Comparative analysis reveals a role for TGF-β in shaping the residency-related transcriptional signature in tissue-resident memory CD8+ T cells.

Tissue-resident CD8+ memory T (TRM) cells are immune cells that permanently reside at tissue sites where they play an important role in providing rapid protection against reinfection. They are not only phenotypically and functionally distinct from their circulating memory counterparts, but also exhibit a unique transcriptional profile. To date, the local tissue signals required for their development and long-term residency are not well understood. So far, the best-characterised tissue-derived signal is transforming growth factor-β (TGF-β), which has been shown to promote the development of these cells within tissues. In this study, we aimed to determine to what extent the transcriptional signatures of TRM cells from multiple tissues reflects TGF-β imprinting. We activated murine CD8+ T cells, stimulated them in vitro by TGF-β, and profiled their transcriptomes using RNA-seq. Upon comparison, we identified a TGF-β-induced signature of differentially expressed genes between TGF-β-stimulated and -unstimulated cells. Next, we linked this in vitro TGF-β-induced signature to a previously identified in vivo TRM-specific gene set and found considerable (>50%) overlap between the two gene sets, thus showing that a substantial part of the TRM signature can be attributed to TGF-β signalling. Finally, gene set enrichment analysis further revealed that the altered gene signature following TGF-β exposure reflected transcriptional signatures found in TRM cells from both epithelial and non-epithelial tissues. In summary, these findings show that TGF-β has a broad footprint in establishing the residency-specific transcriptional profile of TRM cells, which is detectable in TRM cells from diverse tissues. They further suggest that constitutive TGF-β signaling might be involved for their long-term persistence at tissue sites

Elevated serum alpha-1 antitrypsin is a major component of GlycA-associated risk for future morbidity and mortality

Background GlycA is a nuclear magnetic resonance (NMR) spectroscopy biomarker that predicts risk of disease from myriad causes. It is heterogeneous; arising from five circulating glycoproteins with dynamic concentrations: alpha-1 antitrypsin (AAT), alpha-1-acid glycoprotein (AGP), haptoglobin (HP), transferrin (TF), and alpha-1-antichymotrypsin (AACT). The contributions of each glycoprotein to the disease and mortality risks predicted by GlycA remain unknown. Methods We trained imputation models for AAT, AGP, HP, and TF from NMR metabolite measurements in 626 adults from a population cohort with matched NMR and immunoassay data. Levels of AAT, AGP, and HP were estimated in 11,861 adults from two population cohorts with eight years of follow-up, then each biomarker was tested for association with all common endpoints. Whole blood gene expression data was used to identify cellular processes associated with elevated AAT. Results Accurate imputation models were obtained for AAT, AGP, and HP but not for TF. While AGP had the strongest correlation with GlycA, our analysis revealed variation in imputed AAT levels was the most predictive of morbidity and mortality for the widest range of diseases over the eight year follow-up period, including heart failure (meta-analysis hazard ratio = 1.60 per standard deviation increase of AAT, P-value = 1×10−10), influenza and pneumonia (HR = 1.37, P = 6×10−10), and liver diseases (HR = 1.81, P = 1×10−6). Transcriptional analyses revealed association of elevated AAT with diverse inflammatory immune pathways. Conclusions This study clarifies the molecular underpinnings of the GlycA biomarker’s associated disease risk, and indicates a previously unrecognised association between elevated AAT and severe disease onset and mortality.Peer reviewe

Neonatal genetics of gene expression reveal potential origins of autoimmune and allergic disease risk

Abstract: Chronic immune-mediated diseases of adulthood often originate in early childhood. To investigate genetic associations between neonatal immunity and disease, we map expression quantitative trait loci (eQTLs) in resting myeloid cells and CD4+ T cells from cord blood samples, as well as in response to lipopolysaccharide (LPS) or phytohemagglutinin (PHA) stimulation, respectively. Cis-eQTLs are largely specific to cell type or stimulation, and 31% and 52% of genes with cis-eQTLs have response eQTLs (reQTLs) in myeloid cells and T cells, respectively. We identified cis regulatory factors acting as mediators of trans effects. There is extensive colocalisation between condition-specific neonatal cis-eQTLs and variants associated with immune-mediated diseases, in particular CTSH had widespread colocalisation across diseases. Mendelian randomisation shows causal neonatal gene expression effects on disease risk for BTN3A2, HLA-C and others. Our study elucidates the genetics of gene expression in neonatal immune cells, and aetiological origins of autoimmune and allergic diseases

Deletion of Trim28 in committed adipocytes promotes obesity but preserves glucose tolerance.

