@article {156901, title = {Combining lineage correlations and a small molecule inhibitor to detect circadian control of the cell cycle.}, journal = {iScience}, volume = {28}, year = {2025}, month = {2025 Apr 18}, pages = {112269}, abstract = {

Chronotherapy offers an exciting possibility for improving cancer treatments by leveraging the influence of the circadian clock on the cell cycle. While several molecular interactions coupling the two oscillators have been identified, whether they lead to emergent control of cellular proliferation remains unclear. Using stochastic simulations, we demonstrate that the established gene networks underlying the two oscillators are sufficient to generate lineage correlations in cell cycle times, as observed in single-cell microscopy data. The interactions also create a {\textquoteright}therapeutic window{\textquoteright} between cancer and normal cell proliferation peaks that can be leveraged for chronotherapy. Surprisingly, our model predicts that KL001, a clock inhibitor, minimally affects population growth but significantly alters lineage correlations. Our results suggest that clock control of the cell cycle may not be detectable by measuring changes in population dynamics, but combining measurements of lineage correlations with KL001 treatment may provide a more sensitive approach to detecting the coupling.

}, keywords = {Cell biology; Small molecule.}, issn = {2589-0042}, doi = {10.1016/j.isci.2025.112269}, url = {https://www.sciencedirect.com/science/article/pii/S2589004225005309?via\%3Dihub}, author = {Nikhat, Anjoom and Shaikh, Arsh and Chakrabarti, Shaon} } @article {160489, title = {Inheritable cell-states shape drug-persister correlations and population dynamics in cancer cells.}, journal = {PLoS Comput Biol}, volume = {21}, year = {2025}, month = {2025 Sep}, pages = {e1013446}, abstract = {

Drug tolerant persisters (DTPs) drive cancer therapy resistance by temporarily evading drug action, allowing multiple routes to eventual permanent resistance. Despite clear evidence for DTPs, the timing of their emergence, proliferative nature, and how their population dynamics arise from measured single-cell kinetics remain poorly understood. Here we use time-lapse microscopy data from two cancer cell lines, integrating single-cell and population measurements, to develop a quantitative description of drug persistence. Contrary to the expectation that increasing levels of genotoxic stress should lead to slower times to division and faster times to death, we observe minor changes in the single-cell intermitotic and death time distributions upon increasing cisplatin concentration. Yet, population decay rates increase 3-fold, suggesting a surprising independence of the overall dynamics from the measured birth and death rates. To explain this phenomenon, we argue that the observed lineage correlations and concentration-dependent decay rates imply cell-state dependent fate choices made both pre and post-cisplatin as opposed to just post-drug birth/death rate-based competitive fate choices. We demonstrate that these cell-states, present in the pre-drug ancestors of DTP and sensitive cells, exhibit no difference in cycling speed and are inherited across at least 2-3 cellular generations. Post-drug survival versus death fates are decided with high probability by these pre-existing cell-states, but get modulated to some extent by the drug, leading to a drug concentration dependent state-fate map. A stochastic model implementing these rules simultaneously recapitulates the observed decay rates and cell-fate lineage correlations. The model also demonstrates how the use of barcode diversity change before and after drug might lead to misleading interpretations of the timing of persister fate decisions. Our results provide a conceptual framework for quantifying pre versus post-drug contributions to cell fate, without requiring knowledge of the underlying molecular architecture of the heterogeneous cell states.

}, keywords = {Antineoplastic Agents, Cell Line, Tumor, Cisplatin, Computational Biology, Drug Resistance, Neoplasm, Humans, Models, Biological, Neoplasms, Single-Cell Analysis, Time-Lapse Imaging}, issn = {1553-7358}, doi = {10.1371/journal.pcbi.1013446}, author = {Iyer, Anton and Alva, Adrian and Granada, Adri{\'a}n E and Chakrabarti, Shaon} } @article {150950, title = {Identifying memory genes in cancer drug tolerance.}, journal = {Nat Rev Cancer}, year = {2024}, month = {2024 Dec 02}, issn = {1474-1768}, doi = {10.1038/s41568-024-00780-w}, author = {Chakrabarti, Shaon} } @article {142318, title = {Reflections on Several Landmark Advances in Circadian Biology.}, journal = {J Circadian Rhythms}, volume = {22}, year = {2024}, month = {2024}, pages = {1}, abstract = {

