A toolbox of machine learning software to support microbiome analysis.

The human microbiome has become an area of intense research due to its potential impact on human health. However, the analysis and interpretation of this data have proven to be challenging due to its complexity and high dimensionality. Machine learning (ML) algorithms can process vast amounts of data to uncover informative patterns and relationships within the data, even with limited prior knowledge. Therefore, there has been a rapid growth in the development of software specifically designed for the analysis and interpretation of microbiome data using ML techniques. These software incorporate a wide range of ML algorithms for clustering, classification, regression, or feature selection, to identify microbial patterns and relationships within the data and generate predictive models. This rapid development with a constant need for new developments and integration of new features require efforts into compile, catalog and classify these tools to create infrastructures and services with easy, transparent, and trustable standards. Here we review the state-of-the-art for ML tools applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on ML based software and framework resources currently available for the analysis of microbiome data in humans. The aim is to support microbiologists and biomedical scientists to go deeper into specialized resources that integrate ML techniques and facilitate future benchmarking to create standards for the analysis of microbiome data. The software resources are organized based on the type of analysis they were developed for and the ML techniques they implement. A description of each software with examples of usage is provided including comments about pitfalls and lacks in the usage of software based on ML methods in relation to microbiome data that need to be considered by developers and users. This review represents an extensive compilation to date, offering valuable insights and guidance for researchers interested in leveraging ML approaches for microbiome analysis.

FooDrugs: a comprehensive food-drug interactions database with text documents and transcriptional data.

Food-drug interactions (FDIs) occur when a food item alters the pharmacokinetics or pharmacodynamics of a drug. FDIs can be clinically relevant, as they can hamper or enhance the therapeutic effects of a drug and impact both their efficacy and their safety. However, knowledge of FDIs in clinical practice is limited. This is partially due to the lack of resources focused on FDIs. Here, we describe FooDrugs, a database that centralizes FDI knowledge retrieved from two different approaches: a natural processing language pipeline that extracts potential FDIs from scientific documents and clinical trials and a molecular similarity approach based on the comparison of gene expression alterations caused by foods and drugs. FooDrugs database stores a total of 3 430 062 potential FDIs, with 1 108 429 retrieved from scientific documents and 2 321 633 inferred from molecular data. This resource aims to provide researchers and clinicians with a centralized repository for potential FDI information that is free and easy to use. Database URL:  https://zenodo.org/records/8192515 Database DOI:  https://doi.org/10.5281/zenodo.6638469.

Citizen science helps to raise awareness about gut microbiome health in people at risk of developing non-communicable diseases.

Citizens lack knowledge about the impact of gut microbiota on health and how lifestyle and dietary choices can influence it, leading to Non-Communicable Diseases (NCDs) and affecting overall well-being. Participatory action research (PAR) is a promising approach to enhance communication and encourage individuals to adopt healthier behaviors and improve their health. In this study, we explored the feasibility of integrating the photovoice method with citizen science approaches to assess the impact of social and environmental factors on gut microbiota health. In this context, citizen science approaches entailed the involvement of participants in the collection of samples for subsequent analysis, specifically gut microbiome assessment via 16S rRNA gene sequencing. We recruited 70 volunteers and organized six photovoice groups based on age and educational background. Participants selected 64 photographs that represented the influence of daily habits on gut microbiota health and created four photovoice themes. Analysis of the gut microbiome using 16S rRNA gene sequencing identified 474 taxa, and in-depth microbial analysis revealed three clusters of people based on gut microbiome diversity and body mass index (BMI). Our findings indicate that participants enhanced their knowledge of gut microbiome health through PAR activities, and we found a correlation between lower microbial diversity, higher BMI, and better achievement of learning outcomes. Using PAR as a methodology is an effective way to increase citizens' awareness and engagement in self-care, maintain healthy gut microbiota, and prevent NCD development. These interventions are particularly beneficial for individuals at higher risk of developing NCDs.

AI4FoodDB: a database for personalized e-Health nutrition and lifestyle through wearable devices and artificial intelligence.

