Artemisinins Target GABAA Receptor Signaling and Impair α Cell Identity.

Type 1 diabetes is characterized by the destruction of pancreatic ß cells, and generating new insulin-producing cells from other cell types is a major aim of regenerative medicine. One promising approach is transdifferentiation of developmentally related pancreatic cell types, including glucagon-producing α cells. In a genetic model, loss of the master regulatory transcription factor Arx is sufficient to induce the conversion of α cells to functional ß-like cells. Here, we identify artemisinins as small molecules that functionally repress Arx by causing its translocation to the cytoplasm. We show that the protein gephyrin is the mammalian target of these antimalarial drugs and that the mechanism of action of these molecules depends on the enhancement of GABAA receptor signaling. Our results in zebrafish, rodents, and primary human pancreatic islets identify gephyrin as a druggable target for the regeneration of pancreatic ß cell mass from α cells.

Heme drives hemolysis-induced susceptibility to infection via disruption of phagocyte functions.

Hemolysis drives susceptibility to bacterial infections and predicts poor outcome from sepsis. These detrimental effects are commonly considered to be a consequence of heme-iron serving as a nutrient for bacteria. We employed a Gram-negative sepsis model and found that elevated heme levels impaired the control of bacterial proliferation independently of heme-iron acquisition by pathogens. Heme strongly inhibited phagocytosis and the migration of human and mouse phagocytes by disrupting actin cytoskeletal dynamics via activation of the GTP-binding Rho family protein Cdc42 by the guanine nucleotide exchange factor DOCK8. A chemical screening approach revealed that quinine effectively prevented heme effects on the cytoskeleton, restored phagocytosis and improved survival in sepsis. These mechanistic insights provide potential therapeutic targets for patients with sepsis or hemolytic disorders.

CD4(+) T cell lineage integrity is controlled by the histone deacetylases HDAC1 and HDAC2.

Molecular mechanisms that maintain lineage integrity of helper T cells are largely unknown. Here we show histone deacetylases 1 and 2 (HDAC1 and HDAC2) as crucial regulators of this process. Loss of HDAC1 and HDAC2 during late T cell development led to the appearance of major histocompatibility complex (MHC) class II-selected CD4(+) helper T cells that expressed CD8-lineage genes such as Cd8a and Cd8b1. HDAC1 and HDAC2-deficient T helper type 0 (TH0) and TH1 cells further upregulated CD8-lineage genes and acquired a CD8(+) effector T cell program in a manner dependent on Runx-CBFß complexes, whereas TH2 cells repressed features of the CD8(+) lineage independently of HDAC1 and HDAC2. These results demonstrate that HDAC1 and HDAC2 maintain integrity of the CD4 lineage by repressing Runx-CBFß complexes that otherwise induce a CD8(+) effector T cell-like program in CD4(+) T cells.

Mechanisms underlying the cooperation between loss of epithelial polarity and Notch signaling during neoplastic growth in Drosophila.

Aggressive neoplastic growth can be initiated by a limited number of genetic alterations, such as the well-established cooperation between loss of cell architecture and hyperactive signaling pathways. However, our understanding of how these different alterations interact and influence each other remains very incomplete. Using Drosophila paradigms of imaginal wing disc epithelial growth, we have monitored the changes in Notch pathway activity according to the polarity status of cells (scrib mutant). We show that the scrib mutation impacts the direct transcriptional output of the Notch pathway, without altering the global distribution of Su(H), the Notch-dedicated transcription factor. The Notch-dependent neoplasms require, however, the action of a group of transcription factors, similar to those previously identified for Ras/scrib neoplasm (namely AP-1, Stat92E, Ftz-F1 and basic leucine zipper factors), further suggesting the importance of this transcription factor network during neoplastic growth. Finally, our work highlights some Notch/scrib specificities, in particular the role of the PAR domain-containing basic leucine zipper transcription factor and Notch direct target Pdp1 for neoplastic growth.

Enabling population protein dynamics through Bayesian modeling.

MOTIVATION: The knowledge of protein dynamics, or turnover, in patients provides invaluable information related to certain diseases, drug efficacy, or biological processes. A great corpus of experimental and computational methods has been developed, including by us, in the case of human patients followed in vivo. Moving one step further, we propose a novel modeling approach to capture population protein dynamics using Bayesian methods. RESULTS: Using two datasets, we demonstrate that models inspired by population pharmacokinetics can accurately capture protein turnover within a cohort and account for inter-individual variability. Such models pave the way for comparative studies searching for altered dynamics or biomarkers in diseases. AVAILABILITY AND IMPLEMENTATION: R code and preprocessed data are available from zenodo.org. Raw data are available from panoramaweb.org.

