Identification of blood protein biomarkers associated with prostate cancer risk using genetic prediction models: analysis of over 140,000 subjects.

Prostate cancer (PCa) brings huge public health burden in men. A growing number of conventional observational studies report associations of multiple circulating proteins with PCa risk. However, the existing findings may be subject to incoherent biases of conventional epidemiologic studies. To better characterize their associations, herein, we evaluated associations of genetically predicted concentrations of plasma proteins with PCa risk. We developed comprehensive genetic prediction models for protein levels in plasma. After testing 1308 proteins in 79 194 cases and 61 112 controls of European ancestry included in the consortia of BPC3, CAPS, CRUK, PEGASUS, and PRACTICAL, 24 proteins showed significant associations with PCa risk, including 16 previously reported proteins and eight novel proteins. Of them, 14 proteins showed negative associations and 10 showed positive associations with PCa risk. For 18 of the identified proteins, potential functional somatic changes of encoding genes were detected in PCa patients in The Cancer Genome Atlas (TCGA). Genes encoding these proteins were significantly involved in cancer-related pathways. We further identified drugs targeting the identified proteins, which may serve as candidates for drug repurposing for treating PCa. In conclusion, this study identifies novel protein biomarker candidates for PCa risk, which may provide new perspectives on the etiology of PCa and improve its therapeutic strategies.

Variability in performance of genetic-enhanced DXA-BMD prediction models across diverse ethnic and geographic populations: A risk prediction study.

BACKGROUND: Osteoporosis is a major global health issue, weakening bones and increasing fracture risk. Dual-energy X-ray absorptiometry (DXA) is the standard for measuring bone mineral density (BMD) and diagnosing osteoporosis, but its costliness and complexity impede widespread screening adoption. Predictive modeling using genetic and clinical data offers a cost-effective alternative for assessing osteoporosis and fracture risk. This study aims to develop BMD prediction models using data from the UK Biobank (UKBB) and test their performance across different ethnic and geographical populations. METHODS AND FINDINGS: We developed BMD prediction models for the femoral neck (FNK) and lumbar spine (SPN) using both genetic variants and clinical factors (such as sex, age, height, and weight), within 17,964 British white individuals from UKBB. Models based on regression with least absolute shrinkage and selection operator (LASSO), selected based on the coefficient of determination (R2) from a model selection subset of 5,973 individuals from British white population. These models were tested on 5 UKBB test sets and 12 independent cohorts of diverse ancestries, totaling over 15,000 individuals. Furthermore, we assessed the correlation of predicted BMDs with fragility fractures risk in 10 years in a case-control set of 287,183 European white participants without DXA-BMDs in the UKBB. With single-nucleotide polymorphism (SNP) inclusion thresholds at 5×10-6 and 5×10-7, the prediction models for FNK-BMD and SPN-BMD achieved the highest R2 of 27.70% with a 95% confidence interval (CI) of [27.56%, 27.84%] and 48.28% (95% CI [48.23%, 48.34%]), respectively. Adding genetic factors improved predictions slightly, explaining an additional 2.3% variation for FNK-BMD and 3% for SPN-BMD over clinical factors alone. Survival analysis revealed that the predicted FNK-BMD and SPN-BMD were significantly associated with fragility fracture risk in the European white population (P < 0.001). The hazard ratios (HRs) of the predicted FNK-BMD and SPN-BMD were 0.83 (95% CI [0.79, 0.88], corresponding to a 1.44% difference in 10-year absolute risk) and 0.72 (95% CI [0.68, 0.76], corresponding to a 1.64% difference in 10-year absolute risk), respectively, indicating that for every increase of one standard deviation in BMD, the fracture risk will decrease by 17% and 28%, respectively. However, the model's performance declined in other ethnic groups and independent cohorts. The limitations of this study include differences in clinical factors distribution and the use of only SNPs as genetic factors. CONCLUSIONS: In this study, we observed that combining genetic and clinical factors improves BMD prediction compared to clinical factors alone. Adjusting inclusion thresholds for genetic variants (e.g., 5×10-6 or 5×10-7) rather than solely considering genome-wide association study (GWAS)-significant variants can enhance the model's explanatory power. The study highlights the need for training models on diverse populations to improve predictive performance across various ethnic and geographical groups.

