Circulating Metabolite Abundances Associated With Risks of Bipolar Disorder, Schizophrenia, and Depression: A Mendelian Randomization Study.

BACKGROUND: Preventive measures and treatments for psychiatric disorders are limited. Circulating metabolites are potential candidates for biomarker and therapeutic target identification, given their measurability and essential roles in biological processes. METHODS: Leveraging large-scale genome-wide association studies, we conducted Mendelian randomization analyses to assess the associations between circulating metabolite abundances and the risks of bipolar disorder, schizophrenia, and depression. Genetic instruments were selected for 94 metabolites measured in the Canadian Longitudinal Study on Aging cohort (N = 8299). We repeated Mendelian randomization analyses based on the UK Biobank, INTERVAL, and EPIC (European Prospective Investigation into Cancer)-Norfolk studies. RESULTS: After validating Mendelian randomization assumptions and colocalization evidence, we found that a 1 SD increase in genetically predicted circulating abundances of eicosapentaenoate and docosapentaenoate was associated with odds ratios of 0.72 (95% CI, 0.65-0.79) and 0.63 (95% CI, 0.55-0.72), respectively, for bipolar disorder. Genetically increased Ω-3 unsaturated fatty acids abundance and Ω-3-to-total fatty acids ratio, as well as genetically decreased Ω-6-to-Ω-3 ratio, were negatively associated with the risk of bipolar disorder in the UK Biobank. Genetically increased circulating abundances of 3 N-acetyl-amino acids were associated with an increased risk of schizophrenia with a maximum odds ratio of 1.31 (95% CI, 1.18-1.44) per 1 SD increase. Furthermore, a 1 SD increase in genetically predicted circulating abundance of hypotaurine was associated with an odds ratio of 0.85 (95% CI, 0.78-0.93) for depression. CONCLUSIONS: The biological mechanisms that underlie Ω-3 unsaturated fatty acids, NAT8-catalyzed N-acetyl-amino acids, and hypotaurine warrant exploration to identify new biomarkers and potential therapeutic targets.

Transcriptional reprogramming of skeletal muscle stem cells by the niche environment.

Adult stem cells are indispensable for tissue regeneration, but their function declines with age. The niche environment in which the stem cells reside plays a critical role in their function. However, quantification of the niche effect on stem cell function is lacking. Using muscle stem cells (MuSC) as a model, we show that aging leads to a significant transcriptomic shift in their subpopulations accompanied by locus-specific gain and loss of chromatin accessibility and DNA methylation. By combining in vivo MuSC transplantation and computational methods, we show that the expression of approximately half of all age-altered genes in MuSCs from aged male mice can be restored by exposure to a young niche environment. While there is a correlation between gene reversibility and epigenetic alterations, restoration of gene expression occurs primarily at the level of transcription. The stem cell niche environment therefore represents an important therapeutic target to enhance tissue regeneration in aging.

Pan-cancer analysis of mRNA stability for decoding tumour post-transcriptional programs.

Measuring mRNA decay in tumours is a prohibitive challenge, limiting our ability to map the post-transcriptional programs of cancer. Here, using a statistical framework to decouple transcriptional and post-transcriptional effects in RNA-seq data, we uncover the mRNA stability changes that accompany tumour development and progression. Analysis of 7760 samples across 18 cancer types suggests that mRNA stability changes are ~30% as frequent as transcriptional events, highlighting their widespread role in shaping the tumour transcriptome. Dysregulation of programs associated with >80 RNA-binding proteins (RBPs) and microRNAs (miRNAs) drive these changes, including multi-cancer inactivation of RBFOX and miR-29 families. Phenotypic activation or inhibition of RBFOX1 highlights its role in calcium signaling dysregulation, while modulation of miR-29 shows its impact on extracellular matrix organization and stemness genes. Overall, our study underlines the integral role of mRNA stability in shaping the cancer transcriptome, and provides a resource for systematic interrogation of cancer-associated stability pathways.

Polygenic risk score as a possible tool for identifying familial monogenic causes of complex diseases.

