Integrated whole exome sequencing and functional approach delineate genetic heterogeneity in cerebellar ataxias

PurposeDisease deconvolution in heterogeneous cerebellar ataxias (CAs) needs a focussed approach to overcome the diagnostic challenges. A diverse clinical presentation with over 100 reported genetic loci, in addition to the various challenges associated with genotype-phenotype correlation complicate the genetic diagnosis in 40-60% of the CA cases that remain uncharacterized. We present here an integrated whole exome sequencing combined with a functional validation approach to delineate the genetic etiology in Indian CA patients.MethodA total of 50 familial and sporadic progressive CA families (negative for CNG expansion) including 101 subjects were recruited for this study. Index patients from 50 families were subjected to singleton whole exome sequencing (S-WES). Family-based WES (F-WES) was carried out for seven S-WES selected families. Protein simulation and docking studies were performed for seven genetic variants identified through WES. A Cell line-based model was used to assess disease signatures for variants in KCNC3 and a new candidate gene, SPTB.ResultsClinically relevant variants identified in 70% (35/50) of the selected families. We achieved a 50% (25/50) definitive diagnostic yield and 14% (7/50) probable diagnostic yield while 6% (3/50) of the families showed variants of uncertain significance. We prioritized compound heterozygous variants in a candidate gene, SPTB for cerebellar ataxia with hereditary spherocytosis. Lymphoblastoid cell line derived from a patient with a KCNC3 variant showed altered disease signatures with induced ROS and elevated unfolded protein response markers at the basal level.ConclusionOur results highlight an extensive experimental design for the genetic diagnosis of CA. Through targeted analysis of ataxia phenotype-derived gene panel in S-WES, new gene identification through F-WES, and revaluation of unsolved families’ WES data, we identified novel, reported and other clinically relevant variants in CA patients. Bioinformatic protein modeling along with the cellular insights into the pathogenicity of novel variants enabled delineation of genetic diagnostics and enhanced the mechanistic understanding of CAs.

Combinatorial Analysis of Phenotypic and Clinical Risk Factors Associated With Hospitalized COVID-19 Patients

Characterization of the risk factors associated with variability in the clinical outcomes of COVID-19 is important. Our previous study using genomic data identified a potential role of calcium and lipid homeostasis in severe COVID-19. This study aimed to identify similar combinations of features (disease signatures) associated with severe disease in a separate patient population with purely clinical and phenotypic data. The PrecisionLife combinatorial analytics platform was used to analyze features derived from de-identified health records in the UnitedHealth Group COVID-19 Data Suite. The platform identified and analyzed 836 disease signatures in two cohorts associated with an increased risk of COVID-19 hospitalization. Cohort 1 was formed of cases hospitalized with COVID-19 and a set of controls who developed mild symptoms. Cohort 2 included Cohort 1 individuals for whom additional laboratory test data was available. We found several disease signatures where lower levels of lipids were found co-occurring with lower levels of serum calcium and leukocytes. Many of the low lipid signatures were independent of statin use and 50% of cases with hypocalcemia signatures were reported with vitamin D deficiency. These signatures may be attributed to similar mechanisms linking calcium and lipid signaling where changes in cellular lipid levels during inflammation and infection affect calcium signaling in host cells. This study and our previous genomics analysis demonstrate that combinatorial analysis can identify disease signatures associated with the risk of developing severe COVID-19 separately from genomic or clinical data in different populations. Both studies suggest associations between calcium and lipid signaling in severe COVID-19.

Identification of SARS-CoV-2–induced pathways reveals drug repurposing strategies

The global outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) necessitates the rapid development of new therapies against coronavirus disease 2019 (COVID-19) infection. Here, we present the identification of 200 approved drugs, appropriate for repurposing against COVID-19. We constructed a SARS-CoV-2–induced protein network, based on disease signatures defined by COVID-19 multiomics datasets, and cross-examined these pathways against approved drugs. This analysis identified 200 drugs predicted to target SARS-CoV-2–induced pathways, 40 of which are already in COVID-19 clinical trials, testifying to the validity of the approach. Using artificial neural network analysis, we classified these 200 drugs into nine distinct pathways, within two overarching mechanisms of action (MoAs): viral replication (126) and immune response (74). Two drugs (proguanil and sulfasalazine) implicated in viral replication were shown to inhibit replication in cell assays. This unbiased and validated analysis opens new avenues for the rapid repurposing of approved drugs into clinical trials.