Circadian Biology intersects with diverse scientific domains, intricately woven into the fabric of organismal physiology and behavior. The rhythmic orchestration of life by the circadian clock serves as a focal point for researchers across disciplines. This retrospective examination delves into several of the scientific milestones that have fundamentally shaped our contemporary understanding of circadian rhythms. From deciphering the complexities of clock genes at a cellular level to exploring the nuances of coupled oscillators in whole organism responses to stimuli. The field has undergone significant evolution lately guided by genetics approaches. Our exploration here considers key moments in the circadian-research landscape, elucidating the trajectory of this discipline with a keen eye on scientific advancements and paradigm shifts.

}, issn = {1740-3391}, doi = {10.5334/jcr.236}, author = {Chawla, Sangeeta and Oster, Henrik and Duffield, Giles E and Maronde, Erik and Guido, Mario E and Chabot, Christopher and Dkhissi-Benyahya, Ouria and Provencio, Ignacio and Goel, Namni and Youngstedt, Shawn D and Zi-Ching Mak, Natalie and Caba, Mario and Nikhat, Anjoom and Chakrabarti, Shaon and Wang, Lei and Davis, Seth J} } @article {142316, title = {Timely Questions Emerging in Chronobiology: The Circadian Clock Keeps on Ticking.}, journal = {J Circadian Rhythms}, volume = {22}, year = {2024}, month = {2024}, pages = {2}, abstract = {

Chronobiology investigations have revealed much about cellular and physiological clockworks but we are far from having a complete mechanistic understanding of the physiological and ecological implications. Here we present some unresolved questions in circadian biology research as posed by the editorial staff and guest contributors to the Journal of Circadian Rhythms. This collection of ideas is not meant to be comprehensive but does reveal the breadth of our observations on emerging trends in chronobiology and circadian biology. It is amazing what could be achieved with various expected innovations in technologies, techniques, and mathematical tools that are being developed. We fully expect strengthening mechanistic work will be linked to health care and environmental understandings of circadian function. Now that most clock genes are known, linking these to physiological, metabolic, and developmental traits requires investigations from the single molecule to the terrestrial ecological scales. Real answers are expected for these questions over the next decade. Where are the circadian clocks at a cellular level? How are clocks coupled cellularly to generate organism level outcomes? How do communities of circadian organisms rhythmically interact with each other? In what way does the natural genetic variation in populations sculpt community behaviors? How will methods development for circadian research be used in disparate academic and commercial endeavors? These and other questions make it a very exciting time to be working as a chronobiologist.

}, issn = {1740-3391}, doi = {10.5334/jcr.237}, author = {Chawla, Sangeeta and O{\textquoteright}Neill, John and Knight, Marina I and He, Yuqing and Wang, Lei and Maronde, Erik and Rodr{\'\i}guez, Sergio Gil and van Ooijen, Gerben and Garbarino-Pico, Eduardo and Wolf, Eva and Dkhissi-Benyahya, Ouria and Nikhat, Anjoom and Chakrabarti, Shaon and Youngstedt, Shawn D and Zi-Ching Mak, Natalie and Provencio, Ignacio and Oster, Henrik and Goel, Namni and Caba, Mario and Oosthuizen, Maria and Duffield, Giles E and Chabot, Christopher and Davis, Seth J} } @article {137348, title = {Diffusion controls local versus dispersed inheritance of histones during replication and shapes epigenomic architecture.}, journal = {PLoS Comput Biol}, volume = {19}, year = {2023}, month = {2023 Dec 18}, pages = {e1011725}, abstract = {