Inferring ligand-receptor cellular networks from bulk and spatial transcriptomic datasets with BulkSignalR.

The study of cellular networks mediated by ligand-receptor interactions has attracted much attention recently owing to single-cell omics. However, rich collections of bulk data accompanied with clinical information exists and continue to be generated with no equivalent in single-cell so far. In parallel, spatial transcriptomic (ST) analyses represent a revolutionary tool in biology. A large number of ST projects rely on multicellular resolution, for instance the Visium™ platform, where several cells are analyzed at each location, thus producing localized bulk data. Here, we describe BulkSignalR, a R package to infer ligand-receptor networks from bulk data. BulkSignalR integrates ligand-receptor interactions with downstream pathways to estimate statistical significance. A range of visualization methods complement the statistics, including functions dedicated to spatial data. We demonstrate BulkSignalR relevance using different datasets, including new Visium liver metastasis ST data, with experimental validation of protein colocalization. A comparison with other ST packages shows the significantly higher quality of BulkSignalR inferences. BulkSignalR can be applied to any species thanks to its built-in generic ortholog mapping functionality.

Modeling the Simultaneous Dynamics of Proteins in Blood Plasma and the Cerebrospinal Fluid in Human In Vivo.

The analysis of protein dynamics or turnover in patients has the potential to reveal altered protein recycling, such as in Alzheimer's disease, and to provide informative data regarding drug efficacy or certain biological processes. The observed protein dynamics in a solid tissue or a fluid is the net result of not only protein synthesis and degradation but also transport across biological compartments. We report an accurate 3-biological compartment model able to simultaneously account for the protein dynamics observed in blood plasma and the cerebrospinal fluid (CSF) including a hidden central nervous system (CNS) compartment. We successfully applied this model to 69 proteins of a single individual displaying similar or very different dynamics in plasma and CSF. This study puts a strong emphasis on the methods and tools needed to develop this type of model. We believe that it will be useful to any researcher dealing with protein dynamics data modeling.

Modular response analysis reformulated as a multilinear regression problem.

MOTIVATION: Modular response analysis (MRA) is a well-established method to infer biological networks from perturbation data. Classically, MRA requires the solution of a linear system, and results are sensitive to noise in the data and perturbation intensities. Due to noise propagation, applications to networks of 10 nodes or more are difficult. RESULTS: We propose a new formulation of MRA as a multilinear regression problem. This enables to integrate all the replicates and potential additional perturbations in a larger, over-determined, and more stable system of equations. More relevant confidence intervals on network parameters can be obtained, and we show competitive performance for networks of size up to 1000. Prior knowledge integration in the form of known null edges further improves these results. AVAILABILITY AND IMPLEMENTATION: The R code used to obtain the presented results is available from GitHub: https://github.com/J-P-Borg/BioInformatics.

Superoxide Dismutase 1 Protects Hepatocytes from Type I Interferon-Driven Oxidative Damage.

Tissue damage caused by viral hepatitis is a major cause of morbidity and mortality worldwide. Using a mouse model of viral hepatitis, we identified virus-induced early transcriptional changes in the redox pathways in the liver, including downregulation of superoxide dismutase 1 (Sod1). Sod1(-/-) mice exhibited increased inflammation and aggravated liver damage upon viral infection, which was independent of T and NK cells and could be ameliorated by antioxidant treatment. Type I interferon (IFN-I) led to a downregulation of Sod1 and caused oxidative liver damage in Sod1(-/-) and wild-type mice. Genetic and pharmacological ablation of the IFN-I signaling pathway protected against virus-induced liver damage. These results delineate IFN-I mediated oxidative stress as a key mediator of virus-induced liver damage and describe a mechanism of innate-immunity-driven pathology, linking IFN-I signaling with antioxidant host defense and infection-associated tissue damage. VIDEO ABSTRACT.

Prediction of response to promising first-line chemotherapy in ovarian cancer patients with residual peritoneal tumors: practical biomarkers and robust multiplex models.

