Identification of rare-disease genes using blood transcriptome sequencing and large control cohorts.

It is estimated that 350 million individuals worldwide suffer from rare diseases, which are predominantly caused by mutation in a single gene1. The current molecular diagnostic rate is estimated at 50%, with whole-exome sequencing (WES) among the most successful approaches2-5. For patients in whom WES is uninformative, RNA sequencing (RNA-seq) has shown diagnostic utility in specific tissues and diseases6-8. This includes muscle biopsies from patients with undiagnosed rare muscle disorders6,9, and cultured fibroblasts from patients with mitochondrial disorders7. However, for many individuals, biopsies are not performed for clinical care, and tissues are difficult to access. We sought to assess the utility of RNA-seq from blood as a diagnostic tool for rare diseases of different pathophysiologies. We generated whole-blood RNA-seq from 94 individuals with undiagnosed rare diseases spanning 16 diverse disease categories. We developed a robust approach to compare data from these individuals with large sets of RNA-seq data for controls (n = 1,594 unrelated controls and n = 49 family members) and demonstrated the impacts of expression, splicing, gene and variant filtering strategies on disease gene identification. Across our cohort, we observed that RNA-seq yields a 7.5% diagnostic rate, and an additional 16.7% with improved candidate gene resolution

Expansion of the Human Phenotype Ontology (HPO) knowledge base and resources.

The Human Phenotype Ontology (HPO)-a standardized vocabulary of phenotypic abnormalities associated with 7000+ diseases-is used by thousands of researchers, clinicians, informaticians and electronic health record systems around the world. Its detailed descriptions of clinical abnormalities and computable disease definitions have made HPO the de facto standard for deep phenotyping in the field of rare disease. The HPO\u27s interoperability with other ontologies has enabled it to be used to improve diagnostic accuracy by incorporating model organism data. It also plays a key role in the popular Exomiser tool, which identifies potential disease-causing variants from whole-exome or whole-genome sequencing data. Since the HPO was first introduced in 2008, its users have become both more numerous and more diverse. To meet these emerging needs, the project has added new content, language translations, mappings and computational tooling, as well as integrations with external community data. The HPO continues to collaborate with clinical adopters to improve specific areas of the ontology and extend standardized disease descriptions. The newly redesigned HPO website (www.human-phenotype-ontology.org) simplifies browsing terms and exploring clinical features, diseases, and human genes

Crosstalks between Cytokines and Sonic Hedgehog in <i>Helicobacter pylori</i> Infection: A Mathematical Model

<div><p><i>Helicobacter pylori</i> infection of gastric tissue results in an immune response dominated by Th1 cytokines and has also been linked with dysregulation of Sonic Hedgehog (SHH) signaling pathway in gastric tissue. However, since interactions between the cytokines and SHH during <i>H. pylori</i> infection are not well understood, any mechanistic understanding achieved through interpretation of the statistical analysis of experimental results in the context of currently known circuit must be carefully scrutinized. Here, we use mathematical modeling aided by restraints of experimental data to evaluate the consistency between experimental results and temporal behavior of <i>H. pylori</i> activated cytokine circuit model. Statistical analysis of qPCR data from uninfected and <i>H. pylori</i> infected wild-type and parietal cell-specific SHH knockout (PC-SHH^KO) mice for day 7 and 180 indicate significant changes that suggest role of SHH in cytokine regulation. The experimentally observed changes are further investigated using a mathematical model that examines dynamic crosstalks among pro-inflammatory (IL1β, IL-12, IFNγ, MIP-2) cytokines, anti-inflammatory (IL-10) cytokines and SHH during <i>H. pylori</i> infection. Response analysis of the resulting model demonstrates that circuitry, as currently known, is inadequate for explaining of the experimental observations; suggesting the need for additional specific regulatory interactions. A key advantage of a computational model is the ability to propose putative circuit models for <i>in-silico</i> experimentation. We use this approach to propose a parsimonious model that incorporates crosstalks between NFĸB, SHH, IL-1β and IL-10, resulting in a feedback loop capable of exhibiting cyclic behavior. Separately, we show that analysis of an independent time-series GEO microarray data for IL-1β, IFNγ and IL-10 in mock and <i>H. pylori</i> infected mice further supports the proposed hypothesis that these cytokines may follow a cyclic trend. Predictions from the <i>in-silico</i> model provide useful insights for generating new hypothesis and design of subsequent experimental studies.</p></div

Trajectories of cytokines in mock-infected and <i>H. pylori</i> infected mice from GEO microarray dataset.

<p>(A) IL-1β, (B) IL-10 and (C) IFNγ for day 2, 7, 14 and 28 from chief cell of mock-infected and <i>H. pylori</i> infected mice. The temporal profiles indicate that these cytokines potentially display a cyclic expression pattern in response to <i>H. pylori</i> infection.</p

Diagram of mathematical model of cytokine-SHH network during <i>H. pylori</i> infection.

