corto: a lightweight R package for gene network inference and master regulator analysis

Cross-species transcriptome-wide meta-analysis of anterior cruciate ligament rupture

BACKGROUND

The Anterior Cruciate Ligament (ACL) plays a critical role in maintaining the musculoskeletal stability of the knee. Its injury has been linked to an increased risk of developing osteoarthritis. This study aims to identify cross-species responses to ACL rupture providing insights on its molecular basis. We analyzed five publicly available transcriptomic datasets from Homo sapiens, Mus musculus, Canis lupus familiaris, and Oryctolagus cuniculus. Differential gene expression analysis was performed for each dataset, producing a genome-wide transcriptional signature of fold-change significance for individual genes. Stouffer's method was used to integrate the results, identifying genes significantly deregulated across all species. Additionally, gene-set enrichment analysis revealed pathways that were consistently upregulated or downregulated.

RESULTS

A positive correlation in expression was observed between human and the other three species (r2 = 0.177-0.305, p-value ≤ 2.7 × 10- 113), identifying 210 genes as the most consistently up- and down-regulated in response to ACL rupture (p-adjusted ≤ 1.27 × 10- 23). These genes are primarily involved in cellular mitosis, collagen pathways, and cartilage development. Furthermore, 60 pathways were found to be significantly up- or down-regulated across all species (p-adjusted ≤ 4.57 × 10- 4). Among these, the upregulation of inhibition of bone mineralization (p-adjusted ≤ 2.99 × 10- 6) aligns with previous findings on the reduction of subchondral bone mineral density following ACL rupture.

CONCLUSIONS

This study highlights that distinct species exhibit common molecular responses to ACL rupture, underscoring the value of mice, dogs, and rabbits as potential translational model organisms for ACL rupture research. Furthermore, the identified genes and pathways highlight the molecular mechanisms underlying ACL rupture.

Dissecting the role of bHLH transcription factors in the potato spindle tuber viroid (PSTVd)-tomato pathosystem using network approaches

Viroids, minimalist plant pathogens, pose significant threats to crops by causing severe diseases. Transcriptome profiling technologies have significantly advanced the analysis of viroid-infected host plants, providing critical insights into gene regulation by these pathogens. Despite these advancements, the presence of numerous genes of unknown function continues to limit a complete understanding of the transcriptome data. Co-expression analysis addresses this issue by clustering genes into modules based on global gene expression levels, with genes in the same cluster likely participating in the same biological pathways. In a previous study, we emphasized the importance of basic helix-loop-helix (bHLH) proteins in transcriptional reprogramming in tomato host in response to different potato spindle tuber viroid (PSTVd) strains. In the current research, we delve into tissue-specific gene modules, particularly in root and leaf tissues, governed by bHLH transcription factors (TFs) during PSTVd infections. Utilizing public datasets that span Control (C), mock-inoculated, PSTVd-mild (M), and PSTVd-severe (S23) strains in time-course infections, we uncovered differentially expressed gene modules. These modules were functionally characterized to identify essential hub genes, notably highlighting the regulatory coordination of bHLH TFs, depicted through the significant bifan motif found in these interactions. Expanding on these findings, we explored bipartite networks, discerning both common and unique bHLH TF regulatory roles. Our findings reveal that bHLH TFs play pivotal roles in regulating processes such as energy metabolism and facilitating rapid membrane repair in infected roots. In leaves, changes in the external layers affected photosynthesis, linking bHLH TFs to distinct metabolic functions. Through this holistic approach, we deepen our understanding of viroid-host interactions and the intricate regulatory mechanisms underpinning them.

Master of disguise: ribosomal protein L5 beyond translation

Ribosomal proteins (RPs), key components of ribosomes, are traditionally associated with protein synthesis. However, emerging evidence suggests their involvement in diverse cellular functions beyond ribosomal biogenesis and translation, including transcriptional regulation. This study aimed at investigating the potential of RPs as transcriptional regulators by analyzing their interacting protein network. A subset of RP interactors exhibiting transcriptional regulatory functions was subjected to Gene Ontology analysis to identify enriched functional pathways. The results indicated that these interactions may play a role in different cellular pathways relevant to a number of biological processes, including cancer. To further explore this hypothesis, a virtual knockdown of RPL5 was performed in ovarian and breast cancer public data. As proof of concept the same experiments were conducted in vitro to validate the computational findings, confirming the potential of RPL5 in transcriptional regulation in cancer. This seminal study provides a foundation for future investigations into the multifaceted roles of RPs in the regulation of gene expression in physiological and pathological contexts.

4D Biofabrication of Magnetically Augmented Callus Assembloid Implants Enables Rapid Endochondral Ossification via Activation of Mechanosensitive Pathways

The use of magnetic-driven strategies for non-contact manipulation of engineered living modules opens up new possibilities for tissue engineering. The integration of magnetic nanoparticles (MNPs) with cartilaginous microtissues enables model-driven 4D bottom-up biofabrication of remotely actuated assembloids, providing unique properties to mechanoresponsive tissues, particularly skeletal constructs. However, for clinical use, the long-term effects of magnetic stimulation on phenotype and in vivo functionality need further exploration. Magnetic-driven biofabrication includes both rapid processes, such as guided microtissue assembly, and slower biological processes, like extracellular matrix secretion. This work explores the interplay between magnetic fields and MNP-loaded cartilaginous microtissues through mathematical modeling and experimental approaches, investigating long-term stimulation effects on ECM maturation and chondrogenic hypertrophy. Transcriptomic analysis reveal that magnetic stimulation activated mechanosensitive pathways and catabolic processes, driving accelerated cartilage-to-bone transitions via endochondral ossification, outcomes not observed in non-stimulated controls. This study paves the way for pre-programmed, remotely actuated skeletal assembloids with superior bone-forming capacity for regenerating challenging bone fractures.

Clonorchis sinensis Promotes Intrahepatic Cholangiocarcinoma Progression by Activating FASN-Mediated Fatty Acid Metabolism

BACKGROUND

Clonorchis sinensis infection is an important risk factor for intrahepatic cholangiocarcinoma (ICC). C. sinensis positive (C.s+) ICC patients had much shorter overall survival (OS) compared with C. sinensis negative (C.s-) group. This study aims to explore the impact and underlying mechanism of C. sinensis infection on ICC progression.

METHODS

In this study, ICC patients underwent surgery from two medical centers enrolled. RNA sequencing was used to determine the downstream activated pathways and genes. Furthermore, we demonstrated the potential mechanism of C. sinensis infection in promoting ICC progression through in vitro co culture systems and two animal models.

RESULTS

Through RNA sequencing, we found fatty acid metabolism and the expression of fatty acid synthase (FASN), a key enzyme catalyzing long-chain fatty acid synthesis, were significantly elevated in C.s+ ICCs. Then, we found excretory/secretory products (ESPs) secreted by C. sinensis could significantly upregulate the expression of transcription factor E2F1, thereby promoting FASN expression and fatty acid synthesis in tumor cells, which ultimately accelerating tumor progression. However, the promotive effect disappeared when FASN was knocked down. Meanwhile, ESPs could promote tumor growth, increasing FASN expression and free fatty acid level in both subcutaneous and orthotopic mouse models.

CONCLUSION

This study indicates that C. sinensis infection could upregulate the level of FASN and activate fatty acid synthesis pathway, thereby accelerating ICC progression. This provides a new insight for the clinical treatment of ICC with C. sinensis infection.

