Matthew L. Bendall

Composition, taxonomy and functional diversity of the oropharynx microbiome in individuals with schizophrenia and controls

The role of the human microbiome in schizophrenia remains largely unexplored. The microbiome has been shown to alter brain development and modulate behavior and cognition in animals through gut-brain connections, and research in humans suggests that it may be a modulating factor in many disorders. This study reports findings from a shotgun metagenomic analysis of the oropharyngeal microbiome in 16 individuals with schizophrenia and 16 controls. High-level differences were evident at both the phylum and genus levels, with Proteobacteria, Firmicutes, Bacteroidetes, and Actinobacteria dominating both schizophrenia patients and controls, and Ascomycota being more abundant in schizophrenia patients than controls. Controls were richer in species but less even in their distributions, i.e., dominated by fewer species, as opposed to schizophrenia patients. Lactic acid bacteria were relatively more abundant in schizophrenia, including species of Lactobacilli and Bifidobacterium, which have been shown to modulate chronic inflammation. We also found Eubacterium halii, a lactate-utilizing species. Functionally, the microbiome of schizophrenia patients was characterized by an increased number of metabolic pathways related to metabolite transport systems including siderophores, glutamate, and vitamin B12. In contrast, carbohydrate and lipid pathways and energy metabolism were abundant in controls. These findings suggest that the oropharyngeal microbiome in individuals with schizophrenia is significantly different compared to controls, and that particular microbial species and metabolic pathways differentiate both groups. Confirmation of these findings in larger and more diverse samples, e.g., gut microbiome, will contribute to elucidating potential links between schizophrenia and the human microbiota.

Dual Transcriptomic Profiling of Host and Microbiota during Health and Disease in Pediatric Asthma

Background High-throughput sequencing (HTS) analysis of microbial communities from the respiratory airways has heavily relied on the 16S rRNA gene. Given the intrinsic limitations of this approach, airway microbiome research has focused on assessing bacterial composition during health and disease, and its variation in relation to clinical and environmental factors, or other microbiomes. Consequently, very little effort has been dedicated to describing the functional characteristics of the airway microbiota and even less to explore the microbe-host interactions. Here we present a simultaneous assessment of microbiome and host functional diversity and host-microbe interactions from the same RNA-seq experiment, while accounting for variation in clinical metadata. Methods Transcriptomic (host) and metatranscriptomic (microbiota) sequences from the nasal epithelium of 8 asthmatics and 6 healthy controls were separated in silico and mapped to available human and NCBI-NR protein reference databases. Human genes differentially expressed in asthmatics and controls were then used to infer upstream regulators involved in immune and inflammatory responses. Concomitantly, microbial genes were mapped to metabolic databases (COG, SEED, and KEGG) to infer microbial functions differentially expressed in asthmatics and controls. Finally, multivariate analysis was applied to find associations between microbiome characteristics and host upstream regulators while accounting for clinical variation. Results and Discussion Our study showed significant differences in the metabolism of microbiomes from asthmatic and non-asthmatic children for up to 25% of the functional properties tested. Enrichment analysis of 499 differentially expressed host genes for inflammatory and immune responses revealed 43 upstream regulators differentially activated in asthma. Microbial adhesion (virulence) and Proteobacteria abundance were significantly associated with variation in the expression of the upstream regulator IL1A; suggesting that microbiome characteristics modulate host inflammatory and immune systems during asthma.

Phylogenetic Relationships and Molecular Adaptation Dynamics of Human Rhinoviruses

Human rhinoviruses (HRVs) are responsible for nearly 50% of all common cold infections. Ordinarily, HRV infections are mild and self-limiting; nonetheless, every year they result in significant loss of economic productivity and substantial inappropriate antibiotic use. Development of effective vaccine and antiviral prophylaxis against HRV has been hampered by the extensive antigenic diversity present among the nearly 100 serotypes. To gain new insights into the evolutionary processes that create the genetic diversity present among HRVs, we tested for recombination and selection for individual genes and the coding genome for 45 HRV serotypes using estimated phylogenetic relationships. Although the structural capsid genes and nonstructural genes recovered incongruent tree topologies, no recombination was detected using substitution methods. Therefore, the coding genome was determined to be appropriate for phylogenetic tests. Results of the Shimodaira-Hasegawa (SH) test support the hypothesis that the capsid genes recover a different evolutionary history than the nonstructural genes. Our best phylogenetic estimate based on the coding genome suggests that HRV-B is more closely related to enterovirus than to HRV-A; however, several alternative phylogenetic hypotheses were not rejected by the SH test. Positive selection was examined by using two different approaches; d(N)/d(S) rate ratio and the physicochemical phenotypes for 31 amino acid properties. Analyses using d(N)/d(S) failed to detect positive selection. However, protein phenotypic expression appears to be a more sensitive approach. There was extensive stabilizing and destabilizing positive selection in HRV-A major and HRV-B serotypes for all proteins, except in 3A in HRV-B, which overlapped with functional, structural, and to a greater extent in uncharacterized genomic regions. In contrast, the evolution of HRV-A minor serotypes appears to be driven primarily by destabilizing selection. Our results demonstrate that HRV-A major, HRV-A minor, and HRV-B serotypes have not been similarly influenced by purifying selection.

