Causal relationship between blood metabolites and risk of five infections: a Mendelian randomization study.

OBJECTIVE: Infectious diseases continue to pose a significant threat in the field of global public health, and our understanding of their metabolic pathogenesis remains limited. However, the advent of genome-wide association studies (GWAS) offers an unprecedented opportunity to unravel the relationship between metabolites and infections. METHODS: Univariable and multivariable Mendelian randomization (MR) was commandeered to elucidate the causal relationship between blood metabolism and five high-frequency infection phenotypes: sepsis, pneumonia, upper respiratory tract infections (URTI), urinary tract infections (UTI), and skin and subcutaneous tissue infection (SSTI). GWAS data for infections were derived from UK Biobank and the FinnGen consortium. The primary analysis was conducted using the inverse variance weighted method on the UK Biobank data, along with a series of sensitivity analyses. Subsequently, replication and meta-analysis were performed on the FinnGen consortium data. RESULTS: After primary analysis and a series of sensitivity analyses, 17 metabolites were identified from UK Biobank that have a causal relationship with five infections. Upon joint analysis with the FinGen cohort, 7 of these metabolites demonstrated consistent associations. Subsequently, we conducted a multivariable Mendelian randomization analysis to confirm the independent effects of these metabolites. Among known metabolites, genetically predicted 1-stearoylglycerol (1-SG) (odds ratio [OR] = 0.561, 95% confidence interval [CI]: 0.403-0.780, P < 0.001) and 3-carboxy-4-methyl-5-propyl-2-furanpropanoate (CMPF) (OR = 0.780, 95%CI: 0.689-0.883, P < 0.001) was causatively associated with a lower risk of sepsis, and genetically predicted phenylacetate (PA) (OR = 1.426, 95%CI: 1.152-1.765, P = 0.001) and cysteine (OR = 1.522, 95%CI: 1.170-1.980, P = 0.002) were associated with an increased risk of UTI. Ursodeoxycholate (UDCA) (OR = 0.906, 95%CI: 0.829-0.990, P = 0.029) is a protective factor against pneumonia. Two unknown metabolites, X-12407 (OR = 1.294, 95%CI: 1.131-1.481, P < 0.001), and X-12847 (OR = 1.344, 95%CI: 1.152-1.568, P < 0.001), were also identified as independent risk factors for sepsis. CONCLUSIONS: In this MR study, we demonstrated a causal relationship between blood metabolites and the risk of developing sepsis, pneumonia, and UTI. However, there was no evidence of a causal connection between blood metabolites and the risk of URTI or SSTI, indicating a need for larger-scale studies to further investigate susceptibility to certain infection phenotypes.

Sfcnn: a novel scoring function based on 3D convolutional neural network for accurate and stable protein-ligand affinity prediction.

BACKGROUND: Computer-aided drug design provides an effective method of identifying lead compounds. However, success rates are significantly bottlenecked by the lack of accurate and reliable scoring functions needed to evaluate binding affinities of protein-ligand complexes. Therefore, many scoring functions based on machine learning or deep learning have been developed to improve prediction accuracies in recent years. In this work, we proposed a novel featurization method, generating a new scoring function model based on 3D convolutional neural network. RESULTS: This work showed the results from testing four architectures and three featurization methods, and outlined the development of a novel deep 3D convolutional neural network scoring function model. This model simplified feature engineering, and in combination with Grad-CAM made the intermediate layers of the neural network more interpretable. This model was evaluated and compared with other scoring functions on multiple independent datasets. The Pearson correlation coefficients between the predicted binding affinities by our model and the experimental data achieved 0.7928, 0.7946, 0.6758, and 0.6474 on CASF-2016 dataset, CASF-2013 dataset, CSAR_HiQ_NRC_set, and Astex_diverse_set, respectively. Overall, our model performed accurately and stably enough in the scoring power to predict the binding affinity of a protein-ligand complex. CONCLUSIONS: These results indicate our model is an excellent scoring function, and performs well in scoring power for accurately and stably predicting the protein-ligand affinity. Our model will contribute towards improving the success rate of virtual screening, thus will accelerate the development of potential drugs or novel biologically active lead compounds.

A Review of Bayesian Hypothesis Testing and Its Practical Implementations.

We discuss hypothesis testing and compare different theories in light of observed or experimental data as fundamental endeavors in the sciences. Issues associated with the p-value approach and null hypothesis significance testing are reviewed, and the Bayesian alternative based on the Bayes factor is introduced, along with a review of computational methods and sensitivity related to prior distributions. We demonstrate how Bayesian testing can be practically implemented in several examples, such as the t-test, two-sample comparisons, linear mixed models, and Poisson mixed models by using existing software. Caveats and potential problems associated with Bayesian testing are also discussed. We aim to inform researchers in the many fields where Bayesian testing is not in common use of a well-developed alternative to null hypothesis significance testing and to demonstrate its standard implementation.