Structural and cellular transcriptome foundations of human brain disease

The intersection of human genetics and brain transcriptomics promises to reveal the structural and cellular locus of brain diseases via selective co-expression of risk genes. We find that adult brain-wide transcriptomic profiles of 40 human brain diseases identify four major transcriptional patterns, represented by tumor-related, neurodegenerative, psychiatric and substance abuse, and a mixed group of diseases, with some unexpected disease associations. Based on differential co-expression using bulk transcriptomics, the majority of brain diseases exhibit unique regional transcriptomic signatures that strongly reflect neuronal versus non-neuronal divisions and variation in excitatory and inhibitory neurons across the brain. Single cell transcriptomic data confirms and refines the relationship of different diseases to specific neuronal and non-neuronal subclasses. Disease signatures are largely conserved between mouse and human at the higher cell class level, but with significant species differences emerging at the finer subclass level that may help explain human-specific disease susceptibility.

Stratified Management for Bacterial Infections in Late Preterm and Term Neonates: Current Strategies and Future Opportunities Toward Precision Medicine

Bacterial infections remain a major cause of morbidity and mortality in the neonatal period. Therefore, many neonates, including late preterm and term neonates, are exposed to antibiotics in the first weeks of life. Data on the importance of inter-individual differences and disease signatures are accumulating. Differences that may potentially influence treatment requirement and success rate. However, currently, many neonates are treated following a “one size fits all” approach, based on general protocols and standard antibiotic treatment regimens. Precision medicine has emerged in the last years and is perceived as a new, holistic, way of stratifying patients based on large-scale data including patient characteristics and disease specific features. Specific to sepsis, differences in disease susceptibility, disease severity, immune response and pharmacokinetics and -dynamics can be used for the development of treatment algorithms helping clinicians decide when and how to treat a specific patient or a specific subpopulation. In this review, we highlight the current and future developments that could allow transition to a more precise manner of antibiotic treatment in late preterm and term neonates, and propose a research agenda toward precision medicine for neonatal bacterial infections.

A contained Mycobacterium tuberculosis mouse infection model predicts active disease and containment in humans

Previous studies have identified whole-blood transcriptional risk and disease signatures for Tuberculosis (TB); however, several lines of evidence suggest that these signatures primarily reflect bacterial burden, which increases prior to symptomatic disease. We found that the peripheral blood transcriptome of mice with contained Mycobacterium tuberculosis infection (CMTB) has striking similarities to that of humans with active TB and that a signature derived from these mice predicts human disease with comparable accuracy to signatures derived directly from humans. A set of genes associated with immune defense are upregulated in CMTB mice but not in humans with active TB suggesting that their upregulation is associated with bacterial containment. A signature comprised of these genes predicts both protection from TB disease and successful treatment at early time points where current signatures are not predictive. These results suggest that detailed study of the CMTB mouse model may enable identification of biomarkers for human TB.

Novel targeted therapies for Parkinson’s disease

Parkinson’s disease (PD) is the second more common neurodegenerative disease with increasing incidence worldwide associated to the population ageing. Despite increasing awareness and significant research advancements, treatment options comprise dopamine repleting, symptomatic therapies that have significantly increased quality of life and life expectancy, but no therapies that halt or reverse disease progression, which remain a great, unmet goal in PD research. Large biomarker development programs are undertaken to identify disease signatures that will improve patient selection and outcome measures in clinical trials. In this review, we summarize PD-related mechanisms that can serve as targets of therapeutic interventions aiming to slow or modify disease progression, as well as previous and ongoing clinical trials in each field, and discuss future perspectives.

Large Scale Profiling of SARS-CoV-2-Infected Patients Identified Potential Therapeutic Host Targets and Drug Candidates for COVID-19

Given the rapid spread of SARS-CoV-2 and rising death toll of COVID-19 in the current absence of effective treatments, it is imperative that therapeutics are developed and made available to patients as quickly as possible. Publicly available COVID-19 patient data can be used to identify host therapeutic targets, tailoring treatments to the disease signatures observed in patients. In this study, we identify potential host therapeutic targets based on gene expression alterations observed in COVID-19 patients. We analyzed RNAseq data from airway samples of COVID-19 patients and healthy controls to detect significantly differentially expressed genes and pathways that present potential therapeutic targets. Our analysis revealed expression changes in key genes involved in activation of immune pathways, as well as genes targeted by SARS-CoV2 to interfere with normal host cell functioning. Critical changes were observed in a number of genes, including EIF2AK2, which was shown to play important roles in activating the interferon response and interfering with host cell translational machinery in SARS-CoV-2 infection, presenting a prospective therapeutic target. We also identified drugs with potential to modulate multiple therapeutic targets within the most significant pathways. Our results both validate key genes, pathways, and drug candidates that have been reported by other studies and suggest others that have not been well-characterized and warrant further investigation by future studies. Further investigation of these therapeutic targets and their drug interactions may lead to effective therapeutic strategies to combat the current COVID-19 pandemic and protect against future outbreaks.<br>