Combined transcriptomics and proteomics unveil the impact of vitamin C in modulating specific protein abundance in the mouse liver.

BACKGROUND: Vitamin C (ascorbate) is a water-soluble antioxidant and an important cofactor for various biosynthetic and regulatory enzymes. Mice can synthesize vitamin C thanks to the key enzyme gulonolactone oxidase (Gulo) unlike humans. In the current investigation, we used Gulo-/- mice, which cannot synthesize their own ascorbate to determine the impact of this vitamin on both the transcriptomics and proteomics profiles in the whole liver. The study included Gulo-/- mouse groups treated with either sub-optimal or optimal ascorbate concentrations in drinking water. Liver tissues of females and males were collected at the age of four months and divided for transcriptomics and proteomics analysis. Immunoblotting, quantitative RT-PCR, and polysome profiling experiments were also conducted to complement our combined omics studies. RESULTS: Principal component analyses revealed distinctive differences in the mRNA and protein profiles as a function of sex between all the mouse cohorts. Despite such sexual dimorphism, Spearman analyses of transcriptomics data from females and males revealed correlations of hepatic ascorbate levels with transcripts encoding a wide array of biological processes involved in glucose and lipid metabolisms as well as in the acute-phase immune response. Moreover, integration of the proteomics data showed that ascorbate modulates the abundance of various enzymes involved in lipid, xenobiotic, organic acid, acetyl-CoA, and steroid metabolism mainly at the transcriptional level, especially in females. However, several proteins of the mitochondrial complex III significantly correlated with ascorbate concentrations in both males and females unlike their corresponding transcripts. Finally, poly(ribo)some profiling did not reveal significant enrichment difference for these mitochondrial complex III mRNAs between Gulo-/- mice treated with sub-optimal and optimal ascorbate levels. CONCLUSIONS: Thus, the abundance of several subunits of the mitochondrial complex III are regulated by ascorbate at the post-transcriptional levels. Our extensive omics analyses provide a novel resource of altered gene expression patterns at the transcriptional and post-transcriptional levels under ascorbate deficiency.

BERNN: Enhancing classification of Liquid Chromatography Mass Spectrometry data with batch effect removal neural networks.

Liquid Chromatography Mass Spectrometry (LC-MS) is a powerful method for profiling complex biological samples. However, batch effects typically arise from differences in sample processing protocols, experimental conditions, and data acquisition techniques, significantly impacting the interpretability of results. Correcting batch effects is crucial for the reproducibility of omics research, but current methods are not optimal for the removal of batch effects without compressing the genuine biological variation under study. We propose a suite of Batch Effect Removal Neural Networks (BERNN) to remove batch effects in large LC-MS experiments, with the goal of maximizing sample classification performance between conditions. More importantly, these models must efficiently generalize in batches not seen during training. A comparison of batch effect correction methods across five diverse datasets demonstrated that BERNN models consistently showed the strongest sample classification performance. However, the model producing the greatest classification improvements did not always perform best in terms of batch effect removal. Finally, we show that the overcorrection of batch effects resulted in the loss of some essential biological variability. These findings highlight the importance of balancing batch effect removal while preserving valuable biological diversity in large-scale LC-MS experiments.

Non-canonical transcriptional regulation of the poor prognostic factor UGT2B17 in chronic lymphocytic leukemic and normal B cells.

