The domestication of the probiotic bacterium Lactobacillus acidophilus

Lactobacillus acidophilus is a Gram-positive lactic acid bacterium that has had widespread historical use in the dairy industry and more recently as a probiotic. Although L. acidophilus has been designated as safe for human consumption, increasing commercial regulation and clinical demands for probiotic validation has resulted in a need to understand its genetic diversity. By drawing on large, well-characterised collections of lactic acid bacteria, we examined L. acidophilus isolates spanning 92 years and including multiple strains in current commercial use. Analysis of the whole genome sequence data set (34 isolate genomes) demonstrated L. acidophilus was a low diversity, monophyletic species with commercial isolates essentially identical at the sequence level. Our results indicate that commercial use has domesticated L. acidophilus with genetically stable, invariant strains being consumed globally by the human population

Metabolic network percolation quantifies biosynthetic capabilities across the human oral microbiome

The biosynthetic capabilities of microbes underlie their growth and interactions, playing a prominent role in microbial community structure. For large, diverse microbial communities, prediction of these capabilities is limited by uncertainty about metabolic functions and environmental conditions. To address this challenge, we propose a probabilistic method, inspired by percolation theory, to computationally quantify how robustly a genome-derived metabolic network produces a given set of metabolites under an ensemble of variable environments. We used this method to compile an atlas of predicted biosynthetic capabilities for 97 metabolites across 456 human oral microbes. This atlas captures taxonomically-related trends in biomass composition, and makes it possible to estimate inter-microbial metabolic distances that correlate with microbial co-occurrences. We also found a distinct cluster of fastidious/uncultivated taxa, including several Saccharibacteria (TM7) species, characterized by their abundant metabolic deficiencies. By embracing uncertainty, our approach can be broadly applied to understanding metabolic interactions in complex microbial ecosystems.T32GM008764 - NIGMS NIH HHS; T32 GM008764 - NIGMS NIH HHS; R01 DE024468 - NIDCR NIH HHS; R01 GM121950 - NIGMS NIH HHS; DE-SC0012627 - Biological and Environmental Research; RGP0020/2016 - Human Frontier Science Program; NSFOCE-BSF 1635070 - National Science Foundation; HR0011-15-C-0091 - Defense Advanced Research Projects Agency; R37DE016937 - NIDCR NIH HHS; R37 DE016937 - NIDCR NIH HHS; R01GM121950 - NIGMS NIH HHS; R01DE024468 - NIDCR NIH HHS; 1457695 - National Science FoundationPublished versio

The Metagenomics and Metadesign of the Subways and Urban Biomes (MetaSUB) International Consortium inaugural meeting report.

The Metagenomics and Metadesign of the Subways and Urban Biomes (MetaSUB) International Consortium is a novel, interdisciplinary initiative comprised of experts across many fields, including genomics, data analysis, engineering, public health, and architecture. The ultimate goal of the MetaSUB Consortium is to improve city utilization and planning through the detection, measurement, and design of metagenomics within urban environments. Although continual measures occur for temperature, air pressure, weather, and human activity, including longitudinal, cross-kingdom ecosystem dynamics can alter and improve the design of cities. The MetaSUB Consortium is aiding these efforts by developing and testing metagenomic methods and standards, including optimized methods for sample collection, DNA/RNA isolation, taxa characterization, and data visualization. The data produced by the consortium can aid city planners, public health officials, and architectural designers. In addition, the study will continue to lead to the discovery of new species, global maps of antimicrobial resistance (AMR) markers, and novel biosynthetic gene clusters (BGCs). Finally, we note that engineered metagenomic ecosystems can help enable more responsive, safer, and quantified cities

From Genes to Ecosystems: Resource Availability and DNA Methylation Drive the Diversity and Abundance of Restriction Modification Systems in Prokaryotes