<p>This reduced network derived from interaction map, represents the key cytokines activated as host's immune response to <i>H. pylori</i>. Blue arrows represent activation whereas while red arrows depict inhibition. Model species with suffix “i” represent the inactive form. The link between SHH and cytokines, as predicted by our experimental data is modelled through unknown model species “X” (grey colored). Detailed interaction network of host immune response to <i>H. pylori</i> is available in MethodsS1.</p

In-silico SHH KO results show a decrease in cytokines as comared to WT.

<p>SHH KO condition was simulated by setting SHHi to zero. Graph A–F shows profiles of (A) SHH (B) IL-1β (C) IL12 (D) IFNγ (E) MIP2 (F) IL10. Wild type condition (SHHi  = 1) is shown in green and <i>in-silico</i> SHH KO condition (SHHi  = 0) is represented in red.</p

Temporal profiles of model species in uninfected and infected conditions.

<p>Simulation results comparing temporal profiles of model species in (A) absence and (B) presence of <i>H. pylori</i>. Graph C–F show temporal profiles of (C) SHH (D) MIP-2 (E) IL-1β and (F) IL-10 in absence and presence of <i>H. pylori</i>.</p

Effect of <i>H. pylori</i> on SHH and cytokines' expression in WT and PC-SHH^KO mouse stomachs, day 7 and day 180 post-inoculation.

<p>RNA was extracted from stomachs of uninfected and <i>H. pylori</i>-infected wild type (WT) and parietal cell-specific SHH knock out (PC-SHH-KO) mice 7 and 180 days post-inoculation. Expression of genes was measured by qPCR and two-way ANOVA test was performed, followed by Bonferroni test to compare uninfected (-HP) with <i>H. pylori</i> infected group (+HP) in each genotype. The graphs show average fold change in expression of IL-1β (A, B), IL-12 (C, D), MIP-2 (E, F), IL-10 (G, H) and SHH (I, J) upon <i>H. pylori</i> infection relative to uninfected condition. Bars represent the mean ± SEM, n = 3-4 per group.</p

Interaction between infection status and genotype.

<p>RNA was extracted from stomachs of uninfected (-HP) and <i>H. pylori</i>-infected (+HP) wild type (WT) and parietal cell specific SHH knock out (PC-SHH-KO) mice 7 and 180 days post-inoculation. Expression of genes was measured by qPCR and interaction test was performed. Parallel lines imply that <i>H. pylori</i> has same effect on gene's expression in WT and PC-SHH^KO mice whereas intersecting or non-parallel lines indicate an interaction between genotype and infection. The graphs show interaction plot between infection status and genotype for (A) IL1β on day 7, (B) IL1β on day 180, (C) IL-12 on day 7, (D) IL-12 on day 180, (E) MIP-2 on day 7 (F) MIP-2 on day 180 (G) IL-10 on day 7 and (H) IL-10 on day 180. P-value for interaction between infection and genotype were calculated by two-way ANOVA test. Y-axis: mean of negative dCT value of cytokine, X-axis: infection status, trace-factor: genotype.</p

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Not AvailableSustainable development of the national food system must ensure the introduction of adequate food security interventions and policies. However, several high-end technological developments remain unexplored, which can be used to gain explicit information regarding agricultural problems. In this direction, the presented work proposes AgrIntel, a framework consisting of multiple AI-based pipelines to process nationwide farmers’ helpline data and obtain spatiotemporal insights regarding food-production problems on an extensive scale. AgrIntel overcomes several limitations of the existing methods used for similar objectives, including limited scalability, low frequency, and high cost. The call-logs dataset used in the study is obtained from the nationwide network of farmers’ helpline centers, managed by the Ministry of Agriculture & Farmers’ Welfare, Government of India. The article demonstrates the Spatio-temporal profile of one of India’s highest food grain-affecting diseases, i.e., ‘‘blast in rice crop’’, to demonstrate the utility of the AgrIntel pipelines. First, the proposed framework extracts and clusters the precise geographical locations of farmers calling for help corresponding to the target agricultural problem. Next, the temporal modeling of the problem helps extract the critical dates corresponding to the crop disease/pest spread. Furthermore, by incorporating the historical agroclimatological data, the article introduces a new medium to extract the favorable weather conditions corresponding to the targeted disease/pest outbreak. In addition, the study explores the potential of Deep Learning models (based on Artificial Neural Network, Convolutional Neural Network, Gated Recurrent Unit and Long short-term memory unit) to efficiently predict the futuristic demand for assistance regarding target problems (RMSE of ≈1.5 and MAE of ≈0.9 query calls). The obtained results expose unrevealed insights regarding food production problems, significantly boosting the food security policy-designing procedure.Not Availabl