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Chakraborty, P.

Publications and source records attributed to Chakraborty, P..

At least 55 records · Page 3Linked to original sources

P4HA2: A link between tumor-intrinsic hypoxia, partial EMT and collective migration

Epithelial-to-mesenchymal transition (EMT), a well-established phenomenon studied across pan-cancer types, has long been known to be a major player in driving tumor invasion and metastasis. Recent studies have highlighted the importance of partial EMT phenotypes in metastasis [1]. Initially thought as a transitional state between epithelial and mesenchymal phenotypic states, partial EMT state is now widely recognized as a key driver of intra-tumoral heterogeneity and phenotypic plasticity, further accelerating tumor metastasis and therapeutic resistance. However, how tumor microenvironments regulate partial EMT phenotypes remains unclear. We have developed unique size-controlled three-dimensional microtumor models that recapitulate tumor-intrinsic hypoxia and the emergence of collectively migrating cells. In this study, we further interrogate these microtumor models to understand how tumor-intrinsic hypoxia regulates partial EMT and collective migration in hypoxic large microtumors fabricated from T47D breast cancer cells. We also compared global gene expression profiles of hypoxic, migratory microtumors to that of non-hypoxic, non-migratory microtumors at early and late time-points. Using our microtumor models, we identified unique gene signatures for tumor-intrinsic hypoxia (early versus late), partial EMT and migration (pre-migratory versus migratory phenotype). Through differential gene expression analysis between the microtumor models with an overlap of hypoxia, partial EMT and migration signatures, we identified prolyl 4-hydroxylase subunit 2 (P4HA2), a hypoxia responsive gene, as a central regulator common to hypoxia, partial EMT and collective migration. Further, the inhibition of P4HA2 significantly blocked collective migration in hypoxic microtumors. Thus, using the integrated computational-experimental analysis, we identify the key role of P4HA2 in tumor-intrinsic hypoxia-driven partial EMT and collective migration.

cancer biology↗

Single-cell analysis of skeletal muscle macrophages reveals age- associated functional subpopulations

Tissue-resident macrophages represent a group of highly responsive innate immune cells that acquire diverse functions by polarizing towards distinct subgroups. The subgroups of macrophages that reside in skeletal muscle (SKM) and their changes during aging are poorly characterized. By single-cell transcriptomic analysis, we found that mouse SKM macrophages primarily comprise two large populations, "healing" LYVE1+ and "proinflammatory" LYVE1-macrophages. SKM macrophages were further classified into four functional subgroups based on the expression levels of another cell-surface marker, MHCII: LYVE1+/MHCII-lo (similar to alternatively activated M2), LYVE1-/MHCII-hi (similar to classically activated M1), and two new subgroups, LYVE1+/MHCII-hi and LYVE1-/MHCII-lo. Notably, the new subgroup LYVE1+/MHCII-hi had traits of both M2 and M1 macrophages, while the other new subgroup, LYVE1-/MHCII-lo, expressed high levels of mRNAs encoding cytotoxicity proteins. Flow cytometric analysis validated the presence of the four macrophage subgroups in SKM. In old SKM, LYVE1-macrophages were more abundant than LYVE1+ macrophages. Furthermore, complementary unsupervised classification revealed the emergence of specific macrophage subclusters expressing abundant proinflammatory markers, including S100a8 and S100a9 in aged SKM. In sum, our study has identified dynamically polarized mouse SKM macrophages and further uncovered the contribution of specific macrophage subpopulations to the proinflammatory status in old SKM.

cell biology↗

IDSL.UFA assigns high confidence molecular formula annotations for untargeted LC/HRMS datasets in metabolomics and exposomics

