Biology subjects

Padi, M.

Publications and source records attributed to Padi, M..

15 recordsLinked to original sources

bioRxiv2026

PARROT: Phase-Altering Regulatory Rewiring Over Time

MotivationGene regulatory networks undergo dynamic restructuring during development and disease. Identifying when and how these networks change is crucial for understanding developmental and disease transitions, yet existing change-point detection methods often ignore network structure or lack interpretable community assignments. ResultsWe present PARROT (Phase-Altering Regulatory Rewiring Over Time), a framework for detecting change-points in dynamic networks using Stochastic Block Models. PARROT jointly estimates change-point locations and community structure across four network classes: unipartite and bipartite with either Gaussian or Bernoulli edge models. Simulations demonstrate improved performance and community recovery compared to other methods. Applications to human cardiac differentiation and mouse lung development data successfully recovered known phase boundaries. PARROT identifies both which genes are reassigned across modules and how the connections change between states. AvailabilityPARROT is available as an R package at https://github.com/cchen22/PARROT. Contactchenchen9945@gmail.com Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2026

Probabilistic network modeling identifies RBPJ as a driver of stemness in Merkel cell carcinoma

Merkel cell carcinoma (MCC) is a poorly differentiated neuroendocrine carcinoma with limited treatment options, primarily immunotherapy, to which only [~]50% of patients respond. Lineage plasticity drives its poorly differentiated phenotype, in turn promoting tumor aggressiveness and treatment resistance. Targeting the mechanisms underlying lineage plasticity could help induce differentiation, reduce proliferation, and potentially sensitize the tumor to existing therapies, yet such strategies are underdeveloped in MCC. Here, we integrate scRNA-seq and bulk ATAC-seq data to generate Boolean networks, simulate their dynamics, and predict a key regulator of differentiation, which we validated in vitro. Using CytoTRACE2 across two independent datasets, we revealed the existence of tumor subpopulations with distinct developmental potency states. We then constructed and refined transcription factor regulatory networks using BooleaBayes and expanded them with ATAC-seq inferred regulatory interactions. Across multiple network constructions, simulations of single-gene perturbations consistently identified the Notch effector RBPJ as the key regulator predicted to shift MCC cells toward a more differentiated state. Experimental knockdown of RBPJ in an MCC cell line altered expression of differentiation-associated genes, reduced the expression of MCC markers, and drastically reduced cell growth. These findings identify RBPJ as a regulator of MCC lineage plasticity and candidate for targeted treatment, while highlighting the utility of probabilistic network modeling for prioritizing therapeutic targets in translational cancer research.

Systems Biology↗

bioRxiv2025

Transient centrosome loss in cultured prostate epithelial cells induces chromosomal instability to produce an oncogenic genotype that correlates with poor clinical outcomes

Chromosomal instability (CIN) is a hallmark of prostate cancer that strongly correlates with metastatic burden and appears prominently in both primary cancer and metastatic disease. Low Gleason score primary prostate tumors display pervasive centrosome loss, a known mechanistic driver of CIN, that disrupts normal spindle assembly and increases mitotic errors. Previously, we found that transient depletion of centrosomes in immortalized, non-tumorigenic prostate epithelial cells (PrEC) induced a burst of CIN, generating cell lines capable of forming xenograft tumors. Here, we use a multi-omics approach to identify the oncogenic alterations caused by transient centrosome loss. By integrating genomic and transcriptomic data of the prostate lines, we identified a consensus set of focal copy-number variations (CNVs) induced by centrosome loss in cultured cells that are also detectable within a subset of samples from a prostate cancer patient cohort. Using this CNV signature, we were able to derive a unique transcriptomic signature from prostate cancer patient samples that showed strong predictive value for adverse clinical outcomes. In summary, our experimental system uses centrosome loss to promote a punctuated burst of genomic crisis that is characteristic of genome evolution during prostate cancer progression. Consequently, this prostate cancer model produced recurrent structural variations that are detectable in patient samples and associate with worse outcomes. Graphic Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=140 SRC="FIGDIR/small/653266v1_ufig1.gif" ALT="Figure 1"> View larger version (55K): org.highwire.dtl.DTLVardef@ac4c9org.highwire.dtl.DTLVardef@fd9961org.highwire.dtl.DTLVardef@160e20corg.highwire.dtl.DTLVardef@198bf0e_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2025