PURPOSE: The study aimed to evaluate whether polygenic risk scores could be helpful in addition to family history for triaging individuals to undergo deep-depth diagnostic sequencing for identifying monogenic causes of complex diseases. METHODS: Among 44,550 exome-sequenced European ancestry UK Biobank participants, we identified individuals with a clinically reported or computationally predicted monogenic pathogenic variant for breast cancer, bowel cancer, heart disease, diabetes, or Alzheimer disease. We derived polygenic risk scores for these diseases. We tested whether a polygenic risk score could identify rare pathogenic variant heterozygotes among individuals with a parental disease history. RESULTS: Monogenic causes of complex diseases were more prevalent among individuals with a parental disease history than in the rest of the population. Polygenic risk scores showed moderate discriminative power to identify familial monogenic causes. For instance, we showed that prescreening the patients with a polygenic risk score for type 2 diabetes can prioritize individuals to undergo diagnostic sequencing for monogenic diabetes variants and reduce needs for such sequencing by up to 37%. CONCLUSION: Among individuals with a family history of complex diseases, those with a low polygenic risk score are more likely to have monogenic causes of the disease and could be prioritized to undergo genetic testing.

Reshuffling of the ancestral core-eudicot genome shaped chromatin topology and epigenetic modification in Panax.

All extant core-eudicot plants share a common ancestral genome that has experienced cyclic polyploidizations and (re)diploidizations. Reshuffling of the ancestral core-eudicot genome generates abundant genomic diversity, but the role of this diversity in shaping the hierarchical genome architecture, such as chromatin topology and gene expression, remains poorly understood. Here, we assemble chromosome-level genomes of one diploid and three tetraploid Panax species and conduct in-depth comparative genomic and epigenomic analyses. We show that chromosomal interactions within each duplicated ancestral chromosome largely maintain in extant Panax species, albeit experiencing ca. 100-150 million years of evolution from a shared ancestor. Biased genetic fractionation and epigenetic regulation divergence during polyploidization/(re)diploidization processes generate remarkable biochemical diversity of secondary metabolites in the Panax genus. Our study provides a paleo-polyploidization perspective of how reshuffling of the ancestral core-eudicot genome leads to a highly dynamic genome and to the metabolic diversification of extant eudicot plants.

Evolutionary Contribution of Duplicated Genes to Genome Evolution in the Ginseng Species Complex.

Genes duplicated by whole genome duplication (WGD) and small-scale duplication (SSD) have played important roles in adaptive evolution of all flowering plants. However, it still remains underinvestigated how the distinct models of duplication events and their contending evolutionary patterns have shaped the genome and epigenomes of extant plant species. In this study, we investigated the contribution of the WGD- and SSD-derived duplicate genes to the genome evolution of one diploid and three closely related allotetraploid Panax species based on genome, methylome, and proteome data sets. Our genome-wide comparative analyses revealed that although the ginseng species complex was recently diverged, they have evolved distinct overall patterns of nucleotide variation, cytosine methylation, and protein-level expression. In particular, genetic and epigenetic asymmetries observed in the recent WGD-derived genes are largely consistent across the ginseng species complex. In addition, our results revealed that gene duplicates generated by ancient WGD and SSD mechanisms exhibited distinct evolutionary patterns. We found the ancient WGD-derived genes (i.e., ancient collinear gene) are genetically more conserved and hypomethylated at the cytosine sites. In contrast, some of the SSD-derived genes (i.e., dispersal duplicated gene) showed hypermethylation and high variance in nucleotide variation pattern. Functional enrichment analyses of the duplicated genes indicated that adaptation-related traits (i.e., photosynthesis) created during the distant ancient WGDs are further strengthened by both the more recent WGD and SSD. Together, our findings suggest that different types of duplicated genes may have played distinct but relaying evolutionary roles in the polyploidization and speciation processes in the ginseng species complex.

Improved prediction of fracture risk leveraging a genome-wide polygenic risk score.

