SysInflam HuDB, a Web Resource for Mining Human Blood Cells Transcriptomic Data Associated with Systemic Inflammatory Responses to Sepsis.

Sepsis develops after a dysregulated host inflammatory response to a systemic infection. Identification of sepsis biomarkers has been challenging because of the multifactorial causes of disease susceptibility and progression. Public transcriptomic data are a valuable resource for mechanistic discoveries and cross-studies concordance of heterogeneous diseases. Nonetheless, the approach requires structured methodologies and effective visualization tools for meaningful data interpretation. Currently, no such database exists for sepsis or systemic inflammatory diseases in human. Hence we curated SysInflam HuDB (http://sepsis.gxbsidra.org/dm3/geneBrowser/list), a unique collection of human blood transcriptomic datasets associated with systemic inflammatory responses to sepsis. The transcriptome collection and the associated clinical metadata are integrated onto a user-friendly and Web-based interface that allows the simultaneous exploration, visualization, and interpretation of multiple datasets stemming from different study designs. To date, the collection encompasses 62 datasets and 5719 individual profiles. Concordance of gene expression changes with the associated literature was assessed, and additional analyses are presented to showcase database utility. Combined with custom data visualization at the group and individual levels, SysInflam HuDB facilitates the identification of specific human blood gene signatures in response to infection (e.g., patients with sepsis versus healthy control subjects) and the delineation of major genetic drivers associated with inflammation onset and progression under various conditions.

Transcriptomic profile investigations highlight a putative role for NUDT16 in sepsis.

Sepsis is an aberrant systemic inflammatory response mediated by the acute activation of the innate immune system. Neutrophils are important contributors to the innate immune response that controls the infection, but harbour the risk of collateral tissue damage such as thrombosis and organ dysfunction. A better understanding of the modulations of cellular processes in neutrophils and other blood cells during sepsis is needed and can be initiated via transcriptomic profile investigations. To that point, the growing repertoire of publicly accessible transcriptomic datasets serves as a valuable resource for discovering and/or assessing the robustness of biomarkers. We employed systematic literature mining, reductionist approach to gene expression profile and empirical in vitro work to highlight the role of a Nudix hydrolase family member, NUDT16, in sepsis. The relevance and implication of the expression of NUDT16 under septic conditions and the putative functional roles of this enzyme are discussed.

Precision medicine in the era of artificial intelligence: implications in chronic disease management.

Aberrant metabolism is the root cause of several serious health issues, creating a huge burden to health and leading to diminished life expectancy. A dysregulated metabolism induces the secretion of several molecules which in turn trigger the inflammatory pathway. Inflammation is the natural reaction of the immune system to a variety of stimuli, such as pathogens, damaged cells, and harmful substances. Metabolically triggered inflammation, also called metaflammation or low-grade chronic inflammation, is the consequence of a synergic interaction between the host and the exposome-a combination of environmental drivers, including diet, lifestyle, pollutants and other factors throughout the life span of an individual. Various levels of chronic inflammation are associated with several lifestyle-related diseases such as diabetes, obesity, metabolic associated fatty liver disease (MAFLD), cancers, cardiovascular disorders (CVDs), autoimmune diseases, and chronic lung diseases. Chronic diseases are a growing concern worldwide, placing a heavy burden on individuals, families, governments, and health-care systems. New strategies are needed to empower communities worldwide to prevent and treat these diseases. Precision medicine provides a model for the next generation of lifestyle modification. This will capitalize on the dynamic interaction between an individual's biology, lifestyle, behavior, and environment. The aim of precision medicine is to design and improve diagnosis, therapeutics and prognostication through the use of large complex datasets that incorporate individual gene, function, and environmental variations. The implementation of high-performance computing (HPC) and artificial intelligence (AI) can predict risks with greater accuracy based on available multidimensional clinical and biological datasets. AI-powered precision medicine provides clinicians with an opportunity to specifically tailor early interventions to each individual. In this article, we discuss the strengths and limitations of existing and evolving recent, data-driven technologies, such as AI, in preventing, treating and reversing lifestyle-related diseases.

Inherited human IRAK-1 deficiency selectively impairs TLR signaling in fibroblasts.

