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Inouye, M.

Publications and source records attributed to Inouye, M..

At least 19 recordsLinked to original sources

An Injectable Antisense Oligonucleotide (ASO) Approach to Treat Obesity in Preclinical Models

Obesity leads to metabolic dysregulation and thus drives complications including type 2 diabetes, cardiovascular disease, steatotic liver disease and some cancers. Obesity is primarily driven by lifestyle factors, which impacts individuals differently depending on their genetic susceptibility. Studies on this genetic risk have provided major insights into gene networks that modulate obesity development, including incretins and their regulatory circuits. Indeed, incretin-based therapeutics (i.e. GLP1 RA) have proven to be enormously successful in promoting weight loss, but do so with several ongoing challenges including significant weight rebound upon cessation, loss of lean mass, and side effects that reduce therapeutic longevity. Thus, ongoing research is necessary to identify additional interventions that overcome or minimise these concerns. To this end, in our previous work we have identified a novel lipid regulatory protein with potential to influence lipid storage. In the current study, we use antisense oligonucleotides (ASO) to silence this protein in mouse models of diet induced obesity, and demonstrate that once weekly IP injections of ASO prevents the development of obesity, and defends against the progression of existing obesity. This is shown to be effective in multiple mouse strains fed different diets over various time frames, with gene targeting ASOs leading to reductions in fat mass, no change in lean mass, along with improvements in whole body glucose tolerance and steatotic liver disease. Additional studies demonstrate that this phenotype is likely driven by effects in adipose tissue, but not liver, and is not linked to activation of the Type 1 Interferon response, a previously implicated off target effect of ASO administration. Thus, these studies demonstrate that our injectable ASO approach in mice can successfully treat obesity in the absence of major change in lean mass, thus positioning this intervention as a therapeutic program that works alternatively to, or in conjunction with current weight loss medicines.

physiology↗

Bacterial diversity and strain dynamics in the infant respiratory microbiome during the first year of life

BackgroundMicrobial colonisation of the human body begins immediately after birth, with each body site forming a distinct ecological niche. While early gut microbial dynamics and their links to paediatric health have been considerably studied, the bacterial colonisation of the early infant respiratory system remains poorly understood, particularly at the species and strain level. ResultsHere, we generated and analysed whole genome sequencing of 925 isolates from six dominant genera in nasopharyngeal samples from 58 healthy infants enrolled in the Childhood Asthma Study (CAS) in Western Australia, collected longitudinally from birth (2, 6, and 12 months old). Our results expanded genomic reference catalogues and uncovered substantial strain-level diversity in dominant infant airway taxa. Plate-sweep metagenomics identified four microbiome profile groups (MPGs) with age-dependent membership, confirming prior observations while enabling high-resolution species and strain analyses. Community maturation was characterised by a shift from early Staphylococcus aureus dominance to increased Moraxella catarrhalis dominance by 12 months of age, alongside marked temporal changes in prevalence and cohort-level strain diversity. ConclusionsThese findings resolve infant nasopharyngeal microbiota composition at the species and strain level, revealing taxon-specific colonisation patterns. By substantially expanding publicly available reference genomes for underrepresented airway taxa, this work also provides a foundation for functional follow-up studies of the early respiratory microbiota and respiratory outcomes.

microbiology↗

The NHGRI-EBI GWAS Catalog: standards for reusability, sustainability and diversity

The NHGRI-EBI GWAS Catalog serves as a vital resource for the genetic research community, providing access to the most comprehensive database of human GWAS results. Currently, it contains close to 7,000 publications for more than 15,000 traits, from which more than 625,000 lead associations have been curated. Additionally, 85,000 full genome-wide summary statistics datasets - containing association data for all variants in the analysis - are available for downstream analyses such as meta-analysis, fine-mapping, Mendelian randomisation or development of polygenic risk scores. As a centralised repository for GWAS results, the GWAS Catalog sets and implements standards for data submission and harmonisation, and encourages the use of consistent descriptors for traits, samples and methodologies. We share processes and vocabulary with the PGS Catalog, improving interoperability for a growing user group. Here, we describe the latest changes in data content, improvements in our user interface, and the implementation of the GWAS-SSF standard format for summary statistics. We address the challenges of handling the rapid increase in large-scale molecular quantitative trait GWAS and the need for sensitivity in the use of population and cohort descriptors while maintaining data interoperability and reusability.