BACKGROUND: Accurately quantifying the risk of osteoporotic fracture is important for directing appropriate clinical interventions. While skeletal measures such as heel quantitative speed of sound (SOS) and dual-energy X-ray absorptiometry bone mineral density are able to predict the risk of osteoporotic fracture, the utility of such measurements is subject to the availability of equipment and human resources. Using data from 341,449 individuals of white British ancestry, we previously developed a genome-wide polygenic risk score (PRS), called gSOS, that captured 25.0% of the total variance in SOS. Here, we test whether gSOS can improve fracture risk prediction. METHODS: We examined the predictive power of gSOS in five genome-wide genotyped cohorts, including 90,172 individuals of European ancestry and 25,034 individuals of Asian ancestry. We calculated gSOS for each individual and tested for the association between gSOS and incident major osteoporotic fracture and hip fracture. We tested whether adding gSOS to the risk prediction models had added value over models using other commonly used clinical risk factors. RESULTS: A standard deviation decrease in gSOS was associated with an increased odds of incident major osteoporotic fracture in populations of European ancestry, with odds ratios ranging from 1.35 to 1.46 in four cohorts. It was also associated with a 1.26-fold (95% confidence interval (CI) 1.13-1.41) increased odds of incident major osteoporotic fracture in the Asian population. We demonstrated that gSOS was more predictive of incident major osteoporotic fracture (area under the receiver operating characteristic curve (AUROC) = 0.734; 95% CI 0.727-0.740) and incident hip fracture (AUROC = 0.798; 95% CI 0.791-0.805) than most traditional clinical risk factors, including prior fracture, use of corticosteroids, rheumatoid arthritis, and smoking. We also showed that adding gSOS to the Fracture Risk Assessment Tool (FRAX) could refine the risk prediction with a positive net reclassification index ranging from 0.024 to 0.072. CONCLUSIONS: We generated and validated a PRS for SOS which was associated with the risk of fracture. This score was more strongly associated with the risk of fracture than many clinical risk factors and provided an improvement in risk prediction. gSOS should be explored as a tool to improve risk stratification to identify individuals at high risk of fracture.

H3K27M in Gliomas Causes a One-Step Decrease in H3K27 Methylation and Reduced Spreading within the Constraints of H3K36 Methylation.

The discovery of H3K27M mutations in pediatric gliomas marked a new chapter in cancer epigenomics. Numerous studies have investigated the effect of this mutation on H3K27 trimethylation, but only recently have we started to realize its additional effects on the epigenome. Here, we use isogenic glioma H3K27M+/- cell lines to investigate H3K27 methylation and its interaction with H3K36 and H3K9 modifications. We describe a "step down" effect of H3K27M on the distribution of H3K27 methylation: me3 is reduced to me2, me2 is reduced to me1, whereas H3K36me2/3 delineates the boundaries for the spread of H3K27me marks. We also observe a replacement of H3K27me2/3 silencing by H3K9me3. Using a computational simulation, we explain our observations by reduced effectiveness of PRC2 and constraints imposed on the deposition of H3K27me by antagonistic H3K36 modifications. Our work further elucidates the effects of H3K27M in gliomas as well as the general principles of deposition in H3K27 methylation.

Polygenic risk for coronary heart disease acts through atherosclerosis in type 2 diabetes.

BACKGROUND: Type 2 diabetes increases the risk of coronary heart disease (CHD), yet the mechanisms involved remain poorly described. Polygenic risk scores (PRS) provide an opportunity to understand risk factors since they reflect etiologic pathways from the entire genome. We therefore tested whether a PRS for CHD influenced risk of CHD in individuals with type 2 diabetes and which risk factors were associated with this PRS. METHODS: We tested the association of a CHD PRS with CHD and its traditional clinical risk factors amongst individuals with type 2 diabetes in UK Biobank (N = 21,102). We next tested the association of the CHD PRS with atherosclerotic burden in a cohort of 352 genome-wide genotyped participants with type 2 diabetes who had undergone coronary angiograms. RESULTS: In the UK Biobank we found that the CHD PRS was strongly associated with CHD amongst individuals with type 2 diabetes (OR per standard deviation increase = 1.50; p = 1.5 × 10- 59). But this CHD PRS was, at best, only weakly associated with traditional clinical risk factors, such as hypertension, hyperlipidemia, glycemic control, obesity and smoking. Conversely, in the angiographic cohort, the CHD PRS was strongly associated with multivessel stenosis (OR = 1.65; p = 4.9 × 10- 4) and increased number of major stenotic lesions (OR = 1.35; p = 9.4 × 10- 3). CONCLUSIONS: Polygenic predisposition to CHD is strongly associated with atherosclerotic burden in individuals with type 2 diabetes and this effect is largely independent of traditional clinical risk factors. This suggests that genetic risk for CHD acts through atherosclerosis with little effect on most traditional risk factors, providing the opportunity to explore new biological pathways.