A multiethnic whole genome sequencing study to identify novel loci for bone mineral density.

At present, there have only been a few DNA sequencing-based studies to explore the genetic determinants of bone mineral density (BMD). We carried out the largest whole genome sequencing analysis to date for femoral neck and spine BMD (n = 4981), with one of the highest average sequencing depths implemented thus far at 22×, in a multiethnic sample (58% Caucasian and 42% African American) from the Louisiana Osteoporosis Study (LOS). The LOS samples were combined with summary statistics from the GEFOS consortium and several independent samples of various ethnicities to perform GWAS meta-analysis (n = 44 506). We identified 31 and 30 genomic risk loci for femoral neck and spine BMD, respectively. The findings substantiate many previously reported susceptibility loci (e.g. WNT16 and ESR1) and reveal several others that are either novel or have not been widely replicated in GWAS for BMD, including two for femoral neck (IGF2 and ZNF423) and one for spine (SIPA1). Although we were not able to uncover ethnicity specific differences in the genetic determinants of BMD, we did identify several loci which demonstrated sex-specific associations, including two for women (PDE4D and PIGN) and three for men (TRAF3IP2, NFIB and LYSMD4). Gene-based rare variant association testing detected MAML2, a regulator of the Notch signaling pathway, which has not previously been suggested, for association with spine BMD. The findings provide novel insights into the pathophysiological mechanisms of osteoporosis.

BERT6mA: prediction of DNA N6-methyladenine site using deep learning-based approaches.

N6-methyladenine (6mA) is associated with important roles in DNA replication, DNA repair, transcription, regulation of gene expression. Several experimental methods were used to identify DNA modifications. However, these experimental methods are costly and time-consuming. To detect the 6mA and complement these shortcomings of experimental methods, we proposed a novel, deep leaning approach called BERT6mA. To compare the BERT6mA with other deep learning approaches, we used the benchmark datasets including 11 species. The BERT6mA presented the highest AUCs in eight species in independent tests. Furthermore, BERT6mA showed higher and comparable performance with the state-of-the-art models while the BERT6mA showed poor performances in a few species with a small sample size. To overcome this issue, pretraining and fine-tuning between two species were applied to the BERT6mA. The pretrained and fine-tuned models on specific species presented higher performances than other models even for the species with a small sample size. In addition to the prediction, we analyzed the attention weights generated by BERT6mA to reveal how the BERT6mA model extracts critical features responsible for the 6mA prediction. To facilitate biological sciences, the BERT6mA online web server and its source codes are freely accessible at https://github.com/kuratahiroyuki/BERT6mA.git, respectively.

A new hip fracture risk index derived from FEA-computed proximal femur fracture loads and energies-to-failure.

Hip fracture risk assessment is an important but challenging task. Quantitative CT-based patient-specific finite element (FE) analysis (FEA) incorporates bone geometry and bone density in the proximal femur. We developed a global FEA-computed fracture risk index to increase the prediction accuracy of hip fracture incidence. PURPOSE: Quantitative CT-based patient-specific finite element (FE) analysis (FEA) incorporates bone geometry and bone density in the proximal femur to compute the force (fracture load) and energy necessary to break the proximal femur in a particular loading condition. The fracture loads and energies-to-failure are individually associated with incident hip fracture, and provide different structural information about the proximal femur. METHODS: We used principal component analysis (PCA) to develop a global FEA-computed fracture risk index that incorporates the FEA-computed yield and ultimate failure loads and energies-to-failure in four loading conditions of 110 hip fracture subjects and 235 age- and sex-matched control subjects from the AGES-Reykjavik study. Using a logistic regression model, we compared the prediction performance for hip fracture based on the stratified resampling. RESULTS: We referred the first principal component (PC1) of the FE parameters as the global FEA-computed fracture risk index, which was the significant predictor of hip fracture (p-value < 0.001). The area under the receiver operating characteristic curve (AUC) using PC1 (0.776) was higher than that using all FE parameters combined (0.737) in the males (p-value < 0.001). CONCLUSIONS: The global FEA-computed fracture risk index increased hip fracture risk prediction accuracy in males.

