Cytidine Deaminase Resolves Replicative Stress and Protects Pancreatic Cancer from DNA-Targeting Drugs.

Cytidine deaminase (CDA) functions in the pyrimidine salvage pathway for DNA and RNA syntheses and has been shown to protect cancer cells from deoxycytidine-based chemotherapies. In this study, we observed that CDA was overexpressed in pancreatic adenocarcinoma from patients at baseline and was essential for experimental tumor growth. Mechanistic investigations revealed that CDA localized to replication forks where it increased replication speed, improved replication fork restart efficiency, reduced endogenous replication stress, minimized DNA breaks, and regulated genetic stability during DNA replication. In cellular pancreatic cancer models, high CDA expression correlated with resistance to DNA-damaging agents. Silencing CDA in patient-derived primary cultures in vitro and in orthotopic xenografts in vivo increased replication stress and sensitized pancreatic adenocarcinoma cells to oxaliplatin. This study sheds light on the role of CDA in pancreatic adenocarcinoma, offering insights into how this tumor type modulates replication stress. These findings suggest that CDA expression could potentially predict therapeutic efficacy and that targeting CDA induces intolerable levels of replication stress in cancer cells, particularly when combined with DNA-targeted therapies. SIGNIFICANCE: Cytidine deaminase reduces replication stress and regulates DNA replication to confer resistance to DNA-damaging drugs in pancreatic cancer, unveiling a molecular vulnerability that could enhance treatment response.

3D chromatin interactions involving Drosophila insulators are infrequent but preferential and arise before TADs and transcription.

In mammals, insulators contribute to the regulation of loop extrusion to organize chromatin into topologically associating domains. In Drosophila the role of insulators in 3D genome organization is, however, under current debate. Here, we addressed this question by combining bioinformatics analysis and multiplexed chromatin imaging. We describe a class of Drosophila insulators enriched at regions forming preferential chromatin interactions genome-wide. Notably, most of these 3D interactions do not involve TAD borders. Multiplexed imaging shows that these interactions occur infrequently, and only rarely involve multiple genomic regions coalescing together in space in single cells. Finally, we show that non-border preferential 3D interactions enriched in this class of insulators are present before TADs and transcription during Drosophila development. Our results are inconsistent with insulators forming stable hubs in single cells, and instead suggest that they fine-tune existing 3D chromatin interactions, providing an additional regulatory layer for transcriptional regulation.

From time-series transcriptomics to gene regulatory networks: A review on inference methods.

Inference of gene regulatory networks has been an active area of research for around 20 years, leading to the development of sophisticated inference algorithms based on a variety of assumptions and approaches. With the ever increasing demand for more accurate and powerful models, the inference problem remains of broad scientific interest. The abstract representation of biological systems through gene regulatory networks represents a powerful method to study such systems, encoding different amounts and types of information. In this review, we summarize the different types of inference algorithms specifically based on time-series transcriptomics, giving an overview of the main applications of gene regulatory networks in computational biology. This review is intended to give an updated reference of regulatory networks inference tools to biologists and researchers new to the topic and guide them in selecting the appropriate inference method that best fits their questions, aims, and experimental data.

Integrated Multi-omics Analysis of Early Lung Adenocarcinoma Links Tumor Biological Features with Predicted Indolence or Aggressiveness.

Lung adenocarcinoma (LUAD) is a heterogeneous group of tumors associated with different survival rates, even when detected at an early stage. Here, we aim to investigate the biological determinants of early LUAD indolence or aggressiveness using radiomics as a surrogate of behavior. We present a set of 92 patients with LUAD with data collected across different methodologies. Patients were risk-stratified using the CT-based Score Indicative of Lung cancer Aggression (SILA) tool (0 = least aggressive, 1 = most aggressive). We grouped the patients as indolent (x ≤ 0.4, n = 14), intermediate (0.4 > x ≤ 0.6, n = 27), and aggressive (0.6 > x ≤ 1, n = 52). Using Cytometry by time of flight (CyTOF), we identified subpopulations with high HLA-DR expression that were associated with indolent behavior. In the RNA sequencing (RNA-seq) dataset, pathways related to immune response were associated with indolent behavior, while pathways associated with cell cycle and proliferation were associated with aggressive behavior. We extracted quantitative radiomics features from the CT scans of the patients. Integrating these datasets, we identified four feature signatures and four patient clusters that were associated with survival. Using single-cell RNA-seq, we found that indolent tumors had significantly more T cells and less B cells than aggressive tumors, and that the latter had a higher abundance of regulatory T cells and Th cells. In conclusion, we were able to uncover a correspondence between radiomics and tumor biology, which could improve the discrimination between indolent and aggressive LUAD tumors, enhance our knowledge in the biology of these tumors, and offer novel and personalized avenues for intervention. Significance: This study provides a comprehensive profiling of LUAD indolence and aggressiveness at the biological bulk and single-cell levels, as well as at the clinical and radiomics levels. This hypothesis generating study uncovers several potential future research avenues. It also highlights the importance and power of data integration to improve our systemic understanding of LUAD and to help reduce the gap between basic science research and clinical practice.

