Systems Biology

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 GA 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 proteome-scale map of the SARS-CoV-2–human contactome

1. Dae-Kyum Kim
2. Benjamin Weller
3. Chung-Wen Lin
4. Dayag Sheykhkarimli
5. Jennifer J. Knapp
6. Guillaume Dugied
7. Andreas Zanzoni
8. Carles Pons
9. Marie J. Tofaute
10. Sibusiso B. Maseko
11. Kerstin Spirohn
12. Florent Laval
13. Luke Lambourne
14. Nishka Kishore
15. Ashyad Rayhan
16. Mayra Sauer
17. Veronika Young
18. Hridi Halder
19. Nora Marín-de la Rosa
20. Oxana Pogoutse
21. Alexandra Strobel
22. Patrick Schwehn
23. Roujia Li
24. Simin T. Rothballer
25. Melina Altmann
26. Patricia Cassonnet
27. Atina G. Coté
28. Lena Elorduy Vergara
29. Isaiah Hazelwood
30. Betty B. Liu
31. Maria Nguyen
32. Ramakrishnan Pandiarajan
33. Bushra Dohai
34. Patricia A. Rodriguez Coloma
35. Juline Poirson
36. Paolo Giuliana
37. Luc Willems
38. Mikko Taipale
39. Yves Jacob
40. Tong Hao
41. David E. Hill
42. Christine Brun
43. Jean-Claude Twizere
44. Daniel Krappmann
45. Matthias Heinig
46. Claudia Falter
47. Patrick Aloy
48. Caroline Demeret
49. Marc Vidal
50. Michael A. Calderwood
51. Frederick P. Roth
52. 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 Expression by SARS-CoV-2 in Contrast to SARS-CoV

1. Marica Grossegesse
2. Daniel Bourquain
3. Markus Neumann
4. Lars Schaade
5. Jessica Schulze
6. Christin Mache
7. Thorsten Wolff
8. Andreas Nitsche
9. 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 phase-based transmissibility of a novel coronavirus

1. Tian-Mu Chen
2. Jia Rui
3. Qiu-Peng Wang
4. Ze-Yu 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

Predicting hosts based on early SARS-CoV-2 samples and analyzing the 2020 pandemic

1. Qian Guo
2. Mo Li
3. Chunhui Wang
4. Jinyuan Guo
5. Xiaoqing Jiang
6. Jie Tan
7. Shufang Wu
8. Peihong Wang
9. Tingting Xiao
10. Man Zhou
11. Zhencheng Fang
12. Yonghong Xiao
13. Huaiqiu Zhu

Reviewed by ScreenIT