Gut microbial genomes with paired isolates from China illustrate probiotic and cardiometabolic effects.

The gut microbiome displays genetic differences among populations, and characterization of the genomic landscape of the gut microbiome in China remains limited. Here, we present the Chinese Gut Microbial Reference (CGMR) set, comprising 101,060 high-quality metagenomic assembled genomes (MAGs) of 3,707 nonredundant species from 3,234 fecal samples across primarily rural Chinese locations, 1,376 live isolates mainly from lactic acid bacteria, and 987 novel species relative to worldwide databases. We observed region-specific coexisting MAGs and MAGs with probiotic and cardiometabolic functionalities. Preliminary mouse experiments suggest a probiotic effect of two Faecalibacillus intestinalis isolates in alleviating constipation, cardiometabolic influences of three Bacteroides fragilis_A isolates in obesity, and isolates from the genera Parabacteroides and Lactobacillus in host lipid metabolism. Our study expands the current microbial genomes with paired isolates and demonstrates potential host effects, contributing to the mechanistic understanding of host-microbe interactions.

Temporal colonization and metabolic regulation of the gut microbiome in neonatal oxen at single nucleotide resolution.

The colonization of microbes in the gut is key to establishing a healthy host-microbiome symbiosis for newborns. We longitudinally profiled the gut microbiome in a model consisting of 36 neonatal oxen from birth up to 2 months postpartum and carried out microbial transplantation to reshape their gut microbiome. Genomic reconstruction of deeply sequenced fecal samples resulted in a total of 3931 metagenomic-assembled genomes from 472 representative species, of which 184 were identified as new species when compared with existing databases of oxen. Single nucleotide level metagenomic profiling shows a rapid influx of microbes after birth, followed by dynamic shifts during the first few weeks of life. Microbial transplantation was found to reshape the genetic makeup of 33 metagenomic-assembled genomes (FDR < 0.05), mainly from Prevotella and Bacteroides species. We further linked over 20 million microbial single nucleotide variations to 736 plasma metabolites, which enabled us to characterize 24 study-wide significant associations (P < 4.4 × 10-9) that identify the potential microbial genetic regulation of host immune and neuro-related metabolites, including glutathione and L-dopa. Our integration analyses further revealed that microbial genetic variations may influence the health status and growth performance by modulating metabolites via structural regulation of their encoded proteins. For instance, we found that the albumin levels and total antioxidant capacity were correlated with L-dopa, which was determined by single nucleotide variations via structural regulations of metabolic enzymes. The current results indicate that temporal colonization and transplantation-driven strain replacement are crucial for newborn gut development, offering insights for enhancing newborn health and growth.

Early microbial intervention reshapes phenotypes of newborn Bos taurus through metabolic regulations.

BACKGROUND: The rumen of neonatal calves has limited functionality, and establishing intestinal microbiota may play a crucial role in their health and performance. Thus, we aim to explore the temporal colonization of the gut microbiome and the benefits of early microbial transplantation (MT) in newborn calves. RESULTS: We followed 36 newborn calves for 2 months and found that the composition and ecological interactions of their gut microbiomes likely reached maturity 1 month after birth. Temporal changes in the gut microbiome of newborn calves are widely associated with changes in their physiological statuses, such as growth and fiber digestion. Importantly, we observed that MT reshapes the gut microbiome of newborns by altering the abundance and interaction of Bacteroides species, as well as amino acid pathways, such as arginine biosynthesis. Two-year follow-up of those calves further showed that MT improves their later milk production. Notably, MT improves fiber digestion and antioxidant capacity of newborns while reducing diarrhea. MT also contributes to significant changes in the metabolomic landscape, and with putative causal mediation analysis, we suggest that altered gut microbial composition in newborns may influence physiological status through microbial-derived metabolites. CONCLUSIONS: Our study provides a metagenomic and metabolomic atlas of the temporal development of the gut microbiome in newborn calves. MT can alter the gut microbiome of newborns, leading to improved physiological status and later milk production. The data may help develop strategies to manipulate the gut microbiota during early life, which may be relevant to the health and production of newborn calves.

NAT10 Is Involved in Cardiac Remodeling Through ac4C-Mediated Transcriptomic Regulation.

