Biology subjects

Khalili, B.

Publications and source records attributed to Khalili, B..

6 recordsLinked to original sources

bioRxiv2024

Associations between common genetic variants and income provide insights about the socioeconomic health gradient

We conducted a genome-wide association study (GWAS) on income among individuals of European descent and leveraged the results to investigate the socio-economic health gradient (N=668,288). We found 162 genomic loci associated with a common genetic factor underlying various income measures, all with small effect sizes. Our GWAS-derived polygenic index captures 1 - 4% of income variance, with only one-fourth attributed to direct genetic effects. A phenome-wide association study using this polygenic index showed reduced risks for a broad spectrum of diseases, including hypertension, obesity, type 2 diabetes, coronary atherosclerosis, depression, asthma, and back pain. The income factor showed a substantial genetic correlation (0.92, s.e. = .006) with educational attainment (EA). Accounting for EAs genetic overlap with income revealed that the remaining genetic signal for higher income related to better mental health but reduced physical health benefits and increased participation in risky behaviours such as drinking and smoking.

genetics↗

bioRxiv2020

Untargeted metabolome- and transcriptome-wide association study identifies causal genes modulating metabolite concentrations in urine

In this study we investigate the results of a metabolome- and transcriptome-wide association study to identify genes influencing the human metabolome. We used RNAseq data from lymphoblastoid cell lines (LCLs) derived from 555 Caucasian individuals to characterize their transcriptome. As for the metabolome we took an untargeted approach using binned features from 1H nuclear magnetic resonance spectroscopy (NMR) of urine samples from the same subjects allowing for data-driven discovery of associated compounds (rather than working with a limited set of quantified metabolites). Using pairwise linear regression we identified 21 study-wide significant associations between metabolome features and gene expression levels. We observed the most significant association between the gene ALMS1 and two adjacent metabolome features at 2.0325 and 2.0375 ppm. By using our previously developed metabomatching methodology, we found N-Acetylaspartate (NAA) as the potential underlying metabolite whose urine concentration is correlated with ALMS1 expression. Indeed, a number of metabolome- and genome-wide association studies (mGWAS) had already suggested the locus of this gene to be involved in regulation of N-acetylated compounds, yet were not able to identify unambiguously the exact metabolite, nor to disambiguate between ALMS1 and NAT8, another gene found in the same locus as the mediator gene. The second highest significant association was observed between HPS1 and two metabolome features at 2.8575 and 2.8725 ppm. Metabomatching of the association profile of HPS1 with all metabolite features pointed at trimethylamine (TMA) as the most likely underlying metabolite. mGWAS had previously implicated a locus containing HPS1 to be associated with TMA concentrations in urine but could not disambiguate this association signal from PYROXD2, a gene in the same locus. We used Mendelian randomization to show for both ALMS1 and HPS1 that their expression is causally linked to the respective metabolite concentrations. Our study provides evidence that the integration of metabolomics with gene expression data can support mQTL analysis, helping to identify the most likely gene involved in the modulation of the metabolite concentration.

genomics↗

bioRxiv2019

The concentration of heterotrophic growth substrates in culture medium is a crucial determinant of the culturability of subsurface soil microbes

The vast majority of microbes inhabiting oligotrophic shallow subsurface soil environments have not been isolated or studied under controlled laboratory conditions. In part, the challenges associated with isolating shallow subsurface microbes may persist because microbes in deeper soils are adapted to low nutrient availability or quality. Here we use high-throughput dilution-to-extinction culturing to isolate shallow subsurface microbes from a conifer forest in Arizona, USA. We hypothesized that the concentration of heterotrophic substrates in microbiological growth medium would affect which microbial taxa were culturable from these soils. To test this, we diluted extracted cells into one of two custom-designed defined growth media that differed only by a 100-fold difference in the concentration of amino acids and organic carbon. Across both media, we isolated a total of 133 pure cultures, all of which were classified as Actinobacteria or Alphaproteobacteria. The substrate availability dictated which actinobacterial phylotypes were culturable but had no significant effect on the culturability of Alphaproteobacteria. We isolated cultures that were representative of the most abundant phylotype in the soil microbial community (Bradyrhizobium spp.) and representatives of five of the top 10 most abundant Actinobacteria phylotypes, including Nocardioides spp., Mycobacterium spp., and several other phylogenetically-divergent lineages. Flow cytometry of nucleic acid-stained cells showed that cultures isolated on low-substrate medium had significantly lower nucleic-acid fluorescence than those isolated on high-substrate medium. These results show that dilution-to-extinction is an effective method to isolate abundant soil microbes and the concentration of substrates in culture medium influences the culturability of specific microbial lineages. ImportanceIsolating environmental microbes and studying their physiology under controlled conditions is an essential aspect of understanding their ecology. Subsurface ecosystems are typically nutrient-poor environments that harbor diverse microbial communities--the majority of which are thus far uncultured. In this study, we use modified high-throughput cultivation methods to isolate subsurface soil microbes. We show that a component of whether a microbe is culturable from subsurface soils is the concentration of growth substrates in the culture medium. Our results offer new insight into technical approaches and growth medium design that can be used to access the uncultured diversity of soil microbes.

