Biology subjects

Katira, P.

Publications and source records attributed to Katira, P..

7 recordsLinked to original sources

bioRxiv2025

The impact of patient biology on racial disparities in breast cancer outcome

Hormone receptor positive (HR+) breast cancer is the most common subtype of breast cancer diagnosed globally. Despite effective targeted therapies, HR+ breast cancer remains a leading cause of cancer-related death in women. Long-standing epidemiological research identifies significantly worse outcomes for Black women diagnosed with HR+ breast cancer relative to White women. While structural factors such as access to healthcare and education level contribute to this outcome disparity, it persists even in analyses where these factors are controlled. In-depth analyses of the somatic molecular biology that may underlie these outcome disparities are hampered by a lack of datasets that represent Black patient populations. Here, we generate a HR+ breast cancer patient transcriptomic dataset that overrepresents Black women and controls for access to healthcare and education level. We find that signatures relating to the tumor microenvironment, i.e. collagen deposition and prognostically unfavorable T-cell landscapes are enriched in HR+ tumors from Black women. Importantly, we find, using experimental model systems in vitro and in vivo, that race-aligned collagen deposition patterns are at least partially attributable to tumor cell-intrinsic signaling and critical for Black breast cancer metastasis. We also find that unfavorable T-cell signatures in HR+ tumors from Black women, which have previously been attributed to race and ancestry, are more strongly poverty-aligned. Using multiple independent datasets, we identify STAT4 as a potential master regulator of this poverty-associated tumor immune signature. Together, these findings provide new evidence that somatic molecular biology of breast cancer patients can be modified by multiple structural factors such as self-identified race and poverty burden to promote poor patient outcomes. Integrating an understanding of structural factors into molecular cancer research is critical for implementing truly personalized, and maximally effective, oncology systems.

cancer biology↗

bioRxiv2023

Interactions between ploidy and resource availability shape clonal interference at initiation and recurrence of glioblastoma

Glioblastoma (GBM) is the most aggressive form of primary brain tumor. Complete surgical resection of GBM is almost impossible due to the infiltrative nature of the cancer. While no evidence for recent selection events have been found after diagnosis, the selective forces that govern gliomagenesis are strong, shaping the tumors cell composition during the initial progression to malignancy with late consequences for invasiveness and therapy response. We present a mathematical model that simulates the growth and invasion of a glioma, given its ploidy level and the nature of its brain tissue micro-environment (TME), and use it to make inferences about GBM initiation and response to standard-of-care treatment. We approximate the spatial distribution of resource access in the TME through integration of in-silico modelling, multi-omics data and image analysis of primary and recurrent GBM. In the pre-malignant setting, our in-silico results suggest that low ploidy cancer cells are more resistant to starvation-induced cell death. In the malignant setting, between first and second surgery, simulated tumors with different ploidy compositions progressed at different rates. Whether higher ploidy predicted fast recurrence, however, depended on the TME. Historical data supports this dependence on TME resources, as shown by a significant correlation between the median glucose uptake rates in human tissues and the median ploidy of cancer types that arise in the respective tissues (Spearman r = -0.70; P = 0.026). Taken together our findings suggest that availability of metabolic substrates in the TME drives different cell fate decisions for cancer cells with different ploidy and shapes GBM disease initiation and relapse characteristics.

cancer biology↗

bioRxiv2023

"Patchiness" in Mechanical Stiffness across a Tumor as an Early-Stage Marker for Malignancy

Mechanical phenotyping of tumors, either at an individual cell level or tumor cell population level is gaining traction as a diagnostic tool. However, the extent of diagnostic and prognostic information that can be gained through these measurements is still unclear. In this work, we focus on the heterogeneity in mechanical properties of cells obtained from a single source such as a tissue or tumor as a potential novel biomarker. We believe that this heterogeneity is a conventionally overlooked source of information in mechanical phenotyping data. We use mechanics-based in-silico models of cell-cell interactions and cell population dynamics within 3D environments to probe how heterogeneity in cell mechanics drives tissue and tumor dynamics. Our simulations show that the initial heterogeneity in the mechanical properties of individual cells and the arrangement of these heterogenous sub-populations within the environment can dictate overall cell population dynamics and cause a shift towards the growth of malignant cell phenotypes within healthy tissue environments. The overall heterogeneity in the cellular mechanotype and their spatial distributions is quantified by a "patchiness" index, which is the ratio of the global to local heterogeneity in cell populations. We observe that there exists a threshold value of the patchiness index beyond which an overall healthy cell population of cells will show a steady shift towards a more malignant phenotype. Based on these results, we propose that the "patchiness" of a tumor or tissue sample, can be an early indicator for malignant transformation and cancer occurrence in benign tumors or healthy tissues. Additionally, we suggest that tissue patchiness, measured either by biochemical or biophysical markers, can become an important metric in predicting tissue health and disease likelihood just as landscape patchiness is an important metric in ecology.

cancer biology↗

bioRxiv2023

Predicting phenotype to mechanotype relationships in cells based on intra-cellular signaling network