BACKGROUND: Heart failure, characterized by cardiac remodeling, is associated with abnormal epigenetic processes and aberrant gene expression. Here, we aimed to elucidate the effects and mechanisms of NAT10 (N-acetyltransferase 10)-mediated N4-acetylcytidine (ac4C) acetylation during cardiac remodeling. METHODS: NAT10 and ac4C expression were detected in both human and mouse subjects with cardiac remodeling through multiple assays. Subsequently, acetylated RNA immunoprecipitation and sequencing, thiol-linked alkylation for the metabolic sequencing of RNA (SLAM-seq), and ribosome sequencing (Ribo-seq) were employed to elucidate the role of ac4C-modified posttranscriptional regulation in cardiac remodeling. Additionally, functional experiments involving the overexpression or knockdown of NAT10 were conducted in mice models challenged with Ang II (angiotensin II) and transverse aortic constriction. RESULTS: NAT10 expression and RNA ac4C levels were increased in in vitro and in vivo cardiac remodeling models, as well as in patients with cardiac hypertrophy. Silencing and inhibiting NAT10 attenuated Ang II-induced cardiomyocyte hypertrophy and cardiofibroblast activation. Next-generation sequencing revealed ac4C changes in both mice and humans with cardiac hypertrophy were associated with changes in global mRNA abundance, stability, and translation efficiency. Mechanistically, NAT10 could enhance the stability and translation efficiency of CD47 and ROCK2 transcripts by upregulating their mRNA ac4C modification, thereby resulting in an increase in their protein expression during cardiac remodeling. Furthermore, the administration of Remodelin, a NAT10 inhibitor, has been shown to prevent cardiac functional impairments in mice subjected to transverse aortic constriction by suppressing cardiac fibrosis, hypertrophy, and inflammatory responses, while also regulating the expression levels of CD47 and ROCK2 (Rho associated coiled-coil containing protein kinase 2). CONCLUSIONS: Therefore, our data suggest that modulating epitranscriptomic processes, such as ac4C acetylation through NAT10, may be a promising therapeutic target against cardiac remodeling.

Oxidative stress gene expression, DNA methylation, and gut microbiota interaction trigger Crohn's disease: a multi-omics Mendelian randomization study.

BACKGROUND: Oxidative stress (OS) is a key pathophysiological mechanism in Crohn's disease (CD). OS-related genes can be affected by environmental factors, intestinal inflammation, gut microbiota, and epigenetic changes. However, the role of OS as a potential CD etiological factor or triggering factor is unknown, as differentially expressed OS genes in CD can be either a cause or a subsequent change of intestinal inflammation. Herein, we used a multi-omics summary data-based Mendelian randomization (SMR) approach to identify putative causal effects and underlying mechanisms of OS genes in CD. METHODS: OS-related genes were extracted from the GeneCards database. Intestinal transcriptome datasets were collected from the Gene Expression Omnibus (GEO) database and meta-analyzed to identify differentially expressed genes (DEGs) related to OS in CD. Integration analyses of the largest CD genome-wide association study (GWAS) summaries with expression quantitative trait loci (eQTLs) and DNA methylation QTLs (mQTLs) from the blood were performed using SMR methods to prioritize putative blood OS genes and their regulatory elements associated with CD risk. Up-to-date intestinal eQTLs and fecal microbial QTLs (mbQTLs) were integrated to uncover potential interactions between host OS gene expression and gut microbiota through SMR and colocalization analysis. Two additional Mendelian randomization (MR) methods were used as sensitivity analyses. Putative results were validated in an independent multi-omics cohort from the First Affiliated Hospital of Sun Yat-sen University (FAH-SYS). RESULTS: A meta-analysis from six datasets identified 438 OS-related DEGs enriched in intestinal enterocytes in CD from 817 OS-related genes. Five genes from blood tissue were prioritized as candidate CD-causal genes using three-step SMR methods: BAD, SHC1, STAT3, MUC1, and GPX3. Furthermore, SMR analysis also identified five putative intestinal genes, three of which were involved in gene-microbiota interactions through colocalization analysis: MUC1, CD40, and PRKAB1. Validation results showed that 88.79% of DEGs were replicated in the FAH-SYS cohort. Associations between pairs of MUC1-Bacillus aciditolerans and PRKAB1-Escherichia coli in the FAH-SYS cohort were consistent with eQTL-mbQTL colocalization. CONCLUSIONS: This multi-omics integration study highlighted that OS genes causal to CD are regulated by DNA methylation and host-microbiota interactions. This provides evidence for future targeted functional research aimed at developing suitable therapeutic interventions and disease prevention.

