Fast Context-Aware Analysis of Genome Annotation Colocalization.

An annotation is a set of genomic intervals sharing a particular function or property. Examples include genes or their exons, sequence repeats, regions with a particular epigenetic state, and copy number variants. A common task is to compare two annotations to determine if one is enriched or depleted in the regions covered by the other. We study the problem of assigning statistical significance to such a comparison based on a null model representing random unrelated annotations. To incorporate more background information into such analyses, we propose a new null model based on a Markov chain that differentiates among several genomic contexts. These contexts can capture various confounding factors, such as GC content or assembly gaps. We then develop a new algorithm for estimating p-values by computing the exact expectation and variance of the test statistic and then estimating the p-value using a normal approximation. Compared to the previous algorithm by Gafurov et al., the new algorithm provides three advances: (1) the running time is improved from quadratic to linear or quasi-linear, (2) the algorithm can handle two different test statistics, and (3) the algorithm can handle both simple and context-dependent Markov chain null models. We demonstrate the efficiency and accuracy of our algorithm on synthetic and real data sets, including the recent human telomere-to-telomere assembly. In particular, our algorithm computed p-values for 450 pairs of human genome annotations using 24 threads in under three hours. Moreover, the use of genomic contexts to correct for GC bias resulted in the reversal of some previously published findings.

Measuring protein stoichiometry with single-molecule imaging in Xenopus egg extracts.

In human cells, DNA double-strand breaks are rapidly bound by the highly abundant non-homologous end joining (NHEJ) factor Ku70/Ku80 (Ku). Cellular imaging and structural data revealed a single Ku molecule is bound to a free DNA end and yet the mechanism regulating Ku remains unclear. Here, we describe how to utilize the cell-free Xenopus laevis egg extract system in conjunction with single-molecule microscopy to investigate regulation of Ku stoichiometry during non-homologous end joining. Egg extract is an excellent model system to study DNA repair as it contains the soluble proteome including core and accessory NHEJ factors, and efficiently repairs double-strand breaks in an NHEJ-dependent manner. To examine the Ku stoichiometry in the extract system, we developed a single-molecule photobleaching assay, which reports on the number of stable associated Ku molecules by monitoring the intensity of fluorescently labeled Ku molecules bound to double-stranded DNA over time. Photobleaching is distinguishable as step decreases in fluorescence intensity and the number of photobleaching events indicate fluorophore stoichiometry. In this paper we describe sample preparation, experimental methodology, and data analysis to discern Ku stoichiometry and the regulatory mechanism controlling its loading. These approaches can be readily adopted to determine stoichiometry of molecular factors within other macromolecular complexes.

Causal relationships between cerebral cortical structure and preeclampsia: insights from bidirectional Mendelian randomization and colocalization analysis.

Preeclampsia, a multifaceted condition characterized by high blood pressure during pregnancy, is linked to substantial health risks for both the mother and the fetus. Previous studies suggest potential neurological impacts, but the causal relationships between cortical structural changes and preeclampsia remain unclear. We utilized genome-wide association study data for cortical thickness (TH) and surface area (SA) across multiple brain regions and preeclampsia. Bidirectional Mendelian randomization (MR) analyses were conducted to assess causality, followed by co-localization analyses to confirm shared genetic architecture. Increased cortical TH in the inferior parietal and supramarginal regions, and an enlarged SA in the postcentral region, were significantly associated with higher preeclampsia risk. Conversely, preeclampsia was linked to increased SA in the supramarginal and middle temporal gyri, and decreased SA in the lingual gyrus. Co-localization analyses indicated distinct genetic determinants for cortical structures and preeclampsia. Our findings reveal bidirectional influences between cortical structural features and preeclampsia, suggesting neuroinflammatory and vascular mechanisms as potential pathways. These insights underscore the importance of considering brain structure in preeclampsia risk assessment and highlight the need for further research into neuroprotective strategies.

Utilize proteomic analysis to identify potential therapeutic targets for combating sepsis and sepsis-related death.

