Biology subjects

Tsirigos, A.

Publications and source records attributed to Tsirigos, A..

At least 37 records · Page 2Linked to original sources

bioRxiv2024

Previously unknown regulatory role of extracellular RNA on bacterial directional migration

Bacterial directional migration plays a significant role in bacterial adaptation. However, the regulation of this process, particularly in young biofilms, remains unclear. Here, we demonstrated the critical role of extracellular RNA as part of the Universal Receptive System in bacterial directional migration using a multidisciplinary approach, including bacterial culture, biochemistry, and genetics. We found that the destruction or inactivation of extracellular RNA with RNase or RNA-specific antibodies in the presence of the chemoattractant triggered the formation of bacterial "runner cells>> in what we call a "panic state" capable of directional migration. These cells quickly migrated even on the surface of 1.5% agar and formed evolved colonies that were transcriptionally and biochemically different from the ancestral cells. We have also shown that cell-free DNA from blood plasma can act as a potent bacterial chemoattractant. Our data revealed a previously unknown role of bacterial extracellular RNA in the regulation of bacterial migration and have shown that its destruction or inhibition triggered the directional migration of developing and mature biofilms towards the chemoattractant.

microbiology↗

bioRxiv2024

Self-Supervised Learning Reveals Clinically Relevant Histomorphological Patterns for Therapeutic Strategies in Colon Cancer

Self-supervised learning (SSL) automates the extraction and interpretation of histopathology features on unannotated hematoxylin-and-eosin-stained whole-slide images (WSIs). We trained an SSL Barlow Twins-encoder on 435 TCGA colon adenocarcinoma WSIs to extract features from small image patches. Leiden community detection then grouped tiles into histomorphological phenotype clusters (HPCs). HPC reproducibility and predictive ability for overall survival was confirmed in an independent clinical trial cohort (N=1213 WSIs). This unbiased atlas resulted in 47 HPCs displaying unique and sharing clinically significant histomorphological traits, highlighting tissue type, quantity, and architecture, especially in the context of tumor stroma. Through in-depth analysis of these HPCs, including immune landscape and gene set enrichment analysis, and association to clinical outcomes, we shed light on the factors influencing survival and responses to treatments like standard adjuvant chemotherapy and experimental therapies. Further exploration of HPCs may unveil new insights and aid decision-making and personalized treatments for colon cancer patients.

bioinformatics↗

bioRxiv2023

Connecting Chromatin Structures to Gene Regulation Using Dynamic Polymer Simulations

The transfer of regulatory information between distal loci on chromatin is thought to involve physical proximity, but key biophysical features of these contacts remain unclear. For instance, it is unknown how close and for how long two loci need to be in order to productively interact. The main challenge is that it is currently impossible to measure chromatin dynamics with high spatiotemporal resolution at scale. Polymer simulations provide an accessible and rigorous way to test biophysical models of chromatin regulation, yet there is a lack of simple and general methods for extracting the values of model parameters. Here we adapt the Nelder-Mead simplex optimization algorithm to select the best polymer model matching a given Hi-C dataset, using the MYC locus as an example. The models biophysical parameters predict a compartmental rearrangement of the MYC locus in leukemia, which we validate with single-cell measurements. Leveraging trajectories predicted by the model, we find that loci with similar Hi-C contact frequencies can exhibit widely different contact dynamics. Interestingly, the frequency of productive interactions between loci exhibits a non-linear relationship with their Hi-C contact frequency when we enforce a specific capture radius and contact duration. These observations are consistent with recent experimental observations and suggest that the dynamic ensemble of chromatin configurations, rather than average contact matrices, is required to fully predict productive long-range chromatin interactions.

biophysics↗

bioRxiv2023

MetFinder: a neural network-based tool for automated quantitation of metastatic burden in histological sections from animal models