Integrated multi-omics approach to distinct molecular characterization and classification of early-onset colorectal cancer.

Incidence of early-onset colorectal cancer (EOCRC), defined by a diagnosed age under 50 years, is increasing, but its heterogeneous etiologies that differ from general CRC remain undetermined. We initially characterize the genome, epigenome, transcriptome, and proteome of tumors from 79 patients in a Chinese CRC cohort. Data for an additional 126 EOCRC subjects are obtained from the International Cancer Genome Consortium Chinese cohort and The Cancer Genome Atlas European cohort. We observe that early-onset tumors have a high tumor mutation burden; increased DNA repair features by mutational signature 3 and multi-layer pathway enrichments; strong perturbations at effects of DNA methylation and somatic copy-number alteration on gene expression; and upregulated immune infiltration as hot tumors underlying immunophenotypes. Notably, LMTK3 exhibits ancestral mutation disparity, potentially being a functional modulator and biomarker that drives molecular alterations in EOCRC development and immunotherapies. This integrative omics study provides valuable knowledge for precision oncology of CRC.

gutSMASH predicts specialized primary metabolic pathways from the human gut microbiota.

The gut microbiota produce hundreds of small molecules, many of which modulate host physiology. Although efforts have been made to identify biosynthetic genes for secondary metabolites, the chemical output of the gut microbiome consists predominantly of primary metabolites. Here we introduce the gutSMASH algorithm for identification of primary metabolic gene clusters, and we used it to systematically profile gut microbiome metabolism, identifying 19,890 gene clusters in 4,240 high-quality microbial genomes. We found marked differences in pathway distribution among phyla, reflecting distinct strategies for energy capture. These data explain taxonomic differences in short-chain fatty acid production and suggest a characteristic metabolic niche for each taxon. Analysis of 1,135 individuals from a Dutch population-based cohort shows that the level of microbiome-derived metabolites in plasma and feces is almost completely uncorrelated with the metagenomic abundance of corresponding metabolic genes, indicating a crucial role for pathway-specific gene regulation and metabolite flux. This work is a starting point for understanding differences in how bacterial taxa contribute to the chemistry of the microbiome.

Systematic analysis of relationships between plasma branched-chain amino acid concentrations and cardiometabolic parameters: an association and Mendelian randomization study.

BACKGROUND: Branched-chain amino acids (BCAAs; valine, leucine, and isoleucine) are essential amino acids that are associated with an increased risk of cardiometabolic diseases (CMD). However, there are still only limited insights into potential direct associations between BCAAs and a wide range of CMD parameters, especially those remaining after correcting for covariates and underlying causal relationships. METHODS: To shed light on these relationships, we systematically characterized the associations between plasma BCAA concentrations and a large panel of 537 CMD parameters (including atherosclerosis-related parameters, fat distribution, plasma cytokine concentrations and cell counts, circulating concentrations of cardiovascular-related proteins and plasma metabolites) in 1400 individuals from the Dutch population cohort LifeLines DEEP and 294 overweight individuals from the 300OB cohort. After correcting for age, sex, and BMI, we assessed associations between individual BCAAs and CMD parameters. We further assessed the underlying causality using Mendelian randomization. RESULTS: A total of 838 significant associations were detected for 409 CMD parameters. BCAAs showed both common and specific associations, with the most specific associations being detected for isoleucine. Further, we found that obesity status substantially affected the strength and direction of associations for valine, which cannot be corrected for using BMI as a covariate. Subsequent univariable Mendelian randomization (UVMR), after removing BMI-associated SNPs, identified seven significant causal relationships from four CMD traits to BCAA levels, mostly for diabetes-related parameters. However, no causal effects of BCAAs on CMD parameters were supported. CONCLUSIONS: Our cross-sectional association study reports a large number of associations between BCAAs and CMD parameters. Our results highlight some specific associations for isoleucine, as well as obesity-specific effects for valine. MR-based causality analysis suggests that altered BCAA levels can be a consequence of diabetes and alteration in lipid metabolism. We found no MR evidence to support a causal role for BCAAs in CMD. These findings provide evidence to (re)evaluate the clinical importance of individual BCAAs in CMD diagnosis, prevention, and treatment.