Urine Proteome Profiling Predicts Lung Cancer from Control Cases and Other Tumors.

Development of noninvasive, reliable biomarkers for lung cancer diagnosis has many clinical benefits knowing that most of lung cancer patients are diagnosed at the late stage. For this purpose, we conducted proteomic analyses of 231 human urine samples in healthy individuals (n=33), benign pulmonary diseases (n=40), lung cancer (n=33), bladder cancer (n=17), cervical cancer (n=25), colorectal cancer (n=22), esophageal cancer (n=14), and gastric cancer (n=47) patients collected from multiple medical centers. By random forest modeling, we nominated a list of urine proteins that could separate lung cancers from other cases. With a feature selection algorithm, we selected a panel of five urinary biomarkers (FTL: Ferritin light chain; MAPK1IP1L: Mitogen-Activated Protein Kinase 1 Interacting Protein 1 Like; FGB: Fibrinogen Beta Chain; RAB33B: RAB33B, Member RAS Oncogene Family; RAB15: RAB15, Member RAS Oncogene Family) and established a combinatorial model that can correctly classify the majority of lung cancer cases both in the training set (n=46) and the test sets (n=14-47 per set) with an AUC ranging from 0.8747 to 0.9853. A combination of five urinary biomarkers not only discriminates lung cancer patients from control groups but also differentiates lung cancer from other common tumors. The biomarker panel and the predictive model, when validated by more samples in a multi-center setting, may be used as an auxiliary diagnostic tool along with imaging technology for lung cancer detection.

Proof-of-Concept Workflow for Establishing Reference Intervals of Human Urine Proteome for Monitoring Physiological and Pathological Changes.

Urine as a true non-invasive sampling source holds great potential for biomarker discovery. While approximately 2000 proteins can be detected by mass spectrometry in urine from healthy people, the amount of these proteins vary considerably. A systematic evaluation of a large number of samples is needed to determine the range of the variations. Current biomarker studies often measure limited number of urine samples in the discovery phase, which makes it difficult to determine whether proteins differentially expressed between control and disease groups represent actual difference, or are just physiological variations among the individuals, leads to failures in the validation phase with the increased sample numbers. Here, we report a streamlined workflow with capacity of measuring 8 urine proteomes per day at the coverage of >1500 proteins. With this workflow, we evaluated variations in 497 urine proteomes from 167 healthy donors, establishing reference intervals (RIs) that covered urine protein variations. We demonstrated that RIs could be used to monitor physiological changes by detecting transient outlier proteins. Furthermore, we provided a RIs-based algorithm for biomarker discovery and validation to screen for diseases such as cancer. This study provided a proof-of-principle workflow for the use of urine proteome for health monitoring and disease screening.

A fast workflow for identification and quantification of proteomes.

The current in-depth proteomics makes use of long chromatography gradient to get access to more peptides for protein identification, resulting in covering of as many as 8000 mammalian gene products in 3 days of mass spectrometer running time. Here we report a fast sequencing (Fast-seq) workflow of the use of dual reverse phase high performance liquid chromatography - mass spectrometry (HPLC-MS) with a short gradient to achieve the same proteome coverage in 0.5 day. We adapted this workflow to a quantitative version (Fast quantification, Fast-quan) that was compatible to large-scale protein quantification. We subjected two identical samples to the Fast-quan workflow, which allowed us to systematically evaluate different parameters that impact the sensitivity and accuracy of the workflow. Using the statistics of significant test, we unraveled the existence of substantial falsely quantified differential proteins and estimated correlation of false quantification rate and parameters that are applied in label-free quantification. We optimized the setting of parameters that may substantially minimize the rate of falsely quantified differential proteins, and further applied them on a real biological process. With improved efficiency and throughput, we expect that the Fast-seq/Fast-quan workflow, allowing pair wise comparison of two proteomes in 1 day may make MS available to the masses and impact biomedical research in a positive way.