Diagnosis of most diseases relies on expert histopathological evaluation of tissue sections by an experienced pathologist. By using standardized staining techniques and an expanding repertoire of markers, a trained eye is able to recognize disease-specific patterns with high accuracy and determine a diagnosis. As efforts to study mechanisms of metastasis and novel therapeutic approaches multiply, researchers need accurate, high-throughput methods to evaluate effects on tumor burden resulting from specific interventions. However, current methods of quantifying tumor burden are low in either resolution or throughput. Artificial neural networks, which can perform in-depth image analyses of tissue sections, provide an opportunity for automated recognition of consistent histopathological patterns. In order to increase the outflow of data collection from preclinical studies, we trained a deep neural network for quantitative analysis of melanoma tumor content on histopathological sections of murine models. This AI-based algorithm, made freely available to academic labs through a web-interface called MetFinder, promises to become an asset for researchers and pathologists interested in accurate, quantitative assessment of metastasis burden.

cancer biology↗

bioRxiv2023

The expression profile and tumorigenic mechanisms of CD97 (ADGRE5) in glioblastoma render it a targetable vulnerability

Glioblastoma (GBM) is the most common and aggressive primary brain malignancy. Adhesion G protein-coupled receptors (aGPCRs) have attracted interest for their functional role in gliomagenesis and their potential as treatment targets. To identify therapeutically targetable opportunities among aGPCR family members in unbiased fashion, we analyzed expression levels of all aGPCRs in GBM and non-neoplastic brain tissue. Using bulk and single cell transcriptomic and proteomic data, we show that CD97 (ADGRE5), an aGPCR previously implicated in GBM pathogenesis, is the most promising aGPCR target in GBM, by virtue of its abundance in all GBM tumors and its de novo expression profile in GBM compared to normal brain tissue and neural progenitors. CD97 knockdown or knockout significantly reduces the tumor initiation capacity of patient-derived GBM cultures (PDGC) in vitro and in vivo. Transcriptomic and metabolomic data from PDGCs suggest that CD97 promotes glycolytic metabolism. The oncogenic and metabolic effects of CD97 are mediated by the MAPK pathway. Activation of MAPK signaling depends on phosphorylation of the cytosolic C-terminus of CD97 and recruitment of {beta}-arrestin. Using single-cell RNA-sequencing and biochemical assays, we demonstrate that THY1/CD90 is the most likely CD97 ligand in GBM. Lastly, we show that targeting of PDGCs with an anti-CD97 antibody-drug conjugate in vitro selectively kills tumor cells but not human astrocytes or neural stem cells. Our studies identify CD97 as an important regulator of tumor metabolism in GBM, elucidate mechanisms of receptor activation and signaling, and provide strong scientific rationale for developing biologics to target it for therapeutic purposes.

cancer biology↗

bioRxiv2023

Systematic mapping and modeling of 3D enhancer-promoter interactions in early mouse embryonic lineages reveal regulatory principles that determine the levels and cell-type specificity of gene expression

Mammalian embryogenesis commences with two pivotal and binary cell fate decisions that give rise to three essential lineages, the trophectoderm (TE), the epiblast (EPI) and the primitive endoderm (PrE). Although key signaling pathways and transcription factors that control these early embryonic decisions have been identified, the non-coding regulatory elements via which transcriptional regulators enact these fates remain understudied. To address this gap, we have characterized, at a genome-wide scale, enhancer activity and 3D connectivity in embryo-derived stem cell lines that represent each of the early developmental fates. We observed extensive enhancer remodeling and fine-scale 3D chromatin rewiring among the three lineages, which strongly associate with transcriptional changes, although there are distinct groups of genes that are irresponsive to topological changes. In each lineage, a high degree of connectivity or "hubness" positively correlates with levels of gene expression and enriches for cell-type specific and essential genes. Genes within 3D hubs also show a significantly stronger probability of coregulation across lineages, compared to genes in linear proximity or within the same contact domains. By incorporating 3D chromatin features, we build a novel predictive model for transcriptional regulation (3D-HiChAT), which outperformed models that use only 1D promoter or proximal variables in predicting levels and cell-type specificity of gene expression. Using 3D-HiChAT, we performed genome-wide in silico perturbations to nominate candidate functional enhancers and hubs in each cell lineage, and with CRISPRi experiments we validated several novel enhancers that control expression of one or more genes in their respective lineages. Our study comprehensively identifies 3D regulatory hubs associated with the earliest mammalian lineages and describes their relationship to gene expression and cell identity, providing a framework to understand lineage-specific transcriptional behaviors. HIGHLIGHTS- Cell lines representing early embryonic lineages undergo drastic enhancer remodeling and fine-scale 3D chromatin reorganization - Highly interacting 3D hubs strongly enrich for highly expressed, cell-type specific and essential genes - 3D chromatin features greatly improve prediction of cell-type specific gene expression compared to 1D promoter features - In silico and experimental perturbations identify novel enhancers regulating the expression of two or more genes in early embryonic lineages