Causality of metabolites and metabolic pathways on cholestatic liver diseases: a Mendelian randomization study.

Background and Aims: Blood metabolite abnormalities have revealed an association with cholestatic liver diseases (CLDs), while the underlying metabolic mechanisms have remained sluggish yet. Accordingly, the present evaluation aims to investigate the causal relationship between blood metabolites and the risk of two major CLDs, including primary biliary cholangitis (PBC) and primary sclerosing cholangitis (PSC). Methods: Univariable and multivariable Mendelian randomization (MR) approaches were employed to uncover potential causal associations between blood metabolites and 2 CLDs, including PBS and PSC, through extracting instrumental variables (IVs) for metabolites from genome-wide association studies (GWAS) conducted on European individuals. The GWAS summary data of PBC or PSC were sourced from two distinct datasets. The initial analysis employed inverse variance weighted (IVW) and an array of sensitivity analyses, followed by replication and meta-analysis utilizing FinnGen consortium data. Finally, a multivariable MR analysis was carried out to ascertain the independent effects of each metabolite. Furthermore, the web-based tool MetaboAnalyst 5.0 was used to perform metabolic pathway examination. Results: A genetic causality between 15 metabolites and CLDs was recognized after preliminary analysis and false discovery rate (FDR) correction. Subsequently, 9 metabolites consistently represented an association through replication and meta-analysis. Additionally, the independent causal effects of 7 metabolites were corroborated by multivariable MR analysis. Specifically, the metabolites isovalerylcarnitine (odds ratio [OR] = 3.146, 95% confidence intervals [CI]: 1.471-6.726, p = 0.003), valine (OR = 192.44, 95%CI: 4.949-7483.27, p = 0.005), and mannose (OR = 0.184, 95%CI: 0.068-0.499, p < 0.001) were found to have a causal relationship with the occurrence of PBC. Furthermore, erythrose (OR = 5.504, 95%CI: 1.801-16.821, p = 0.003), 1-stearoylglycerophosphocholine (OR = 6.753, 95%CI: 2.621-17.399, p = 7.64 × 10-5), X-11847 (OR = 0.478, 95%CI: 0.352-0.650, p = 2.28 × 10-6), and X-12405 (OR = 3.765, 95%CI: 1.771-8.005, p = 5.71 × 10-4) were independently associated with the occurrence of PSC. Furthermore, the analysis of metabolic pathways identified seven significant pathways in two CLDs. Conclusion: The findings of the present study have unveiled robust causal relationships between 7 metabolites and 2 CLDs, thereby providing novel insights into the metabolic mechanisms and therapeutic strategies for these disorders.

Circulating micronutrient levels and their association with sepsis susceptibility and severity: a Mendelian randomization study.

Background: Sepsis, a global health challenge, necessitates a nuanced understanding of modifiable factors for effective prevention and intervention. The role of trace micronutrients in sepsis pathogenesis remains unclear, and their potential connection, especially with genetic influences, warrants exploration. Methods: We employed Mendelian randomization (MR) analyses to assess the causal relationship between genetically predicted blood levels of nine micronutrients (calcium, ß-carotene, iron, magnesium, phosphorus, vitamin C, vitamin B6, vitamin D, and zinc) and sepsis susceptibility, severity, and subtypes. The instrumental variables for circulating micronutrients were derived from nine published genome-wide association studies (GWAS). In the primary MR analysis, we utilized summary statistics for sepsis from two independent databases (UK Biobank and FinnGen consortium), for initial and replication analyses. Subsequently, a meta-analysis was conducted to merge the results. In secondary MR analyses, we assessed the causal effects of micronutrients on five sepsis-related outcomes (severe sepsis, sepsis-related death within 28 days, severe sepsis-related death within 28 days, streptococcal septicaemia, and puerperal sepsis), incorporating multiple sensitivity analyses and multivariable MR to address potential heterogeneity and pleiotropy. Results: The study revealed a significant causal link between genetically forecasted zinc levels and reduced risk of severe sepsis-related death within 28 days (odds ratio [OR] = 0.450; 95% confidence interval [CI]: 0.263, 0.770; p = 3.58 × 10-3). Additionally, suggestive associations were found for iron (increased risk of sepsis), ß-carotene (reduced risk of sepsis death) and vitamin C (decreased risk of puerperal sepsis). No significant connections were observed for other micronutrients. Conclusion: Our study highlighted that zinc may emerges as a potential protective factor against severe sepsis-related death within 28 days, providing theoretical support for supplementing zinc in high-risk critically ill sepsis patients. In the future, larger-scale data are needed to validate our findings.

Investigating the relationship between the Bayes factor and the separation of credible intervals.

