Systems Biology

Systems genomics of salinity stress response in rice

1. Sonal Gupta
2. Simon Niels Groen
3. Maricris L Zaidem
4. Andres Godwin C Sajise
5. Irina Calic
6. Mignon Natividad
7. Kenneth McNally
8. Georgina V Vergara
9. Rahul Satija
10. Steven J Franks
11. Rakesh K Singh
12. Zoé Joly-Lopez
13. Michael D Purugganan

• Curated by eLife

  eLife Assessment

  Working with a diverse panel of rice accessions grown in field conditions, this valuable study measures changes in transcript abundance, tests for patterns of selection on gene expression, and maps the genetic basic of variation in gene expression in normal and elevated salinity treatments. The manuscript provides solid evidence that mean gene expression levels are further from the optimum abundance for more genes under the elevated salinity treatment compared to normal treatment, and that a relatively small number of genes are hotspots that harbor genetic variants which affect broader genome-wide patterns of natural variation in gene expression under high salinity conditions. However, the design, clarity, and interpretation of several statistical analyses can be improved, some opportunities for integration among datasets and analyses could yet be realized, and genetic manipulation is required to confirm functional involvement of any specific genes in regulatory networks or organismal traits that confer adaptation to higher salinity conditions. The manuscript will be of interest to evolutionary biologists studying the genetics of complex traits and a resource for plant biologists studying mechanisms of abiotic stress tolerance.

Reviewed by eLife

Hormone circuit analysis explains why most HPA drugs fail for mood disorders and predicts the few that work

1. Tomer Milo
2. Shiraz Nir Halber
3. Moriya Raz
4. Avi Mayo
5. Uri Alon

Reviewed by Review Commons

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 valuable theoretical predictions on how horizontal gene transfer (HGT) can lead to alternative stable states in microbial communities. Using a modeling framework, solid theoretical evidence is provided to support the claimed role of HGT. However, given that the model has many degrees of freedom, a more comprehensive analysis of the role of different parameters could strengthen the study. Additionally, potential interactions between plasmids that carry out HGT are not discussed in the model. This paper would be of interest to researchers in microbiology, ecology, and evolutionary biology.

Reviewed by eLife

A statistical framework for quantifying the nuclear export rate of influenza viral mRNAs

1. Michi Miura
2. Naho Kiuchi
3. Siu-Ying Lau
4. Bobo Wing-Yee Mok
5. Hiroshi Ushirogawa
6. Tadasuke Naito
7. Honglin Chen
8. Mineki Saito

• Curated by eLife

  eLife Assessment

  This important study combines virology experiments and mathematical modeling to determine the nuclear export rate of each of the eight RNA segments of the influenza A virus, leading to the proposal that a specific retention of mRNA within the nucleus delays the expression of antigenic viral proteins. The proposed model for explaining the differential rate of export is compelling, going beyond the state of the art, but the experimental setup is only in partial support and further studies will be needed to confirm the proposed mechanism.

Reviewed by eLife

Dynamic multi-omics and mechanistic modeling approach uncovers novel mechanisms of kidney fibrosis progression

1. Nadine Tuechler
2. Mira Lea Burtscher
3. Martin Garrido-Rodriguez
4. Muzamil Majid Khan
5. Denes Turei
6. Christian Tischer
7. Sarah Kaspar
8. Jennifer Jasmin Schwarz
9. Frank Stein
10. Mandy Rettel
11. Rafael Kramann
12. Mikhail M Savitski
13. Julio Saez-Rodriguez
14. Rainer Pepperkok

Reviewed by Review Commons

Evaluating Study Design Rigor in Preclinical Cardiovascular Research: A Replication Study

1. Isaiah C Jimenez
2. Gabrielle C Montenegro
3. Keyana Zahiri
4. Damini Patel
5. Adrienne Mueller

• Curated by eLife

  eLife Assessment

  The objective of this important study is to assess the study design and rigor, enhance the quality of clinical research studies, and emphasize crucial design elements in basic science research. It specifically tackles the ongoing problem of experimental design deficiencies that obstruct the effective translation of research findings into clinical applications. This paper is particularly convincing as it highlights the lack of progress in addressing these issues over the past decade, despite a substantial body of existing research. It serves as a strong call to action for the broader scientific community to improve research practices.

Reviewed by eLife

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. Herve 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