Untargeted LC/HRMS assays in metabolomics and exposomics aim to characterize the small molecule chemical space in a biospecimen. To gain maximum biological insights from these datasets, LC/HRMS peaks should be annotated with chemical and functional information including molecular formula, structure, chemical class and metabolic pathways. Among these, molecular formulas may be assigned to LC/HRMS peaks through matching theoretical and observed isotopic profiles (MS1) of the underlying ionized compound. For this, we have developed the Integrated Data Science Laboratory for Metabolomics and Exposomics - United Formula Annotation (IDSL.UFA) R package. In the untargeted metabolomics validation tests, IDSL.UFA assigned 54.31%-85.51% molecular formula for true positive annotations as the top hit, and 90.58%-100% within the top five hits. Molecular formula annotations were also supported by MS/MS data. We have implemented new strategies to 1) generate formula sources and their theoretical isotopic profiles 2) optimize the formula hits ranking for the individual and the aligned peak lists and 3) scale IDSL.UFA-based workflows for studies with larger sample sizes. Annotating the raw data for a publicly available pregnancy metabolome study using IDSL.UFA highlighted hundreds of new pregnancy related compounds, and also suggested presence of chlorinated perfluorotriether alcohols (Cl-PFTrEAs) in human specimens. IDSL.UFA is useful for human metabolomics and exposomics studies where we need to minimize the loss of biological insights in untargeted LC/HRMS datasets. The IDSL.UFA package is available in the R CRAN repository https://cran.r-project.org/package=IDSL.UFA. Detailed documentation and tutorials are also provided at www.ufa.idsl.me.

bioinformatics↗

How synchrony and metastable network dynamics are affected in fast and slow timescales with aging: Implication for Cognition

Both healthy and pathological aging exhibits gradual deterioration of structure but in-terestingly in healthy aging adults often maintains a high level of cognitive performance in a variety of cognitively demanding task till late age. What are the relevant network measures that could possibly track these dynamic changes which may be critically relevant for maintenance of cognitive functions through lifespan and how does these measures affected by the specific alterations in underlying anatomical connectivity till day remains an open question. In this work, we propose that whole-brain computational models are required to test the hypothesis that aging affects the brain network dynamics through two highly relevant network measures synchrony and metastability. Since aging entails complex processes involving multiple timescales we test the additional hypothesis that whether these two network measures remain invariant or exhibit different behavior in the fast and slow timescales respectively. The altered global synchrony and metastability with aging can be related to shifts in the dynamic working point of the system based on biophysical parameters e.g., time delay, and inter-areal coupling constrained by the underlying structural connectivity matrix.Using diffusion tensor imaging (DTI) data, we estimate structural connectivity (SC) of individual group of participants and obtain network level synchrony, metastability indexing network dynamics from resting state functional MRI data for both young and elderly participants in the age range of 18-89 years. Subsequently, we simulate a whole-brain Kuramoto model of coupled oscillators with appropriate conduction delay and interareal coupling strength to test the hypothesis of shifting of dynamic working point with age-associated alteration in network dynamics in both neural and ultraslow BOLD signal time scales. Specifically, we investigate the age-associated difference in metastable brain dynamics across large-scale neurocognitive brain networks e.g., salience network (SN), default mode network (DMN), and central executive network (CEN) to test spatio-temporal changes in default to executive coupling hypothesis with age. Interestingly, we find that the metastability of the SN increases substantially with age, whereas the metastability of the CEN and DMN networks do not substantially vary with age suggesting a clear role of conduction delay and global coupling in mediating altered dynamics in these networks. Moreover, our finding suggests that the metastability changes from slow to fast timescales confirming previous findings that variability of brain signals relates differently in slower and faster time scales with aging. However, synchrony remains invariant network measure across timescales and agnostic to the filtering of fast signals. Finally, we demonstrate both numerically and analytically longrange anatomical connections as oppose to shot-range or mid-range connection alterations is responsible for the overall neural difference in large-scale brain network dynamics captured by the network measure metastability. In summary, we propose a theoretical framework providing a systematic account of tracking age-associated variability and synchrony at multiple time scales across lifespan which may pave the way for developing dynamical theories of cognitive aging.

neuroscience↗

Transcriptomic-based quantification of the epithelial-hybrid-mesenchymal spectrum across biological contexts

