Biology subjects

Forman, J.

Publications and source records attributed to Forman, J..

7 recordsLinked to original sources

bioRxiv2026

Integrating Single-Cell Experiments and Stochastic Models to Understand and Predict Glucocorticoid Receptor Transport and DUSP1 mRNA Expression Dynamics

Glucocorticoids activate the glucocorticoid receptor (GR) to suppress inflammation, yet it remains unclear how GR transport dynamics and downstream gene regulation are coordinated within single cells. We combine immunocytochemistry (ICC) and single-molecule fluorescence in situ hybridization (smFISH) to quantify endogenous GR transport and DUSP1 transcription dynamics across thousands of individual cells following dexamethasone (Dex) stimulation. Performing multiple rounds of statistical inference based on Chemical Master Equations (CME), we determine the most likely mechanisms and reaction rates for Dex-driven GR nuclear import; compartment-specific GR degradation; GR-dependent control of the DUSP1 promoter; and DUSP1 transcription, elongation, transport, and degradation. Our inferred model suggests that nuclear GR degradation is the dominant mechanism of receptor clearance, that GR primarily regulates promoter activation, and that time-dependent AU-rich element (ARE)-mediated mRNA degradation contributes heavily to DUSP1 clearance. With these mechanisms, the fully-parameterized model quantitatively predicts joint distributions of GR translocation and decay dynamics, DUSP1 transcription site activity, and nuclear and cytoplasmic DUSP1 mRNA heterogeneity among clonal cells as functions of time and across seven orders of magnitude for Dex induction concentrations. Our results establish an integrated quantitative framework to link receptor dynamics to gene expression heterogeneity and predict single-cell hormone-responsive transcription programs.

cell biology↗

bioRxiv2026

The Stochastic System Identification Toolkit (SSIT) to model, fit, predict, and design experiments

Biological data is prone to both intrinsic and extrinsic noise and variability between experimental replicas. That same stochasticity and heterogeneity can carry information about underlying biochemical mechanisms but, if not incorporated in modeling and probabilistic inference, can also bias parameter estimates and misguide predictions and, subsequently, experiment design. Mechanistic inference typically requires lengthy simulations (e.g., the Stochastic Simulation Algorithm (SSA)); approximations to chemical master equation (CME) solutions that lack rigorous error tracking; or deterministic averaging that lacks the complexity necessary to reflect the data. We introduce the Stochastic System Identification Toolkit (SSIT) - a fast, flexible, and open-source software package available on GitHub that makes use of MATLABs efficient and diverse computational architecture. The SSIT is designed for building, simulating, and solving chemical reaction models using ODEs, moments, SSA, Finite State Projection truncations of the CME, or hybrid methods; sensitivity analysis and Fisher information quantification; parameter fitting using likelihood- or Bayesian-based methods; handling of experimental noise and measurement errors using probabilistic distortion operators; and sequential experiment design that empowers users to save time and resources while gaining the most information possible out of their data. The SSIT also offers advanced modeling tools, including model reduction methods for increased efficiency and joint fitting of models and datasets with overlapping reactions or parameters. To facilitate the ease and speed of use, the SSIT provides a graphical user interface and ready-made, adaptable pipelines that can be run in the background from commandline or high-performance computing clusters. We demonstrate features of the SSIT on two experimental datasets: the first consists of published mRNA count data that reflect Saccharomyces cerevisiae yeast cell response to osmotic shock using single-cell single-molecule fluorescence in situ hybridization; the second consists of single-cell RNA sequencing measurements of 151 activating genes in breast cancer cells following treatment with dexamethasone. Author summaryWe present the Stochastic System Identification Toolkit (SSIT) to model, fit, and predict any data that can be interpreted as changing populations or counts through time, including but not limited to single-cell experiments, economics, epidemiology, ecology, sociology, agriculture, and biotechnology. The SSIT was constructed particularly for stochastic modeling, which is important for systems whose states may experience significant fluctuations from mean behavior, thus affecting the inference of the underlying rate parameters and predictions of subsequent behavior. The SSIT provides statistical inference tools for parameter estimation; sensitivity analysis and information calculation; handling of distortions to probability distributions caused by experimental or measurement processes (e.g., dropout in single-cell RNA sequence data and total fluorescence intensities versus spot counting/puncta analysis); and quantitative design of experiments. The SSIT also offers a variety of complex modeling tools, including model reduction methods and fitting of combined models/datasets that share some behavior but remain distinct (e.g., different genes responding a single stimulus). The SSIT generates pipelines for easy, efficient analyses to run in the MATLAB environment, in the background on commandline, or on high-performance computing clusters, thus facilitating users to make informed, time- and cost-effective decisions about their next set of experiments.

