Joseph N. Paulson

Diarrhea in young children from low-income countries leads to large-scale alterations in intestinal microbiota composition

BackgroundDiarrheal diseases continue to contribute significantly to morbidity and mortality in infants and young children in developing countries. There is an urgent need to better understand the contributions of novel, potentially uncultured, diarrheal pathogens to severe diarrheal disease, as well as distortions in normal gut microbiota composition that might facilitate severe disease.ResultsWe use high throughput 16S rRNA gene sequencing to compare fecal microbiota composition in children under five years of age who have been diagnosed with moderate to severe diarrhea (MSD) with the microbiota from diarrhea-free controls. Our study includes 992 children from four low-income countries in West and East Africa, and Southeast Asia. Known pathogens, as well as bacteria currently not considered as important diarrhea-causing pathogens, are positively associated with MSD, and these include Escherichia/Shigella, and Granulicatella species, and Streptococcus mitis/pneumoniae groups. In both cases and controls, there tend to be distinct negative correlations between facultative anaerobic lineages and obligate anaerobic lineages. Overall genus-level microbiota composition exhibit a shift in controls from low to high levels of Prevotella and in MSD cases from high to low levels of Escherichia/Shigella in younger versus older children; however, there was significant variation among many genera by both site and age.ConclusionsOur findings expand the current understanding of microbiota-associated diarrhea pathogenicity in young children from developing countries. Our findings are necessarily based on correlative analyses and must be further validated through epidemiological and molecular techniques.

Metastats: an improved statistical method for analysis of metagenomic data

Metagenomic studies were originally focused on exploratory/validation projects but are rapidly being applied in a clinical setting. In this setting, researchers are interested in finding characteristics of the microbiome that correlate with the clinical status of the corresponding sample. Comparatively few computational/statistical tools have been developed that can assist in this process. Rather, most developments in the metagenomics community have focused on methods that compare samples as a whole. Specifically, the focus has been on developing robust methods for determining the level of similarity or difference between samples, rather than on identifying the specific characteristics that distinguish different samples from each other. Metastats [1] was the first statistical method developed specifically to address the questions asked in clinical studies. Metastats allows a comparison of metagenomic samples (represented as counts of individual features such as organisms, genes and functional groups) from two treatment populations (for example, healthy versus disease) and identifies those features that statistically distinguish the two populations. n nHere, we present major improvements to the Metastats software and the underlying statistical methods. First, we describe new approaches for data normalization that allow a more accurate assessment of differential abundance by reducing the covariance between individual features implicitly introduced by the traditionally used ratio-based normalization. These normalization techniques are also of interest for time-series analyses or in the estimation of microbial networks. A second extension of Metastats is a mixed-model zero-inflated Gaussian distribution that allows Metastats to account for a common characteristic of metagenomic data: the presence of many features with zero counts owing to undersampling of the community. The number of ‘missing features’ (zero counts) correlates with the amount of sequencing performed, thereby biasing abundance measurements and the differential abundance statistics derived from them. n nUsing simulated and real data, we show that these methods significantly improve the accuracy of Metastats. We also describe the addition of several new statistical tests to our code (including presence/absence and the corresponding odds ratio, and penetrance calculations) that improve the usability of our software in clinical practice.

Survey of Culture, GoldenGate Assay, Universal Biosensor Assay, and 16S rRNA Gene Sequencing as Alternative Methods of Bacterial Pathogen Detection

ABSTRACT Cultivation-based assays combined with PCR or enzyme-linked immunosorbent assay (ELISA)-based methods for finding virulence factors are standard methods for detecting bacterial pathogens in stools; however, with emerging molecular technologies, new methods have become available. The aim of this study was to compare four distinct detection technologies for the identification of pathogens in stools from children under 5 years of age in The Gambia, Mali, Kenya, and Bangladesh. The children were identified, using currently accepted clinical protocols, as either controls or cases with moderate to severe diarrhea. A total of 3,610 stool samples were tested by established clinical culture techniques: 3,179 DNA samples by the Universal Biosensor assay (Ibis Biosciences, Inc.), 1,466 DNA samples by the GoldenGate assay (Illumina), and 1,006 DNA samples by sequencing of 16S rRNA genes. Each method detected different proportions of samples testing positive for each of seven enteric pathogens, enteroaggregative Escherichia coli (EAEC), enterotoxigenic E. coli (ETEC), enteropathogenic E. coli (EPEC), Shigella spp., Campylobacter jejuni, Salmonella enterica, and Aeromonas spp. The comparisons among detection methods included the frequency of positive stool samples and kappa values for making pairwise comparisons. Overall, the standard culture methods detected Shigella spp., EPEC, ETEC, and EAEC in smaller proportions of the samples than either of the methods based on detection of the virulence genes from DNA in whole stools. The GoldenGate method revealed the greatest agreement with the other methods. The agreement among methods was higher in cases than in controls. The new molecular technologies have a high potential for highly sensitive identification of bacterial diarrheal pathogens.