The impact of glucocorticoid receptor transactivation on context-dependent cell migration dynamics

The glucocorticoid receptor (GR) plays a significant role in breast cancer cell behaviour, although data on its effects are conflicting. The impact of GR agonist dexamethasone (dex) and antagonist mifepristone (mif) on oestrogen-positive (ER+) and triple-negative (TN) breast cancer cell lines in both 2D and 3D cultures was studied using multiple in vitro functional assays and transcriptome sequencing. GR activation increased cell motility in TN but not in ER + tumour cells, as observed in both collective and single-cell migration tests. Time-lapse analysis showed enhanced motility after 4-6 h in wound healing, despite dex inhibiting migration initially. This inhibition was observed at 2 h in single-cell tracking migration assays. Cell proliferation increased in TN and decreased in ER + cells upon GR activation, reversed by GR antagonist. RNA sequencing revealed dex's impact on cell adhesion and extracellular matrix signalling in TN cells and on DNA replication in ER + cells. Based on data from 1085 human breast cancer specimens, GR pathway expression correlated with migratory, extracellular matrix, and angiogenesis gene signatures. Additionally, higher expression of GR and increased GR signature were observed in fast-migrating cells compared to slow-migrating ones. Positive correlation between the GR signature and migration signature at the single-cell level indicated an association between GR activity and cell migration. For the first time, we assessed altered time-lapse migration dynamics in TN breast cancer cells, potentially contributing to cancer progression and prognosis, highlighting that the effects of dexamethasone on breast cancer cell migration are influenced by ER status and treatment duration.

EMT-driven plasticity prospectively increases cell-cell variability to promote therapeutic adaptation in breast cancer

Cellular plasticity enables cancer cells to adapt non-genetically, thereby preventing therapeutic success. The epithelial-mesenchymal transition (EMT) is a type of plasticity linked to resistance and metastasis. However, its exact impact on population diversity and its dynamics under chemotherapy is unknown. We used single-cell transcriptomics to investigate phenotypic diversity dynamics upon treatment in two in vitro models of triple negative breast cancer (TNBC), where EMT-driven plasticity is either induced or spontaneously occurring. We report that EMT-driven plasticity confers higher phenotypic cell-cell variability (p < 0.001) while enriching for stem-like cells. Genetic and phenotypic cell-cell variability were not consistently correlated. High-plasticity populations displayed more pre-adapted cells before treatment (p = 0.03). In a population displaying spontaneous EMT and phenotypic variation, pre-adapted cells were a rare minority of high-scoring outliers whose expression patterns correlated with survival in TNBC patients subjected to chemotherapy (p = 0.03). Higher plasticity was not associated with a partial EMT status. Our results provide novel insights on how EMT-driven plasticity promotes a prospective diversification process increasing population phenotypic diversity, which can yield rare pre-adapted states before treatment. This highlights the need to tackle phenotypic diversity prior to treatment in high-plasticity tumours.

Pan-Cancer Upregulation of the FOXM1 Transcription Factor

BACKGROUND

The human FOXM1 transcription factor controls cell cycle progression and genome stability, and it has been correlated to the onset and progression of many tumor types.

METHODS

In our study, we collected all recent sequence and quantitative transcriptomics data about FOXM1, testing its presence across vertebrate evolution and its upregulation in cancer, both in bulk tissue contexts (by comparing the TCGA tumor dataset and the GTEx normal tissue dataset) and in single-cell contexts.

RESULTS

FOXM1 is significantly and consistently upregulated in all tested tumor types, as well as in tumor cells within a cancer microenvironment. Its upregulation reverberates in the upregulation of its target genes and can be used as a biomarker for poor cancer outcome in at least four tumor types.

CONCLUSIONS

Despite its lack of cancer-related mutations and amplifications, the recurring upregulation of FOXM1 in all tumors puts a focusing lens on this gene as a candidate pan-cancer master regulator.

Integrating Molecular Perspectives: Strategies for Comprehensive Multi-Omics Integrative Data Analysis and Machine Learning Applications in Transcriptomics, Proteomics, and Metabolomics

With the advent of high-throughput technologies, the field of omics has made significant strides in characterizing biological systems at various levels of complexity. Transcriptomics, proteomics, and metabolomics are the three most widely used omics technologies, each providing unique insights into different layers of a biological system. However, analyzing each omics data set separately may not provide a comprehensive understanding of the subject under study. Therefore, integrating multi-omics data has become increasingly important in bioinformatics research. In this article, we review strategies for integrating transcriptomics, proteomics, and metabolomics data, including co-expression analysis, metabolite-gene networks, constraint-based models, pathway enrichment analysis, and interactome analysis. We discuss combined omics integration approaches, correlation-based strategies, and machine learning techniques that utilize one or more types of omics data. By presenting these methods, we aim to provide researchers with a better understanding of how to integrate omics data to gain a more comprehensive view of a biological system, facilitating the identification of complex patterns and interactions that might be missed by single-omics analyses.

Transcriptional Patterns in Stages of Alzheimer's Disease Are Cell-Type-Specific and Partially Converge with the Effects of Alcohol Use Disorder in Humans

Advances in single-cell technologies have led to the discovery and characterization of new brain cell types, which in turn lead to a better understanding of the pathogenesis of Alzheimer's disease (AD). Here, we present a detailed analysis of single-nucleus (sn)RNA-seq data for three stages of AD from middle temporal gyrus and compare it with snRNA-seq data from the prefrontal cortices from individuals with alcohol use disorder (AUD). We observed a significant decrease in both inhibitory and excitatory neurons, in general agreement with previous reports. We observed several cell-type-specific gene expressions and pathway dysregulations that delineate AD stages. Endothelial and vascular leptomeningeal cells showed the greatest degree of gene expression changes. Cell-type-specific evidence of neurodegeneration was seen in multiple neuronal cell types particularly in somatostatin and Layer 5 extratelencephalic neurons, among others. Evidence of inflammatory responses was seen in non-neuronal cells, particularly in intermediate and advanced AD. We observed common perturbations in AD and AUD, particularly in pathways, like transcription, translation, apoptosis, autophagy, calcium signaling, neuroinflammation, and phosphorylation, that imply shared transcriptional pathogenic mechanisms and support the role of excessive alcohol intake in AD progression. Major AUD gene markers form and perturb a network of genes significantly associated with intermediate and advanced AD. Master regulator analysis from AUD gene markers revealed significant correlation with advanced AD of transcription factors that have implications in intellectual disability, neuroinflammation, and other neurodegenerative conditions, further suggesting a shared nexus of transcriptional changes between AD and AUD.

SignalingProfiler 2.0 a network-based approach to bridge multi-omics data to phenotypic hallmarks

Unraveling how cellular signaling is remodeled upon perturbation is crucial for understanding disease mechanisms and identifying potential drug targets. In this pursuit, computational tools generating mechanistic hypotheses from multi-omics data have invaluable potential. Here, we present a newly implemented version (2.0) of SignalingProfiler, a multi-step pipeline to draw mechanistic hypotheses on the signaling events impacting cellular phenotypes. SignalingProfiler 2.0 derives context-specific signaling networks by integrating proteogenomic data with the prior knowledge-causal network. This is a freely accessible and flexible tool that incorporates statistical, footprint-based, and graph algorithms to accelerate the integration and interpretation of multi-omics data. Through a benchmarking process on three proof-of-concept studies, we demonstrate the tool's ability to generate hierarchical mechanistic networks recapitulating novel and known perturbed signaling and phenotypic outcomes, in both human and mice contexts. In summary, SignalingProfiler 2.0 addresses the emergent need to derive biologically relevant information from complex multi-omics data by extracting interpretable networks.