BACKGROUND: Platinum/taxane (TC) chemotherapy with debulking surgery stays the mainstay of the treatment in ovarian cancer patients with peritoneal metastasis, and recently its novel modality, intraperitoneal carboplatin with dose-dense paclitaxel (ddTCip), was shown to have greater therapeutic impact. Nevertheless, the response varies among patients and consequent recurrence, or relapse often occurs. Discovery of therapeutic response predictor to ddTCip and/or TC therapy is eagerly awaited to improve the treatment outcome. METHODS: Using datasets in 76 participants in our ddTCip study and published databases on patients received TC therapy, we first validated a total of 75 previously suggested markers, sought out more active biomarkers through the association analyses of genome-wide transcriptome and genotyping data with progression-free survival (PFS) and adverse events, and then developed multiplex statistical prediction models for PFS and toxicity by mainly using multiple regression analysis and the classification and regression tree (CART) algorithm. RESULTS: The association analyses revealed that SPINK1 could be a possible biomarker of ddTCip efficacy, while ABCB1 rs1045642 and ERCC1 rs11615 would be a predictor of hematologic toxicity and peripheral neuropathy, respectively. Multiple regression analyses and CART algorithm finally provided a potent efficacy prediction model using 5 gene expression data and robust multiplex toxicity prediction models-CART models using a total of 4 genotype combinations and multiple regression models using 15 polymorphisms on 12 genes. CONCLUSION: Biomarkers and multiplex models composed here could work well in the response prediction of ddTCip and/or TC therapy, which might contribute to realize optimal selection of the key therapy.

Circulating DNA reveals a specific and higher fragmentation of the Y chromosome.

Chromosome stability is a key point in genome evolution, particularly that of the Y chromosome. The Y chromosome loss in blood and tumor cells is well established. Through processes that are common to other chromosomes too, the Y chromosome undergoes degradation and fragmentation in the blood stream before elimination. This process gives rise to circulating DNA (cirDNA) fragments, whose examination may provide potential insight into the role of DNA fragmentation in blood for the Y chromosome elimination. In this study, we employed shallow whole genome sequencing (sWGS) to comprehensively assess the total cirDNA and the individual chromosome fragment size profiles in the plasma of healthy male individuals. Here, we show that (i) the fragment size profiles of total circulating DNA (cirDNA) and DNA fragments originating from autosomes and the X chromosome in blood plasma are homogeneous, and have a remarkably low variability (mean CV = 7%) among healthy individuals, (ii) the Y chromosome has a distinct fragment size profile with the accumulation of the fragment < 145 bp and depletion of the dinucleosome-associated fragments (290-390 bp), and its fragment fraction in blood decreases with age. These results indicate a higher fragmentation of the Y chromosome compared to other chromosomes and this in turn might be due to its increased susceptibility to degradation. Our findings pave the way for an elucidation of the impact of chromosomal origin on DNA degradation and the Y chromosome biology.

IFIT1 is an antiviral protein that recognizes 5'-triphosphate RNA.

Antiviral innate immunity relies on the recognition of microbial structures. One such structure is viral RNA that carries a triphosphate group on its 5' terminus (PPP-RNA). By an affinity proteomics approach with PPP-RNA as the 'bait', we found that the antiviral protein IFIT1 (interferon-induced protein with tetratricopeptide repeats 1) mediated binding of a larger protein complex containing other IFIT family members. IFIT1 bound PPP-RNA with nanomolar affinity and required the arginine at position 187 in a highly charged carboxy-terminal groove of the protein. In the absence of IFIT1, the growth and pathogenicity of viruses containing PPP-RNA was much greater. In contrast, IFIT proteins were dispensable for the clearance of pathogens that did not generate PPP-RNA. On the basis of this specificity and the great abundance of IFIT proteins after infection, we propose that the IFIT complex antagonizes viruses by sequestering specific viral nucleic acids.

Application of modular response analysis to medium- to large-size biological systems.

The development of high-throughput genomic technologies associated with recent genetic perturbation techniques such as short hairpin RNA (shRNA), gene trapping, or gene editing (CRISPR/Cas9) has made it possible to obtain large perturbation data sets. These data sets are invaluable sources of information regarding the function of genes, and they offer unique opportunities to reverse engineer gene regulatory networks in specific cell types. Modular response analysis (MRA) is a well-accepted mathematical modeling method that is precisely aimed at such network inference tasks, but its use has been limited to rather small biological systems so far. In this study, we show that MRA can be employed on large systems with almost 1,000 network components. In particular, we show that MRA performance surpasses general-purpose mutual information-based algorithms. Part of these competitive results was obtained by the application of a novel heuristic that pruned MRA-inferred interactions a posteriori. We also exploited a block structure in MRA linear algebra to parallelize large system resolutions.

