Biology subjects

Jolly, M. K.

Publications and source records attributed to Jolly, M. K..

At least 55 records · Page 3Linked to original sources

bioRxiv2025

Emergent dynamics of cellular decision making in multi-node mutually repressive regulatory networks

Stem cell differentiation during development is governed by the dynamics of the underlying gene regulatory networks (GRNs). Mutually inhibiting nodes/collection of nodes encompass the GRNs that govern differentiation to two distinct fates. But the properties of GRNs that can allow differentiation into n-terminal phenotypes are not understood. In this study, we examine toggle-n networks, encompassing mutual inhibitions among multiple transcription factors, to derive generalized conclusions regarding the dynamics underlying differentiation into n-terminal phenotypes. We show that in steady-state distributions of gene expression, multiple cell state-specific transcription factors are co-expressed, indicating an obligatory multi-step process of multi-lineage differentiation. Furthermore, we show that cytokine signaling and specific asymmetry of regulatory links can lead to directed differentiation towards a particular cell state. Our findings provide valuable insights into the mechanistic aspects of directed differentiation of stem cells.

systems biology↗

bioRxiv2025

Topological conditions of gene regulatory networks enabling robust binary cell-fate decision-making

Phenotypic plasticity--the reversible switching of cell-states--is a central tenet of development, regeneration, and cancer progression. These transitions are governed by gene regulatory networks (GRNs), whose topological features strongly influence their dynamics. While toggle switches (mutually inhibitory feedback loops between two transcription factors) are a common motif observed for binary cell-fate decisions, GRNs across diverse contexts often exhibit a more general structure: two mutually inhibiting teams of nodes. Here, we investigate the teams of nodes as a potential topological design principle of GRNs. We first analyze GRNs from the Cell Collective database and introduce a metric, impurity, which quantifies the fraction of edges inconsistent with an idealized two-team architecture. Impurity correlates strongly with statistical properties of GRN phenotypic landscapes, highlighting its predictive value. To further probe this relationship, we simulate artificial two-team networks (TTNs) using both continuous (RACIPE) and discrete (Boolean) formalisms across varying impurity, density, and network size values. TTNs exhibit toggle-switch-like robustness under perturbations and enable accurate prediction of dynamical features such as inter-team correlations and steady-state entropy. Together, our findings establish the teams paradigm as a unifying principle linking GRN topology to dynamics, with broad implications for inferring coarse-grained network properties from high-throughput sequencing data.

systems biology↗

bioRxiv2025

An integrative molecular systems approach unravels mechanisms underlying biphasic nitrate uptake by plant nitrate transporter NRT1.1

Elucidating the mechanisms of transport kinetics in plants is crucial to develop crops that can use nutrients efficiently. The plant nitrate transporter NRT1.1 rapidly switches between high- and low-affinity transport modes to maintain an optimal uptake amidst fluctuations in nitrate levels. This functional switch is regulated by NRT1.1 phosphorylation, but the precise mechanisms remain poorly understood. Here, using an integrated molecular and systems-level modeling, we identify mechanisms underlying biphasic behaviour of NRT1.1. Phosphorylation of NRT1.1 and its binding to nitrate impacts its overall flexibility and synergistically modulates its global conformation, impacting the nitrate transport rate. Integrating these observations with a regulatory network involving kinases CIPK8/CIPK23 and calcium binding proteins CBL1/9, reveals that in high nitrate conditions, CIPK8-mediated sequestration of CBL1 disrupts the CIPK23-CBL complex required for NRT1.1 phosphorylation, switching NRT1.1 to a low-affinity mode. Together, our findings untangle the molecular complexity enabling NRT1.1 phosphorylation switch with broader implications in nitrate sensing and molecular-level adaption to fluctuating external nutrient levels.

systems biology↗

bioRxiv2025

Bioenergetic reprogramming of macrophages reduces drug tolerance in Mycobacterium tuberculosis