Application of Graph Models to the Identification of Transcriptomic Oncometabolic Pathways in Human Hepatocellular Carcinoma

Whole-tissue transcriptomic analyses have been helpful to characterize molecular subtypes of hepatocellular carcinoma (HCC). Metabolic subtypes of human HCC have been defined, yet whether these different metabolic classes are clinically relevant or derive in actionable cancer vulnerabilities is still an unanswered question. Publicly available gene sets or gene signatures have been used to infer functional changes through gene set enrichment methods. However, metabolism-related gene signatures are poorly co-expressed when applied to a biological context. Here, we apply a simple method to infer highly consistent signatures using graph-based statistics. Using the Cancer Genome Atlas Liver Hepatocellular cohort (LIHC), we describe the main metabolic clusters and their relationship with commonly used molecular classes, and with the presence of TP53 or CTNNB1 driver mutations. We find similar results in our validation cohort, the LIRI-JP cohort. We describe how previously described metabolic subtypes could not have therapeutic relevance due to their overall downregulation when compared to non-tumoral liver, and identify N-glycan, mevalonate and sphingolipid biosynthetic pathways as the hallmark of the oncogenic shift of the use of acetyl-coenzyme A in HCC metabolism. Finally, using DepMap data, we demonstrate metabolic vulnerabilities in HCC cell lines.

Systematic dissection of tumor-normal single-cell ecosystems across a thousand tumors of 30 cancer types

The complexity of the tumor microenvironment poses significant challenges in cancer therapy. Here, to comprehensively investigate the tumor-normal ecosystems, we perform an integrative analysis of 4.9 million single-cell transcriptomes from 1070 tumor and 493 normal samples in combination with pan-cancer 137 spatial transcriptomics, 8887 TCGA, and 1261 checkpoint inhibitor-treated bulk tumors. We define a myriad of cell states constituting the tumor-normal ecosystems and also identify hallmark gene signatures across different cell types and organs. Our atlas characterizes distinctions between inflammatory fibroblasts marked by AKR1C1 or WNT5A in terms of cellular interactions and spatial co-localization patterns. Co-occurrence analysis reveals interferon-enriched community states including tertiary lymphoid structure (TLS) components, which exhibit differential rewiring between tumor, adjacent normal, and healthy normal tissues. The favorable response of interferon-enriched community states to immunotherapy is validated using immunotherapy-treated cancers (n = 1261) including our lung cancer cohort (n = 497). Deconvolution of spatial transcriptomes discriminates TLS-enriched from non-enriched cell types among immunotherapy-favorable components. Our systematic dissection of tumor-normal ecosystems provides a deeper understanding of inter- and intra-tumoral heterogeneity.

The pRb/RBL2-E2F1/4-GCN5 axis regulates cancer stem cell formation and G0 phase entry/exit by paracrine mechanisms

The lethality, chemoresistance and metastatic characteristics of cancers are associated with phenotypically plastic cancer stem cells (CSCs). How the non-cell autonomous signalling pathways and cell-autonomous transcriptional machinery orchestrate the stem cell-like characteristics of CSCs is still poorly understood. Here we use a quantitative proteomic approach for identifying secreted proteins of CSCs in pancreatic cancer. We uncover that the cell-autonomous E2F1/4-pRb/RBL2 axis balances non-cell-autonomous signalling in healthy ductal cells but becomes deregulated upon KRAS mutation. E2F1 and E2F4 induce whereas pRb/RBL2 reduce WNT ligand expression (e.g. WNT7A, WNT7B, WNT10A, WNT4) thereby regulating self-renewal, chemoresistance and invasiveness of CSCs in both PDAC and breast cancer, and fibroblast proliferation. Screening for epigenetic enzymes identifies GCN5 as a regulator of CSCs that deposits H3K9ac onto WNT promoters and enhancers. Collectively, paracrine signalling pathways are controlled by the E2F-GCN5-RB axis in diverse cancers and this could be a therapeutic target for eliminating CSCs.

Transcriptional and metabolic effects of aspartate-glutamate carrier isoform 1 (AGC1) downregulation in mouse oligodendrocyte precursor cells (OPCs)

Aspartate-glutamate carrier isoform 1 (AGC1) is a carrier responsible for the export of mitochondrial aspartate in exchange for cytosolic glutamate and is part of the malate-aspartate shuttle, essential for the balance of reducing equivalents in the cells. In the brain, mutations in SLC25A12 gene, encoding for AGC1, cause an ultra-rare genetic disease, reported as a neurodevelopmental encephalopathy, whose symptoms include global hypomyelination, arrested psychomotor development, hypotonia and seizures. Among the biological components most affected by AGC1 deficiency are oligodendrocytes, glial cells responsible for myelination processes, and their precursors [oligodendrocyte progenitor cells (OPCs)]. The AGC1 silencing in an in vitro model of OPCs was documented to cause defects of proliferation and differentiation, mediated by alterations of histone acetylation/deacetylation. Disrupting AGC1 activity could possibly reduce the availability of acetyl groups, leading to perturbation of many biological pathways, such as histone modifications and fatty acids formation for myelin production. Here, we explore the transcriptome of mouse OPCs partially silenced for AGC1, reporting results of canonical analyses (differential expression) and pathway enrichment analyses, which highlight a disruption in fatty acids synthesis from both a regulatory and enzymatic stand. We further investigate the cellular effects of AGC1 deficiency through the identification of most affected transcriptional networks and altered alternative splicing. Transcriptional data were integrated with differential metabolite abundance analysis, showing downregulation of several amino acids, including glutamine and aspartate. Taken together, our results provide a molecular foundation for the effects of AGC1 deficiency in OPCs, highlighting the molecular mechanisms affected and providing a list of actionable targets to mitigate the effects of this pathology.

Genomic and transcriptomic analysis of the recent Mpox outbreak

The Mpox (formerly named Monkeypox) virus is the etiological cause of a recent multi-country outbreak, with thousands of distinct cases detected outside the endemic areas of Africa as of December 2023. In this article, we analyze the sequences of full genomes of Mpox virus from Europe and compare them with all available Mpox sequences of historical relevance, annotated by year and geographic origin, as well as related Cowpox and Variola (smallpox) virus sequences. Our results show that the recent outbreak is most likely originating from the West African clade of Mpox, with >99 % sequence identity with sequences derived from historical and recent cases, dating from 1971 to 2017. We analyze specific mutations occurring in viral proteins between the current outbreak, previous Mpox and Cowpox sequences, and the historical Variola virus. Genome-wide sequence analysis of the recent outbreak and other Mpox/Cowpox/Variola viruses shows a very high conservation, with 97.9 % (protein-based) and 97.8 % (nucleotide-based) sequence identity. We identified significant correlation in human transcriptional responses as well, with a conserved immune pathway response induced in human cell cultures by the three families of Pox virus. The similarities identified between the major strains of Pox viruses, as well as within the Mpox clades, both at the genomic and transcriptomic levels, provide a molecular basis for the observed efficacy of Variola vaccines in other Poxviruses.