FT-GPI, a highly sensitive and accurate predictor of GPI-anchored proteins, reveals the composition and evolution of the GPI proteome in Plasmodium species.

BACKGROUND: Protozoan parasites are known to attach specific and diverse group of proteins to their plasma membrane via a GPI anchor. In malaria parasites, GPI-anchored proteins (GPI-APs) have been shown to play an important role in host-pathogen interactions and a key function in host cell invasion and immune evasion. Because of their immunogenic properties, some of these proteins have been considered as malaria vaccine candidates. However, identification of all possible GPI-APs encoded by these parasites remains challenging due to their sequence diversity and limitations of the tools used for their characterization. METHODS: The FT-GPI software was developed to detect GPI-APs based on the presence of a hydrophobic helix at both ends of the premature peptide. FT-GPI was implemented in C ++and applied to study the GPI-proteome of 46 isolates of the order Haemosporida. Using the GPI proteome of Plasmodium falciparum strain 3D7 and Plasmodium vivax strain Sal-1, a heuristic method was defined to select the most sensitive and specific FT-GPI software parameters. RESULTS: FT-GPI enabled revision of the GPI-proteome of P. falciparum and P. vivax, including the identification of novel GPI-APs. Orthology- and synteny-based analyses showed that 19 of the 37 GPI-APs found in the order Haemosporida are conserved among Plasmodium species. Our analyses suggest that gene duplication and deletion events may have contributed significantly to the evolution of the GPI proteome, and its composition correlates with speciation. CONCLUSION: FT-GPI-based prediction is a useful tool for mining GPI-APs and gaining further insights into their evolution and sequence diversity. This resource may also help identify new protein candidates for the development of vaccines for malaria and other parasitic diseases.

Detection of soluble biomarkers of pancreatic cancer in endoscopic ultrasound-guided fine-needle aspiration samples.

BACKGROUND: Biomarkers are urgently needed for pancreatic ductal adenocarcinoma (PDAC). Endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA) is the cornerstone for diagnosing PDAC. We developed a method for discovery of PDAC biomarkers using the discarded EUS-FNA liquid. METHODS: This retrospective study included 58 patients with suspected pancreatic lesions who underwent EUS-FNA. Protein extracts from EUS-FNA liquid were analyzed by mass spectrometry. Proteomic and clinical data were modeled by supervised statistical learning to identify protein markers and clinical variables that distinguish PDAC. RESULTS: Statistical modeling revealed a protein signature for PDAC screening that achieved high sensitivity and specificity (0.92, 95 % confidence interval [CI] 0.79-0.98, and 0.85, 95 %CI 0.67-0.93, respectively). We also developed a protein signature score (PSS) to guide PDAC diagnosis. In combination with patient age, the PSS achieved 100 % certainty in correctly identifying PDAC patients > 54 years. In addition, 3 /4 inconclusive EUS-FNA biopsies were correctly identified using PSS. CONCLUSIONS: EUS-FNA-derived fluid is a rich source of PDAC proteins with biomarker potential. The PSS requires further validation and verification of the feasibility of measuring these proteins in patient sera.

SingleCellSignalR: inference of intercellular networks from single-cell transcriptomics.

Single-cell transcriptomics offers unprecedented opportunities to infer the ligand-receptor (LR) interactions underlying cellular networks. We introduce a new, curated LR database and a novel regularized score to perform such inferences. For the first time, we try to assess the confidence in predicted LR interactions and show that our regularized score outperforms other scoring schemes while controlling false positives. SingleCellSignalR is implemented as an open-access R package accessible to entry-level users and available from https://github.com/SCA-IRCM. Analysis results come in a variety of tabular and graphical formats. For instance, we provide a unique network view integrating all the intercellular interactions, and a function relating receptors to expressed intracellular pathways. A detailed comparison of related tools is conducted. Among various examples, we demonstrate SingleCellSignalR on mouse epidermis data and discover an oriented communication structure from external to basal layers.

An orthogonal proteomic-genomic screen identifies AIM2 as a cytoplasmic DNA sensor for the inflammasome.

