Improved detection of disease-associated gut microbes using 16S sequence-based biomarkers.

BACKGROUND: Sequencing partial 16S rRNA genes is a cost effective method for quantifying the microbial composition of an environment, such as the human gut. However, downstream analysis relies on binning reads into microbial groups by either considering each unique sequence as a different microbe, querying a database to get taxonomic labels from sequences, or clustering similar sequences together. However, these approaches do not fully capture evolutionary relationships between microbes, limiting the ability to identify differentially abundant groups of microbes between a diseased and control cohort. We present sequence-based biomarkers (SBBs), an aggregation method that groups and aggregates microbes using single variants and combinations of variants within their 16S sequences. We compare SBBs against other existing aggregation methods (OTU clustering and Microphenoor DiTaxa features) in several benchmarking tasks: biomarker discovery via permutation test, biomarker discovery via linear discriminant analysis, and phenotype prediction power. We demonstrate the SBBs perform on-par or better than the state-of-the-art methods in biomarker discovery and phenotype prediction. RESULTS: On two independent datasets, SBBs identify differentially abundant groups of microbes with similar or higher statistical significance than existing methods in both a permutation-test-based analysis and using linear discriminant analysis effect size. . By grouping microbes by SBB, we can identify several differentially abundant microbial groups (FDR <.1) between children with autism and neurotypical controls in a set of 115 discordant siblings. Porphyromonadaceae, Ruminococcaceae, and an unnamed species of Blastocystis were significantly enriched in autism, while Veillonellaceae was significantly depleted. Likewise, aggregating microbes by SBB on a dataset of obese and lean twins, we find several significantly differentially abundant microbial groups (FDR<.1). We observed Megasphaera andSutterellaceae highly enriched in obesity, and Phocaeicola significantly depleted. SBBs also perform on bar with or better than existing aggregation methods as features in a phenotype prediction model, predicting the autism phenotype with an ROC-AUC score of .64 and the obesity phenotype with an ROC-AUC score of .84. CONCLUSIONS: SBBs provide a powerful method for aggregating microbes to perform differential abundance analysis as well as phenotype prediction. Our source code can be freely downloaded from http://github.com/briannachrisman/16s_biomarkers .

Piphillin predicts metagenomic composition and dynamics from DADA2-corrected 16S rDNA sequences.

BACKGROUND: Shotgun metagenomic sequencing reveals the potential in microbial communities. However, lower-cost 16S ribosomal RNA (rRNA) gene sequencing provides taxonomic, not functional, observations. To remedy this, we previously introduced Piphillin, a software package that predicts functional metagenomic content based on the frequency of detected 16S rRNA gene sequences corresponding to genomes in regularly updated, functionally annotated genome databases. Piphillin (and similar tools) have previously been evaluated on 16S rRNA data processed by the clustering of sequences into operational taxonomic units (OTUs). New techniques such as amplicon sequence variant error correction are in increased use, but it is unknown if these techniques perform better in metagenomic content prediction pipelines, or if they should be treated the same as OTU data in respect to optimal pipeline parameters. RESULTS: To evaluate the effect of 16S rRNA sequence analysis method (clustering sequences into OTUs vs amplicon sequence variant error correction into amplicon sequence variants (ASVs)) on the ability of Piphillin to predict functional metagenomic content, we evaluated Piphillin-predicted functional content from 16S rRNA sequence data processed through OTU clustering and error correction into ASVs compared to corresponding shotgun metagenomic data. We show a strong correlation between metagenomic data and Piphillin-predicted functional content resulting from both 16S rRNA sequence analysis methods. Differential abundance testing with Piphillin-predicted functional content exhibited a low false positive rate (< 0.05) while capturing a large fraction of the differentially abundant features resulting from corresponding metagenomic data. However, Piphillin prediction performance was optimal at different cutoff parameters depending on 16S rRNA sequence analysis method. Using data analyzed with amplicon sequence variant error correction, Piphillin outperformed comparable tools, for instance exhibiting 19% greater balanced accuracy and 54% greater precision compared to PICRUSt2. CONCLUSIONS: Our results demonstrate that raw Illumina sequences should be processed for subsequent Piphillin analysis using amplicon sequence variant error correction (with DADA2 or similar methods) and run using a 99% ID cutoff for Piphillin, while sequences generated on platforms other than Illumina should be processed via OTU clustering (e.g., UPARSE) and run using a 96% ID cutoff for Piphillin. Piphillin is publicly available for academic users (Piphillin server. http://piphillin.secondgenome.com/.).