Comparative transcriptomics coupled to developmental grading via transgenic zebrafish reporter strains identifies conserved features in neutrophil maturation

Neutrophils are evolutionarily conserved innate immune cells playing pivotal roles in host defense. Zebrafish models have contributed substantially to our understanding of neutrophil functions but similarities to human neutrophil maturation have not been systematically characterized, which limits their applicability to studying human disease. Here we show, by generating and analysing transgenic zebrafish strains representing distinct neutrophil differentiation stages, a high-resolution transcriptional profile of neutrophil maturation. We link gene expression at each stage to characteristic transcription factors, including C/ebp-β, which is important for late neutrophil maturation. Cross-species comparison of zebrafish, mouse, and human samples confirms high molecular similarity of immature stages and discriminates zebrafish-specific from pan-species gene signatures. Applying the pan-species neutrophil maturation signature to RNA-sequencing data from human neuroblastoma patients reveals association between metastatic tumor cell infiltration in the bone marrow and an overall increase in mature neutrophils. Our detailed neutrophil maturation atlas thus provides a valuable resource for studying neutrophil function at different stages across species in health and disease.

Metabolomic Profiles of Human Glioma Inform Patient Survival

Aims: Targeting tumor metabolism may improve the outcomes for patients with glioblastoma (GBM). To further preclinical efforts targeting metabolism in GBM, we tested the hypothesis that brain tumors can be stratified into distinct metabolic groups with different patient outcomes. Therefore, to determine if tumor metabolites relate to patient survival, we profiled the metabolomes of human gliomas and correlated metabolic information with clinical data. Results: We found that isocitrate dehydrogenase-wildtype (IDHwt) GBMs are metabolically distinguishable from IDH mutated (IDHmut) astrocytomas and oligodendrogliomas. Survival of patients with IDHmut gliomas was expectedly more favorable than those with IDHwt GBM, and metabolic signatures can stratify IDHwt GBMs subtypes with varying prognoses. Patients whose GBMs were enriched in amino acids had improved survival, while those whose tumors were enriched for nucleotides, redox molecules, and lipid metabolites fared more poorly. These findings were recapitulated in validation cohorts using both metabolomic and transcriptomic data. Innovation: Our results suggest the existence of metabolic subtypes of GBM with differing prognoses, and further support the concept that metabolism may drive the aggressiveness of human gliomas. Conclusions: Our data show that metabolic signatures of human gliomas can inform patient survival. These findings may be used clinically to tailor novel metabolically targeted agents for GBM patients with different metabolic phenotypes. Antioxid. Redox Signal. 39, 942-956.

Deconvolution of immune cell composition and biological age of hepatocellular carcinoma using DNA methylation

Hepatocellular carcinoma (HCC) has been an approved indication for the administration of immunotherapy since 2017, but biomarkers that predict therapeutic response have remained limited. Understanding and characterizing the tumor immune microenvironment enables better classification of these tumors and may reveal biomarkers that predict immunotherapeutic efficacy. In this paper, we applied a cell-type deconvolution algorithm using DNA methylation array data to investigate the composition of the tumor microenvironment in HCC. Using publicly available and in-house datasets with a total cohort size of 57 patients, each with tumor and matched normal tissue samples, we identified key differences in immune cell composition. We found that NK cell abundance was significantly decreased in HCC tumors compared to adjacent normal tissue. We also applied DNA methylation "clocks" which estimate phenotypic aging and compared these findings to expression-based determinations of cellular senescence. Senescence and epigenetic aging were significantly increased in HCC tumors, and the degree of age acceleration and senescence was strongly associated with decreased NK cell abundance. In summary, we found that NK cell infiltration in the tumor microenvironment is significantly diminished, and that this loss of NK abundance is strongly associated with increased senescence and age-related phenotype. These findings point to key interactions between NK cells and the senescent tumor microenvironment and offer insights into the pathogenesis of HCC as well as potential biomarkers of therapeutic efficacy.

Copper chelation suppresses epithelial-mesenchymal transition by inhibition of canonical and non-canonical TGF-β signaling pathways in cancer

BACKGROUND

Metastatic cancer cells exploit Epithelial-mesenchymal-transition (EMT) to enhance their migration, invasion, and resistance to treatments. Recent studies highlight that elevated levels of copper are implicated in cancer progression and metastasis. Clinical trials using copper chelators are associated with improved patient survival; however, the molecular mechanisms by which copper depletion inhibits tumor progression and metastasis are poorly understood. This remains a major hurdle to the clinical translation of copper chelators. Here, we propose that copper chelation inhibits metastasis by reducing TGF-β levels and EMT signaling. Given that many drugs targeting TGF-β have failed in clinical trials, partly because of severe side effects arising in patients, we hypothesized that copper chelation therapy might be a less toxic alternative to target the TGF-β/EMT axis.

RESULTS

Our cytokine array and RNA-seq data suggested a link between copper homeostasis, TGF-β and EMT process. To validate this hypothesis, we performed single-cell imaging, protein assays, and in vivo studies. Here, we used the copper chelating agent TEPA to block copper trafficking. Our in vivo study showed a reduction of TGF-β levels and metastasis to the lung in the TNBC mouse model. Mechanistically, TEPA significantly downregulated canonical (TGF-β/SMAD2&3) and non-canonical (TGF-β/PI3K/AKT, TGF-β/RAS/RAF/MEK/ERK, and TGF-β/WNT/β-catenin) TGF-β signaling pathways. Additionally, EMT markers of MMP-9, MMP-14, Vimentin, β-catenin, ZEB1, and p-SMAD2 were downregulated, and EMT transcription factors of SNAI1, ZEB1, and p-SMAD2 accumulated in the cytoplasm after treatment.

CONCLUSIONS

Our study suggests that copper chelation therapy represents a potentially effective therapeutic approach for targeting TGF-β and inhibiting EMT in a diverse range of cancers.

Cross-Species Transcriptomics Analysis Highlights Conserved Molecular Responses to Per- and Polyfluoroalkyl Substances

In recent decades, per- and polyfluoroalkyl substances (PFASs) have garnered widespread public attention due to their persistence in the environment and detrimental effects on the health of living organisms, spurring the generation of several transcriptome-centered investigations to understand the biological basis of their mechanism. In this study, we collected 2144 publicly available samples from seven distinct animal species to examine the molecular responses to PFAS exposure and to determine if there are conserved responses. Our comparative transcriptional analysis revealed that exposure to PFAS is conserved across different tissues, molecules and species. We identified and reported several genes exhibiting consistent and evolutionarily conserved transcriptional response to PFASs, such as ESR1, HADHA and ID1, as well as several pathways including lipid metabolism, immune response and hormone pathways. This study provides the first evidence that distinct PFAS molecules induce comparable transcriptional changes and affect the same metabolic processes across inter-species borders. Our findings have significant implications for understanding the impact of PFAS exposure on living organisms and the environment. We believe that this study offers a novel perspective on the molecular responses to PFAS exposure and provides a foundation for future research into developing strategies for mitigating the detrimental effects of these substances in the ecosystem.