The increasing prevalence of diet-related diseases calls for an improvement in nutritional advice. Personalized nutrition aims to solve this problem by adapting dietary and lifestyle guidelines to the unique circumstances of each individual. With the latest advances in technology and data science, researchers can now automatically collect and analyze large amounts of data from a variety of sources, including wearable and smart devices. By combining these diverse data, more comprehensive insights of the human body and its diseases can be achieved. However, there are still major challenges to overcome, including the need for more robust data and standardization of methodologies for better subject monitoring and assessment. Here, we present the AI4Food database (AI4FoodDB), which gathers data from a nutritional weight loss intervention monitoring 100 overweight and obese participants during 1 month. Data acquisition involved manual traditional approaches, novel digital methods and the collection of biological samples, obtaining: (i) biological samples at the beginning and the end of the intervention, (ii) anthropometric measurements every 2 weeks, (iii) lifestyle and nutritional questionnaires at two different time points and (iv) continuous digital measurements for 2 weeks. To the best of our knowledge, AI4FoodDB is the first public database that centralizes food images, wearable sensors, validated questionnaires and biological samples from the same intervention. AI4FoodDB thus has immense potential for fostering the advancement of automatic and novel artificial intelligence techniques in the field of personalized care. Moreover, the collected information will yield valuable insights into the relationships between different variables and health outcomes, allowing researchers to generate and test new hypotheses, identify novel biomarkers and digital endpoints, and explore how different lifestyle, biological and digital factors impact health. The aim of this article is to describe the datasets included in AI4FoodDB and to outline the potential that they hold for precision health research. Database URL https://github.com/AI4Food/AI4FoodDB.

A genome-wide computational approach to define microRNA-Polycomb/trithorax gene regulatory circuits in Drosophila.

Characterization of gene regulatory networks is fundamental to understanding homeostatic development. This process can be simplified by analyzing relatively simple genomes such as the genome of Drosophila melanogaster. In this work we have developed a computational framework in Drosophila to explore for the presence of gene regulatory circuits between two large groups of transcriptional regulators: the epigenetic group of the Polycomb/trithorax (PcG/trxG) proteins and the microRNAs (miRNAs). We have searched genome-wide for miRNA targets in PcG/trxG transcripts as well as for Polycomb Response Elements (PREs) in miRNA genes. Our results show that 10% of the analyzed miRNAs could be controlling PcG/trxG gene expression, while 40% of those miRNAs are putatively controlled by the selected set of PcG/trxG proteins. The integration of these analyses has resulted in the predicted existence of 3 classes of miRNA-PcG/trxG crosstalk interactions that define potential regulatory circuits. In the first class, miRNA-PcG circuits are defined by miRNAs that reciprocally crosstalk with PcG. In the second, miRNA-trxG circuits are defined by miRNAs that reciprocally crosstalk with trxG. In the third class, miRNA-PcG/trxG shared circuits are defined by miRNAs that crosstalk with both PcG and trxG regulators. These putative regulatory circuits may uncover a novel mechanism in Drosophila for the control of PcG/trxG and miRNAs levels of expression. The computational framework developed here for Drosophila melanogaster can serve as a model case for similar analyses in other species. Moreover, our work provides, for the first time, a new and useful resource for the Drosophila community to consult prior to experimental studies investigating the epigenetic regulatory networks of miRNA-PcG/trxG mediated gene expression.

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

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

A comprehensive map of microbial biomarkers along the gastrointestinal tract for celiac disease patients.

Dysbiosis of the microbiome has been related to Celiac disease (CeD) progress, an autoimmune disease characterized by gluten intolerance developed in genetically susceptible individuals under certain environmental factors. The microbiome contributes to CeD pathophysiology, modulating the immune response by the action of short-chain fatty acids (SCFA), affecting gut barrier integrity allowing the entrance of gluten-derived proteins, and degrading immunogenic peptides of gluten through endoprolyl peptidase enzymes. Despite the evidence suggesting the implication of gut microbiome over CeD pathogenesis, there is no consensus about the specific microbial changes observed in this pathology. Here, we compiled the largest dataset of 16S prokaryotic ribosomal RNA gene high-throughput sequencing for consensus profiling. We present for the first time an integrative analysis of metataxonomic data from patients with CeD, including samples from different body sites (saliva, pharynx, duodenum, and stool). We found the presence of coordinated changes through the gastrointestinal tract (GIT) characterized by an increase in Actinobacteria species in the upper GIT (pharynx and duodenum) and an increase in Proteobacteria in the lower GIT (duodenum and stool), as well as site-specific changes evidencing a dysbiosis in patients with CeD' microbiota. Moreover, we described the effect of adherence to a gluten-free diet (GFD) evidenced by an increase in beneficial bacteria and a decrease in some Betaproteobacteriales but not fully restoring CeD-related dysbiosis. Finally, we built a Random Forest model to classify patients based on the lower GIT composition achieving good performance.