Eradication of Mycobacterium tuberculosis (Mtb) requires strategies targeting bacteria inside the host. Mtb exhibits heterogeneity in redox metabolism inside macrophages to evade killing by anti-TB drugs. If and how macrophage physiology correlates with bacterial redox heterogeneity and drug tolerance remains unclear. Using a fluorescent reporter of mycobacterial redox potential, flow sorting, and RNA sequencing of infected macrophages, we characterized transcriptional and metabolic responses of macrophages harboring redox-diverse Mtb populations. We found that macrophages with suppressed glycolysis and elevated oxidative phosphorylation (OXPHOS) correlated with Mtb populations exhibiting reductive stress and drug tolerance. Conversely, macrophages with elevated glycolysis and suppressed OXPHOS displayed higher mitochondrial reactive oxygen species through reverse electron transport, resulting in oxidative stress in Mtb and enhancing drug efficacy. Computational and genetic approaches identified Nrf2 as a key regulator of macrophage bioenergetics driving redox heterogeneity and drug tolerance in Mtb. Redirecting macrophage metabolism from OXPHOS to glycolysis using an FDA- approved antiemetic drug, meclizine, subverted redox heterogeneity and diminished drug tolerance in macrophages and mice. The pharmacological profile of meclizine (Cmax and AUClast) indicated no adverse interactions with first-line anti-TB drugs in mice. Our data demonstrate the feasibility of reprogramming macrophage metabolism to reduce drug tolerance in Mtb infection.

microbiology↗

bioRxiv2025

Epithelial to Mesenchymal Transition in the Endometrium Mediated by HOXA10 drives Embryo Implantation

In mammalian reproduction, a significant proportion of embryos fail to implant despite a receptive uterus, suggesting that defects in epithelial remodelling at the embryo-uterine interface contribute to implantation failure. The molecular programs enabling such remodelling remain incompletely understood. Here, we identify a conserved transcriptional circuit involving HOXA10 and TWIST2 that regulates epithelial plasticity in the endometrium via partial epithelial-to-mesenchymal transition (pEMT). HOXA10, a transcription factor essential for uterine receptivity, is specifically downregulated in the luminal epithelium at implantation in mice, hamsters, and monkeys. Integrated CUT&RUN and transcriptomic profiling in human endometrial epithelial cells reveal that HOXA10 directly activates epithelial gene networks and represses mesenchymal programs. HOXA10 loss, both in vitro and in vivo, induces a pEMT state with increased cell motility. Mechanistically, HOXA10 represses TWIST2, a core EMT regulator; its derepression promotes mesenchymal gene expression and epithelial cell displacement. TWIST2 knockdown restores epithelial identity and impairs implantation. These findings establish a mutually antagonistic HOXA10-TWIST2 circuit as a key regulator of pEMT and epithelial remodelling during implantation. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=144 SRC="FIGDIR/small/631632v2_ufig1.gif" ALT="Figure 1"> View larger version (46K): org.highwire.dtl.DTLVardef@1ea7b16org.highwire.dtl.DTLVardef@136f3c5org.highwire.dtl.DTLVardef@be47a8org.highwire.dtl.DTLVardef@5283b2_HPS_FORMAT_FIGEXP M_FIG C_FIG

developmental biology↗

bioRxiv2024

A multi-level formalism to capture the characteristics of hybrid phenotypes in Epithelial-Mesenchymal Plasticity

Epithelial-mesenchymal plasticity (EMP) is a cell-fate switching program that enables cells to adopt a spectrum of phenotypes ranging from epithelial (E) to mesenchymal (M), including intermediate hybrid E/M states. Hybrid E/M phenotypes are conducive to cancer metastasis, as they are associated with metastatic initiation, cancer stemness, drug resistance, and collective migration. Boolean models of the gene regulatory networks (GRNs) underlying EMP have yielded valuable insights into the dynamics of E and M phenotypes. However, these models are limited in their ability to capture hybrid phenotypes effectively, as they restrict gene expression to binary states. In contrast, hybrid E/M cells often exhibit partial expression of epithelial and mesenchymal markers. To overcome this limitation, we modified a threshold-based Boolean formalism to incorporate intermediate gene expression levels. The resulting multilevel model reveals novel hybrid steady-states characterized by partial expression of both E and M genes, thereby expanding the phenotypic landscape beyond that represented by traditional Boolean approaches. Notably, these hybrid states exhibit lower frustration compared to their counterparts in classical Boolean models. Furthermore, by resolving dynamical degeneracy, we demonstrate that the hybrid states identified by the multilevel model are more stable. These findings suggest that introducing minimal additional complexity into Boolean models can uncover previously hidden qualitative features of phenotypic landscapes governed by GRNs. Significance StatementHybrid epithelial-mesenchymal phenotypes drive metastasis through collective migration, stemness, and drug resistance. However, traditional Boolean models of epithelial-mesenchymal plasticity (EMP) networks cannot capture the partial gene expression characteristic of these biologically crucial hybrid states. We developed a multilevel Boolean formalism that extends classical binary models to include intermediate expression levels. This approach successfully captures hybrid states with partial expression of both epithelial and mesenchymal genes, exhibiting lower frustration and greater stability than traditional Boolean hybrid phenotypes. While conventional Boolean hybrid states depend on modeling artifacts, multilevel hybrid states emerge independently of such uncertainties. Critically, plasticity experiments revealed that multilevel hybrid states show significantly higher transition probabilities to other hybrid states compared to traditional models, creating a stable "hybrid cloud" that maintains metastatic properties while allowing adaptive responses to environmental stress. This multilevel framework provides a biologically meaningful characterization of hybrid phenotypes essential for understanding metastasis.