Computational approaches to understand transcription regulation in development

Gene regulatory networks (GRNs) serve as useful abstractions to understand transcriptional dynamics in developmental systems. Computational prediction of GRNs has been successfully applied to genome-wide gene expression measurements with the advent of microarrays and RNA-sequencing. However, these inferred networks are inaccurate and mostly based on correlative rather than causative interactions. In this review, we highlight three approaches that significantly impact GRN inference: (1) moving from one genome-wide functional modality, gene expression, to multi-omics, (2) single cell sequencing, to measure cell type-specific signals and predict context-specific GRNs, and (3) neural networks as flexible models. Together, these experimental and computational developments have the potential to significantly impact the quality of inferred GRNs. Ultimately, accurately modeling the regulatory interactions between transcription factors and their target genes will be essential to understand the role of transcription factors in driving developmental gene expression programs and to derive testable hypotheses for validation.

Clustering by antigen-presenting genes reveals immune landscapes and predicts response to checkpoint immunotherapy

Immune checkpoint blockade (ICB) has demonstrated efficacy by reinvigorating immune cytotoxicity against tumors. However, the mechanisms underlying how ICB induces responses in a subset of patients remain unclear. Using bulk and single-cell transcriptomic cohorts of melanoma patients receiving ICB, we proposed a clustering model based on the expression of an antigen-presenting machinery (APM) signature consisting of 23 genes in a forward-selection manner. We characterized four APM clusters associated with distinct immune characteristics, cancer hallmarks, and patient prognosis in melanoma. The model predicts differential regulation of APM genes during ICB, which shaped ICB responsiveness. Surprisingly, while immunogenically hot tumors with high baseline APM expression prior to treatment are correlated with a better response to ICB than cold tumors with low APM expression, a subset of hot tumors with the highest pre-ICB APM expression fail to upregulate APM expression during treatment. In addition, they undergo immunoediting and display infiltration of exhausted T cells. In comparison, tumors associated with the best patient prognosis demonstrate significant APM upregulation and immune infiltration following ICB. They also show infiltration of tissue-resident memory T cells, shaping prolonged antitumor immunity. Using only pre-treatment transcriptomic data, our model predicts the dynamic APM-mediated tumor-immune interactions in response to ICB and provides insights into the immune escape mechanisms in hot tumors that compromise the ICB efficacy. We highlight the prognostic value of APM expression in predicting immune response in chronic diseases.

Integrated analysis of endoplasmic reticulum stress regulators' expression identifies distinct subtypes of autism spectrum disorder

Endoplasmic reticulum (ER) stress has been demonstrated to play important roles in a variety of human diseases. However, their relevance to autism spectrum disorder (ASD) remains largely unknown. Herein, we aimed to investigate the expression patterns and potential roles of the ER stress regulators in ASD. The ASD expression profiles GSE111176 and GSE77103 were compiled from the Gene Expression Omnibus (GEO) database. ER stress score determined by the single sample gene set enrichment analysis (ssGSEA) was significantly higher in ASD patients. Differential analysis revealed that there were 37 ER stress regulators dysregulated in ASD. Based on their expression profile, the random forest and artificial neuron network techniques were applied to build a classifier that can effectively distinguish ASD from control samples among independent datasets. Weighted gene co-expression network analysis (WGCNA) screened out the turquoise module with 774 genes was closely related to the ER stress score. Through the overlapping results of the turquoise module and differential expression ER stress genes, hub regulators were gathered. The TF/miRNA-hub gene interaction networks were created. Furthermore, the consensus clustering algorithm was performed to cluster the ASD patients, and there were two ASD subclusters. Each subcluster has unique expression profiles, biological functions, and immunological characteristics. In ASD subcluster 1, the FAS pathway was more enriched, while subcluster 2 had a higher level of plasma cell infiltration as well as the BCR signaling pathway and interleukin receptor reaction reactivity. Finally, the Connectivity map (CMap) database was used to find prospective compounds that target various ASD subclusters. A total of 136 compounds were significantly enriched. In addition to some specific drugs which can effectively reverse the differential gene expression of each subcluster, we found that the PKC inhibitor BRD-K09991945 that targets Glycogen synthase kinase 3β (GSK3B) might have a therapeutic effect on both ASD subtypes that worth of the experimental validation. Our finding proved that ER stress plays a crucial role in the diversity and complexity of ASD, which may inform both mechanistic and therapeutic assessments of the disorder.

In Silico Prediction of Hub Genes Involved in Diabetic Kidney and COVID-19 Related Disease by Differential Gene Expression and Interactome Analysis

Diabetic kidney disease (DKD) is a frequently chronic kidney pathology derived from diabetes comorbidity. This condition has irreversible damage and its risk factor increases with SARS-CoV-2 infection. The prognostic outcome for diabetic patients with COVID-19 is dismal, even with intensive medical treatment. However, there is still scarce information on critical genes involved in the pathophysiological impact of COVID-19 on DKD. Herein, we characterize differential expression gene (DEG) profiles and determine hub genes undergoing transcriptional reprogramming in both disease conditions. Out of 995 DEGs, we identified 42 shared with COVID-19 pathways. Enrichment analysis elucidated that they are significantly induced with implications for immune and inflammatory responses. By performing a protein-protein interaction (PPI) network and applying topological methods, we determine the following five hub genes: STAT1, IRF7, ISG15, MX1 and OAS1. Then, by network deconvolution, we determine their co-expressed gene modules. Moreover, we validate the conservancy of their upregulation using the Coronascape database (DB). Finally, tissue-specific regulation of the five predictive hub genes indicates that OAS1 and MX1 expression levels are lower in healthy kidney tissue. Altogether, our results suggest that these genes could play an essential role in developing severe outcomes of COVID-19 in DKD patients.

Cell-type Deconvolution and Age Estimation Using DNA Methylation Reveals NK Cell Deficiency in the Hepatocellular Carcinoma Microenvironment

Hepatocellular carcinoma (HCC) has been an approved indication for the administration of immunotherapy since 2017, but biomarkers that predict therapeutic response have remained limited. Understanding and characterizing the tumor immune microenvironment enables better classification of these tumors and may reveal biomarkers that predict immunotherapeutic efficacy. In this paper, we applied a cell-type deconvolution algorithm using DNA methylation array data to investigate the composition of the tumor microenvironment in HCC. Using two publicly available datasets with a total cohort size of 57 patients, each with tumor and matched normal tissue samples, we identified key differences in immune cell composition. We found that NK cell abundance was significantly decreased in HCC tumors compared to adjacent normal tissue. We also applied DNA methylation "clocks" which estimate phenotypic aging and compared these findings to expression-based determinations of cellular senescence. Senescence and epigenetic aging was significantly increased in HCC tumors, and the degree of age acceleration and senescence was strongly associated with decreased NK cell abundance. In summary, we found that NK cell infiltration in the tumor microenvironment is significantly diminished, and that this loss of NK abundance is strongly associated with increased senescence and age-related phenotype. These findings point to key interactions between NK cells and the senescent tumor microenvironment and offer insights into the pathogenesis of HCC as well as potential biomarkers of therapeutic efficacy.

Single-Cell Sequencing Identifies Master Regulators Affected by Panobinostat in Neuroblastoma Cells

The molecular mechanisms and gene regulatory networks sustaining cell proliferation in neuroblastoma (NBL) cells are still not fully understood. In this tumor context, it has been proposed that anti-proliferative drugs, such as the pan-HDAC inhibitor panobinostat, could be tested to mitigate tumor progression. Here, we set out to investigate the effects of panobinostat treatment at the unprecedented resolution offered by single-cell sequencing. We identified a global senescence signature paired with reduction in proliferation in treated Kelly cells and more isolated transcriptional responses compatible with early neuronal differentiation. Using master regulator analysis, we identified BAZ1A, HCFC1, MAZ, and ZNF146 as the transcriptional regulators most significantly repressed by panobinostat. Experimental silencing of these transcription factors (TFs) confirmed their role in sustaining NBL cell proliferation in vitro.

