Sept8/SEPTIN8 involvement in cellular structure and kidney damage is identified by genetic mapping and a novel human tubule hypoxic model

Plasma proteins and psoriatic arthritis: a proteome-wide Mendelian randomization study

Background

Previous epidemiological studies have identified a correlation between serum protein levels and Psoriatic Arthritis (PsA). However, the precise nature of this relationship remains uncertain. Therefore, our objective was to assess whether circulating levels of 2,923 plasma proteins are associated with the risk of PsA, utilizing the Mendelian randomization (MR) approach.

Methods

Two-sample MR analysis was performed to assess the causal impact of proteins on PsA risk. Exposure data for plasma proteins were sourced from a genome-wide association study (GWAS) conducted within the UK Biobank Pharma Proteomics Project, which encompassed 2,923 unique plasma proteins. The outcome data for PsA were sourced from the FinnGen study, a large-scale genomics initiative, comprising 3,537 cases and 262,844 controls. Additionally, colocalization analysis, Phenome-wide MR analysis, and candidate drug prediction were employed to identify potential causal circulating proteins and novel drug targets.

Results

We thoroughly assessed the association between 1,837 plasma proteins and PsA risk, identifying seven proteins associated with PsA risk. An inverse association of Interleukin-10 (IL-10) with PsA risk was observed [odds ratio (OR)=0.45, 95% confidence interval (CI), 0.28 to 0.70, P FDR=0.072]. Additionally, Apolipoprotein F (APOF) has a positive effect on PsA risk (OR=2.08, 95% CI, 1.51 to 2.86, P FDR=0.005). Subsequently, we found strong evidence indicating that IL-10 and APOF were colocalized with PsA associations (PP.H4 = 0.834 for IL-10 and PP.H4 = 0.900 for APOF). Phenome-wide association analysis suggested that these two proteins may have dual effects on other clinical traits (P FDR<0.1).

Conclusion

This study identified 7 plasma proteins associated with PsA risk, particularly IL-10 and APOF, which offer new insights into its etiology. Further studies are needed to assess the utility and effectiveness of these candidate proteins.

Allogeneic bone marrow mesenchymal stem cell-derived exosomes alleviate human hypoxic AKI-on-a-Chip within a tight treatment window

BACKGROUND

Acute hypoxic proximal tubule (PT) injury and subsequent maladaptive repair present high mortality and increased risk of acute kidney injury (AKI) - chronic kidney disease (CKD) transition. Human bone marrow mesenchymal stem cell-derived exosomes (hBMMSC-Exos) as potential cell therapeutics can be translated into clinics if drawbacks on safety and efficacy are clarified. Here, we determined the real-time effective dose and treatment window of allogeneic hBMMSC-Exos, evaluated their performance on the structural and functional integrity of 3D microfluidic acute hypoxic PT injury platform.

METHODS

hBMMSC-Exos were isolated and characterized. Real-time impedance-based cell proliferation analysis (RTCA) determined the effective dose and treatment window for acute hypoxic PT injury. A 2-lane 3D gravity-driven microfluidic platform was set to mimic PT in vitro. ZO-1, acetylated α-tubulin immunolabelling, and permeability index assessed structural; cell proliferation by WST-1 measured functional integrity of PT.

RESULTS

hBMMSC-Exos induced PT proliferation with ED50 of 172,582 µg/ml at the 26th hour. Hypoxia significantly decreased ZO-1, increased permeability index, and decreased cell proliferation rate on 24-48 h in the microfluidic platform. hBMMSC-Exos reinforced polarity by a 1.72-fold increase in ZO-1, restored permeability by 20/45-fold against 20/155 kDa dextran and increased epithelial proliferation 3-fold compared to control.

CONCLUSIONS

The real-time potency assay and 3D gravity-driven microfluidic acute hypoxic PT injury platform precisely demonstrated the therapeutic performance window of allogeneic hBMMSC-Exos on ischemic AKI based on structural and functional cellular data. The novel standardized, non-invasive two-step system validates the cell-based personalized theragnostic tool in a real-time physiological microenvironment prior to safe and efficient clinical usage in nephrology.

