PCIF1 is partly cytoplasmic, dynamically localizes to stress granules and binds mRNA coding regions upon oxidative stress.

PCIF1 (Phosphorylated CTD-Interacting Factor 1) is the mRNA (2'-O-methyladenosine-N(6)-)-methyltransferase that catalyzes the formation of cap-adjacent N6,2'-O-dimethyladenosine (m6Am) by methylating adenosines at the first transcribed position of capped mRNAs. While previous studies assumed that PCIF1 was nuclear, cell fractionation and immunofluorescence both show that a population of PCIF1 is localized to the cytoplasm. Further, PCIF1 redistributes to stress granules upon oxidative stress. Immunoprecipitation studies with stressed cells show that PCIF1 also physically interacts with G3BP and other stress granule components. In addition, PCIF1 behaves as a stress granule component as it disassociates from stress granules upon recovery from stress. Overexpressing full-length PCIF1 also inhibits stress granule formation, while knocking out PCIF1 slows stress granule disassembly. Next, our enhanced crosslinking and immunoprecipitation (eCLIP) data show that PCIF1 binds mRNAs in their coding sequences rather than cap-proximal regions. Further PCIF1's association with mRNAs increased upon NaAsO2 stress. In contrast to eCLIP data, ChIP-Seq experiments show that PCIF1 is predominantly associated with transcription start sites rather than gene bodies, indicating that PCIF1's association with mature mRNA is not co-transcriptional. Collectively, our data suggest that PCIF1 has cytoplasmic RNA surveillance role(s) independent of transcription-associated cap-adjacent mRNA modification, particularly during the stress response.

The Genboree Microbiome Toolset and the analysis of 16S rRNA microbial sequences.

BACKGROUND: Microbial metagenomic analyses rely on an increasing number of publicly available tools. Installation, integration, and maintenance of the tools poses significant burden on many researchers and creates a barrier to adoption of microbiome analysis, particularly in translational settings. METHODS: To address this need we have integrated a rich collection of microbiome analysis tools into the Genboree Microbiome Toolset and exposed them to the scientific community using the Software-as-a-Service model via the Genboree Workbench. The Genboree Microbiome Toolset provides an interactive environment for users at all bioinformatic experience levels in which to conduct microbiome analysis. The Toolset drives hypothesis generation by providing a wide range of analyses including alpha diversity and beta diversity, phylogenetic profiling, supervised machine learning, and feature selection. RESULTS: We validate the Toolset in two studies of the gut microbiota, one involving obese and lean twins, and the other involving children suffering from the irritable bowel syndrome. CONCLUSIONS: By lowering the barrier to performing a comprehensive set of microbiome analyses, the Toolset empowers investigators to translate high-volume sequencing data into valuable biomedical discoveries.

Gastrointestinal microbiome signatures of pediatric patients with irritable bowel syndrome.

BACKGROUND & AIMS: The intestinal microbiomes of healthy children and pediatric patients with irritable bowel syndrome (IBS) are not well defined. Studies in adults have indicated that the gastrointestinal microbiota could be involved in IBS. METHODS: We analyzed 71 samples from 22 children with IBS (pediatric Rome III criteria) and 22 healthy children, ages 7-12 years, by 16S ribosomal RNA gene sequencing, with an average of 54,287 reads/stool sample (average 454 read length = 503 bases). Data were analyzed using phylogenetic-based clustering (Unifrac), or an operational taxonomic unit (OTU) approach using a supervised machine learning tool (randomForest). Most samples were also hybridized to a microarray that can detect 8741 bacterial taxa (16S rRNA PhyloChip). RESULTS: Microbiomes associated with pediatric IBS were characterized by a significantly greater percentage of the class γ-proteobacteria (0.07% vs 0.89% of total bacteria, respectively; P < .05); 1 prominent component of this group was Haemophilus parainfluenzae. Differences highlighted by 454 sequencing were confirmed by high-resolution PhyloChip analysis. Using supervised learning techniques, we were able to classify different subtypes of IBS with a success rate of 98.5%, using limited sets of discriminant bacterial species. A novel Ruminococcus-like microbe was associated with IBS, indicating the potential utility of microbe discovery for gastrointestinal disorders. A greater frequency of pain correlated with an increased abundance of several bacterial taxa from the genus Alistipes. CONCLUSIONS: Using 16S metagenomics by PhyloChip DNA hybridization and deep 454 pyrosequencing, we associated specific microbiome signatures with pediatric IBS. These findings indicate the important association between gastrointestinal microbes and IBS in children; these approaches might be used in diagnosis of functional bowel disorders in pediatric patients.