Gene Regulatory Network Characterization of Gastric Cancer's Histological Subtypes: Distinctive Biological and Clinically Relevant Master Regulators

Gastric cancer (GC) molecular heterogeneity represents a major determinant for clinical outcomes, and although new molecular classifications have been introduced, they are not easy to translate from bench to bedside. We explored the data from GC public databases by performing differential gene expression analysis (DEGs) and gene network reconstruction to identify master regulators (MRs), as well as a gene set analysis (GSA) to reveal their biological features. Moreover, we evaluated the association of MRs with clinicopathological parameters. According to the GSA, the Diffuse group was characterized by an epithelial-mesenchymal transition (EMT) and inflammatory response, while the Intestinal group was associated with a cell cycle and drug resistance pathways. In particular, the regulons of Diffuse MRs, such as Vgll3 and Ciita, overlapped with the EMT and interferon-gamma response, while the regulons Top2a and Foxm1 were shared with the cell cycle pathways in the Intestinal group. We also found a strict association between MR activity and several clinicopathological features, such as survival. Our approach led to the identification of genes and pathways differentially regulated in the Intestinal and Diffuse GC histotypes, highlighting biologically interesting MRs and subnetworks associated with clinical features and prognosis, suggesting putative actionable candidates.

Lineage-coupled clonal capture identifies clonal evolution mechanisms and vulnerabilities of BRAFV600E inhibition resistance in melanoma

Targeted cancer therapies have revolutionized treatment but their efficacies are limited by the development of resistance driven by clonal evolution within tumors. We developed "CAPTURE", a single-cell barcoding approach to comprehensively trace clonal dynamics and capture live lineage-coupled resistant cells for in-depth multi-omics analysis and functional exploration. We demonstrate that heterogeneous clones, either preexisting or emerging from drug-tolerant persister cells, dominated resistance to vemurafenib in BRAFV600E melanoma. Further integrative studies uncovered diverse resistance mechanisms. This includes a previously unrecognized and clinically relevant mechanism, chromosome 18q21 gain, which leads to vulnerability of the cells to BCL2 inhibitor. We also identified targetable common dependencies of captured resistant clones, such as oxidative phosphorylation and E2F pathways. Our study provides new therapeutic insights into overcoming therapy resistance in BRAFV600E melanoma and presents a platform for exploring clonal evolution dynamics and vulnerabilities that can be applied to study treatment resistance in other cancers.

Detection of pan-cancer surface protein biomarkers via a network-based approach on transcriptomics data

Cell surface proteins have been used as diagnostic and prognostic markers in cancer research and as targets for the development of anticancer agents. Many of these proteins lie at the top of signaling cascades regulating cell responses and gene expression, therefore acting as ‘signaling hubs’. It has been previously demonstrated that the integrated network analysis on transcriptomic data is able to infer cell surface protein activity in breast cancer. Such an approach has been implemented in a publicly available method called ‘SURFACER’. SURFACER implements a network-based analysis of transcriptomic data focusing on the overall activity of curated surface proteins, with the final aim to identify those proteins driving major phenotypic changes at a network level, named surface signaling hubs. Here, we show the ability of SURFACER to discover relevant knowledge within and across cancer datasets. We also show how different cancers can be stratified in surface-activity-specific groups. Our strategy may identify cancer-wide markers to design targeted therapies and biomarker-based diagnostic approaches.

Structural genetics of circulating variants affecting the SARS-CoV-2 spike/human ACE2 complex

SARS-CoV-2 entry in human cells is mediated by the interaction between the viral Spike protein and the human ACE2 receptor. This mechanism evolved from the ancestor bat coronavirus and is currently one of the main targets for antiviral strategies. However, there currently exist several Spike protein variants in the SARS-CoV-2 population as the result of mutations, and it is unclear if these variants may exert a specific effect on the affinity with ACE2 which, in turn, is also characterized by multiple alleles in the human population. In the current study, the GBPM analysis, originally developed for highlighting host-guest interaction features, has been applied to define the key amino acids responsible for the Spike/ACE2 molecular recognition, using four different crystallographic structures. Then, we intersected these structural results with the current mutational status, based on more than 295,000 sequenced cases, in the SARS-CoV-2 population. We identified several Spike mutations interacting with ACE2 and mutated in at least 20 distinct patients: S477N, N439K, N501Y, Y453F, E484K, K417N, S477I and G476S. Among these, mutation N501Y in particular is one of the events characterizing SARS-CoV-2 lineage B.1.1.7, which has recently risen in frequency in Europe. We also identified five ACE2 rare variants that may affect interaction with Spike and susceptibility to infection: S19P, E37K, M82I, E329G and G352V.Communicated by Ramaswamy H. Sarma.

Deep learning-based transcription factor activity for stratification of breast cancer patients

Transcription factors directly bind to DNA and regulate the expression of the gene, causing epigenetic modification of the DNA. They often mediate epigenetic parameters of transcriptional and posttranscriptional mechanisms, and their expression activities can be used to characterize genomic aberrations in cancer cell. In this study, the activity profile of transcription factors inferred by VIPER algorithm. The autoencoder model was applied for compressing the transcription factor activity profile for obtaining more useful transformed features for stratifying patients into two different breast cancer subtypes. The deep learning-based subtypes exhibited superior prognostic value and yielded better risk-stratification than the transcription factor activity-based method. Importantly, according to transformed features, a deep neural network was constructed to predict the subtypes, and achieved the accuracy of 94.98% and area under the ROC curve of 0.9663, respectively. The proposed subtypes were found to be significantly associated with immune infiltration, tumor immunogenicity and so on. Furthermore, the ceRNA network was constructed for the breast cancer subtypes. Besides, 11 master regulators were found to be associated with patients in cluster 1. Given the robustness performance of our deep learning model over multiple breast cancer cohorts, we expected this model may be useful in the area of prognosis prediction and lead some possibility for personalized medicine in breast cancer patients.

Insights into the Transcriptional Reprogramming in Tomato Response to PSTVd Variants Using Network Approaches

Viroids are the smallest pathogens of angiosperms, consisting of non-coding RNAs that cause severe diseases in agronomic crops. Symptoms associated with viroid infection are linked to developmental alterations due to genetic regulation. To understand the global mechanisms of host viroid response, we implemented network approaches to identify master transcription regulators and their differentially expressed targets in tomato infected with mild and severe variants of PSTVd. Our approach integrates root and leaf transcriptomic data, gene regulatory network analysis, and identification of affected biological processes. Our results reveal that specific bHLH, MYB, and ERF transcription factors regulate genes involved in molecular mechanisms underlying critical signaling pathways. Functional enrichment of regulons shows that bHLH-MTRs are linked to metabolism and plant defense, while MYB-MTRs are involved in signaling and hormone-related processes. Strikingly, a member of the bHLH-TF family has a specific potential role as a microprotein involved in the post-translational regulation of hormone signaling events. We found that ERF-MTRs are characteristic of severe symptoms, while ZNF-TF, tf3a-TF, BZIP-TFs, and NAC-TF act as unique MTRs. Altogether, our results lay a foundation for further research on the PSTVd and host genome interaction, providing evidence for identifying potential key genes that influence symptom development in tomato plants.

