Integrated Proteogenomic Characterization across Major Histological Types of Pediatric Brain Cancer.

We report a comprehensive proteogenomics analysis, including whole-genome sequencing, RNA sequencing, and proteomics and phosphoproteomics profiling, of 218 tumors across 7 histological types of childhood brain cancer: low-grade glioma (n = 93), ependymoma (32), high-grade glioma (25), medulloblastoma (22), ganglioglioma (18), craniopharyngioma (16), and atypical teratoid rhabdoid tumor (12). Proteomics data identify common biological themes that span histological boundaries, suggesting that treatments used for one histological type may be applied effectively to other tumors sharing similar proteomics features. Immune landscape characterization reveals diverse tumor microenvironments across and within diagnoses. Proteomics data further reveal functional effects of somatic mutations and copy number variations (CNVs) not evident in transcriptomics data. Kinase-substrate association and co-expression network analysis identify important biological mechanisms of tumorigenesis. This is the first large-scale proteogenomics analysis across traditional histological boundaries to uncover foundational pediatric brain tumor biology and inform rational treatment selection.

Elucidating Compound Mechanism of Action by Network Perturbation Analysis.

Genome-wide identification of the mechanism of action (MoA) of small-molecule compounds characterizing their targets, effectors, and activity modulators represents a highly relevant yet elusive goal, with critical implications for assessment of compound efficacy and toxicity. Current approaches are labor intensive and mostly limited to elucidating high-affinity binding target proteins. We introduce a regulatory network-based approach that elucidates genome-wide MoA proteins based on the assessment of the global dysregulation of their molecular interactions following compound perturbation. Analysis of cellular perturbation profiles identified established MoA proteins for 70% of the tested compounds and elucidated novel proteins that were experimentally validated. Finally, unknown-MoA compound analysis revealed altretamine, an anticancer drug, as an inhibitor of glutathione peroxidase 4 lipid repair activity, which was experimentally confirmed, thus revealing unexpected similarity to the activity of sulfasalazine. This suggests that regulatory network analysis can provide valuable mechanistic insight into the elucidation of small-molecule MoA and compound similarity.

Programmed cell senescence during mammalian embryonic development.

Cellular senescence disables proliferation in damaged cells, and it is relevant for cancer and aging. Here, we show that senescence occurs during mammalian embryonic development at multiple locations, including the mesonephros and the endolymphatic sac of the inner ear, which we have analyzed in detail. Mechanistically, senescence in both structures is strictly dependent on p21, but independent of DNA damage, p53, or other cell-cycle inhibitors, and it is regulated by the TGF-ß/SMAD and PI3K/FOXO pathways. Developmentally programmed senescence is followed by macrophage infiltration, clearance of senescent cells, and tissue remodeling. Loss of senescence due to the absence of p21 is partially compensated by apoptosis but still results in detectable developmental abnormalities. Importantly, the mesonephros and endolymphatic sac of human embryos also show evidence of senescence. We conclude that the role of developmentally programmed senescence is to promote tissue remodeling and propose that this is the evolutionary origin of damage-induced senescence.

PanDrugs2: prioritizing cancer therapies using integrated individual multi-omics data.

Genomics studies routinely confront researchers with long lists of tumor alterations detected in patients. Such lists are difficult to interpret since only a minority of the alterations are relevant biomarkers for diagnosis and for designing therapeutic strategies. PanDrugs is a methodology that facilitates the interpretation of tumor molecular alterations and guides the selection of personalized treatments. To do so, PanDrugs scores gene actionability and drug feasibility to provide a prioritized evidence-based list of drugs. Here, we introduce PanDrugs2, a major upgrade of PanDrugs that, in addition to somatic variant analysis, supports a new integrated multi-omics analysis which simultaneously combines somatic and germline variants, copy number variation and gene expression data. Moreover, PanDrugs2 now considers cancer genetic dependencies to extend tumor vulnerabilities providing therapeutic options for untargetable genes. Importantly, a novel intuitive report to support clinical decision-making is generated. PanDrugs database has been updated, integrating 23 primary sources that support >74K drug-gene associations obtained from 4642 genes and 14 659 unique compounds. The database has also been reimplemented to allow semi-automatic updates to facilitate maintenance and release of future versions. PanDrugs2 does not require login and is freely available at https://www.pandrugs.org/.