Correction to: Piphillin predicts metagenomic composition and dynamics from DADA2- corrected 16S rDNA sequences.

Following the publication of this article [1], the authors reported errors in Figs. 1, 2 and 5. Due to a typesetting error the asterisks denoting significance were missing from the published figures.

Immune Response and Mortality Risk Relate to Distinct Lung Microbiomes in Patients with HIV and Pneumonia.

RATIONALE: The potential role of the airway microbiota in dictating immune responses and infection outcomes in HIV-associated pneumonia is largely unknown. OBJECTIVES: To investigate whether microbiologically and immunologically distinct subsets of patients with HIV and pneumonia exist and are related to mortality. METHODS: Bronchoalveolar lavage samples from Ugandan patients with HIV and pneumonia (n = 182) were obtained at study enrollment (following antibiotic treatment); patient demographics including 8- and 70-day mortality were collected. Lower airway bacterial community composition was assessed via amplification and sequencing of the V4 region of the 16S ribosomal RNA gene. Host immune response gene expression profiles were generated by quantitative polymerase chain reaction using RNA extracted from bronchoalveolar lavage fluid. Liquid and gas chromatography mass spectrometry was used to profile serum metabolites. MEASUREMENTS AND MAIN RESULTS: Based on airway microbiome composition, most patients segregated into three distinct groups, each of which were predicted to encode metagenomes capable of producing metabolites characteristically enriched in paired serum samples from these patients. These three groups also exhibited differences in mortality; those with the highest rate had increased ceftriaxone administration and culturable Aspergillus, and demonstrated significantly increased induction of airway T-helper cell type 2 responses. The group with the lowest mortality was characterized by increased expression of T-cell immunoglobulin and mucin domain 3, which down-regulates T-helper cell type 1 proinflammatory responses and is associated with chronic viral infection. CONCLUSIONS: These data provide evidence that compositionally and structurally distinct lower airway microbiomes are associated with discrete local host immune responses, peripheral metabolic reprogramming, and different rates of mortality.

StrainSelect: A novel microbiome reference database that disambiguates all bacterial strains, genome assemblies and extant cultures worldwide.

Motivation: Microbial metagenomic profiling software and databases are advancing rapidly for development of novel disease biomarkers and therapeutics yet three problems impede analyses: 1) the conflation of "genome assembly" and "strain" in reference databases; 2) difficulty connecting DNA biomarkers to a procurable strain for laboratory experimentation; and 3) absence of a comprehensive and unified strain-resolved reference database for integrating both shotgun metagenomics and 16S rRNA gene data. Results: We demarcated 681,087 strains, the largest collection of its kind, by filtering public data into a knowledge graph of vertices representing contiguous DNA sequences, genome assemblies, strain monikers and bio-resource center (BRC) catalog numbers then adding inter-vertex edges only for synonyms or direct derivatives. Surprisingly, for 10,043 important strains, we found replicate RefSeq genome assemblies obstructing interpretation of database searches. We organized each strain into eight taxonomic ranks with bootstrap confidence inversely correlated with genome assembly contamination. The StrainSelect database is suited for applications where a taxonomic, functional or procurement reference is needed for shotgun or amplicon metagenomics since 636,568 strains have at least one 16S rRNA gene, 245,005 have at least one annotated genome assembly, and 36,671 are procurable from at least one BRC. The database overcomes all three aforementioned problems since it disambiguates strains from assemblies, locates strains at BRCs, and unifies a taxonomic reference for both 16S rRNA and shotgun metagenomics. Availability: The StrainSelect database is available in igraph and tabular vertex-edge formats compatible with Neo4J. Dereplicated MinHash and fasta databases are distributed for sourmash and usearch pipelines at http://strainselect.secondgenome.com. Contact:todd.desantis@gmail.com. Supplementary information: Supplementary data are available online.

Topic modeling for multi-omic integration in the human gut microbiome and implications for Autism.