An agent-based model of monocyte differentiation into tumour-associated macrophages in chronic lymphocytic leukemia.

Monocyte-derived macrophages help maintain tissue homeostasis and defend the organism against pathogens. In tumors, recent studies have uncovered complex macrophage populations, including tumor-associated macrophages, which support tumorigenesis through cancer hallmarks such as immunosuppression, angiogenesis, or matrix remodeling. In the case of chronic lymphocytic leukemia, these macrophages are known as nurse-like cells (NLCs) and they protect leukemic cells from spontaneous apoptosis, contributing to their chemoresistance. We propose an agent-based model of monocyte differentiation into NLCs upon contact with leukemic B cells in vitro. We performed patient-specific model optimization using cultures of peripheral blood mononuclear cells from patients. Using our model, we were able to reproduce the temporal survival dynamics of cancer cells in a patient-specific manner and to identify patient groups related to distinct macrophage phenotypes. Our results show a potentially important role of phagocytosis in the polarization process of NLCs and in promoting cancer cells' enhanced survival.

Network models of chromatin structure.

Increasing numbers of datasets and experimental assays that capture the organization of chromatin inside the nucleus warrant an effort to develop tools to visualize and analyze these structures. Alongside polymer physics or constraint-based modeling, network theory approaches to describe 3D epigenome organization have gained in popularity. Representing genomic regions as nodes in a network enables visualization of 1D epigenomics datasets in the context of chromatin structure maps, while network theory metrics can be used to describe 3D epigenome organization and dynamics. In this review, we summarize the most salient applications of network theory to the study of chromatin contact maps, demonstrating its potential in revealing epigenomic patterns and relating them to cellular phenotypes.

mosna reveals different types of cellular interactions predictive of response to immunotherapies in cancer.

Single-cell spatially resolved proteomic or transcriptomic methods offer the opportunity to discover cell types interactions of biological or clinical importance. To extract relevant information from these data, we present mosna, a Python package to analyze spatially resolved experiments and discover patterns of cellular spatial organization. It includes the detection of preferential interactions between specific cell types and the discovery of cellular niches. We exemplify the proposed analysis pipeline on spatially resolved proteomic data from cancer patient samples annotated with clinical response to immunotherapy, and we show that mosna can identify a number of features describing cellular composition and spatial distribution that can provide biological hypotheses regarding factors that affect response to therapies.

3D chromatin connectivity underlies replication origin efficiency in mouse embryonic stem cells.

In mammalian cells, chromosomal replication starts at thousands of origins at which replisomes are assembled. Replicative stress triggers additional initiation events from 'dormant' origins whose genomic distribution and regulation are not well understood. In this study, we have analyzed origin activity in mouse embryonic stem cells in the absence or presence of mild replicative stress induced by aphidicolin, a DNA polymerase inhibitor, or by deregulation of origin licensing factor CDC6. In both cases, we observe that the majority of stress-responsive origins are also active in a small fraction of the cell population in a normal S phase, and stress increases their frequency of activation. In a search for the molecular determinants of origin efficiency, we compared the genetic and epigenetic features of origins displaying different levels of activation, and integrated their genomic positions in three-dimensional chromatin interaction networks derived from high-depth Hi-C and promoter-capture Hi-C data. We report that origin efficiency is directly proportional to the proximity to transcriptional start sites and to the number of contacts established between origin-containing chromatin fragments, supporting the organization of origins in higher-level DNA replication factories.

Epigenome-wide analysis of T-cell large granular lymphocytic leukemia identifies BCL11B as a potential biomarker.