genetics↗

Variant-to-function dissection of rare non-coding GWAS loci with high impact on blood traits

Understanding the function of genetic variants associated with human traits and diseases remains a significant challenge. Here we combined analyses based on natural genetic variation and genetic engineering to dissect the function of 94 non-coding variants associated with haematological traits. We describe 22 genetic variants impacting haematological variation through gene expression. Further, through in-depth functional analysis, we illustrate how a rare, non-coding variant near the CUX1 transcription factor impacts megakaryopoiesis through modulation of the CUX1 transcriptional cascade. Collectively, our findings enhance the functional interpretation of genetic association studies and advance understanding of how non-coding variants contribute to blood and immune system variation.

genetics↗

Skeletal Muscle Specific PolG Dysfunction Activates the Integrated Stress Response and Promotes a Cachexia like Phenotype

During mitochondrial damage, information is relayed between the mitochondria and nucleus to coordinate precise responses to preserve cellular health. One such pathway is the mitochondrial integrated stress response (mtISR), which is specifically activated by mitochondrial DNA (mtDNA) damage. However, the causal molecular signals responsible for this activation remain elusive. A gene often associated with mtDNA mutations/deletions is Polg1, which encodes the mitochondrial DNA Polymerase {gamma} (PolG). Here, we describe what is to our knowledge the first conditional muscle specific model of PolG mutation, and demonstrate a rapid development of mitochondrial dysfunction and cachexia in these animals from [~]5 months of age. Detailed molecular profiling of muscles demonstrated robust activation of the mtISR, likely mediated upstream by the HRI/DELE1 stress pathway. This was accompanied by striking alterations to the mitochondrial folate cycle and its metabolites, strongly implying imbalanced folate intermediates as a previously unrecognised pathology driving the mtISR in mitochondrial disease.

physiology↗

Misexpression of inactive genes in whole blood is associated with nearby rare structural variants

Gene misexpression is the aberrant transcription of a gene in a context where it is usually inactive. Despite its known pathological consequences in specific rare diseases, we have a limited understanding of its wider prevalence and mechanisms in humans. To address this, we analyzed gene misexpression in 4,568 whole blood bulk RNA sequencing samples from INTERVAL study blood donors. We found that while individual misexpression events occur rarely, in aggregate they were found in almost all samples and over half of inactive genes. Using 2,821 paired whole genome and RNA sequencing samples, we identified that misexpression events are enriched in cis for rare structural variants. We established putative mechanisms through which a subset of SVs lead to gene misexpression, including transcriptional readthrough, transcript fusions and gene inversion. Overall, we develop misexpression as a novel type of transcriptomic outlier analysis and extend our understanding of the variety of mechanisms by which genetic variants can influence gene expression.

genetics↗

Whole blood transcriptional profiles and the pathogenesis of tuberculous meningitis

BackgroundMortality and morbidity from tuberculous meningitis (TBM) are frequent and strongly associated with the inflammatory response to Mycobacterium tuberculosis infection. However, the mechanisms driving the associations are uncertain. We sought to identify the gene modules, hubs and pathways associated with the pathogenesis and mortality from TBM, and to identify which best-predicted death. MethodsWe used whole blood RNA sequencing to obtain transcriptional profiles from 281 Vietnamese adults with TBM (207 HIV-negative; 74 HIV-positive), 295 with pulmonary TB (PTB), and 30 healthy controls. The TBM cohort was divided randomly into a discovery cohort (n=142) and a validation cohort (n=139). Weighted gene co-expression network analysis identified clusters of genes (or modules) and hub genes associated with death or disease severity. An overrepresentation analysis identified pathways associated with TBM mortality, with a consensus analysis identifying consensual patterns between HIV-positive and HIV-negative individuals. A multivariate elastic-net Cox regression model selected the candidate predictors of TBM mortality, then model prediction performance using logistic regression and internal bootstrap validation to choose best predictors. ResultsOverall, TBM mortality was associated with increased neutrophil activation and decreased T and B cell activation pathways. Death from TBM was associated with increased angiogenesis in HIV-positive adults, and with activated TNF signaling and down-regulated extracellular matrix organization in HIV-negative adults. PTB and TBM have similar transcriptional profiles compared to healthy controls, although inflammatory genes were more activated in HIV-positive than HIV-negative TBM. The expression of four hub genes - MCEMP1, NELL2, ZNF354C and CD4 - were strongly predictive of death from TBM (AUC 0.80 and 0.86 for HIV-negative and HIV-positive, respectively). ConclusionsWhole blood transcriptional profiling revealed that TBM is associated with a characteristic systemic inflammatory response, similar to that invoked by pulmonary tuberculosis, but with key gene modules, hubs and pathways strongly associated with death. Our analysis suggests a novel 4-gene biomarker for predicting death from TBM, but also opens a new window into TBM pathogenesis that may reveal novel therapeutic targets for this lethal disease.