The regulatory landscape of retinoblastoma: a pathway analysis perspective

Retinoblastoma (Rb) is a rare intraocular tumour in early childhood, with an approximate incidence of 1 in 18 000 live births. Experimental studies for Rb are complex due to the challenges associated with obtaining a normal retina to contrast with diseased tissue. In this work, we reanalyse a dataset that contains normal retina samples. We identified the individual genes whose expression is different in Rb in contrast with normal tissue, determined the pathways whose global expression pattern is more distant from the global expression observed in normal tissue, and finally, we identified which transcription factors regulate the highest number of differentially expressed genes (DEGs) and proposed as transcriptional master regulators (TMRs). The enrichment of DEGs in the phototransduction and retrograde endocannabinoid signalling pathways could be associated with abnormal behaviour of the processes leading to cellular differentiation and cellular proliferation. On the other hand, the TMRs nuclear receptor subfamily 5 group A member 2 and hepatocyte nuclear factor 4 gamma are involved in hepatocyte differentiation. Therefore, the enrichment of aberrant expression in these transcription factors could suggest an abnormal retina development that could be involved in Rb origin and progression.

Corticosteroid sensitization drives opioid addiction

The global crisis of opioid overdose fatalities has led to an urgent search to discover the neurobiological mechanisms of opioid use disorder (OUD). A driving force for OUD is the dysphoric and emotionally painful state (hyperkatifeia) that is produced during acute and protracted opioid withdrawal. Here, we explored a mechanistic role for extrahypothalamic stress systems in driving opioid addiction. We found that glucocorticoid receptor (GR) antagonism with mifepristone reduced opioid addiction-like behaviors in rats and zebrafish of both sexes and decreased the firing of corticotropin-releasing factor neurons in the rat amygdala (i.e., a marker of brain stress system activation). In support of the hypothesized role of glucocorticoid transcriptional regulation of extrahypothalamic GRs in addiction-like behavior, an intra-amygdala infusion of an antisense oligonucleotide that blocked GR transcriptional activity reduced addiction-like behaviors. Finally, we identified transcriptional adaptations of GR signaling in the amygdala of humans with OUD. Thus, GRs, their coregulators, and downstream systems may represent viable therapeutic targets to treat the "stress side" of OUD.

The R Language: An Engine for Bioinformatics and Data Science

The R programming language is approaching its 30th birthday, and in the last three decades it has achieved a prominent role in statistics, bioinformatics, and data science in general. It currently ranks among the top 10 most popular languages worldwide, and its community has produced tens of thousands of extensions and packages, with scopes ranging from machine learning to transcriptome data analysis. In this review, we provide an historical chronicle of how R became what it is today, describing all its current features and capabilities. We also illustrate the major tools of R, such as the current R editors and integrated development environments (IDEs), the R Shiny web server, the R methods for machine learning, and its relationship with other programming languages. We also discuss the role of R in science in general as a driver for reproducibility. Overall, we hope to provide both a complete snapshot of R today and a practical compendium of the major features and applications of this programming language.

Introduction to Genomic Network Reconstruction for Cancer Research

High-throughput genomic technologies have revolutionized the study of cancer. Current research in oncology is now limited more for the capacity of analyzing and interpreting data, rather than the availability of data itself. Integrative approaches to obtain functional information from data are at the core of the disciplines gathered under the systems biology banner. In this context, network models have been used to study cancer, from the identification of key molecules involved in the disease to the discovery of functional alterations associated with specific manifestations of the disease.In this chapter, we describe the state of the art of network reconstruction from genomic data, with an emphasis in gene expression experiments. We explore the strengths and limitations of correlation, Bayesian, and information theoretic approaches to network reconstruction. We present tools that leverage the flexibility of network science to gain a deeper understanding of cancer biology.

Prediction of Metabolic Profiles from Transcriptomics Data in Human Cancer Cell Lines

The Metabolome and Transcriptome are mutually communicating within cancer cells, and this interplay is translated into the existence of quantifiable correlation structures between gene expression and metabolite abundance levels. Studying these correlations could provide a novel venue of understanding cancer and the discovery of novel biomarkers and pharmacological strategies, as well as laying the foundation for the prediction of metabolite quantities by leveraging information from the more widespread transcriptomics data. In the current paper, we investigate the correlation between gene expression and metabolite levels in the Cancer Cell Line Encyclopedia dataset, building a direct correlation network between the two molecular ensembles. We show that a metabolite/transcript correlation network can be used to predict metabolite levels in different samples and datasets, such as the NCI-60 cancer cell line dataset, both on a sample-by-sample basis and in differential contrasts. We also show that metabolite levels can be predicted in principle on any sample and dataset for which transcriptomics data are available, such as the Cancer Genome Atlas (TCGA).

Detection of subtype-specific breast cancer surface protein biomarkers via a novel transcriptomics approach

BACKGROUND

Cell-surface proteins have been widely used as diagnostic and prognostic markers in cancer research and as targets for the development of anticancer agents. So far, very few attempts have been made to characterize the surfaceome of patients with breast cancer, particularly in relation with the current molecular breast cancer (BRCA) classification. In this view, we developed a new computational method to infer cell-surface protein activities from transcriptomics data, termed 'SURFACER'.

METHODS

Gene expression data from GTEx were used to build a normal breast network model as input to infer differential cell-surface proteins activity in BRCA tissue samples retrieved from TCGA versus normal samples. Data were stratified according to the PAM50 transcriptional subtypes (Luminal A, Luminal B, HER2 and Basal), while unsupervised clustering techniques were applied to define BRCA subtypes according to cell-surface proteins activity.

RESULTS

Our approach led to the identification of 213 PAM50 subtypes-specific deregulated surface genes and the definition of five BRCA subtypes, whose prognostic value was assessed by survival analysis, identifying a cell-surface activity configuration at increased risk. The value of the SURFACER method in BRCA genotyping was tested by evaluating the performance of 11 different machine learning classification algorithms.

CONCLUSIONS

BRCA patients can be stratified into five surface activity-specific groups having the potential to identify subtype-specific actionable targets to design tailored targeted therapies or for diagnostic purposes. SURFACER-defined subtypes show also a prognostic value, identifying surface-activity profiles at higher risk.

ANANSE: an enhancer network-based computational approach for predicting key transcription factors in cell fate determination

Proper cell fate determination is largely orchestrated by complex gene regulatory networks centered around transcription factors. However, experimental elucidation of key transcription factors that drive cellular identity is currently often intractable. Here, we present ANANSE (ANalysis Algorithm for Networks Specified by Enhancers), a network-based method that exploits enhancer-encoded regulatory information to identify the key transcription factors in cell fate determination. As cell type-specific transcription factors predominantly bind to enhancers, we use regulatory networks based on enhancer properties to prioritize transcription factors. First, we predict genome-wide binding profiles of transcription factors in various cell types using enhancer activity and transcription factor binding motifs. Subsequently, applying these inferred binding profiles, we construct cell type-specific gene regulatory networks, and then predict key transcription factors controlling cell fate transitions using differential networks between cell types. This method outperforms existing approaches in correctly predicting major transcription factors previously identified to be sufficient for trans-differentiation. Finally, we apply ANANSE to define an atlas of key transcription factors in 18 normal human tissues. In conclusion, we present a ready-to-implement computational tool for efficient prediction of transcription factors in cell fate determination and to study transcription factor-mediated regulatory mechanisms. ANANSE is freely available at https://github.com/vanheeringen-lab/ANANSE.