Most members of the Toll-like receptor (TLR) and interleukin-1 receptor (IL-1R) families transduce signals via a canonical pathway involving the MyD88 adapter and the interleukin-1 receptor-associated kinase (IRAK) complex. This complex contains four molecules, including at least two (IRAK-1 and IRAK-4) active kinases. In mice and humans, deficiencies of IRAK-4 or MyD88 abolish most TLR (except for TLR3 and some TLR4) and IL-1R signaling in both leukocytes and fibroblasts. TLR and IL-1R responses are weak but not abolished in mice lacking IRAK-1, whereas the role of IRAK-1 in humans remains unclear. We describe here a boy with X-linked MECP2 deficiency-related syndrome due to a large de novo Xq28 chromosomal deletion encompassing both MECP2 and IRAK1 Like many boys with MECP2 null mutations, this child died very early, at the age of 7 mo. Unlike most IRAK-4- or MyD88-deficient patients, he did not suffer from invasive bacterial diseases during his short life. The IRAK-1 protein was completely absent from the patient's fibroblasts, which responded very poorly to all TLR2/6 (PAM2CSK4, LTA, FSL-1), TLR1/2 (PAM3CSK4), and TLR4 (LPS, MPLA) agonists tested but had almost unimpaired responses to IL-1ß. By contrast, the patient's peripheral blood mononuclear cells responded normally to all TLR1/2, TLR2/6, TLR4, TLR7, and TLR8 (R848) agonists tested, and to IL-1ß. The death of this child precluded long-term evaluations of the clinical consequences of inherited IRAK-1 deficiency. However, these findings suggest that human IRAK-1 is essential downstream from TLRs but not IL-1Rs in fibroblasts, whereas it plays a redundant role downstream from both TLRs and IL-1Rs in leukocytes.

Interpreting patient-Specific risk prediction using contextual decomposition of BiLSTMs: application to children with asthma.

BACKGROUND: Predictive modeling with longitudinal electronic health record (EHR) data offers great promise for accelerating personalized medicine and better informs clinical decision-making. Recently, deep learning models have achieved state-of-the-art performance for many healthcare prediction tasks. However, deep models lack interpretability, which is integral to successful decision-making and can lead to better patient care. In this paper, we build upon the contextual decomposition (CD) method, an algorithm for producing importance scores from long short-term memory networks (LSTMs). We extend the method to bidirectional LSTMs (BiLSTMs) and use it in the context of predicting future clinical outcomes using patients' EHR historical visits. METHODS: We use a real EHR dataset comprising 11071 patients, to evaluate and compare CD interpretations from LSTM and BiLSTM models. First, we train LSTM and BiLSTM models for the task of predicting which pre-school children with respiratory system-related complications will have asthma at school-age. After that, we conduct quantitative and qualitative analysis to evaluate the CD interpretations produced by the contextual decomposition of the trained models. In addition, we develop an interactive visualization to demonstrate the utility of CD scores in explaining predicted outcomes. RESULTS: Our experimental evaluation demonstrate that whenever a clear visit-level pattern exists, the models learn that pattern and the contextual decomposition can appropriately attribute the prediction to the correct pattern. In addition, the results confirm that the CD scores agree to a large extent with the importance scores generated using logistic regression coefficients. Our main insight was that rather than interpreting the attribution of individual visits to the predicted outcome, we could instead attribute a model's prediction to a group of visits. CONCLUSION: We presented a quantitative and qualitative evidence that CD interpretations can explain patient-specific predictions using CD attributions of individual visits or a group of visits.

The role of alternative Polyadenylation in regulation of rhythmic gene expression.

BACKGROUND: Alternative transcription is common in eukaryotic cells and plays important role in regulation of cellular processes. Alternative polyadenylation results from ambiguous PolyA signals in 3' untranslated region (UTR) of a gene. Such alternative transcripts share the same coding part, but differ by a stretch of UTR that may contain important functional sites. METHODS: The methodoogy of this study is based on mathematical modeling, analytical solution, and subsequent validation by datamining in multiple independent experimental data from previously published studies. RESULTS: In this study we propose a mathematical model that describes the population dynamics of alternatively polyadenylated transcripts in conjunction with rhythmic expression such as transcription oscillation driven by circadian or metabolic oscillators. Analysis of the model shows that alternative transcripts with different turnover rates acquire a phase shift if the transcript decay rate is different. Difference in decay rate is one of the consequences of alternative polyadenylation. Phase shift can reach values equal to half the period of oscillation, which makes alternative transcripts oscillate in abundance in counter-phase to each other. Since counter-phased transcripts share the coding part, the rate of translation becomes constant. We have analyzed a few data sets collected in circadian timeline for the occurrence of transcript behavior that fits the mathematical model. CONCLUSION: Alternative transcripts with different turnover rate create the effect of rectifier. This "molecular diode" moderates or completely eliminates oscillation of individual transcripts and stabilizes overall protein production rate. In our observation this phenomenon is very common in different tissues in plants, mice, and humans. The occurrence of counter-phased alternative transcripts is also tissue-specific and affects functions of multiple biological pathways. Accounting for this mechanism is important for understanding the natural and engineering the synthetic cellular circuits.

