Levi Waldron

Metagenomic biomarker discovery and explanation

This study describes and validates a new method for metagenomic biomarker discovery by way of class comparison, tests of biological consistency and effect size estimation. This addresses the challenge of finding organisms, genes, or pathways that consistently explain the differences between two or more microbial communities, which is a central problem to the study of metagenomics. We extensively validate our method on several microbiomes and a convenient online interface for the method is provided at http://huttenhower.sph.harvard.edu/lefse/.

Orchestrating high-throughput genomic analysis with Bioconductor

Bioconductor is an open-source, open-development software project for the analysis and comprehension of high-throughput data in genomics and molecular biology. The project aims to enable interdisciplinary research, collaboration and rapid development of scientific software. Based on the statistical programming language R, Bioconductor comprises 934 interoperable packages contributed by a large, diverse community of scientists. Packages cover a range of bioinformatic and statistical applications. They undergo formal initial review and continuous automated testing. We present an overview for prospective users and contributors.

Metagenomic microbial community profiling using unique clade-specific marker genes

Metagenomic shotgun sequencing data can identify microbes populating a microbial community and their proportions, but existing taxonomic profiling methods are inefficient for increasingly large data sets. We present an approach that uses clade-specific marker genes to unambiguously assign reads to microbial clades more accurately and >50× faster than current approaches. We validated our metagenomic phylogenetic analysis tool, MetaPhlAn, on terabases of short reads and provide the largest metagenomic profiling to date of the human gut. It can be accessed at http://huttenhower.sph.harvard.edu/metaphlan/.

Composition of the adult digestive tract bacterial microbiome based on seven mouth surfaces, tonsils, throat and stool samples

BackgroundTo understand the relationship between our bacterial microbiome and health, it is essential to define the microbiome in the absence of disease. The digestive tract includes diverse habitats and hosts the human bodys greatest bacterial density. We describe the bacterial community composition of ten digestive tract sites from more than 200 normal adults enrolled in the Human Microbiome Project, and metagenomically determined metabolic potentials of four representative sites.ResultsThe microbiota of these diverse habitats formed four groups based on similar community compositions: buccal mucosa, keratinized gingiva, hard palate; saliva, tongue, tonsils, throat; sub- and supra-gingival plaques; and stool. Phyla initially identified from environmental samples were detected throughout this population, primarily TM7, SR1, and Synergistetes. Genera with pathogenic members were well-represented among this disease-free cohort. Tooth-associated communities were distinct, but not entirely dissimilar, from other oral surfaces. The Porphyromonadaceae, Veillonellaceae and Lachnospiraceae families were common to all sites, but the distributions of their genera varied significantly. Most metabolic processes were distributed widely throughout the digestive tract microbiota, with variations in metagenomic abundance between body habitats. These included shifts in sugar transporter types between the supragingival plaque, other oral surfaces, and stool; hydrogen and hydrogen sulfide production were also differentially distributed.ConclusionsThe microbiomes of ten digestive tract sites separated into four types based on composition. A core set of metabolic pathways was present across these diverse digestive tract habitats. These data provide a critical baseline for future studies investigating local and systemic diseases affecting human health.

Assessment of colorectal cancer molecular features along bowel subsites challenges the conception of distinct dichotomy of proximal versus distal colorectum

Objective Colorectal cancer is typically classified into proximal colon, distal colon and rectal cancer. Tumour genetic and epigenetic features differ by tumour location. Considering a possible role of bowel contents (including microbiome) in carcinogenesis, this study hypothesised that tumour molecular features might gradually change along bowel subsites, rather than change abruptly at splenic flexure. Design Utilising 1443 colorectal cancers in two US nationwide prospective cohort studies, the frequencies of molecular features (CpG island methylator phenotype (CIMP), microsatellite instability (MSI), LINE-1 methylation and BRAF, KRAS and PIK3CA mutations) were examined along bowel subsites (rectum, rectosigmoid junction, sigmoid, descending colon, splenic flexure, transverse colon, hepatic flexure, ascending colon and caecum). The linearity and non-linearity of molecular relations along subsites were statistically tested by multivariate logistic or linear regression analysis. Results The frequencies of CIMP-high, MSI-high and BRAF mutations gradually increased from the rectum (<2.3%) to ascending colon (36–40%), followed by falls in the caecum (12–22%). By linearity tests, these molecular relations were significantly linear from rectum to ascending colon (p<0.0001), and there was little evidence of non-linearity (p>0.09). Caecal cancers exhibited the highest frequency of KRAS mutations (52% vs 27–35% in other sites; p<0.0001). Conclusions The frequencies of CIMP-high, MSI-high and BRAF mutations in cancer increased gradually along colorectum subsites from the rectum to ascending colon. These novel data challenge the common conception of discrete molecular features of proximal versus distal colorectal cancers, and have a substantial impact on clinical, translational and epidemiology research, which has typically been performed with the dichotomous classification of proximal versus distal tumours.

