Systems Biology

Deep learning linking mechanistic models to single-cell transcriptomics data reveals transcriptional bursting in response to DNA damage

1. Zhiwei Huang
2. Songhao Luo
3. Zihao Wang
4. Zhenquan Zhang
5. Benyuan Jiang
6. Qing Nie
7. Jiajun Zhang

• Curated by eLife

  eLife Assessment

  The paper introduces DeepTX, a valuable deep-learning framework linking stochastic, mechanistic modelling with single-cell RNA sequencing data to investigate transcriptional burst kinetics on a genome-wide scale. This tool has been employed by the authors to evaluate transcriptional changes under DNA-damaging treatments, with observations that are of value to the systems biology and bioinformatics communities. The evidence supporting these findings is solid, though some concerns remain regarding specific technical details. This methodological advancement holds potential for application in diverse contexts, such as linking mechanistic models of signalling pathways to transcriptional data.

Reviewed by eLife

Cell type-specific network analysis in Diversity Outbred mice identifies genes potentially responsible for human bone mineral density GWAS associations

1. Luke J Dillard
2. Gina M Calabrese
3. Larry D Mesner
4. Charles R Farber

• Curated by eLife

  eLife Assessment

  This important study provides a framework for applying single-cell transcriptome data and network analysis from genetically diverse mouse cells to identify novel driver genes underlying the role of genetic loci associated with bone mineral density. The evidence supporting the identification of the driver genes and the conclusion of the paper is convincing. Overall, this approach may be broadly applicable and of interest to researchers investigating the genetics of complex diseases.

Reviewed by eLife

CausalXtract: a flexible pipeline to extract causal effects from live-cell time-lapse imaging data

1. Franck Simon
2. Maria Colomba Comes
3. Tiziana Tocci
4. Louise Dupuis
5. Vincent Cabeli
6. Nikita Lagrange
7. Arianna Mencattini
8. Maria Carla Parrini
9. Eugenio Martinelli
10. Hervé Isambert

• Curated by eLife

  eLife Assessment

  This important study represents a data processing pipeline to discover causal interactions from time-lapse imaging data, and convincingly illustrates it on a challenging application for the analysis of tumor-on-chip ecosystem data. The authors describe the raw data they used (imaging data), go through a step-by-step description on how to extract the features they are interested in from the raw data, and how to perform the causal discovery process. This paper tackles the problem of learning causal interactions from temporal data, which is applicable to many biological applications.

Reviewed by eLife

Exploration of the Metabolomic Mechanisms of Postmenopausal Hypertension Induced by Low Estrogen State

1. Yao Li
2. Hui Xin
3. Zhexun Lian
4. Wei Zhang

• Curated by eLife

  eLife Assessment

  This useful study provides incomplete evidence regarding the pathophysiological role of low estrogen levels post-menopause in hypertension, focusing on L-AABA as a key mediator. The results describe a novel hypothesis for the pathophysiology of hypertension in this population and are of interest to experts in hypertension and vascular biology.

Reviewed by eLife

Emergence of power law distributions in protein-protein interaction networks through study bias

1. David B Blumenthal
2. Marta Lucchetta
3. Linda Kleist
4. Sándor P Fekete
5. Markus List
6. Martin H Schaefer

• Curated by eLife

  eLife Assessment

  This manuscript makes an important contribution to the understanding of protein-protein interaction (PPI) networks by challenging the widely held assumption that their degree distributions uniformly follow a power law. The authors present convincing evidence that biases in study design, such as data aggregation and selective research focus, may contribute to the appearance of power-law-like distributions. While the power law assumption has already been questioned in network biology, the methodological rigor and correction procedures introduced here are valuable for advancing our understanding of PPI network structure.

Reviewed by eLife

Emergence of alternative stable states in microbial communities undergoing horizontal gene transfer

1. Juken Hong
2. Wenzhi Xue
3. Teng Wang

• Curated by eLife

  eLife Assessment

  This manuscript offers a modeling platform in which horizontal gene transfer (HGT) is incorporated into the ecological dynamics of microbial communities. The investigation is valuable as it brings to the forefront a potentially significant process and highlights its implications. However, the investigation in its current form is incomplete because it is based on a narrow range of parameters and assumptions. As a result, the scope and relevance of the findings are not fully clear. A more in-depth description of model assumptions and the formulation structure and a more thorough analysis of the impact of different parameters would strengthen the manuscript. This work will be of interest to microbiologists as well as researchers in ecological and evolutionary biology.

Reviewed by eLife

Abundant clock proteins point to missing molecular regulation in the plant circadian clock

1. Uriel Urquiza-García
2. Nacho Molina
3. Karen J. Halliday
4. Andrew J. Millar

Reviewed by Review Commons

Mitochondrial respiration atlas reveals differential changes in mitochondrial function across sex and age

1. Dylan C Sarver
2. Muzna Saqib
3. Fangluo Chen
4. G William Wong

• Curated by eLife

  eLife Assessment

  This important study provides a comprehensive assessment of mitochondrial function across age and sex in mice. The strength of evidence supporting this resource is compelling, given the exhaustive number of tissues profiled and in-depth analyses performed.

Reviewed by eLife

DNA O-MAP uncovers the molecular neighborhoods associated with specific genomic loci

1. Yuzhen Liu
2. Christopher D McGann
3. Mary Krebs
4. Thomas A Perkins
5. Rose Fields
6. Conor K Camplisson
7. David Z Nwizugbo
8. Chris Hsu
9. Shayan C Avanessian
10. Ashley F Tsue
11. Evan E Kania
12. David M Shechner
13. Brian J Beliveau
14. Devin K Schweppe

• Curated by eLife

  eLife Assessment

  This study presents a valuable new method for probing the DNA and proteins associated with targeted genomic elements in cells. The authors present solid evidence that the method can map DNA-DNA interactions for individual loci and can detect enriched proteins at repetitive DNA loci such as telomeres, but benchmarks of the method's resolution and specificity remain incomplete. The methodological details of this study will be of particular interest and utility to chromatin biologists.

Reviewed by eLife

Perturbation-response analysis of in silico metabolic dynamics in nonlinear regime: Hard-coded responsiveness in the cofactors and network sparsity

1. Yusuke Himeoka
2. Chikara Furusawa

• Curated by eLife

  eLife Assessment

  This valuable study uses dynamic metabolic models to compare perturbation responses in a bacterial system, analyzing whether they return to their steady state or amplify beyond the initial perturbation. The evidence supporting the emergent properties of perturbed metabolic systems to network topology and sensitivity to specific metabolites is solid, although the authors do not explain the origin of some significant inconsistencies between models.

Reviewed by eLife