Comparison of Whole Genome Sequencing and Repetitive Element PCR for Multidrug-Resistant Pseudomonas aeruginosa Strain Typing.

Hospital-acquired infections pose significant costly global challenges to patient care. Rapid and sensitive methods to identify potential outbreaks are integral to infection control measures. Whole-genome sequencing (WGS)-based bacterial strain typing provides higher discriminatory power over standard nucleotide banding pattern-based methods such as repetitive sequence-based PCR (rep-PCR). However, integration of WGS into clinical epidemiology is limited by the lack of consensus in methodology and data analysis/interpretation. In this study, WGS was performed on genomic DNA extracted from 22 multidrug-resistant Pseudomonas aeruginosa (MDR-PA) isolates using next-generation sequencing. Resulting high-quality reads were analyzed for phylogenetic relatedness using a whole-genome multilocus sequence typing (wgMLST)-based software program and single-nucleotide variant phylogenomics (SNVPhyl). WGS-based results were compared with conventional MLST and archived rep-PCR results. Rep-PCR identified three independent clonal clusters of MDR-PA. Only one clonal cluster identified by rep-PCR, an endemic strain within the pediatric cystic fibrosis population at Texas Children's Hospital, was concordantly identified using wgMLST and SNVPhyl. Results were highly consistent between the three sequence-based analyses (conventional MLST, wgMLST, and SNVPhyl), and these results remained consistent with the addition of 74 MDR-PA genomes. These WGS-based methods provided greater resolution for strain discrimination than rep-PCR or standard MLST classification, and the ease of use of wgMLST software renders it clinically viable for analysis, interpretation, and reporting of WGS-based strain typing.

Complete Genome Sequence of the Multidrug-Resistant Pseudomonas aeruginosa Endemic Houston-1 Strain, Isolated from a Pediatric Patient with Cystic Fibrosis and Assembled Using Oxford Nanopore and Illumina Sequencing.

Hybrid de novo assembly of Illumina/Nanopore sequence data produced complete circular sequences of the chromosome and a plasmid for the multidrug-resistant Pseudomonas aeruginosa Houston-1 strain. This provides a high-quality representative sequence for a lineage endemic to a pediatric cystic fibrosis care center at Texas Children's Hospital.

Complete Genome Sequence of Clostridioides difficile Ribotype 255 Strain Mta-79, Assembled Using Oxford Nanopore and Illumina Sequencing.

Hybrid de novo assembly of Illumina/Nanopore sequence data produced a complete circular sequence of the chromosome for a Clostridioides difficile ribotype 255 (RT255) isolate from an elderly patient with recurrent C. difficile infection (CDI). This provides a high-quality representative sequence for the RT255 lineage.

Leveraging Human Microbiome Features to Diagnose and Stratify Children with Irritable Bowel Syndrome.

Accurate diagnosis and stratification of children with irritable bowel syndrome (IBS) remain challenging. Given the central role of recurrent abdominal pain in IBS, we evaluated the relationships of pediatric IBS and abdominal pain with intestinal microbes and fecal metabolites using a comprehensive clinical characterization and multiomics strategy. Using rigorous clinical phenotyping, we identified preadolescent children (aged 7 to 12 years) with Rome III IBS (n = 23) and healthy controls (n = 22) and characterized their fecal microbial communities using whole-genome shotgun metagenomics and global unbiased fecal metabolomic profiling. Correlation-based approaches and machine learning algorithms identified associations between microbes, metabolites, and abdominal pain. IBS cases differed from controls with respect to key bacterial taxa (eg, Flavonifractor plautii and Lachnospiraceae bacterium 7_1_58FAA), metagenomic functions (eg, carbohydrate metabolism and amino acid metabolism), and higher-order metabolites (eg, secondary bile acids, sterols, and steroid-like compounds). Significant associations between abdominal pain frequency and severity and intestinal microbial features were identified. A random forest classifier built on metagenomic and metabolic markers successfully distinguished IBS cases from controls (area under the curve, 0.93). Leveraging multiple lines of evidence, intestinal microbes, genes/pathways, and metabolites were associated with IBS, and these features were capable of distinguishing children with IBS from healthy children. These multi-omics features, and their links to childhood IBS coupled with nutritional interventions, may lead to new microbiome-guided diagnostic and therapeutic strategies.

Structure and function of the healthy pre-adolescent pediatric gut microbiome.