systems biology↗

bioRxiv2024

Mechano-sensing role of oncogenic lncRNA NEAT1 on soft vs. stiff substrates and its implications for glioblastoma progression

Long non-coding RNAs (lncRNAs) have gained increasing recognition as key regulators of cancer biology, overturning their earlier classification as non-functional genomic elements. Among them, NEAT1 (Nuclear-Enriched Abundant Transcript 1) has emerged as a prominent oncogenic lncRNA involved in multiple solid tumors, including liver, prostate, and gastric cancers, as well as gliomas. Although the role of NEAT1 in cancer has been extensively studied, most of these investigations have focused on biochemical factors, leaving the mechano-sensitivity of NEAT1 to mechanical alterations in the tumor microenvironment (TME) largely unexplored. As tumors progress, the TME undergoes significant mechanical changes, particularly extracellular matrix (ECM) stiffening, which modulates cellular behavior through mechano-transduction pathways. While much research has focused on how these mechanical cues influence protein-coding gene expression, their impact on lncRNAs such as NEAT1 remains understudied. This study aims to address this gap by investigating the mechano-sensitivity of NEAT1 in glioblastoma (GBM) cells cultured on polyacrylamide (PAA) gels that mimic the brains physiological stiffness (0.5 kPa) versus conventional, stiff tissue culture plastic (TCP). NEAT1 expression, quantified using qRT-PCR, revealed a significant increase on softer, brain-mimicking substrates compared to stiffer TCP. Overexpression of NEAT1 in brain-mimicking stiffness also showed higher expression levels of cancer progression markers, quantified by qRT-PCR. To further explore NEAT1s role in oncogenesis due to its mechano-sensing capabilities, siRNA-mediated downregulation of NEAT1 in GBM cells cultured on soft PAA gels was performed to reduce NEAT1 levels to those comparable to TCP. The results showed reduced cancer aggressiveness, characterized by decreased expression of invasion, stemness, and epithelial-mesenchymal transition (EMT) markers, further supported by migration and invasion assays. These findings highlight NEAT1 as a mechanosensitive regulator in response to substrate stiffness, underscoring its role in tumor progression.

cancer biology↗

bioRxiv2024

Metastatic organotropism in small cell lung cancer

Metastasis is the leading cause of cancer-related deaths, yet its regulatory mechanisms are not fully understood. Small-cell lung cancer (SCLC) is the most metastatic form of lung cancer, with most patients presenting with widespread disease, making it an ideal model for studying metastasis. However, the lack of suitable preclinical models has limited such studies. We utilized rapid autopsy-derived tumors to develop xenograft models that mimic key features of SCLC, including histopathology, rapid and widespread development of metastasis to the liver, brain, adrenal, bone marrow, and kidneys within weeks, and response to chemotherapy. By integrating in vivo lineage selection with comprehensive bulk and single cell multiomic profiling of transcriptomes and chromatin accessibility, we identified critical cellular programs driving metastatic organotropism to the liver and brain, the most common sites of SCLC metastasis. Our findings reveal the key role of nuclear-cytoskeletal interactions in SCLC liver metastasis. Specifically, the loss of the nuclear envelope protein lamin A/C, encoded by the LMNA gene, increased nuclear deformability and significantly increased the incidence of liver metastasis. Human liver metastases exhibited reduced LMNA expression compared to other metastatic sites, correlating with poorer patient outcomes and increased mortality. This study introduces novel preclinical models for SCLC metastasis and highlights pathways critical for organ-specific metastasis, offering new avenues for the development of targeted therapies to prevent or treat metastatic disease.

