Biophysics

Mechanistic insight for T-cell exclusion by cancer-associated fibroblasts in human lung cancer

1. Joseph Ackermann
2. Chiara Bernard
3. Philemon Sirven
4. Helene Salmon
5. Massimiliano Fraldi
6. Martine D Ben Amar

• Curated by eLife

  eLife Assessment

  This is a valuable report of a spatially-extended model to study the complex interactions between immune cells, fibroblasts, and cancer cells, providing insights into how fibroblast activation can influence tumor progression. The model opens up new possibilities for studying fibroblast-driven effects in diverse settings, which is crucial for understanding potential tumor microenvironment manipulations that could enhance immunotherapy efficacy. While the results presented are convincing and follow logically from the model's assumptions, some of these assumptions, as acknowledged by the authors, may oversimplify certain aspects in light of complex experimental findings, system geometry, and general principles of active matter research. Nonetheless, the authors provide justification for their work as a meaningful step towards more comprehensive modeling approaches.

Reviewed by eLife

Wide transition-state ensemble as key component for enzyme catalysis

1. Gabriel E Jara
2. Francesco Pontiggia
3. Renee Otten
4. Roman V Agafonov
5. Marcelo A Martí
6. Dorothee Kern

• Curated by eLife

  eLife Assessment

  In this potentially important study, the authors report results of QM/MM simulations and kinetic measurements for the phosphoryl-transfer step in adenylate kinase. The results point to the mechanistic proposal that the transition state ensemble is broader in the most efficient form of the enzyme (i.e., in the presence of Mg2+ in the active site) and thus a different activation entropy. With a broad set of computations and experimental analyses, the level of evidence is considered solid by some reviewers. On the other hand, there remain limitations in the computational analyses, especially regarding free energy profiles using different methodologies (shape of free energy profiles with DFTB vs. PBE QM/MM, and barriers with steered MD and umbrella sampling) and the activation entropy, leading some reviewers to the evaluation that the level of evidence is incomplete.

Reviewed by eLife

Mechanism of dimer selectivity and binding cooperativity of BRAF inhibitors

1. Joseph Clayton
2. Aarion Romany
3. Evangelia Matenoglou
4. Evripidis Gavathiotis
5. Poulikos I Poulikakos
6. Jana Shen

• Curated by eLife

  eLife Assessment

  This fundamental study illuminates the dynamics of BRAF in its monomeric and dimeric forms, both in the absence and presence of inhibitors, through a convincing combination of traditional experiments and sophisticated computational analyses. By revealing novel insights into the selectivity and cooperative processes of BRAF inhibitors, it holds significant promise for the development of future therapeutics, particularly against mutant isoforms in cancer. Overall, these findings will be of great interest to structural biologists, medicinal chemists, and pharmacologists.

Reviewed by eLife

Huntingtin S421 phosphorylation increases kinesin and dynein engagement on early endosomes and lysosomes

1. Emily N.P. Prowse
2. Abdullah R. Chaudhary
3. David Sharon
4. Adam G. Hendricks

Reviewed by preLights

Glucokinase activity controls peripherally located subpopulations of β-cells that lead islet Ca2+ oscillations

1. Erli Jin
2. Jennifer K Briggs
3. Richard KP Benninger
4. Matthew J Merrins

• Curated by eLife

  eLife Assessment

  This study provides compelling evidence for functional subpopulations of β-cells responsible for Ca2+ signal initiation and maintenance using novel three-dimensional light sheet microscopy imaging and analysis of pancreatic islets. The findings are important as they help decode mechanistic underpinnings of islet calcium oscillations and the resulting pulsatile insulin secretion. The work will be of general interest to cell biologists and particular interest to islet biologists.

Reviewed by eLife

Target cell tension regulates macrophage trogocytosis

1. Caitlin E. Cornell
2. Aymeric Chorlay
3. Deepak Krishnamurthy
4. Nicholas R. Martin
5. Lucia Baldauf
6. Daniel A. Fletcher

Reviewed by preLights

Decoding phase separation of prion-like domains through data-driven scaling laws

1. M Julia Maristany
2. Anne Aguirre Gonzalez
3. Jorge R Espinosa
4. Jan Huertas
5. Rosana Collepardo-Guevara
6. Jerelle A Joseph

• Curated by eLife

  eLife Assessment

  The authors performed extensive coarse-grained molecular dynamics simulations of 140 different prion-like domain variants to interrogate how specific amino acid substitutions determine the driving forces for phase separation. The analyses are solid, and the derived predictive scaling laws can aid in identifying potential phase-separating regions in uncharacterized proteins. Overall, this is a valuable contribution to the field of biomolecular condensates. It exemplifies how data-driven methodologies can uncover new insights into complex biological phenomena.

Reviewed by eLife

Mechanical imbalance between normal and transformed cells drives epithelial homeostasis through cell competition

1. Praver Gupta
2. Sayantani Kayal
3. Nobuyuki Tanimura
4. Shilpa P Pothapragada
5. Harish K Senapati
6. Padmashree Devendran
7. Yasuyuki Fujita
8. Dapeng Bi
9. Tamal Das

• Curated by eLife

  eLife Assessment

  In this important study, the authors combine innovative experimental approaches, including direct compressibility measurements and traction force analyses, with theoretical modeling to propose that wild-type cells exert compressive forces on softer HRasV12-transformed cells, influencing competition outcomes. The data generally provide solid evidence that transformed epithelial cells exhibit higher compressibility than wild-type cells, a property linked to their compaction during mechanical cell competition. However, the study would benefit from further characterization of how compression affects the behavior of HRasV12 cells and clearer causal links between compressibility and competition outcomes.

Reviewed by eLife

Cluster size determines morphology of transcription factories in human cells

1. Massimiliano Semeraro
2. Giuseppe Negro
3. Giada Forte
4. Antonio Suma
5. Giuseppe Gonnella
6. Peter R Cook
7. Davide Marenduzzo

• Curated by eLife

  eLife Assessment

  This is a valuable polymer model that provides insight into the origin of macromolecular mixed and demixed states within transcription clusters. The well-performed and clearly presented simulations will be of interest to those studying gene expression in the context of chromatin. While the study is generally solid, it could benefit from a more direct comparison with existing experimental data sets as well as further discussion of the limits of the underlying model assumptions.

Reviewed by eLife

De novo identification of universal cell mechanics gene signatures

1. Marta Urbanska
2. Yan Ge
3. Maria Winzi
4. Shada Abuhattum
5. Syed Shafat Ali
6. Maik Herbig
7. Martin Kräter
8. Nicole Toepfner
9. Joanne Durgan
10. Oliver Florey
11. Martina Dori
12. Federico Calegari
13. Fidel-Nicolás Lolo
14. Miguel Ángel del Pozo
15. Anna Taubenberger
16. Carlo Vittorio Cannistraci
17. Jochen Guck

• Curated by eLife

  eLife Assessment

  This important study uses machine learning-based network analysis on transcriptomic data from different tissue cell types to identify a small set of conserved (pan-tissue) genes associated with changes in cell mechanics. The new method, which provides a new type of approach for mechanobiology, is accessible, compelling, and well-validated using in silico and experimental approaches. The study provides motivation for researchers to test hypotheses concerning the identified five-gene network, and the method will be strengthened over time with expanded sets of validations, such as testing genes with hitherto unknown roles and different perturbation techniques.

Reviewed by eLife