BACKGROUND: High expression of the glycosyltransferase UGT2B17 represents an independent adverse prognostic marker in chronic lymphocytic leukemia (CLL). It also constitutes a predictive marker for therapeutic response and a drug resistance mechanism. The key determinants driving expression of the UGT2B17 gene in normal and leukemic B-cells remain undefined. The UGT2B17 transcriptome is complex and is comprised of at least 10 alternative transcripts, identified by previous RNA-sequencing of liver and intestine. We hypothesized that the transcriptional program regulating UGT2B17 in B-lymphocytes is distinct from the canonical expression previously characterized in the liver. RESULTS: RNA-sequencing and genomics data revealed a specific genomic landscape at the UGT2B17 locus in normal and leukemic B-cells. RNA-sequencing and quantitative PCR data indicated that the UGT2B17 enzyme is solely encoded by alternative transcripts expressed in CLL patient cells and not by the canonical transcript widely expressed in the liver and intestine. Chromatin accessible regions (ATAC-Seq) in CLL cells mapped with alternative promoters and non-coding exons, which may be derived from endogenous retrotransposon elements. By luciferase reporter assays, we identified key cis-regulatory STAT3, RELA and interferon regulatory factor (IRF) binding sequences driving the expression of UGT2B17 in lymphoblastoid and leukemic B-cells. Electrophoretic mobility shift assays and pharmacological inhibition demonstrated key roles for the CLL prosurvival transcription factors STAT3 and NF-κB in the leukemic expression of UGT2B17. CONCLUSIONS: UGT2B17 expression in B-CLL is driven by key regulators of CLL progression. Our data suggest that a NF-κB/STAT3/IRF/UGT2B17 axis may represent a novel B-cell pathway promoting disease progression and drug resistance.

SOS genes are rapidly induced while translesion synthesis polymerase activity is temporally regulated.

The DNA damage inducible SOS response in bacteria serves to increase survival of the species at the cost of mutagenesis. The SOS response first initiates error-free repair followed by error-prone repair. Here, we have employed a multi-omics approach to elucidate the temporal coordination of the SOS response. Escherichia coli was grown in batch cultivation in bioreactors to ensure highly controlled conditions, and a low dose of the antibiotic ciprofloxacin was used to activate the SOS response while avoiding extensive cell death. Our results show that expression of genes involved in error-free and error-prone repair were both induced shortly after DNA damage, thus, challenging the established perception that the expression of error-prone repair genes is delayed. By combining transcriptomics and a sub-proteomics approach termed signalomics, we found that the temporal segregation of error-free and error-prone repair is primarily regulated after transcription, supporting the current literature. Furthermore, the heterology index (i.e., the binding affinity of LexA to the SOS box) was correlated to the maximum increase in gene expression and not to the time of induction of SOS genes. Finally, quantification of metabolites revealed increasing pyrimidine pools as a late feature of the SOS response. Our results elucidate how the SOS response is coordinated, showing a rapid transcriptional response and temporal regulation of mutagenesis on the protein and metabolite levels.

SARS-CoV-2 infection modifies the transcriptome of the megakaryocytes in the bone marrow.

Megakaryocytes, integral to platelet production, predominantly reside in the bone marrow and undergo regulated fragmentation within sinusoid vessels to release platelets into the bloodstream. Inflammatory states and infections influence megakaryocyte transcription, potentially affecting platelet functionality. Notably, COVID-19 has been associated with altered platelet transcriptomes. In this study, we investigated the hypothesis that SARS-CoV-2 infection could impact the transcriptome of bone marrow megakaryocytes. Utilizing spatial transcriptomics to discriminate subpopulations of megakaryocytes based on proximity to bone marrow sinusoids, we identified approximately 19,000 genes in megakaryocytes. Machine learning techniques revealed that the transcriptome of healthy murine bone marrow megakaryocytes exhibited minimal differences based on proximity to sinusoid vessels. Further, at peak SARS-CoV-2 viremia, when the disease primarily affected the lungs, megakaryocytes were not significantly different from those from healthy mice. Conversely, a significant divergence in the megakaryocyte transcriptome was observed during systemic inflammation, although SARS-CoV-2 RNA was never detected in bone marrow and it was no longer detectable in the lungs. Under these conditions, the megakaryocyte transcriptional landscape was enriched in pathways associated with histone modifications, megakaryocyte differentiation, NETosis, and autoimmunity, which could not be explained by cell proximity to sinusoid vessels. Notably, the type-I interferon signature and calprotectin (S100A8/A9) were not induced in megakaryocytes under any condition. However, inflammatory cytokines induced in the blood and lungs of COVID-19 mice were different from those found in the bone marrow, suggesting a discriminating impact of inflammation on this specific subset of cells. Collectively, our data indicate that a new population of bone marrow megakaryocytes may emerge through COVID-19-related pathogenesis.