Temporal self-attention for risk prediction from electronic health records using non-stationary kernel approximation.

Effective modeling of patient representation from electronic health records (EHRs) is increasingly becoming a vital research topic. Yet, modeling the non-stationarity in EHR data has received less attention. Most existing studies follow a strong assumption of stationarity in patient representation from EHRs. However, in practice, a patient's visits are irregularly spaced over a relatively long period of time, and disease progression patterns exhibit non-stationarity. Furthermore, the time gaps between patient visits often encapsulate significant domain knowledge, potentially revealing undiscovered patterns that characterize specific medical conditions. To address these challenges, we introduce a new method which combines the self-attention mechanism with non-stationary kernel approximation to capture both contextual information and temporal relationships between patient visits in EHRs. To assess the effectiveness of our proposed approach, we use two real-world EHR datasets, comprising a total of 76,925 patients, for the task of predicting the next diagnosis code for a patient, given their EHR history. The first dataset is a general EHR cohort and consists of 11,451 patients with a total of 3,485 unique diagnosis codes. The second dataset is a disease-specific cohort that includes 65,474 pregnant patients and encompasses a total of 9,782 unique diagnosis codes. Our experimental evaluation involved nine prediction models, categorized into three distinct groups. Group 1 comprises the baselines: original self-attention with positional encoding model, RETAIN model, and LSTM model. Group 2 includes models employing self-attention with stationary kernel approximations, specifically incorporating three variations of Bochner's feature maps. Lastly, Group 3 consists of models utilizing self-attention with non-stationary kernel approximations, including quadratic, cubic, and bi-quadratic polynomials. The experimental results demonstrate that non-stationary kernels significantly outperformed baseline methods for NDCG@10 and Hit@10 metrics in both datasets. The performance boost was more substantial in dataset 1 for the NDCG@10 metric. On the other hand, stationary Kernels showed significant but smaller gains over baselines and were nearly as effective as Non-stationary Kernels for Hit@10 in dataset 2. These findings robustly validate the efficacy of employing non-stationary kernels for temporal modeling of EHR data, and emphasize the importance of modeling non-stationary temporal information in healthcare prediction tasks.

PredictPTB: an interpretable preterm birth prediction model using attention-based recurrent neural networks.

BACKGROUND: Early identification of pregnant women at risk for preterm birth (PTB), a major cause of infant mortality and morbidity, has a significant potential to improve prenatal care. However, we lack effective predictive models which can accurately forecast PTB and complement these predictions with appropriate interpretations for clinicians. In this work, we introduce a clinical prediction model (PredictPTB) which combines variables (medical codes) readily accessible through electronic health record (EHR) to accurately predict the risk of preterm birth at 1, 3, 6, and 9 months prior to delivery. METHODS: The architecture of PredictPTB employs recurrent neural networks (RNNs) to model the longitudinal patient's EHR visits and exploits a single code-level attention mechanism to improve the predictive performance, while providing temporal code-level and visit-level explanations for the prediction results. We compare the performance of different combinations of prediction time-points, data modalities, and data windows. We also present a case-study of our model's interpretability illustrating how clinicians can gain some transparency into the predictions. RESULTS: Leveraging a large cohort of 222,436 deliveries, comprising a total of 27,100 unique clinical concepts, our model was able to predict preterm birth with an ROC-AUC of 0.82, 0.79, 0.78, and PR-AUC of 0.40, 0.31, 0.24, at 1, 3, and 6 months prior to delivery, respectively. Results also confirm that observational data modalities (such as diagnoses) are more predictive for preterm birth than interventional data modalities (e.g., medications and procedures). CONCLUSIONS: Our results demonstrate that PredictPTB can be utilized to achieve accurate and scalable predictions for preterm birth, complemented by explanations that directly highlight evidence in the patient's EHR timeline.