The Peptidoglycan Recognition Protein 1 confers immune evasive properties on pancreatic cancer stem cells.

OBJECTIVE: Pancreatic ductal adenocarcinoma (PDAC) has limited therapeutic options, particularly with immune checkpoint inhibitors. Highly chemoresistant 'stem-like' cells, known as cancer stem cells (CSCs), are implicated in PDAC aggressiveness. Thus, comprehending how this subset of cells evades the immune system is crucial for advancing novel therapies. DESIGN: We used the KPC mouse model (LSL-KrasG12D/+; LSL-Trp53R172H/+; Pdx-1-Cre) and primary tumour cell lines to investigate putative CSC populations. Transcriptomic analyses were conducted to pinpoint new genes involved in immune evasion. Overexpressing and knockout cell lines were established with lentiviral vectors. Subsequent in vitro coculture assays, in vivo mouse and zebrafish tumorigenesis studies, and in silico database approaches were performed. RESULTS: Using the KPC mouse model, we functionally confirmed a population of cells marked by EpCAM, Sca-1 and CD133 as authentic CSCs and investigated their transcriptional profile. Immune evasion signatures/genes, notably the gene peptidoglycan recognition protein 1 (PGLYRP1), were significantly overexpressed in these CSCs. Modulating PGLYRP1 impacted CSC immune evasion, affecting their resistance to macrophage-mediated and T-cell-mediated killing and their tumourigenesis in immunocompetent mice. Mechanistically, tumour necrosis factor alpha (TNFα)-regulated PGLYRP1 expression interferes with the immune tumour microenvironment (TME) landscape, promoting myeloid cell-derived immunosuppression and activated T-cell death. Importantly, these findings were not only replicated in human models, but clinically, secreted PGLYRP1 levels were significantly elevated in patients with PDAC. CONCLUSIONS: This study establishes PGLYRP1 as a novel CSC-associated marker crucial for immune evasion, particularly against macrophage phagocytosis and T-cell killing, presenting it as a promising target for PDAC immunotherapy.

Mathematical Modeling of Mating Probability and Fertile Egg Production in Helminth Parasites.

In this work, we obtained a general formulation for the mating probability and fertile egg production in helminth parasites, focusing on the reproductive behavior of polygamous parasites and its implications for transmission dynamics. By exploring various reproductive variables in parasites with density-dependent fecundity, such as helminth parasites, we departed from the traditional assumptions of Poisson and negative binomial distributions to adopt an arbitrary distribution model. Our analysis considered critical factors such as mating probability, fertile egg production, and the distribution of female and male parasites among hosts, whether they are distributed together or separately. We show that the distribution of parasites within hosts significantly influences transmission dynamics, with implications for parasite persistence and, therefore, with implications in parasite control. Using statistical models and empirical data from Monte Carlo simulations, we provide insights into the complex interplay of reproductive variables in helminth parasites, enhancing our understanding of parasite dynamics and the transmission of parasitic diseases.

Somatic structural variation targets neurodevelopmental genes and identifies SHANK2 as a tumor suppressor in neuroblastoma.

