Genomics in the long-read sequencing era.

Long-read sequencing (LRS) technologies have provided extremely powerful tools to explore genomes. While in the early years these methods suffered technical limitations, they have recently made significant progress in terms of read length, throughput, and accuracy and bioinformatics tools have strongly improved. Here, we aim to review the current status of LRS technologies, the development of novel methods, and the impact on genomics research. We will explore the most impactful recent findings made possible by these technologies focusing on high-resolution sequencing of genomes and transcriptomes and the direct detection of DNA and RNA modifications. We will also discuss how LRS methods promise a more comprehensive understanding of human genetic variation, transcriptomics, and epigenetics for the coming years.

The Drosophila Fab-7 boundary modulates Abd-B gene activity by guiding an inversion of collinear chromatin organization and alternate promoter use.

Hox genes encode transcription factors that specify segmental identities along the anteroposterior body axis. These genes are organized in clusters, where their order corresponds to their activity along the body axis, a feature known as collinearity. In Drosophila, the BX-C cluster contains the three most posterior Hox genes, where their collinear activation incorporates progressive changes in histone modifications, chromatin architecture, and use of boundary elements and cis-regulatory regions. To dissect functional hierarchies, we compare chromatin organization in cell lines and larvae, with a focus on the Abd-B gene. Our work establishes the importance of the Fab-7 boundary for insulation between 3D domains carrying different histone modifications. Interestingly, we detect a non-canonical inversion of collinear chromatin dynamics at Abd-B, with the domain of active histone modifications progressively decreasing in size. This dynamic chromatin organization differentially activates the alternative promoters of the Abd-B gene, thereby expanding the possibilities for fine-tuning of transcriptional output.

ClustMMRA v2: A Scalable Computational Pipeline for the Identification of MicroRNA Clusters Acting Cooperatively on Tumor Molecular Subgroups.

In recent cancer genomics programs, large-scale profiling of microRNAs has been routinely used in order to better understand the role of microRNAs in gene regulation and disease. To support the analysis of such amount of data, scalability of bioinformatics pipelines is increasingly important to handle larger datasets.Here, we describe a scalable implementation of the clustered miRNA Master Regulator Analysis (clustMMRA) pipeline, developed to search for genomic clusters of microRNAs potentially driving cancer molecular subtyping. Genomically clustered microRNAs can be simultaneously expressed to work in a combined manner and jointly regulate cell phenotypes. However, the majority of computational approaches for the identification of microRNA master regulators are typically designed to detect the regulatory effect of a single microRNA.We have applied the clustMMRA pipeline to multiple pediatric tumor datasets, up to a hundred samples in size, demonstrating very satisfying performances of the software on large datasets. Results have highlighted genomic clusters of microRNAs potentially involved in several subgroups of the different pediatric cancers or specifically involved in the phenotype of a subgroup. In particular, we confirmed the cluster of microRNAs at the 14q32 locus to be involved in multiple pediatric cancers, showing its specific downregulation in tumor subgroups with aggressive phenotype.

The purinergic P2Y14 receptor links hepatocyte death to hepatic stellate cell activation and fibrogenesis in the liver.

Fibrosis contributes to ~45% of deaths in western countries. In chronic liver disease, fibrosis is a major factor determining outcomes, but efficient antifibrotic therapies are lacking. Although platelet-derived growth factor and transforming growth factor-ß constitute key fibrogenic mediators, they do not account for the well-established link between cell death and fibrosis in the liver. Here, we hypothesized that damage-associated molecular patterns (DAMPs) may link epithelial cell death to fibrogenesis in the injured liver. DAMP receptor screening identified purinergic receptor P2Y14 among several candidates as highly enriched in hepatic stellate cells (HSCs), the main fibrogenic cell type of the liver. Conversely, P2Y14 ligands uridine 5'-diphosphate (UDP)-glucose and UDP-galactose were enriched in hepatocytes and were released upon different modes of cell death. Accordingly, ligand-receptor interaction analysis that combined proteomic and single-cell RNA sequencing data revealed P2Y14 ligands and P2Y14 receptor as a link between dying cells and HSCs, respectively. Treatment with P2Y14 ligands or coculture with dying hepatocytes promoted HSC activation in a P2Y14-dependent manner. P2Y14 ligands activated extracellular signal-regulated kinase (ERK) and Yes-associated protein (YAP) signaling in HSCs, resulting in ERK-dependent HSC activation. Global and HSC-selective P2Y14 deficiency attenuated liver fibrosis in multiple mouse models of liver injury. Functional expression of P2Y14 was confirmed in healthy and diseased human liver and human HSCs. In conclusion, P2Y14 ligands and their receptor constitute a profibrogenic DAMP pathway that directly links cell death to fibrogenesis.