cancer biology↗

bioRxiv2024

Collective amoeboid dynamics drives colonization of drug-resistant ovarian cancer cells

Epithelial ovarian cancer (EOC) is characterized by resistance to platinum-based therapy, resulting in rapid progression and poor survival. Here, we ask whether drug resistance and invasiveness coevolve to drive metastasis. Selection experiments involving pulsed carboplatin exposure established isogenic chemoresistant variants of lines, which typify high-grade serous ovarian carcinoma (HGSOC), the most aggressive type of EOC. Time-lapse imaging showed enhanced migration of resistant single cells and their collectives. Resistant cell spheroids spread faster on Collagen I substrata than sensitive controls. The resistant OVCAR-3 transcriptome was ontologically enriched for migration and showed overlap with previously reported markers of resistance in EOC patients and other evolved lines. Gene set enrichment predicted transition between epithelial, mesenchymal, and amoeboid states is higher in resistance compared to control lines. Lower matrix adhesion, weak focal adhesion, and highly deformable and translatory dynamics of cell collectives indicated that resistant cancer cells displayed a unique collective amoeboid-like migration. When injected intraperitoneally into immunodeficient mice, resistant cells colonized to a greater extent on parietal mucosae. Ex vivo, suspended resistant cells formed moruloids associated with quicker peritoneal adhesion, clearing human coelomic mesothelial monolayers with higher efficiency. Knockdown in resistant OVCAR-3 cells of two upregulated proteins, E-cadherin and LGALS3BP, had distinct consequences. E-cadherin knockdown partially restored sensitivity to carboplatin but did not affect invasion. In contrast, silencing LGALS3BP decreased invasion but not resistance. Our results suggest that drug resistance and invasiveness could coevolve through the upregulation of distinct trait drivers in EOC.

cancer biology↗

bioRxiv2024

DNA walk of specific fused oncogenes exhibit distinct fractal geometric characteristics in nucleotide patterns

Background/ObjectivesThe complex system of cancer has led to an emphasis on understanding the more general causal relationship within the disease. In this context, concepts of symmetry and symmetry-breaking in distinct biological cell features or components have been examined as an approach to cancer investigation. However, there can be possible limitations in directly interpreting the symmetry-based approach from a physical viewpoint due to the lack of understanding of physical laws governing symmetry in complex systems like cancer. MethodsFractal geometry and DNA walk representation were employed to investigate the geometric features i.e., self-similarity and heterogeneity in DNA nucleotide coding sequences of wild-type and mutated oncogenes, tumour-suppressor, and other unclassified genes. The mutation-facilitated self-similar and heterogenous features were quantified by the fractal dimension and lacunarity coefficient measures, respectively. Additionally, the geometrical orderedness and disorderedness in the analyzed sequences were interpreted from the combination of the fractal measures. ResultsThe findings showed distinct fractal geometric features in the case of fusion mutations. It also highlights the possible interpretation of the observed fractal features as geometric analogues concerning explicit observations corresponding to specific cancer types. In addition, the two-dimensional multi-fractal analysis highlighted the presence of a single exponent in the scaling of mutation-mediated gene sequence self-similarity/complexity and heterogeneity. ConclusionsThe approach identified mutation-induced geometric features in gene sequences, demonstrating the potential of DNA walks and fractal analysis in translational research regarding cancer. The findings suggest that investigating fractal parameters can capture unique geometric features in nucleotide sequences, contributing to the understanding of cancers molecular complexity.

bioinformatics↗

bioRxiv2024

TGFb-ANGPT2-Tie2 axis in cancer-associated fibroblasts reprograms oral cancer cells to embryonic-like cell state with predictive significance of poor prognosis