molecular biology↗

bioRxiv2023

Tumor-intrinsic LKB1-LIF signaling axis establishes a myeloid niche to promote immune evasion and tumor growth

Tumor mutations can influence the surrounding microenvironment leading to suppression of anti-tumor immune responses and thereby contributing to tumor progression and failure of cancer therapies. Here we use genetically engineered lung cancer mouse models and patient samples to dissect how LKB1 mutations accelerate tumor growth by reshaping the immune microenvironment. Comprehensive immune profiling of LKB1-mutant vs wildtype tumors revealed dramatic changes in myeloid cells, specifically enrichment of Arg1+ interstitial macrophages and SiglecFHi neutrophils. We discovered a novel mechanism whereby autocrine LIF signaling in Lkb1-mutant tumors drives tumorigenesis by reprogramming myeloid cells in the immune microenvironment. Inhibiting LIF signaling in Lkb1-mutant tumors, via gene targeting or with a neutralizing antibody, resulted in a striking reduction in Arg1+ interstitial macrophages and SiglecFHi neutrophils, expansion of antigen specific T cells, and inhibition of tumor progression. Thus, targeting LIF signaling provides a new therapeutic approach to reverse the immunosuppressive microenvironment of LKB1-mutant tumors.

cancer biology↗

bioRxiv2023

MethNet: a robust approach to identify regulatory hubs and their distal targets in cancer

Aberrations in the capacity of DNA/chromatin modifiers and transcription factors to bind non-coding regions can lead to changes in gene regulation and impact disease phenotypes. However, identifying distal regulatory elements and connecting them with their target genes remains challenging. Here, we present MethNet, a pipeline that integrates large-scale DNA methylation and gene expression data across multiple cancers, to uncover novel cis regulatory elements (CREs) in a 1Mb region around every promoter in the genome. MethNet identifies clusters of highly ranked CREs, referred to as hubs, which contribute to the regulation of multiple genes and significantly affect patient survival. Promoter-capture Hi-C confirmed that highly ranked associations involve physical interactions between CREs and their gene targets, and CRISPRi based scRNA Perturb-seq validated the functional impact of CREs. Thus, MethNet-identified CREs represent a valuable resource for unraveling complex mechanisms underlying gene expression, and for prioritizing the verification of predicted non-coding disease hotspots.

cancer biology↗

bioRxiv2023

Glutamine antagonist DRP-104 suppresses tumor growth and enhances response to checkpoint blockade in KEAP1 mutant lung cancer