Identifying Sex Differences in Lung Adenocarcinoma Using Multi-Omics Integrative Protein Signaling Networks

Lung adenocarcinoma (LUAD) exhibits differences between the sexes in incidence, prognosis, and therapy, suggesting underexplored molecular mechanisms. We conducted an integrative multi-omics analysis using the Clinical Proteomic Tumor Analysis Consortium (CPTAC) and The Cancer Genome Atlas (TCGA) datasets to contrast transcriptomes and proteomes between sexes. We used TIGER to analyze TCGA-LUAD expression data and found sex-biased activity of transcription factors (TFs); we used PTM-SEA with CPTAC-LUAD proteomics data and found sex-biased kinase activity. We combined these to construct a kinase-TF signaling network and discovered druggable pathways linked to cancer-related processes. We also found significant sex biases in clinically relevant TFs and kinases, including NR3C1, AR, and AURKA. Using the PRISM drug screening database, we identified potential sex-specific drugs, such as glucocorticoid receptor agonists and aurora kinase inhibitors. Our findings emphasize the importance of considering sex and using multi-omics network methods to discover personalized cancer therapies.

cancer biology↗

bioRxiv2024

Viral and host network analysis of the human cytomegalovirus transcriptome in latency.

HCMV genes UL135 and UL138 play opposing roles regulating latency and reactivation in CD34+ human progenitor cells (HPCs). Using the THP-1 cell line model for latency and reactivation, we designed an RNA sequencing study to compare the transcriptional profile of HCMV infection in the presence and absence of these genes. The loss of UL138 results in elevated levels of viral gene expression and increased differentiation of cell populations that support HCMV gene expression and genome synthesis. The loss of UL135 results in diminished viral gene expression during an initial burst that occurs as latency is established and no expression of eleven viral genes from the ULb region even following stimulation for differentiation and reactivation. Transcriptional network analysis revealed host transcription factors with potential to regulate the ULb genes in coordination with pUL135. These results reveal roles for UL135 and UL138 in regulation of viral gene expression and potentially hematopoietic differentiation.

microbiology↗

bioRxiv2023

Integrative analysis reveals therapeutic potential of pyrvinium pamoate in Merkel cell carcinoma

Merkel Cell Carcinoma (MCC) is an aggressive neuroendocrine cutaneous malignancy arising from either ultraviolet-induced mutagenesis or Merkel cell polyomavirus (MCPyV) integration. Despite extensive research, our understanding of the molecular mechanisms driving the transition from normal cells to MCC remains limited. To address this knowledge gap, we assessed the impact of inducible MCPyV T antigens on normal human fibroblasts by performing RNA sequencing. Our data uncovered changes in expression and regulation of Wnt signaling pathway members. Building on this observation, we bioinformatically evaluated various Wnt pathway perturbagens for their ability to reverse the MCC gene expression signature and identified pyrvinium pamoate, an FDA-approved anthelminthic drug known for its anti-tumor activity in other cancers. Leveraging transcriptomic, network, and molecular analyses, we found that pyrvinium targets multiple MCC vulnerabilities. Pyrvinium not only reverses the neuroendocrine features of MCC by modulating canonical and non-canonical Wnt signaling but also inhibits cancer cell growth by activating p53-mediated apoptosis, disrupting mitochondrial function, and inducing endoplasmic reticulum stress. Finally, we demonstrated that pyrvinium reduces tumor growth in an MCC mouse xenograft model. These findings offer a new understanding of the role of Wnt signaling in MCC and highlight the utility of pyrvinium as a potential treatment for MCC.

cancer biology↗

bioRxiv2022

Joint inference of transcription factor activity and context-specific regulatory networks

Transcriptional regulation is a critical process that determines cell fate and disease. One of the challenges in understanding transcriptional regulation is that there is no easy way to infer the main regulators from gene expression data. Many existing methods focus on estimating the activity of individual transcription factors (TFs) using static TF-gene interaction databases, but regulomes are often altered in different cell types and disease conditions. To address this problem, we developed a new algorithm - Transcriptional Inference using Gene Expression and Regulatory data (TIGER) - that leverages Bayesian matrix factorization to simultaneously infer TF regulomes and transcription factor (TF) activities from RNA-seq data. We show that, when applied to yeast, A375, and MCF7 TF knock-out datasets, TIGER can provide more accurate predictions than comparable methods. The application to single-cell RNA-seq data reveals TIGERs potential for uncovering cell differentiation mechanisms. Our results reinforce the importance of incorporating context-specific regulation when studying the mechanisms driving disease in different cell types.