Transcriptomic Analysis of Mineralized Adipose-Derived Stem Cell Tissues for Calcific Valve Disease Modelling.

Calcific aortic valve disease (CAVD) is characterized by the fibrosis and mineralization of the aortic valve, which leads to aortic stenosis and heart failure. At the cellular level, this is due to the osteoblastic-like differentiation of valve interstitial cells (VICs), resulting in the calcification of the tissue. Unfortunately, human VICs are not readily available to study CAVD pathogenesis and the implicated mechanisms in vitro; however, adipose-derived stromal/stem cells (ASCs), carrying the patient's specific genomic features, have emerged as a promising cell source to model cardiovascular diseases due to their multipotent nature, availability, and patient-specific characteristics. In this study, we describe a comprehensive transcriptomic analysis of tissue-engineered, scaffold-free, ASC-embedded mineralized tissue sheets using bulk RNA sequencing. Bioinformatic and gene set enrichment analyses revealed the up-regulation of genes associated with the organization of the extracellular matrix (ECM), suggesting that the ECM could play a vital role in the enhanced mineralization observed in these tissue-engineered ASC-embedded sheets. Upon comparison with publicly available gene expression datasets from CAVD patients, striking similarities emerged regarding cardiovascular diseases and ECM functions, suggesting a potential link between ECM gene expression and CAVDs pathogenesis. A matrisome-related sub-analysis revealed the ECM microenvironment promotes the transcriptional activation of the master gene runt-related transcription factor 2 (RUNX2), which is essential in CAVD development. Tissue-engineered ASC-embedded sheets with enhanced mineralization could be a valuable tool for research and a promising avenue for the identification of more effective aortic valve replacement therapies.

Loss of testosterone induces postprandial insulin resistance and increases the expression of the hepatic antioxidant flavin-containing monooxygenases in mice exposed to intermittent hypoxia.

AIM: We tested the hypothesis that low testosterone alters the effects of intermittent hypoxia (IH) on glucose homeostasis, hepatic oxidative stress, and transcriptomic profile in male mice. METHODS: We used sham-operated or orchiectomized (ORX) mice exposed to normoxia (Nx) or IH for 2 weeks. We performed fasting insulin and glucose tolerance tests and assessed fasting and postprandial insulin resistance with the HOMA-IR. The activity of hepatic prooxidant (NADPH oxidase-NOX), antioxidant enzymes (superoxide dismutase, catalase, and glutathione peroxidase-SOD, Cat, GPx), lipid peroxidation (MDA concentration), and the total concentration of glutathione (GSH) were measured under postprandial conditions. mRNA sequencing and pathway enrichment analyses were used to identify hepatic genes underlying the interactions between IH and testosterone. RESULTS: In Sham mice, IH improves fasting insulin sensitivity and glucose tolerance, while there are no effects of IH in ORX mice. In ORX mice, IH induces postprandial hyperinsulinemia, insulin resistance, and a prooxidant profile of enzyme activity (low SOD activity) without altering hepatic MDA and GSH content. ORX and IH altered the expression of genes involved in oxidoreductase activities, cytochromes-dependent pathways, and glutathione metabolism. Among the genes upregulated in ORX-IH mice, the flavin-containing monooxygenases (FMO) are particularly relevant since these are potent hepatic antioxidants that could help prevent overt oxidative stress in ORX-IH mice. CONCLUSION: Low levels of testosterone in male mice exposed to IH induce post-prandial hyperinsulinemia and insulin resistance and determine the mechanisms by which the liver handles IH-induced oxidative stress.

Immune-related transcriptomic and epigenetic reconfiguration in BV2 cells after lipopolysaccharide exposure: an in vitro omics integrative study.