Background: Sepsis is an inflammatory disease that leads to severe mortality, highlighting the urgent need to identify new therapeutic strategies for sepsis. Proteomic research serves as a primary source for drug target identification. We employed proteome-wide Mendelian randomization (MR), genetic correlation analysis, and colocalization analysis to identify potential targets for sepsis and sepsis-related death. Methods: Genetic data for plasma proteomics were obtained from 35,559 Icelandic individuals and an initial MR analysis was conducted using 13,531 sepsis cases from the FinnGen R10 cohort to identify associations between plasma proteins and sepsis. Subsequently, significant proteins underwent genetic correlation analysis, followed by replication in 54,306 participants from the UK Biobank Pharma Proteomics Project and validation in 11,643 sepsis cases from the UK Biobank. The identified proteins were then subjected to colocalization analysis, enrichment analysis, and protein-protein interaction network analysis. Additionally, we also investigated a MR analysis using plasma proteins on 1,896 sepsis cases with 28-day mortality from the UK Biobank. Results: After FDR correction, MR analysis results showed a significant causal relationship between 113 plasma proteins and sepsis. Genetic correlation analysis revealed that only 8 proteins had genetic correlations with sepsis. In the UKB-PPP replication analysis, only 4 proteins were found to be closely associated with sepsis, while validation in the UK Biobank sepsis cases found overlaps for 21 proteins. In total, 30 proteins were identified in the aforementioned analyses, and colocalization analysis revealed that only 2 of these proteins were closely associated with sepsis. Additionally, in the 28-day mortality MR analysis of sepsis, we also found that only 2 proteins were significant. Conclusions: The identified plasma proteins and their associated metabolic pathways have enhanced our understanding of the complex relationship between proteins and sepsis. This provides new avenues for the development of drug targets and paves the way for further research in this field.

Multi-scale transcriptomics reveals that specific tumor cells promote lung adenocarcinoma metastasis through crosstalk with the microenvironment.

Most advanced lung adenocarcinoma (LUAD) patient deaths are attributed to metastasis. However, the complete understanding of the metastatic mechanism in LUAD remains elusive. Single-cell RNA-seq (scRNA-seq), spatial RNA-seq (stRNA-seq) and bulk RNA-seq of primary LUAD were integrated to investigate metastatic driver genes, cell-cell interactions, and spatial colocalization of cells and ligand-receptor pairs. A lung adenocarcinoma metastasis risk scoring model (LMRS) was established to estimate the risk of metastasis in LUAD. Forty-two metastasis driver genes were identified and tumor epithelial cells were classified into two subtypes. Epithelial cell subclass characterized by susceptibility to metastasis are referred to as Epithelial_LM, and the remaining as Epithelial_LL. Epithelial_LM subtype has intimate ligand-receptor interactions with inflammatory endothelial cells (iendo), inflammatory cancer-associated fibroblasts (iCAF), and NKT cells. Epithelial_LM cells have a spatial colocalization relationship with these three types of cells. The LMRS was established and its efficacy was verified in bulk RNA-seq. We identified a subclass of epithelial cells prone to metastasis and demonstrated the contribution of inflammatory stromal cells and NKT cells in facilitating tumor metastasis.

Identifying new biomarkers and potential therapeutic targets for breast cancer through the integration of human plasma proteomics: a Mendelian randomization study and colocalization analysis.

Background: The proteome is a crucial reservoir of targets for cancer treatment. While some targeted therapies have been developed, there are still significant challenges in early diagnosis and treatment, highlighting the need to identify new biomarkers and therapeutic targets for breast cancer. Therefore, we conducted a comprehensive proteome-wide Mendelian randomization (MR) study to identify novel biomarkers and potential therapeutic targets for breast cancer. Methods: Protein quantitative trait locus (pQTL) data were extracted from two published plasma proteome-wide association studies. Genetic variants associated with breast cancer were obtained from the Breast Cancer Association Consortium, which included 133,384 cases and 113,789 controls, and the Finnish cohort study, comprising 18,786 cases and 182,927 controls. We employed summary-based MR and colocalization methods to identify potential drug targets for breast cancer, which were subsequently validated using a two-sample MR approach. Finally, a protein-protein interaction (PPI) network was constructed to detect interactions between the identified proteins and existing cancer drug targets. Results: Gene-predicted levels of ten proteins were associated with breast cancer risk. Decreased levels of CASP8, DDX58, CPNE1, ULK3, PARK7, and BTN2A1, as well as increased levels of TNFRSF9, TNXB, DNPH1, and TLR1, were linked to an elevated risk of breast cancer. Among these, CASP8 and DDX58 were supported by tier-one evidence, while CPNE1, ULK3, PARK7, and TNFRSF9 received tier-two evidence support. The remaining proteins, TNXB, BTN2A1, DNPH1, and TLR1, were supported by tier-three evidence. CASP8, DDX58, CPNE1, ULK3, PARK7, and TNFRSF9 have already been identified as targets in drug development and potential therapeutic targets for breast cancer treatment. Additionally, ULK3 showed promise as a prognostic biomarker for breast cancer. Conclusions: The present study identified several novel potential drug targets and biomarkers for breast cancer, providing new insights into its diagnosis and treatment. The integration of PPI and druggability evaluations enhances the prioritization of these therapeutic targets, paving the way for future drug development efforts.