Myofibroblastic cancer-associated fibroblasts (CAFs) in tumor stroma is identified as poor-prognostic indicator in oral cancer; however, biological mechanisms are largely unexplored. Here, we discovered the role of autocrine or exogenous transforming growth factor beta (TGF{beta}) in inducing Tunica Interna Endothelial cell kinase 2 (Tie2) -signaling through histone deacetylase-mediated downregulation of Tie2-antagonist, Angiopoietin-2 in CAFs, responsible for induction and maintenance of myofibroblastic differentiation. To understand the influence of CAF-specific Tie2-signaling on cancer cell properties, we performed CAF-Cancer cell co-culture and its single-cell RNA sequencing (scRNA-Seq). Distinct clustering of CAFs suggested their transcriptional heterogeneity, driven by TGF{beta}-Tie2 activation. Interestingly, CAF-specific Tie2-signaling was responsible to reprogram cancer cells, producing embryonic-like cell state with increased stemness and EMT signatures. Importantly, both the Tie2-specific gene expression signature as well as reprogrammed cancer cell specific gene expression modules were validated respectively in fibroblasts clusters and malignant cell clusters in two independent earlier reported scRNAseq studies of HNSCC tumors. Highlighting the translatability of our study, the gene expression signature derived from reprogrammed cancer cells showed significant association with poor prognosis in HNSCC patient of TCGA cohort. Pharmacological inhibition of Tie2-signaling in CAFs, significantly abrogated the tumor initiating ability of co-cultured oral cancer cell lines. Overall, combining our molecular and computational analysis, we may propose Tie2 as a novel factor responsible for CAF mediated cancer cell plasticity, associated with aggressive nature of oral cancer. TeaserTie2-signaling is activated in myofibroblasts which impacts the behaviour of malignant cells by inducing cancer cell plasticity to acquire stemness.

cancer biology↗

bioRxiv2024

Exploring the role of EMT in Ovarian Cancer Progression: Insights from a multiscale mathematical model

Epithelial-to-mesenchymal transition (EMT) plays a key role in the progression of cancer tumours and can make treatment significantly less successful for patients. EMT occurs when a cell gains a different phenotype and possesses different behaviours to those previously exhibited. This may result in enhanced drug resistance, higher cell plasticity, and increased metastatic abilities. It has therefore has become essential to encapsulate this change and study tumour progression and its response to treatments. Here, we use a 3D agent-based multiscale modelling framework based on Physicell to investigate the role of EMT over time in two cell lines, OVCAR-3 and SKOV-3. The impact of conditions in the microenvironment are incorporated into the model by modifying cellular behaviours dependant on variables such as substrate concentrations and proximity to neighbouring cells. OVCAR-3 and SKOV-3 cell lines possess highly contrasting tumour layouts, allowing a vast array of different tumour dynamics and morphologies to be tested and studied. The model encapsulates the biological observations and trends seen in tumour growth and development, thus can help to obtain further insights into OVCAR-3 and SKOV-3 cell line dynamics. Sensitivity analysis was performed to investigate the impact of parameter sensitivity on model outcome. Sensitivity analysis showed that parameters used in generating the rate of EMT and cycle rates within the cells are relatively more sensitive than other parameters used.

cancer biology↗

bioRxiv2024

Multistability and predominant double-positive states in a four node mutually repressive network: a case study of Th1/Th2/Th17/T-reg differentiation

Elucidating the emergent dynamics of complex regulatory networks enabling cellular differentiation is crucial to understand embryonic development and suggest strategies for synthetic circuit design. A well-studied network motif often driving cellular decisions is a toggle switch - a set of two mutually inhibitory lineage-specific transcription factors A and B. A toggle switch often enables two possible mutually exclusive states - (high A, low B) and (low A, high B) - from a common progenitor cell. However, the dynamics of networks enabling differentiation of more than two cell types from a progenitor cell is not well-studied. Here, we investigate the dynamics of four master regulators A, B, C and D inhibiting each other, thus forming a toggle tetrahedron. Our simulations show that a toggle tetrahedron predominantly allows for co-existence of six double positive or hybrid states where two of the nodes are expressed relatively high as compared to the remaining two - (high A, high B, low C, low D), (high A, low B, high C, low D), (high A, low B, low C, high D), (low A, high B, high C, low D), (low A, low B, high C, high D) and (low A, high B, low C, high D). Stochastic simulations showed state-switching among these phenotypes, indicating phenotypic plasticity. Finally, we apply our results to understand the differentiation of naive CD4+ T cells into Th1, Th2, Th17 and Treg subsets, suggesting Th1/Th2/Th17/Treg decision-making to be a two-step process. Our results reveal multistable dynamics and establish the stable co-existence of hybrid cell-states, offering a potential explanation for simultaneous differentiation of multipotent naive CD4+ T cells.