Neuroblastoma is a malignancy of the developing sympathetic nervous system that accounts for 12% of childhood cancer deaths. Like many childhood cancers, neuroblastoma shows a relative paucity of somatic single-nucleotide variants (SNVs) and small insertions and deletions (indels) compared to adult cancers. Here, we assessed the contribution of somatic structural variation (SV) in neuroblastoma using a combination of whole-genome sequencing (WGS) of tumor-normal pairs (n = 135) and single-nucleotide polymorphism (SNP) genotyping of primary tumors (n = 914). Our study design allowed for orthogonal validation and replication across platforms. SV frequency, type, and localization varied significantly among high-risk tumors. MYCN nonamplified high-risk tumors harbored an increased SV burden overall, including a significant excess of tandem duplication events across the genome. Genes disrupted by SV breakpoints were enriched in neuronal lineages and associated with phenotypes such as autism spectrum disorder (ASD). The postsynaptic adapter protein-coding gene, SHANK2, located on Chromosome 11q13, was disrupted by SVs in 14% of MYCN nonamplified high-risk tumors based on WGS and 10% in the SNP array cohort. Expression of SHANK2 was low across human-derived neuroblastoma cell lines and high-risk neuroblastoma tumors. Forced expression of SHANK2 in neuroblastoma cells resulted in significant growth inhibition (P = 2.6 × 10-2 to 3.4 × 10-5) and accelerated neuronal differentiation following treatment with all-trans retinoic acid (P = 3.1 × 10-13 to 2.4 × 10-30). These data further define the complex landscape of somatic structural variation in neuroblastoma and suggest that events leading to deregulation of neurodevelopmental processes, such as inactivation of SHANK2, are key mediators of tumorigenesis in this childhood cancer.

bollito: a flexible pipeline for comprehensive single-cell RNA-seq analyses.

SUMMARY: bollito is an automated, flexible and parallelizable computational pipeline for the comprehensive analysis of single-cell RNA-seq data. Starting from FASTQ files or preprocessed expression matrices, bollito performs both basic and advanced tasks in single-cell analysis integrating >30 state-of-the-art tools. This includes quality control, read alignment, dimensionality reduction, clustering, cell-marker detection, differential expression, functional analysis, trajectory inference and RNA velocity. bollito is built using the Snakemake workflow management system, which easily connects each execution step and facilitates the reproducibility of results. bollito's modular design makes it easy to incorporate other packages into the pipeline enabling its expansion with new functionalities. AVAILABILITY AND IMPLEMENTATION: Source code is freely available at https://gitlab.com/bu_cnio/bollito under the MIT license. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A Braf kinase-inactive mutant induces lung adenocarcinoma.

The initiating oncogenic event in almost half of human lung adenocarcinomas is still unknown, a fact that complicates the development of selective targeted therapies. Yet these tumours harbour a number of alterations without obvious oncogenic function including BRAF-inactivating mutations. Inactivating BRAF mutants in lung predominate over the activating V600E mutant that is frequently observed in other tumour types. Here we demonstrate that the expression of an endogenous Braf(D631A) kinase-inactive isoform in mice (corresponding to the human BRAF(D594A) mutation) triggers lung adenocarcinoma in vivo, indicating that BRAF-inactivating mutations are initiating events in lung oncogenesis. Moreover, inactivating BRAF mutations have also been identified in a subset of KRAS-driven human lung tumours. Co-expression of Kras(G12V) and Braf(D631A) in mouse lung cells markedly enhances tumour initiation, a phenomenon mediated by Craf kinase activity, and effectively accelerates tumour progression when activated in advanced lung adenocarcinomas. We also report a key role for the wild-type Braf kinase in sustaining Kras(G12V)/Braf(D631A)-driven tumours. Ablation of the wild-type Braf allele prevents the development of lung adenocarcinoma by inducing a further increase in MAPK signalling that results in oncogenic toxicity; this effect can be abolished by pharmacological inhibition of Mek to restore tumour growth. However, the loss of wild-type Braf also induces transdifferentiation of club cells, which leads to the rapid development of lethal intrabronchiolar lesions. These observations indicate that the signal intensity of the MAPK pathway is a critical determinant not only in tumour development, but also in dictating the nature of the cancer-initiating cell and ultimately the resulting tumour phenotype.