Regulon active landscape reveals cell development and functional state changes of human primary osteoblasts in vivo.

BACKGROUND: While transcription factor (TF) regulation is known to play an important role in osteoblast development, differentiation, and bone metabolism, the molecular features of TFs in human osteoblasts at the single-cell resolution level have not yet been characterized. Here, we identified modules (regulons) of co-regulated genes by applying single-cell regulatory network inference and clustering to the single-cell RNA sequencing profiles of human osteoblasts. We also performed cell-specific network (CSN) analysis, reconstructed regulon activity-based osteoblast development trajectories, and validated the functions of important regulons both in vivo and in vitro. RESULTS: We identified four cell clusters: preosteoblast-S1, preosteoblast-S2, intermediate osteoblasts, and mature osteoblasts. CSN analysis results and regulon activity-based osteoblast development trajectories revealed cell development and functional state changes of osteoblasts. CREM and FOSL2 regulons were mainly active in preosteoblast-S1, FOXC2 regulons were mainly active in intermediate osteoblast, and RUNX2 and CREB3L1 regulons were most active in mature osteoblasts. CONCLUSIONS: This is the first study to describe the unique features of human osteoblasts in vivo based on cellular regulon active landscapes. Functional state changes of CREM, FOSL2, FOXC2, RUNX2, and CREB3L1 regulons regarding immunity, cell proliferation, and differentiation identified the important cell stages or subtypes that may be predominantly affected by bone metabolism disorders. These findings may lead to a deeper understanding of the mechanisms underlying bone metabolism and associated diseases.

Dissecting the shared genetic architecture between anti-Müllerian hormone and age at menopause based on genome-wide association study.

BACKGROUND: While the phenotypic association between anti-Müllerian hormoneand age at menopause has been widely studied, the role of anti-Müllerian hormone in predicting the age at menopause is currently controversial, and the genetic architecture or causal relationships underlying these 2 traits is not well understood. AIM: We aimed to explore the shared genetic architecture between anti-Müllerian hormone and age at menopause, to identify shared pleiotropic loci and genes, and to investigate causal association and potential causal mediators. STUDY DESIGN: Using summary statistics from publicly available genome-wide association studies on anti-Müllerian hormone (N=7049) and age at menopause (N=201,323) in Europeans, we investigated the global genetic architecture between anti-Müllerian hormone and age at menopause through linkage disequilibrium score regression. We employed pleiotropic analysis under composite null hypothesis, Functional Mapping and Annotation of Genetic Associations, multimarker analysis of GenoMic annotation, and colocalization analysis to identify loci and genes with pleiotropic effects. Tissue enrichment analysis based on Genotype-Tissue Expression data was conducted using the Linkage Disequilibrium Score for the specific expression of genes analysis. Functional genes that were shared were additionally identified through summary data-based Mendelian randomization. The relationship between anti-Müllerian hormone and age at menopause was examined through 2-sample Mendelian randomization, and potential mediators were further explored using colocalization and metabolite-mediated analysis. RESULTS: A positive genetic association (correlation coefficient=0.88, P=1.33×10-5) was observed between anti-Müllerian hormone and age at menopause. By using pleiotropic analysis under composite null hypothesis and Functional Mapping and Annotation of Genetic Associations, 42 significant pleiotropic loci were identified that were associated with anti-Müllerian hormone and age at menopause, and 10 of these (rs10734411, rs61913600, rs2277339, rs75770066, rs28416520, rs9796, rs11668344, rs403727, rs6011452, and rs62237617) had colocalized loci. Additionally, 245 significant pleiotropic genes were identified by multimarker analysis of GenoMic annotation. Genetic associations between anti-Müllerian hormone and age at menopause were markedly concentrated in various tissues including whole blood, brain, heart, liver, muscle, pancreas, and kidneys. Further, summary data-based Mendelian randomization analysis revealed 9 genes that may have a causative effect on both anti-Müllerian hormone and age at menopause. A potential causal effect of age at menopause on anti-Müllerian hormone was suggested by 2-sample Mendelian randomization analysis, with very-low-density lipoprotein identified as a potential mediator. CONCLUSION: Our study revealed a shared genetic architecture between anti-Müllerian hormone and age at menopause, providing a basis for experimental investigations and individual therapies to enhance reproductive outcomes. Furthermore, our findings emphasized that relying solely on anti-Müllerian hormone is not sufficient for accurately predicting the age at menopause, and a combination of other factors needs to be considered. Exploring new therapeutics aimed at delaying at the onset of menopause holds promise, particularly when targeting shared genes based on their shared genetic architecture.