BACKGROUND: Molecular alterations affecting microglia have been consistently associated with the inflammatory response. These cells can have pro- or anti-inflammatory activity, phenotypes thought to be regulated by epigenetic mechanisms. Still, little is known about the details on how epigenetic marks regulate the expression of genes in the context of an inflammatory response. METHODS: Through CUT&RUN, we profiled four genome-wide histone marks (HM) (H3K4me1, H3K4me3, H3K27ac, and H3K27me3) in lipopolysaccharide-exposed cells and compared their distributions to control cells. Transcriptomic profiles were determined through RNA-seq and differentially expressed genes were identified and contrasted with the epigenetic landscapes. Other downstream analyses were also included in this study. RESULTS: Our results illustrate an effectively induced M1 phenotype in microglial cells derived from LPS exposure. We observed differential bound regions associated with the genes classically involved in the inflammatory response in the expected direction according to each histone modification. Consistently, our transcriptomic analysis yielded a conspicuous illustration of the LPS-induced immune activity showing the up-regulation of Nf-κB-induced mRNAs (TNF-α, nfκbiz, nfκbia) and other important genes (Marco, Il-6, etc.). Furthermore, we integrated both omics profiles and identified an important reconfiguration of the genome induced by LPS. The latter was depicted by 8 different chromatin states that changed between conditions and that associated with unique clusters of differentially expressed genes, which not only represented regulatory elements, but also underlined distinct biological functions (inhibition of morphogenesis; changes in metabolism, homeostasis, and cytokine regulation; activation of the inflammatory response). CONCLUSION: This study exhibits important differences in the distribution of histone modifications in treated and control BV2 cells, constituting an epigenetic reconfiguration that leads to the inflammatory response. Also, it highlights the importance of these marks' regulatory role in gene expression and provides possible targets for further studies in the context of inflammation.

Behavioral as well as hippocampal transcriptomic and microglial responses differ across sexes in adult mouse offspring exposed to a dual genetic and environmental challenge.

INTRODUCTION: A wide range of positive, negative, and cognitive symptoms compose the clinical presentation of schizophrenia. Schizophrenia is a multifactorial disorder in which genetic and environmental risk factors interact for a full emergence of the disorder. Infectious challenges during pregnancy are a well-known environmental risk factor for schizophrenia. Also, genetic variants affecting the function of fractalkine signaling between neurons and microglia were linked to schizophrenia. Translational animal models recapitulating these complex gene-environment associations have a great potential to untangle schizophrenia neurobiology and propose new therapeutic strategies. METHODS: Given that genetic variants affecting the function of fractalkine signaling between neurons and microglia were linked to schizophrenia, we compared the outcomes of a well-characterized model of maternal immune activation induced using the viral mimetic polyinosinic:polycytidylic acid (Poly I:C) in wild-type versus fractalkine receptor knockout mice. Possible behavioral and immune alterations were assessed in male and female offspring during adulthood. Considering the role of the hippocampus in schizophrenia, microglial analyses and bulk RNA sequencing were performed within this region to assess the neuroimmune dynamics at play. Males and females were examined separately. RESULTS: Offspring exposed to the dual challenge paradigm exhibited symptoms relevant to schizophrenia and unpredictably to mood disorders. Males displayed social and cognitive deficits related to schizophrenia, while females mainly presented anxiety-like behaviors related to mood disorders. Hippocampal microglia in females exposed to the dual challenge were hypertrophic, indicative of an increased surveillance, whereas those in males showed on the other end of the spectrum blunted morphologies with a reduced phagocytosis. Hippocampal bulk-RNA sequencing further revealed a downregulation in females of genes related to GABAergic transmission, which represents one of the main proposed causes of mood disorders. CONCLUSIONS: Building on previous results, we identified in the current study distinctive behavioral phenotypes in female mice exposed to a dual genetic and environmental challenge, thus proposing a new model of neurodevelopmentally-associated mood and affective symptoms. This paves the way to future sex-specific investigations into the susceptibility to developmental challenges using animal models based on genetic and immune vulnerability as presented here.

Predictive model for vitamin C levels in hyperinsulinemic individuals based on age, sex, waist circumference, low-density lipoprotein, and immune-associated serum proteins.