molecular biology↗

Direct inference and control of genetic population structure from RNA sequencing data

RNAseq data can be used to infer genetic variants, yet its use for estimating genetic population structure remains underexplored. Here, we construct a freely available computational tool (RGStraP) to estimate RNAseq-based genetic principal components (RG-PCs) and assess whether RG-PCs can be used to control for population structure in gene expression analyses. Using whole blood samples from understudied Nepalese populations and the Geuvadis study, we show that RG-PCs had comparable results to paired array-based genotypes, with high genotype concordance and high correlations of genetic principal components, capturing subpopulations within the dataset. In differential gene expression analysis, we found that inclusion of RG-PCs as covariates reduced test statistic inflation. Our paper demonstrates that genetic population structure can be directly inferred and controlled for using RNAseq data, thus facilitating improved retrospective and future analyses of transcriptomic data.

bioinformatics↗

An atlas of genetic scores to predict multi-omic traits

Genetically predicted levels of multi-omic traits can uncover the molecular underpinnings of common phenotypes in a highly efficient manner. Here, we utilised a large cohort (INTERVAL; 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). We used machine learning to train genetic scores for 17,227 molecular traits, including 10,521 which reached Bonferroni-adjusted significance. We evaluated genetic score performances in external validation across European, Asian and African American ancestries, and assessed their longitudinal stability within diverse individuals. We demonstrated 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 UK Biobank to identify disease associations using a phenome-wide scan. Finally, we developed a portal (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.

genomics↗

Construction of in silico protein-protein interaction networks across different topologies using machine learning

Protein-protein interactions (PPIs) are essential to understanding biological pathways as well as their roles in development and disease. Computational tools, based on classic machine learning, have been successful at predicting PPIs in silico, but the lack of consistent and reliable frameworks for this task has led to network models that are difficult to compare and discrepancies between algorithms that remain unexplained. To better understand the underlying inference mechanisms that underpin these models, we designed an open-source framework for benchmarking that accounts for a range of biological and statistical pitfalls while facilitating reproducibility. We use it to shed light on the impact of network topology and how different algorithms deal with highly connected proteins. By studying functional genomics-based and sequence-based models on human PPIs, we show their complementarity as the former performs best on lone proteins while the latter specialises in interactions involving hubs. We also show that algorithm design has little impact on performance with functional genomic data. We replicate our results between both human and S. cerevisiae data and demonstrate that models using functional genomics are better suited to PPI prediction across species. With rapidly increasing amounts of sequence and functional genomics data, our study provides a principled foundation for future construction, comparison and application of PPI networks.

bioinformatics↗

Assessing and removing the effect of unwanted technical variations in microbiome data