[Construction of Aspergillus oryzae strains with organelles labeled with fluorescence and observation of organelle morphology].

The organelles in the multi-nucleated filamentous fungus Aspergillus oryzae present polymorphism. To observe the organelle morphology in A. oryzae and provide references for the localization prediction of unknown proteins and the disclosure of biological reaction pathways in A. oryzae, we fused different subcellular localization signals with green fluorescent protein (GFP) to obtain different subcellular localization vectors, which were then transferred into A. oryzae by Agrobacterium tumefaciens-mediated transformation. The A. oryzae reporter strains with fluorescence-labeled nuclei, mitochondria, endoplasmic reticulum, vacuole, lipid droplets, peroxisome, and Golgi apparatus were successfully constructed. Furthermore, staining with small-molecule specific dyes was carried out to validate the co-localization of fluorescence-labeled mitochondria, nuclei, and lipid droplets in the reporter strains, which further confirmed that the reporter strains were successfully constructed. The distribution and morphology of fluorescence-labeled organelles were observed at different growth stages and under different culture conditions. The constructed reporter strains provide basic tools for studying the organelle morphology, localization of unknown target proteins, and subcellular localization in A. oryzae.

Causal relationship between Brugada syndrome and electrocardiogram traits: A bidirectional Mendelian randomization study.

INTRODUCTION: Observational studies have suggested associations between Brugada syndrome (BrS) and electrocardiograms traits. Nonetheless, the causal relationships remains uncertain in observational studies. This study aims to investigate the causal relationships between BrS phenotypic risk and electrocardiogram traits using Mendelian randomization (MR) analysis and colocalization analysis. METHODS: MR analysis was performed to investigate the causal relationships between BrS phenotype risk and electrocardiogram traits (P wave duration, PR interval, QRS wave duration, ST segment duration, T wave duration, QT interval, heart rate (HR) and heart rate variability). The genetic instruments for BrS (number of cases = 12,821) were obtained from the latest GWAS. GWAS summary data of electrocardiogram traits were obtained from the MRC-IEU and GWAS catalog databases. The causal relationships were obtained through MR methods, and sensitivity analyses (e.g. Cochran's Q test, MR-PRESSO). Furthermore, the causal relationships were evaluated whether they were driven by one linkage disequilibrium using colocalization analysis. RESULTS: We found that there are positive causal relationships between BrS phenotypic risk and P wave duration, PR interval, QRS wave duration and QT interval, respectively (IVWP: ß = 1.238, 95 % CI = 0.857-1.619, P<0.001; IVWPR: ß = 2.199, 95 % CI = 1.358-3.039, P<0.001; IVWQRS: ß = 0.157, 95 % CI = 0.115-0.198, P<0.001; IVWQT: ß = 0.593, 95 % CI = 0.391-0.796, P<0.001), and there is a negative causal relationship between BrS phenotypic risk and heart rate (IVWHR: ß = -0.023, 95 % CI = -0.03 âˆ¼ -0.015, P<0.001). Additionally, there are bidirectional causal relationships between BrS phenotypic risk and P wave duration and PR interval, respectively (IVWP: OR = 1.217, 95 % CI = 1.118-1.325, P<0.001; IVWPR: OR = 1.02, 95 % CI = 1.008-1.032, P = 0.001). Furthermore, colocalization analysis identified that the causal relationships between BrS phenotype risk and P wave duration, PR interval and QRS wave duration were driven by rs6790396, rs6801957 and rs6801957, respectively. CONCLUSIONS: Bidirectional causal relationships were identified between BrS phenotypic risk and P wave duration and PR interval, respectively. There were positive causal relationships between BrS phenotypic risk and QRS wave duration and QT interval, respectively, and there is a negative causal relationship between BrS phenotypic risk and heart rate.