bioinformatics↗

bioRxiv2024

Object permanence in domestic cats (Felis catus) using violation-of-expectancy by owner and stranger

Object permanence, the understanding that objects continue to exist while out of sight, has been extensively researched in human infants and animals. Cats have been shown to reach Stage 5 object permanence by passing successive visible displacement tests, but their understanding of Stage 6 object permanence is less clear. We tested 18 domestic cats on their understanding of Stage 4 (single visible displacement; SVD) and Stage 6 (invisible displacement; IVD) of object permanence in their home environment. Additionally, we investigated how person familiarity may influence study engagement. In SVD tests, the box manipulator (owner or researcher) hid a toy in one of two cardboard boxes for the cat to find. In IVD tests, we implemented a violation-of-expectancy procedure in which the box manipulator showed a toy re-appearing out of either a) the same box it was hidden in (consistent trials) or b) the box it was not hidden in (violation trials). Approximately half of the cats (56%) did not find the hidden toy in SVD trials, with 42% of these cats not attempting to find the toy, despite previous research demonstrating that cats can retrieve hidden objects in successive SVD tests. No predictors significantly influenced whether cats found the toy, or which box was checked first (toy or empty box). In IVD trials, we unexpectedly found that cats were more likely to play with and had longer gaze durations towards the toy and display more toy box-directed behaviours in consistent trials than violation trials. Similarly, we found that cats were more likely to display toy and empty box- directed behaviours in trials where the researcher acted as the box manipulator. Other factors such as breed, outdoor access, cat sex and the first person to act as the box manipulator also influenced toy-directed behaviours. We discuss the complexity of person familiarity in research contexts and highlight some methodological challenges in studying cat cognition.

animal behavior and cognition↗

bioRxiv2023

Chromatin Remodeling and Epigenetic Modifications in Planar Cell Migration

The cell nucleus plays a critical role in cell migration by its deformability under forces, by acting as a piston that activates mechanosensitive channels, and also by serving as a ruler tailoring cell response to spatial constraints. Cell nuclear mechanics determine the mechanobiology of static and migrating cells. Here, we report that intranuclear chromatin architecture plays a previously unknown role during cell migration on planar substrates. By inhibiting histone deacetylation, which compacts chromatin, and a methyltransferase, which affects chromatin remodeling and chromatin compaction, we showed that cell migration speed and persistence drastically slowed wound closure efficiency in an in vitro scratch wound assay. Chromatin remodeling was visualized and quantified during cell migration, which may be intertwined with nuclear mechanics and shape. Inhibition disrupts remodeling and other unknown roles in chromatin, thus affecting the speed and persistence of cell migration. Interestingly, cytoskeletal stress fiber formation and cell shape were not visibly affected by chromatin modifications, suggesting an exclusive nuclear mechanobiological role in cell migration. These findings provide new insights into how aging and other degenerative conditions affect the plasticity of chromatin architecture and hence its effect on cell migration efficiency.

biophysics↗

bioRxiv2020

Integrative Molecular Characterization of Sarcomatoid and Rhabdoid Renal Cell Carcinoma Reveals Determinants of Poor Prognosis and Response to Immune Checkpoint Inhibitors