Prenatal diagnosis and treatment for fetal angiotensin converting enzyme deficiency.

OBJECTIVE: To elucidate an etiology in a case with persistent oligohydramnios by prenatal diagnosis and actively treat the case to achieve good prognosis. METHODS: We performed whole exome sequencing (WES) of DNA from the fetus and parents. Serial amnioinfusions were conducted until birth. Pressors were required to maintain normal blood pressure. The infant angiotensin-converting enzyme (ACE) activity, angiotensin II (Ang II, a downstream product of ACE), and compensatory enzymes (CEs) activities were measured. Compensatory enzyme activities in plasma from age-matched healthy controls were also detected. RESULTS: We identified a fetus with a severe ACE mutation prenatally. The infant was born prematurely without pulmonary dysplasia. Hypotension and anuria resolved spontaneously. He had almost no ACE activity, but his Ang II level and CE activity exceeded the upper limit of the normal range and the upper limit of the 95% confidence interval of controls, respectively. His renal function also largely recovered. CONCLUSION: Fetuses with ACE mutations can be diagnosed prenatally through WES. Serial amnioinfusion permits the continuation of pregnancy in fetal ACE deficiency. Compensatory enzymes for defective ACE appeared postnatally. Renal function may be spared by preterm delivery; furthermore, for postnatal vasopressor therapy to begin, improving renal perfusion pressure before nephrogenesis has been completed.

Most synonymous allelic variants in HIV tat are not silent.

The genetic code has degenerate codons that produce no change in the translated protein sequence and are generally thought to be silent. However, some synonymous variants are clearly not silent. Herein, we questioned the frequency of non-silent synonymous variants. We tested how random synonymous variants in the HIV Tat transcription factor effect transcription of an LTR-GFP reporter. Our model system has the advantage of directly measuring the function of the gene in human cells. Approximately, 67% of synonymous variants in Tat were non-silent, either having reduced activity or were full loss-of-function alleles. Eight mutant codons had higher codon usage than wild type, accompanied by reduced transcriptional activity. These were clustered on a loop in the Tat structure. We conclude that most synonymous Tat variants are not silent in human cells, and 25% are associated with changes in codon usage, likely effecting protein folding.

Identification of the serum metabolites associated with cow milk consumption in Chinese Peri-/Postmenopausal women.