Analysis of risk factors progression of preterm delivery using electronic health records.

BACKGROUND: Preterm deliveries have many negative health implications on both mother and child. Identifying the population level factors that increase the risk of preterm deliveries is an important step in the direction of mitigating the impact and reducing the frequency of occurrence of preterm deliveries. The purpose of this work is to identify preterm delivery risk factors and their progression throughout the pregnancy from a large collection of Electronic Health Records (EHR). RESULTS: The study cohort includes about 60,000 deliveries in the USA with the complete medical history from EHR for diagnoses, medications and procedures. We propose a temporal analysis of risk factors by estimating and comparing risk ratios and variable importance at different time points prior to the delivery event. We selected the following time points before delivery: 0, 12 and 24 week(s) of gestation. We did so by conducting a retrospective cohort study of patient history for a selected set of mothers who delivered preterm and a control group of mothers that delivered full-term. We analyzed the extracted data using logistic regression and random forests models. The results of our analyses showed that the highest risk ratio and variable importance corresponds to history of previous preterm delivery. Other risk factors were identified, some of which are consistent with those that are reported in the literature, others need further investigation. CONCLUSIONS: The comparative analysis of the risk factors at different time points showed that risk factors in the early pregnancy related to patient history and chronic condition, while the risk factors in late pregnancy are specific to the current pregnancy. Our analysis unifies several previously reported studies on preterm risk factors. It also gives important insights on the changes of risk factors in the course of pregnancy. The code used for data analysis will be made available on github.

Epidemiology, genetic landscape and classification of childhood diabetes mellitus in the State of Qatar.

AIMS/INTRODUCTION: To study the epidemiology, genetic landscape and causes of childhood diabetes mellitus in the State of Qatar. MATERIALS AND METHODS: All patients (aged 0-18 years) with diabetes mellitus underwent biochemical, immunological and genetic testing. American Diabetes Association guidelines were used to classify types of diabetes mellitus. The incidence and prevalence of all the different types of diabetes mellitus were calculated. RESULTS: Total number of children with diabetes mellitus was 1,325 (type 1 n = 1,096, ≥1 antibody; type 2 n = 104, type 1B n = 53; maturity onset diabetes of the young n = 20; monogenic autoimmune n = 4; neonatal diabetes mellitus n = 10;, syndromic diabetes mellitus n = 23; and double diabetes mellitus n = 15). The incidence and prevalence of type 1 diabetes were 38.05 and 249.73 per 100,000, respectively, and for type 2 were 2.51 and 23.7 per 100,000, respectively. The incidence of neonatal diabetes mellitus was 34.4 per 1,000,000 live births, and in indigenous Qataris the incidence was 43.6 per 1,000,000 live births. The prevalence of type 1 diabetes and type 2 diabetes in Qatari children was double compared with other nationalities. The prevalence of maturity onset diabetes of the young in Qatar was 4.56 per 100,000. CONCLUSIONS: This is the first prospective and comprehensive study to document the epidemiology and genetic landscape of childhood diabetes mellitus in this region. Qatar has the fourth highest incidence of type 1 diabetes mellitus, with the incidence and prevalence being higher in Qatari compared with non-Qatari. The prevalence of type 2 diabetes mellitus is also higher in Qatar than in Western countries. The incidence of neonatal diabetes mellitus is the second highest in the world. GCK is the most common form of maturity onset diabetes of the young, and a large number of patients have type 1B diabetes mellitus.

Privacy-Preserving Analysis of Distributed Biomedical Data: Designing Efficient and Secure Multiparty Computations Using Distributed Statistical Learning Theory.