Methylome Analysis in Chickens Immunized with Infectious Laryngotracheitis Vaccine

In this study we investigated the methylome of chickens immunized with Infectious laryngotracheitis (ILT) vaccine derived from chicken embryos. Methyl-CpG binding domain protein-enriched genome sequencing (MBD-Seq) method was employed in the detection of the 1,155 differentially methylated regions (DMRs) across the entire genome. After validation, we ascertained the genomic DMRs distribution and annotated them regarding genes, transcription start sites (TSS) and CpG islands. We found that global DNA methylation decreased in vaccinated birds, presenting 704 hypomethylated and 451 hypermethylated DMRs, respectively. Additionally, we performed an enrichment analysis detecting gene networks, in which cancer and RNA post-transcriptional modification appeared in the first place, followed by humoral immune response, immunological disease and inflammatory disease. The top four identified canonical pathways were EIF2 signaling, regulation of EIF4 and p70S6K signaling, axonal guidance signaling and mTOR signaling, providing new insight regarding the mechanisms of ILT etiology. Lastly, the association between DNA methylation and differentially expressed genes was examined, and detected negative correlation in seventeen of the eighteen genes.

Mentholation affects the cigarette microbiota by selecting for bacteria resistant to harsh environmental conditions and selecting against potential bacterial pathogens

BackgroundThere is a paucity of data regarding the microbial constituents of tobacco products and their impacts on public health. Moreover, there has been no comparative characterization performed on the bacterial microbiota associated with the addition of menthol, an additive that has been used by tobacco manufacturers for nearly a century. To address this knowledge gap, we conducted bacterial community profiling on tobacco from user- and custom-mentholated/non-mentholated cigarette pairs, as well as a commercially-mentholated product. Total genomic DNA was extracted using a multi-step enzymatic and mechanical lysis protocol followed by PCR amplification of the V3-V4 hypervariable regions of the 16S rRNA gene from five cigarette products (18 cigarettes per product for a total of 90 samples): Camel Crush, user-mentholated Camel Crush, Camel Kings, custom-mentholated Camel Kings, and Newport Menthols. Sequencing was performed on the Illumina MiSeq platform and sequences were processed using the Quantitative Insights Into Microbial Ecology (QIIME) software package.ResultsIn all products, Pseudomonas was the most abundant genera and included Pseudomonas oryzihabitans and Pseudomonas putida, regardless of mentholation status. However, further comparative analysis of the five products revealed significant differences in the bacterial compositions across products. Bacterial community richness was higher among non-mentholated products compared to those that were mentholated, particularly those that were custom-mentholated. In addition, mentholation appeared to be correlated with a reduction in potential human bacterial pathogens and an increase in bacterial species resistant to harsh environmental conditions.ConclusionsTaken together, these data provide preliminary evidence that the mentholation of commercially available cigarettes can impact the bacterial community of these products.

Meta-analysis of the lung microbiota in pulmonary tuberculosis.

The lung microbiota has received less attention compared to other body sites, in part because its study carries special technological difficulties related to obtaining reliable samples as compared to other body niches. The limited number of studies on the sputum microbiota on TB patients and controls available so far have reported inconsistent, and sometimes, contradictory results. Aiming to clarify if changes in the lung microbiota composition are associated with pulmonary TB, we performed a meta-analysis of available data on microbiota of the lower respiratory tract in TB patients and healthy controls. Re-processing next generation sequencing data under uniform parameters and utilizing state-of-the-art bioinformatics analysis, we obtained distinct clusterings of microbiota between TB cases vs. controls across multiple studies. We identified Tumebacillus ginsengisoli, Propionibacterium acnes, Haemophilus parahaemolyticus as differentially abundant species signature in healthy controls while Caulobacter henricii, Actinomyces graevenitzii, Rothia mucilaginosa, in addition to Mycobacterium tuberculosis as differentially abundant species signature in TB cases, and described R. mucilaginosa as the anchoring species in a network of bacteria co-occurring with Mycobacterium tuberculosis (Mtb) infection.