Loss-of-function mutations in KEAP1 frequently occur in lung cancer and are associated with resistance to standard of care treatment, highlighting the need for the development of targeted therapies. We have previously shown that KEAP1 mutant tumors have increased glutamine consumption to support the metabolic rewiring associated with NRF2 activation. Here, using patient-derived xenograft models and antigenic orthotopic lung cancer models, we show that the novel glutamine antagonist DRP-104 impairs the growth of KEAP1 mutant tumors. We find that DRP-104 suppresses KEAP1 mutant tumor growth by inhibiting glutamine-dependent nucleotide synthesis and promoting anti-tumor CD4 and CD8 T cell responses. Using multimodal single-cell sequencing and ex vivo functional assays, we discover that DRP-104 reverses T cell exhaustion and enhances the function of CD4 and CD8 T cells culminating in an improved response to anti-PD1 therapy. Our pre-clinical findings provide compelling evidence that DRP-104, currently in phase 1 clinical trials, offers a promising therapeutic approach for treating patients with KEAP1 mutant lung cancer. Furthermore, we demonstrate that by combining DRP-104 with checkpoint inhibition, we can achieve suppression of tumor intrinsic metabolism and augmentation of anti-tumor T cell responses.

cancer biology↗

bioRxiv2023

Novel Chromatin Insulating Activities Uncovered upon Eliminating KnownInsulators in vivo

Partitioning of repressive from actively transcribed chromatin in mammalian cells fosters cell-type specific gene expression patterns. While this partitioning is reconstructed during differentiation, the chromatin occupancy of the key insulator, CTCF, is unchanged at the developmentally important Hox clusters. Thus, dynamic changes in chromatin boundaries must entail other activities. Given its requirement for chromatin loop formation, we examined cohesin-based chromatin occupancy without known insulators, CTCF and MAZ, and identified a family of zinc finger proteins (ZNFs), some of which exhibit tissue-specific expression. Two such ZNFs foster chromatin boundaries at the Hox clusters that are distinct from each other and from MAZ. PATZ1 was critical to the thoracolumbar boundary in differentiating motor neurons and mouse skeleton, while ZNF263 contributed to cervicothoracic boundaries. We propose that these insulating activities act with cohesin, alone or combinatorially, with or without CTCF, to implement precise positional identity and cell fate during development.

genetics↗

bioRxiv2022

Inflammation in the tumor-adjacent lung as a predictor of clinical outcome in lung adenocarcinoma

Early-stage lung adenocarcinoma is typically treated by surgical resection of the tumor. While in the majority of cases surgery can lead to cure, approximately 30% of patients progress. Despite intense efforts to map the genetic landscape of early-stage lung tumors, there has been limited success in discovering accurate biomarkers that can predict clinical outcomes. Meanwhile, the role of the tumor-adjacent tissue in cancer progression has been largely ignored. To test whether tumor-adjacent tissue can be informative of progression-free survival and to probe the underlying molecular pathways involved, we designed a multi-omic study in both tumor and matched tumor-adjacent histologically normal lung tissue from the same patient. Our study includes 143 treatment naive stage I cases with long-term patient follow-up and is, to our knowledge, the largest such study with the longest follow-up. We performed a comprehensive histologic characterization of all tumors, mapped the mutational landscape and probed the transcriptome of both tumor and adjacent normal tissue. We evaluated the predictive power of each data modality and showed that the transcriptome of tumor-adjacent histologically normal lung tissue is the only reliable predictor of clinical outcome. Unbiased discovery of co-expressed gene modules revealed that inflammatory pathways are upregulated in the tumor-adjacent tissue of patients at high risk for disease progression. Furthermore, single-cell transcriptome analysis in the tumor-adjacent lung demonstrated that progression-associated inflammatory signatures were broadly expressed by both immune and non-immune cells including mesothelial cells, alveolar type 2 cells and fibroblasts, CD1 dendritic cells and MAST cells. Collectively, our studies suggest that molecular profiling of tumor-adjacent tissue can identify patients that are at high risk for disease progression.

bioinformatics↗

bioRxiv2022

Single-Cell RNA Sequencing Reveals the Effects of Chemotherapy on Human Pancreatic Adenocarcinoma and its Tumor Microenvironment