Epithelial-mesenchymal plasticity (EMP) underlies embryonic development, wound healing, and cancer metastasis and fibrosis. Cancer cells exhibiting EMP often have more aggressive behavior, characterized by drug resistance, and tumor-initiating and immuno-evasive traits. Thus, the EMP status of cancer cells can be a critical indicator of patient prognosis. Here, we compare three distinct transcriptomic-based metrics - each derived using a different gene list and algorithm - that quantify the EMP spectrum. Our results for 96 cancer-related RNA-seq datasets reveal a high degree of concordance among these metrics in quantifying the extent of EMP. Moreover, each metric, despite being trained on cancer expression profiles, recapitulates the expected changes in EMP scores for non-cancer contexts such as lung fibrosis and cellular reprogramming into induced pluripotent stem cells. Thus, we offer a scoring platform to quantify the extent of EMP in vitro and in vivo for diverse biological applications including cancer.

bioinformatics↗

Spontaneous emergence of non-convertible cell states with CD24-High phenotype results in phenotypic heterogeneity that associates with poor prognosis in oral cancer

PurposeCellular dynamics between phenotypically heterogeneous subpopulations of cancer cells within individual tumor is shown to be responsible for drug tolerance and overall poor prognosis; however, evidences were largely missing in oral cancer. Therefore, this study was undertaken to describe the dynamic phenotypic states among oral cancer cells, its influence on transcriptomic heterogeneity as well as its clinical significance. Experimental DesignWe multiplexed phenotypic markers of putative oral-stem-like cancer cells (SLCCs) and characterized diversity among CD44-positive oral cancer cell subpopulations with respect to distinct expression of CD24 and aldehyde dehydrogenase (ALDH)-activity in multiple cell lines. Population trajectories were characterized by Markov model and cell states were defined based on the population specific RNA sequencing (RNAseq). ssGSEA based gene expression signatures were explore for prognostic significance. ResultsOral cancer cells followed two distinct patterns of spontaneous repopulation dynamics with stochastic inter-conversions on ALDH-axis, however a strict non-interconvertible transition on CD24-axis. Interestingly, plastic ALDH-axis was harnessed to enrich ALDHHigh subpopulations in response to Cisplatin treatment, to adapt a drug tolerant state. Phenotype-specific RNAseq results suggested the organization of subpopulations into hierarchical structure with possible maintenance of intermediate states of stemness within the differentiating oral cancer cells. Further, survival analysis with each subpopulation-specific gene signature strongly suggested that the cell-state dynamics may act as possible mechanism to drive ITH, resulting in poor prognosis in patient. ConclusionsOur results emphasized the prognostic power of the population dynamics in oral cancer. Importantly, we have described the phenotypic-composition of heterogeneous subpopulations critical for global tumor behaviour in oral cancer; which is a prerequisite knowledge important for precision treatment, however largely lacking for most solid tumors. Graphical AbstractWe have characterized diversity among CD44-positive oral cancer cells lines with respect to distinct expression of CD24 and ALDH-activity. Subpopulations showed stochastic inter-conversions on ALDH-axis but a strict non-interconvertible transition of CD24Low to CD24High phenotype, even in response to chemotherapy-induced stress. RNAseq study suggested the organization of subpopulations into hierarchical structure with possible maintenance of intermediate alternate states of stemness within the differentiating oral cancer cells. The described population dynamics demonstrtaed influence tumor behaviour possibly by increasing intratumoral heterogeneity in aggressive oral tumors. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=112 SRC="FIGDIR/small/457509v3_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@1dc6a7org.highwire.dtl.DTLVardef@dd0948org.highwire.dtl.DTLVardef@18c9603org.highwire.dtl.DTLVardef@cca8fb_HPS_FORMAT_FIGEXP M_FIG C_FIG Translational relevanceIntratumoral heterogeneity (ITH) has been the clinically important factor, impacting aggressive cancer behaviour, drug tolerance and overall poor prognosis. Recent high-throughput studies have provided better cellular and molecular resolution of ITH; however, the prerequisite knowledge which defines the composition of subpopulations critical for global tumor behaviour is majorly lacking for most of the solid tumors. By combining phenotypic markers, we have defined four subpopulations of oral cancer cells. These subpopulations showed stochastic inter-conversions as well as a strict non-interconvertible transition among them to acheive heterogeneity. Importantly, transcriptional states of each subpopulations indicated a clinically relevant signatures for patient prognosis. Also, we observed interconversions of these subpopulations in response to Cisplatin to accumulate drug-tolerant cell state, as rapid and reversible strategy to respond to chemotherapy induced stress. Thus, the characteristics of described phenotypic subgroups may be translated to the clinic for estimating the extent of intratumoral heterogeneity in oral cancer patients.