Remnant cholesterol is associated with cardiovascular mortality.

Background: Genetic, observational, and clinical intervention studies indicate that circulating levels of remnant cholesterol (RC) are associated with cardiovascular diseases. However, the predictive value of RC for cardiovascular mortality in the general population remains unclear. Methods: Our study population comprised 19,650 adults in the United States from the National Health and Nutrition Examination Survey (NHANES) (1999-2014). RC was calculated from non-high-density lipoprotein cholesterol (non-HDL-C) minus low-density lipoprotein cholesterol (LDL-C) determined by the Sampson formula. Multivariate Cox regression, restricted cubic spline analysis, and subgroup analysis were applied to explore the relationship of RC with cardiovascular mortality. Results: The mean age of the study cohort was 46.4 ± 19.2 years, and 48.7% of participants were male. During a median follow-up of 93 months, 382 (1.9%) cardiovascular deaths occurred. In a fully adjusted Cox regression model, log RC was significantly associated with cardiovascular mortality [hazard ratio (HR) 2.82; 95% confidence interval (CI) 1.17-6.81]. The restricted cubic spline curve indicated that log RC had a linear association with cardiovascular mortality (p for non-linearity = 0.899). People with higher LDL-C (≥130 mg/dL), higher RC [≥25.7/23.7 mg/dL in males/females corresponding to the LDL-C clinical cutoff point (130 mg/dL)] and abnormal HDL-C (<40/50 mg/dL in males/females) levels had a higher risk of cardiovascular mortality (HR 2.18; 95% CI 1.13-4.21 in males and HR 2.19; 95% CI 1.24-3.88 in females) than the reference group (lower LDL-C, lower RC and normal HDL-C levels). Conclusions: Elevated RC levels were associated with cardiovascular mortality independent of traditional risk factors.

Influence of the microbiome, diet and genetics on inter-individual variation in the human plasma metabolome.

The levels of the thousands of metabolites in the human plasma metabolome are strongly influenced by an individual's genetics and the composition of their diet and gut microbiome. Here, by assessing 1,183 plasma metabolites in 1,368 extensively phenotyped individuals from the Lifelines DEEP and Genome of the Netherlands cohorts, we quantified the proportion of inter-individual variation in the plasma metabolome explained by different factors, characterizing 610, 85 and 38 metabolites as dominantly associated with diet, the gut microbiome and genetics, respectively. Moreover, a diet quality score derived from metabolite levels was significantly associated with diet quality, as assessed by a detailed food frequency questionnaire. Through Mendelian randomization and mediation analyses, we revealed putative causal relationships between diet, the gut microbiome and metabolites. For example, Mendelian randomization analyses support a potential causal effect of Eubacterium rectale in decreasing plasma levels of hydrogen sulfite-a toxin that affects cardiovascular function. Lastly, based on analysis of the plasma metabolome of 311 individuals at two time points separated by 4 years, we observed a positive correlation between the stability of metabolite levels and the amount of variance in the levels of that metabolite that could be explained in our analysis. Altogether, characterization of factors that explain inter-individual variation in the plasma metabolome can help design approaches for modulating diet or the gut microbiome to shape a healthy metabolome.

Effect of host genetics on the gut microbiome in 7,738 participants of the Dutch Microbiome Project.

Host genetics are known to influence the gut microbiome, yet their role remains poorly understood. To robustly characterize these effects, we performed a genome-wide association study of 207 taxa and 205 pathways representing microbial composition and function in 7,738 participants of the Dutch Microbiome Project. Two robust, study-wide significant (P < 1.89 × 10-10) signals near the LCT and ABO genes were found to be associated with multiple microbial taxa and pathways and were replicated in two independent cohorts. The LCT locus associations seemed modulated by lactose intake, whereas those at ABO could be explained by participant secretor status determined by their FUT2 genotype. Twenty-two other loci showed suggestive evidence (P < 5 × 10-8) of association with microbial taxa and pathways. At a more lenient threshold, the number of loci we identified strongly correlated with trait heritability, suggesting that much larger sample sizes are needed to elucidate the remaining effects of host genetics on the gut microbiome.