microbiology↗

bioRxiv2019

Automated analysis of large-scale NMR data generates metabolomic signatures and links them to candidate metabolites

Identification of metabolites in large-scale 1H NMR data from human biofluids remains challenging due to the complexity of the spectra and their sensitivity to pH and ionic concentrations. In this work, we test the capacity of three analysis tools to extract metabolite signatures from 968 NMR profiles of human urine samples. Specifically, we studied sets of co-varying features derived from Principal Component Analysis (PCA), the Iterative Signature Algorithm (ISA) and Averaged Correlation Profiles (ACP), a new method we devised inspired by the STOCSY approach. We used our previously developed metabomatching method to match the sets generated by these algorithms to NMR spectra of individual metabolites available in public databases. Based on the number and quality of the matches we concluded that both ISA and ACP can robustly identify about a dozen metabolites, half of which were shared, while PCA did not produce any signatures with robust matches.

bioinformatics↗

bioRxiv2018

PhenoMeNal: Processing and analysis of Metabolomics data in the Cloud

BackgroundMetabolomics is the comprehensive study of a multitude of small molecules to gain insight into an organisms metabolism. The research field is dynamic and expanding with applications across biomedical, biotechnological and many other applied biological domains. Its computationally-intensive nature has driven requirements for open data formats, data repositories and data analysis tools. However, the rapid progress has resulted in a mosaic of independent - and sometimes incompatible - analysis methods that are difficult to connect into a useful and complete data analysis solution.\n\nFindingsThe PhenoMeNal (Phenome and Metabolome aNalysis) e-infrastructure provides a complete, workflow-oriented, interoperable metabolomics data analysis solution for a modern infrastructure-as-a-service (IaaS) cloud platform. PhenoMeNal seamlessly integrates a wide array of existing open source tools which are tested and packaged as Docker containers through the projects continuous integration process and deployed based on a kubernetes orchestration framework. It also provides a number of standardized, automated and published analysis workflows in the user interfaces Galaxy, Jupyter, Luigi and Pachyderm.\n\nConclusionsPhenoMeNal constitutes a keystone solution in cloud infrastructures available for metabolomics. It provides scientists with a ready-to-use, workflow-driven, reproducible and shareable data analysis platform harmonizing the software installation and configuration through user-friendly web interfaces. The deployed cloud environments can be dynamically scaled to enable large-scale analyses which are interfaced through standard data formats, versioned, and have been tested for reproducibility and interoperability. The flexible implementation of PhenoMeNal allows easy adaptation of the infrastructure to other application areas and omics research domains.

bioinformatics↗

bioRxiv2017

Feedback inhibition of Ras activity coordinates cell fusion with cell-cell contact

In fission yeast Schizosaccharomyces pombe, pheromone signalling engages a GPCR-Ras-MAPK cascade to trigger sexual differentiation leading to gamete fusion. Cell-cell fusion necessitates local cell wall digestion, the location of which relies on an initially dynamic actin fusion focus that becomes stabilized upon local enrichment of the signalling cascade. We constructed a live-reporter of active Ras1 (Ras1-GTP), also functional in S. cerevisiae, which revealed Ras activity at polarity sites peaking on the fusion structure before fusion. Remarkably, constitutive Ras1 activation promoted fusion focus stabilization and fusion attempts irrespective of cell-cell pairing, leading to cell lysis. Ras1 activity is restricted by the GTPase activating protein (GAP) Gap1, itself recruited to sites of Ras1-GTP. While the GAP domain on its own does not suffice for this localization, its recruitment to Ras1-GTP sites is essential to block untimely fusion attempts. We conclude that negative feedback control of Ras activity restrains the MAPK signal and couples fusion with cell-cell engagement.

cell biology↗