Studies on the characteristics of polycyclic aromatic hydrocarbons accumulation in lipids and the disturbance of lipid metabolism of Ruditapes philippinarum.

Polycyclic aromatic hydrocarbons (PAHs) constitute a class of persistent organic pollutants with strong lipophilicity, which readily accumulate within organisms and have the effect to induce disorders in lipid metabolism. The present study aimed to investigate the accumulation localization and pattern of PAHs in Ruditapes philippinarum, and to reveal the association between PAHs and lipids metabolism. The 21-day exposure experiment was conducted using a mixture of phenanthrene, chrysene, and benzo[a]pyrene (the proportion is 1:1:1) at concentrations of 0.4 µg/L, 2 µg/L, and 10 µg/L. The tissue distribution of PAHs indicated that the digestive gland was the primary site of PAHs accumulation. Meanwhile, fluorescence colocalization suggested that PAHs primarily accumulated within the lipid droplets of digestive gland cells. This study further determined the transcriptomic and lipidomic profiles of the digestive gland to analyze the key genes involved in disrupted lipid metabolism and the major lipids affected. Lipidomic analysis identified the key differential metabolites as triglycerides (TGs). Furthermore, TGs were upregulated in the digestive gland had a total carbon atom number of 50-64 and a total number of 3-9 double bonds in the acyl side chains. Biochemical analysis experiments and oil red O stained frozen sections confirmed that the content of TGs steadily increased in various tissues during the experiment, leading to an elevated digestive gland index. Changes of lipid metabolism associated genes expression level also indicated that the synthesis of lipid in digestive gland were up-regulated while the decomposition was down-regulated. This study is the first to demonstrate the cellular localization of PAHs accumulation in bivalves and confirms the pattern of variation in TGs, providing new insights into the mechanisms of PAHs bioaccumulation and lipid metabolism disruption.

Bivariate genome-wide association study of circulating fibrinogen and C-reactive protein levels.

BACKGROUND: Fibrinogen and C-reactive protein (CRP) play an important role in inflammatory pathways and share multiple genetic loci reported in previously published genome-wide association studies (GWAS), highlighting their common genetic background. Leveraging the shared biology may identify further loci pleiotropically associated with both fibrinogen and CRP. OBJECTIVES: To identify novel genetic variants that are pleiotropic and associated with both fibrinogen and CRP, by integrating both phenotypes in a bivariate GWAS by using a multitrait GWAS. METHODS: We performed a bivariate GWAS to identify further pleiotropic genetic loci, using summary statistics of previously published GWAS on fibrinogen (n = 120 246) from the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium, consisting of European ancestry samples and CRP (n = 363 228) from UK Biobank, including 5 different population groups. The main analysis was performed using metaUSAT and N-GWAMA. We conducted replication for novel CRP associations to test the robustness of the findings using an independent GWAS for CRP (n = 148 164). We also performed colocalization analysis to compare the associations in identified loci for the 2 traits and Genotype-Tissue Expression data. RESULTS: We identified 87 pleiotropic loci that overlapped between metaUSAT and N-GWAMA, including 23 previously known for either fibrinogen or CRP, 58 novel loci for fibrinogen, and 6 novel loci for both fibrinogen and CRP. Overall, there were 30 pleiotropic and novel loci for both traits, and 7 of these showed evidence of colocalization, located in or near ZZZ3, NR1I2, RP11-72L22.1, MICU1, ARL14EP, SOCS2, and PGM5. Among these 30 loci, 13 replicated for CRP in an independent CRP GWAS. CONCLUSION: Bivariate GWAS identified additional associated loci for fibrinogen and CRP. This analysis suggests fibrinogen and CRP share a common genetic architecture with many pleiotropic loci.