BACKGROUND: Biomedical research often requires large cohorts and necessitates the sharing of biomedical data with researchers around the world, which raises many privacy, ethical, and legal concerns. In the face of these concerns, privacy experts are trying to explore approaches to analyzing the distributed data while protecting its privacy. Many of these approaches are based on secure multiparty computations (SMCs). SMC is an attractive approach allowing multiple parties to collectively carry out calculations on their datasets without having to reveal their own raw data; however, it incurs heavy computation time and requires extensive communication between the involved parties. OBJECTIVE: This study aimed to develop usable and efficient SMC applications that meet the needs of the potential end-users and to raise general awareness about SMC as a tool that supports data sharing. METHODS: We have introduced distributed statistical computing (DSC) into the design of secure multiparty protocols, which allows us to conduct computations on each of the parties' sites independently and then combine these computations to form 1 estimator for the collective dataset, thus limiting communication to the final step and reducing complexity. The effectiveness of our privacy-preserving model is demonstrated through a linear regression application. RESULTS: Our secure linear regression algorithm was tested for accuracy and performance using real and synthetic datasets. The results showed no loss of accuracy (over nonsecure regression) and very good performance (20 min for 100 million records). CONCLUSIONS: We used DSC to securely calculate a linear regression model over multiple datasets. Our experiments showed very good performance (in terms of the number of records it can handle). We plan to extend our method to other estimators such as logistic regression.

A curated transcriptome dataset collection to investigate inborn errors of immunity.

Primary immunodeficiencies (PIDs) are a heterogeneous group of inherited disorders, frequently caused by loss-of-function and less commonly by gain-of-function mutations, which can result in susceptibility to a broad or a very narrow range of infections but also in inflammatory, allergic or malignant diseases. Owing to the wide range in clinical manifestations and variability in penetrance and expressivity, there is an urgent need to better understand the underlying molecular, cellular and immunological phenotypes in PID patients in order to improve clinical diagnosis and management. Here we have compiled a manually curated collection of public transcriptome datasets mainly obtained from human whole blood, peripheral blood mononuclear cells (PBMCs) or fibroblasts of patients with PIDs and of control subjects for subsequent meta-analysis, query and interpretation. A total of nineteen (19) datasets derived from studies of PID patients were identified and retrieved from the NCBI Gene Expression Omnibus (GEO) database and loaded in GXB, a custom web application designed for interactive query and visualization of integrated large-scale data. The dataset collection includes samples from well characterized PID patients that were stimulated ex vivo under a variety of conditions to assess the molecular consequences of the underlying, naturally occurring gene defects on a genome-wide scale. Multiple sample groupings and rank lists were generated to facilitate comparisons of the transcriptional responses between different PID patients and control subjects. The GXB tool enables browsing of a single transcript across studies, thereby providing new perspectives on the role of a given molecule across biological systems and PID patients. This dataset collection is available at http://pid.gxbsidra.org/dm3/geneBrowser/list.

A curated transcriptome dataset collection to investigate the blood transcriptional response to viral respiratory tract infection and vaccination.

The human immune defense mechanisms and factors associated with good versus poor health outcomes following viral respiratory tract infections (VRTI), as well as correlates of protection following vaccination against respiratory viruses, remain incompletely understood. To shed further light into these mechanisms, a number of systems-scale studies have been conducted to measure transcriptional changes in blood leukocytes of either naturally or experimentally infected individuals, or in individual's post-vaccination. Here we are making available a public repository, for research investigators for interpretation, a collection of transcriptome datasets obtained from human whole blood and peripheral blood mononuclear cells (PBMC) to investigate the transcriptional responses following viral respiratory tract infection or vaccination against respiratory viruses. In total, Thirty one31 datasets, associated to viral respiratory tract infections and their related vaccination studies, were identified and retrieved from the NCBI Gene Expression Omnibus (GEO) and loaded in a custom web application designed for interactive query and visualization of integrated large-scale data. Quality control checks, using relevant biological markers, were performed. Multiple sample groupings and rank lists were created to facilitate dataset query and interpretation. Via this interface, users can generate web links to customized graphical views, which may be subsequently inserted into manuscripts to report novel findings. The GXB tool enables browsing of a single gene across projects, providing new perspectives on the role of a given molecule across biological systems in the diagnostic and prognostic following VRTI but also in identifying new correlates of protection. This dataset collection is available at: http://vri1.gxbsidra.org/dm3/geneBrowser/list.

A curated collection of transcriptome datasets to investigate the molecular mechanisms of immunoglobulin E-mediated atopic diseases.

