Systems Biology

Human interpretable grammar encodes multicellular systems biology models to democratize virtual cell laboratories

1. Jeanette A.I. Johnson
2. Daniel R. Bergman
3. Heber L. Rocha
4. David L. Zhou
5. Eric Cramer
6. Ian C. Mclean
7. Yoseph W. Dance
8. Max Booth
9. Zachary Nicholas
10. Tamara Lopez-Vidal
11. Atul Deshpande
12. Randy Heiland
13. Elmar Bucher
14. Fatemeh Shojaeian
15. Matthew Dunworth
16. André Forjaz
17. Michael Getz
18. Inês Godet
19. Furkan Kurtoglu
20. Melissa Lyman
21. John Metzcar
22. Jacob T. Mitchell
23. Andrew Raddatz
24. Jacobo Solorzano
25. Aneequa Sundus
26. Yafei Wang
27. David G. DeNardo
28. Andrew J. Ewald
29. Daniele M. Gilkes
30. Luciane T. Kagohara
31. Ashley L. Kiemen
32. Elizabeth D. Thompson
33. Denis Wirtz
34. Laura D. Wood
35. Pei-Hsun Wu
36. Neeha Zaidi
37. Lei Zheng
38. Jacquelyn W. Zimmerman
39. Jude M. Phillip
40. Elizabeth M. Jaffee
41. Joe W. Gray
42. Lisa M. Coussens
43. Young Hwan Chang
44. Laura M. Heiser
45. Genevieve L. Stein-O’Brien
46. Elana J. Fertig
47. Paul Macklin

Reviewed by preLights

Shared and distinct pathways and networks genetically linked to coronary artery disease between human and mouse

1. Zeyneb Kurt
2. Jenny Cheng
3. Rio Barrere-Cain
4. Caden N McQuillen
5. Zara Saleem
6. Neil Hsu
7. Nuoya Jiang
8. Calvin Pan
9. Oscar Franzén
10. Simon Koplev
11. Susanna Wang
12. Johan Björkegren
13. Aldons J Lusis
14. Montgomery Blencowe
15. Xia Yang

• Curated by eLife

  eLife assessment

  In this important study, the authors integrated genetic and genomic datasets from humans and mice to unveil shared networks and pathways associated with coronary artery disease. Their compelling analysis led to the identification of new regulatory genes and pathways in vascular tissues and in the liver, allowing for a more in-depth understanding of the pathogenesis of coronary artery disease.

Reviewed by eLife

The PP2A-like phosphatase Ppg1 mediates assembly of the Far complex to balance gluconeogenic outputs and enables adaptation to glucose depletion

1. Shreyas Niphadkar
2. Lavanya Karinje
3. Sunil Laxman

Reviewed by Review Commons

Quantitative Geometric Modeling of Blood Cells from X-ray Histotomograms of Whole Zebrafish Larvae

1. Maksim A. Yakovlev
2. Ke Liang
3. Carolyn R. Zaino
4. Daniel J. Vanselow
5. Andrew L. Sugarman
6. Alex Y. Lin
7. Patrick J. La Riviere
8. Yuxi Zheng
9. Justin D. Silverman
10. John C. Leichty
11. Sharon X. Huang
12. Keith C. Cheng

• Curated by eLife

  eLife assessment

  Tissue phenotyping is central to nearly all areas of biology. In this study, the authors use an advanced form of micro-CT (X-ray histotomography) in zebrafish to phenotype blood cells in the intact animal. These approaches build upon prior work from this group and others showing this is a scalable imaging method that could readily be applied to other cell types, and provide an excellent complement to histological analysis of tissues. This is important work, as it demonstrates that the method can provide an approach that is orthogonal to conventional histology. The strength of the presented data is compelling, with description of both the hardware and software needed to implement the protocol, which will make it accessible to other researchers in the field.

Reviewed by eLife

Affected cell types for hundreds of Mendelian diseases revealed by analysis of human and mouse single-cell data

1. Idan Hekselman
2. Assaf Vital
3. Maya Ziv-Agam
4. Lior Kerber
5. Ido Yairi
6. Esti Yeger-Lotem

• Curated by eLife

  eLife assessment

  The study presents analyses linking cell-types to monogenic disorders using over-expression of known disease-associated genes in single-cell data to identify 110 disease-affected cell types for 714 Mendelian diseases. Overall this important study combines multiple data analyses to quantify the connection between cell types and human disorders. While some of the analyses are compelling, updates to the method are needed to ensure that statistical inference is appropriately stringent and rigorous.

Reviewed by eLife

Multicellular factor analysis of single-cell data for a tissue-centric understanding of disease

1. Ricardo Omar Ramirez Flores
2. Jan David Lanzer
3. Daniel Dimitrov
4. Britta Velten
5. Julio Saez-Rodriguez

Reviewed by Review Commons

Homeostasis, injury, and recovery dynamics at multiple scales in a self-organizing mouse intestinal crypt

1. Louis Gall
2. Carrie Duckworth
3. Ferran Jardi
4. Lieve Lammens
5. Aimee Parker
6. Ambra Bianco
7. Holly Kimko
8. David Mark Pritchard
9. Carmen Pin

• Curated by eLife

  eLife assessment

  The authors developed a valuable mathematical model that describes the spatiotemporal dynamics of cells in the intestinal crypt. The proposed model makes an important contribution to the field, allowing a better understanding of the formation and response dynamics of the intestinal crypt through the effective evaluation of health, disease, and treatment conditions. The authors provided solid evidence of the validity of their model and their conclusions, but some minor claims are not properly justified in the current manuscript. This paper is meant for computational biologists and cancer researchers working on oncotherapies for the intestinal epithelium.

Reviewed by eLife

Systems level identification of a matrisome-associated macrophage polarisation state in multi-organ fibrosis

1. John F Ouyang
2. Kunal Mishra
3. Yi Xie
4. Harry Park
5. Kevin Y Huang
6. Enrico Petretto
7. Jacques Behmoaras

• Curated by eLife

  eLife assessment

  This important study deepens our understanding of macrophage phenotypes in pathological contexts and identifies a new macrophage state associated with tissue fibrosis, as well as putative drivers of this cellular state. The authors provide convincing evidence and performed a well-thought-out and thoroughly described computational analysis of single-cell RNA-sequencing data. This work will be of broad interest to the fields of tissue inflammation, fibrosis, macrophage biology, and immunology.

Reviewed by eLife

A Phosphoproteomics Data Resource for Systems-level Modeling of Kinase Signaling Networks

1. Song Feng
2. James A. Sanford
3. Thomas Weber
4. Chelsea M. Hutchinson-Bunch
5. Panshak P. Dakup
6. Vanessa L. Paurus
7. Kwame Attah
8. Herbert M. Sauro
9. Wei-Jun Qian
10. H. Steven Wiley

Reviewed by preLights

A new pipeline SPICE identifies novel JUN-IKZF1 composite elements

1. Peng Li
2. Sree Pulugulla
3. Sonali Das
4. Jangsuk Oh
5. Rosanne Spolski
6. Jian-Xin Lin
7. Warren J Leonard

• Curated by eLife

  eLife assessment

  This valuable study presents a screening pipeline (SPICE) for detecting DNA motif spacing preferences between TF partners. SPICE predicts previously known composite elements, but experiments to elucidate the nature of the predicted novel interaction between JUN and IKZF1 are incomplete. These experiments would benefit from more rigorous approaches using other databases to explore additional relevant data. The work will be of broad interest to those involved in dissecting the regulatory logic of mammalian enhancers and promoters.

Reviewed by eLife