Identification of Potential Causal Genes for Neurodegenerative Diseases by Mitochondria-Related Genome-Wide Mendelian Randomization.

Current research lacks comprehensive investigations into the potential causal link between mitochondrial-related genes and the risk of neurodegenerative diseases (NDDs). We aimed to identify potential causative genes for five NDDs through an examination of mitochondrial-related gene expression levels. Through the integration of summary statistics from expression quantitative trait loci (eQTL) datasets (human blood and brain tissue), mitochondrial DNA copy number (mtDNA-CN), and genome-wide association studies (GWAS) datasets of five NDDs from European ancestry, we conducted a Mendelian randomization (MR) analysis to explore the potential causal relationship between mitochondrial-related genes and Alzheimer's disease (AD), Parkinson's disease (PD), amyotrophic lateral sclerosis (ALS), frontotemporal dementia (FTD), and Lewy body dementia (LBD). Sensitivity analysis and Bayesian colocalization were employed to validate this causal relationship. Through MR analysis, we have identified potential causal relationships between 12 mitochondria-related genes and AD, PD, ALS, and FTD overlapping with motor neuron disease (FTD_MND) in human blood or brain tissue. Bayesian colocalization analysis further confirms 9 causal genes, including NDUFS2, EARS2, and MRPL41 for AD; NDUFAF2, MALSU1, and METTL8 for PD; MYO19 and MRM1 for ALS; and FASTKD1 for FTD_MND. Importantly, in both human blood and brain tissue, NDUFS2 exhibits a significant pathogenic effect on AD, while NDUFAF2 demonstrates a robust protective effect on PD. Additionally, the mtDNA-CN plays a protected role in LBD (OR = 0.62, p = 0.031). This study presents evidence establishing a causal relationship between mitochondrial dysfunction and NDDs. Furthermore, the identified candidate genes may serve as potential targets for drug development aimed at preventing NDDs.

Characterizing cell-type spatial relationships across length scales in spatially resolved omics data.

Spatially resolved omics (SRO) technologies enable the identification of cell types while preserving their organization within tissues. Application of such technologies offers the opportunity to delineate cell-type spatial relationships, particularly across different length scales, and enhance our understanding of tissue organization and function. To quantify such multi-scale cell-type spatial relationships, we developed CRAWDAD, Cell-type Relationship Analysis Workflow Done Across Distances, as an open-source R package with source code and additional documentation at https://jef.works/CRAWDAD/. To demonstrate the utility of such multi-scale characterization, recapitulate expected cell-type spatial relationships, and evaluate against other cell-type spatial analyses, we applied CRAWDAD to various simulated and real SRO datasets of diverse tissues assayed by diverse SRO technologies. We further demonstrate how such multi-scale characterization enabled by CRAWDAD can be used to compare cell-type spatial relationships across multiple samples. Finally, we applied CRAWDAD to SRO datasets of the human spleen to identify consistent as well as patient and sample-specific cell-type spatial relationships. In general, we anticipate such multi-scale analysis of SRO data enabled by CRAWDAD will provide useful quantitative metrics to facilitate the identification, characterization, and comparison of cell-type spatial relationships across axes of interest.

ColocZStats: a z-stack signal colocalization extension tool for 3D slicer.

Confocal microscopy has evolved to be a widely adopted imaging technique in molecular biology and is frequently utilized to achieve accurate subcellular localization of proteins. Applying colocalization analysis on image z-stacks obtained from confocal fluorescence microscopes is a dependable method of revealing the relationship between different molecules. In addition, despite the established advantages and growing adoption of 3D visualization software in various microscopy research domains, there have been few systems that can support colocalization analysis within a user-specified region of interest (ROI). In this context, several broadly employed biological image visualization platforms are meticulously explored in this study to understand the current landscape. It has been observed that while these applications can generate three-dimensional (3D) reconstructions for z-stacks, and in some cases transfer them into an immersive virtual reality (VR) scene, there is still little support for performing quantitative colocalization analysis on such images based on a user-defined ROI and thresholding levels. To address these issues, an extension called ColocZStats (pronounced Coloc-Zee-Stats) has been developed for 3D Slicer, a widely used free and open-source software package for image analysis and scientific visualization. With a custom-designed user-friendly interface, ColocZStats allows investigators to conduct intensity thresholding and ROI selection on imported 3D image stacks. It can deliver several essential colocalization metrics for structures of interest and produce reports in the form of diagrams and spreadsheets.

