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Omics data can be integrated into a reference model using various model extraction methods (MEMs) to yield context-specific genome-scale metabolic models (GEMs). How to chose the appropriate MEM, thresholding rule and threshold remains a challenge. We integrated mouse transcriptomic data from a Cyp51 knockout mice diet experiment (GSE58271) using five MEMs (GIMME, iMAT, FASTCORE, INIT an tINIT) in a combination with a recently published mouse GEM iMM1865. Except for INIT and tINIT, the size of extracted models varied with the MEM used (t-test p-value 90%) in PC1 for FASTCORE. In iMAT, each of the three factors explained less than 40% of the variability within PC1, PC2 and PC3. Among all the MEMs, FASTCORE captured the most of the true variability in the data by clustering samples by gender. Our results show that for the efficient use of MEMs in the context of omics data integration and analysis, one should apply various MEMs, thresholding rules, and thresholding values to select the MEM and its configuration that best captures the true variability in the data. This selection can be guided by the methodology as proposed and used in this paper. Moreover, we describe certain approaches that can be used to analyse the results obtained with the selected MEM and to put these results in a biological context.Adverse effects of spaceflight on musculoskeletal health increase the risk of bone injury and impairment of fracture healing. Its yet elusive molecular comprehension warrants immediate attention, since space travel is becoming more frequent. Here we examined the effects of spaceflight on bone fracture healing using a 2 mm femoral segmental bone defect (SBD) model. Forty, 9-week-old, male C57BL/6J mice were randomized into 4 groups 1) Sham surgery on Ground (G-Sham); 2) Sham surgery housed in Spaceflight (FLT-Sham); 3) SBD surgery on Ground (G-Surgery); and 4) SBD surgery housed in Spaceflight (FLT-Surgery). Surgery procedures occurred 4 days prior to launch; post-launch, the spaceflight mice were house in the rodent habitats on the International Space Station (ISS) for approximately 4 weeks before euthanasia. Mice remaining on the Earth were subjected to identical housing and experimental conditions. The right femur from half of the spaceflight and ground groups was investigated by micro-computed tomography (µCT). In the remaining mice, the callus regions from surgery groups and corresponding femoral segments in sham mice were probed by global transcriptomic and metabolomic assays. µCT confirmed escalated bone loss in FLT-Sham compared to G-Sham mice. Comparing to their respective on-ground counterparts, the morbidity gene-network signal was inhibited in sham spaceflight mice but activated in the spaceflight callus. µCT analyses of spaceflight callus revealed increased trabecular spacing and decreased trabecular connectivity. Activated apoptotic signals in spaceflight callus were synchronized with inhibited cell migration signals that potentially hindered the wound site to recruit growth factors. A major pro-apoptotic and anti-migration gene network, namely the RANK-NFκB axis, emerged as the central node in spaceflight callus. Concluding, spaceflight suppressed a unique biomolecular mechanism in callus tissue to facilitate a failed regeneration, which merits a customized intervention strategy.The L-arginine biosynthesis pathway consists of eight enzymes that catalyse the conversion of L-glutamate to L-arginine. Arginine auxotrophs (argB/argF deletion mutants) of Mycobacterium tuberculosis are rapidly sterilised in mice, while inhibition of ArgJ with Pranlukast was found to clear chronic M. tuberculosis infection in a mouse model. Enzymes in the arginine biosynthetic pathway have therefore emerged as promising targets for anti-tuberculosis drug discovery. In this work, the ligandability of four enzymes of the pathway ArgB, ArgC, ArgD and ArgF is assessed using a fragment-based approach. We identify several hits against these enzymes validated with biochemical and biophysical assays, as well as X-ray crystallographic data, which in the case of ArgB were further confirmed to have on-target activity against M. tuberculosis. These results demonstrate the potential for more enzymes in this pathway to be targeted with dedicated drug discovery programmes.

Polydactyly is a highly heterogeneous group of skeletal deformities in clinical and genetic background. The variation spectrum in Chinese sporadic polydactyly has not been comprehensively analyzed. To elucidate genetic variation spectrum and genotype-phenotype correlations in Chinese patients with polydactyly, we conducted comprehensive genetic analysis of patients nationwide using targeted sequencing.

A total of 181 patients diagnosed with polydactylies were recruited. We designed a targeted capture panel for sequencing 721 genes that are associated with the pathogenesis of skeletal dysplasia. We performed rigorous variant- and gene-level filtrations to identify potentially damaging variants, followed by enrichment analysis and gene prioritization.

A total of 568 deleterious variants of 293 genes were identified in 173 of 181 patients with a positive rate of 95.6% by targeted sequencing. For each sample, an average of 3.17 deleterious variants were identified. Especially, 14 pathogenic or likely pathogenic variants were identified in 10 genes in 14 patients out of the 181 patients, providing a positive molecular diagnostic rate of 7.7%.

Targeted sequencing analysis provides a high efficiency approach for the genetic diagnosis of polydactyly. This is the largest next generation sequencing study performed to date in patients with polydactyly and represents the genetic basis of polydactyly typically encountered in genetics clinics.

Targeted sequencing analysis provides a high efficiency approach for the genetic diagnosis of polydactyly. This is the largest next generation sequencing study performed to date in patients with polydactyly and represents the genetic basis of polydactyly typically encountered in genetics clinics.Genome-wide association studies (GWAS) have identified numerous common genetic variants associated with complex human traits and diseases. However, the translation of GWAS discoveries into biological and clinical insights is highly challenging. In this study, we present a novel bioinformatics approach for enhancing the functional interpretation of GWAS signals, based on their integration with single-cell (sc)RNA-seq datasets that examine developmental processes. this website Our approach performs three tasks (1) Identification of links between cell differentiation trajectories and traits; (2) Elucidation of biological processes and molecular pathways that underlie such trajectory-trait links; and (3) Prioritization of target genes that carry the links between trajectories, pathways and traits. We applied our method to a set of 11 traits of various pathologies, and 12 scRNA-seq datasets of diverse developmental processes, and it readily detected well-established biological connections, including those between the maturation of cortical inhibitory interneurons and schizophrenia, hepatocytes and cholesterol levels, and pancreatic beta-islet cells and type-2 diabetes.