Cow milk consumption (CMC) and downstream alterations of serum metabolites are commonly considered important factors regulating human health status. Foods may lead to metabolic changes directly or indirectly through remodelling gut microbiota (GM). We sought to identify the metabolic alterations in Chinese Peri-/Postmenopausal women with habitual CMC and explore if the GM mediates the CMC-metabolite associations. 346 Chinese Peri-/Postmenopausal women participants were recruited in this study. Fixed effects regression and partial least squares discriminant analysis (PLS-DA) were applied to reveal alterations of serum metabolic features in different CMC groups. Spearman correlation coefficient was computed to detect metabolome-metagenome association. 36 CMC-associated metabolites including palmitic acid (FA(16:0)), 7alpha-hydroxy-4-cholesterin-3-one (7alphaC4), citrulline were identified by both fixed effects regression (FDR < 0.05) and PLS-DA (VIP score > 2). Some significant metabolite-GM associations were observed, including FA(16:0) with gut species Bacteroides ovatus, Bacteroides sp.D2. These findings would further prompt our understanding of the effect of cow milk on human health.

Beyond Amyloid: A Machine Learning-Driven Approach Reveals Properties of Potent GSK-3ß Inhibitors Targeting Neurofibrillary Tangles.

Current treatments for Alzheimer's disease (AD) focus on slowing memory and cognitive decline, but none offer curative outcomes. This study aims to explore and curate the common properties of active, drug-like molecules that modulate glycogen synthase kinase 3ß (GSK-3ß), a well-documented kinase with increased activity in tau hyperphosphorylation and neurofibrillary tangles-hallmarks of AD pathology. Leveraging quantitative structure-activity relationship (QSAR) data from the PubChem and ChEMBL databases, we employed seven machine learning models: logistic regression (LogR), k-nearest neighbors (KNN), random forest (RF), support vector machine (SVM), extreme gradient boosting (XGB), neural networks (NNs), and ensemble majority voting. Our goal was to correctly predict active and inactive compounds that inhibit GSK-3ß activity and identify their key properties. Among the six individual models, the NN demonstrated the highest performance with a 79% AUC-ROC on unbalanced external validation data, while the SVM model was superior in accurately classifying the compounds. The SVM and RF models surpassed NN in terms of Kappa values, and the ensemble majority voting model demonstrated slightly better accuracy to the NN on the external validation data. Feature importance analysis revealed that hydrogen bonds, phenol groups, and specific electronic characteristics are important features of molecular descriptors that positively correlate with active GSK-3ß inhibition. Conversely, structural features like imidazole rings, sulfides, and methoxy groups showed a negative correlation. Our study highlights the significance of structural, electronic, and physicochemical descriptors in screening active candidates against GSK-3ß. These predictive features could prove useful in therapeutic strategies to understand the important properties of GSK-3ß candidate inhibitors that may potentially benefit non-amyloid-based AD treatments targeting neurofibrillary tangles.

Identification of potential genetic causal variants for obesity-related traits using statistical fine mapping.

BACKGROUND: Obesity is highly influenced by heritability and variant effects. While previous genome-wide association studies (GWASs) have successfully identified numerous genetic loci associated with obesity-related traits [body mass index (BMI) and waist-to-hip ratio (WHR)], most causal variants remain unidentified. The high degree of linkage disequilibrium (LD) throughout the genome makes it extremely difficult to distinguish the GWAS-associated SNPs that exert a true biological effect. OBJECTIVE: This study was to identify the potential causal variants having a biological effect on obesity-related traits. METHODS: We used Probabilistic Annotation INTegratOR, a Bayesian fine-mapping method, which incorporated genetic association data (GWAS summary statistics), LD structure, and functional annotations to calculate a posterior probability of causality for SNPs across all loci of interest. Moreover, we performed gene expression analysis using the available public transcriptomic data to validate the corresponding genes of the potential causal SNPs partially. RESULTS: We identified 96 SNPs for BMI and 43 SNPs for WHR with a high posterior probability of causality (> 99%), including 49 BMI SNPs and 24 WHR SNPs which did not reach genome-wide significance in the original GWAS. Finally, we partially validated some genes corresponding to the potential causal SNPs. CONCLUSION: Using a statistical fine-mapping approach, we identified a set of potential causal variants to be prioritized for future functional validation and also detected some novel trait-associated variants. These results provided novel insight into our understanding of the genetics of obesity and also demonstrated that fine mapping may improve upon the results identified by the original GWASs.