The tumor microenvironment (TME) in pancreatic ductal adenocarcinoma (PDAC) is a complex ecosystem that drives tumor progression; however, in-depth single cell characterization of the PDAC TME and its role in response to therapy is lacking. We performed single-cell RNA sequencing on freshly collected human PDAC samples either before or after chemotherapy. Overall, we found a heterogeneous mixture of basal and classical cancer cell subtypes, along with distinct cancer-associated fibroblast and macrophage subpopulations. Strikingly, classical and basal-like cancer cells exhibited similar transcriptional responses to chemotherapy, and did not demonstrate a shift towards a basal-like transcriptional program among treated samples. We observed decreased ligand-receptor interactions in treated samples, particularly TIGIT on CD8+ T cells and its receptor on cancer cells, and identified TIGIT as the major inhibitory checkpoint molecule of CD8+ T cells. Our results suggest that chemotherapy profoundly impacts the PDAC TME and may promote resistance to immunotherapy.

cancer biology↗

bioRxiv2022

Genome-wide analysis of dendritic cell differentiation

Dendritic cells (DCs) are immune sentinel cells that comprise antigen-presenting conventional DCs (cDCs) and cytokine-producing plasmacytoid DCs (pDCs). Cytokine Flt3 ligand (Flt3L) supports the proliferation of hematopoietic progenitors, and is also necessary and sufficient for DC differentiation. Here we characterized the spontaneous differentiation of a Flt3L-dependent murine progenitor cell line into pDCs and "myeloid" cDCs (cDC2s), and interrogated it using a genome-wide CRISPR/Cas9 dropout screen. The screen revealed multiple regulators of DC differentiation including the glycosylphosphatidylinositol transamidase complex, the Nieman-Pick type C cholesterol transporter and arginine methyltransferase Carm1; the role of Carm1 in pDC and cDC2 differentiation was confirmed by conditional targeting in vivo. We also found that negative regulators of mTOR signaling, including the subunits of TSC and GATOR1 complexes, restricted progenitor growth but enabled DC differentiation. The results provide a comprehensive forward genetic analysis of DC differentiation, and help explain how the opposing processes of proliferation and differentiation could be driven by the same cytokine.

immunology↗

bioRxiv2022

Cell type-specific prediction of 3D chromatin architecture

The mammalian genome is spatially organized in the nucleus to enable cell type-specific gene expression. Investigating how chromatin organization determines this specificity remains a challenge. Methods for measuring the 3D chromatin organization, such as Hi-C, are costly and bear strong technical limitations, restricting their broad application particularly in high-throughput genetic perturbations. In this study, we present C.Origami, a deep neural network model that performs de novo prediction of cell type-specific chromatin organization. The C.Origami model enables in silico experiments to examine the impact of genetic perturbations on chromatin interactions in cancer genomes and beyond. In addition, we propose an in silico genetic screening framework that enables high-throughput identification of impactful genomic regions on 3D chromatin organization. We demonstrate that cell type-specific in silico genetic perturbation and screening, enabled by C.Origami, can be used to systematically discover novel chromatin regulatory mechanisms in both normal and disease-related biological systems.

genomics↗

bioRxiv2022

NSD2 E1099K drives relapse in pediatric acute lymphoblastic leukemia by disrupting 3D chromatin organization

The NSD2 p.E1099K (EK) mutation has been shown to be enriched in patients with relapsed ALL and found to play a role in clonal fitness dependent on the underlying genetic/epigenetic landscape of the cells. To uncover 3D chromatin architecture-related mechanisms underlying drug resistance, we systematically integrated Hi-C, ATAC-seq, RNA-seq and ChIP-seq data from three B-ALL cell lines heterozygous for NSD2 EK (RS4;11, RCH-ACV, SEM) and assessed changes upon knockdown. NSD2 knockdown revealed widespread remodeling of the 3D genome, specifically in terms of compartmentalization. Systematic integration of these datasets revealed significant switches in A/B compartments with a strong bias towards B compartments upon knockdown, suggesting that NSD2 EK plays a prominent role in maintaining A compartments through enrichment of H3K36me2 epigenetic marks. In contrast, we identified few changes in intra-TAD activity suggesting that the NSD2 EK impacts transcriptional changes through a remarkable dependence on compartmentalization. Furthermore, EK-mediated reorganization of compartments highlights the existence of a common core of compacting loci shared across the three cell lines that explain previously described phenotypes as well as serve as targets for therapeutic intervention. This study offers a novel mechanism by which NSD2 EK drives clonal evolution and drug resistance.