Varying technologies and experimental approaches used in microbiome studies often lead to irreproducible results due to unwanted technical variations. Such variations, often unaccounted for and of unknown source, may interfere with true biological signals, resulting in misleading biological conclusions. In this work, we aim to characterize the major sources of technical variations in microbiome data and demonstrate how a state-of-the art approach can minimize their impact on downstream analyses. We analyzed 184 pig faecal metagenomes encompassing 21 specific combinations of deliberately introduced factors of technical and biological variations. We identify several known experimental factors, specifically storage conditions and freeze-thaw cycles, as a likely major source of unwanted variation in metagenomes. We also observed that these unwanted technical variations do not affect taxa uniformly, with freezing samples affecting taxa of class Bacteroidia the most, for example. Additionally, we benchmarked the performance of a novel batch correcting tool used in this study, RUV-III-NB (https://github.com/limfuxing/ruvIIInb/), to other popular batch correction methods, including ComBat, ComBat-seq, RUVg, and RUVs. While RUV-III-NB performed consistently robustly across our sensitivity and specificity metrics, most other methods did not remove unwanted variations optimally, with RUVg even overcorrecting and removing some of the true biological signals from the samples. Our analyses suggests that a careful consideration of possible technical confounders is critical in the experimental design of microbiome studies to ensure accurate biological reading of microbial taxa of interest, and that the inclusion of technical replicates is necessary to efficiently remove unwanted variations computationally.

bioinformatics↗

OGUs enable effective, phylogeny-aware analysis of even shallow metagenome community structures

We introduce Operational Genomic Unit (OGU), a metagenome analysis strategy that directly exploits sequence alignment hits to individual reference genomes as the minimum unit for assessing the diversity of microbial communities and their relevance to environmental factors. This approach is independent from taxonomic classification, granting the possibility of maximal resolution of community composition, and organizes features into an accurate hierarchy using a phylogenomic tree. The outputs are suitable for contemporary analytical protocols for community ecology, differential abundance and supervised learning while supporting phylogenetic methods, such as UniFrac and phylofactorization, that are seldomly applied to shotgun metagenomics despite being prevalent in 16S rRNA gene amplicon studies. As demonstrated in one synthetic and two real-world case studies, the OGU method produces biologically meaningful patterns from microbiome datasets. Such patterns further remain detectable at very low metagenomic sequencing depths. Compared with taxonomic unit-based analyses implemented in currently adopted metagenomics tools, and the analysis of 16S rRNA gene amplicon sequence variants, this method shows superiority in informing biologically relevant insights, including stronger correlation with body environment and host sex on the Human Microbiome Project dataset, and more accurate prediction of human age by the gut microbiomes in the Finnish population. We provide Woltka, a bioinformatics tool to implement this method, with full integration with the QIIME 2 package and the Qiita web platform, to facilitate OGU adoption in future metagenomics studies. ImportanceShotgun metagenomics is a powerful, yet computationally challenging, technique compared to 16S rRNA gene amplicon sequencing for decoding the composition and structure of microbial communities. However, current analyses of metagenomic data are primarily based on taxonomic classification, which is limited in feature resolution compared to 16S rRNA amplicon sequence variant analysis. To solve these challenges, we introduce Operational Genomic Units (OGUs), which are the individual reference genomes derived from sequence alignment results, without further assigning them taxonomy. The OGU method advances current read-based metagenomics in two dimensions: (i) providing maximal resolution of community composition while (ii) permitting use of phylogeny-aware tools. Our analysis of real-world datasets shows several advantages over currently adopted metagenomic analysis methods and the finest-grained 16S rRNA analysis methods in predicting biological traits. We thus propose the adoption of OGU as standard practice in metagenomic studies.

bioinformatics↗

The carbon footprint of bioinformatics

Bioinformatic research relies on large-scale computational infrastructures which have a non-zero carbon footprint. So far, no study has quantified the environmental costs of bioinformatic tools and commonly run analyses. In this study, we estimate the bioinformatic carbon footprint (in kilograms of CO2 equivalent units, kgCO2e) using the freely available Green Algorithms calculator (www.green-algorithms.org). We assess (i) bioinformatic approaches in genome-wide association studies (GWAS), RNA sequencing, genome assembly, metagenomics, phylogenetics and molecular simulations, as well as (ii) computation strategies, such as parallelisation, CPU (central processing unit) vs GPU (graphics processing unit), cloud vs. local computing infrastructure and geography. In particular, for GWAS, we found that biobank-scale analyses emitted substantial kgCO2e and simple software upgrades could make GWAS greener, e.g. upgrading from BOLT-LMM v1 to v2.3 reduced carbon footprint by 73%. Switching from the average data centre to a more efficient data centres can reduce carbon footprint by ~34%. Memory over-allocation can be a substantial contributor to an algorithms carbon footprint. The use of faster processors or greater parallelisation reduces run time but can lead to, sometimes substantially, greater carbon footprint. Finally, we provide guidance on how researchers can reduce power consumption and minimise kgCO2e. Overall, this work elucidates the carbon footprint of common analyses in bioinformatics and provides solutions which empower a move toward greener research.