Genetic background in the rat affects endocrine and metabolic outcomes of bisphenol F exposure

Environmental bisphenol compounds like bisphenol F (BPF) are endocrine-disrupting chemicals (EDCs) affecting adipose and classical endocrine systems. Genetic factors that influence EDC exposure outcomes are poorly understood and are unaccounted variables that may contribute to the large range of reported outcomes in the human population. We previously demonstrated that BPF exposure increased body growth and adiposity in male N/NIH heterogeneous stock (HS) rats, a genetically heterogeneous outbred population. We hypothesize that the founder strains of the HS rat exhibit EDC effects that were strain- and sex-dependent. Weanling littermate pairs of male and female ACI, BN, BUF, F344, M520, and WKY rats randomly received either vehicle (0.1% EtOH) or 1.125 mg BPF/l in 0.1% EtOH for 10 weeks in drinking water. Body weight and fluid intake were measured weekly, metabolic parameters were assessed, and blood and tissues were collected. BPF increased thyroid weight in ACI males, thymus and kidney weight in BUF females, adrenal weight in WKY males, and possibly increased pituitary weight in BN males. BUF females also developed a disruption in activity and metabolic rate with BPF exposure. These sex- and strain-specific exposure outcomes illustrate that HS rat founders possess diverse bisphenol-exposure risk alleles and suggest that BPF exposure may intensify inherent organ system dysfunction existing in the HS rat founders. We propose that the HS rat will be an invaluable model for dissecting gene EDC interactions on health.

2022 updates to the Rat Genome Database: a Findable, Accessible, Interoperable, and Reusable (FAIR) resource

The Rat Genome Database (RGD, https://rgd.mcw.edu) has evolved from simply a resource for rat genetic markers, maps, and genes, by adding multiple genomic data types and extensive disease and phenotype annotations and developing tools to effectively mine, analyze, and visualize the available data, to empower investigators in their hypothesis-driven research. Leveraging its robust and flexible infrastructure, RGD has added data for human and eight other model organisms (mouse, 13-lined ground squirrel, chinchilla, naked mole-rat, dog, pig, African green monkey/vervet, and bonobo) besides rat to enhance its translational aspect. This article presents an overview of the database with the most recent additions to RGD's genome, variant, and quantitative phenotype data. We also briefly introduce Virtual Comparative Map (VCMap), an updated tool that explores synteny between species as an improvement to RGD's suite of tools, followed by a discussion regarding the refinements to the existing PhenoMiner tool that assists researchers in finding and comparing quantitative data across rat strains. Collectively, RGD focuses on providing a continuously improving, consistent, and high-quality data resource for researchers while advancing data reproducibility and fulfilling Findable, Accessible, Interoperable, and Reusable (FAIR) data principles.

Genetic Mapping of Multiple Traits Identifies Novel Genes for Adiposity, Lipids, and Insulin Secretory Capacity in Outbred Rats

Despite the successes of human genome-wide association studies, the causal genes underlying most metabolic traits remain unclear. We used outbred heterogeneous stock (HS) rats, coupled with expression data and mediation analysis, to identify quantitative trait loci (QTLs) and candidate gene mediators for adiposity, glucose tolerance, serum lipids, and other metabolic traits. Physiological traits were measured in 1,519 male HS rats, with liver and adipose transcriptomes measured in >410 rats. Genotypes were imputed from low-coverage whole-genome sequencing. Linear mixed models were used to detect physiological and expression QTLs (pQTLs and eQTLs, respectively), using both single nucleotide polymorphism (SNP)- and haplotype-based models for pQTL mapping. Genes with cis-eQTLs that overlapped pQTLs were assessed as causal candidates through mediation analysis. We identified 14 SNP-based pQTLs and 19 haplotype-based pQTLs, of which 10 were in common. Using mediation, we identified the following genes as candidate mediators of pQTLs: Grk5 for fat pad weight and serum triglyceride pQTLs on Chr1, Krtcap3 for fat pad weight and serum triglyceride pQTLs on Chr6, Ilrun for a fat pad weight pQTL on Chr20, and Rfx6 for a whole pancreatic insulin content pQTL on Chr20. Furthermore, we verified Grk5 and Ktrcap3 using gene knockdown/out models, thereby shedding light on novel regulators of obesity.

Air pollutants, genetic factors, and risk of chronic kidney disease: Findings from the UK Biobank

BACKGROUND

Experiment studies have suggested the emerging role of air pollutants in chronic kidney disease (CKD). However, only a few population studies conducted in Asia and North America have assessed their association, and the conclusions remained controversial. This study aims to investigate the effect of air pollutants exposure on CKD in the European population and first explores the modification effect of genetic risk on this association.