BACKGROUND: The gut microbiome influences myriad host functions, including nutrient acquisition, immune modulation, brain development, and behavior. Although human gut microbiota are recognized to change as we age, information regarding the structure and function of the gut microbiome during childhood is limited. Using 16S rRNA gene and shotgun metagenomic sequencing, we characterized the structure, function, and variation of the healthy pediatric gut microbiome in a cohort of school-aged, pre-adolescent children (ages 7-12 years). We compared the healthy pediatric gut microbiome with that of healthy adults previously recruited from the same region (Houston, TX, USA). RESULTS: Although healthy children and adults harbored similar numbers of taxa and functional genes, their composition and functional potential differed significantly. Children were enriched in Bifidobacterium spp., Faecalibacterium spp., and members of the Lachnospiraceae, while adults harbored greater abundances of Bacteroides spp. From a functional perspective, significant differences were detected with respect to the relative abundances of genes involved in vitamin synthesis, amino acid degradation, oxidative phosphorylation, and triggering mucosal inflammation. Children's gut communities were enriched in functions which may support ongoing development, while adult communities were enriched in functions associated with inflammation, obesity, and increased risk of adiposity. CONCLUSIONS: Previous studies suggest that the human gut microbiome is relatively stable and adult-like after the first 1 to 3 years of life. Our results suggest that the healthy pediatric gut microbiome harbors compositional and functional qualities that differ from those of healthy adults and that the gut microbiome may undergo a more prolonged development than previously suspected.

A metagenomic approach to characterization of the vaginal microbiome signature in pregnancy.

While current major national research efforts (i.e., the NIH Human Microbiome Project) will enable comprehensive metagenomic characterization of the adult human microbiota, how and when these diverse microbial communities take up residence in the host and during reproductive life are unexplored at a population level. Because microbial abundance and diversity might differ in pregnancy, we sought to generate comparative metagenomic signatures across gestational age strata. DNA was isolated from the vagina (introitus, posterior fornix, midvagina) and the V5V3 region of bacterial 16S rRNA genes were sequenced (454FLX Titanium platform). Sixty-eight samples from 24 healthy gravidae (18 to 40 confirmed weeks) were compared with 301 non-pregnant controls (60 subjects). Generated sequence data were quality filtered, taxonomically binned, normalized, and organized by phylogeny and into operational taxonomic units (OTU); principal coordinates analysis (PCoA) of the resultant beta diversity measures were used for visualization and analysis in association with sample clinical metadata. Altogether, 1.4 gigabytes of data containing >2.5 million reads (averaging 6,837 sequences/sample of 493 nt in length) were generated for computational analyses. Although gravidae were not excluded by virtue of a posterior fornix pH >4.5 at the time of screening, unique vaginal microbiome signature encompassing several specific OTUs and higher-level clades was nevertheless observed and confirmed using a combination of phylogenetic, non-phylogenetic, supervised, and unsupervised approaches. Both overall diversity and richness were reduced in pregnancy, with dominance of Lactobacillus species (L. iners crispatus, jensenii and johnsonii, and the orders Lactobacillales (and Lactobacillaceae family), Clostridiales, Bacteroidales, and Actinomycetales. This intergroup comparison using rigorous standardized sampling protocols and analytical methodologies provides robust initial evidence that the vaginal microbial 16S rRNA gene catalogue uniquely differs in pregnancy, with variance of taxa across vaginal subsite and gestational age.

Microbial DNA typing by automated repetitive-sequence-based PCR.

Repetitive sequence-based PCR (rep-PCR) has been recognized as an effective method for bacterial strain typing. Recently, rep-PCR has been commercially adapted to an automated format known as the DiversiLab system to provide a reliable PCR-based typing system for clinical laboratories. We describe the adaptations made to automate rep-PCR and explore the performance and reproducibility of the system as a molecular genotyping tool for bacterial strain typing. The modifications for automation included changes in rep-PCR chemistry and thermal cycling parameters, incorporation of microfluidics-based DNA amplicon fractionation and detection, and Internet-based computer-assisted analysis, reporting, and data storage. The performance and reproducibility of the automated rep-PCR were examined by performing DNA typing and replicate testing with multiple laboratories, personnel, instruments, DNA template concentrations, and culture conditions prior to DNA isolation. Finally, we demonstrated the use of automated rep-PCR for clinical laboratory applications by using isolates from an outbreak of Neisseria meningitidis infections. N. meningitidis outbreak-related strains were distinguished from other isolates. The DiversiLab system is a highly integrated, convenient, and rapid testing platform that may allow clinical laboratories to realize the potential of microbial DNA typing.