A large number of studies have highlighted the importance of gut microbiome composition in shaping fat deposition in mammals. Several studies have also highlighted how host genome controls the abundance of certain species that make up the gut microbiota. We propose a systematic approach to infer how the host genome can control the gut microbiome, which in turn contributes to the host phenotype determination. We implemented a mediation test that can be applied to measured and latent dependent variables to describe fat deposition in swine (Sus scrofa). In this study, we identify several host genomic features having a microbiome-mediated effects on fat deposition. This demonstrates how the host genome can affect the phenotypic trait by inducing a change in gut microbiome composition that leads to a change in the phenotype. Host genomic variants identified through our analysis are different than the ones detected in a traditional genome-wide association study. In addition, the use of latent dependent variables allows for the discovery of additional host genomic features that do not show a significant effect on the measured variables. Microbiome-mediated host genomic effects can help understand the genetic determination of fat deposition. Since their contribution to the overall genetic variance is usually not included in association studies, they can contribute to filling the missing heritability gap and provide further insights into the host genome - gut microbiome interplay. Further studies should focus on the portability of these effects to other populations as well as their preservation when pro-/pre-/anti-biotics are used (i.e. remediation).The technology of noninvasive prenatal testing (NIPT) enables risk-free detection of genetic conditions in the fetus, by analysis of cell-free DNA (cfDNA) in maternal blood. For chromosomal abnormalities, NIPT often effectively replaces invasive tests (e.g. amniocentesis), although it is considered as screening rather than diagnostics. Most recently, the NIPT has been applied to genome-wide, comprehensive genotyping of the fetus using cfDNA, i.e. identifying all its genetic variants and mutations. Previously, we suggested that NIPD should be treated as a special case of variant calling, and presented Hoobari, the first software tool for noninvasive fetal variant calling. Using a unique pipeline, we were able to comprehensively decipher the inheritance of SNPs and indels. A few caveats still exist in this pipeline. Performance was lower for indels and biparental loci (i.e. where both parents carry the same mutation), and performance was not uniform across the genome. Here we utilized standardized methods for benchmarking of variant calling pipelines and applied them to noninvasive fetal variant calling. https://www.selleckchem.com/products/auranofin.html By using the best performing pipeline and by focusing on coding regions, we showed that noninvasive fetal genotyping greatly improves performance, particularly in indels and biparental loci. These results emphasize the importance of using widely accepted concepts to describe the challenge of genome-wide NIPT of point mutations; and demonstrate a benchmarking process for the first time in this field. This study brings genome-wide and complete NIPD closer to the clinic; while potentially alleviating uncertainty and anxiety during pregnancy, and promoting informed choices among families and physicians.Interaction among different pathways, such as metabolic, signaling and gene regulatory networks, of cellular system is responsible to maintain homeostasis in a mammalian cell. Malfunctioning of this cooperation may lead to many complex diseases, such as cancer and type 2 diabetes. Timescale differences among these pathways make their integration a daunting task. Metabolic, signaling and gene regulatory networks have three different timescales, such as, ultrafast, fast and slow respectively. The article deals with this problem by developing a support vector regression (SVR) based three timescale model with the application of genetic algorithm based nonlinear controller. The proposed model can successfully capture the nonlinear transient dynamics and regulations of such integrated biochemical pathway under consideration. Besides, the model is quite capable of predicting the effects of certain drug targets for many types of complex diseases. Here, energy and cell proliferation management of mammalian cancer cells have been explored and analyzed with the help of the proposed novel approach. Previous investigations including in silico/in vivo/in vitro experiments have validated the results (the regulations of glucose transporter 1 (glut1), hexokinase (HK), and hypoxia-inducible factor-1 α (HIF-1 α ) among others, and the switching of pyruvate kinase (M2 isoform) between dimer and tetramer) generated by this model proving its effectiveness. Subsequently, the model predicts the effects of six selected drug targets, such as, the deactivation of transketolase and glucose-6-phosphate isomerase among others, in the case of mammalian malignant cells in terms of growth, proliferation, fermentation, and energy supply in the form of adenosine triphosphate (ATP).Growing evidence suggests that prebiotics may induce weight loss and alleviate non-alcoholic fatty liver disease (NAFLD) via modulation of the gut microbiota. However, key members of the gut microbiota that may mediate the beneficial effects of prebiotics remain elusive. Here, we find that restricted prebiotic feeding during active phase (HF-ARP) induced weight-independent alleviation of liver steatosis and reduced serum cholesterol in high-fat diet (HF) fed mice more significantly than unrestricted feeding (HF-UP). HF-ARP mice also showed concomitantly altered gut microbiota structure that was different from HF-UP group along with significantly increased production of total short-chain fatty-acids (SCFAs). Amplicon sequence variants (ASVs) were clustered into co-abundant groups (CAGs) as potential functional groups that may respond distinctively to prebiotic consumption and prebiotic feeding regime. Prebiotic feeding induces significant alterations in CAG abundances by day 7. Eight of 32 CAGs were promoted by prebiotics, including CAG17 with the most abundant ASV from Parabacteroides, CAG22 with Bacteroides thetaiotamicron and CAG32 with Fecalibaculum and Akkermansia.