NeuroPred-FRL: an interpretable prediction model for identifying neuropeptide using feature representation learning.

Neuropeptides (NPs) are the most versatile neurotransmitters in the immune systems that regulate various central anxious hormones. An efficient and effective bioinformatics tool for rapid and accurate large-scale identification of NPs is critical in immunoinformatics, which is indispensable for basic research and drug development. Although a few NP prediction tools have been developed, it is mandatory to improve their NPs' prediction performances. In this study, we have developed a machine learning-based meta-predictor called NeuroPred-FRL by employing the feature representation learning approach. First, we generated 66 optimal baseline models by employing 11 different encodings, six different classifiers and a two-step feature selection approach. The predicted probability scores of NPs based on the 66 baseline models were combined to be deemed as the input feature vector. Second, in order to enhance the feature representation ability, we applied the two-step feature selection approach to optimize the 66-D probability feature vector and then inputted the optimal one into a random forest classifier for the final meta-model (NeuroPred-FRL) construction. Benchmarking experiments based on both cross-validation and independent tests indicate that the NeuroPred-FRL achieves a superior prediction performance of NPs compared with the other state-of-the-art predictors. We believe that the proposed NeuroPred-FRL can serve as a powerful tool for large-scale identification of NPs, facilitating the characterization of their functional mechanisms and expediting their applications in clinical therapy. Moreover, we interpreted some model mechanisms of NeuroPred-FRL by leveraging the robust SHapley Additive exPlanation algorithm.

Integrative analysis of multi-omics data to detect the underlying molecular mechanisms for obesity in vivo in humans.

BACKGROUND: Obesity is a complex, multifactorial condition in which genetic play an important role. Most of the systematic studies currently focuses on individual omics aspect and provide insightful yet limited knowledge about the comprehensive and complex crosstalk between various omics levels. SUBJECTS AND METHODS: Therefore, we performed a most comprehensive trans-omics study with various omics data from 104 subjects, to identify interactions/networks and particularly causal regulatory relationships within and especially those between omic molecules with the purpose to discover molecular genetic mechanisms underlying obesity etiology in vivo in humans. RESULTS: By applying differentially analysis, we identified 8 differentially expressed hub genes (DEHGs), 14 differentially methylated regions (DMRs) and 12 differentially accumulated metabolites (DAMs) for obesity individually. By integrating those multi-omics biomarkers using Mendelian Randomization (MR) and network MR analyses, we identified 18 causal pathways with mediation effect. For the 20 biomarkers involved in those 18 pairs, 17 biomarkers were implicated in the pathophysiology of obesity or related diseases. CONCLUSIONS: The integration of trans-omics and MR analyses may provide us a holistic understanding of the underlying functional mechanisms, molecular regulatory information flow and the interactive molecular systems among different omic molecules for obesity risk and other complex diseases/traits.

Deepm5C: A deep-learning-based hybrid framework for identifying human RNA N5-methylcytosine sites using a stacking strategy.

As one of the most prevalent post-transcriptional epigenetic modifications, N5-methylcytosine (m5C) plays an essential role in various cellular processes and disease pathogenesis. Therefore, it is important accurately identify m5C modifications in order to gain a deeper understanding of cellular processes and other possible functional mechanisms. Although a few computational methods have been proposed, their respective models have been developed using small training datasets. Hence, their practical application is quite limited in genome-wide detection. To overcome the existing limitations, we propose Deepm5C, a bioinformatics method for identifying RNA m5C sites throughout the human genome. To develop Deepm5C, we constructed a novel benchmarking dataset and investigated a mixture of three conventional feature-encoding algorithms and a feature derived from word-embedding approaches. Afterward, four variants of deep-learning classifiers and four commonly used conventional classifiers were employed and trained with the four encodings, ultimately obtaining 32 baseline models. A stacking strategy is effectively utilized by integrating the predicted output of the optimal baseline models and trained with a one-dimensional (1D) convolutional neural network. As a result, the Deepm5C predictor achieved excellent performance during cross-validation with a Matthews correlation coefficient and an accuracy of 0.697 and 0.855, respectively. The corresponding metrics during the independent test were 0.691 and 0.852, respectively. Overall, Deepm5C achieved a more accurate and stable performance than the baseline models and significantly outperformed the existing predictors, demonstrating the effectiveness of our proposed hybrid framework. Furthermore, Deepm5C is expected to assist community-wide efforts in identifying putative m5Cs and to formulate the novel testable biological hypothesis.