Tumor regression and resistance mechanisms upon CDK4 and RAF1 inactivation in KRAS/P53 mutant lung adenocarcinomas.

KRAS mutant lung adenocarcinomas remain intractable for targeted therapies. Genetic interrogation of KRAS downstream effectors, including the MAPK pathway and the interphase CDKs, identified CDK4 and RAF1 as the only targets whose genetic inactivation induces therapeutic responses without causing unacceptable toxicities. Concomitant CDK4 inactivation and RAF1 ablation prevented tumor progression and induced complete regression in 25% of KRAS/p53-driven advanced lung tumors, yet a significant percentage of those tumors that underwent partial regression retained a population of CDK4/RAF1-resistant cells. Characterization of these cells revealed two independent resistance mechanisms implicating hypermethylation of several tumor suppressors and increased PI3K activity. Importantly, these CDK4/RAF1-resistant cells can be pharmacologically controlled. These studies open the door to new therapeutic strategies to treat KRAS mutant lung cancer, including resistant tumors.

Germline 16p11.2 Microdeletion Predisposes to Neuroblastoma.

Neuroblastoma is a cancer of the developing sympathetic nervous system. It is diagnosed in 600-700 children per year in the United States and accounts for 12% of pediatric cancer deaths. Despite recent advances in our understanding of this malignancy's complex genetic architecture, the contribution of rare germline variants remains undefined. Here, we conducted a genome-wide analysis of large (>500 kb), rare (<1%) germline copy number variants (CNVs) in two independent, multi-ethnic cohorts totaling 5,585 children with neuroblastoma and 23,505 cancer-free control children. We identified a 550-kb deletion on chromosome 16p11.2 significantly enriched in neuroblastoma cases (0.39% of cases and 0.03% of controls; p = 3.34 × 10-9). Notably, this CNV corresponds to a known microdeletion syndrome that affects approximately one in 3,000 children and confers risk for diverse developmental phenotypes including autism spectrum disorder and other neurodevelopmental disorders. The CNV had a substantial impact on neuroblastoma risk, with an odds ratio of 13.9 (95% confidence interval = 5.8-33.4). The association remained significant when we restricted our analysis to individuals of European ancestry in order to mitigate potential confounding by population stratification (0.42% of cases and 0.03% of controls; p = 4.10 × 10-8). We used whole-genome sequencing (WGS) to validate the deletion in paired germline and tumor DNA from 12 cases. Finally, WGS of four parent-child trios revealed that the deletion primarily arose de novo without maternal or paternal bias. This finding expands the clinical phenotypes associated with 16p11.2 microdeletion syndrome to include cancer, and it suggests that disruption of the 16p11.2 region may dysregulate neurodevelopmental pathways that influence both neurological phenotypes and neuroblastoma.

svpluscnv: analysis and visualization of complex structural variation data.

MOTIVATION: Despite widespread prevalence of somatic structural variations (SVs) across most tumor types, understanding of their molecular implications often remains poor. SVs are extremely heterogeneous in size and complexity, hindering the interpretation of their pathogenic role. Tools integrating large SV datasets across platforms are required to fully characterize the cancer's somatic landscape. RESULTS: svpluscnv R package is a swiss army knife for the integration and interpretation of orthogonal datasets including copy number variant segmentation profiles and sequencing-based structural variant calls. The package implements analysis and visualization tools to evaluate chromosomal instability and ploidy, identify genes harboring recurrent SVs and detects complex rearrangements such as chromothripsis and chromoplexia. Further, it allows systematic identification of hot-spot shattered genomic regions, showing reproducibility across alternative detection methods and datasets. AVAILABILITY AND IMPLEMENTATION: https://github.com/ccbiolab/svpluscnv. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

DREIMT: a drug repositioning database and prioritization tool for immunomodulation.