METHODS

458,968 participants from the UK Biobank were included in this study. Cox proportional hazards model was used to assess the associations of air pollutants (PM_2.5, PM₁₀, NO₂, and NO_x) with incident CKD. A genetic risk score of 53 single nucleotide polymorphisms was constructed to represent the genetic susceptibility to CKD. To assess the interaction effect between air pollutants and the genetic risk, we added a multiplicative interaction term and did a stratified analysis.

RESULTS

During a median follow-up of 11.7 years, 16,637 incidents of CKD were identified. We observed positive associations between air pollutants exposure and CKD risk with the HRs for CKD were 1.09 (1.07, 1.11), 1.08 (1.06, 1.10), 1.05 (1.03, 1.07), 1.06 (1.04, 1.08) with per IQR (interquartile range) increment in PM_2.5, PM₁₀, NO₂, and NO_x, respectively. Stratified analysis showed that the associations between air pollutants and CKD were modest and marginal in the high genetic risk population (P > 0.05), while the associations were statistically significant in the low and intermediate genetic risk groups.

CONCLUSIONS

Our study indicated that exposure to various air pollutants, including PM_2.5, PM₁₀, NO₂, and NO_x, was associated with an elevated risk of CKD. This finding provide evidence that formulating strategies to improve air quality can be helpful to reduce the burden of CKD.

The complex, dynamic SpliceOme of the small GTPase transcripts altered by technique, sex, genetics, tissue specificity, and RNA base editing

The small GTPase family is well-studied in cancer and cellular physiology. With 162 annotated human genes, the family has a broad expression throughout cells of the body. Members of the family have multiple exons that require splicing. Yet, the role of splicing within the family has been underexplored. We have studied the splicing dynamics of small GTPases throughout 41,671 samples by integrating Nanopore and Illumina sequencing techniques. Within this work, we have made several discoveries. 1). Using the GTEx long read data of 92 samples, each small GTPase gene averages two transcripts, with 83 genes (51%) expressing two or more isoforms. 2). Cross-tissue analysis of GTEx from 17,382 samples shows 41 genes (25%) expressing two or more protein-coding isoforms. These include protein-changing transcripts in genes such as RHOA, RAB37, RAB40C, RAB4B, RAB5C, RHOC, RAB1A, RAN, RHEB, RAC1, and KRAS. 3). The isolation and library technique of the RNAseq influences the abundance of non-sense-mediated decay and retained intron transcripts of small GTPases, which are observed more often in genes than appreciated. 4). Analysis of 16,243 samples of "Blood PAXgene" identified seven genes (3.7%; RHOA, RAB40C, RAB4B, RAB37, RAB5B, RAB5C, RHOC) with two or more transcripts expressed as the major isoform (75% of the total gene), suggesting a role of genetics in altering splicing. 5). Rare (ARL6, RAB23, ARL13B, HRAS, NRAS) and common variants (GEM, RHOC, MRAS, RAB5B, RERG, ARL16) can influence splicing and have an impact on phenotypes and diseases. 6). Multiple genes (RAB9A, RAP2C, ARL4A, RAB3A, RAB26, RAB3C, RASL10A, RAB40B, and HRAS) have sex differences in transcript expression. 7). Several exons are included or excluded for small GTPase genes (RASEF, KRAS, RAC1, RHEB, ARL4A, RHOA, RAB30, RHOBTB1, ARL16, RAP1A) in one or more forms of cancer. 8). Ten transcripts are altered in hypoxia (SAR1B, IFT27, ARL14, RAB11A, RAB10, RAB38, RAN, RIT1, RAB9A) with RHOA identified to have a transient 3'UTR RNA base editing at a conserved site found in all of its transcripts. Overall, we show a remarkable and dynamic role of splicing within the small GTPase family that requires future explorations.

The regulatory landscape of multiple brain regions in outbred heterogeneous stock rats

Heterogeneous Stock (HS) rats are a genetically diverse outbred rat population that is widely used for studying genetics of behavioral and physiological traits. Mapping Quantitative Trait Loci (QTL) associated with transcriptional changes would help to identify mechanisms underlying these traits. We generated genotype and transcriptome data for five brain regions from 88 HS rats. We identified 21 392 cis-QTLs associated with expression and splicing changes across all five brain regions and validated their effects using allele specific expression data. We identified 80 cases where eQTLs were colocalized with genome-wide association study (GWAS) results from nine physiological traits. Comparing our dataset to human data from the Genotype-Tissue Expression (GTEx) project, we found that the HS rat data yields twice as many significant eQTLs as a similarly sized human dataset. We also identified a modest but highly significant correlation between genetic regulatory variation among orthologous genes. Surprisingly, we found less genetic variation in gene regulation in HS rats relative to humans, though we still found eQTLs for the orthologs of many human genes for which eQTLs had not been found. These data are available from the RatGTEx data portal (RatGTEx.org) and will enable new discoveries of the genetic influences of complex traits.