Large HDL particles negatively associate with leukocyte counts independent of cholesterol efflux capacity: A cross sectional study in the population-based LifeLines DEEP cohort.

BACKGROUND AND AIMS: Leukocytosis, the expansion of white blood cells, is associated with increased cardiovascular risk. Studies in animal models have shown that high-density lipoprotein cholesterol (HDL-c) suppresses leukocytosis by mediating cholesterol efflux from hematopoietic stem and progenitor cells. HDL-c showed a moderate negative association with leukocyte numbers in the UK Biobank and Multi-Ethnic Study of Atherosclerosis. Cholesterol efflux capacity of HDL (HDL-CEC) or HDL particle (HDL-P) number has been proposed as improved inverse predictor of CVD compared to plasma HDL-c. In the LifeLines DEEP (LLD) cohort (n = 962), a sub-cohort representing the prospective population-based LL cohort from the North of The Netherlands, we tested the hypothesis that HDL-CEC and HDL-P were associated with lower leukocyte counts. METHODS: We carried out multivariable regression and causal mediation analyses (CMA) to test associations between HDL-c, HDL-CEC, or HDL-P and leukocyte counts. We measured HDL-CEC in THP-1 macrophages and HDL-P and composition using nuclear magnetic resonance. RESULTS: HDL-c associated negatively with leukocyte counts, as did extra-large and large HDL-P, while HDL-CEC showed no association. Each one-standard deviation (SD) increase in extra-large HDL-P was associated with 3.0% and 4.8% lower leukocytes and neutrophils, respectively (q < 0.001). In contrast, plasma concentration of small HDL-P associated positively with leukocyte and neutrophil counts, as did small HDL-P triglycerides (TG) and total plasma TG. CMA showed that the association between S-HDL-P and leukocytes was mediated by S-HDL-TG. CONCLUSIONS: The association between HDL-P and leukocyte counts in the general population is dependent on HDL-P size and composition, but not HDL-CEC.

Characterization of gut microbial structural variations as determinants of human bile acid metabolism.

Bile acids (BAs) facilitate intestinal fat absorption and act as important signaling molecules in host-gut microbiota crosstalk. BA-metabolizing pathways in the microbial community have been identified, but it remains largely unknown how the highly variable genomes of gut bacteria interact with host BA metabolism. We characterized 8,282 structural variants (SVs) of 55 bacterial species in the gut microbiomes of 1,437 individuals from two cohorts and performed a systematic association study with 39 plasma BA parameters. Both variations in SV-based continuous genetic makeup and discrete clusters showed correlations with BA metabolism. Metagenome-wide association analysis identified 809 replicable associations between bacterial SVs and BAs and SV regulators that mediate the effects of lifestyle factors on BA metabolism. This is the largest microbial genetic association analysis to demonstrate the impact of bacterial SVs on human BA composition, and it highlights the potential of targeting gut microbiota to regulate BA metabolism through lifestyle intervention.

Periodontal breakdown inter-tooth relationships in estimating periodontitis-related tooth loss.

OBJECTIVES: The reasons for tooth extraction are rarely recorded in epidemiological datasets. It poses a diagnostic challenge to determine if tooth loss is related to periodontal disease (TLPD). The present study aimed to assess the inter-tooth relationships based on the periodontal characteristics of existing teeth. METHODS: A cross-sectional dataset of 8,978 participants with complete periodontal examination (including probing pocket depth [PPD] and clinical attachment loss [CAL]) in the NHANES 2009-2014 was used in this study. Spearman rank correlation was applied to assess the inter-tooth correlations of PPD/CAL among 28 teeth after adjustment for relevant confounders. We further verify our findings in the Java Project on Periodontal Disease with TLPD information available (the number of TLPD = 12). RESULTS: Strong PPD/CAL correlations were observed in adjacent teeth (r for PPD = 0.652, r for CAL = 0.597; false discovery rate [FDR] <0.05) rather than those on non-adjacent teeth (r for PPD = 0.515, r for CAL = 0.476; FDR <0.05). The correlations increased among severe periodontitis cases (CAL ≥5 mm or PPD ≥6 mm). In line with this, we further observed that the teeth adjacent to the TLPD tooth had the most alveolar bone loss in the Java dataset. CONCLUSION: The periodontitis parameters (PPD/CAL) of adjacent teeth could be a potential indicator to estimate TLPD when actual reasons for tooth extraction are unknown. CLINICAL SIGNIFICANCE: Periodontally compromised teeth adjacent to a lost tooth may help estimate whether the loss could be related to periodontal disease when the actual extraction reasons are unknown.