Prevalence of allergies has reached ~20% of population in developed countries and sensitization rate to one or more allergens among school age children are approaching 50%. However, the combination of the complexity of atopic allergy susceptibility/development and environmental factors has made identification of gene biomarkers challenging. The amount of publicly accessible transcriptomic data presents an unprecedented opportunity for mechanistic discoveries and validation of complex disease signatures across studies. However, this necessitates structured methodologies and visual tools for the interpretation of results. Here, we present a curated collection of transcriptomic datasets relevant to immunoglobin E-mediated atopic diseases (ranging from allergies to primary immunodeficiencies). Thirty-three datasets from the Gene Expression Omnibus, encompassing 1860 transcriptome profiles, were made available on the Gene Expression Browser (GXB), an online and open-source web application that allows for the query, visualization and annotation of metadata. The thematic compositions, disease categories, sample number and platforms of the collection are described. Ranked gene lists and sample grouping are used to facilitate data visualization/interpretation and are available online via GXB (http://ige.gxbsidra.org/dm3/geneBrowser/list). Dataset validation using associated publications showed good concordance in GXB gene expression trend and fold-change.

Long-Chain Acyl-CoA Synthetase 1 Role in Sepsis and Immunity: Perspectives From a Parallel Review of Public Transcriptome Datasets and of the Literature.

A potential role for the long-chain acyl-CoA synthetase family member 1 (ACSL1) in the immunobiology of sepsis was explored during a hands-on training workshop. Participants first assessed the robustness of the potential gap in biomedical knowledge identified via an initial screen of public transcriptome data and of the literature associated with ACSL1. Increase in ACSL1 transcript abundance during sepsis was confirmed in several independent datasets. Querying the ACSL1 literature also confirmed the absence of reports associating ACSL1 with sepsis. Inferences drawn from both the literature (via indirect associations) and public transcriptome data (via correlation) point to the likely participation of ACSL1 and ACSL4, another family member, in inflammasome activation in neutrophils during sepsis. Furthermore, available clinical data indicate that levels of ACSL1 and ACSL4 induction was significantly higher in fatal cases of sepsis. This denotes potential translational relevance and is consistent with involvement in pathways driving potentially deleterious systemic inflammation. Finally, while ACSL1 expression was induced in blood in vitro by a wide range of pathogen-derived factors as well as TNF, induction of ACSL4 appeared restricted to flagellated bacteria and pathogen-derived TLR5 agonists and IFNG. Taken together, this joint review of public literature and omics data records points to two members of the acyl-CoA synthetase family potentially playing a role in inflammasome activation in neutrophils. Translational relevance of these observations in the context of sepsis and other inflammatory conditions remain to be investigated.

Chronic mucocutaneous candidiasis and connective tissue disorder in humans with impaired JNK1-dependent responses to IL-17A/F and TGF-ß.

Genetic etiologies of chronic mucocutaneous candidiasis (CMC) disrupt human IL-17A/F-dependent immunity at mucosal surfaces, whereas those of connective tissue disorders (CTDs) often impair the TGF-ß-dependent homeostasis of connective tissues. The signaling pathways involved are incompletely understood. We report a three-generation family with an autosomal dominant (AD) combination of CMC and a previously undescribed form of CTD that clinically overlaps with Ehlers-Danlos syndrome (EDS). The patients are heterozygous for a private splice-site variant of MAPK8, the gene encoding c-Jun N-terminal kinase 1 (JNK1), a component of the MAPK signaling pathway. This variant is loss-of-expression and loss-of-function in the patients' fibroblasts, which display AD JNK1 deficiency by haploinsufficiency. These cells have impaired, but not abolished, responses to IL-17A and IL-17F. Moreover, the development of the patients' TH17 cells was impaired ex vivo and in vitro, probably due to the involvement of JNK1 in the TGF-ß-responsive pathway and further accounting for the patients' CMC. Consistently, the patients' fibroblasts displayed impaired JNK1- and c-Jun/ATF-2-dependent induction of key extracellular matrix (ECM) components and regulators, but not of EDS-causing gene products, in response to TGF-ß. Furthermore, they displayed a transcriptional pattern in response to TGF-ß different from that of fibroblasts from patients with Loeys-Dietz syndrome caused by mutations of TGFBR2 or SMAD3, further accounting for the patients' complex and unusual CTD phenotype. This experiment of nature indicates that the integrity of the human JNK1-dependent MAPK signaling pathway is essential for IL-17A- and IL-17F-dependent mucocutaneous immunity to Candida and for the TGF-ß-dependent homeostasis of connective tissues.

IRF4 haploinsufficiency in a family with Whipple's disease.