bioinformatics↗

Loss of the Long Non-coding RNA OIP5-AS1 Exacerbates Heart Failure in a Sex-Specific Manner

BackgroundLong ncRNAs (lncRNAs) are known to influence numerous biological processes including cellular differentiation and tissue development. They are also implicated in the maintenance, health and physiological function of many tissues including the heart. Indeed, manipulating the expression of specific lncRNAs has been shown to improve pathological cardiac phenotypes such as heart failure. One lncRNA studied in various settings is OIP5-AS1 (also known as 1700020I14Rik and Cyrano), however its role in cardiac pathologies remains mostly uncharacterised. MethodsWe used data generated from FACS sorted murine cardiomyocytes, human iPSC derived cardiomyocytes, as well as heart tissue from various animal models to investigate OIP5-AS1 expression in health and disease. Using CRISPR we engineered a global OIP5-AS1 knock out (KO) mouse model and performed cardiac pressure overload experiments to study heart failure in these animals. RNA-sequencing of left ventricles provided mechanistic insight between WT and KO mice. ResultsWe demonstrate that OIP5-AS1 expression is regulated during cardiac development and cardiac specific pathologies in both rodent and human models. Moreover, we demonstrate that global female OIP5-AS1 KO mice develop exacerbated heart failure, but male mice do not. Transcriptomics and gene set enrichment analysis suggests that OIP5-AS1 may regulate pathways that impact mitochondrial function. ConclusionsOIP5-AS1 is regulated in cardiac tissue and its deletion leads to worsening heart function under pressure overload in female mice. This may be due to impairments in mitochondrial function, highlighting OIP5-AS1 as a gene of interest in sex-specific differences in heart failure.

physiology↗

Bacterial modification of the host glycosaminoglycan heparan sulfate modulates SARS-CoV-2 infectivity

The human microbiota has a close relationship with human disease and it remodels components of the glycocalyx including heparan sulfate (HS). Studies of the severe acute respiratory syndrome coronavirus (SARS-CoV-2) spike protein receptor binding domain suggest that infection requires binding to HS and angiotensin converting enzyme 2 (ACE2) in a codependent manner. Here, we show that commensal host bacterial communities can modify HS and thereby modulate SARS-CoV-2 spike protein binding and that these communities change with host age and sex. Common human-associated commensal bacteria whose genomes encode HS-modifying enzymes were identified. The prevalence of these bacteria and the expression of key microbial glycosidases in bronchoalveolar lavage fluid (BALF) was lower in adult COVID-19 patients than in healthy controls. The presence of HS-modifying bacteria decreased with age in two large survey datasets, FINRISK 2002 and American Gut, revealing one possible mechanism for the observed increase in COVID-19 susceptibility with age. In vitro, bacterial glycosidases from unpurified culture media supernatants fully blocked SARS-CoV-2 spike binding to human H1299 protein lung adenocarcinoma cells. HS-modifying bacteria in human microbial communities may regulate viral adhesion, and loss of these commensals could predispose individuals to infection. Understanding the impact of shifts in microbial community composition and bacterial lyases on SARS-CoV-2 infection may lead to new therapeutics and diagnosis of susceptibility. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=136 SRC="FIGDIR/small/238444v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@14ff1ecorg.highwire.dtl.DTLVardef@193d84corg.highwire.dtl.DTLVardef@15d6f9eorg.highwire.dtl.DTLVardef@14b16c6_HPS_FORMAT_FIGEXP M_FIG Graphical Abstract. Diagram of hypothesis for bacterial mediation of SARS-CoV-2 infection through heparan sulfate (HS).It is well known that host microbes groom the mucosa where they reside. Recent investigations have shown that HS, a major component of mucosal layers, is necessary for SARS-CoV-2 infection. In this study we examine the impact of microbial modification of HS on viral attachment. C_FIG

microbiology↗

GeneMates: an R package for Detecting Horizontal Gene Co-transfer between Bacteria Using Gene-gene Associations Controlled for Population Structure