Association of Insulin Resistance and Type 2 Diabetes With Gut Microbial Diversity: A Microbiome-Wide Analysis From Population Studies.

Importance: Previous studies have indicated that gut microbiome may be associated with development of type 2 diabetes. However, these studies are limited by small sample size and insufficient for confounding. Furthermore, which specific taxa play a role in the development of type 2 diabetes remains unclear. Objective: To examine associations of gut microbiome composition with insulin resistance and type 2 diabetes in a large population-based setting controlling for various sociodemographic and lifestyle factors. Design, Setting, and Participants: This cross-sectional analysis included 2166 participants from 2 Dutch population-based prospective cohorts: the Rotterdam Study and the LifeLines-DEEP study. Exposures: The 16S ribosomal RNA method was used to measure microbiome composition in stool samples collected between January 1, 2012, and December 31, 2013. The α diversity (Shannon, richness, and Inverse Simpson indexes), ß diversity (Bray-Curtis dissimilarity matrix), and taxa (from domain to genus level) were identified to reflect gut microbiome composition. Main Outcomes and Measures: Associations among α diversity, ß diversity, and taxa with the Homeostatic Model Assessment of Insulin Resistance (HOMA-IR) and with type 2 diabetes were examined. Glucose and insulin were measured to calculate the HOMA-IR. Type 2 diabetes cases were identified based on glucose levels and medical records from January 2012 to December 2013. Analyses were adjusted for technical covariates, lifestyle, sociodemographic, and medical factors. Data analysis was performed from January 1, 2018, to December 31, 2020. Results: There were 2166 participants in this study: 1418 from the Rotterdam Study (mean [SD] age, 62.4 [5.9] years; 815 [57.5%] male) and 748 from the LifeLines-DEEP study (mean [SD] age, 44.7 [13.4] years; 431 [57.6%] male); from this total, 193 type 2 diabetes cases were identified. Lower microbiome Shannon index and richness were associated with higher HOMA-IR (eg, Shannon index, -0.06; 95% CI, -0.10 to -0.02), and patients with type 2 diabetes had a lower richness than participants without diabetes (odds ratio [OR], 0.93; 95% CI, 0.88-0.99). The ß diversity (Bray-Curtis dissimilarity matrix) was associated with insulin resistance (R2 = 0.004, P = .001 in the Rotterdam Study and R2 = 0.005, P = .002 in the LifeLines-DEEP study). A total of 12 groups of bacteria were associated with HOMA-IR or type 2 diabetes. Specifically, a higher abundance of Christensenellaceae (ß = -0.08; 95% CI, -0.12 to -0.03: P < .001), Christensenellaceae R7 group (ß = -0.07; 95% CI, -0.12 to -0.03; P < .001), Marvinbryantia (ß = -0.07; 95% CI, -0.11 to -0.03; P < .001), Ruminococcaceae UCG005 (ß = -0.09; 95% CI, -0.13 to -0.05; P < .001), Ruminococcaceae UCG008 (ß = -0.07; 95% CI, -0.11 to -0.03; P < .001), Ruminococcaceae UCG010 (ß = -0.08; 95% CI, -0.12 to -0.04; P < .001), or Ruminococcaceae NK4A214 group (ß = -0.09; 95% CI, -0.13 to -0.05; P < .001) was associated with lower HOMA-IR. A higher abundance of Clostridiaceae 1 (OR, 0.51; 95% CI, 0.41-0.65; P < .001), Peptostreptococcaceae (OR, 0.56; 95% CI, 0.45-0.70; P < .001), C sensu stricto 1 (OR, 0.51; 95% CI, 0.40-0.65; P < .001), Intestinibacter (OR, 0.60; 95% CI, 0.48-0.76; P < .001), or Romboutsia (OR, 0.55; 95% CI, 0.44-0.70; P < .001) was associated with less type 2 diabetes. These bacteria are all known to produce butyrate. Conclusions and Relevance: In this cross-sectional study, higher microbiome α diversity, along with more butyrate-producing gut bacteria, was associated with less type 2 diabetes and with lower insulin resistance among individuals without diabetes. These findings could help provide insight into the etiology, pathogenesis, and treatment of type 2 diabetes.