Phylogenetics applied to genotype/phenotype association and selection analyses with sequence data from angptl4 in humans.

Genotype/phenotype association analyses (Treescan) with plasma lipid levels and functional site prediction methods (TreeSAAP and PolyPhen) were performed using sequence data for ANGPTL4 from 3,551 patients in the Dallas Heart Study. Biological assays of rare variants in phenotypic tails and results from a Treescan analysis were used as “known” variants to assess the site prediction abilities of PolyPhen and TreeSAAP. The E40K variant in European Americans and the R278Q variant in African Americans were significantly associated with multiple lipid phenotypes. Combining TreeSAAP and PolyPhen performed well to predict “known” functional variants while reducing noise from false positives.

Telescope: Characterization of the retrotranscriptome by accurate estimation of transposable element expression

Characterization of Human Endogenous Retrovirus (HERV) expression within the transcriptomic landscape using RNA-seq is complicated by uncertainty in fragment assignment because of sequence similarity. We present Telescope, a computational software tool that provides accurate estimation of transposable element expression (retrotranscriptome) resolved to specific genomic locations. Telescope directly addresses uncertainty in fragment assignment by reassigning ambiguously mapped fragments to the most probable source transcript as determined within a Bayesian statistical model. We demonstrate the utility of our approach through single locus analysis of HERV expression in 13 ENCODE cell types. When examined at this resolution, we find that the magnitude and breadth of the retrotranscriptome can be vastly different among cell types. Furthermore, our approach is robust to differences in sequencing technology, and demonstrates that the retrotranscriptome has potential to be used for cell type identification. Telescope performs highly accurate quantification of the retrotranscriptomic landscape in RNA-seq experiments, revealing a differential complexity in the transposable element biology of complex systems not previously observed. Telescope is available at github.com/mlbendall/telescope. Author Summary Almost half of the human genome is composed of Transposable elements (TEs), but their contribution to the transcriptome, their cell-type specific expression patterns, and their role in disease remains poorly understood. Recent studies have found many elements to be actively expressed and involved in key cellular processes. For example, human endogenous retroviruses (HERVs) are reported to be involved in human embryonic stem cell differentiation. Discovering which exact HERVs are differentially expressed in RNA-seq data would be a major advance in understanding such processes. However, because HERVs have a high level of sequence similarity it is hard to identify which exact HERV is differentially expressed. To solve this problem, we developed a computer program which addressed uncertainty in fragment assignment by reassigning ambiguously mapped fragments to the most probable source transcript as determined within a Bayesian statistical model. We call this program, “Telescope”. We then used Telescope to identify HERV expression in 13 well-studied cell types from the ENCODE consortium and found that different cell types could be characterized by enrichment for different HERV families, and for locus specific expression. We also showed that Telescope performed better than other methods currently used to determine TE expression. The use of this computational tool to examine new and existing RNA-seq data sets may lead to new understanding of the roles of TEs in health and disease.

Benchmark Evaluation of True Single Molecular Sequencing to Determine Cystic Fibrosis Airway Microbiome Diversity

Cystic fibrosis (CF) is an autosomal recessive disease associated with recurrent lung infections that can lead to morbidity and mortality. The impact of antibiotics for treatment of acute pulmonary exacerbations on the CF airway microbiome remains unclear with prior studies giving conflicting results and being limited by their use of 16S ribosomal RNA sequencing. Our primary objective was to validate the use of true single molecular sequencing (tSMS) and PathoScope in the analysis of the CF airway microbiome. Three control samples were created with differing amounts of Burkholderia cepacia, Pseudomonas aeruginosa, and Prevotella melaninogenica, three common bacteria found in cystic fibrosis lungs. Paired sputa were also obtained from three study participants with CF before and >6 days after initiation of antibiotics. Antibiotic resistant B. cepacia and P. aeruginosa were identified in concurrently obtained respiratory cultures. Direct sequencing was performed using tSMS, and filtered reads were aligned to reference genomes from NCBI using PathoScope and Kraken and unique clade-specific marker genes using MetaPhlAn. A total of 180–518 K of 6–12 million filtered reads were aligned for each sample. Detection of known pathogens in control samples was most successful using PathoScope. In the CF sputa, alpha diversity measures varied based on the alignment method used, but similar trends were found between pre- and post-antibiotic samples. PathoScope outperformed Kraken and MetaPhlAn in our validation study of artificial bacterial community controls and also has advantages over Kraken and MetaPhlAn of being able to determine bacterial strains and the presence of fungal organisms. PathoScope can be confidently used when evaluating metagenomic data to determine CF airway microbiome diversity.