Association between GATM gene polymorphism and progression of chronic kidney disease: a mitochondrial related genome-wide Mendelian randomization study.

Chronic Kidney Disease (CKD) stands as a substantial challenge within the global health landscape. The elevated metabolic demands essential for sustaining normal kidney function have propelled an increasing interest in unraveling the intricate relationship between mitochondrial dysfunction and CKD. However, the authentic causal relationship between these two factors remains to be conclusively elucidated. This study endeavors to address this knowledge gap through the Mendelian Randomization (MR) method. We utilized large-scale QTL datasets (including 31,684 eQTLs samples, 1980 mQTLs samples, and 35,559 pQTLs samples) to precisely identify key genes related to mitochondrial function as exposure factors. Subsequently, we employed GWAS datasets (comprising 480,698 CKD samples and 1,004,040 eGFRcrea samples) as outcome factors. Through a comprehensive multi-level analysis (encompassing expression, methylation, and protein quantification loci), we evaluated the causal impact of these genes on CKD and estimated glomerular filtration rate (eGFR). The integration and validation of diverse genetic data, complemented by the application of co-localization analysis, bi-directional MR analysis, and various MR methods, notably including inverse variance weighted, have collectively strengthened our confidence in the robustness of these findings. Lastly, we validate the outcomes through examination in human RNA sequencing datasets encompassing various subtypes of CKD. This study unveils significant associations between the glycine amidinotransferase (GATM) and CKD, as well as eGFR. Notably, an augmentation in GATM gene and protein expression corresponds to a diminished risk of CKD, whereas distinct methylation patterns imply an increased risk. Furthermore, a discernible reduction in GATM expression is observed across diverse pathological subtypes of CKD, exhibiting a noteworthy positive correlation with GFR. These findings establish a causal relationship between GATM and CKD, thereby highlighting its potential as a therapeutic target. This insight lays the foundation for the development of potential therapeutic interventions for CKD, presenting substantial clinical promise.

Identification of novel drug targets for osteoarthritis by integrating genetics and proteomes from blood.

BACKGROUND: Osteoarthritis (OA) is a degenerative osteoarticular disease, involving genetic predisposition. How the risk variants confer the risk of OA through their effects on proteins remains largely unknown. Therefore, we aimed to discover new and effective drug targets for OA and its subtypes. METHODS: A proteome-wide association study (PWAS) was performed based on OA and its subtypes genome-wide association studies (GWAS) summary datasets and the protein quantitative trait loci (pQTL) data. Subsequently, Mendelian randomization (MR) and colocalization analysis was conducted to estimate the associations between protein and OA risk. The replication analysis was performed in an independent dataset of human plasma pQTL data. RESULTS: The abundance of seven proteins was causally related to OA, two proteins to knee OA and six proteins to hip OA, respectively. We replicated 2 of these proteins using an independent pQTL dataset. With the further support of colocalization, and higher ECM1 level was causally associated with a higher risk of OA and hip OA. Higher PCSK1 level was causally associated with a lower risk of OA. And higher levels of ITIH1, EFEMP1, and ERLEC1 were associated with decreased risk of hip OA. CONCLUSION: Our study provides new insights into the genetic component of protein abundance in OA and a promising therapeutic target for future drug development.

Advances in multi-enzyme co-localization strategies for the construction of microbial cell factory.

Biomanufacturing, driven by technologies such as synthetic biology, offers significant potential to advance the bioeconomy and promote sustainable development. It is anticipated to transform traditional manufacturing and become a key industry in future strategies. Cell factories are the core of biomanufacturing. The advancement of synthetic biology and growing market demand have led to the production of a greater variety of natural products and increasingly complex metabolic pathways. However, this progress also presents challenges, notably the conflict between natural product production and chassis cell growth. This conflict results in low productivity and yield, adverse side effects, metabolic imbalances, and growth retardation. Enzyme co-localization strategies have emerged as a promising solution. This article reviews recent progress and applications of these strategies in constructing cell factories for efficient natural product production. It comprehensively describes the applications of enzyme-based compartmentalization, metabolic pathway-based compartmentalization, and synthetic organelle-based compartmentalization in improving product titers. The article also explores future research directions and the prospects of combining multiple strategies with advanced technologies.

