Systems Biology

A systematic, complexity-reduction approach to dissect the kombucha tea microbiome

1. Xiaoning Huang
2. Yongping Xin
3. Ting Lu

• Curated by eLife

  Evaluation Summary:

  This work will be of interest for researchers studying the functions of microbial communities, microbial ecology and interactions. Using the Kombucha tea (KT) microbiome as a case study, Huang et al. provide a framework for simplifying complex communities into core communities that capture aspects of complex communities. Authors demonstrated that core communities can facilitate a mechanistic understanding of how microbes interact, especially when member species are individually culturable. The work presents a fresh, novel approach for the coarse-grained analysis of complex microbiomes.

  (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 #1 agreed to share their name with the authors.)

Reviewed by eLife

Machine learning-assisted discovery of growth decision elements by relating bacterial population dynamics to environmental diversity

1. Honoka Aida
2. Takamasa Hashizume
3. Kazuha Ashino
4. Bei-Wen Ying

• Curated by eLife

  Evaluation Summary:

  Aida et al use a combination of novel experimental measurements and data processing to wrangle the complexity of bacterial growth for different media conditions. This study represents a clear example tackling the complexity of biological systems from the condition sides (~13,000 growth curves were measured) influencing the growth of a well-defined single specie of bacterium and with a reasonable first pass at data processing. The findings are ultimately simple (with essentially 3 conditions accounting all variability in the system) and easily interpretable.

  (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 agreed to share their name with the authors.)

Reviewed by eLife

A spatiotemporal reconstruction of the C. elegans pharyngeal cuticle reveals a structure rich in phase-separating proteins

1. Muntasir Kamal
2. Levon Tokmakjian
3. Jessica Knox
4. Peter Mastrangelo
5. Jingxiu Ji
6. Hao Cai
7. Jakub W Wojciechowski
8. Michael P Hughes
9. Kristóf Takács
10. Xiaoquan Chu
11. Jianfeng Pei
12. Vince Grolmusz
13. Malgorzata Kotulska
14. Julie Deborah Forman-Kay
15. Peter J Roy

• Curated by eLife

  Evaluation Summary:

  Cuticles are specialized extracellular matrices that cover the bodies of ecdysozoans, which make up 85% of all animals. How cuticles are formed is very poorly understood, in particular in light of the fact that cuticles are shed and regrown as animals grow. The authors present a comprehensively and carefully curated resource of the components of the pharyngeal cuticle of C. elegans and provide a spatio-temporal framework to understand cuticle assembly. In doing so, the authors propose a function for a large class of intrinsically disordered proteins (IDPs). The significance of this work is high because our understanding of both cuticle formation and of IDPs is poor.

  (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. The reviewers remained anonymous to the authors.)

Reviewed by eLife

Individualized discovery of rare cancer drivers in global network context

1. Iurii Petrov
2. Andrey Alexeyenko

• Curated by eLife

  Evaluation Summary:

  In this work, Petrov and Alexeyenko present a novel network-based method, NEADriver, aimed at the identification of mutational (point mutations and copy number variants) driver genes across tumors. The authors evaluate ten large cancer cohorts and assess the overlap of their results with established cancer genes or datasets that are enriched for cancer genes. This manuscript addresses a topic of high interest in the cancer genomics community.

  (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 agreed to share their name with the authors.)

Reviewed by eLife

Comprehensive analysis of pathways in Coronavirus 2019 (COVID-19) using an unsupervised machine learning method

1. Golnaz Taheri
2. Mahnaz Habibi

Reviewed by ScreenIT

Measurements of damage and repair of binary health attributes in aging mice and humans reveal that robustness and resilience decrease with age, operate over broad timescales, and are affected differently by interventions

1. Spencer Farrell
2. Alice E Kane
3. Elise Bisset
4. Susan E Howlett
5. Andrew D Rutenberg

• Curated by eLife

  Evaluation Summary:

  The key contribution of this paper is to measure frailty longitudinally in mice and humans to model 'robustness' (the ability to resist damage) and 'resilience' (the ability to recover from damage). To model these concepts, a frailty index (FI) composed of multiple binary parameters is calculated, but with the novel contribution that newly detected changes represent damage and that the parameters that have previously been detected but are not detected currently represent damage repair. Statistical steps then derive resilience and robustness and their changes over time. The sophisticated attempts to effectively model longitudinal data and rigorous analytic approach are strengths, as is the use of both human and animal species and intervention studies. A few overarching concerns were raised, primarily pertaining to the potential risk of over-conceptualized links between deficit index and biologic constructs of 'damage' and 'repair', but it nonetheless advances a growing field interested in measuring the longitudinal change in biologic age.

  (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 #3 agreed to share their name with the authors.)

Reviewed by eLife

Modelling the within-host spread of SARS-CoV-2 infection, and the subsequent immune response, using a hybrid, multiscale, individual-based model. Part I: Macrophages

1. C. F. Rowlatt
2. M. A. J. Chaplain
3. D. J. Hughes
4. S. H. Gillespie
5. D. H. Dockrell
6. I. Johannessen
7. R. Bowness

Reviewed by ScreenIT

Robotic search for optimal cell culture in regenerative medicine

1. Genki N Kanda
2. Taku Tsuzuki
3. Motoki Terada
4. Noriko Sakai
5. Naohiro Motozawa
6. Tomohiro Masuda
7. Mitsuhiro Nishida
8. Chihaya T Watanabe
9. Tatsuki Higashi
10. Shuhei A Horiguchi
11. Taku Kudo
12. Motohisa Kamei
13. Genshiro A Sunagawa
14. Kenji Matsukuma
15. Takeshi Sakurada
16. Yosuke Ozawa
17. Masayo Takahashi
18. Koichi Takahashi
19. Tohru Natsume

• Curated by eLife

  Evaluation Summary:

  The manuscript by Kanda GN, Natsume T et al. describes a robotic artificial intelligence system with a batch Bayesian optimization algorithm that allows to optimise and reliably repeat cell culture protocols. The authors utilise induced pluripotent stem cell-derived retinal pigment epithelial cells as a model culture system of broad interest in regenerative medicine. They demonstrate that the robotic system with Bayesian algorithm accelerates the optimisation of cell culture protocols and increases the quality and quantity of cell products, compared with manual operations - these results will likely inform and strongly impact modern cell culture strategies in regenerative medicine.

  (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 #1, Reviewer #2 and Reviewer #3 agreed to share their name with the authors.)

Reviewed by eLife

Modeling COVID-19 disease biology to identify drug treatment candidates

1. Walter J. Jessen
2. Stefan Diaz Gaisenband
3. M’Lissa Quintanilla
4. Sadiq Lula
5. Patrick McLeroth
6. Adam Sullivan
7. Stanley Letovsky

Reviewed by ScreenIT

Whole-body metabolic modelling predicts isoleucine dependency of SARS-CoV-2 replication

1. Ines Thiele
2. Ronan M.T. Fleming

Reviewed by ScreenIT