Genetic and molecular analysis of rare disease is made difficult by the small numbers of affected patients. Phenotypic comorbidity analysis can help rectify this by combining information from ...individuals with similar phenotypes and looking for overlap in terms of shared genes and underlying functional systems. However, few studies have combined comorbidity analysis with genomic data. We present a computational approach that connects patient phenotypes based on phenotypic co-occurence and uses genomic information related to the patient mutations to assign genes to the phenotypes, which are used to detect enriched functional systems. These phenotypes are clustered using network analysis to obtain functionally coherent phenotype clusters. We applied the approach to the DECIPHER database, containing phenotypic and genomic information for thousands of patients with heterogeneous rare disorders and copy number variants. Validity was demonstrated through overlap with known diseases, co-mention within the biomedical literature, semantic similarity measures, and patient cluster membership. These connected pairs formed multiple phenotype clusters, showing functional coherence, and mapped to genes and systems involved in similar pathological processes. Examples include claudin genes from the 22q11 genomic region associated with a cluster of phenotypes related to DiGeorge syndrome and genes related to the GO term anterior/posterior pattern specification associated with abnormal development. The clusters generated can help with the diagnosis of rare diseases, by suggesting additional phenotypes for a given patient and potential underlying functional systems. Other tools to find causal genes based on phenotype were also investigated. The approach has been implemented as a workflow, named PhenCo, which can be adapted to any set of patients for which phenomic and genomic data is available. Full details of the analysis, including the clusters formed, their constituent functional systems and underlying genes are given. Code to implement the workflow is available from GitHub.
The development of equine immunity from the fetus to adulthood is complex. The foal's immune response and the immune mechanisms that they are equipped with, along with changes over the first months ...of life until the immune system becomes adult‐like, are only partially understood. While several innate immune responses seem to be fully functional from birth, the onset of adaptive immune response is delayed. For some adaptive immune parameters, such as immunoglobin (Ig)G1, IgG3, IgG5 and IgA antibodies, the immune response starts before or at birth and matures within 3 months of life. Other antibody responses, such as IgG4, IgG7 and IgE production, slowly develop within the first year of life until they reach adult levels. Similar differences have been observed for adaptive T cell responses. Interferon‐gamma (IFN‐γ) production by T helper 1 (Th1)‐cells and cytotoxic T cells starts shortly after birth with low level production that gradually increases during the first year of life. In contrast, interleukin‐4 (IL‐4) produced by Th2‐cells is almost undetectable in the first 3 months of life. These findings offer some explanation for the increased susceptibility of foals to certain pathogens such as Rhodococcus equi. The delay in Th‐cell development and in particular Th2 immunity during the first months of life also provides an explanation for the reduced responsiveness of young horses to most traditional vaccines. In summary, all immune components of adult horses seem to exist in foals but the orchestrating and regulation of the immune response in immature horses is strikingly different. Young foals are fully competent and can perform certain immune responses but many mechanisms have yet to mature. Additional work is needed to improve our understanding of immunity and immune regulation in young horses, to identify the preferred immune pathways that they are using and ultimately provide new preventive strategies to protect against infectious disease.
The dwarf spheroidal satellite galaxies (dSphs) of the Milky Way are some of the most dark matter (DM) dominated objects known. We report on γ-ray observations of Milky Way dSphs based on six years ...of Fermi Large Area Telescope data processed with the new Pass8 event-level analysis. None of the dSphs are significantly detected in γ rays, and we present upper limits on the DM annihilation cross section from a combined analysis of 15 dSphs. These constraints are among the strongest and most robust to date and lie below the canonical thermal relic cross section for DM of mass ≲100 GeV annihilating via quark and τ-lepton channels.