Transcriptional and Mutational Profiling of B-Other Acute Lymphoblastic Leukemia for Improved Diagnostics.

B-cell precursor acute lymphoblastic leukemia (BCP-ALL) is the most common cancer in children, and significant progress has been made in diagnostics and the treatment of this disease based on the subtypes of BCP-ALL. However, in a large proportion of cases (B-other), recurrent BCP-ALL-associated genomic alterations remain unidentifiable by current diagnostic procedures. In this study, we performed RNA sequencing and analyzed gene fusions, expression profiles, and mutations in diagnostic samples of 185 children with BCP-ALL. Gene expression clustering showed that a subset of B-other samples partially clusters with some of the known subgroups, particularly DUX4-positive. Mutation analysis coupled with gene expression profiling revealed the presence of distinctive BCP-ALL subgroups, characterized by the presence of mutations in known ALL driver genes, e.g., PAX5 and IKZF1. Moreover, we identified novel fusion partners of lymphoid lineage transcriptional factors ETV6, IKZF1 and PAX5. In addition, we report on low blast count detection thresholds and show that the use of EDTA tubes for sample collection does not have adverse effects on sequencing and downstream analysis. Taken together, our findings demonstrate the applicability of whole-transcriptome sequencing for personalized diagnostics in pediatric ALL, including tentative classification of the B-other cases that are difficult to diagnose using conventional methods.

Gut Microbiota Induced by Pterostilbene and Resveratrol in High-Fat-High-Fructose Fed Rats: Putative Role in Steatohepatitis Onset.

Resveratrol and its 2-methoxy derivative pterostilbene are two phenolic compounds that occur in foodstuffs and feature hepato-protective effects. This study is devoted to analysing and comparing the metabolic effects of pterostilbene and resveratrol on gut microbiota composition in rats displaying NAFLD induced by a diet rich in saturated fat and fructose. The associations among changes induced by both phenolic compounds in liver status and those induced in gut microbiota composition were also analysed. For this purpose, fifty Wistar rats were distributed in five experimental groups: a group of animals fed a standard diet (CC group) and four additional groups fed a high-fat high-fructose diet alone (HFHF group) or supplemented with 15 or 30 mg/kg bw/d of pterostilbene (PT15 and PT30 groups, respectively) or 30 mg/kg bw/d of resveratrol (RSV30 group). The dramatic changes induced by high-fat high-fructose feeding in the gut microbiota were poorly ameliorated by pterostilbene or resveratrol. These results suggest that the specific changes in microbiota composition induced by pterostilbene (increased abundances of Akkermansia and Erysipelatoclostridium, and lowered abundance of Clostridum sensu stricto 1) may not entirely explain the putative preventive effects on steatohepatitis.

Mutational mechanisms shaping the coding and noncoding genome of germinal center derived B-cell lymphomas.

B cells have the unique property to somatically alter their immunoglobulin (IG) genes by V(D)J recombination, somatic hypermutation (SHM) and class-switch recombination (CSR). Aberrant targeting of these mechanisms is implicated in lymphomagenesis, but the mutational processes are poorly understood. By performing whole genome and transcriptome sequencing of 181 germinal center derived B-cell lymphomas (gcBCL) we identified distinct mutational signatures linked to SHM and CSR. We show that not only SHM, but presumably also CSR causes off-target mutations in non-IG genes. Kataegis clusters with high mutational density mainly affected early replicating regions and were enriched for SHM- and CSR-mediated off-target mutations. Moreover, they often co-occurred in loci physically interacting in the nucleus, suggesting that mutation hotspots promote increased mutation targeting of spatially co-localized loci (termed hypermutation by proxy). Only around 1% of somatic small variants were in protein coding sequences, but in about half of the driver genes, a contribution of B-cell specific mutational processes to their mutations was found. The B-cell-specific mutational processes contribute to both lymphoma initiation and intratumoral heterogeneity. Overall, we demonstrate that mutational processes involved in the development of gcBCL are more complex than previously appreciated, and that B cell-specific mutational processes contribute via diverse mechanisms to lymphomagenesis.

Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment.

