Single-cell study of neural stem cells derived from human iPSCs reveals distinct progenitor populations with neurogenic and gliogenic potential.

We used single-cell RNA sequencing (seq) on several human induced pluripotent stem (iPS) cell-derived neural stem cell (NSC) lines and one fetal brain-derived NSC line to study inherent cell type heterogeneity at proliferating neural stem cell stage and uncovered predisposed presence of neurogenic and gliogenic progenitors. We observed heterogeneity in neurogenic progenitors that differed between the iPS cell-derived NSC lines and the fetal-derived NSC line, and we also observed differences in spontaneous differentiation potential for inhibitory and excitatory neurons between the iPS cell-derived NSC lines and the fetal-derived NSC line. In addition, using a recently published glia patterning protocol we enriched for gliogenic progenitors and generated glial cells from an iPS cell-derived NSC line.

Strategies to improve usability and preserve accuracy in biological sequence databases.

Biology is increasingly dependent on large-scale analysis, such as proteomics, creating a requirement for efficient bioinformatics. Bioinformatic predictions of biological functions rely upon correctly annotated database sequences, and the presence of inaccurately annotated or otherwise poorly described sequences introduces noise and bias to biological analyses. Accurate annotations are, for example, pivotal for correct identification of polypeptide fragments. However, standards for how sequence databases are organized and presented are currently insufficient. Here, we propose five strategies to address fundamental issues in the annotation of sequence databases: (i) to clearly separate experimentally verified and unverified sequence entries; (ii) to enable a system for tracing the origins of annotations; (iii) to separate entries with high-quality, informative annotation from less useful ones; (iv) to integrate automated quality-control software whenever such tools exist; and (v) to facilitate postsubmission editing of annotations and metadata associated with sequences. We believe that implementation of these strategies, for example as requirements for publication of database papers, would enable biology to better take advantage of large-scale data.

Metagenomic data utilization and analysis (MEDUSA) and construction of a global gut microbial gene catalogue.

Metagenomic sequencing has contributed important new knowledge about the microbes that live in a symbiotic relationship with humans. With modern sequencing technology it is possible to generate large numbers of sequencing reads from a metagenome but analysis of the data is challenging. Here we present the bioinformatics pipeline MEDUSA that facilitates analysis of metagenomic reads at the gene and taxonomic level. We also constructed a global human gut microbial gene catalogue by combining data from 4 studies spanning 3 continents. Using MEDUSA we mapped 782 gut metagenomes to the global gene catalogue and a catalogue of sequenced microbial species. Hereby we find that all studies share about half a million genes and that on average 300,000 genes are shared by half the studied subjects. The gene richness is higher in the European studies compared to Chinese and American and this is also reflected in the species richness. Even though it is possible to identify common species and a core set of genes, we find that there are large variations in abundance of species and genes.

Genome-scale metabolic reconstructions of Bifidobacterium adolescentis L2-32 and Faecalibacterium prausnitzii A2-165 and their interaction.

BACKGROUND: The gut microbiota plays an important role in human health and disease by acting as a metabolic organ. Metagenomic sequencing has shown how dysbiosis in the gut microbiota is associated with human metabolic diseases such as obesity and diabetes. Modeling may assist to gain insight into the metabolic implication of an altered microbiota. Fast and accurate reconstruction of metabolic models for members of the gut microbiota, as well as methods to simulate a community of microorganisms, are therefore needed. The Integrated Microbial Genomes (IMG) database contains functional annotation for nearly 4,650 bacterial genomes. This tremendous new genomic information adds new opportunities for systems biology to reconstruct accurate genome scale metabolic models (GEMs). RESULTS: Here we assembled a reaction data set containing 2,340 reactions obtained from existing genome-scale metabolic models, where each reaction is assigned with KEGG Orthology. The reaction data set was then used to reconstruct two genome scale metabolic models for gut microorganisms available in the IMG database Bifidobacterium adolescentis L2-32, which produces acetate during fermentation, and Faecalibacterium prausnitzii A2-165, which consumes acetate and produces butyrate. F. prausnitzii is less abundant in patients with Crohn's disease and has been suggested to play an anti-inflammatory role in the gut ecosystem. The B. adolescentis model, iBif452, comprises 699 reactions and 611 unique metabolites. The F. prausnitzii model, iFap484, comprises 713 reactions and 621 unique metabolites. Each model was validated with in vivo data. We used OptCom and Flux Balance Analysis to simulate how both organisms interact. CONCLUSIONS: The consortium of iBif452 and iFap484 was applied to predict F. prausnitzii's demand for acetate and production of butyrate which plays an essential role in colonic homeostasis and cancer prevention. The assembled reaction set is a useful tool to generate bacterial draft models from KEGG Orthology.

