Systems Biology

Quantifying how post-transcriptional noise and gene copy number variation bias transcriptional parameter inference from mRNA distributions

1. Xiaoming Fu
2. Heta P Patel
3. Stefano Coppola
4. Libin Xu
5. Zhixing Cao
6. Tineke L Lenstra
7. Ramon Grima

Reviewed by Review Commons

Decoding mechanism of action and sensitivity to drug candidates from integrated transcriptome and chromatin state

1. Caterina Carraro
2. Lorenzo Bonaguro
3. Jonas Schulte-Schrepping
4. Arik Horne
5. Marie Oestreich
6. Stefanie Warnat-Herresthal
7. Tim Helbing
8. Michele De Franco
9. Kristian Haendler
10. Sach Mukherjee
11. Thomas Ulas
12. Valentina Gandin
13. Richard Goettlich
14. Anna C Aschenbrenner
15. Joachim L Schultze
16. Barbara Gatto

• Curated by eLife

  Evaluation Summary:

  This article applies a systems biology approach to understand the mechanism of action of 3-chloropiperidines (a class of anticancer drugs) in cancer cells and evaluate their sensitivity to drugs. It integrates transcriptomic and open-chromatin data and utilizes sound statistical frameworks for building a sensitivity model. The author's methodology can be applied to early-stage drug discovery. This paper will be of interest to the large class of people who tried to understand how omics data will help drug discovery. It sets a new framework to integrate transcriptome and chromatin accessibility data to identify the key mechanisms of action and provide potential disease targets, which will help speed up the early phases of drug discovery.

  (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. All three reviewers agreed to share their names with the authors.)

Reviewed by eLife

Concentration fluctuations in growing and dividing cells: Insights into the emergence of concentration homeostasis

1. Chen Jia
2. Abhyudai Singh
3. Ramon Grima

Reviewed by Review Commons

On the flexibility of the cellular amination network in E coli

1. Helena Schulz-Mirbach
2. Alexandra Müller
3. Tong Wu
4. Pascal Pfister
5. Selçuk Aslan
6. Lennart Schada von Borzyskowski
7. Tobias J Erb
8. Arren Bar-Even
9. Steffen N Lindner

• Curated by eLife

  Evaluation Summary:

  In this paper, the authors demonstrate that a reversible amination network that allows nitrogen transfer via transaminases for synthesis of several amino acids can be constructed in laboratory strains through clever and carefully designed experiments. As a result, this work should be of interest to microbiologists, biochemists, synthetic biologists, and biotechnologists.

  (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 name with the authors.)

Reviewed by eLife

Information content differentiates enhancers from silencers in mouse photoreceptors

1. Ryan Z Friedman
2. David M Granas
3. Connie A Myers
4. Joseph C Corbo
5. Barak A Cohen
6. Michael A White

• Curated by eLife

  Evaluation Summary:

  This manuscript will be of interest to geneticists seeking to establish rules that govern gene regulation. To explain why a sequence enhances, rather than silences, gene transcription the authors draw our attention away from the binding of a single transcription factor, to focus instead on the number and diversity of transcription factor molecules that bind to it. Using a relatively simple metric called sequence information content they appear to be able to improve the prediction of enhancer over silencer sequences. A concern is whether the silencers are true silencers, or whether they only act as such in this specific experimental paradigm.

  (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

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