bioinformatics↗

bioRxiv2022

The Network Zoo: a multilingual package for the inference and analysis of biological networks

Inference and analysis of cellular biological networks requires software tools that integrate multi-omic data from various sources. The Network Zoo (netZoo; netzoo.github.io) is an open-source software suite to model biological networks, including context-specific gene regulatory networks and multi-omics partial correlation networks, to conduct differential analyses, estimate community structure, and model the transitions between biological states. The netZoo builds on our ongoing development of network methods, harmonizing the implementations in various computing languages (R, Python, MATLAB, and C) and between methods to allow a better integration of these tools into analytical pipelines. To demonstrate the value of this integrated toolkit, we analyzed the multi-omic data from the Cancer Cell Line Encyclopedia (CCLE) by inferring gene regulatory networks for each cancer cell line and associating network features with other phenotypic attributes such as drug sensitivity. This allowed us to identify transcription factors that play a critical role in both drug resistance and cancer development in melanoma. We also used netZoo to build a pan-cancer, multi-tiered CCLE map and used it to identify known metabolic hallmarks of cancer and to estimate novel context-specific elements that mediate post-transcriptional regulation. Because the netZoo tools are open-source and there is a growing community of both users and developers, we built an ecosystem to support community contributions, share use cases, and visualize networks online. As additional data types become available and our suite of methods grows, we will expand "the zoo" to incorporate an increasingly sophisticated collection of tools for network inference and analysis.

genomics↗

bioRxiv2021

Molecular Drivers of Tumor Progression in Microsatellite Stable APC Mutation-Negative Colorectal Cancers

BackgroundThe tumor suppressor gene adenomatous polyposis coli (APC) is the initiating mutation in approximately 80% of all colorectal cancers (CRC), underscoring the importance of aberrant regulation of intracellular WNT signaling in CRC development. Recent studies have found that early-onset CRC exhibits an increased proportion of tumors lacking an APC mutation. We set out to identify mechanisms underlying APC mutation-negative (APCmut-) CRCs. MethodsWe analyzed data from The Cancer Genome Atlas to compare clinical phenotypes, somatic mutations, copy number variations, gene fusions, RNA expression, and DNA methylation profiles between APCmut- and APC mutation-positive (APCmut+) microsatellite stable CRCs. ResultsTranscriptionally, APCmut- CRCs clustered into two approximately equal groups. Cluster One was associated with enhanced mitochondrial activation. Cluster Two was strikingly associated with genetic inactivation or decreased RNA expression of the WNT antagonist RNF43, increased expression of the WNT agonist RSPO3, activating mutation of BRAF, or increased methylation and decreased expression of AXIN2. APCmut- CRCs exhibited evidence of increased immune cell infiltration, with significant correlation between M2 macrophages and RSPO3. ConclusionsAPCmut- CRCs comprise two groups of tumors characterized by enhanced mitochondrial activation or increased sensitivity to extracellular WNT, suggesting that they could be respectively susceptible to inhibition of these pathways.

cancer biology↗

bioRxiv2021

EVL and MIM/MTSS1 regulate actin cytoskeletal remodeling to promote dendritic filopodia in developing neurons

Dendritic spines are the postsynaptic compartment of a functional neuronal synapse, and are critical for synaptic connectivity and plasticity. The developmental precursor to dendritic spines, dendritic filopodia, are highly motile protrusions that facilitate synapse formation by sampling the environment for suitable axon partners during development and learning. Despite the significance of the actin cytoskeleton in driving these protrusions, the actin remodeling factors involved in this process are not fully characterized. In this work, we identify a critical function for the Ena/VASP protein EVL in the regulation of dendritic filopodia. Amongst the Ena/VASP proteins, EVL is uniquely required for the characteristic morphology and dynamics of dendritic filopodia. Using a combination of genetic and optogenetic manipulations, we demonstrate that EVL promotes protrusive motility through membrane-direct actin polymerization at dendritic filopodia tips. EVL forms a complex at nascent protrusions and dendritic filopodia tips with MIM/MTSS1, an I-BAR protein recently discovered to be important for initiation of dendritic filopodia. We propose a model in which EVL cooperates with MIM to elongate and coalesce branched actin filaments, establishing the dynamic lamellipodia-like architecture of dendritic filopodia in developing neurons.