GigaAssay - An adaptable high-throughput saturation mutagenesis assay platform.

High-throughput assay systems have had a large impact on understanding the mechanisms of basic cell functions. However, high-throughput assays that directly assess molecular functions are limited. Herein, we describe the "GigaAssay", a modular high-throughput one-pot assay system for measuring molecular functions of thousands of genetic variants at once. In this system, each cell was infected with one virus from a library encoding thousands of Tat mutant proteins, with each viral particle encoding a random unique molecular identifier (UMI). We demonstrate proof of concept by measuring transcription of a GFP reporter in an engineered reporter cell line driven by binding of the HIV Tat transcription factor to the HIV long terminal repeat. Infected cells were flow-sorted into 3 bins based on their GFP fluorescence readout. The transcriptional activity of each Tat mutant was calculated from the ratio of signals from each bin. The use of UMIs in the GigaAssay produced a high average accuracy (95%) and positive predictive value (98%) determined by comparison to literature benchmark data, known C-terminal truncations, and blinded independent mutant tests. Including the substitution tolerance with structure/function analysis shows restricted substitution types spatially concentrated in the Cys-rich region. Tat has abundant intragenic epistasis (10%) when single and double mutants are compared.

Alkynyl-Protected Chiral Bimetallic Ag22 Cu7 Superatom with Multiple Chirality Origins.

Understanding the origin of chirality in the nanostructured materials is essential for chiroptical and catalytic applications. Here we report a chiral AgCu superatomic cluster, [Ag22 Cu7 (C≡CR)16 (PPh3 )5 Cl6 ](PPh4 ), Ag22 Cu7 , protected by an achiral alkynyl ligand (HC≡CR: 3,5-bis(trifluoromethyl)phenylacetylene). Its crystal structure comprises a rare interpenetrating biicosahedral Ag17 Cu2 core, which is stabilized by four different types of motifs: one Cu(C≡CR)2 , four -C≡CR, two chlorides and one helical Ag5 Cu4 (C≡CR)10 (PPh3 )5 Cl4 . Structural analysis reveals that Ag22 Cu7 exhibits multiple chirality origins, including the metal core, the metal-ligand interface and the ligand layer. Furthermore, the circular dichroism spectra of R/S-Ag22 Cu7 are obtained by employing appropriate chiral molecules as optical enrichment agents. DFT calculations show that Ag22 Cu7 is an eight-electron superatom, confirm that the cluster is chirally active, and help to analyze the origins of the circular dichroism.

Identification of genetic loci shared between Alzheimer's disease and hypertension.

Alzheimer's disease (AD) and high blood pressure (BP) are prevalent age-related diseases with significant unexplained heritability. A thorough analysis of genetic pleiotropy between AD and BP will lay a foundation for the study of the associated molecular mechanisms, leading to a better understanding of the development of each phenotype. We used the conditional false discovery rate (cFDR) method to identify novel genetic loci associated with both AD and BP. The cFDR approach improves the effective sample size for association testing by combining GWAS summary statistics for correlated phenotypes. We identified 50 pleiotropic SNPs for AD and BP, 7 of which are novel and have not previously been reported to be associated with either AD or BP. The novel SNPs located at STK3 are particularly noteworthy, as this gene may influence AD risk via the Hippo signaling network, which regulates cell death. Bayesian colocalization analysis demonstrated that although AD and BP are associated, they do not appear to share the same causal variants. We further performed two sample Mendelian randomization analysis, but could not detect a causal effect of BP on AD. Despite the inability to establish a causal link between AD and BP, our findings report some potential novel pleiotropic loci that may influence disease susceptibility. In summary, we identified 7 SNPs that annotate to 4 novel genes which have not previously been reported to be associated with AD nor with BP and discuss the possible role of one of these genes, STK3 in the Hippo signaling network.