We report on the search for spectral irregularities induced by oscillations between photons and axion-like particles (ALPs) in the gamma-ray spectrum of NGC 1275, the central galaxy of the Perseus ...cluster. Using 6 years of Fermi Large Area Telescope data, we find no evidence for ALPs and exclude couplings above 5 times 10 (sup -12) per gigaelectronvolt for ALP masses less than or approximately equal to 0.5 apparent magnitude (m (sub a)) less than or approximately equal to 5 nanoelectronvolts at 95 percent confidence. The limits are competitive withthe sensitivity of planned laboratory experiments, and, together with other bounds, strongly constrain thepossibility that ALPs can reduce the gamma-ray opacity of the Universe.
Abstract
Variants within non-coding genomic regions can greatly affect disease. In recent years, increasing focus has been given to these variants, and how they can alter regulatory elements, such as ...enhancers, transcription factor binding sites and DNA methylation regions. Such variants can be considered regulatory variants. Concurrently, much effort has been put into establishing international consortia to undertake large projects aimed at discovering regulatory elements in different tissues, cell lines and organisms, and probing the effects of genetic variants on regulation by measuring gene expression. Here, we describe methods and techniques for discovering disease-associated non-coding variants using sequencing technologies. We then explain the computational procedures that can be used for annotating these variants using the information from the aforementioned projects, and prediction of their putative effects, including potential pathogenicity, based on rule-based and machine learning approaches. We provide the details of techniques to validate these predictions, by mapping chromatin–chromatin and chromatin–protein interactions, and introduce Clustered Regularly Interspaced Short Palindromic Repeats-Associated Protein 9 (CRISPR-Cas9) technology, which has already been used in this field and is likely to have a big impact on its future evolution.
We also give examples of regulatory variants associated with multiple complex diseases. This review is aimed at bioinformaticians interested in the characterization of regulatory variants, molecular biologists and geneticists interested in understanding more about the nature and potential role of such variants from a functional point of views, and clinicians who may wish to learn about variants in non-coding genomic regions associated with a given disease and find out what to do next to uncover how they impact on the underlying mechanisms.
Protein function prediction remains a key challenge. Domain composition affects protein function. Here we present DomFun, a Ruby gem that uses associations between protein domains and functions, ...calculated using multiple indices based on tripartite network analysis. These domain-function associations are combined at the protein level, to generate protein-function predictions.
We analysed 16 tripartite networks connecting homologous superfamily and FunFam domains from CATH-Gene3D with functional annotations from the three Gene Ontology (GO) sub-ontologies, KEGG, and Reactome. We validated the results using the CAFA 3 benchmark platform for GO annotation, finding that out of the multiple association metrics and domain datasets tested, Simpson index for FunFam domain-function associations combined with Stouffer's method leads to the best performance in almost all scenarios. We also found that using FunFams led to better performance than superfamilies, and better results were found for GO molecular function compared to GO biological process terms. DomFun performed as well as the highest-performing method in certain CAFA 3 evaluation procedures in terms of Formula: see text and Formula: see text We also implemented our own benchmark procedure, Pathway Prediction Performance (PPP), which can be used to validate function prediction for additional annotations sources, such as KEGG and Reactome. Using PPP, we found similar results to those found with CAFA 3 for GO, moreover we found good performance for the other annotation sources. As with CAFA 3, Simpson index with Stouffer's method led to the top performance in almost all scenarios.
DomFun shows competitive performance with other methods evaluated in CAFA 3 when predicting proteins function with GO, although results vary depending on the evaluation procedure. Through our own benchmark procedure, PPP, we have shown it can also make accurate predictions for KEGG and Reactome. It performs best when using FunFams, combining Simpson index derived domain-function associations using Stouffer's method. The tool has been implemented so that it can be easily adapted to incorporate other protein features, such as domain data from other sources, amino acid k-mers and motifs. The DomFun Ruby gem is available from https://rubygems.org/gems/DomFun . Code maintained at https://github.com/ElenaRojano/DomFun . Validation procedure scripts can be found at https://github.com/ElenaRojano/DomFun_project .