Vitamin C (ascorbic acid) is an important water-soluble antioxidant associated with decreased oxidative stress in type 2 diabetes (T2D) patients. A previous targeted plasma proteomic study has indicated that ascorbic acid is associated with markers of the immune system in healthy subjects. However, the association between the levels of ascorbic acid and blood biomarkers in subjects at risk of developing T2D is still unknown. Serum ascorbic acid was measured by ultra-performance liquid chromatography and serum proteins were quantified by untargeted liquid-chromatography mass spectrometry in 25 hyperinsulinemia subjects that were randomly assigned a high dairy intake diet or an adequate dairy intake diet for 6 weeks, then crossed-over after a 6-week washout period. Spearman correlation followed by gene ontology analyses were performed to identify biological pathways associated with ascorbic acid. Finally, machine learning analysis was performed to obtain a specific serum protein signature that could predict ascorbic acid levels. After adjustments for waist circumference, LDL, HDL, fasting insulin, fasting blood glucose, age, gender, and dairy intake; serum ascorbic acid correlated positively with different aspects of the immune system. Machine learning analysis indicated that a signature composed of 21 features that included 17 proteins (mainly from the immune system), age, sex, waist circumference, and LDL could predict serum ascorbic acid levels in hyperinsulinemia subjects. In conclusion, the result reveals a correlation as well as modulation between serum ascorbic acid levels and proteins that play vital roles in regulating different aspects of the immune response in individuals at risk of T2D. The development of a predictive signature for ascorbic acid will further help the assessment of ascorbic acid status in clinical settings.

Mass spectrometry in cerebrospinal fluid uncovers association of glycolysis biomarkers with Alzheimer's disease in a large clinical sample.

Alzheimer's disease (AD) is a complex and heterogeneous neurodegenerative disorder with contributions from multiple pathophysiological pathways. One of the long-recognized and important features of AD is disrupted cerebral glucose metabolism, but the underlying molecular basis remains unclear. In this study, unbiased mass spectrometry was used to survey CSF from a large clinical cohort, comparing patients who are either cognitively unimpaired (CU; n = 68), suffering from mild-cognitive impairment or dementia from AD (MCI-AD, n = 95; DEM-AD, n = 72), or other causes (MCI-other, n = 77; DEM-other, n = 23), or Normal Pressure Hydrocephalus (NPH, n = 57). The results revealed changes related to altered glucose metabolism. In particular, two glycolytic enzymes, pyruvate kinase (PKM) and aldolase A (ALDOA), were found to be upregulated in CSF from patients with AD compared to those with other neurological conditions. Increases in full-length PKM and ALDOA levels in CSF were confirmed with immunoblotting. Levels of these enzymes furthermore correlated negatively with CSF glucose in matching CSF samples. PKM levels were also found to be increased in AD in publicly available brain-tissue data. These results indicate that ALDOA and PKM may act as technically-robust potential biomarkers of glucose metabolism dysregulation in AD.

BioDiscViz: A visualization support and consensus signature selector for BioDiscML results.

Machine learning (ML) algorithms are powerful tools to find complex patterns and biomarker signatures when conventional statistical methods fail to identify them. While the ML field made significant progress, state of the art methodologies to build efficient and non-overfitting models are not always applied in the literature. To this purpose, automatic programs, such as BioDiscML, were designed to identify biomarker signatures and correlated features while escaping overfitting using multiple evaluation strategies, such as cross validation, bootstrapping and repeated holdout. To further improve BioDiscML and reach a broader audience, better visualization support and flexibility in choosing the best models and signatures are needed. Thus, to provide researchers with an easily accessible and usable tool for in depth investigation of the results from BioDiscML outputs, we developed a visual interaction tool called BioDiscViz. This tool provides summaries, tables and graphics, in the form of Principal Component Analysis (PCA) plots, UMAP, t-SNE, heatmaps and boxplots for the best model and the correlated features. Furthermore, this tool also provides visual support to extract a consensus signature from BioDiscML models using a combination of filters. BioDiscViz will be a great visual support for research using ML, hence new opportunities in this field by opening it to a broader community.