Utility of in vitro systems and preclinical data for the prediction of human intestinal first-pass metabolism during drug discovery and preclinical development.

A growing awareness of the risks associated with extensive intestinal metabolism has triggered an interest in developing robust methods for its quantitative assessment. This study explored the utility of intestinal S9 fractions, human liver microsomes, and recombinant cytochromes P450 to quantify CYP3A-mediated intestinal extraction in humans for a selection of marketed drugs that are predominantly metabolized by CYP3A4. A simple competing rates model is used to estimate the fraction of drug escaping gut wall metabolism (fg) from in vitro intrinsic clearance in humans. The fg values extrapolated from the three in vitro systems used in this study, together with literature-derived fg from human intestinal microsomes, were validated against fg extracted from human in vivo pharmacokinetic (PK) profiles using a generic whole-body physiologically-based pharmacokinetic (PBPK) model. The utility of the rat as a model for human CYP3A-mediated intestinal metabolism was also evaluated. Human fg from PBPK compares well with that from the grapefruit juice method, justifying its use for the evaluation of human in vitro systems. Predictive performance of all human in vitro systems was comparable [root mean square error (RMSE) = 0.22-0.27; n = 10]. Rat fg derived from in vivo PK profiles using PBPK has the lowest RMSE (0.19; n = 11) for the prediction of human fg for the selected compounds, most of which have a fraction absorbed close to 1. On the basis of these evaluations, the combined use of fg from human in vitro systems and rats is recommended for the estimation of CYP3A4-mediated intestinal metabolism in lead optimization and preclinical development phases.

Gut metagenome in European women with normal, impaired and diabetic glucose control.

Type 2 diabetes (T2D) is a result of complex gene-environment interactions, and several risk factors have been identified, including age, family history, diet, sedentary lifestyle and obesity. Statistical models that combine known risk factors for T2D can partly identify individuals at high risk of developing the disease. However, these studies have so far indicated that human genetics contributes little to the models, whereas socio-demographic and environmental factors have greater influence. Recent evidence suggests the importance of the gut microbiota as an environmental factor, and an altered gut microbiota has been linked to metabolic diseases including obesity, diabetes and cardiovascular disease. Here we use shotgun sequencing to characterize the faecal metagenome of 145 European women with normal, impaired or diabetic glucose control. We observe compositional and functional alterations in the metagenomes of women with T2D, and develop a mathematical model based on metagenomic profiles that identified T2D with high accuracy. We applied this model to women with impaired glucose tolerance, and show that it can identify women who have a diabetes-like metabolism. Furthermore, glucose control and medication were unlikely to have major confounding effects. We also applied our model to a recently described Chinese cohort and show that the discriminant metagenomic markers for T2D differ between the European and Chinese cohorts. Therefore, metagenomic predictive tools for T2D should be specific for the age and geographical location of the populations studied.

FANTOM: Functional and taxonomic analysis of metagenomes.