Graphical abstract Immune response of EHV-1 naïve mares after repeated vaccination with an inactivated EHV-vaccine. The mares were first vaccinated 5 days after foaling (0) and were subsequently bred ...and pregnant again. They foaled again at 12 months.
Pharmacokinetics of an i.v. prodrug of acetaminophen (propacetamol) in neonates after repeat dosing are reported, with scant data for i.v. acetaminophen formulation.
Neonates from an intensive care ...unit received 6-hourly prn i.v. acetaminophen dosed according to postmenstrual age (PMA): 28–32 weeks, 10 mg kg−1; 32–36 weeks, 12.5 mg kg−1; and ≥36 weeks, 15 mg kg−1. A maximum of five blood samples for assay and liver function tests (LFTs) were collected. A one-compartment linear disposition model (zero-order input; first-order elimination) was used to describe time–concentration profiles using population modelling (NONMEM).
Fifty neonates, median (range) PMA 38.6 (32–45) weeks, mean (sd) weight 2.9 (0.7) kg, received a mean of 15 doses over a median 4 days with 189 serum acetaminophen and 231 LFT measurements. Standardized population parameter estimates for a term neonate were clearance (CL) 5.24 (CV 30.5%) litre h−1 70 kg−1 and volume of distribution (V) 76 (29.6%) litre 70 kg−1. CL increased with PMA from 4.4 litre h−1 70 kg−1 at 34 weeks to 6.3 litre h−1 70 kg−1 at 46 weeks. The presence of unconjugated hyperbilirubinaemia was associated with reduced CL: 150 μmol litre−1 associated with 40% CL reduction. Acetaminophen concentrations between 10 and 23 mg litre−1 at steady state are predicted after 15 mg kg−1 6-hourly for a neonate of PMA 40 weeks. Hepatic enzyme analysis of daily samples changed significantly for one patient whose alanine aminotransferase concentration tripled.
The parameter estimates are similar to those described for propacetamol. There was no evidence of hepatotoxicity. Unconjugated hyperbilirubinaemia impacts upon CL, dictating dose reduction.
We conducted prospective, active population-based surveillance for candidemia (defined as any Candida species isolated from blood) in Atlanta and San Francisco (total population, 5.34 million) during ...1992–1993. The average annual incidence of candidemia at both sites was 8 per 100,000 population. The highest incidence (75 per 100,000) occurred among infants ⩽1 year old. In 19% of patients, candidemia developed prior to or on the day of admission. Underlying medical conditions included cancer (26%), abdominal surgery (14%), diabetes mellitus (13%), and human immunodeficiency virus infection (10%). In 47% of cases, species of Candida other than Candida albicans were isolated, most commonly Candida parapsilosis, Candida glabrata, and Candida tropicalis. Antifungal susceptibility testing of 394 isolates revealed minimal levels of azole resistance among C. albicans, C. tropicalis, and C. parapsilosis. These data document the substantial burden of candidemia and its changing epidemiology. Continued surveillance will be important to monitor the epidemiology of candidemia and to detect emergence of resistance to azoles.
Mitochondrial outer membrane permeabilization and cytochrome c release promote caspase activation and execution of apoptosis through cleavage of specific caspase substrates in the cell. Among the ...first targets of activated caspases are the permeabilized mitochondria themselves, leading to disruption of electron transport, loss of mitochondrial transmembrane potential (ΔΨm), decline in ATP levels, production of reactive oxygen species (ROS), and loss of mitochondrial structural integrity. Here, we identify NDUFS1, the 75 kDa subunit of respiratory complex I, as a critical caspase substrate in the mitochondria. Cells expressing a noncleavable mutant of p75 sustain ΔΨm and ATP levels during apoptosis, and ROS production in response to apoptotic stimuli is dampened. While cytochrome c release and DNA fragmentation are unaffected by the noncleavable p75 mutant, mitochondrial morphology of dying cells is maintained, and loss of plasma membrane integrity is delayed. Therefore, caspase cleavage of NDUFS1 is required for several mitochondrial changes associated with apoptosis.
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