MOTIVATION: Drug immunomodulation modifies the response of the immune system and can be therapeutically exploited in pathologies such as cancer and autoimmune diseases. RESULTS: DREIMT is a new hypothesis-generation web tool, which performs drug prioritization analysis for immunomodulation. DREIMT provides significant immunomodulatory drugs targeting up to 70 immune cells subtypes through a curated database that integrates 4960 drug profiles and ∼2600 immune gene expression signatures. The tool also suggests potential immunomodulatory drugs targeting user-supplied gene expression signatures. Final output includes drug-signature association scores, FDRs and downloadable plots and results tables. AVAILABILITYAND IMPLEMENTATION: http://www.dreimt.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Autoimmune gene expression profiling of fingerstick whole blood in Chronic Fatigue Syndrome.

BACKGROUND: Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a debilitating condition that can lead to severe impairment of physical, psychological, cognitive, social, and occupational functions. The cause of ME/CFS remains incompletely understood. There is no clinical diagnostic test for ME/CFS. Although many therapies have been used off-label to manage symptoms of ME/CFS, there are limited, if any, specific therapies or cure for ME/CFS. In this study, we investigated the expression of genes specific to key immune functions, and viral infection status in ME/CFS patients with an aim of identifying biomarkers for characterization and/or treatment of the disease. METHODS: In 2021, one-hundred and sixty-six (166) patients diagnosed with ME/CFS and 83 healthy controls in the US participated in this study via a social media-based application (app). The patients and heathy volunteers consented to the study and provided self-collected finger-stick blood and first morning void urine samples from home. RNA from the fingerstick blood was tested using DxTerity's 51-gene autoimmune RNA expression panel (AIP). In addition, DNA from the same fingerstick blood sample was extracted to detect viral load of 4 known ME/CFS associated viruses (HHV6, HHV7, CMV and EBV) using a real-time PCR method. RESULTS: Among the 166 ME/CFS participants in the study, approximately half (49%) of the ME/CFS patients reported being house-bound or bedridden due to severe symptoms of the disease. From the AIP testing, ME/CFS patients with severe, bedridden conditions displayed significant increases in gene expression of IKZF2, IKZF3, HSPA8, BACH2, ABCE1 and CD3D, as compared to patients with mild to moderate disease conditions. These six aforementioned genes were further upregulated in the 22 bedridden participants who suffer not only from ME/CFS but also from other autoimmune diseases. These genes are involved in T cell, B cell and autoimmunity functions. Furthermore, IKZF3 (Aiolos) and IKZF2 (Helios), and BACH2 have been implicated in other autoimmune diseases such as systemic lupus erythematosus (SLE) and Rheumatoid Arthritis (RA). Among the 240 participants tested with the viral assays, 9 samples showed positive results (including 1 EBV positive and 8 HHV6 positives). CONCLUSIONS: Our study indicates that gene expression biomarkers may be used in identifying or differentiating subsets of ME/CFS patients having different levels of disease severity. These gene targets may also represent opportunities for new therapeutic modalities for the treatment of ME/CFS. The use of social media engaged patient recruitment and at-home sample collection represents a novel approach for conducting clinical research which saves cost, time and eliminates travel for office visits.

Evidence, and replication thereof, that molecular-genetic and environmental risks for psychosis impact through an affective pathway.