cancer biology↗

Epithelial-mesenchymal plasticity through loss of CTCF motif accessibility and protein expression

Epithelial-mesenchymal transition (EMT) and its reversal, mesenchymal-epithelial transition (MET) drive tissue reorganization critical for early development. In carcinomas, processing through EMT, MET or partial states promotes migration, invasion, dormancy, and metastatic colonization. As a reversible process, EMT is inherently regulated at epigenetic and epigenomic levels. To understand the epigenomic nature of reversible EMT and its partial states, we characterized chromatin accessibility dynamics, transcriptomic output, protein expression, and cellular phenotypes during stepwise reversible EMT. We found that the chromatin insulating protein machinery, including CTCF, is suppressed and re-expressed, coincident with broad alterations in chromatin accessibility, during EMT/MET and is lower in triple-negative breast cancer cell lines with EMT features. Through analysis of chromatin accessibility using ATAC-seq, we identify that early phases of EMT are characterized by enrichment for AP-1 family member binding motifs but also by diminished enrichment for CTCF binding motifs. Through loss-of-function analysis we demonstrate that suppression of CTCF alters cellular plasticity, facilitating entrance into a partial EMT state. These findings are indicative of a role of CTCF and chromatin reorganization for epithelial-mesenchymal plasticity.

cell biology↗

Analysis of immune subtypes across the epithelial-mesenchymal plasticity spectrum

Epithelial-mesenchymal plasticity plays a critical role in many solid tumor types as a mediator of metastatic dissemination and treatment resistance. In addition, there is also a growing appreciation that the epithelial/mesenchymal status of a tumor plays a role in immune evasion and immune suppression. A deeper understanding of the immunological features of different tumor types has been facilitated by the availability of large gene expression datasets and the development of methods to deconvolute bulk RNA-Seq data. These resources have generated powerful new ways of characterizing tumors, including classification of immune subtypes based on differential expression of immunological genes. In the present work, we combine scoring algorithms to quantify epithelial-mesenchymal plasticity with immune subtype analysis to understand the relationship between epithelial plasticity and immune subtype across cancers. We find heterogeneity of epithelial-mesenchymal transition (EMT) status both within and between cancer types, with greater heterogeneity in the expression of EMT-related factors than of MET-related factors. We also find that specific immune subtypes have associated EMT scores and differential expression of immune checkpoint markers.

cancer biology↗

Dynamic plasticity within the EMT spectrum, rather than static mesenchymal traits, drives tumor heterogeneity and metastatic progression of breast cancers