The number of microbiome-related studies has notably increased the availability of data on human microbiome composition and function. These studies provide the essential material to deeply explore host-microbiome associations and their relation to the development and progression of various complex diseases. Improved data-analytical tools are needed to exploit all information from these biological datasets, taking into account the peculiarities of microbiome data, i.e., compositional, heterogeneous and sparse nature of these datasets. The possibility of predicting host-phenotypes based on taxonomy-informed feature selection to establish an association between microbiome and predict disease states is beneficial for personalized medicine. In this regard, machine learning (ML) provides new insights into the development of models that can be used to predict outputs, such as classification and prediction in microbiology, infer host phenotypes to predict diseases and use microbial communities to stratify patients by their characterization of state-specific microbial signatures. Here we review the state-of-the-art ML methods and respective software applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on the application of ML in microbiome studies related to association and clinical use for diagnostics, prognostics, and therapeutics. Although the data presented here is more related to the bacterial community, many algorithms could be applied in general, regardless of the feature type. This literature and software review covering this broad topic is aligned with the scoping review methodology. The manual identification of data sources has been complemented with: (1) automated publication search through digital libraries of the three major publishers using natural language processing (NLP) Toolkit, and (2) an automated identification of relevant software repositories on GitHub and ranking of the related research papers relying on learning to rank approach.

Combined MEK and PI3K/p110ß Inhibition as a Novel Targeted Therapy for Malignant Mesothelioma Displaying Sarcomatoid Features.

Among malignant mesotheliomas (MM), the sarcomatoid subtype is associated with higher chemoresistance and worst survival. Due to its low incidence, there has been little progress in the knowledge of the molecular mechanisms associated with sarcomatoid MM, which might help to define novel therapeutic targets. In this work, we show that loss of PTEN expression is frequent in human sarcomatoid MM and PTEN expression levels are lower in sarcomatoid MM than in the biphasic and epithelioid subtypes. Combined Pten and Trp53 deletion in mouse mesothelium led to nonepithelioid MM development. In Pten;Trp53-null mice developing MM, the Gαi2-coupled receptor subunit activated MEK/ERK and PI3K, resulting in aggressive, immune-suppressed tumors. Combined inhibition of MEK and p110ß/PI3K reduced mouse tumor cell growth in vitro. Therapeutic inhibition of MEK and p110ß/PI3K using selumetinib (AZD6244, ARRY-142886) and AZD8186, two drugs that are currently in clinical trials, increased the survival of Pten;Trp53-null mice without major toxicity. This drug combination effectively reduced the proliferation of primary cultures of human pleural (Pl) MM, implicating nonepithelioid histology and high vimentin, AKT1/2, and Gαi2 expression levels as predictive markers of response to combined MEK and p110ß/PI3K inhibition. Our findings provide a rationale for the use of selumetinib and AZD8186 in patients with MM with sarcomatoid features. This constitutes a novel targeted therapy for a poor prognosis and frequently chemoresistant group of patients with MM, for whom therapeutic options are currently lacking. SIGNIFICANCE: Mesothelioma is highly aggressive; its sarcomatoid variants have worse prognosis. Building on a genetic mouse model, a novel combination therapy is uncovered that is relevant to human tumors.

Prevalence of depression in patients with type 2 diabetes mellitus in Spain (the DIADEMA Study) : results from the MADIABETES cohort.

OBJECTIVE: To estimate the prevalence of depression in patients diagnosed with type 2 diabetes mellitus (T2DM), and to identify sociodemographic, clinical and psychological factors associated with depression in this population. Additionally, we examine the annual incidence rate of depression among patients with T2DM. METHODS: We performed a large prospective cohort study of patients with T2DM from the Madrid Diabetes Study. The first recruitment drive included 3443 patients. The second recruitment drive included 727 new patients. Data have been collected since 2007 (baseline visit) and annually during the follow-up period (since 2008). RESULTS: Depression was prevalent in 20.03% of patients (n=592; 95% CI 18.6% to 21.5%) and was associated with previous personal history of depression (OR 6.482; 95% CI 5.138 to 8.178), mental health status below mean (OR 1.423; 95% CI 1.452 to 2.577), neuropathy (OR 1.951; 95% CI 1.423 to 2.674), fair or poor self-reported health status (OR 1.509; 95% CI 1.209 to 1.882), treatment with oral antidiabetic agents plus insulin (OR 1.802; 95% CI 1.364 to 2.380), female gender (OR 1.333; 95% CI 1.009 to 1.761) and blood cholesterol level (OR 1.005; 95% CI 1.002 to 1.009). The variables inversely associated with depression were: being in employment (OR 0.595; 95% CI 0.397 to 0.894), low physical activity (OR 0.552; 95% CI 0.408 to 0.746), systolic blood pressure (OR 0.982; 95% CI 0.971 to 0.992) and social support (OR 0.978; 95% CI 0.963 to 0.993). In patients without depression at baseline, the incidence of depression after 1 year of follow-up was 1.20% (95% CI 1.11% to 2.81%). CONCLUSIONS: Depression is very prevalent among patients with T2DM and is associated with several key diabetes-related outcomes. Our results suggest that previous mental status, self-reported health status, gender and several diabetes-related complications are associated with differences in the degree of depression. These findings should alert practitioners to the importance of detecting depression in patients with T2DM.