Benchmarking joint multi-omics dimensionality reduction approaches for the study of cancer.

High-dimensional multi-omics data are now standard in biology. They can greatly enhance our understanding of biological systems when effectively integrated. To achieve proper integration, joint Dimensionality Reduction (jDR) methods are among the most efficient approaches. However, several jDR methods are available, urging the need for a comprehensive benchmark with practical guidelines. We perform a systematic evaluation of nine representative jDR methods using three complementary benchmarks. First, we evaluate their performances in retrieving ground-truth sample clustering from simulated multi-omics datasets. Second, we use TCGA cancer data to assess their strengths in predicting survival, clinical annotations and known pathways/biological processes. Finally, we assess their classification of multi-omics single-cell data. From these in-depth comparisons, we observe that intNMF performs best in clustering, while MCIA offers an effective behavior across many contexts. The code developed for this benchmark study is implemented in a Jupyter notebook-multi-omics mix (momix)-to foster reproducibility, and support users and future developers.

Deciphering and modelling the TGF-ß signalling interplays specifying the dorsal-ventral axis of the sea urchin embryo.

During sea urchin development, secretion of Nodal and BMP2/4 ligands and their antagonists Lefty and Chordin from a ventral organiser region specifies the ventral and dorsal territories. This process relies on a complex interplay between the Nodal and BMP pathways through numerous regulatory circuits. To decipher the interplay between these pathways, we used a combination of treatments with recombinant Nodal and BMP2/4 proteins and a computational modelling approach. We assembled a logical model focusing on cell responses to signalling inputs along the dorsal-ventral axis, which was extended to cover ligand diffusion and enable multicellular simulations. Our model simulations accurately recapitulate gene expression in wild-type embryos, accounting for the specification of ventral ectoderm, ciliary band and dorsal ectoderm. Our model simulations further recapitulate various morphant phenotypes, reveal a dominance of the BMP pathway over the Nodal pathway and stress the crucial impact of the rate of Smad activation in dorsal-ventral patterning. These results emphasise the key role of the mutual antagonism between the Nodal and BMP2/4 pathways in driving early dorsal-ventral patterning of the sea urchin embryo.

Computational Verification of Large Logical Models-Application to the Prediction of T Cell Response to Checkpoint Inhibitors.

At the crossroad between biology and mathematical modeling, computational systems biology can contribute to a mechanistic understanding of high-level biological phenomenon. But as knowledge accumulates, the size and complexity of mathematical models increase, calling for the development of efficient dynamical analysis methods. Here, we propose the use of two approaches for the development and analysis of complex cellular network models. A first approach, called "model verification" and inspired by unitary testing in software development, enables the formalization and automated verification of validation criteria for whole models or selected sub-parts. When combined with efficient analysis methods, this approach is suitable for continuous testing, thereby greatly facilitating model development. A second approach, called "value propagation," enables efficient analytical computation of the impact of specific environmental or genetic conditions on the dynamical behavior of some models. We apply these two approaches to the delineation and the analysis of a comprehensive model for T cell activation, taking into account CTLA4 and PD-1 checkpoint inhibitory pathways. While model verification greatly eases the delineation of logical rules complying with a set of dynamical specifications, propagation provides interesting insights into the different potential of CTLA4 and PD-1 immunotherapies. Both methods are implemented and made available in the all-inclusive CoLoMoTo Docker image, while the different steps of the model analysis are fully reported in two companion interactive jupyter notebooks, thereby ensuring the reproduction of our results.

Impact of hepatitis C virus and alcohol, alone and combined, on the unfolded protein response in primary human hepatocytes.