A Guide to Enterotypes across the Human Body: Meta-Analysis of Microbial Community Structures in Human Microbiome Datasets

Recent analyses of human-associated bacterial diversity have categorized individuals into ‘enterotypes’ or clusters based on the abundances of key bacterial genera in the gut microbiota. There is a lack of consensus, however, on the analytical basis for enterotypes and on the interpretation of these results. We tested how the following factors influenced the detection of enterotypes: clustering methodology, distance metrics, OTU-picking approaches, sequencing depth, data type (whole genome shotgun (WGS) vs.16S rRNA gene sequence data), and 16S rRNA region. We included 16S rRNA gene sequences from the Human Microbiome Project (HMP) and from 16 additional studies and WGS sequences from the HMP and MetaHIT. In most body sites, we observed smooth abundance gradients of key genera without discrete clustering of samples. Some body habitats displayed bimodal (e.g., gut) or multimodal (e.g., vagina) distributions of sample abundances, but not all clustering methods and workflows accurately highlight such clusters. Because identifying enterotypes in datasets depends not only on the structure of the data but is also sensitive to the methods applied to identifying clustering strength, we recommend that multiple approaches be used and compared when testing for enterotypes.

Relating the metatranscriptome and metagenome of the human gut.

Significance Recent years have seen incredible growth in both the scale and specificity of projects analyzing the microbial organisms living in and on the human body (the human microbiome). Such studies typically require subjects to report to clinics for sample collection, a complicated practice that is impractical for large studies. To address these issues, we developed a protocol that allows subjects to collect microbiome samples at home and ship them to laboratories for multiple different types of molecular analysis. Measurements of microbial species, gene, and gene transcript composition within self-collected samples were consistent across sampling methods. In addition, our subsequent analysis of these samples revealed interesting similarities and differences between the measured functional potential and functional activity of the human microbiome. Although the composition of the human microbiome is now well-studied, the microbiota’s >8 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 (<5%) 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.

mRNA transcript quantification in archival samples using multiplexed, color-coded probes

BackgroundA recently developed probe-based technology, the NanoString nCounter™ gene expression system, has been shown to allow accurate mRNA transcript quantification using low amounts of total RNA. We assessed the ability of this technology for mRNA expression quantification in archived formalin-fixed, paraffin-embedded (FFPE) oral carcinoma samples.ResultsWe measured the mRNA transcript abundance of 20 genes (COL3A1, COL4A1, COL5A1, COL5A2, CTHRC1, CXCL1, CXCL13, MMP1, P4HA2, PDPN, PLOD2, POSTN, SDHA, SERPINE1, SERPINE2, SERPINH1, THBS2, TNC, GAPDH, RPS18) in 38 samples (19 paired fresh-frozen and FFPE oral carcinoma tissues, archived from 1997-2008) by both NanoString and SYBR Green I fluorescent dye-based quantitative real-time PCR (RQ-PCR). We compared gene expression data obtained by NanoString vs. RQ-PCR in both fresh-frozen and FFPE samples. Fresh-frozen samples showed a good overall Pearson correlation of 0.78, and FFPE samples showed a lower overall correlation coefficient of 0.59, which is likely due to sample quality. We found a higher correlation coefficient between fresh-frozen and FFPE samples analyzed by NanoString (r = 0.90) compared to fresh-frozen and FFPE samples analyzed by RQ-PCR (r = 0.50). In addition, NanoString data showed a higher mean correlation (r = 0.94) between individual fresh-frozen and FFPE sample pairs compared to RQ-PCR (r = 0.53).ConclusionsBased on our results, we conclude that both technologies are useful for gene expression quantification in fresh-frozen or FFPE tissues; however, the probe-based NanoString method achieved superior gene expression quantification results when compared to RQ-PCR in archived FFPE samples. We believe that this newly developed technique is optimal for large-scale validation studies using total RNA isolated from archived, FFPE samples.

MicroRNA-301 Mediates Proliferation and Invasion in Human Breast Cancer

Several microRNAs have been implicated in human breast cancer but none to date have been validated or utilized consistently in clinical management. MicroRNA-301 (miR-301) overexpression has been implicated as a negative prognostic indicator in lymph node negative (LNN) invasive ductal breast cancer, but its potential functional impact has not been determined. Here we report that in breast cancer cells, miR-301 attenuation decreased cell proliferation, clonogenicity, migration, invasion, tamoxifen resistance, tumor growth, and microvessel density, establishing an important oncogenic role for this gene. Algorithm-based and experimental strategies identified FOXF2, BBC3, PTEN, and COL2A1 as candidate miR-301 targets, all of which were verified as direct targets through luciferase reporter assays. We noted that miR-301 is located in an intron of the SKA2 gene which is responsible for kinetochore assembly, and both genes were found to be coexpressed in primary breast cancer samples. In summary, our findings define miR-301 as a crucial oncogene in human breast cancer that acts through multiple pathways and mechanisms to promote nodal or distant relapses.

Genotype and time of day shape the Populus drought response.

As exposure to episodic drought can impinge significantly on forest health and the establishment of productive tree plantations, there is great interest in understanding the mechanisms of drought response in trees. The ecologically dominant and economically important genus Populus, with its sequenced genome, provides an ideal opportunity to examine transcriptome level changes in trees in response to a drought stimulus. The transcriptome level drought response of two commercially important Populus clones (P. deltoides x P. nigra, DN34, and P. nigra x P. maximowiczii, NM6) was characterized over a diurnal period using a 4 x 2 x 2 complete randomized factorial anova experimental design (four time points, two genotypes and two treatment conditions), using Affymetrix Poplar GeneChip microarrays. Notably, the specific genes that exhibited changes in transcript abundance in response to drought differed between the genotypes and/or the time of day that they exhibited their greatest differences. This study emphasizes the fact that it is not possible to draw simple, generalized conclusions about the drought response of the genus Populus on the basis of one species, nor on the basis of results collected at a single time point. The data derived from our studies provide insights into the variety of genetic mechanisms underpinning the Populus drought response, and provide candidates for future experiments aimed at understanding this response across this economically and ecologically important genus.