Mitochondrial dysfunction and onset of type 2 diabetes along with its complications: a multi-omics Mendelian randomization and colocalization study.

Background: Mitochondrial dysfunction plays a crucial role in Type 2 Diabetes Mellitus (T2DM) and its complications. However, the genetic pathophysiology remains under investigation. Through multi-omics Mendelian Randomization (MR) and colocalization analyses, we identified mitochondrial-related genes causally linked with T2DM and its complications. Methods: Summary-level quantitative trait loci data at methylation, RNA, and protein levels were retrieved from European cohort studies. GWAS summary statistics for T2DM and its complications were collected from the DIAGRAM and FinnGen consortiums, respectively. Summary-data-based MR was utilized to estimate the causal effects. The heterogeneity in dependent instrument test assessed horizontal pleiotropy, while colocalization analysis determined whether genes and diseases share the same causal variant. Enrichment analysis, drug target analysis, and phenome-wide MR were conducted to further explore the biological functions, potential drugs, and causal associations with other diseases. Results: Integrating evidence from multi-omics, we identified 18 causal mitochondrial-related genes. Enrichment analysis revealed they were not only related to nutrient metabolisms but also to the processes like mitophagy, autophagy, and apoptosis. Among these genes, Tu translation elongation factor mitochondrial (TUFM), 3-hydroxyisobutyryl-CoA hydrolase (HIBCH), and iron-sulfur cluster assembly 2 (ISCA2) were identified as Tier 1 genes, showing causal links with T2DM and strong colocalization evidence. TUFM and ISCA2 were causally associated with an increased risk of T2DM, while HIBCH showed an inverse causal relationship. The causal associations and colocalization effects for TUFM and HIBCH were validated in specific tissues. TUFM was also found to be a risk factor for microvascular complications in T2DM patients including retinopathy, nephropathy, and neuropathy. Furthermore, drug target analysis and phenome-wide MR underscored their significance as potential therapeutic targets. Conclusions: This study identified 18 mitochondrial-related genes causally associated with T2DM at multi-omics levels, enhancing the understanding of mitochondrial dysfunction in T2DM and its complications. TUFM, HIBCH, and ISCA2 emerge as potential therapeutic targets for T2DM and its complications.

Identification of genetic association between mitochondrial dysfunction and knee osteoarthritis through integrating multi-omics: a summary data-based Mendelian randomization study.

OBJECTIVE: Association between mitochondrial dysfunction and osteoarthritis (OA) has been consistently investigated, yet their genetic association remains obscure. In this study, mitochondrial-related genes were used as instrumental variables to proxy for mitochondrial dysfunction, and summary data of knee OA (KOA) were used as outcome to examine their genetic association. METHODS: We obtained 1136 mitochondrial-related genes from the human MitoCarta3.0 database. Genetic proxy instruments for mitochondrial-related genes from studies of corresponding gene expression (n = 31,684) and protein (n = 35,559) quantitative trait locus (eQTLs and pQTLs), respectively. Aggregated data for KOA (62,497 KOA cases and 333,557 controls) were extracted from the largest OA genome-wide association study (GWAS). We integrated QTL data with KOA GWAS data to estimate their genetic association using summary data-based Mendelian randomization analysis (SMR). Additionally, we implemented Bayesian colocalization analysis to reveal whether suggestive mitochondrial-related genes and KOA were driven by a same genetic variant. Finally, to validate the primary findings, replication study (24,955 cases and 378,169 controls) and multi-SNP-based SMR (SMR-multi) test was performed. RESULTS: Through SMR analysis, we found that the expression levels of 2 mitochondrial-related genes were associated with KOA risk. Specifically, elevated gene expression levels of the IMMP2L (odds ratio [OR] = 1.056; 95% confidence interval [CI] = 1.030-1.082; P-FDR = 0.004) increased the risk of KOA. Conversely, increased gene expression levels of AKAP10 decreased the risk of KOA (OR = 0.955; 95% CI, 0.934-0.977; P-FDR = 0.019). Colocalization analysis demonstrated that AKAP10 (PP.H4 = 0.84) and IMMP2L (PP.H4 = 0.91) shared the same genetic variant with KOA. In addition, consistent results were found in replication study and SMR-multi test, further demonstrating the reliability of our findings. CONCLUSIONS: In summary, our analyses revealed the genetic association between mitochondrial dysfunction proxied by mitochondrial-related genes and KOA, providing new insight into potential pathogenesis of KOA. Furthermore, these identified candidate genes offer the possibility of clinical drug target development for KOA. Key points • This is the first SMR study to explore the genetic association between mitochondrial dysfunction proxied by mitochondrial-related genes and KOA. • Sufficient evidence to support genetic association between the expression levels of AKAP10 and IMMP2L, and KOA • Our MR analysis may provide novel new insight into potential pathogenesis of KOA. • These identified candidate genes offer the possibility of clinical drug target development for KOA.