Hepatitis C virus (HCV) infection and alcohol abuse are leading causes of chronic liver disease and frequently coexist in patients. The unfolded protein response (UPR), a cellular stress response ranging along a spectrum from cytoprotection to apoptosis commitment, has emerged as a major contributor to human diseases including liver injuries. However, the literature contains conflicting reports as to whether HCV and ethanol activate the UPR and which UPR genes are involved. Here we have used primary human hepatocytes (PHH) to reassess this issue and address combined impacts. In this physiologically relevant model, either stressor activated a chronic complete UPR. However, the levels of UPR gene induction were only modest in the case of HCV infection. Moreover, when combined to the strong stressor thapsigargin, ethanol exacerbated the activation of pro-apoptotic genes whereas HCV tended to limit the induction of key UPR genes. The UPR resulting from HCV plus ethanol was comparable to that induced by ethanol alone with the notable exception of three pro-survival genes the expressions of which were selectively enhanced by HCV. Interestingly, HCV genome replication was maintained at similar levels in PHH exposed to ethanol. In conclusion, while both HCV and alcohol activate the hepatocellular UPR, only HCV manipulates UPR signalling in the direction of a cytoprotective response, which appears as a viral strategy to spare its own replication.

Contribution of ROS and metabolic status to neonatal and adult CD8+ T cell activation.

In neonatal T cells, a low response to infection contributes to a high incidence of morbidity and mortality of neonates. Here we have evaluated the impact of the cytoplasmic and mitochondrial levels of Reactive Oxygen Species of adult and neonatal CD8+ T cells on their activation potential. We have also constructed a logical model connecting metabolism and ROS with T cell signaling. Our model indicates the interplay between antigen recognition, ROS and metabolic status in T cell responses. This model displays alternative stable states corresponding to different cell fates, i.e. quiescent, activated and anergic states, depending on ROS levels. Stochastic simulations with this model further indicate that differences in ROS status at the cell population level contribute to the lower activation rate of neonatal, compared to adult, CD8+ T cells upon TCR engagement. These results are relevant for neonatal health care. Our model can serve to analyze the impact of metabolic shift during cancer in which, similar to neonatal cells, a high glycolytic rate and low concentrations of glutamine and arginine promote tumor tolerance.

Cooperation between T cell receptor and Toll-like receptor 5 signaling for CD4+ T cell activation.

CD4+ T cells recognize antigens through their T cell receptors (TCRs); however, additional signals involving costimulatory receptors, for example, CD28, are required for proper T cell activation. Alternative costimulatory receptors have been proposed, including members of the Toll-like receptor (TLR) family, such as TLR5 and TLR2. To understand the molecular mechanism underlying a potential costimulatory role for TLR5, we generated detailed molecular maps and logical models for the TCR and TLR5 signaling pathways and a merged model for cross-interactions between the two pathways. Furthermore, we validated the resulting model by analyzing how T cells responded to the activation of these pathways alone or in combination, in terms of the activation of the transcriptional regulators CREB, AP-1 (c-Jun), and NF-κB (p65). Our merged model accurately predicted the experimental results, showing that the activation of TLR5 can play a similar role to that of CD28 activation with respect to AP-1, CREB, and NF-κB activation, thereby providing insights regarding the cross-regulation of these pathways in CD4+ T cells.

Proteo-Transcriptomic Dynamics of Cellular Response to HIV-1 Infection.

Throughout the HIV-1 replication cycle, complex host-pathogen interactions take place in the infected cell, leading to the production of new virions. The virus modulates the host cellular machinery in order to support its life cycle, while counteracting intracellular defense mechanisms. We investigated the dynamic host response to HIV-1 infection by systematically measuring transcriptomic, proteomic, and phosphoproteomic expression changes in infected and uninfected SupT1 CD4+ T cells at five time points of the viral replication process. By means of a Gaussian mixed-effects model implemented in the new R/Bioconductor package TMixClust, we clustered host genes based on their temporal expression patterns. We identified a proteo-transcriptomic gene expression signature of 388 host genes specific for HIV-1 replication. Comprehensive functional analyses of these genes confirmed the previously described roles of some of the genes and revealed novel key virus-host interactions affecting multiple molecular processes within the host cell, including signal transduction, metabolism, cell cycle, and immune system. The results of our analysis are accessible through a freely available, dedicated and user-friendly R/Shiny application, called PEACHi2.0. This resource constitutes a catalogue of dynamic host responses to HIV-1 infection that provides a basis for a more comprehensive understanding of virus-host interactions.