systems biology↗

bioRxiv2023

Cell-state transitions and frequency-dependent interactions among subpopulations together explain the dynamics of spontaneous epithelial-mesenchymal heterogeneity in breast cancer

Individual cells in a tumour can be distributed among Epithelial (E) and Mesenchymal (M) cell-states, as characterised by the levels of canonical E and M markers. Even after E and M (E-M) subpopulations are isolated and then cultured independently, E-M heterogeneity can re-equilibrate in each population over time, sometimes regaining the initial distribution of the parental cell population. However, it remains unclear which population-level processes give rise to the dynamical changes in E-M heterogeneity observed experimentally, including 1) differential growth, 2) cell-state switching, and 3) frequency-dependent growth or state-transition rates. Here, we analyse the necessity of these three processes in explaining the dynamics of E-M population distributions as observed in PMC42-LA and HCC38 breast cancer cells. We find that growth differences among E and M subpopulations, with and without any frequency-dependent interactions (cooperation or suppression) among E-M sub-populations, are insufficient to explain the observed population dynamics. This insufficiency is ameliorated by including cell-state transitions, albeit at slow rates, in explaining both PMC42-LA and HCC38 cells data. Further, our models predict that treatment of HCC38 cells with TGF{beta} signalling and JAK2/3 inhibitors could significantly enhance the transition rates from M state to E state, but does not prevent transitions from E to M. Finally, we devise a selection criterion to identify the next most informative time points for which future experimental data can optimally improve the identifiability of our estimated best fit model parameters. Overall, our study identifies the necessary population-level processes shaping the dynamics of E-M heterogeneity in breast cancer cells.

systems biology↗

bioRxiv2023

Dynamic Plasticity Systems Direct Early Adaptation to Treatment in Neuroblastoma

Neuroblastoma, like many aggressive cancers, exhibits phenotypic heterogeneity, contributing to therapy resistance and disease progression. However, direct evidence of how phenotypic plasticity influences tumour evolution remains limited. Using a multi-resolution quantitative approach, we define the principles, types, and dynamics of plasticity in neuroblastoma at unprecedented resolution. We demonstrate that intrinsic plasticity is a model-dependent process that enables the coexistence of drug-sensitive and drug-resistant states in environmentally stable conditions, positioning plasticity as a bet-hedging strategy that allows tumours to anticipate environmental changes. Additionally, we show plasticity varies across lineages and single-cell-derived clones, establishing that it is not merely an induced response but a heritable and selectable trait. By simultaneously mapping plasticity at and clonal dynamics in evolving neuroblastoma populations, we show plasticity is shaped by selective pressures, reinforcing its role as a fundamental driver of neuroblastoma evolution in a treatment- and genetic background-dependent manner. We define three distinct modes of plasticityled adaptation. In the selection of the plasticity model, strong selective pressure temporarily constrains phenotypic transitions, but they reemerge with greater dynamics once the stressor is removed, favouring the selection of highly plastic clones. In the adaptive plasticity model, phenotypic transitions actively reshape tumour heterogeneity, allowing for rapid adaptation to treatment while minimising the impact of clonal selection. Finally, in the plasticity equilibrium model, phenotypic transitions persist at baseline, maintaining a state of phenotypic fluidity, with clonal selection ultimately dictating tumour evolution. These findings highlight the diverse, context-dependent strategies that neuroblastoma populations employ to navigate selective pressures and therapy resistance, emphasizing the need for plasticity-targeting therapeutic approaches to disrupt tumour adaptation and improve treatment outcomes. TeaserNatural selection and phenotypic transitions shape adaptive evolution, guiding neuroblastoma survival under treatment pressure.

cancer biology↗

bioRxiv2023

Development of adaptive anoikis resistance promotes metastasis that can be overcome by CDK8/19 Mediator kinase inhibition