We examined the relationship between the Bayes factor and the separation of credible intervals in between- and within-subject designs under a range of effect and sample sizes. For the within-subject case, we considered five intervals: (1) the within-subject confidence interval of Loftus and Masson (1994); (2) the within-subject Bayesian interval developed by Nathoo et al. (2018), whose derivation conditions on estimated random effects; (3) and (4) two modifications of (2) based on a proposal by Heck (2019) to allow for shrinkage and account for uncertainty in the estimation of random effects; and (5) the standard Bayesian highest-density interval. We derived and observed through simulations a clear and consistent relationship between the Bayes factor and the separation of credible intervals. Remarkably, for a given sample size, this relationship is described well by a simple quadratic exponential curve and is most precise in case (4). In contrast, interval (5) is relatively wide due to between-subjects variability and is likely to obscure effects when used in within-subject designs, rendering its relationship with the Bayes factor unclear in that case. We discuss how the separation percentage of (4), combined with knowledge of the sample size, could provide evidence in support of either a null or an alternative hypothesis. We also present a case study with example data and provide an R package 'rmBayes' to enable computation of each of the within-subject credible intervals investigated here using a number of possible prior distributions.

High expression of Ran binding protein 1 predicts poor outcomes in hepatocellular carcinoma patients: a Cancer Genome Atlas database analysis.

BACKGROUND: Ran-specific binding protein 1 (RANBP1) is involved in the regulation of the cell cycle, while its role in hepatocellular carcinoma (HCC) is unknown. Therefore, we aimed to demonstrate the association of RANBP1 with clinicopathologic features and potential biological functions in HCC based on The Cancer Genome Atlas (TCGA) data. METHODS: We assessed RANBP1 expression and its correlation with clinicopathologic features and evaluated the prognostic value of RANBP1 with Kaplan-Meier survival analysis and the MethSurv database. Univariate and multivariate Cox regression analyses were conducted to elucidate the factors responsible for prognosis. The identification of a co-expression network and the analysis of related biological events with RANBP1 in HCC were assessed using LinkedOmics. Moreover, gene set enrichment analysis (GSEA) was employed to annotate the biological function of RANBP1. We also explored the correlation between RANBP1 and tumor immune infiltrates using a single sample GSEA (ssGSEA). RESULTS: The expression of RANBP1 was found significantly elevated in HCC and linked to advanced T stage and histopathological grade. Up-regulated RANBP1 expression was linked to poor prognosis. High DNA methylation levels of RANBP1 were significantly linked to very poor overall survival (OS). Co-expression network analysis revealed that RANBP1 was involved in ribosome, spliceosome, deoxyribonucleic acid (DNA) replication, ribonucleic acid (RNA) transport, and cell cycle. GSEA showed enrichment of G2M-checkpoint, Wingless and Int-1 (Wnt) cell signaling, and DNA repair in the RANBP1 high-expression phenotype. By using ssGSEA analysis, the increased RANBP1 expression was positively linked to the immune infiltration level of T helper cell type-1 (Th1) and negatively linked to the immune infiltration levels of T helper cell type-17 (Th17). CONCLUSIONS: Findings suggest that RANBP1 may play a pivotal role in HCC prognosis and can potentially serve as a candidate biosignature and as a therapeutic target for HCC.

The microRNA miR-19a-3p suppresses cell growth, migration, and invasion in multiple myeloma via the Wnt/ß-catenin pathway.

BACKGROUND: MicroRNAs have been suggested as potential regulators in the development of multiple myeloma (MM) through affecting the expression of their target genes. This study aimed to investigate the effects of miR-19a-3p in MM, and its underlying mechanisms in regulating cell proliferation and invasion. METHODS: Bone marrow samples from 25 MM patients and 12 healthy donors were collected and miR-19a-3p and Wnt1 mRNA expression was assessed. The effects of miR-19a-3p on cell proliferation, migration, and invasion in U226 and RPMI-8226 MM cells were evaluated by miR-19a-3p overexpression. Luciferase assays were performed to explore the potential target genes. Knock down or overexpression of Wnt1 was used to explore the effects of miR-19a-3p on cell growth, migration, and invasion. RESULTS: The expression of miR-19a-3p was downregulated in MM and cell lines, while Wnt1 mRNA levels were increased. Overexpression of miR-19a-3p inhibited cell proliferation, migration, and invasion in U226 and RPMI-8226 cells. Additionally, western blot assays revealed that miR-19a-3p could suppress Wnt1, ß-catenin, cyclin D1, and c-Myc expression. Knockdown of Wnt1 also inhibited cell growth, migration, and invasion. Moreover, luciferase reporter assay revealed direct binding between Wnt1 and miR-19a-3p. Wnt1 overexpression partially reversed the suppressive effects of miR-19a-3p on cell proliferation, migration, and invasion in U266 cells. CONCLUSIONS: The expression of miR-19a-3p was downregulated in MM patients and MM cell lines. Overexpression of miR-19a-3p inhibited proliferation, migration, and invasion by targeting Wnt1 via the Wnt/ß-catenin signaling pathway.