cell biology↗

bioRxiv2020

Cell intrinsic signaling in MEN1 mutant pancreatic neuroendocrine tumors unveils novel signaling pathways associated with de-differentiation

ObjectivesPreliminary genomic analysis of primary pancreatic neuroendocrine tumors revealed a complex mutational landscape with four common oncogenic events; however, critical activation pathways responsible for pancreatic neuroendocrine tumor progression and metastasis have yet to be elucidated. Here, we analyzed six primary pancreatic neuroendocrine tumors to determine which pathways are deregulated and responsible for progression. MethodsSelected genomic profiling of six primary pancreatic neuroendocrine tumors was performed using the Ion Torrent Comprehensive Cancer Panel with matched transcriptomes analyzed by Affymetrix Clariom D arrays. Validation of gene expression changes were measured by quantitative PCR using TaqMan assays and immunohistochemistry on tumor specimens. ResultsMEN1 was mutated in half (50%) of our sequenced tumors while FGFR3 was mutated in 2/6 (33%). Transcriptome analysis revealed that ITGA2 and EZH2 were overexpressed in MEN1 mutant tumors whereas ALK and VEGFA were overexpressed in FGFR3 mutant tumors. Immunohistochemistry revealed increased nuclear ITGA2 and EZH2 staining along with increased VE-Cadherin staining and loss of membranous E-cadherin localization in MEN1 and FGFR3 mutant tumors. ConclusionsOur results suggest that pancreatic neuroendocrine tumors containing MEN1 and FGFR3 mutations are more aggressive and de-differentiated than their wild-type counterparts. Additionally, we provide novel chemotherapeutic target FGFR3 for patients with this disease.

cancer biology↗

bioRxiv2019

Breast tumor stiffness instructs bone metastasis via mechanical memory

The mechanical microenvironment of primary breast tumors plays a substantial role in promoting tumor progression. While the transitory response of cancer cells to pathological stiffness in their native microenvironment has been well described, it is still unclear how mechanical stimuli in the primary tumor influence distant, late-stage metastatic phenotypes across time and space in absentia. Here, we show that primary tumor stiffness promotes stable, non-genetically heritable phenotypes in breast cancer cells. This mechanical memory instructs cancer cells to adopt and maintain increased cytoskeletal dynamics, traction force, and 3D invasion in vitro, in addition to promoting osteolytic bone metastasis in vivo. Furthermore, we established a mechanical conditioning (MeCo) score comprised of mechanically-regulated genes as a global gene expression measurement of tumor stiffness response. Clinically, we show that a high MeCo score is strongly associated with bone metastasis in patients. Using a discovery approach, we mechanistically traced mechanical memory in part to ERK-mediated mechanotransductive activation of RUNX2, an osteogenic gene bookmarker and bone metastasis driver. The combination of these RUNX2 traits permits the stable transactivation of osteolytic target genes that remain upregulated after cancer cells disseminate from their activating microenvironment in order to modify a distant microenvironment. Using genetic, epigenetic, and functional approaches, we were able to simulate, repress, select and extend RUNX2-mediated mechanical memory and alter cancer cell behavior accordingly. In concert with previous studies detailing the influence of biochemical properties of the primary tumor stroma on distinct metastatic phenotypes, our findings detailing the influence of biomechanical properties support a generalized model of cancer progression in which the integrated properties of the primary tumor microenvironment govern the secondary tumor microenvironment, i.e., soil instructs soil.

cancer biology↗

bioRxiv2018

Interrogating Mutant Allele Expression via Customized Reference Genomes to Define Influential Cancer Mutations