The Epithelial-to-Mesenchymal Transition (EMT) is a developmental cellular program frequently coopted by cancer cells1 and is a key contributor to both heterogeneity in solid tumors2-4 and later stage chemo-resistance and metastasis5,6. Rather than being a switch from an epithelial to a mesenchymal state, increasing evidence points to the existence of intermediate EMT states, wherein cells co-express both epithelial and mesenchymal traits7-13. Multiple stable intermediate EMT states possessing unique characteristics exist across the EMT spectrum7,8,14,15, contributing to the complex heterogeneity of tumors to promote metastasis16. While much work has been carried out identifying and characterizing EMT-inducing transcription factors17-20, the transcriptional and epigenetic networks responsible for the stability and maintenance of the midpoints along the EMT spectrum are poorly defined. In addition, there are currently no approaches to identifying and quantifying intermediate EMT subpopulations within patient tumors to evaluate their prognostic significance. Using clonally isolated derivatives of the SUM149PT breast cancer cell line, we systematically interrogate how each EMT state independently contributes to heterogeneity and influences metastatic progression, uncovering the role of RUNX2 in stabilizing certain intermediate states. Using SUM149PT-derived tumors as a training set, we develop an entropy-based model to quantify phenotypic heterogeneity and EMT status. Remarkably, the majority of cell states captured in the SUM149PT model are represented in a selection of patient tumors, laying the foundation for quantification of epithelial-mesenchymal heterogeneity and understanding the role of the intermediate EMT state in tumor progression.

cancer biology↗

A theoretical approach to coupling the epithelial-mesenchymal transition (EMT) to extracellular matrix (ECM) stiffness via LOXL2

The epithelial-mesenchymal transition (EMT) plays a critical role in cancer progression, being responsible in many cases for the onset of the metastatic cascade and being integral in the ability of cells to resist drug treatment. Most studies of EMT focus on its induction via chemical signals such as TGF-{beta} or Notch ligands, but it has become increasingly clear that biomechanical features of the microenvironment such as ECM (extracellular matrix) stiffness can be equally important. Here, we introduce a coupled feedback loop connecting stiffness to the EMT transcription factor ZEB1, which acts via increasing the secretion of LOXL2 that leads to increased cross-linking of collagen fibers in the ECM. This increased cross-linking can effectively increase ECM stiffness and increase ZEB1 levels, thus setting a positive feedback loop between ZEB1 and ECM stiffness. To investigate the impact of this non-cell-autonomous effect, we introduce a computational approach capable of connecting LOXL2 concentration to increased stiffness and thereby to higher ZEB1 levels. Our results indicate that this positive feedback loop, once activated, can effectively lock the cells in a mesenchymal state.

cancer biology↗

Immunodominant T-cell epitopes from the SARS-CoV-2 spike antigen reveal robust pre-existing T-cell immunity in unexposed individuals

The COVID-19 pandemic has revealed a range of disease phenotypes in infected patients with asymptomatic, mild or severe clinical outcomes, but the mechanisms that determine such variable outcomes remain unresolved. In this study, we identified immunodominant CD8 T-cell epitopes in the RBD and the non-RBD domain of the spike antigen using a novel TCR-binding algorithm. A selected pool of 11 predicted epitopes induced robust T-cell activation in unexposed donors demonstrating pre-existing CD4 and CD8 T-cell immunity to SARS-CoV-2 antigen. The T-cell reactivity to the predicted epitopes was higher than the Spike-S1 and S2 peptide pools containing 157 and 158 peptides both in unexposed donors and in convalescent patients suggesting that strong T-cell epitopes are likely to be missed when larger peptide pools are used in assays. A key finding of our study is that pre-existing T-cell immunity to SARS-CoV-2 is contributed by TCRs that recognize common viral antigens such as Influenza and CMV, even though the viral epitopes lack sequence identity to the SARS-CoV-2 epitopes. This finding is in contrast to multiple published studies in which pre-existing T-cell immunity is suggested to arise from shared epitopes between SARS-CoV-2 and other common cold-causing coronaviruses. Whether the presence of pre-existing T-cell immunity provides protection against COVID-19 or contributes to severe disease phenotype remains to be determined in a larger cohort. However, our findings raise the expectation that a significant majority of the global population is likely to have SARS-CoV-2 reactive T-cells because of prior exposure to flu and CMV viruses, in addition to common cold-causing coronaviruses.

immunology↗

Gene expression profiles of inflammatory breast cancer reveal high heterogeneity across the epithelial-hybrid-mesenchymal spectrum