Together, prokaryotic hosts and their viruses numerically dominate the planet and are engaged in an eternal struggle of hosts evading viral predation and viruses overcoming defensive mechanisms employed by their hosts. Prokaryotic hosts have been found to carry several viral defense systems in recent years with Restriction Modification systems (RMs) were the first discovered in the 1950s. While we have biochemically elucidated many of these systems in the last 70 years, we still struggle to understand what drives their gain and loss in prokaryotic genomes. In this work, we take a computational approach to understand the underlying evolutionary drivers of RMs by assessing ‘big data’ signals of RMs in prokaryotic genomes and incorporating molecular data in trait-based mathematical models. Focusing on the Cyanobacteria, we found a large discrepancy in the frequency of RMs per genome in different environmental contexts, where Cyanobacteria that live in oligotrophic nutrient conditions have few to no RMs and those in nutrient-rich conditions consistently have many RMs. While our models agree with the observation that increased nutrient inputs make the selective pressure of RMs more intense, they were unable to reconcile the high numbers of RMs per genome with their potent defensive properties- a situation of apparent overkill. By incorporating viral methylation, an unavoidable effect of RMs, we were able to explain how organisms could carry over 15 RMs. With this discovery, we then tried and reassess the distribution of methyltransferases, an essential component of RMs that can also have alternate physiological rolls in the cell. We expand on conventional wisdom, that methyltransferases that are widely phylogenetically conserved are associated with global cellular regulation. However, we also find that organisms with high numbers of RMs also have a surprising amount of conservation in the methyltransferases that they carry. This data suggests caution should be used in associating phylogenic signals with functional rolls in methyltransferases as different functional rolls seem to overlap in their phylogenetic signal. Indeed, we suggest trait-based modeling may be the best tool in elucidating why organisms with a high selective pressure to maintain RMs appear to have conserved methyltransferase

The Human Microbiome Project: A Community Resource for the Healthy Human Microbiome

The Human Microbiome Project (HMP) [1],[2] is a concept that was long in the making. After the Human Genome Project, interest grew in sequencing the “other genome" of microbes carried in and on the human body [3],[4]. Microbial ecologists, realizing that >99% of environmental microbes could not be easily cultured, developed approaches to study microorganisms in situ [5], primarily by sequencing the 16S ribosomal RNA gene (16S) as a phylogenetic and taxonomic marker to identify members of microbial communities [6]. The need to develop corresponding new methods for culture-independent studies [7],[8] in turn precipitated a sea change in the study of microbes and human health, inspiring the new term “metagenomics" [9] both to describe a technological approach—sequencing and analysis of the genes from whole communities rather than from individual genomes—and to emphasize that microbes function within communities rather than as individual species. This shift from a focus on individual organisms to microbial interactions [10] culminated in a National Academy of Science report [11], which outlined challenges and promises for metagenomics as a way of understanding the foundational role of microbial communities both in the environment and in human health.National Institutes of Health (U.S.) (grant U54HG004969)National Institutes of Health (U.S.) (grant U54HG004973)National Institutes of Health (U.S.) (grant U54AI084844)National Institutes of Health (U.S.) (grant U01HG004866)National Institutes of Health (U.S.) (grant R01HG005969)National Institutes of Health (U.S.) (grant R01HG004872)United States. Army Research Office (grant W911NF-11-1-0473)National Science Foundation (U.S.) (NSF DBI-1053486)Howard Hughes Medical Institute (Early Career Scientist

Assessment of complex microbial assemblages: description of their diversity and characterisation of individual members: Assessment of complex microbial assemblages: description of their diversity and characterisation of individual members

