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Ajami, N. J.

Publications and source records attributed to Ajami, N. J..

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TARO: tree-aggregated factor regression for microbiome data integration

MotivationAlthough the human microbiome plays a key role in health and disease, the biological mechanisms underlying the interaction between the microbiome and its host are incompletely understood. Integration with other molecular profiling data offers an opportunity to characterize the role of the microbiome and elucidate therapeutic targets. However, this remains challenging to the high dimensionality, compositionality, and rare features found in microbiome profiling data. These challenges necessitate the use of methods that can achieve structured sparsity in learning cross-platform association patterns. ResultsWe propose Tree-Aggregated factor RegressiOn (TARO) for the integration of microbiome and metabolomic data. We leverage information on the phylogenetic tree structure to flexibly aggregate rare features. We demonstrate through simulation studies that TARO accurately recovers a low-rank coefficient matrix and identifies relevant features. We applied TARO to microbiome and metabolomic profiles gathered from subjects being screened for colorectal cancer to understand how gut microrganisms shape intestinal metabolite abundances. Availability and implementationThe R package TARO implementing the proposed methods is available online at https://github.com/amishra-stats/taro-package.

bioinformatics↗

Diet-derived metabolites and mucus link the gut microbiome to fever after cytotoxic cancer treatment

Not all cancer patients with severe neutropenia develop fever, and the fecal microbiome may play a role. In neutropenic hematopoietic cell transplant patients (n=119), 63 (53%) developed a subsequent fever and had increased fecal Akkermansia muciniphila, a mucus-degrading bacteria (p=0.006, corrected for multiple comparisons). In mouse models, two therapies, irradiation and melphalan, similarly expanded A. muciniphila. Dietary restriction of unirradiated mice also expanded A. muciniphila and thinned the colonic mucus layer. Azithromycin treatment depleted A. muciniphila and preserved colonic mucus. Dietary restriction raised colonic luminal pH and reduced acetate, propionate, and butyrate. Culturing A. muciniphila with lower pH and increased propionate prevented utilization of mucin. Treating irradiated mice with azithromycin or propionate preserved the mucus layer, lessened hypothermia, and reduced inflammatory cytokines in the colon. These results suggest that diet, metabolites and colonic mucus link the microbiome to neutropenic fever, and could guide future microbiome-based preventive strategies.

microbiology↗

Diversity and composition of gut microbiome of cervical cancer patients by 16S rRNA and whole-metagenome sequencing

PurposeNext generation sequencing has progressed rapidly, characterizing microbial communities beyond culture-based or biochemical techniques. 16S ribosomal RNA gene sequencing (16S) produces reliable taxonomic classifications and relative abundances, while shotgun metagenome sequencing (WMS) allows higher taxonomic and functional resolution at greater cost. The purpose of this study was to determine if 16S and WMS provide congruent information for our patient population from paired fecal microbiome samples. MethodsPatients with locally advanced cervical cancers were enrolled on a prospective, observational clinical trial with a rectal swab sample collected prior to chemoradiation. Bacterial DNA was extracted from each sample and divided in two parts for 16S or WMS sequencing. We used measures of diversity richness and evenness as comparators of 16S and WMS sequencing. Relative abundances of the most common taxa were also compared between both datasets. Both techniques were tested against baseline patient demographics to assess associations identified with either or both methods. ResultsComparative indices were highly congruent between 16S and WMS. The most abundant genera for 16S and WMS data did not overlap. Overlap was observed at the Phylum level, as expected. However, relative abundances correlated poorly between the two methodologies (all p>0.05). Hierarchical clustering of both sequencing analyses identified overlapping enterotypes. Both approaches were in agreement with regard to demographic variables. ConclusionDiversity, evenness and richness are comparable when using 16S and WMS techniques, however relative abundances of individual genera are not. Clinical associations with diversity and evenness metrics were similarly identified with WMS or 16S. ImportanceThe gut microbiome plays an important role in regulating human health and disease. 16S rRNA gene sequencing (16S) and the whole-metagenome shotgun DNA sequencing (WMS) are two approaches to describe the microbial community. 16S sequencing via any amplicon sequencing-based method offers advantages over WMS in terms of precision (specific gene targeting). Additionally, 16S has historically been less costly due to the simplicity of library preparation and it does not require the same level read coverage as WMS. In this study, we performed both sequencing methods on a single rectal swab sample obtained from each cervical cancer patient prior to treatment. We showed that these two methods provide comparable information for diversity, evenness, and richness at higher taxonomic resolution, but are discrepant at a lower resolution. These methodological findings provide valuable information for the design and interpretation of future investigations of the role of the gut microbiome in cancer. Tweet(optional: 256 words, please submit a Tweet that conveys the essential message of your manuscript.) 16S may be sufficient for most initial studies of the gut microbiome in cancer patients, but WMS may be required for analysis of lower level taxonomy. Research supportThis research was supported in part by the Radiological Society of North America Resident/Fellow Award (to L.E.C.), the National Institutes of Health (NIH) through MD Andersons Cancer Center Support Grant P30CA016672, the Emerson Collective and the National Institutes of Health T32 grant #5T32 CA101642-14 (T.T.S). This study was partially funded by The University of Texas MD Anderson Cancer Center HPV-related Cancers Moonshot (L.E.C and A.K.).