ASVmaker: A New Tool to Improve Taxonomic Identifications for Amplicon Sequencing Data.

The taxonomic assignment of sequences obtained by high throughput amplicon sequencing poses a limitation for various applications in the biomedical, environmental, and agricultural fields. Identifications are constrained by the length of the obtained sequences and the computational processes employed to efficiently assign taxonomy. Arriving at a consensus is often preferable to uncertain identification for ecological purposes. To address this issue, a new tool called "ASVmaker" has been developed to facilitate the creation of custom databases, thereby enhancing the precision of specific identifications. ASVmaker is specifically designed to generate reference databases for allocating amplicon sequencing data. It uses publicly available reference data and generates specific sequences derived from the primers used to create amplicon sequencing libraries. This versatile tool can complete taxonomic assignments performed with pre-trained classifiers from the SILVA and UNITE databases. Moreover, it enables the generation of comprehensive reference databases for specific genes in cases where no directly applicable database exists for taxonomic classification tools.

Developing a cluster-based approach for deciphering complexity in individuals with neurodevelopmental differences.

Objective: Individuals with neurodevelopmental disorders such as global developmental delay (GDD) present both genotypic and phenotypic heterogeneity. This diversity has hampered developing of targeted interventions given the relative rarity of each individual genetic etiology. Novel approaches to clinical trials where distinct, but related diseases can be treated by a common drug, known as basket trials, which have shown benefits in oncology but have yet to be used in GDD. Nonetheless, it remains unclear how individuals with GDD could be clustered. Here, we assess two different approaches: agglomerative and divisive clustering. Methods: Using the largest cohort of individuals with GDD, which is the Deciphering Developmental Disorders (DDD), characterized using a systematic approach, we extracted genotypic and phenotypic information from 6,588 individuals with GDD. We then used a k-means clustering (divisive) and hierarchical agglomerative clustering (HAC) to identify subgroups of individuals. Next, we extracted gene network and molecular function information with regard to the clusters identified by each approach. Results: HAC based on phenotypes identified in individuals with GDD revealed 16 clusters, each presenting with one dominant phenotype displayed by most individuals in the cluster, along with other minor phenotypes. Among the most common phenotypes reported were delayed speech, absent speech, and seizure. Interestingly, each phenotypic cluster molecularly included several (3-12) gene sub-networks of more closely related genes with diverse molecular function. k-means clustering also segregated individuals harboring those phenotypes, but the genetic pathways identified were different from the ones identified from HAC. Conclusion: Our study illustrates how divisive (k-means) and agglomerative clustering can be used in order to group individuals with GDD for future basket trials. Moreover, the result of our analysis suggests that phenotypic clusters should be subdivided into molecular sub-networks for an increased likelihood of successful treatment. Finally, a combination of both agglomerative and divisive clustering may be required for developing of a comprehensive treatment.

Use of Elasticsearch-based business intelligence tools for integration and visualization of biological data.

The emergence of massive datasets exploring the multiple levels of molecular biology has made their analysis and knowledge transfer more complex. Flexible tools to manage big biological datasets could be of great help for standardizing the usage of developed data visualizations and integration methods. Business intelligence (BI) tools have been used in many fields as exploratory tools. They have numerous connectors to link numerous data repositories with a unified graphic interface, offering an overview of data and facilitating interpretation for decision makers. BI tools could be a flexible and user-friendly way of handling molecular biological data with interactive visualizations. However, it is rather uncommon to see such tools used for the exploration of massive and complex datasets in biological fields. We believe that two main obstacles could be the reason. Firstly, we posit that the way to import data into BI tools are not compatible with biological databases. Secondly, BI tools may not be adapted to certain particularities of complex biological data, namely, the size, the variability of datasets and the availability of specialized visualizations. This paper highlights the use of five BI tools (Elastic Kibana, Siren Investigate, Microsoft Power BI, Salesforce Tableau and Apache Superset) onto which the massive data management repository engine called Elasticsearch is compatible. Four case studies will be discussed in which these BI tools were applied on biological datasets with different characteristics. We conclude that the performance of the tools depends on the complexity of the biological questions and the size of the datasets.