1. Microbial ecology According to Caumette et al. (2015) the term ecology is derived from the Greek words “oikos” (the house and its operation) and “logos” (the word, knowledge or discourse) and can, therefore, be defined as the scientific field engaged in the “knowledge of the laws governing the house”. This, in extension, results in the simple conclusion that microbial ecology represents the study of the relationship between microorganisms, their co-occurring biota and the prevailing environmental conditions (Caumette et al. 2015). The term microbial ecology has been in use since the early 1960s (Caumette et al. 2015) and microbial ecologists have made astonishing discoveries since. Microbial life at extremes such as in the hydrothermal vents (see Dubilier et al. 2008 and references therein) or the abundance of picophytoplankton (Waterbury et al. 1979; Chisholm et al. 1988) in the deep and surface waters of the oceans, respectively, are only a few of many highlights. Nevertheless, a microbial ecologist who, after leaving the field early in their career, now intends to return would hardly recognise again their former scientific field. The main reason for this hypothesis is to be found in the advances made to the methodologies employed in the field. Most of these were developed for biomedical research and were subsequently hijacked, sometimes followed by minor modifications, by microbial ecologists. The Author presents in this thesis scientific findings which, although spanning only a fraction of the era of research into microbial ecology, have been obtained using various modern tools of the trade. These studies were undertaken by the Author during his employment as postdoctoral scientist at Warwick University (UK), as member of staff at Plymouth Marine Laboratory (UK) and as scientist at the TU Bergakademie Freiberg. Although the scientific issues and the environmental habitats investigated by the Author changed due to funding constraints or due to change of work place (i.e. from the marine to the mining environment) the research shared, by and large, a common aim: to further the existing understanding of microbial communities. The methodological approach chosen to achieve this aim employed both isolation followed by the characterisation of microorganisms and culture independent techniques. Both of these strategies utilised again a variety of methods, but techniques in molecular biology represent a common theme. In particular, the polymerase chain reaction (PCR) formed the work horse for much of the research since it has been routinely used for the amplification of a marker gene for strain identification or analysis of the microbial diversity. To achieve this, the amplicons were either directly sequenced by the Sanger approach or analysed via the application of genetic fingerprint techniques or through Sanger sequencing of individual amplicons cloned into a heterologous host. However, the Author did not remain at idle while with these ‘classical’ approaches for the analysis of microbial communities, but utilised the advances made in the development of nucleotide sequence analysis. In particular, the highly parallelised sequencing techniques (e.g. 454 pyrosequencing, Illumina sequencing) offered the chance to obtain both high genetic resolution of the microbial diversity present in a sample and identification of many individuals through sequence comparison with appropriate sequence repositories. Moreover, these next generation sequencing (NGS) techniques also provided a cost-effective opportunity to extent the characterisation of microbial strains to non-clonal cultures and to even complex microbial assemblages (metagenomics). The work involving the high throughput sequencing techniques has been undertaken in collaboration with Dr Jack Gilbert (PML, lateron at Argonne National Laboratory, USA) and, since at Freiberg, with Dr Anja Poehlein (Goettingen University). These colleagues are thanked for their support with sequence data handling and analyses

MultiMetEval: comparative and multi-objective analysis of genome-scale metabolic models

Comparative metabolic modelling is emerging as a novel field, supported by the development of reliable and standardized approaches for constructing genome-scale metabolic models in high throughput. New software solutions are needed to allow efficient comparative analysis of multiple models in the context of multiple cellular objectives. Here, we present the user-friendly software framework Multi-Metabolic Evaluator (MultiMetEval), built upon SurreyFBA, which allows the user to compose collections of metabolic models that together can be subjected to flux balance analysis. Additionally, MultiMetEval implements functionalities for multi-objective analysis by calculating the Pareto front between two cellular objectives. Using a previously generated dataset of 38 actinobacterial genome-scale metabolic models, we show how these approaches can lead to exciting novel insights. Firstly, after incorporating several pathways for the biosynthesis of natural products into each of these models, comparative flux balance analysis predicted that species like Streptomyces that harbour the highest diversity of secondary metabolite biosynthetic gene clusters in their genomes do not necessarily have the metabolic network topology most suitable for compound overproduction. Secondly, multi-objective analysis of biomass production and natural product biosynthesis in these actinobacteria shows that the well-studied occurrence of discrete metabolic switches during the change of cellular objectives is inherent to their metabolic network architecture. Comparative and multi-objective modelling can lead to insights that could not be obtained by normal flux balance analyses. MultiMetEval provides a powerful platform that makes these analyses straightforward for biologists. Sources and binaries of MultiMetEval are freely available from https://github.com/PiotrZakrzewski/MetEv​al/downloads