The effective storage of lipids in white adipose tissue (WAT) critically impacts whole body energy homeostasis. Many genes have been implicated in WAT lipid metabolism, including tripartite motif containing 28 (Trim28), a gene proposed to primarily influence adiposity via epigenetic mechanisms in embryonic development. However, in the current study we demonstrate that mice with deletion of Trim28 specifically in committed adipocytes, also develop obesity similar to global Trim28 deletion models, highlighting a post-developmental role for Trim28. These effects were exacerbated in female mice, contributing to the growing notion that Trim28 is a sex-specific regulator of obesity. Mechanistically, this phenotype involves alterations in lipolysis and triglyceride metabolism, explained in part by loss of Klf14 expression, a gene previously demonstrated to modulate adipocyte size and body composition in a sex-specific manner. Thus, these findings provide evidence that Trim28 is a bona fide, sex specific regulator of post-developmental adiposity and WAT function

An atlas of genetic scores to predict multi-omic traits

The use of omic modalities to dissect the molecular underpinnings of common diseases and traits is becoming increasingly common. But multi-omic traits can be genetically predicted, which enables highly cost-effective and powerful analyses for studies that do not have multi-omics. Here we examine a large cohort (the INTERVAL study; n = 50,000 participants) with extensive multi-omic data for plasma proteomics (SomaScan, n = 3,175; Olink, n = 4,822), plasma metabolomics (Metabolon HD4, n = 8,153), serum metabolomics (Nightingale, n = 37,359) and whole-blood Illumina RNA sequencing (n = 4,136), and use machine learning to train genetic scores for 17,227 molecular traits, including 10,521 that reach Bonferroni-adjusted significance. We evaluate the performance of genetic scores through external validation across cohorts of individuals of European, Asian and African American ancestries. In addition, we show the utility of these multi-omic genetic scores by quantifying the genetic control of biological pathways and by generating a synthetic multi-omic dataset of the UK Biobank to identify disease associations using a phenome-wide scan. We highlight a series of biological insights with regard to genetic mechanisms in metabolism and canonical pathway associations with disease; for example, JAK-STAT signalling and coronary atherosclerosis. Finally, we develop a portal ( https://www.omicspred.org/ ) to facilitate public access to all genetic scores and validation results, as well as to serve as a platform for future extensions and enhancements of multi-omic genetic scores

Evolution and transmission of antibiotic resistance is driven by Beijing lineage Mycobacterium tuberculosis in Vietnam

A previous investigation has elucidated the landscape of Mtb genomic diversity and transmission dynamics in Ho Chi Minh City, Vietnam. Here, we expand the scope of this survey by adding a substantial number of additional genomes (total sample size: 2,542) and phenotypic drug susceptibility data for the majority of isolates. We aim to explore the prevalence and evolutionary dynamics of drug resistance and our ability to predict drug resistance from sequencing data. Among isolates tested phenotypically against first-line drugs, we observed high rates of streptomycin [STR, 37.7% ( N = 573/1,520)] and isoniazid resistance [INH, 25.7% ( N = 459/1,786)] and lower rates of resistance to rifampicin [RIF, 4.9% ( N = 87/1,786)] and ethambutol [EMB, 4.2% ( N = 75/1,785)]. Relative to global benchmarks, resistance to STR and INH was predicted accurately when applying the TB-Profiler algorithm to whole genome sequencing data (sensitivities of 0.81 and 0.87, respectively), while resistance to RIF and EMB was predicted relatively poorly (sensitivities of 0.70 and 0.44, respectively). Exploring the evolution of drug resistance revealed the main phylogenetic lineages to display differing dynamics and tendencies to evolve resistance via mutations in certain genes. The Beijing sublineage L2.2.1 was found to acquire de novo resistance mutations more frequently than isolates from other lineages and to suffer no apparent fitness cost acting to impede the transmission of resistance. Mutations conferring resistance to INH and STR arose earlier, on average, than those conferring resistance to RIF and are now more widespread across the phylogeny. The high prevalence of “background” INH resistance, combined with high rates of RIF mono-resistance (20.7%, N = 18/87), suggests that rapid assays for INH resistance will be valuable in this setting. These tests will allow the detection of INH mono-resistance and will allow multi-drug-resistant isolates to be distinguished from isolates with RIF mono-resistance. IMPORTANCE Drug-resistant tuberculosis (TB) infection is a growing and potent concern, and combating it will be necessary to achieve the WHO’s goal of a 95% reduction in TB deaths by 2035. While prior studies have explored the evolution and spread of drug resistance, we still lack a clear understanding of the fitness costs (if any) imposed by resistance-conferring mutations and the role that Mtb genetic lineage plays in determining the likelihood of resistance evolution. This study offers insight into these questions by assessing the dynamics of resistance evolution in a high-burden Southeast Asian setting with a diverse lineage composition. It demonstrates that there are clear lineage-specific differences in the dynamics of resistance acquisition and transmission and shows that different lineages evolve resistance via characteristic mutational pathways

An interaction map of circulating metabolites, immune gene networks, and their genetic regulation