A Benchmark of Genetic Variant Calling Pipelines Using Metagenomic Short-Read Sequencing.

Microbes live in complex communities that are of major importance for environmental ecology, public health, and animal physiology and pathology. Short-read metagenomic shotgun sequencing is currently the state-of-the-art technique for exploring these communities. With the aid of metagenomics, our understanding of the microbiome is moving from composition toward functionality, even down to the genetic variant level. While the exploration of single-nucleotide variation in a genome is a standard procedure in genomics, and many sophisticated tools exist to perform this task, identification of genetic variation in metagenomes remains challenging. Major factors that hamper the widespread application of variant-calling analysis include low-depth sequencing of individual genomes (which is especially significant for the microorganisms present in low abundance), the existence of large genomic variation even within the same species, the absence of comprehensive reference genomes, and the noise introduced by next-generation sequencing errors. Some bioinformatics tools, such as metaSNV or InStrain, have been created to identify genetic variants in metagenomes, but the performance of these tools has not been systematically assessed or compared with the variant callers commonly used on single or pooled genomes. In this study, we benchmark seven bioinformatic tools for genetic variant calling in metagenomics data and assess their performance. To do so, we simulated metagenomic reads to mimic human microbial composition, sequencing errors, and genetic variability. We also simulated different conditions, including low and high depth of coverage and unique or multiple strains per species. Our analysis of the simulated data shows that probabilistic method-based tools such as HaplotypeCaller and Mutect2 from the GATK toolset show the best performance. By applying these tools to longitudinal gut microbiome data from the Human Microbiome Project, we show that the genetic similarity between longitudinal samples from the same individuals is significantly greater than the similarity between samples from different individuals. Our benchmark shows that probabilistic tools can be used to call metagenomes, and we recommend the use of GATK's tools as reliable variant callers for metagenomic samples.

Highly Transparent, Stretchable, and Conductive Supramolecular Ionogels Integrated with Three-Dimensional Printable, Adhesive, Healable, and Recyclable Character.

In this work, we report the easy fabrication of highly transparent (optical transmittance above 93%), stretchable (1500-2500% elongation at break), and conductive (up to 2.25 S m-1 at 25 °C) supramolecular ionogels that simultaneously integrate with three-dimensional (3D) printable, healable, adhesive, and recyclable character. The supramolecular ionogel is designed using a linear amphiphilic poly(urethane-urea) (PUU) copolymer and ionic liquid (IL) as the elastic scaffold and electrolyte, respectively, via a simple cosolvent method. Intriguingly, the 3D-printed highly conductive (2.25 S m-1 at 25 °C) supramolecular ionogel structure shows record-high mechanical performance with a breaking tensile strain and stress of 945% and 1.51 MPa, respectively, and is able to lift 3400× or bear 10000× its weight without fracture. Furthermore, both the solution casting and 3D-printed ionogel films show high sensitivity and reliability for sensing a wide range of strains, including various human motions. The results present some new insights into the structural, mechanical, and functional design of novel multifunctional ionogels with distinguished mechanical performance and tractable processability, which will extend them to a wide range of flexible electronic applications, including artificial intelligence, wearable/conformable electronics, human/machine interactions, soft robotics, etc.

The long-term genetic stability and individual specificity of the human gut microbiome.

By following up the gut microbiome, 51 human phenotypes and plasma levels of 1,183 metabolites in 338 individuals after 4 years, we characterize microbial stability and variation in relation to host physiology. Using these individual-specific and temporally stable microbial profiles, including bacterial SNPs and structural variations, we develop a microbial fingerprinting method that shows up to 85% accuracy in classifying metagenomic samples taken 4 years apart. Application of our fingerprinting method to the independent HMP cohort results in 95% accuracy for samples taken 1 year apart. We further observe temporal changes in the abundance of multiple bacterial species, metabolic pathways, and structural variation, as well as strain replacement. We report 190 longitudinal microbial associations with host phenotypes and 519 associations with plasma metabolites. These associations are enriched for cardiometabolic traits, vitamin B, and uremic toxins. Finally, mediation analysis suggests that the gut microbiome may influence cardiometabolic health through its metabolites.