Lipid metabolic reprogramming of hepatic CD4+ T cells during SIV infection.

While liver inflammation is associated with AIDS, little is known so far about hepatic CD4+ T cells. By using the simian immunodeficiency virus (SIV)-infected rhesus macaque (RM) model, we aimed to characterize CD4+ T cells. The phenotype of CD4+ T cells was assessed by flow cytometry from uninfected (n = 3) and infected RMs, with either SIVmac251 (n = 6) or SHIVSF162p3 (n = 6). After cell sorting of hepatic CD4+ T cells, viral DNA quantification and RNA sequencing were performed.Thus, we demonstrated that liver CD4+ T cells strongly expressed the SIV coreceptor, CCR5. We showed that viremia was negatively correlated with the percentage of hepatic effector memory CD4+ T cells. Consistent with viral sensing, inflammatory and interferon gene transcripts were increased. We also highlighted the presence of harmful CD4+ T cells expressing GZMA and members of TGFB that could contribute to fuel inflammation and fibrosis. Whereas RNA sequencing demonstrated activated CD4+ T cells displaying higher levels of mitoribosome and membrane lipid synthesis transcripts, few genes were related to glycolysis and oxidative phosphorylation, which are essential to sustain activated T cells. Furthermore, we observed lower levels of mitochondrial DNA and higher levels of genes associated with damaged organelles (reticulophagy and mitophagy). Altogether, our data revealed that activated hepatic CD4+ T cells are reprogrammed to lipid metabolism. Thus, strategies aiming to reprogram T cell metabolism with effector function could be of interest for controlling viral infection and preventing liver disorders.IMPORTANCEHuman immunodeficiency virus (HIV) infection may cause liver diseases, associated with inflammation and tissue injury, contributing to comorbidity in people living with HIV. Paradoxically, the contribution of hepatic CD4+ T cells remains largely underestimated. Herein, we used the model of simian immunodeficiency virus (SIV)-infected rhesus macaques to access liver tissue. Our work demonstrates that hepatic CD4+ T cells express CCR5, the main viral coreceptor, and are infected. Viral infection is associated with the presence of inflamed and activated hepatic CD4+ T cells expressing cytotoxic molecules. Furthermore, hepatic CD4+ T cells are reprogrammed toward lipid metabolism after SIV infection. Altogether, our findings shed new light on hepatic CD4+ T cell profile that could contribute to liver injury following viral infection.

Sex difference contributes to phenotypic diversity in individuals with neurodevelopmental disorders.

Objective: Gain a better understanding of sex-specific differences in individuals with global developmental delay (GDD), with a focus on phenotypes and genotypes. Methods: Using the Deciphering Developmental Disorders (DDD) dataset, we extracted phenotypic information from 6,588 individuals with GDD and then identified statistically significant variations in phenotypes and genotypes based on sex. We compared genes with pathogenic variants between sex and then performed gene network and molecular function enrichment analysis and gene expression profiling between sex. Finally, we contrasted individuals with autism as an associated condition. Results: We identified significantly differentially expressed phenotypes in males vs. females individuals with GDD. Autism and macrocephaly were significantly more common in males whereas microcephaly and stereotypies were more common in females. Importantly, 66% of GDD genes with pathogenic variants overlapped between both sexes. In the cohort, males presented with only slightly increased X-linked genes (9% vs. 8%, respectively). Individuals from both sexes harbored a similar number of pathogenic variants overall (3) but females presented with a significantly higher load for GDD genes with high intolerance to loss of function. Sex difference in gene expression correlated with genes identified in a sex specific manner. While we identified sex-specific GDD gene mutations, their pathways overlapped. Interestingly, individuals with GDD but also co-morbid autism phenotypes, we observed distinct mutation load, pathways and phenotypic presentation. Conclusion: Our study shows for the first time that males and females with GDD present with significantly different phenotypes. Moreover, while most GDD genes overlapped, some genes were found uniquely in each sex. Surprisingly they shared similar molecular functions. Sorting genes by predicted tolerance to loss of function (pLI) led to identifying an increased mutation load in females with GDD, suggesting potentially a tolerance to GDD genes of higher pLI compared to overall GDD genes. Finally, we show that considering associated conditions (for instance autism) may influence the genomic underpinning found in individuals with GDD and highlight the importance of comprehensive phenotyping.