BACKGROUND: There is evidence that environmental and genetic risk factors for schizophrenia spectrum disorders are transdiagnostic and mediated in part through a generic pathway of affective dysregulation. METHODS: We analysed to what degree the impact of schizophrenia polygenic risk (PRS-SZ) and childhood adversity (CA) on psychosis outcomes was contingent on co-presence of affective dysregulation, defined as significant depressive symptoms, in (i) NEMESIS-2 (n = 6646), a representative general population sample, interviewed four times over nine years and (ii) EUGEI (n = 4068) a sample of patients with schizophrenia spectrum disorder, the siblings of these patients and controls. RESULTS: The impact of PRS-SZ on psychosis showed significant dependence on co-presence of affective dysregulation in NEMESIS-2 [relative excess risk due to interaction (RERI): 1.01, p = 0.037] and in EUGEI (RERI = 3.39, p = 0.048). This was particularly evident for delusional ideation (NEMESIS-2: RERI = 1.74, p = 0.003; EUGEI: RERI = 4.16, p = 0.019) and not for hallucinatory experiences (NEMESIS-2: RERI = 0.65, p = 0.284; EUGEI: -0.37, p = 0.547). A similar and stronger pattern of results was evident for CA (RERI delusions and hallucinations: NEMESIS-2: 3.02, p < 0.001; EUGEI: 6.44, p < 0.001; RERI delusional ideation: NEMESIS-2: 3.79, p < 0.001; EUGEI: 5.43, p = 0.001; RERI hallucinatory experiences: NEMESIS-2: 2.46, p < 0.001; EUGEI: 0.54, p = 0.465). CONCLUSIONS: The results, and internal replication, suggest that the effects of known genetic and non-genetic risk factors for psychosis are mediated in part through an affective pathway, from which early states of delusional meaning may arise.

A replication study of JTC bias, genetic liability for psychosis and delusional ideation.

BACKGROUND: This study attempted to replicate whether a bias in probabilistic reasoning, or 'jumping to conclusions'(JTC) bias is associated with being a sibling of a patient with schizophrenia spectrum disorder; and if so, whether this association is contingent on subthreshold delusional ideation. METHODS: Data were derived from the EUGEI project, a 25-centre, 15-country effort to study psychosis spectrum disorder. The current analyses included 1261 patients with schizophrenia spectrum disorder, 1282 siblings of patients and 1525 healthy comparison subjects, recruited in Spain (five centres), Turkey (three centres) and Serbia (one centre). The beads task was used to assess JTC bias. Lifetime experience of delusional ideation and hallucinatory experiences was assessed using the Community Assessment of Psychic Experiences. General cognitive abilities were taken into account in the analyses. RESULTS: JTC bias was positively associated not only with patient status but also with sibling status [adjusted relative risk (aRR) ratio : 4.23 CI 95% 3.46-5.17 for siblings and aRR: 5.07 CI 95% 4.13-6.23 for patients]. The association between JTC bias and sibling status was stronger in those with higher levels of delusional ideation (aRR interaction in siblings: 3.77 CI 95% 1.67-8.51, and in patients: 2.15 CI 95% 0.94-4.92). The association between JTC bias and sibling status was not stronger in those with higher levels of hallucinatory experiences. CONCLUSIONS: These findings replicate earlier findings that JTC bias is associated with familial liability for psychosis and that this is contingent on the degree of delusional ideation but not hallucinations.

Cognitive functioning throughout adulthood and illness stages in individuals with psychotic disorders and their unaffected siblings.

Important questions remain about the profile of cognitive impairment in psychotic disorders across adulthood and illness stages. The age-associated profile of familial impairments also remains unclear, as well as the effect of factors, such as symptoms, functioning, and medication. Using cross-sectional data from the EU-GEI and GROUP studies, comprising 8455 participants aged 18 to 65, we examined cognitive functioning across adulthood in patients with psychotic disorders (n = 2883), and their unaffected siblings (n = 2271), compared to controls (n = 3301). An abbreviated WAIS-III measured verbal knowledge, working memory, visuospatial processing, processing speed, and IQ. Patients showed medium to large deficits across all functions (ES range = -0.45 to -0.73, p < 0.001), while siblings showed small deficits on IQ, verbal knowledge, and working memory (ES = -0.14 to -0.33, p < 0.001). Magnitude of impairment was not associated with participant age, such that the size of impairment in older and younger patients did not significantly differ. However, first-episode patients performed worse than prodromal patients (ES range = -0.88 to -0.60, p < 0.001). Adjusting for cannabis use, symptom severity, and global functioning attenuated impairments in siblings, while deficits in patients remained statistically significant, albeit reduced by half (ES range = -0.13 to -0.38, p < 0.01). Antipsychotic medication also accounted for around half of the impairment in patients (ES range = -0.21 to -0.43, p < 0.01). Deficits in verbal knowledge, and working memory may specifically index familial, i.e., shared genetic and/or shared environmental, liability for psychotic disorders. Nevertheless, potentially modifiable illness-related factors account for a significant portion of the cognitive impairment in psychotic disorders.