Most humans are exposed to Tropheryma whipplei (Tw). Whipple's disease (WD) strikes only a small minority of individuals infected with Tw (<0.01%), whereas asymptomatic chronic carriage is more common (<25%). We studied a multiplex kindred, containing four WD patients and five healthy Tw chronic carriers. We hypothesized that WD displays autosomal dominant (AD) inheritance, with age-dependent incomplete penetrance. We identified a single very rare non-synonymous mutation in the four patients: the private R98W variant of IRF4, a transcription factor involved in immunity. The five Tw carriers were younger, and also heterozygous for R98W. We found that R98W was loss-of-function, modified the transcriptome of heterozygous leukocytes following Tw stimulation, and was not dominant-negative. We also found that only six of the other 153 known non-synonymous IRF4 variants were loss-of-function. Finally, we found that IRF4 had evolved under purifying selection. AD IRF4 deficiency can underlie WD by haploinsufficiency, with age-dependent incomplete penetrance.

Optimal classifier for imbalanced data using Matthews Correlation Coefficient metric.

Data imbalance is frequently encountered in biomedical applications. Resampling techniques can be used in binary classification to tackle this issue. However such solutions are not desired when the number of samples in the small class is limited. Moreover the use of inadequate performance metrics, such as accuracy, lead to poor generalization results because the classifiers tend to predict the largest size class. One of the good approaches to deal with this issue is to optimize performance metrics that are designed to handle data imbalance. Matthews Correlation Coefficient (MCC) is widely used in Bioinformatics as a performance metric. We are interested in developing a new classifier based on the MCC metric to handle imbalanced data. We derive an optimal Bayes classifier for the MCC metric using an approach based on Frechet derivative. We show that the proposed algorithm has the nice theoretical property of consistency. Using simulated data, we verify the correctness of our optimality result by searching in the space of all possible binary classifiers. The proposed classifier is evaluated on 64 datasets from a wide range data imbalance. We compare both classification performance and CPU efficiency for three classifiers: 1) the proposed algorithm (MCC-classifier), the Bayes classifier with a default threshold (MCC-base) and imbalanced SVM (SVM-imba). The experimental evaluation shows that MCC-classifier has a close performance to SVM-imba while being simpler and more efficient.

-A curated transcriptomic dataset collection relevant to embryonic development associated with in vitro fertilization in healthy individuals and patients with polycystic ovary syndrome.

The collection of large-scale datasets available in public repositories is rapidly growing and providing opportunities to identify and fill gaps in different fields of biomedical research. However, users of these datasets should be able to selectively browse datasets related to their field of interest. Here we made available a collection of transcriptome datasets related to human follicular cells from normal individuals or patients with polycystic ovary syndrome, in the process of their development, during in vitro fertilization. After RNA-seq dataset exclusion and careful selection based on study description and sample information, 12 datasets, encompassing a total of 85 unique transcriptome profiles, were identified in NCBI Gene Expression Omnibus and uploaded to the Gene Expression Browser (GXB), a web application specifically designed for interactive query and visualization of integrated large-scale data. Once annotated in GXB, multiple sample grouping has been made in order to create rank lists to allow easy data interpretation and comparison. The GXB tool also allows the users to browse a single gene across multiple projects to evaluate its expression profiles in multiple biological systems/conditions in a web-based customized graphical views. The curated dataset is accessible at the following link: http://ivf.gxbsidra.org/dm3/landing.gsp.

A collection of annotated and harmonized human breast cancer transcriptome datasets, including immunologic classification.

The increased application of high-throughput approaches in translational research has expanded the number of publicly available data repositories. Gathering additional valuable information contained in the datasets represents a crucial opportunity in the biomedical field. To facilitate and stimulate utilization of these datasets, we have recently developed an interactive data browsing and visualization web application, the Gene Expression Browser (GXB). In this note, we describe a curated compendium of 13 public datasets on human breast cancer, representing a total of 2142 transcriptome profiles. We classified the samples according to different immune based classification systems and integrated this information into the datasets. Annotated and harmonized datasets were uploaded to GXB. Study samples were categorized in different groups based on their immunologic tumor response profiles, intrinsic molecular subtypes and multiple clinical parameters. Ranked gene lists were generated based on relevant group comparisons. In this data note, we demonstrate the utility of GXB to evaluate the expression of a gene of interest, find differential gene expression between groups and investigate potential associations between variables with a specific focus on immunologic classification in breast cancer. This interactive resource is publicly available online at: http://breastcancer.gxbsidra.org/dm3/geneBrowser/list.