Dynamics of Transcription Regulation in Human Bone Marrow Myeloid Differentiation to Mature Blood Neutrophils.

Neutrophils are short-lived blood cells that play a critical role in host defense against infections. To better comprehend neutrophil functions and their regulation, we provide a complete epigenetic overview, assessing important functional features of their differentiation stages from bone marrow-residing progenitors to mature circulating cells. Integration of chromatin modifications, methylation, and transcriptome dynamics reveals an enforced regulation of differentiation, for cellular functions such as release of proteases, respiratory burst, cell cycle regulation, and apoptosis. We observe an early establishment of the cytotoxic capability, while the signaling components that activate these antimicrobial mechanisms are transcribed at later stages, outside the bone marrow, thus preventing toxic effects in the bone marrow niche. Altogether, these data reveal how the developmental dynamics of the chromatin landscape orchestrate the daily production of a large number of neutrophils required for innate host defense and provide a comprehensive overview of differentiating human neutrophils.

Transcriptional regulation by NR5A2 links differentiation and inflammation in the pancreas.

Chronic inflammation increases the risk of developing one of several types of cancer. Inflammatory responses are currently thought to be controlled by mechanisms that rely on transcriptional networks that are distinct from those involved in cell differentiation. The orphan nuclear receptor NR5A2 participates in a wide variety of processes, including cholesterol and glucose metabolism in the liver, resolution of endoplasmic reticulum stress, intestinal glucocorticoid production, pancreatic development and acinar differentiation. In genome-wide association studies, single nucleotide polymorphisms in the vicinity of NR5A2 have previously been associated with the risk of pancreatic adenocarcinoma. In mice, Nr5a2 heterozygosity sensitizes the pancreas to damage, impairs regeneration and cooperates with mutant Kras in tumour progression. Here, using a global transcriptomic analysis, we describe an epithelial-cell-autonomous basal pre-inflammatory state in the pancreas of Nr5a2+/- mice that is reminiscent of the early stages of pancreatitis-induced inflammation and is conserved in histologically normal human pancreases with reduced expression of NR5A2 mRNA. In Nr5a2+/-mice, NR5A2 undergoes a marked transcriptional switch, relocating from differentiation-specific to inflammatory genes and thereby promoting gene transcription that is dependent on the AP-1 transcription factor. Pancreatic deletion of Jun rescues the pre-inflammatory phenotype, as well as binding of NR5A2 to inflammatory gene promoters and the defective regenerative response to damage. These findings support the notion that, in the pancreas, the transcriptional networks involved in differentiation-specific functions also suppress inflammatory programmes. Under conditions of genetic or environmental constraint, these networks can be subverted to foster inflammation.

c-Myc downregulation is required for preacinar to acinar maturation and pancreatic homeostasis.