Antimicrobial resistance (AMR) in bacteria has been a global threat to public health for decades. A well-known driving force for the emergence, evolution and dissemination of genetic AMR determinants in bacterial populations is horizontal gene transfer, which is frequently mediated by mobile genetic elements (MGEs). Some MGEs can capture, maintain, and rearrange multiple AMR genes in a donor bacterium before moving them into recipients, giving rise to a phenomenon called horizontal gene co-transfer (HGcoT). This physical linkage or co-localisation between mobile AMR genes is of particular concern because it facilitates rapid dissemination of multidrug resistance within and across bacterial populations, providing opportunities for co-selection of AMR genes and limiting our therapeutic options. The study of HGcoT can be benefited from large-scale whole-genome sequencing (WGS) data, however, by far most published studies of HGcoT only consider simple co-occurrence measures, which can be confounded by strong bacterial population structure due to clonal reproduction, leading to spurious associations. To address this issue, we present GeneMates, an R package implementing a network approach to identification of HGcoT using WGS data. The package enables users to test for associations between presence-absence of bacterial genes using univariate linear mixed models controlling for population structure based on core-genome variation. Furthermore, when physical distances between genes of interest are measurable in bacterial genomes, users can evaluate distance consistency to further support their inference of putative horizontally co-transferred genes, whose co-occurrence cannot be completely explained by the population structure. We demonstrate how this package can be used to identify co-transferred AMR genes and recover known MGEs from Escherichia coli and Salmonella Typhimurium WGS data. GeneMates is accessible at github.com/wanyuac/GeneMates.

genomics↗

Learning polygenic scores for human blood cell traits

Polygenic scores (PGSs) for blood cell traits can be constructed using summary statistics from genome-wide association studies. As the selection of variants and the modelling of their interactions in PGSs may be limited by univariate analysis, therefore, such a conventional method may yield sub-optional performance. This study evaluated the relative effectiveness of four machine learning and deep learning methods, as well as a univariate method, in the construction of PGSs for 26 blood cell traits, using data from UK Biobank (n=~400,000) and INTERVAL (n=~40,000). Our results showed that learning methods can improve PGSs construction for nearly every blood cell trait considered, with this superiority explained by the ability of machine learning methods to capture interactions among variants. This study also demonstrated that populations can be well stratified by the PGSs of these blood cell traits, even for traits that exhibit large differences between ages and sexes, suggesting potential for disease prevention. As our study found genetic correlations between the PGSs for blood cell traits and PGSs for several common human diseases (recapitulating well-known associations between the blood cell traits themselves and certain diseases), it suggests that blood cell traits may be indicators or/and mediators for a variety of common disorders via shared genetic variants and functional pathways.

genetics↗

Integrative analysis of the plasma proteome and polygenic risk of cardiometabolic diseases

Summary ParagraphCommon human diseases are frequently polygenic in architecture, comprising a large number of risk alleles with small effects spread across the genome1-3. Polygenic scores (PGSs) aggregate these alleles into a metric which represents an individuals genetic predisposition to a specific disease. PGSs have shown promise for early risk prediction4-7, and there is potential to use PGSs to understand disease biology in parallel8. Here, we investigate the role plasma protein levels play in cardiometabolic disease risk in a cohort of 3,087 healthy individuals using PGSs. We found PGSs for coronary artery disease (CAD), type 2 diabetes (T2D), chronic kidney disease (CKD), and ischaemic stroke (IS) were associated with levels of 49 plasma proteins. These associations were polygenic in architecture, largely independent of cis protein QTLs, and robust to environmental variation. Over a median 7.7 years follow-up, 28 of these plasma proteins were associated with future myocardial infarction (MI) or T2D events, 16 of which were causal mediators between polygenic risk and incident disease. These protein mediators of polygenic disease risk included targets of approved therapies which may have repurposing potential. Our results demonstrate that PGSs can identify proteins with causal roles in disease, and may have utility in drug development.

genetics↗