Influence of the Cortical Midline Structures on Moral Emotion and Motivation in Moral Decision-Making

The present study aims to examine the relationship between the cortical midline structures (CMS), which have been regarded to be associated with selfhood, and moral decision making processes at the neural level. Traditional moral psychological studies have suggested the role of moral self as the moderator of moral cognition, so activity of moral self would present at the neural level. The present study examined the interaction between the CMS and other moral-related regions by conducting psycho-physiological interaction analysis of functional images acquired while 16 subjects were solving moral dilemmas. Furthermore, we performed Granger causality analysis to demonstrate the direction of influences between activities in the regions in moral decision-making. We first demonstrate there are significant positive interactions between two central CMS seed regions—i.e., the medial prefrontal cortex (MPFC) and posterior cingulate cortex (PCC)—and brain regions associated with moral functioning including the cerebellum, brainstem, midbrain, dorsolateral prefrontal cortex, orbitofrontal cortex and anterior insula (AI); on the other hand, the posterior insula (PI) showed significant negative interaction with the seed regions. Second, several significant Granger causality was found from CMS to insula regions particularly under the moral-personal condition. Furthermore, significant dominant influence from the AI to PI was reported. Moral psychological implications of these findings are discussed. The present study demonstrated the significant interaction and influence between the CMS and morality-related regions while subject were solving moral dilemmas. Given that, activity in the CMS is significantly involved in human moral functioning

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

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.</p

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.</p

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.</p

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.</p

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.</p

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.</p

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