Glucocorticoid stimulation induces regionalized gene responses within topologically associating domains.

Transcription-factor binding to cis-regulatory regions regulates the gene expression program of a cell, but occupancy is often a poor predictor of the gene response. Here, we show that glucocorticoid stimulation led to the reorganization of transcriptional coregulators MED1 and BRD4 within topologically associating domains (TADs), resulting in active or repressive gene environments. Indeed, we observed a bias toward the activation or repression of a TAD when their activities were defined by the number of regions gaining and losing MED1 and BRD4 following dexamethasone (Dex) stimulation. Variations in Dex-responsive genes at the RNA levels were consistent with the redistribution of MED1 and BRD4 at the associated cis-regulatory regions. Interestingly, Dex-responsive genes without the differential recruitment of MED1 and BRD4 or binding by the glucocorticoid receptor were found within TADs, which gained or lost MED1 and BRD4, suggesting a role of the surrounding environment in gene regulation. However, the amplitude of the response of Dex-regulated genes was higher when the differential recruitment of the glucocorticoid receptor and transcriptional coregulators was observed, reaffirming the role of transcription factor-driven gene regulation and attributing a lesser role to the TAD environment. These results support a model where a signal-induced transcription factor induces a regionalized effect throughout the TAD, redefining the notion of direct and indirect effects of transcription factors on target genes.

Early Diagnosis: End-to-End CNN-LSTM Models for Mass Spectrometry Data Classification.

Liquid chromatography-mass spectrometry (LC-MS) is a powerful method for cell profiling. The use of LC-MS technology is a tool of choice for cancer research since it provides molecular fingerprints of analyzed tissues. However, the ubiquitous presence of noise, the peaks shift between acquisitions, and the huge amount of information owing to the high dimensionality of the data make rapid and accurate cancer diagnosis a challenging task. Deep learning (DL) models are not only effective classifiers but are also well suited to jointly learn feature representation and classification tasks. This is particularly relevant when applied to raw LC-MS data and hence avoid the need for costly preprocessing and complicated feature selection. In this study, we propose a new end-to-end DL methodology that addresses all of the above challenges at once, while preserving the high potential of LC-MS data. Our DL model is designed to early discriminate between tumoral and normal tissues. It is a combination of a convolutional neural network (CNN) and a long short-term memory (LSTM) Network. The CNN network allows for significantly reducing the high dimensionality of the data while learning spatially relevant features. The LSTM network enables our model to capture temporal patterns. We show that our model outperforms not only benchmark models but also state-of-the-art models developed on the same data. Our framework is a promising strategy for improving early cancer detection during a diagnostic process.

Pearl shape classification using deep convolutional neural networks from Tahitian pearl rotation in Pinctada margaritifera.

Tahitian pearls, artificially cultivated from the black-lipped pearl oyster Pinctada margaritifera, are renowned for their unique color and large size, making the pearl industry vital for the French Polynesian economy. Understanding the mechanisms of pearl formation is essential for enabling quality and sustainable production. In this paper, we explore the process of pearl formation by studying pearl rotation. Here we show, using a deep convolutional neural network, a direct link between the rotation of the pearl during its formation in the oyster and its final shape. We propose a new method for non-invasive pearl monitoring and a model for predicting the final shape of the pearl from rotation data with 81.9% accuracy. These novel resources provide a fresh perspective to study and enhance our comprehension of the overall mechanism of pearl formation, with potential long-term applications for improving pearl production and quality control in the industry.