Biology subjects

BV, H.

Publications and source records attributed to BV, H..

4 recordsLinked to original sources

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

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

Reconstruction of single cell lineage trajectories and identification of diversity in fates during the epithelial-to-mesenchymal transition

Exploring the complexity of the epithelial-to-mesenchymal transition (EMT) unveils a diversity of potential cell fates; however, the exact timing and intricate mechanisms by which early cell states diverge into distinct EMT trajectories remain unclear. Studying these EMT trajectories through single cell RNA sequencing is challenging due to the necessity of sacrificing cells for each measurement. In this study, we employed optimal-transport (OT) analysis to reconstruct the past trajectories of different cell fates during TGF-beta-induced EMT in the MCF10A cell line. Our analysis revealed three distinct trajectories leading to low EMT, partial EMT, and high EMT states. Cells along partial EMT trajectory showed substantial variations in the EMT signature and exhibited pronounced stemness. Throughout this EMT trajectory, we observed a consistent downregulation of the EED and EZH2 genes. This finding was validated by recent inhibitor screens of EMT regulators and CRISPR screen studies. Moreover, we applied our analysis of early-phase differential gene expression to gene sets associated with stemness and proliferation, pinpointing ITGB4, LAMA3, and LAMB3 as genes differentially expressed in the initial stages of the partial versus high EMT trajectories. We also found that CENPF, CKS1B, and MKI67 showed significant upregulation in the high EMT trajectory. While the first group of genes aligns with findings from previous studies, our work uniquely pinpoints the precise timing of these upregulations. Finally, the latter group of genes represents newly identified regulators, shedding light on potential targets for modulating EMT trajectories. Significance StatementIn our study, we investigated cellular trajectories during EMT using a time-series scRNAseq dataset. OT analysis was used to infer cell-to-cell connections from scRNAseq data, allowing us to predict cell linkages and overcome limitations of sequencing such as the need to sacrifice cells for each measurement. This approach allowed us to identify diverse EMT responses under uniform treatment, a significant advancement over previous studies limited by the static nature of scRNAseq data. Our analysis identified a broad set of genes involved in the EMT process, uncovering novel insights such as the upregulation of cell cycle genes in cells predisposed to a high EMT state and the enhancement of cell adhesion marker genes in cells veering towards a partial EMT state. This work enriches our understanding of the dynamic processes of EMT, showcasing the varied cellular fates within the same experimental setup.

systems biology↗

bioRxiv2023

A resource-based mechanistic framework for castration-resistant prostate cancer (CRPC)

Cancer therapy often leads to the selective elimination of drug-sensitive cells from the tumour. This can favour the growth of cells resistant to the therapeutic agent, ultimately causing a tumour relapse. Castration-resistant prostate cancer (CRPC) is a well-characterised instance of this phenomenon. In CRPC, after systemic androgen deprivation therapy (ADT), a subset of drug-resistant cancer cells autonomously produce testosterone, thus enabling tumour regrowth. A previous theoretical study has shown that such a tumour relapse can be delayed by inhibiting the growth of drug-resistant cells using biotic competition from drug-sensitive cells. In this context, the centrality of resource dynamics to intra-tumour competition in the CRPC system indicates clear scope for the construction of theoretical models that can explicitly incorporate the underlying mechanisms of tumour ecology. In the current study, we use a modified logistic framework to model cell-cell interactions in terms of the production and consumption of resources. Our results show that steady state composition of CRPC can be understood as a composite function of the availability and utilisation efficiency of two resources-oxygen and testosterone. In particular, we show that the effect of changing resource availability or use efficiency is conditioned by their general abundance regimes. Testosterone typically functions in trace amounts and thus affects steady state behaviour of the CRPC system differently from oxygen, which is usually available at higher levels. Our data thus indicate that explicit consideration of resource dynamics can produce novel and useful mechanistic understanding of CRPC. Furthermore, such a modelling approach also incorporates variables into the systems description that can be directly measured in a clinical context. This is therefore a promising avenue of research in cancer ecology that could lead to therapeutic approaches that are more clearly rooted in the biology of CRPC. HighlightsO_LICancer growth and progression can be driven by intra-tumour interactions. C_LIO_LIProstate cancer cells compete among each other for resources like testosterone and oxygen. C_LIO_LIWe model these interactions using a modified logistic model with resource dynamics. C_LIO_LIEquilibrium behaviour can be understood through resource supply and use efficiency. C_LIO_LIExplicitly ecological models could enable better strategies for cancer control. C_LI

cancer biology↗