Building Genomic Analysis Pipelines in a Hackathon Setting with Bioinformatician Teams: DNA-seq, Epigenomics, Metagenomics and RNA-seq

We assembled teams of genomics professionals to assess whether we could rapidly develop pipelines to answer biological questions commonly asked by biologists and others new to bioinformatics by facilitating analysis of high-throughput sequencing data. In January 2015, teams were assembled on the National Institutes of Health (NIH) campus to address questions in the DNA-seq, epigenomics, metagenomics and RNA-seq subfields of genomics. The only two rules for this hackathon were that either the data used were housed at the National Center for Biotechnology Information (NCBI) or would be submitted there by a participant in the next six months, and that all software going into the pipeline was open-source or open-use. Questions proposed by organizers, as well as suggested tools and approaches, were distributed to participants a few days before the event and were refined during the event. Pipelines were published on GitHub, a web service providing publicly available, free-usage tiers for collaborative software development (https://github.com/features/). The code was published at https://github.com/DCGenomics/ with separate repositories for each team, starting with hackathon_v001.

metagenomeFeatures: An R package for working with 16S rRNA reference databases and marker-gene survey feature data.

We developed the metagenomeFeatures R Bioconductor package along with annotation packages for the three primary 16S rRNA databases (Greengenes, RDP, and SILVA) to facilitate working with 16S rRNA sequence databases and marker-gene survey feature data. The metagenomeFeatures package defines two classes, MgDb for working with 16S rRNA sequence databases, and mgFeatures for working with marker-gene survey feature data. The associated annotation packages provide a consistent interface to the different 16S rRNA databases facilitating database comparison and exploration. The mgFeatures represents a crucial step in the development of a common data structure for working with 16S marker-gene survey data in R. Availability https://bioconductor.org/packages/release/bioc/html/metagenomeFeatures.html Contact nolson@nist.gov

Conventional wastewater treatment and reuse site practices modify bacterial community structure but do not eliminate some opportunistic pathogens in reclaimed water

Water recycling continues to expand across the United States, from areas that have access to advanced, potable-level treated reclaimed water, to those having access only to reclaimed water treated at conventional municipal wastewater treatment plants. This expansion makes it important to further characterize the microbial quality of these conventionally-treated water sources. Therefore, we used 16S rRNA gene sequencing to characterize total bacterial communities present in differentially-treated wastewater and reclaimed water (nu202f=u202f67 samples) from four U.S. wastewater treatment plants and one associated spray irrigation site conducting on-site ultraviolet treatment and open-air storage. The number of observed operational taxonomic units was significantly lower (pu202f<u202f0.01) in effluent, compared to influent, after conventional treatment. Effluent community structure was influenced more by treatment method than by influent community structure. The abundance of Legionella spp. increased as treatment progressed in one treatment plant that performed chlorination and in another that seasonally chlorinated. Overall, the alpha-diversity of bacterial communities in reclaimed water decreased (pu202f<u202f0.01) during wastewater treatment and spray irrigation site ultraviolet treatment (pu202f<u202f0.01), but increased (pu202f<u202f0.01) after open-air storage at the spray irrigation site. The abundance of Legionella spp. was higher at the sprinkler system pumphouse at the spray irrigation site than in the influent from the treatment plant supplying the site. Legionella pneumophila was detected in conventionally treated effluent samples and in samples collected after ultraviolet treatment at the spray irrigation site, while Legionella feeleii persisted throughout on-site treatment at the spray irrigation site, and, along with Mycobacterium gordonae, was also detected at the sprinkler system pumphouse at the spray irrigation site. These data could inform the development of future treatment technologies and reuse guidelines that address a broader assemblage of the bacterial community of reclaimed water, resulting in reuse practices that may be more protective of public health.