Relating the metatranscriptome and metagenome of the human gut

Although the composition of the human microbiome is now wellstudied, the microbiota’s \u3e8 million genes and their regulation remain largely uncharacterized. This knowledge gap is in part because of the difficulty of acquiring large numbers of samples amenable to functional studies of the microbiota. We conducted what is, to our knowledge, one of the first human microbiome studies in a well-phenotyped prospective cohort incorporating taxonomic, metagenomic, and metatranscriptomic profiling at multiple body sites using self-collected samples. Stool and saliva were provided by eight healthy subjects, with the former preserved by three different methods (freezing, ethanol, and RNAlater) to validate self-collection. Within-subject microbial species, gene, and transcript abundances were highly concordant across sampling methods, with only a small fraction of transcripts (\u3c5%) displaying between-method variation. Next, we investigated relationships between the oral and gut microbial communities, identifying a subset of abundant oral microbes that routinely survive transit to the gut, but with minimal transcriptional activity there. Finally, systematic comparison of the gut metagenome and metatranscriptome revealed that a substantial fraction (41%) of microbial transcripts were not differentially regulated relative to their genomic abundances. Of the remainder, consistently underexpressed pathways included sporulation and amino acid biosynthesis, whereas up-regulated pathways included ribosome biogenesis and methanogenesis. Across subjects, metatranscriptional profiles were significantly more individualized than DNA-level functional profiles, but less variable than microbial composition, indicative of subject-specific whole-community regulation. The results thus detail relationships between community genomic potential and gene expression in the gut, and establish the feasibility of metatranscriptomic investigations in subject-collected and shipped samples

MLTreeMap - accurate Maximum Likelihood placement of environmental DNA sequences into taxonomic and functional reference phylogenies

BACKGROUND: Shotgun sequencing of environmental DNA is an essential technique for characterizing uncultivated microbes in situ. However, the taxonomic and functional assignment of the obtained sequence fragments remains a pressing problem. RESULTS: Existing algorithms are largely optimized for speed and coverage; in contrast, we present here a software framework that focuses on a restricted set of informative gene families, using Maximum Likelihood to assign these with the best possible accuracy. This framework ('MLTreeMap'; http://mltreemap.org/) uses raw nucleotide sequences as input, and includes hand-curated, extensible reference information. CONCLUSIONS: We discuss how we validated our pipeline using complete genomes as well as simulated and actual environmental sequences

SaDA: From Sampling to Data Analysis—An Extensible Open Source Infrastructure for Rapid, Robust and Automated Management and Analysis of Modern Ecological High-Throughput Microarray Data

One of the most crucial characteristics of day-to-day laboratory information management is the collection, storage and retrieval of information about research subjects and environmental or biomedical samples. An efficient link between sample data and experimental results is absolutely important for the successful outcome of a collaborative project. Currently available software solutions are largely limited to large scale, expensive commercial Laboratory Information Management Systems (LIMS). Acquiring such LIMS indeed can bring laboratory information management to a higher level, but most of the times this requires a sufficient investment of money, time and technical efforts. There is a clear need for a light weighted open source system which can easily be managed on local servers and handled by individual researchers. Here we present a software named SaDA for storing, retrieving and analyzing data originated from microorganism monitoring experiments. SaDA is fully integrated in the management of environmental samples, oligonucleotide sequences, microarray data and the subsequent downstream analysis procedures. It is simple and generic software, and can be extended and customized for various environmental and biomedical studies

Metagenomics, Metatranscriptomics, and Metabolomics Approaches for Microbiome Analysis

Microbiomes are ubiquitous and are found in the ocean, the soil, and in/on other living organisms. Changes in the microbiome can impact the health of the environmental niche in which they reside. In order to learn more about these communities, different approaches based on data from mul-tiple omics have been pursued. Metagenomics produces a taxonomical profile of the sample, metatranscriptomics helps us to obtain a functional profile, and metabolomics completes the picture by determining which byproducts are being released into the environment. Although each approach provides valuable information separately, we show that, when combined, they paint a more comprehensive picture. We conclude with a review of network-based approaches as applied to integrative studies, which we believe holds the key to in-depth understanding of microbiomes

Physiological and predicted metagenomic analysis of soil aggregate microbial communities under different tillage regimes

Soils are spatially heterogeneous environments, and the distribution of microorganisms and carbon is organized at the scale of millimeters in soil aggregates. The physio-chemical environment within macroaggregates and microaggregates differ, which may lead to the selection of microbial communities with different survival and growth strategies- here termed life history strategies. Using an aggregate scale survey of microbial communities in agricultural soils, I show that soil aggregates harbor distinct communities with life history characteristics that align with the Yield, Acquisition, Stress tolerator framework (Y-A-S). Soils collected from an eight- year tillage experiment were isolated into four aggregate size classes and physiological measurements of enzyme activity, multiple substrate induced respiration, and carbon use efficiency were conducted to reveal tradeoffs in community resource allocation. Carbon and nitrogen acquiring enzyme activity was highest in macroaggregates \u3e2mm and this was negatively correlated with carbon use efficiency, which is consistent with an Acquisition- Yield strategy tradeoff. Carbon use efficiency was highest in microaggregate communities. Substrate induced respiration revealed that aggregate microbial communities showed patterns of carbon substrate preference across aggregate size class; however, these patterns were not consistent with the Y-A-S framework. Community stress tolerance was assessed using predictive metagenomics which revealed an enrichment in genes consistent with a Stress tolerator strategy in microaggregates \u3c0.25mm. Together, these findings show that understanding the role of the soil physical environment in shaping microbial life histories may help us to predict how agricultural management affects the fate of carbon in soils