The dynamics of inheritance of histones and their associated modifications across cell divisions can have major consequences on maintenance of the cellular epigenomic state. Recent experiments contradict the long-held notion that histone inheritance during replication is always local, suggesting that active and repressed regions of the genome exhibit fundamentally different histone dynamics independent of transcription-coupled turnover. Here we develop a stochastic model of histone dynamics at the replication fork and demonstrate that differential diffusivity of histones in active versus repressed chromatin is sufficient to quantitatively explain these recent experiments. Further, we use the model to predict patterns in histone mark similarity between pairs of genomic loci that should be developed as a result of diffusion, but cannot originate from either PRC2 mediated mark spreading or transcriptional processes. Interestingly, using a combination of CHIP-seq, replication timing and Hi-C datasets we demonstrate that all the computationally predicted patterns are consistently observed for both active and repressive histone marks in two different cell lines. While direct evidence for histone diffusion remains controversial, our results suggest that dislodged histones in euchromatin and facultative heterochromatin may exhibit some level of diffusion within "Diffusion-Accessible-Domains" (DADs), leading to redistribution of epigenetic marks within and across chromosomes. Preservation of the epigenomic state across cell divisions therefore might be achieved not by passing on strict positional information of histone marks, but by maintaining the marks in somewhat larger DADs of the genome.

}, issn = {1553-7358}, doi = {10.1371/journal.pcbi.1011725}, author = {Singh, Archit and Chakrabarti, Shaon} } @article {134610, title = {An improved rhythmicity analysis method using Gaussian Processes detects cell-density dependent circadian oscillations in stem cells.}, journal = {Bioinformatics}, year = {2023}, month = {2023 Sep 28}, abstract = {

MOTIVATION: Detecting oscillations in time series remains a challenging problem even after decades of research. In chronobiology, rhythms (for instance in gene expression, eclosion, egg-laying and feeding) tend to be low amplitude, display large variations amongst replicates, and often exhibit varying peak-to-peak distances (non-stationarity). Most currently available rhythm detection methods are not specifically designed to handle such datasets, and are also limited by their use of p-values in detecting oscillations.

RESULTS: We introduce a new method, ODeGP (Oscillation Detection using Gaussian Processes), which combines Gaussian Process (GP) regression and Bayesian inference to incorporate measurement errors, non-uniformly sampled data, and a recently developed non-stationary kernel to improve detection of oscillations. By using Bayes factors, ODeGP models both the null (non-rhythmic) and the alternative (rhythmic) hypotheses, thus providing an advantage over p-values. Using synthetic datasets we first demonstrate that ODeGP almost always outperforms eight commonly used methods in detecting stationary as well as non-stationary symmetric oscillations. Next, by analyzing existing qPCR datasets we demonstrate that our method is more sensitive compared to the existing methods at detecting weak and noisy oscillations. Finally, we generate new qPCR data on mouse embryonic stem cells. Surprisingly, we discover using ODeGP that increasing cell density results in rapid generation of oscillations in the Bmal1 gene, thus highlighting our method{\textquoteright}s ability to discover unexpected and new patterns. In its current implementation, ODeGP is meant only for analyzing single or a few time-trajectories, not genome-wide datasets.

AVAILABILITY AND IMPLEMENTATION: ODeGP is available at https://github.com/Shaonlab/ODeGP.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Journal Name online.

}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btad602}, author = {Sahay, Shabnam and Adhikari, Shishir and Hormoz, Sahand and Chakrabarti, Shaon} } @article {87964, title = {Circadian clock effects on cellular proliferation: Insights from theory and experiments.}, journal = {Curr Opin Cell Biol}, volume = {67}, year = {2020}, month = {2020 Dec}, pages = {17-26}, abstract = {

Oscillations of the cellular circadian clock have emerged as an important regulator of many physiological processes, both in health and in disease. One such process, cellular proliferation, is being increasingly recognized to be affected by the circadian clock. Here, we review how a combination of experimental and theoretical work has furthered our understanding of the way circadian clocks couple to the cell cycle and play a role in tissue homeostasis and cancer. Finally, we discuss recently introduced methods for modeling coupling of clocks based on techniques from survival analysis and machine learning and highlight their potential importance for future studies.

}, issn = {1879-0410}, doi = {10.1016/j.ceb.2020.07.003}, author = {Chakrabarti, Shaon and Michor, Franziska} }