BACKGROUND: The molecular pathogenesis of T-cell large granular lymphocytic leukemia (T-LGLL), a mature T-cell leukemia arising commonly from T-cell receptor αß-positive CD8+ memory cytotoxic T cells, is only partly understood. The role of deregulated methylation in T-LGLL is not well known. We analyzed the epigenetic profile of T-LGLL cells of 11 patients compared to their normal counterparts by array-based DNA methylation profiling. For identification of molecular events driving the pathogenesis of T-LGLL, we compared the differentially methylated loci between the T-LGLL cases and normal T cells with chromatin segmentation data of benign T cells from the BLUEPRINT project. Moreover, we analyzed gene expression data of T-LGLL and benign T cells and validated the results by pyrosequencing in an extended cohort of 17 patients, including five patients with sequential samples. RESULTS: We identified dysregulation of DNA methylation associated with altered gene expression in T-LGLL. Since T-LGLL is a rare disease, the samples size is low. But as confirmed for each sample, hypermethylation of T-LGLL cells at various CpG sites located at enhancer regions is a hallmark of this disease. The interaction of BLC11B and C14orf64 as suggested by in silico data analysis could provide a novel pathogenetic mechanism that needs further experimental investigation. CONCLUSIONS: DNA methylation is altered in T-LGLL cells compared to benign T cells. In particular, BCL11B is highly significant differentially methylated in T-LGLL cells. Although our results have to be validated in a larger patient cohort, BCL11B could be considered as a potential biomarker for this leukemia. In addition, altered gene expression and hypermethylation of enhancer regions could serve as potential mechanisms for treatment of this disease. Gene interactions of dysregulated genes, like BLC11B and C14orf64, may play an important role in pathogenic mechanisms and should be further analyzed.

The antitumoral activity of TLR7 ligands is corrupted by the microenvironment of pancreatic tumors.

Toll-like receptors (TLRs) are key players in the innate immune system. Recent studies have suggested that they may affect the growth of pancreatic cancer, a disease with no cure. Among them, TLR7 shows promise for therapy but may also promotes tumor growth. Thus, we aimed to clarify the therapeutic potential of TLR7 ligands in experimental pancreatic cancer models, to open the door for clinical applications. In vitro, we found that TLR7 ligands strongly inhibit the proliferation of both human and murine pancreatic cancer cells, compared with TLR2 agonists. Hence, TLR7 treatment alters cancer cells' cell cycle and induces cell death by apoptosis. In vivo, TLR7 agonist therapy significantly delays the growth of murine pancreatic tumors engrafted in immunodeficient mice. Remarkably, TLR7 ligands administration instead increases tumor growth and accelerates animal death when tumors are engrafted in immunocompetent models. Further investigations revealed that TLR7 agonists modulate the intratumoral content and phenotype of macrophages and that depleting such tumor-associated macrophages strongly hampers TLR7 agonist-induced tumor growth. Collectively, our findings shine a light on the duality of action of TLR7 agonists in experimental cancer models and call into question their use for pancreatic cancer therapy.

STAG2 loss-of-function affects short-range genomic contacts and modulates the basal-luminal transcriptional program of bladder cancer cells.

Cohesin exists in two variants containing STAG1 or STAG2. STAG2 is one of the most mutated genes in cancer and a major bladder tumor suppressor. Little is known about how its inactivation contributes to tumorigenesis. Here, we analyze the genomic distribution of STAG1 and STAG2 and perform STAG2 loss-of-function experiments using RT112 bladder cancer cells; we then analyze the genomic effects by integrating gene expression and chromatin interaction data. Functional compartmentalization exists between the cohesin complexes: cohesin-STAG2 displays a distinctive genomic distribution and mediates short and mid-ranged interactions that engage genes at higher frequency than those established by cohesin-STAG1. STAG2 knockdown results in down-regulation of the luminal urothelial signature and up-regulation of the basal transcriptional program, mirroring differences between STAG2-high and STAG2-low human bladder tumors. This is accompanied by rewiring of DNA contacts within topological domains, while compartments and domain boundaries remain refractive. Contacts lost upon depletion of STAG2 are assortative, preferentially occur within silent chromatin domains, and are associated with de-repression of lineage-specifying genes. Our findings indicate that STAG2 participates in the DNA looping that keeps the basal transcriptional program silent and thus sustains the luminal program. This mechanism may contribute to the tumor suppressor function of STAG2 in the urothelium.

Activation of Vitamin D Receptor Pathway Enhances Differentiating Capacity in Acute Myeloid Leukemia with Isocitrate Dehydrogenase Mutations.

Relapses and resistance to therapeutic agents are major barriers in the treatment of acute myeloid leukemia (AML) patients. These unfavorable outcomes emphasize the need for new strategies targeting drug-resistant cells. As IDH mutations are present in the preleukemic stem cells and systematically conserved at relapse, targeting IDH mutant cells could be essential to achieve a long-term remission in the IDH mutant AML subgroup. Here, using a panel of human AML cell lines and primary AML patient specimens harboring IDH mutations, we showed that the production of an oncometabolite (R)-2-HG by IDH mutant enzymes induces vitamin D receptor-related transcriptional changes, priming these AML cells to differentiate with pharmacological doses of ATRA and/or VD. This activation occurs in a CEBPα-dependent manner. Accordingly, our findings illuminate potent and cooperative effects of IDH mutations and the vitamin D receptor pathway on differentiation in AML, revealing a novel therapeutic approach easily transferable/immediately applicable to this subgroup of AML patients.