Genetic alterations are essential for cancer initiation and progression. However, differentiating mutations that drive the tumor phenotype from mutations that do not affect tumor fitness remains a fundamental challenge in cancer biology. To better understand the impact of a given mutation within cancer, RNA-sequencing data was used to categorize mutations based on their allelic expression. For this purpose, we developed the MAXX (Mutation Allelic Expression Extractor) software, which is highly effective at delineating the allelic expression of both single nucleotide variants and small insertions and deletions. Results from MAXX demonstrated that mutations can be separated into three groups based on their expression of the mutant allele, lack of expression from both alleles, or expression of only the wild-type allele. By taking into consideration the allelic expression patterns of genes that are mutated in PDAC, it was possible to increase the sensitivity of widely used driver mutation detection methods, as well as identify subtypes that have prognostic significance and are associated with sensitivity to select classes of therapeutic agents in cell culture. Thus, differentiating mutations based on their mutant allele expression via MAXX represents a means to parse somatic variants in tumor genomes, helping to elucidate of a genes respective role in cancer.

cancer biology↗

bioRxiv2017

Controllability in an islet specific regulatory network identifies the transcriptional factor NFATC4, which regulates Type 2 Diabetes associated genes

Probing the dynamic control features of biological networks represents a new frontier in capturing the dysregulated pathways in complex diseases. Here, using patient samples obtained from a pancreatic islet transplantation program, we constructed a tissue-specific gene regulatory network and used the control centrality (Cc) concept to identify the high control centrality (HiCc) pathways, which might serve as key pathobiological pathways for Type 2 Diabetes (T2D). We found that HiCc pathway genes were significantly enriched with modest GWAS p-values in the DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) study. We identified variants regulating gene expression (expression quantitative loci, eQTL) of HiCc pathway genes in islet samples. These eQTL genes showed higher levels of differential expression compared to non-eQTL genes in low, medium and high glucose concentrations in rat islets. Among genes with highly significant eQTL evidence, NFATC4 belonged to four HiCc pathways. We asked if the expressions of T2D-associated candidate genes from GWAS and literature are regulated by Nfatc4 in rat islets. Extensive in vitro silencing of Nfatc4 in rat islet cells displayed reduced expression of 16, and increased expression of 4 putative downstream T2D genes. Overall, our approach uncovers the mechanistic connection of NFATC4 with downstream targets including a previously unknown one, TCF7L2, and establishes the HiCc pathways relationship to T2D.

systems biology↗

bioRxiv2017

Phenotype-Driven Transitions In Regulatory Network Structure

Complex traits and diseases like human height or cancer are often not caused by a single mutation or genetic variant, but instead arise from multiple factors that together functionally perturb the underlying molecular network. Biological networks are known to be highly modular and contain dense \"communities\" of genes that carry out cellular processes, but these structures change between tissues, during development, and in disease. While many methods exist for inferring networks, we lack robust methods for quantifying changes in network structure. Here, we describe ALPACA (ALtered Partitions Across Community Architectures), a method for comparing two genome-scale networks derived from different phenotypic states to identify condition-specific modules. In simulations, ALPACA leads to more nuanced, sensitive, and robust module discovery than currently available network comparison methods. We used ALPACA to compare transcriptional networks in three contexts: angiogenic and non-angiogenic subtypes of ovarian cancer, human fibroblasts expressing transforming viral oncogenes, and sexual dimorphism in human breast tissue. In each case, ALPACA identified modules enriched for processes relevant to the phenotype. For example, modules specific to angiogenic ovarian tumors were enriched for genes associated with blood vessel development, interferon signaling, and flavonoid biosynthesis. In comparing the modular structure of networks in female and male breast tissue, we found that female breast has distinct modules enriched for genes involved in estrogen receptor and ERK signaling. The functional relevance of these new modules indicate that not only does phenotypic change correlate with network structural changes, but also that ALPACA can identify such modules in complex networks.\n\nSignificance statementDistinct phenotypes are often thought of in terms of unique patterns of gene expression. But the expression levels of genes and proteins are driven by networks of interacting elements, and changes in expression are driven by changes in the structure of the associated networks. Because of the size and complexity of these networks, identifying functionally significant changes in network topology has been an ongoing challenge. We describe a new method for comparing networks derived from related conditions, such as healthy and disease tissue, and identifying emergent modules associated with the phenotypic differences between the conditions. We show that this method can find both known and previously unreported pathways involved in three contexts: ovarian cancer, tumor viruses, and breast tissue development.

systems biology↗