Inflammatory breast cancer (IBC) is a highly aggressive breast cancer that metastasizes largely via tumor emboli, and has a 5-year survival rate of less than 30%. No unique genomic signature has yet been identified for IBC nor has any specific molecular therapeutic been developed to manage the disease. Thus, identifying gene expression signatures specific to IBC remains crucial. Here, we compare various gene lists that have been proposed as molecular footprints of IBC using different clinical samples as training and validation sets and using independent training algorithms, and determine their accuracy in identifying IBC samples in three independent datasets. We show that these gene lists have little to no mutual overlap, and have limited predictive accuracy in identifying IBC samples. Despite this inconsistency, single-sample gene set enrichment analysis (ssGSEA) of IBC samples correlate with their position on the epithelial-hybrid-mesenchymal spectrum. This positioning, together with ssGSEA scores, improves the accuracy of IBC identification across the three independent datasets. Finally, we observed that IBC samples robustly displayed a higher coefficient of variation in terms of EMT scores, as compared to non-IBC samples. Pending verification that this patient-to-patient variability extends to intratumor heterogeneity within a single patient, these results suggest that higher heterogeneity along the epithelial-hybrid-mesenchymal spectrum can be regarded to be a hallmark of IBC and a possibly useful biomarker.

cancer biology↗

The bakerss yeast Msh4-Msh5 associates with double-strand break hotspots and chromosome axis during meiosis to promote crossovers

Segregation of homologous chromosomes during the first meiotic division requires at least one obligate crossover/exchange event between the homolog pairs. In the bakers yeast Saccharomyces cerevisiae and mammals, the mismatch repair-related factors, Msh4-Msh5 and Mlh1-Mlh3 generate the majority of the meiotic crossovers from programmed double-strand breaks (DSBs). To understand the mechanistic role of Msh4-Msh5 in meiotic crossing over, we performed genome-wide ChIP-sequencing and cytological analysis of the Msh5 protein in cells synchronized for meiosis. We observe that Msh5 associates with DSB hotspots, chromosome axis, and centromeres. We found that the initial recruitment of Msh4-Msh5 occurs following DSB resection. A two-step Msh5 binding pattern was observed: an early weak binding at DSB hotspots followed by enhanced late binding upon the formation of double Holliday junction structures. Msh5 association with the chromosome axis is Red1 dependent, while Msh5 association with the DSB hotspots and axis is dependent on DSB formation by Spo11. Msh5 binding was enhanced at strong DSB hotspots consistent with a role for DSB frequency in promoting Msh5 binding. These data on the in vivo localization of Msh5 during meiosis have implications for how Msh4-Msh5 may work with other crossover and synapsis promoting factors to ensure Holliday junction resolution at the chromosome axis. AUTHOR SUMMARYDuring meiosis, crossovers facilitate physical linkages between homologous chromosomes that ensure their accurate segregation. Meiotic crossovers are initiated from programmed DNA double-strand breaks (DSBs). In the bakers yeast and mammals, DSBs are repaired into crossovers primarily through a pathway involving the highly conserved mismatch repair related Msh4-Msh5 complex along with other crossover promoting factors. In vitro and physical studies suggest that the Msh4-Msh5 heterodimer facilitates meiotic crossover formation by stabilizing Holliday junctions. We investigated the genome-wide in vivo binding sites of Msh5 during meiotic progression. Msh5 was enriched at DSB hotspots, chromosome axis, and centromere sites. Our results suggest Msh5 associates with both DSB sites on the chromosomal loops and with the chromosome axis to promote crossover formation. These results on the in vivo dynamic localization of the Msh5 protein provide novel insights into how the Msh4-Msh5 complex may work with other crossover and synapsis promoting factors to facilitate crossover formation.