Sarcomatoid and rhabdoid (S/R) renal cell carcinoma (RCC) are highly aggressive tumors with limited molecular and clinical characterization. Emerging evidence suggests immune checkpoint inhibitors (ICI) are particularly effective for these tumors1-3, although the biological basis for this property is largely unknown. Here, we evaluate multiple clinical trial and real-world cohorts of S/R RCC to characterize their molecular features, clinical outcomes, and immunologic characteristics. We find that S/R RCC tumors harbor distinctive molecular features that may account for their aggressive behavior, including BAP1 mutations, CDKN2A deletions, and increased expression of MYC transcriptional programs. We show that these tumors are highly responsive to ICI and that they exhibit an immune-inflamed phenotype characterized by immune activation, increased cytotoxic immune infiltration, upregulation of antigen presentation machinery genes, and PD-L1 expression. Our findings shed light on the molecular drivers of aggressivity and responsiveness to immune checkpoint inhibitors of S/R RCC tumors.

cancer biology↗

bioRxiv2020

Distinct evolutionary paths in chronic lymphocytic leukemia during resistance to graft-versus-leukemia

Resistance to the graft-versus-leukemia (GvL) effect remains the major barrier to successful allogeneic hematopoietic stem cell transplantation (allo-HSCT) for aggressive hematologic malignancies. The basis of GvL resistance for advanced lymphoid malignancies remains incompletely understood. We hypothesized that for patients with chronic lymphocytic leukemia (CLL) treated with allo-HSCT, leukemic cell-intrinsic features shape GvL outcomes by directing the evolutionary trajectories of CLL cells. Integrated genetic, transcriptomic and epigenetic analyses of CLL cells from 10 patients revealed that the clinical kinetics of post- HSCT relapse are shaped by distinct molecular dynamics and suggest that the selection pressures of the GvL bottleneck are unlike those imposed by chemotherapy. No selective advantage for HLA loss was observed, even when present in pre-transplant subpopulations. Regardless of post-transplant relapse kinetics, gain of stem cell modules was a common signature associated with leukemia relapse. These data elucidate the biological pathways that underlie GvL resistance and post-transplant relapse. One Sentence SummaryWe find that the clinical kinetics of chronic lymphocytic leukemia relapse after stem cell transplant are underwritten by distinct genetic and epigenetic evolutionary trajectories and suggest that the selection pressures of the post-transplant, immunologic bottleneck are unlike those imposed by chemotherapy.

cancer biology↗

bioRxiv2017

xenoGI: reconstructing the history of genomic island insertions in clades of closely related bacteria

BackgroundGenomic islands play an important role in microbial genome evolution, providing a mechanism for strains to adapt to new ecological conditions. A variety of computational methods, both genome-composition based and comparative have been developed to identify them. Some of these methods are explicitly designed to work in single strains, while others make use of multiple strains. In general, existing methods do not identify islands in the context of the phylogeny in which they evolved. Even multiple strain approaches are best suited to identifying genomic islands that are present in one strain but absent in others. They do not automatically recognize islands which are shared between some strains in the clade or determine the branch on which these islands inserted within the phylogenetic tree.\n\nResultsWe have developed a software package, xenoGI, that identifies genomic islands and maps their origin within a clade of closely related bacteria, determining which branch they inserted on. It takes as input a set of sequenced genomes and a tree specifying their phylogenetic relationships. Making heavy use of synteny information, the package builds gene families in a species-tree-aware way, and then attempts to combine into islands those families whose members are adjacent and whose most recent common ancestor is shared. The package provides a variety of text-based analysis functions, as well as the ability to export genomic islands into formats suitable for viewing in a genome browser. We demonstrate the capabilities of the package with several examples from enteric bacteria, including an examination of the evolution of the acid fitness island in the genus Escherichia. In addition we use output from simulations and a set of known genomic islands from the literature to show that xenoGI can accurately identify genomic islands and place them on a phylogenetic tree.\n\nConclusionsxenoGI is an effective tool for studying the history of genomic island insertions in a clade of microbes. It identifies genomic islands, and determines which branch they inserted on within the phylogenetic tree for the clade. Such information is valuable because it helps us understand the adaptive path that has produced living species. Given the large and growing number of sequenced microbial genomes, this sort of analysis will become increasingly useful in the future.

bioinformatics↗