Addition of clinoptilolite in the diet reduces uterine PMN leukocytes and open days in multiparous lactating dairy cows managed in a mountain tropical pasture-based system.

This study aimed to assess the effect of adding clinoptilolite in the diet on uterine health and reproductive performance in multiparous lactating dairy cows managed in a tropical pasture-based system above 2500 m of altitude. Seventy-seven multiparous Holstein crossbred cows from two farms were allocated randomly into two groups: clinoptilolite supplemented cows (CLG, n = 42) and non-supplemented cows as control (CG, n = 35). Cows from CLG were supplemented with clinoptilolite from 30 days (50 g/cow/day) before to 60 days after calving (200 g/cow/day). In CLG cows, percentages of uterine PMN leukocytes (P < 0.0001) and proportion of subclinical endometritis (P = 0.0187) were lower than in CG. The interval calving to first corpus luteum was shorter (P = 0.0759) in CLG than CG, and calving to first service interval was similar between treatments. Cows from CLG became pregnant 35 days earlier than CG cows (P = 0.0224). On farm A, calving to conception interval was 18.1 days longer in CLG than in CG (P = 0.3750); in farm B, this interval was 86.2 days shorter in CLG than in CG (P = 0.0002). In conclusion, daily addition of clinoptilolite in the diet decreased the percentage of uterine PMN leukocytes, the proportion of cows with subclinical endometritis, and shortened the calving-conception interval in multiparous lactating dairy cows.

A comprehensive database for integrated analysis of omics data in autoimmune diseases.

BACKGROUND: Autoimmune diseases are heterogeneous pathologies with difficult diagnosis and few therapeutic options. In the last decade, several omics studies have provided significant insights into the molecular mechanisms of these diseases. Nevertheless, data from different cohorts and pathologies are stored independently in public repositories and a unified resource is imperative to assist researchers in this field. RESULTS: Here, we present Autoimmune Diseases Explorer ( https://adex.genyo.es ), a database that integrates 82 curated transcriptomics and methylation studies covering 5609 samples for some of the most common autoimmune diseases. The database provides, in an easy-to-use environment, advanced data analysis and statistical methods for exploring omics datasets, including meta-analysis, differential expression or pathway analysis. CONCLUSIONS: This is the first omics database focused on autoimmune diseases. This resource incorporates homogeneously processed data to facilitate integrative analyses among studies.

Precision medicine needs pioneering clinical bioinformaticians.

Success in precision medicine depends on accessing high-quality genetic and molecular data from large, well-annotated patient cohorts that couple biological samples to comprehensive clinical data, which in conjunction can lead to effective therapies. From such a scenario emerges the need for a new professional profile, an expert bioinformatician with training in clinical areas who can make sense of multi-omics data to improve therapeutic interventions in patients, and the design of optimized basket trials. In this review, we first describe the main policies and international initiatives that focus on precision medicine. Secondly, we review the currently ongoing clinical trials in precision medicine, introducing the concept of 'precision bioinformatics', and we describe current pioneering bioinformatics efforts aimed at implementing tools and computational infrastructures for precision medicine in health institutions around the world. Thirdly, we discuss the challenges related to the clinical training of bioinformaticians, and the urgent need for computational specialists capable of assimilating medical terminologies and protocols to address real clinical questions. We also propose some skills required to carry out common tasks in clinical bioinformatics and some tips for emergent groups. Finally, we explore the future perspectives and the challenges faced by precision medicine bioinformatics.