genetics↗

Characterization of systemic genomic instability in budding yeast

Conventional models of genome evolution are centered around the principle that mutations form independently of each other and build up slowly over time. We characterized the occurrence of bursts of genome-wide loss-of-heterozygosity (LOH) in Saccharomyces cerevisiae, providing support for an additional non-independent and faster mode of mutation accumulation. We initially characterized a yeast clone isolated for carrying an LOH event at a specific chromosome site, and surprisingly, found that it also carried multiple unselected rearrangements elsewhere in its genome. Whole genome analysis of over 100 additional clones selected for carrying primary LOH tracts revealed that they too contained unselected structural alterations more often than control clones obtained without any selection. We also measured the rates of coincident LOH at two different chromosomes and found that double LOH formed at rates 14-150 fold higher than expected if the two underlying single LOH events occurred independently of each other. These results were consistent across different strain backgrounds, and in mutants incapable of entering meiosis. Our results indicate that a subset of mitotic cells within a population can experience discrete episodes of systemic genomic instability, when the entire genome becomes vulnerable and multiple chromosomal alterations can form over a narrow time window. They are reminiscent of early reports from the classic yeast genetics literature, as well as recent studies in humans, both in the cancer and genomic disorder contexts. The experimental model we describe provides a system to further dissect the fundamental biological processes responsible for punctuated bursts of structural genomic variation. SIGNIFICANCE STATEMENTMutations are generally thought to accumulate independently and gradually over many generations. Here, we combined complementary experimental approaches in budding yeast to track the appearance of chromosomal changes resulting in loss-of-heterozygosity (LOH). In contrast to the prevailing model, our results provide evidence for the existence of a path for non-independent accumulation of multiple chromosomal alteration events over few generations. These results are analogous to recent reports of bursts of genomic instability in human cells. The experimental model we describe provides a system to further dissect the fundamental biological processes underlying such punctuated bursts of mutation accumulation.

genetics↗

Gene expression and chromatin accessibility during progressive EMT and MET linked to dynamic CTCF engagement

BackgroundEpithelial-mesenchymal transition (EMT) facilitates cellular movements critical for proper development; however, in a carcinoma, EMT promotes metastatic dissemination. Stable intermediate states (partial-EMT) are increasingly implicated in metastatic dissemination while reversal of EMT, termed mesenchymal-epithelial transition (MET), is increasingly implicated in metastatic colonization. To understand the partial and reversible nature of EMT, we characterized chromatin accessibility dynamics, transcriptome changes, protein expression patterns, as well as E-cadherin expression, localization, and gene-level dynamics in mammary epithelial cells undergoing stepwise reversible EMT. ResultsWhile shorter EMT induction induced internalization of E-cadherin protein, surface expression was recovered upon MET without loss of transcript or bulk protein. Conversely, a longer EMT induced stable repression of E-cadherin indicated by loss of chromatin accessibility and induced global expansion of accessible sites across the genome, facilitated by increased engagement of multiple transcription factor families, including AP-1 and SMAD. We observe enrichment for binding sites for the insulator proteins CTCF and BORIS was significantly diminished in both stemness-enriched partial-EMT and partial-MET states and determined that CTCF repression imparts alterations in some histone covalent modifications concomitant with those observed during TGF{beta}-induced EMT. ConclusionsThese findings are indicative of a major role for chromatin looping and reorganization in plasticity, stemness, and partial EMT phenotypes.

cell biology↗

Epigenetic feedback and stochastic partitioning during cell division can drive resistance to EMT

Epithelial-mesenchymal transition (EMT) and its reverse process mesenchymal-epithelial transition (MET) are central to metastatic aggressiveness and therapy resistance in solid tumors. While molecular determinants of both processes have been extensively characterized, the heterogeneity in the response of tumor cells to EMT and MET inducers has come into focus recently, and has been implicated in the failure of anti-cancer therapies. Recent experimental studies have shown that some cells can undergo an irreversible EMT depending on the duration of exposure to EMT-inducing signals. While the irreversibility of MET, or equivalently, resistance to EMT, has not been studied in as much detail, evidence supporting such behavior is slowly emerging. Here, we identify two possible mechanisms that can underlie resistance of cells to undergo EMT: epigenetic feedback in ZEB1/GRHL2 feedback loop and stochastic partitioning of biomolecules during cell division. Identifying the ZEB1/GRHL2 axis as a key determinant of epithelial-mesenchymal plasticity across many cancer types, we use mechanistic mathematical models to show how GRHL2 can be involved in both the abovementioned processes, thus driving an irreversible MET. Our study highlights how an isogenic population may contain subpopulation with varying degrees of susceptibility or resistance to EMT, and proposes a next set of questions for detailed experimental studies characterizing the irreversibility of MET/resistance to EMT.