Tysserand-fast and accurate reconstruction of spatial networks from bioimages.

SUMMARY: Networks provide a powerful framework to analyze spatial omics experiments. However, we lack tools that integrate several methods to easily reconstruct networks for further analyses with dedicated libraries. In addition, choosing the appropriate method and parameters can be challenging. We propose tysserand, a Python library to reconstruct spatial networks from spatially resolved omics experiments. It is intended as a common tool to which the bioinformatics community can add new methods to reconstruct networks, choose appropriate parameters, clean resulting networks and pipe data to other libraries. AVAILABILITY AND IMPLEMENTATION: tysserand software and tutorials with a Jupyter notebook to reproduce the results are available at https://github.com/VeraPancaldiLab/tysserand. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Low Replicative Stress Triggers Cell-Type Specific Inheritable Advanced Replication Timing.

DNA replication timing (RT), reflecting the temporal order of origin activation, is known as a robust and conserved cell-type specific process. Upon low replication stress, the slowing of replication forks induces well-documented RT delays associated to genetic instability, but it can also generate RT advances that are still uncharacterized. In order to characterize these advanced initiation events, we monitored the whole genome RT from six independent human cell lines treated with low doses of aphidicolin. We report that RT advances are cell-type-specific and involve large heterochromatin domains. Importantly, we found that some major late to early RT advances can be inherited by the unstressed next-cellular generation, which is a unique process that correlates with enhanced chromatin accessibility, as well as modified replication origin landscape and gene expression in daughter cells. Collectively, this work highlights how low replication stress may impact cellular identity by RT advances events at a subset of chromosomal domains.

Mitochondrial metabolism supports resistance to IDH mutant inhibitors in acute myeloid leukemia.

Mutations in IDH induce epigenetic and transcriptional reprogramming, differentiation bias, and susceptibility to mitochondrial inhibitors in cancer cells. Here, we first show that cell lines, PDXs, and patients with acute myeloid leukemia (AML) harboring an IDH mutation displayed an enhanced mitochondrial oxidative metabolism. Along with an increase in TCA cycle intermediates, this AML-specific metabolic behavior mechanistically occurred through the increase in electron transport chain complex I activity, mitochondrial respiration, and methylation-driven CEBPα-induced fatty acid ß-oxidation of IDH1 mutant cells. While IDH1 mutant inhibitor reduced 2-HG oncometabolite and CEBPα methylation, it failed to reverse FAO and OxPHOS. These mitochondrial activities were maintained through the inhibition of Akt and enhanced activation of peroxisome proliferator-activated receptor-γ coactivator-1 PGC1α upon IDH1 mutant inhibitor. Accordingly, OxPHOS inhibitors improved anti-AML efficacy of IDH mutant inhibitors in vivo. This work provides a scientific rationale for combinatory mitochondrial-targeted therapies to treat IDH mutant AML patients, especially those unresponsive to or relapsing from IDH mutant inhibitors.

Chromatin Network Analyses: Towards Structure-Function Relationships in Epigenomics.

Recent technological advances have allowed us to map chromatin conformation and uncover the genome's spatial organization of the genome inside the nucleus. These experiments have revealed the complexities of genome folding, characterized by the presence of loops and domains at different scales, which can change across development and in different cell types. There is strong evidence for a relationship between the topological properties of chromatin contacts and cellular phenotype. Chromatin can be represented as a network, in which genomic fragments are the nodes and connections represent experimentally observed spatial proximity of two genomically distant regions in a specific cell type or biological condition. With this approach we can consider a variety of chromatin features in association with the 3D structure, investigating how nuclear chromatin organization can be related to gene regulation, replication, malignancy, phenotypic variability and plasticity. We briefly review the results obtained on genome architecture through network theoretic approaches. As previously observed in protein-protein interaction networks and many types of non-biological networks, external conditions could shape network topology through a yet unidentified structure-function relationship. Similar to scientists studying the brain, we are confronted with a duality between a spatially embedded network of physical contacts, a related network of correlation in the dynamics of network nodes and, finally, an abstract definition of function of this network, related to phenotype. We summarise major developments in the study of networks in other fields, which we think can suggest a path towards better understanding how 3D genome configuration can impact biological function and adaptation to the environment.