Logical Modeling and Analysis of Cellular Regulatory Networks With GINsim 3.0.

The logical formalism is well adapted to model large cellular networks, in particular when detailed kinetic data are scarce. This tutorial focuses on this well-established qualitative framework. Relying on GINsim (release 3.0), a software implementing this formalism, we guide the reader step by step toward the definition, the analysis and the simulation of a four-node model of the mammalian p53-Mdm2 network.

The CoLoMoTo Interactive Notebook: Accessible and Reproducible Computational Analyses for Qualitative Biological Networks.

Analysing models of biological networks typically relies on workflows in which different software tools with sensitive parameters are chained together, many times with additional manual steps. The accessibility and reproducibility of such workflows is challenging, as publications often overlook analysis details, and because some of these tools may be difficult to install, and/or have a steep learning curve. The CoLoMoTo Interactive Notebook provides a unified environment to edit, execute, share, and reproduce analyses of qualitative models of biological networks. This framework combines the power of different technologies to ensure repeatability and to reduce users' learning curve of these technologies. The framework is distributed as a Docker image with the tools ready to be run without any installation step besides Docker, and is available on Linux, macOS, and Microsoft Windows. The embedded computational workflows are edited with a Jupyter web interface, enabling the inclusion of textual annotations, along with the explicit code to execute, as well as the visualization of the results. The resulting notebook files can then be shared and re-executed in the same environment. To date, the CoLoMoTo Interactive Notebook provides access to the software tools GINsim, BioLQM, Pint, MaBoSS, and Cell Collective, for the modeling and analysis of Boolean and multi-valued networks. More tools will be included in the future. We developed a Python interface for each of these tools to offer a seamless integration in the Jupyter web interface and ease the chaining of complementary analyses.

Prediction of Mutations to Control Pathways Enabling Tumor Cell Invasion with the CoLoMoTo Interactive Notebook (Tutorial).

Boolean and multi-valued logical formalisms are increasingly used to model complex cellular networks. To ease the development and analysis of logical models, a series of software tools have been proposed, often with specific assets. However, combining these tools typically implies a series of cumbersome software installation and model conversion steps. In this respect, the CoLoMoTo Interactive Notebook provides a joint distribution of several logical modeling software tools, along with an interactive web Python interface easing the chaining of complementary analyses. Our computational workflow combines (1) the importation of a GINsim model and its display, (2) its format conversion using the Java library BioLQM, (3) the formal prediction of mutations using the OCaml software Pint, (4) the model checking using the C++ software NuSMV, (5) quantitative stochastic simulations using the C++ software MaBoSS, and (6) the visualization of results using the Python library matplotlib. To illustrate our approach, we use a recent Boolean model of the signaling network controlling tumor cell invasion and migration. Our model analysis culminates with the prediction of sets of mutations presumably involved in a metastatic phenotype.

HMGB1 links chronic liver injury to progenitor responses and hepatocarcinogenesis.

Cell death is a key driver of disease progression and carcinogenesis in chronic liver disease (CLD), highlighted by the well-established clinical correlation between hepatocellular death and risk for the development of cirrhosis and hepatocellular carcinoma (HCC). Moreover, hepatocellular death is sufficient to trigger fibrosis and HCC in mice. However, the pathways through which cell death drives CLD progression remain elusive. Here, we tested the hypothesis that high-mobility group box 1 (HMGB1), a damage-associated molecular pattern (DAMP) with key roles in acute liver injury, may link cell death to injury responses and hepatocarcinogenesis in CLD. While liver-specific HMGB1 deficiency did not significantly affect chronic injury responses such as fibrosis, regeneration, and inflammation, it inhibited ductular/progenitor cell expansion and hepatocyte metaplasia. HMGB1 promoted ductular expansion independently of active secretion in a nonautonomous fashion, consistent with its role as a DAMP. Liver-specific HMGB1 deficiency reduced HCC development in 3 mouse models of chronic injury but not in a model lacking chronic liver injury. As with CLD, HMGB1 ablation reduced the expression of progenitor and oncofetal markers, a key determinant of HCC aggressiveness, in tumors. In summary, HMGB1 links hepatocyte death to ductular reaction, progenitor signature, and hepatocarcinogenesis in CLD.