Polygenic risk score improves the accuracy of a clinical risk score for coronary artery disease.

BACKGROUND: The value of polygenic risk scores (PRSs) towards improving guideline-recommended clinical risk models for coronary artery disease (CAD) prediction is controversial. Here we examine whether an integrated polygenic risk score improves the prediction of CAD beyond pooled cohort equations.  METHODS: An observation study of 291,305 unrelated White British UK Biobank participants enrolled from 2006 to 2010 was conducted. A case-control sample of 9499 prevalent CAD cases and an equal number of randomly selected controls was used for tuning and integrating of the polygenic risk scores. A separate cohort of 272,307 individuals (with follow-up to 2020) was used to examine the risk prediction performance of pooled cohort equations, integrated polygenic risk score, and PRS-enhanced pooled cohort equation for incident CAD cases. The performance of each model was analyzed by discrimination and risk reclassification using a 7.5% threshold. RESULTS: In the cohort of 272,307 individuals (mean age, 56.7 years) used to analyze predictive accuracy, there were 7036 incident CAD cases over a 12-year follow-up period. Model discrimination was tested for integrated polygenic risk score, pooled cohort equation, and PRS-enhanced pooled cohort equation with reported C-statistics of 0.640 (95% CI, 0.634-0.646), 0.718 (95% CI, 0.713-0.723), and 0.753 (95% CI, 0.748-0.758), respectively. Risk reclassification for the addition of the integrated polygenic risk score to the pooled cohort equation at a 7.5% risk threshold resulted in a net reclassification improvement of 0.117 (95% CI, 0.102 to 0.129) for cases and - 0.023 (95% CI, - 0.025 to - 0.022) for noncases [overall: 0.093 (95% CI, 0.08 to 0.104)]. For incident CAD cases, this represented 14.2% correctly reclassified to the higher-risk category and 2.6% incorrectly reclassified to the lower-risk category. CONCLUSIONS: Addition of the integrated polygenic risk score for CAD to the pooled cohort questions improves the predictive accuracy for incident CAD and clinical risk classification in the White British from the UK Biobank. These findings suggest that an integrated polygenic risk score may enhance CAD risk prediction and screening in the White British population.

Gut microbiota accelerates obesity in peri-/post-menopausal women via Bacteroides fragilis and acetic acid.

OBJECTIVE: Many animal experiments and epidemiological studies have shown that the gut microbiota (GM) plays an important role in the development of obesity, but the specific biological mechanism involved in the pathogenesis of disease remain unknown. We aimed to examine the relationships and functional mechanisms of GM on obesity in peri- and post-menopausal women. METHODS: We recruited 499 Chinese peri- and post-menopausal women and performed comprehensive analyses of the gut microbiome, targeted metabolomics for short-chain fatty acids in serum, and host whole-genome sequencing by various association analysis methods. RESULTS: Through constrained linear regression analysis, we found that an elevated abundance of Bacteroides fragilis (B. fragilis) was associated with obesity. We also found that serum levels of acetic acid were negatively associated with obesity, and that B. fragilis was negatively associated with serum acetic acid levels by partial Spearman correlation analysis. Mendelian randomization analysis indicated that B. fragilis increases the risk of obesity and may causally down-regulate acetic acid levels. CONCLUSIONS: We found the gut with B. fragilis may accelerate obesity, in part, by suppressing acetic acid levels. Therefore, B. fragilis and acetic acid may represent important therapeutic targets for obesity intervention in peri- and post-menopausal women.