BACKGROUND AND AIMS: c-Myc is highly expressed in pancreatic multipotent progenitor cells (MPC) and in pancreatic cancer. The transition from MPC to unipotent acinar progenitors is associated with c-Myc downregulation; a role for c-Myc in this process, and its possible relationship to a role in cancer, has not been established. DESIGN: Using coimmunoprecipitation assays, we demonstrate that c-Myc and Ptf1a interact. Using reverse transcriptase qPCR, western blot and immunofluorescence, we show the erosion of the acinar programme. To analyse the genomic distribution of c-Myc and Ptf1a and the global transcriptomic profile, we used ChIP-seq and RNA-seq, respectively; validation was performed with ChIP-qPCR and RT-qPCR. Lineage-tracing experiments were used to follow the effect of c-Myc overexpression in preacinar cells on acinar differentiation. RESULTS: c-Myc binds and represses the transcriptional activity of Ptf1a. c-Myc overexpression in preacinar cells leads to a massive erosion of differentiation. In adult Ela1-Myc mice: (1) c-Myc binds to Ptf1a, and Tcf3 is downregulated; (2) Ptf1a and c-Myc display partially overlapping chromatin occupancy but do not bind the same E-boxes; (3) at the proximal promoter of genes coding for digestive enzymes, we find reduced PTF1 binding and increased levels of repressive chromatin marks and PRC2 complex components. Lineage tracing of committed acinar precursors reveals that c-Myc overexpression does not restore multipotency but allows the persistence of a preacinar-like cell population. In addition, mutant KRas can lead to c-Myc overexpression and acinar dysregulation. CONCLUSIONS: c-Myc repression during development is crucial for the maturation of preacinar cells, and c-Myc overexpression can contribute to pancreatic carcinogenesis through the induction of a dedifferentiated state.

Automatic identification of informative regions with epigenomic changes associated to hematopoiesis.

Hematopoiesis is one of the best characterized biological systems but the connection between chromatin changes and lineage differentiation is not yet well understood. We have developed a bioinformatic workflow to generate a chromatin space that allows to classify 42 human healthy blood epigenomes from the BLUEPRINT, NIH ROADMAP and ENCODE consortia by their cell type. This approach let us to distinguish different cells types based on their epigenomic profiles, thus recapitulating important aspects of human hematopoiesis. The analysis of the orthogonal dimension of the chromatin space identify 32,662 chromatin determinant regions (CDRs), genomic regions with different epigenetic characteristics between the cell types. Functional analysis revealed that these regions are linked with cell identities. The inclusion of leukemia epigenomes in the healthy hematological chromatin sample space gives us insights on the healthy cell types that are more epigenetically similar to the disease samples. Further analysis of tumoral epigenetic alterations in hematopoietic CDRs points to sets of genes that are tightly regulated in leukemic transformations and commonly mutated in other tumors. Our method provides an analytical approach to study the relationship between epigenomic changes and cell lineage differentiation. Method availability: https://github.com/david-juan/ChromDet.

Wolf-Hirschhorn Syndrome Candidate 1 Is Necessary for Correct Hematopoietic and B Cell Development.

Immunodeficiency is one of the most important causes of mortality associated with Wolf-Hirschhorn syndrome (WHS), a severe rare disease originated by a deletion in chromosome 4p. The WHS candidate 1 (WHSC1) gene has been proposed as one of the main genes responsible for many of the alterations in WHS, but its mechanism of action is still unknown. Here, we present in vivo genetic evidence showing that Whsc1 plays an important role at several points of hematopoietic development. Particularly, our results demonstrate that both differentiation and function of Whsc1-deficient B cells are impaired at several key developmental stages due to profound molecular defects affecting B cell lineage specification, commitment, fitness, and proliferation, demonstrating a causal role for WHSC1 in the immunodeficiency of WHS patients.

Genome-wide analysis of differential transcriptional and epigenetic variability across human immune cell types.

BACKGROUND: A healthy immune system requires immune cells that adapt rapidly to environmental challenges. This phenotypic plasticity can be mediated by transcriptional and epigenetic variability. RESULTS: We apply a novel analytical approach to measure and compare transcriptional and epigenetic variability genome-wide across CD14+CD16- monocytes, CD66b+CD16+ neutrophils, and CD4+CD45RA+ naïve T cells from the same 125 healthy individuals. We discover substantially increased variability in neutrophils compared to monocytes and T cells. In neutrophils, genes with hypervariable expression are found to be implicated in key immune pathways and are associated with cellular properties and environmental exposure. We also observe increased sex-specific gene expression differences in neutrophils. Neutrophil-specific DNA methylation hypervariable sites are enriched at dynamic chromatin regions and active enhancers. CONCLUSIONS: Our data highlight the importance of transcriptional and epigenetic variability for the key role of neutrophils as the first responders to inflammatory stimuli. We provide a resource to enable further functional studies into the plasticity of immune cells, which can be accessed from: http://blueprint-dev.bioinfo.cnio.es/WP10/hypervariability .