The Transcriptome of SH-SY5Y at Single-Cell Resolution: A CITE-Seq Data Analysis Workflow

Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) is a recently established multimodal single cell analysis technique combining the immunophenotyping capabilities of antibody labeling and cell sorting with the resolution of single-cell RNA sequencing (scRNA-seq). By simply adding a 12-bp nucleotide barcode to antibodies (cell hashing), CITE-seq can be used to sequence antibody-bound tags alongside the cellular mRNA, thus reducing costs of scRNA-seq by performing it at the same time on multiple barcoded samples in a single run. Here, we illustrate an ideal CITE-seq data analysis workflow by characterizing the transcriptome of SH-SY5Y neuroblastoma cell line, a widely used model to study neuronal function and differentiation. We obtained transcriptomes from a total of 2879 single cells, measuring an average of 1600 genes/cell. Along with standard scRNA-seq data handling procedures, such as quality checks and cell filtering procedures, we performed exploratory analyses to identify most stable genes to be possibly used as reference housekeeping genes in qPCR experiments. We also illustrate how to use some popular R packages to investigate cell heterogeneity in scRNA-seq data, namely Seurat, Monocle, and slalom. Both the CITE-seq dataset and the code used to analyze it are freely shared and fully reusable for future research.

A review of COVID-19 biomarkers and drug targets: resources and tools

The stratification of patients at risk of progression of COVID-19 and their molecular characterization is of extreme importance to optimize treatment and to identify therapeutic options. The bioinformatics community has responded to the outbreak emergency with a set of tools and resource to identify biomarkers and drug targets that we review here. Starting from a consolidated corpus of 27 570 papers, we adopt latent Dirichlet analysis to extract relevant topics and select those associated with computational methods for biomarker identification and drug repurposing. The selected topics span from machine learning and artificial intelligence for disease characterization to vaccine development and to therapeutic target identification. Although the way to go for the ultimate defeat of the pandemic is still long, the amount of knowledge, data and tools generated so far constitutes an unprecedented example of global cooperation to this threat.

Single-Cell Gene Network Analysis and Transcriptional Landscape of MYCN-Amplified Neuroblastoma Cell Lines

Neuroblastoma (NBL) is a pediatric cancer responsible for more than 15% of cancer deaths in children, with 800 new cases each year in the United States alone. Genomic amplification of the MYC oncogene family member MYCN characterizes a subset of high-risk pediatric neuroblastomas. Several cellular models have been implemented to study this disease over the years. Two of these, SK-N-BE-2-C (BE2C) and Kelly, are amongst the most used worldwide as models of MYCN-Amplified human NBL. Here, we provide a transcriptome-wide quantitative measurement of gene expression and transcriptional network activity in BE2C and Kelly cell lines at an unprecedented single-cell resolution. We obtained 1105 Kelly and 962 BE2C unsynchronized cells, with an average number of mapped reads/cell of roughly 38,000. The single-cell data recapitulate gene expression signatures previously generated from bulk RNA-Seq. We highlight low variance for commonly used housekeeping genes between different cells (ACTB, B2M and GAPDH), while showing higher than expected variance for metallothionein transcripts in Kelly cells. The high number of samples, despite the relatively low read coverage of single cells, allowed for robust pathway enrichment analysis and master regulator analysis (MRA), both of which highlight the more mesenchymal nature of BE2C cells as compared to Kelly cells, and the upregulation of TWIST1 and DNAJC1 transcriptional networks. We further defined master regulators at the single cell level and showed that MYCN is not constantly active or expressed within Kelly and BE2C cells, independently of cell cycle phase. The dataset, alongside a detailed and commented programming protocol to analyze it, is fully shared and reusable.

Predicting the response of the dental pulp to SARS-CoV2 infection: a transcriptome-wide effect cross-analysis

Pulpitis, inflammation of the dental pulp, is a disease that often necessitates emergency dental care. While pulpitis is considered to be a microbial disease primarily caused by bacteria, viruses have also been implicated in its pathogenesis. Here, we determined the expression of the SARS-CoV2 receptor, angiotensin converting enzyme 2 (ACE2) and its associated cellular serine protease TPMRSS2 in the dental pulp under normal and inflamed conditions. Next, we explored the relationship between the SARS-CoV-2/human interactome and genes expressed in pulpitis. Using existing datasets we show that both ACE2 and TPMRSS2 are expressed in the dental pulp and, that their expression does not change under conditions of inflammation. Furthermore, Master Regulator Analysis of the SARS-CoV2/human interactome identified 75 relevant genes whose expression values are either up-regulated or down-regulated in both the human interactome and pulpitis. Our results suggest that the dental pulp is vulnerable to SARS-CoV2 infection and that SARS-CoV-2 infection of the dental pulp may contribute to worse outcomes of pulpitis.

Multiomics data integration unveils core transcriptional regulatory networks governing cell-type identity

A plethora of computational approaches have been proposed for reconstructing gene regulatory networks (GRNs) from gene expression data. However, gene regulatory processes are often too complex to predict from the transcriptome alone. Here, we present a computational method, Moni, that systematically integrates epigenetics, transcriptomics, and protein–protein interactions to reconstruct GRNs among core transcription factors and their co-factors governing cell identity. We applied Moni to 57 datasets of human cell types and lines and demonstrate that it can accurately infer GRNs, thereby outperforming state-of-the-art methods.

Transcriptional network inference and master regulator analysis of the response to ribosome-inactivating proteins in leukemia cells

Gene-regulatory networks reconstruction has become a very popular approach in applied biology to infer and dissect functional interactions of Transcription Factors (TFs) driving a defined phenotypic state, termed as Master Regulators (MRs). In the present work, cutting-edge bioinformatic methods were applied to re-analyze experimental data on leukemia cells (human myelogenous leukemia cell line THP-1 and acute myeloid leukemia MOLM-13 cells) treated for 6 h with two different Ribosome-Inactivating Proteins (RIPs), namely Shiga toxin type 1 (400 ng/mL) produced by Escherichia coli strains and the plant toxin stenodactylin (60 ng/mL), purified from the caudex of Adenia stenodactyla Harms. This analysis allowed us to identify the common early transcriptional response to 28S rRNA damage based on gene-regulatory network inference and Master Regulator Analysis (MRA). Both toxins induce a common response at 6 h which involves inflammatory mediators triggered by AP-1 family transcriptional factors and ATF3 in leukemia cells. We describe for the first time the involvement of MAFF, KLF2 and KLF6 in regulating RIP-induced apoptotic cell death, while receptor-mediated downstream signaling through ANXA1 and TLR4 is suggested for both toxins.

Master Regulator Analysis of the SARS-CoV-2/Human Interactome

The recent epidemic outbreak of a novel human coronavirus called SARS-CoV-2 causing the respiratory tract disease COVID-19 has reached worldwide resonance and a global effort is being undertaken to characterize the molecular features and evolutionary origins of this virus. In this paper, we set out to shed light on the SARS-CoV-2/host receptor recognition, a crucial factor for successful virus infection. Based on the current knowledge of the interactome between SARS-CoV-2 and host cell proteins, we performed Master Regulator Analysis to detect which parts of the human interactome are most affected by the infection. We detected, amongst others, affected apoptotic and mitochondrial mechanisms, and a downregulation of the ACE2 protein receptor, notions that can be used to develop specific therapies against this new virus.