Anoikis resistance or evasion of cell death triggered by matrix detachment is a hallmark of cancer cell survival and metastasis. We show that repeated exposure to suspension stress followed by recovery under attached conditions leads to development of anoikis resistance. The acquisition of anoikis resistance is associated with enhanced invasion, chemoresistance, and immune evasion in vitro and distant metastasis in vivo. This acquired anoikis resistance is not genetic, persisting for a finite duration without detachment stress, but is sensitive to CDK8/19 Mediator kinase inhibition that can also reverse anoikis resistance. Transcriptomic analysis reveals that CDK8/19 kinase inhibition induces bidirectional transcriptional changes in both sensitive and resistant cells, disrupting the balanced reprogramming required for anoikis adaptation and resistance by reversing some resistance associated pathways and enhancing others. Both anoikis resistance and in vivo metastatic growth of ovarian cancers are sensitive to CDK8/19 inhibition, thereby providing a therapeutic opportunity to both prevent and suppress ovarian cancer metastasis.

cancer biology↗

bioRxiv2023

Proneural - Mesenchymal antagonism dominates the patterns of phenotypic heterogeneity in Glioblastoma

1The aggressive nature of glioblastoma (GBM) - one of the deadliest forms of brain tumours - is majorly attributed to underlying phenotypic heterogeneity. Early attempts to classify this heterogeneity at a transcriptomic level in TCGA GBM cohort proposed the existence of four distinct molecular subtypes: Proneural, Neural, Classical and Mesenchymal. Further, a single-cell RNA-seq analysis of primary tumours also reported similar 4 subtypes mimicking neuro-developmental lineages. However, it remains unclear whether these 4 subtypes identified via bulk and single-cell transcriptomics are mutually exclusive or not. Here, we perform pairwise correlations among individual genes and gene signatures corresponding to these proposed subtypes, and show that the subtypes are not distinctly mutually antagonistic in either TCGA or single-cell RNA-sequencing data. We observed that the proneural (or neural progenitor-like) - mesenchymal axis is the most prominent antagonistic pair, with the other two subtypes lying on this spectrum. These results are reinforced through a meta-analysis of over 100 single-cell and bulk transcriptomic datasets as well as in terms of functional association with metabolic switching, cell cycle and immune evasion pathways. These results suggest rethinking GBM phenotypic characterization for more effective therapeutic targeting efforts.

systems biology↗

bioRxiv2023

Spatial heterogeneity in tumor adhesion qualifies collective cell migration

Collective cell migration, a canon of most invasive solid tumors, is an emergent property of the interactions between cancer cells and their surrounding extracellular matrix (ECM). However, tumor populations invariably consist of cells expressing variable levels of adhesive proteins that mediate such interactions, disallowing an intuitive understanding of how tumor invasiveness at a multicellular scale is influenced by spatial heterogeneity of cell-cell and cell-ECM adhesion. Here, we have used a Cellular Potts model-based multiscale computational framework that is constructed on the histopathological principles of glandular cancers. In earlier efforts on homogenous cancer cell populations, this framework revealed the relative ranges of interactions, including cell-cell and cell-ECM adhesion that drove collective, dispersed, and mixed multimodal migrations. Here, we constitute a tumor core of two separate cell subsets showing distinct intra-subset cell-cell or cell-ECM adhesion strengths. These two subsets of cells are arranged to varying extents of spatial intermingling, which we call the heterogeneity index (HI). Our simulations show that for a given two intra-subset cell-cell adhesions for two subsets of cells, low and high inter-subset cell adhesion favors migration of high HI and low HI intermingled populations, respectively. In addition, for the most explored values of cell-ECM adhesion strengths, populations with high HI values collectively migrate better than those with lower HI values. We then asked how spatial migration is regulated by progressively intermingled cellular subsets that were epithelial, i.e., showed high cell-cell but poor cell-ECM adhesion, and mesenchymal, i.e., with reversed adhesion strengths to the former. Here too, inter-subset adhesion plays an important role in contextualizing the proportionate relationship between HI and migration. We also observe an exception to this relationship for cases of heterogeneous cell-ECM adhesion where sub-maximal HI patterns with higher outer localization of cells with stronger ECM adhesion collectively migrate better than their relatively higher HI counterparts. Our simulations also reveal how adhesion heterogeneity qualifies migrative dynamics through collective cellular unjamming, when either cell-cell or -ECM adhesion type is varied but incorporates dispersion when both adhesion types are simultaneously altered.

systems biology↗