cancer biology↗

bioRxiv2021

KEAP1 mutation in lung adenocarcinoma promotes immune evasion and immunotherapy resistance

Lung cancer treatment has benefited greatly from the development of effective immune-based therapies. However, these strategies still fail in a large subset of patients. Tumor-intrinsic mutations can drive immune evasion via recruiting immunosuppressive populations or suppressing anti-tumor immune responses. KEAP1 is one of the most frequently mutated genes in lung adenocarcinoma patients and is associated with poor prognosis and inferior response to all therapies, including checkpoint blockade. Here, we established a novel antigenic lung cancer model and showed that Keap1-mutant tumors promote dramatic remodeling of the tumor immune microenvironment. Combining single-cell technology and depletion studies, we demonstrate that Keap1-mutant tumors diminish dendritic cell and T cell responses driving immunotherapy resistance. Importantly, analysis of KEAP1 mutant patient tumors revealed analogous decrease in dendritic cell and T cell infiltration. Our study provides new insight into the role of KEAP1 mutations in promoting immune evasion and suggests a path to novel immune-based therapeutic strategies for KEAP1 mutant lung cancer. Statement of significanceThis study establishes that tumor-intrinsic KEAP1 mutations contribute to immune evasion through suppression of dendritic cell and T cell responses, explaining the observed resistance to immunotherapy of KEAP1 mutant tumors. These results highlight the importance of stratifying patients based on KEAP1 status and paves the way for novel therapeutic strategies.

cancer biology↗

bioRxiv2020

A Machine Learning approach for assessing drug development risk

Characterizing drug development risk - the probability that a drug will eventually receive regulatory approval - has been notoriously hard given the complexities of drug biology and clinical trials. This often leads to an inefficient allocation of resources, and an overall reduction in R&D productivity. We propose a Machine Learning (ML) approach that provides a more accurate and unbiased estimate of drug development risk than traditional models.

bioinformatics↗

bioRxiv2020

Ontogeny and Vulnerabilities of Lapatinib Drug-Tolerant Persisters in HER2+ Breast Cancer

Resistance to targeted therapies is an important clinical problem in HER2-positive (HER2+) breast cancer. "Drug-tolerant persisters" (DTPs), a sub-population of cancer cells that survive via reversible, non-genetic mechanisms, are implicated in resistance to tyrosine kinase inhibitors (TKIs) in other malignancies, but DTPs following HER2 TKI exposure have not been well characterized. We found that HER2 TKIs evoke DTPs with a luminal-like or a mesenchymal-like transcriptome. Lentiviral barcoding/single cell RNA-sequencing reveal that HER2+ breast cancer cells cycle stochastically through a "pre-DTP" state, characterized by a G0-like expression signature and enriched for diapause and/or senescence genes. Trajectory analysis/cell sorting show that pre-DTPs preferentially yield DTPs upon HER2 TKI exposure. Cells with similar transcriptomes are present in HER2+ breast tumors and are associated with poor TKI response. Finally, biochemical experiments indicate that luminal-like DTPs survive via estrogen receptor-dependent induction of SGK3,leading to rewiring of the PI3K/AKT/mTORC1 pathway to enable AKT-independent mTORC1 activation. STATEMENT OF SIGNIFICANCEDTPs are implicated in resistance to TKIs, other targeted therapies, and chemotherapy, but their ontogeny and vulnerabilities remain unclear. We find that HER2 TKI-DTPs emerge from stochastically arising primed cells ("pre-DTPs") that preferentially engage either of two distinct transcriptional programs upon TKI exposure. Our results provide new insights into DTP ontogeny and identify potential therapeutic vulnerabilities.

cancer biology↗