Unraveling the Pathogenesis of Asthma and Chronic Obstructive Pulmonary Disease Overlap: Focusing on Epigenetic Mechanisms

Asthma and COPD overlap (ACO) is characterized by patients presenting with persistent airflow limitation and features of both asthma and COPD. It is associated with a higher frequency and severity of exacerbations, a faster lung function decline, and a higher healthcare cost. Systemic inflammation in COPD and asthma is driven by type 1 T helper (Th1) and Th2 immune responses, respectively, both of which may contribute to airway remodeling in ACO. ACO-related biomarkers can be classified into four categories: neutrophil-mediated inflammation, Th2 cell responses, arachidonic acid-eicosanoids pathway, and metabolites. Gene–environment interactions are key contributors to the complexity of ACO and are regulated by epigenetic mechanisms, including DNA methylation, histone modifications, and non-coding RNAs. Thus, this review focuses on the link between epigenetics and ACO, and outlines the following: (I) inheriting epigenotypes without change with environmental stimuli, or epigenetic changes in response to long-term exposure to inhaled particles plus intermittent exposure to specific allergens; (II) epigenetic markers distinguishing ACO from COPD and asthma; (III) potential epigenetic drugs that can reverse oxidative stress, glucocorticoid insensitivity, and cell injury. Improved understanding of the epigenetic regulations holds great value to give deeper insight into the mechanisms, and clarify their implications for biomedical research in ACO.

Transcriptome-wide analyses of adipose tissue in outbred rats reveal genetic regulatory mechanisms relevant for human obesity

Transcriptomic analysis in metabolically active tissues allows a systems genetics approach to identify causal genes and networks involved in metabolic disease. Outbred heterogeneous stock (HS) rats are used for genetic mapping of complex traits, but to-date, a systems genetics analysis of metabolic tissues has not been done. We investigated whether adiposity-associated genes and gene co-expression networks in outbred heterogeneous stock (HS) rats overlap those found in humans. We analyzed RNAseq data from adipose tissue of 415 male HS rats, correlated these transcripts with body weight (BW) and compared transcriptome signatures to two human cohorts: the "African American Genetics of Metabolism and Expression" and "Metabolic Syndrome in Men". We used weighted gene co-expression network analysis to identify adiposity-associated gene networks and mediation analysis to identify genes under genetic control whose expression drives adiposity. We identified 554 orthologous "consensus genes" whose expression correlates with BW in the rat and with body mass index (BMI) in both human cohorts. Consensus genes fell within eight co-expressed networks and were enriched for genes involved in immune system function, cell growth, extracellular matrix organization and lipid metabolic processes. We identified 19 consensus genes for which genetic variation may influence BW via their expression, including those involved in lipolysis (e.g., Hcar1), inflammation (e.g., Rgs1), adipogenesis (e.g., Tmem120b) or no previously known role in obesity (e.g., St14, Msa4a6). Strong concordance between HS rat and human BW/BMI associated transcripts demonstrates translational utility of the rat model, while identification of novel genes expands our knowledge of the genetics underlying obesity.

Computational and Experimental Analysis of Genetic Variants

Genomics has grown exponentially over the last decade. Common variants are associated with physiological changes through statistical strategies such as Genome-Wide Association Studies (GWAS) and quantitative trail loci (QTL). Rare variants are associated with diseases through extensive filtering tools, including population genomics and trio-based sequencing (parents and probands). However, the genomic associations require follow-up analyses to narrow causal variants, identify genes that are influenced, and to determine the physiological changes. Large quantities of data exist that can be used to connect variants to gene changes, cell types, protein pathways, clinical phenotypes, and animal models that establish physiological genomics. This data combined with bioinformatics including evolutionary analysis, structural insights, and gene regulation can yield testable hypotheses for mechanisms of genomic variants. Molecular biology, biochemistry, cell culture, CRISPR editing, and animal models can test the hypotheses to give molecular variant mechanisms. Variant characterizations can be a significant component of educating future professionals at the undergraduate, graduate, or medical training programs through teaching the basic concepts and terminology of genetics while learning independent research hypothesis design. This article goes through the computational and experimental analysis strategies of variant characterization and provides examples of these tools applied in publications. © 2022 American Physiological Society. Compr Physiol 12:3303-3336, 2022.