BACKGROUND: Interpretation of quantitative metagenomics data is important for our understanding of ecosystem functioning and assessing differences between various environmental samples. There is a need for an easy to use tool to explore the often complex metagenomics data in taxonomic and functional context. RESULTS: Here we introduce FANTOM, a tool that allows for exploratory and comparative analysis of metagenomics abundance data integrated with metadata information and biological databases. Importantly, FANTOM can make use of any hierarchical database and it comes supplied with NCBI taxonomic hierarchies as well as KEGG Orthology, COG, PFAM and TIGRFAM databases. CONCLUSIONS: The software is implemented in Python, is platform independent, and is available at http://www.sysbio.se/Fantom.

Symptomatic atherosclerosis is associated with an altered gut metagenome.

Recent findings have implicated the gut microbiota as a contributor of metabolic diseases through the modulation of host metabolism and inflammation. Atherosclerosis is associated with lipid accumulation and inflammation in the arterial wall, and bacteria have been suggested as a causative agent of this disease. Here we use shotgun sequencing of the gut metagenome to demonstrate that the genus Collinsella was enriched in patients with symptomatic atherosclerosis, defined as stenotic atherosclerotic plaques in the carotid artery leading to cerebrovascular events, whereas Roseburia and Eubacterium were enriched in healthy controls. Further characterization of the functional capacity of the metagenomes revealed that patient gut metagenomes were enriched in genes encoding peptidoglycan synthesis and depleted in phytoene dehydrogenase; patients also had reduced serum levels of ß-carotene. Our findings suggest that the gut metagenome is associated with the inflammatory status of the host and patients with symptomatic atherosclerosis harbor characteristic changes in the gut metagenome.

Prospects for systems biology and modeling of the gut microbiome.

Abundant microorganisms that inhabit the human intestine are implicated in health and disease. The gut microbiome has been studied with metagenomic tools, and over 3 million genes have been discovered, constituting a 'parts list' of this ecosystem; further understanding requires studies of the interacting parts. Mouse models have provided a glimpse into the microbiota and host interactions at metabolic and immunologic levels; however, to provide more insight, there is a need to generate mathematical models that can reveal genotype-phenotype relationships and provide scaffolds for integrated analyses. To this end, we propose the use of genome-scale metabolic models that have successfully been used in studying interactions between human hosts and microbes, as well as microbes in isolation and in communities.

A closer look at bacteroides: phylogenetic relationship and genomic implications of a life in the human gut.

The human gut is extremely densely inhabited by bacteria mainly from two phyla, Bacteroidetes and Firmicutes, and there is a great interest in analyzing whole-genome sequences for these species because of their relation to human health and disease. Here, we do whole-genome comparison of 105 Bacteroidetes/Chlorobi genomes to elucidate their phylogenetic relationship and to gain insight into what is separating the gut living Bacteroides and Parabacteroides genera from other Bacteroidetes/Chlorobi species. A comprehensive analysis shows that Bacteroides species have a higher number of extracytoplasmic function σ factors (ECF σ factors) and two component systems for extracellular signal transduction compared to other Bacteroidetes/Chlorobi species. A whole-genome phylogenetic analysis shows a very little difference between the Parabacteroides and Bacteroides genera. Further analysis shows that Bacteroides and Parabacteroides species share a large common core of 1,085 protein families. Genome atlases illustrate that there are few and only small unique areas on the chromosomes of four Bacteroides/Parabacteroides genomes. Functional classification to clusters of othologus groups show that Bacteroides species are enriched in carbohydrate transport and metabolism proteins. Classification of proteins in KEGG metabolic pathways gives a detailed view of the genome's metabolic capabilities that can be linked to its habitat. Bacteroides pectinophilus and Bacteroides capillosus do not cluster together with other Bacteroides species, based on analysis of 16S rRNA sequence, whole-genome protein families and functional content, 16S rRNA sequences of the two species suggest that they belong to the Firmicutes phylum. We have presented a more detailed and precise description of the phylogenetic relationships of members of the Bacteroidetes/Chlorobi phylum by whole genome comparison. Gut living Bacteroides have an enriched set of glycan, vitamin, and cofactor enzymes important for diet digestion.