Kuoyuan Cheng
2. Laura Martin-Sancho
3. Lipika R. Pal
4. Yuan Pu
5. Laura Riva
6. Xin Yin
7. Sanju Sinha
8. Nishanth Ulhas Nair
9. Sumit K. Chanda
10. Eytan Ruppin

Reviewed by ScreenIT

Unsupervised machine learning reveals key immune cell subsets in COVID-19, rhinovirus infection, and cancer therapy

1. Sierra M. Barone
2. Alberta G.A. Paul
3. Lyndsey M. Muehling
4. Joanne A. Lannigan
5. William W. Kwok
6. Ronald B. Turner
7. Judith A. Woodfolk
8. Jonathan M. Irish

• Curated by eLife

  Evaluation Summary:

  The three reviewers were overly positive about the stated goal of your T-REX method to characterize rare populations by cytometry. The potential applications in the context of analyzing antigen-specific T cells (as identified as tetramer-positive cells) were not missed on the reviewers and the use of the 2 timepoints-cohort of samples from rhinovirus-infected patients was judged clever. However, all three reviewers requested some edits and additional tests to really distinguish T-REX from other methods in terms of performance, and to better understand its analysis power. Reviewer #1 enjoined you to clarify the improvements of your method compared to previous methods. Reviewer #2 requested more stringent tests of your methods against functional datasets. Reviewer #3 inquired about corrections for batch effects, the result consistency for repeated down-sampling as well as the scalability of the method (especially when UMAP is being used).

  (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #2 and Reviewer #3 agreed to share their names with the authors.)

Reviewed by eLife, ScreenIT

A map of binary SARS-CoV-2 protein interactions implicates host immune regulation and ubiquitination

1. Dae-Kyum Kim
2. Benjamin Weller
3. Chung-Wen Lin
4. Dayag Sheykhkarimli
5. Jennifer J. Knapp
6. Nishka Kishore
7. Mayra Sauer
8. Ashyad Rayhan
9. Veronika Young
10. Nora Marin-de la Rosa
11. Oxana Pogoutse
12. Kerstin Spirohn
13. Alexandra Strobel
14. Florent Laval
15. Patrick Schwehn
16. Roujia Li
17. Simin Rothballer
18. Melina Altmann
19. Patricia Cassonnet
20. Guillaume Dugied
21. Atina G. Cote
22. Lena Elorduy Vergara
23. Isaiah Hazelwood
24. Bingruo B. Liu
25. Maria Nguyen
26. Ramakrishnan Pandiarajan
27. Patricia A. Rodriguez Coloma
28. Luc Willems
29. Jean-Claude Twizere
30. Caroline Demeret
31. Yves Jacob
32. Tong Hao
33. Dave E. Hill
34. Claudia Falter
35. Marc Vidal
36. Michael A. Calderwood
37. Frederick P. Roth
38. Pascal Falter-Braun

Reviewed by ScreenIT

Deep time course proteomics of SARS-CoV- and SARS-CoV-2-infected human lung epithelial cells (Calu-3) reveals strong induction of interferon-stimulated gene (ISG) expression by SARS-CoV-2 in contrast to SARS-CoV

1. Marica Grossegesse
2. Daniel Bourquain
3. Markus Neumann
4. Lars Schaade
5. Andreas Nitsche
6. Joerg Doellinger

Reviewed by ScreenIT

The Great Deceiver: miR-2392’s Hidden Role in Driving SARS-CoV-2 Infection

1. J. Tyson McDonald
2. Francisco Javier Enguita
3. Deanne Taylor
4. Robert J. Griffin
5. Waldemar Priebe
6. Mark R. Emmett
7. Mohammad M. Sajadi
8. Anthony D. Harris
9. Jean Clement
10. Joseph M. Dybas
11. Nukhet Aykin-Burns
12. Joseph W. Guarnieri
13. Larry N. Singh
14. Peter Grabham
15. Stephen B. Baylin
16. Aliza Yousey
17. Andrea N. Pearson
18. Peter M. Corry
19. Amanda Saravia-Butler
20. Thomas R. Aunins
21. Sadhana Sharma
22. Prashant Nagpal
23. Cem Meydan
24. Jonathan Foox
25. Christopher Mozsary
26. Bianca Cerqueira
27. Viktorija Zaksas
28. Urminder Singh
29. Eve Syrkin Wurtele
30. Sylvain V. Costes
31. Gustavo Gastão Davanzo
32. Diego Galeano
33. Alberto Paccanaro
34. Suzanne L. Meinig
35. Robert S. Hagan
36. Natalie M Bowman
37. UNC COVID-19 Pathobiology Consortium
38. Matthew C. Wolfgang
39. Selin Altinok
40. Nicolae Sapoval
41. Todd J. Treangen
42. Pedro M. Moraes-Vieira
43. Charles Vanderburg
44. Douglas C. Wallace
45. Jonathan Schisler
46. Christopher E. Mason
47. Anushree Chatterjee
48. Robert Meller
49. Afshin Beheshti

Reviewed by ScreenIT

A mathematical model for simulating the transmission of Wuhan novel Coronavirus

1. Tianmu Chen
2. Jia Rui
3. Qiupeng Wang
4. Zeyu Zhao
5. Jing-An Cui
6. Ling Yin

Reviewed by ScreenIT

Complexity in SARS-CoV-2 genome data: Price theory of mutant isolates

1. Saurav Mandal
2. R.K. Sanayaima Singh
3. Saurabh Kumar Sharma
4. Md. Zubbair Malik
5. R.K. Brojen Singh

Reviewed by ScreenIT

Network controllability enrichment analysis reveals that SARS-CoV-2 infection tends to target indispensable nodes of a directed human protein-protein interaction network

1. Ho-Joon Lee

Reviewed by ScreenIT

Machine Learning Identifies Ponatinib as a Potent Inhibitor of SARS-CoV2-induced Cytokine Storm

1. Marina Chan
2. Siddharth Vijay
3. M. Juliana McElrath
4. Eric C. Holland
5. Taranjit S Gujral

Reviewed by ScreenIT