cancer biology↗

Emergent properties of the HNF4α-PPARγ may drive consequent phenotypic plasticity in NAFLD

Non-Alcoholic Fatty Liver Disease (NAFLD) is the most common form of chronic liver disease in adults and children. It is characterized by excessive accumulation of lipids in the hepatocytes of patients without any excess alcohol intake. With a global presence of 24% and limited therapeutic options, the disease burden of NAFLD is increasing. Thus, it becomes imperative to attempt to understand the dynamics of disease progression at a systems-level. Here, we decode the emergent dynamics of underlying gene regulatory networks that have been identified to drive the initiation and progression of NAFLD. We have developed a mathematical model to elucidate the dynamics of the HNF4-PPAR{gamma} gene regulatory network. Our simulations reveal that this network can enable multiple co-existing phenotypes under certain biological conditions: an adipocyte, a hepatocyte, and a "hybrid" adipocyte-like state of the hepatocyte. These phenotypes may also switch among each other, thus enabling phenotypic plasticity and consequently leading to simultaneous deregulation of the levels of molecules that maintain a hepatic identity and/or facilitate a partial or complete acquisition of adipocytic traits. These predicted trends are supported by the analysis of clinical data, further substantiating the putative role of phenotypic plasticity in driving NAFLD. Our results unravel how the emergent dynamics of underlying regulatory networks can promote phenotypic plasticity, thereby propelling the clinically observed changes in gene expression often associated with NAFLD.

systems biology↗

Comparative study of transcriptomics-based scoring metrics for the epithelial-hybrid-mesenchymal spectrum

The Epithelial-mesenchymal transition (EMT) is a cellular process implicated in embryonic development, wound healing, and pathological conditions such as cancer metastasis and fibrosis. Cancer cells undergoing EMT exhibit enhanced aggressive behavior characterized by drug resistance, tumor-initiation potential, and the ability to evade immune system. Recent in silico, in vitro, and in vivo evidence indicates that EMT is not an all-or-none process; instead, cells stably acquire one or more hybrid epithelial/mesenchymal (E/M) phenotypes which often can be more aggressive than purely epithelial or mesenchymal cell populations. Thus, the EMT status of cancer cells can prove to be a critical estimate of patient prognosis. Recent attempts have employed different transcriptomics signatures to quantify EMT status in cell lines and patient tumors. However, a comprehensive comparison of these methods, including their accuracy in identifying cells in the hybrid E/M phenotype(s), is lacking. Here, we compare three distinct metrics that score EMT on a continuum, based on the transcriptomics signature of individual samples. Our results demonstrate that these methods exhibit good concordance among themselves in quantifying the extent of EMT in a given sample. Moreover, scoring EMT using any of the three methods discerned that cells undergo varying extents of EMT across tumor types. Separately, our analysis also identified tumor types with maximum variability in terms of EMT and associated an enrichment of hybrid E/M signatures in these samples. Moreover, we also found that the multinomial logistic regression (MLR) based metric was capable of distinguishing between pure individual hybrid E/M vs. mixtures of epithelial (E) and mesenchymal (M) cells. Our results, thus, suggest that while any of the three methods can indicate a generic trend in the EMT status of a given cell, the MLR method has two additional advantages: a) it uses a small number of predictors to calculate the EMT score, and b) it can predict from the transcriptomic signature of a population whether it is comprised of pure hybrid E/M cells at the single-cell level or is instead an ensemble of E and M cell subpopulations.

cancer biology↗