Systems Biology

Multimodal Single-Cell Omics Analysis of COVID-19 Sex Differences in Human Immune Systems

1. Yuan Hou
2. Yadi Zhou
3. Michaela U. Gack
4. Justin D. Lathia
5. Asha Kallianpur
6. Reena Mehra
7. Timothy Chan
8. Jae U. Jung
9. Lara Jehi
10. Charis Eng
11. Feixiong Cheng

Reviewed by ScreenIT

Novel gene-specific translation mechanism of dysregulated, chronic inflammation reveals promising, multifaceted COVID-19 therapeutics

1. Li Wang
2. Adil Muneer
3. Ling Xie
4. Feng Zhang
5. Bing Wu
6. Liu Mei
7. Erik M Lenarcic
8. Emerald Hillary Feng
9. Juan Song
10. Yan Xiong
11. Xufen Yu
12. Charles Wang
13. Ciprian Gheorghe
14. Karina Torralba
15. Jeanette Gowen Cook
16. Yisong Y. Wan
17. Nathaniel John Moorman
18. Hongjun Song
19. Jian Jin
20. Xian Chen

Reviewed by ScreenIT

Network medicine links SARS-CoV-2/COVID-19 infection to brain microvascular injury and neuroinflammation in dementia-like cognitive impairment

1. Yadi Zhou
2. Jielin Xu
3. Yuan Hou
4. James B. Leverenz
5. Asha Kallianpur
6. Reena Mehra
7. Yunlong Liu
8. Haiyuan Yu
9. Andrew A. Pieper
10. Lara Jehi
11. Feixiong Cheng

Reviewed by ScreenIT

Multilevel proteomics reveals host perturbations by SARS-CoV-2 and SARS-CoV

1. Alexey Stukalov
2. Virginie Girault
3. Vincent Grass
4. Ozge Karayel
5. Valter Bergant
6. Christian Urban
7. Darya A. Haas
8. Yiqi Huang
9. Lila Oubraham
10. Anqi Wang
11. M. Sabri Hamad
12. Antonio Piras
13. Fynn M. Hansen
14. Maria C. Tanzer
15. Igor Paron
16. Luca Zinzula
17. Thomas Enghleitner
18. Maria Reinecke
19. Teresa M. Lavacca
20. Rosina Ehmann
21. Roman Wölfel
22. Jörg Jores
23. Bernhard Kuster
24. Ulrike Protzer
25. Roland Rad
26. John Ziebuhr
27. Volker Thiel
28. Pietro Scaturro
29. Matthias Mann
30. Andreas Pichlmair

Reviewed by ScreenIT

In Silico Modeling of Virus Particle Propagation and Infectivity along the Respiratory Tract: A Case Study for SARS-COV-2

1. Dixon Vimalajeewa
2. Sasitharan Balasubramaniam
3. Donagh P. Berry
4. Gerald Barry

Reviewed by ScreenIT

High-content screening of coronavirus genes for innate immune suppression reveals enhanced potency of SARS-CoV-2 proteins

1. Erika J. Olson
2. David M. Brown
3. Timothy Z. Chang
4. Lin Ding
5. Tai L. Ng
6. H. Sloane Weiss
7. Yukiye Koide
8. Peter Koch
9. Nathan Rollins
10. Pia Mach
11. Tobias Meisinger
12. Trenton Bricken
13. Joshua Rollins
14. Yun Zhang
15. Colin Molloy
16. Yun Zhang
17. Bridget N. Queenan
18. Timothy Mitchison
19. Debora Marks
20. Jeffrey C. Way
21. John I. Glass
22. Pamela A. Silver

Reviewed by ScreenIT

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