Cells originating from the same tissue can respond differently to external signals depending on the genotypic and phenotypic state of the cell and its local environment. We have developed a semi-quantitative-computational model to analyze the intracellular signaling network and its outcome in the presence of multiple external signals including growth factors, hormones, and extracellular matrix. We use this model to analyze the cells mechanical response to external stimuli and identify the key internal elements of the network that drive specific outcomes within the response space. The model is built upon the Boolean approach to network modeling, where the state of any given node is determined using the state of the connecting nodes and Boolean logic. This allows us to analyze the network behavior without the need to estimate all the various interaction rates between different cellular components. However, such an approach is limited in its ability to predict network dynamics and temporal evolution of the cell state. So, we introduce modularity in the model and incorporate dynamical aspects, mass-action kinetics, and chemo-mechanical effects on only certain transition rates within specific modules as required, creating a Boolean-Hybrid-Modular (BoHyM) signal transduction model. We present this model as a comprehensive, cell-type agnostic, user-modifiable tool to investigate how extra-and intra-cellular signaling can regulate cellular cytoskeletal components and consequently influence cell-substrate interactions, force generation, and migration. Using this tool, we show how slight changes in signaling network architectures due to phenotypic changes can alter cellular response to stress hormone signaling in an environment-dependent manner. The tool also allows isolating effector proteins driving specific cellular mechanical responses. Ultimately, we show the utility of the tool in analyzing transient chemo-mechanical dynamics of cells in response to time-varying chemical stimuli.

systems biology↗

bioRxiv2021

Downregulation of RyR and NCX in the neonatal rat ventricular myocyte modulates cytosolic

Calcium (Ca2+) is necessary for cardiac muscle contraction. RyR, NCX, and SERCA are key regulatory protein channels for cytosolic Ca2+ in cardiac myocytes. Expression levels of these proteins are a function of development, with protein expression shifting toward the adult phenotype over time. We investigated how downregulation by siRNAs of RyR and NCX affected expression levels of complimentary proteins and the corresponding intracellular Ca2+ transients. We compared experimentally observed Ca2+ transients to those predicted by mathematical models. Experiments show RyR downregulation decreased SERCA and increased NCX protein levels. The associated Ca2+ transient had a decreased amplitude, increased time-to-peak, 50%, and 90% Ca2+ removal with respect to the control cell. NCX downregulation increased SERCA production without significant changes in RyR expression levels. The corresponding [Ca2+] transient had increased amplitude, no change in time-to-peak and 50% Ca2+ removal, and increased 90% Ca2+ removal with respect to the control cell. Computational models that accurately predict the observed experimental data suggest compensatory changes occurring in the expression levels as well as biochemical activity of the regulatory proteins.

cell biology↗

bioRxiv2020

Phage and bacteria diversification through a prophage acquisition ratchet

Lysogeny is prevalent in the microbial-dense mammalian gut. This contrasts the classical view of lysogeny as a refuge used by phages under poor host growth conditions. Here we hypothesize that as carrying capacity increases, lysogens escape phage top-down control through superinfection exclusion, overcoming the canonical trade-off between competition and resistance. This hypothesis was tested by developing an ecological model that combined lytic and lysogenic communities and a diversification model that estimated the accumulation of prophages in bacterial genomes. The ecological model sampled phage-bacteria traits stochastically for communities ranging from 1 to 1000 phage-bacteria pairs, and it included a fraction of escaping lysogens proportional to the increase in carrying capacity. The diversification model introduced new prophages at each diversification step and estimated the distribution of prophages per bacteria using combinatorics. The ecological model recovered the range of abundances and sublinear relationship between phage and bacteria observed across eleven ecosystems. The diversification model predicted an increase in the number of prophages per genome as bacterial abundances increased, in agreement with the distribution of prophages on 833 genomes from marine and human-associated bacteria. The study of lysogeny presented here offers a framework to interpret viral and microbial abundances and reconciles the Kill-the-Winner and Piggyback-the-Winner paradigms in viral ecology.

ecology↗

bioRxiv2019

β-adrenergic signaling modulates cancer cell mechanotype through a RhoA-ROCK-myosin II axis

The ability of cells to deform and generate forces are key mechanical properties that are implicated in metastasis. While various soluble and mechanical cues are known to regulate cancer cell mechanical phenotype or mechanotype, our knowledge of how cells translate external signals into changes in mechanotype is still emerging. We previously discovered that activation of {beta}-adrenergic signaling, which results from soluble stress hormone cues, causes cancer cells to be stiffer or less deformable; this stiffer mechanotype was associated with increased cell motility and invasion. Here, we characterize how {beta}-adrenergic activation is translated into changes in cellular mechanotype by identifying molecular mediators that regulate key components of mechanotype including cellular deformability, traction forces, and non-muscle myosin II (NMII) activity. Using a micropillar assay and computational modelling, we determine that {beta}AR activation increases cellular force generation by increasing the number of actin-myosin binding events; this mechanism is distinct from how cells increase force production in response to matrix stiffness, suggesting that cells regulate their mechanotype using a complementary mechanism in response to stress hormone cues. To identify the molecules that modulate cellular mechanotype with {beta}AR activation, we use a high throughput filtration platform to screen the effects of pharmacologic and genetic perturbations on {beta}AR regulation of whole cell deformability. Our results indicate that {beta}AR activation decreases cancer cell deformability and increases invasion by signaling through RhoA, ROCK, and NMII. Our findings establish {beta}AR-RhoA-ROCK-NMII as a primary signaling axis that mediates cancer cell mechanotype, which provides a foundation for future interventions to stop metastasis.

cancer biology↗