microbiology↗

Evidence of recombination in coronaviruses implicating pangolin origins of nCoV-2019

A novel coronavirus (nCoV-2019) was the cause of an outbreak of respiratory illness detected in Wuhan, Hubei Province, China in December of 2019. Genomic analyses of nCoV-2019 determined a 96% resemblance with a coronavirus isolated from a bat in 2013 (RaTG13); however, the receptor binding motif (RBM) of these two genomes share low sequence similarity. This divergence suggests a possible alternative source for the RBM coding sequence in nCoV-2019. We identified high sequence similarity in the RBM between nCoV-2019 and a coronavirus genome reconstructed from a viral metagenomic dataset from pangolins possibly indicating a more complex origin for nCoV-2019.

microbiology↗

Influence of fecal collection conditions and 16S rRNA gene sequencing protocols at two centers on human gut microbiota analysis

BackgroundTo optimise fecal sampling and analysis yielding reproducible microbiome data, and gain further insight into sources of its variation, we compared different collection conditions and 16S rRNA gene sequencing protocols in two centers. Fecal samples were collected on three sequential days from six healthy adults and placed in commercial collection tubes (OMNIgeneGut OMR-200) at room temperature or in sterile 5 ml screw-top tubes in a home fridge or home freezer for 6-24 h, before transfer at 4{degrees}C to the laboratory and storage at - 80{degrees}C within 24 hours. Replicate samples were shipped on dry ice to centers in Australia and the USA for DNA extraction and sequencing of the V4 region of the 16S rRNA gene, using different PCR protocols. Sequences were analysed with the QIIME pipeline and Greengenes database at the Australian center and with an in-house pipeline and SILVA database at the USA center.\n\nResultsVariation in gut microbiome composition and diversity was dominated by differences between individuals. Minor differences in the abundance of taxa were found between collection-processing methods and day of collection. Larger differences were evident between the two centers, including in the relative abundances of genus Akkermansia, in phylum Verrucomicrobiales, and Bifidobacteria in Actinobacteria.\n\nConclusionsCollection with storage and transport at 4{degrees}C within 24 h is adequate for 16S rRNA analysis of the gut microbiome. However, variation between sequencing centers suggests that cohort samples should be sequenced by the same method in one center. Differences in handling, shipping and methods of PCR gene amplification and sequence analysis in different centers introduce variation in ways that are not fully understood. These findings are particularly relevant as microbiome studies shift towards larger population-based and multicenter studies.

bioinformatics↗