Liver eQTL meta-analysis illuminates potential molecular mechanisms of cardiometabolic traits.

Understanding the molecular mechanisms of complex traits is essential for developing targeted interventions. We analyzed liver expression quantitative-trait locus (eQTL) meta-analysis data on 1,183 participants to identify conditionally distinct signals. We found 9,013 eQTL signals for 6,564 genes; 23% of eGenes had two signals, and 6% had three or more signals. We then integrated the eQTL results with data from 29 cardiometabolic genome-wide association study (GWAS) traits and identified 1,582 GWAS-eQTL colocalizations for 747 eGenes. Non-primary eQTL signals accounted for 17% of all colocalizations. Isolating signals by conditional analysis prior to coloc resulted in 37% more colocalizations than using marginal eQTL and GWAS data, highlighting the importance of signal isolation. Isolating signals also led to stronger evidence of colocalization: among 343 eQTL-GWAS signal pairs in multi-signal regions, analyses that isolated the signals of interest resulted in higher posterior probability of colocalization for 41% of tests. Leveraging allelic heterogeneity, we predicted causal effects of gene expression on liver traits for four genes. To predict functional variants and regulatory elements, we colocalized eQTL with liver chromatin accessibility QTL (caQTL) and found 391 colocalizations, including 73 with non-primary eQTL signals and 60 eQTL signals that colocalized with both a caQTL and a GWAS signal. Finally, we used publicly available massively parallel reporter assays in HepG2 to highlight 14 eQTL signals that include at least one expression-modulating variant. This multi-faceted approach to unraveling the genetic underpinnings of liver-related traits could lead to therapeutic development.

Investigating the interaction of uranium(VI) with diatoms and their bacterial community: A microscopic and spectroscopic study.

Diatoms and bacteria play a vital role in investigating the ecological effects of heavy metals in the environment. Despite separate studies on metal interactions with diatoms and bacteria, there is a significant gap in research regarding heavy metal interactions within a diatom-bacterium system, which closely mirrors natural conditions. In this study, we aim to address this gap by examining the interaction of uranium(VI) (U(VI)) with Achnanthidium saprophilum freshwater diatoms and their natural bacterial community, primarily consisting of four successfully isolated bacterial strains (Acidovorax facilis, Agrobacterium fabrum, Brevundimonas mediterranea, and Pseudomonas peli) from the diatom culture. Uranium (U) bio-association experiments were performed both on the xenic A. saprophilum culture and on the four bacterial isolates. Scanning electron microscopy and transmission electron microscopy coupled with spectrum imaging analysis based on energy-dispersive X-ray spectroscopy revealed a clear co-localization of U and phosphorus both on the surface and inside A. saprophilum diatoms and the associated bacterial cells. Time-resolved laser-induced fluorescence spectroscopy with parallel factor analysis identified similar U(VI) binding motifs both on A. saprophilum diatoms and the four bacterial isolates. This is the first work providing valuable microscopic and spectroscopic data on U localization and speciation within a diatom-bacterium system, demonstrating the contribution of the co-occurring bacteria to the overall interaction with U, a factor non-negligible for future modeling and assessment of radiological effects on living microorganisms.