Cytoplasmic DNA triggers activation of the innate immune system. Although 'downstream' signaling components have been characterized, the DNA-sensing components remain elusive. Here we present a systematic proteomics screen for proteins that associate with DNA, 'crossed' to a screen for transcripts induced by interferon-beta, which identified AIM2 as a candidate cytoplasmic DNA sensor. AIM2 showed specificity for double-stranded DNA. It also recruited the inflammasome adaptor ASC and localized to ASC 'speckles'. A decrease in AIM2 expression produced by RNA-mediated interference impaired DNA-induced maturation of interleukin 1beta in THP-1 human monocytic cells, which indicated that endogenous AIM2 is required for DNA recognition. Reconstitution of unresponsive HEK293 cells with AIM2, ASC, caspase-1 and interleukin 1beta showed that AIM2 was sufficient for inflammasome activation. Our data suggest that AIM2 is a cytoplasmic DNA sensor for the inflammasome.

Stereospecific targeting of MTH1 by (S)-crizotinib as an anticancer strategy.

Activated RAS GTPase signalling is a critical driver of oncogenic transformation and malignant disease. Cellular models of RAS-dependent cancers have been used to identify experimental small molecules, such as SCH51344, but their molecular mechanism of action remains generally unknown. Here, using a chemical proteomic approach, we identify the target of SCH51344 as the human mutT homologue MTH1 (also known as NUDT1), a nucleotide pool sanitizing enzyme. Loss-of-function of MTH1 impaired growth of KRAS tumour cells, whereas MTH1 overexpression mitigated sensitivity towards SCH51344. Searching for more drug-like inhibitors, we identified the kinase inhibitor crizotinib as a nanomolar suppressor of MTH1 activity. Surprisingly, the clinically used (R)-enantiomer of the drug was inactive, whereas the (S)-enantiomer selectively inhibited MTH1 catalytic activity. Enzymatic assays, chemical proteomic profiling, kinome-wide activity surveys and MTH1 co-crystal structures of both enantiomers provide a rationale for this remarkable stereospecificity. Disruption of nucleotide pool homeostasis via MTH1 inhibition by (S)-crizotinib induced an increase in DNA single-strand breaks, activated DNA repair in human colon carcinoma cells, and effectively suppressed tumour growth in animal models. Our results propose (S)-crizotinib as an attractive chemical entity for further pre-clinical evaluation, and small-molecule inhibitors of MTH1 in general as a promising novel class of anticancer agents.

Distinct oncogenes drive different genome and epigenome alterations in human mammary epithelial cells.

Molecular subtypes of breast cancer are defined on the basis of gene expression and genomic/epigenetic pattern differences. Different subtypes are thought to originate from distinct cell lineages, but the early activation of an oncogene could also play a role. It is difficult to discriminate the respective inputs of oncogene activation or cell type of origin. In this work, we wished to determine whether activation of distinct oncogenic pathways in human mammary epithelial cells (HMEC) could lead to different patterns of genetic and epigenetic changes. To this aim, we transduced shp53 immortalized HMECs in parallel with the CCNE1, WNT1 and RASv12 oncogenes which activate distinct oncogenic pathways and characterized them at sequential stages of transformation for changes in their genetic and epigenetic profiles. We show that initial activation of CCNE1, WNT1 and RASv12, in shp53 HMECs results in different and reproducible changes in mRNA and micro-RNA expression, copy number alterations (CNA) and DNA methylation profiles. Noticeably, HMECs transformed by RAS bore very specific profiles of CNAs and DNA methylation, clearly distinct from those shown by CCNE1 and WNT1 transformed HMECs. Genes impacted by CNAs and CpG methylation in the RAS and the CCNE1/WNT1 clusters showed clear differences, illustrating the activation of distinct pathways. Our data show that early activation of distinct oncogenic pathways leads to active adaptive events resulting in specific sets of CNAs and DNA methylation changes. We, thus, propose that activation of different oncogenes could have a role in reshaping the genetic landscape of breast cancer subtypes.

In Vivo Large-Scale Mapping of Protein Turnover in Human Cerebrospinal Fluid.

The extraction of accurate physiological parameters from clinical samples provides a unique perspective to understand disease etiology and evolution, including under therapy. We introduce a new methodologic framework to map patient proteome dynamics in vivo, either proteome-wide or in large targeted panels. We applied it to ventricular cerebrospinal fluid (CSF) and could determine the turnover parameters of almost 200 proteins, whereas a handful were known previously. We covered a large number of neuron biology- and immune system-related proteins, including many biomarkers and drug targets. This first large data set unraveled a significant relationship between turnover and protein origin that relates to our ability to investigate organ physiology with protein-labeling strategy specifics. Our data constitute the first draft of CSF proteome dynamics as well as a repertoire of peptides for the community to design new analyses. The disclosed methods apply to other fluids or tissues provided sequential sample collection can be performed. We show that the proposed mathematical modeling applies to other analytical methods in the field.