Background: Immunometabolism plays a central role in many cardiometabolic diseases. However, a robust map of immune-related gene networks in circulating human cells, their interactions with metabolites, and their genetic control is still lacking. Here, we integrate blood transcriptomic, metabolomic, and genomic profiles from two population-based cohorts (total N = 2168), including a subset of individuals with matched multi-omic data at 7-year follow-up. Results: We identify topologically replicable gene networks enriched for diverse immune functions including cytotoxicity, viral response, B cell, platelet, neutrophil, and mast cell/basophil activity. These immune gene modules show complex patterns of association with 158 circulating metabolites, including lipoprotein subclasses, lipids, fatty acids, amino acids, small molecules, and CRP. Genome-wide scans for module expression quantitative trait loci (mQTLs) reveal five modules with mQTLs that have both cis and trans effects. The strongest mQTL is in ARHGEF3 (rs1354034) and affects a module enriched for platelet function, independent of platelet counts. Modules of mast cell/basophil and neutrophil function show temporally stable metabolite associations over 7-year follow-up, providing evidence that these modules and their constituent gene products may play central roles in metabolic inflammation. Furthermore, the strongest mQTL in ARHGEF3 also displays clear temporal stability, supporting widespread trans effects at this locus. Conclusions: This study provides a detailed map of natural variation at the blood immunometabolic interface and its genetic basis, and may facilitate subsequent studies to explain inter-individual variation in cardiometabolic disease.Peer reviewe

Integrative genomics to understand immune function and regulation

© 2017 Dr. Artika Praveeta NathCharacterising the mechanistic principles underlying immune function and regulation will help us understand how the immune system provides effective host defence. However, the highly complex and multi-level nature of the immune system requires a systems-level analysis to gain multi-dimensional insight and unravel its complexity. High-throughput profiling technologies allow quantitative measurement of various immunological parameters that capture system-wide information. This has led to the generation of large-scale multi-omics datasets from human populations, experimental set-ups, and a compendium of immune cell types. Developments in bioinformatics offer integrative approaches to explore the functional and regulatory relationships within and between various organisational levels of the immune system as well as across other biological systems. For this thesis, multi-omic analysis was used to characterise immune processes in terms of genetics, transcriptional networks and interactions with metabolism. First, I mapped the genetics and interactions of immune gene networks with circulating metabolites in in a population-based study. I integrated blood transcriptomic, metabolomic, and genomic profiles from two population-based cohorts, including a subset with 7-year follow-up sampling. I identified topologically robust gene networks enriched for immune functions including cytotoxicity, viral response, B cell, platelet, neutrophil, and mast cell/basophil activity. These immune gene modules showed complex patterns of association with 158 circulating metabolites, including lipoprotein subclasses, lipids, fatty acids, amino acids, and CRP. Genome-wide scans for module expression quantitative trait loci (mQTLs) revealed five modules with mQTLs with both cis and trans effects. Then, I explored the underlying shared genetic architecture between correlated cytokines, the regulatory agents of the immune system. Multivariate genome-wide association scan was performed to identify genetic variants regulating circulating cytokines in ~9,000 individuals from three independent population studies. Eight loci were identified as regulating this network, including two previously undetected loci. Expression quantitative loci (eQTL) analysis revealed that these loci harbour eQTLs. Further linking these loci with genetic variants associated with disease risk provided insight into the possible inflammatory pathways underlying these common diseases. Thirdly, I explored a particular component of the immune system, immunological memory; with emphasis on tissue resident memory T-cells (TRM cells). I employed a network-based approach to identify a transcriptional sub-network related to the residency of murine TRM cells isolated from various tissues. Comparative analysis further revealed that expression profiles of tissue resident immune cells from different lineages share transcriptional similarity. Finally, the role of TGF-β, an extrinsic tissue-derived factor in influencing the transcriptional signature of TRM cells was investigated. I compared the global transcription of T-cells induced in vitro by TGF-β with the residency-related transcription signature previously established in TRM cells. This demonstrated that the transcriptional signature of TRM cells is largely driven by TGF-β. Findings from this thesis demonstrate the power of integrative bioinformatics analyses to gain novel insights into the immune system, which can assist in predicting its response to perturbations. It also may help explain how inter-individual variability in immune function contributes to differential disease susceptibility and treatment outcomes. This thesis offers a general framework to systematically integrate and analyse multi-omics data to answer important biological questions

Using blood informative transcripts in geographical genomics: impact of lifestyle on gene expression in Fijians

This Document is Protected by copyright and was first published by Frontiers. All rights reserved. It is reproduced with permission.© 2012 Nath, Arafat and Gibson. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.DOI:10.3389/fgene.2012.00243In previous geographical genomics studies of the impact of lifestyle on gene expression inferred from microarray analysis of peripheral blood samples, we described the complex influences of culture, ethnicity, and gender in Morocco, and of pregnancy in Brisbane. Here we describe the use of nanofluidic Fluidigm quantitative RT-PCR arrays targeted at a set of 96 transcripts that are broadly informative of the major axes of immune gene expression, to explore the population structure of transcription in Fiji. As in Morocco, major differences are seen between the peripheral blood transcriptomes of rural villagers and residents of the capital city, Suva.The effect is much greater in Indian villages than in Melanesian high-landers and appears to be similar with respect to the nature of at least two axes of variation. Gender differences are much smaller than ethnicity or lifestyle effects. Body mass index is shown to associate with one of the axes as it does in Atlanta and Brisbane, establishing a link between the epidemiological transition of human metabolic disease, and gene expression profiles

An interaction map of circulating metabolites, immune gene networks, and their genetic regulation

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