While healthy gut microbiomes are critical to human health, pertinent microbial processes remain largely undefined, partially due to differential bias among profiling techniques. By simultaneously integrating multiple profiling methods, multi-omic analysis can define generalizable microbial processes, and is especially useful in understanding complex conditions such as Autism. Challenges with integrating heterogeneous data produced by multiple profiling methods can be overcome using Latent Dirichlet Allocation (LDA), a promising natural language processing technique that identifies topics in heterogeneous documents. In this study, we apply LDA to multi-omic microbial data (16S rRNA amplicon, shotgun metagenomic, shotgun metatranscriptomic, and untargeted metabolomic profiling) from the stool of 81 children with and without Autism. We identify topics, or microbial processes, that summarize complex phenomena occurring within gut microbial communities. We then subset stool samples by topic distribution, and identify metabolites, specifically neurotransmitter precursors and fatty acid derivatives, that differ significantly between children with and without Autism. We identify clusters of topics, deemed "cross-omic topics", which we hypothesize are representative of generalizable microbial processes observable regardless of profiling method. Interpreting topics, we find each represents a particular diet, and we heuristically label each cross-omic topic as: healthy/general function, age-associated function, transcriptional regulation, and opportunistic pathogenesis.

Oral and airway microbiota in HIV-infected pneumonia patients.

Despite the increased frequency of recurrent pneumonia in HIV-infected patients and recent studies linking the airway bacterial community (microbiota) to acute and chronic respiratory infection, little is known of the oral and airway microbiota that exist in these individuals and their propensity to harbor pathogens despite antimicrobial treatment for acute pneumonia. This pilot study compared paired samples of the oral and airway microbiota from 15 hospitalized HIV-infected patients receiving antimicrobial treatment for acute pneumonia. Total DNA was extracted, bacterial burden was assessed by quantitative PCR, and amplified 16S rRNA was profiled for microbiome composition using a phylogenetic microarray (16S rRNA PhyloChip). Though the bacterial burden of the airway was significantly lower than that of the oral cavity, microbiota in both niches were comparably diverse. However, oral and airway microbiota exhibited niche specificity. Oral microbiota were characterized by significantly increased relative abundance of multiple species associated with the mouth, including members of the Bacteroides, Firmicutes, and TM7 phyla, while airway microbiota were primarily characterized by a relative expansion of the Proteobacteria. Twenty-two taxa were detected in both niches, including Streptococcus bovis and Chryseobacterium species, pathogens associated with HIV-infected populations. In addition, we compared the airway microbiota of five of these patients to those of five non-HIV-infected pneumonia patients from a previous study. Compared to the control population, HIV-infected patients exhibited relative increased abundance of a large number of phylogenetically distinct taxa, which included several known or suspected pathogenic organisms, suggesting that recurrent pneumonia in HIV-infected populations may be related to the presence of these species.

Strain level and comprehensive microbiome analysis in inflammatory bowel disease via multi-technology meta-analysis identifies key bacterial influencers of disease.

Objective: Inflammatory bowel disease (IBD) is a heterogenous disease in which the microbiome has been shown to play an important role. However, the precise homeostatic or pathological functions played by bacteria remain unclear. Most published studies report taxa-disease associations based on single-technology analysis of a single cohort, potentially biasing results to one clinical protocol, cohort, and molecular analysis technology. To begin to address this key question, precise identification of the bacteria implicated in IBD across cohorts is necessary. Methods: We sought to take advantage of the numerous and diverse studies characterizing the microbiome in IBD to develop a multi-technology meta-analysis (MTMA) as a platform for aggregation of independently generated datasets, irrespective of DNA-profiling technique, in order to uncover the consistent microbial modulators of disease. We report the largest strain-level survey of IBD, integrating microbiome profiles from 3,407 samples from 21 datasets spanning 15 cohorts, three of which are presented for the first time in the current study, characterized using three DNA-profiling technologies, mapping all nucleotide data against known, culturable strain reference data. Results: We identify several novel IBD associations with culturable strains that have so far remained elusive, including two genome-sequenced but uncharacterized Lachnospiraceae strains consistently decreased in both the gut luminal and mucosal contents of patients with IBD, and demonstrate that these strains are correlated with inflammation-related pathways that are known mechanisms targeted for treatment. Furthermore, comparative MTMA at the species versus strain level reveals that not all significant strain associations resulted in a corresponding species-level significance and conversely significant species associations are not always re-captured at the strain level. Conclusion: We propose MTMA for uncovering experimentally testable strain-disease associations that, as demonstrated here, are beneficial in discovering mechanisms underpinning microbiome impact on disease or novel targets for therapeutic interventions.

Multi-angle meta-analysis of the gut microbiome in Autism Spectrum Disorder: a step toward understanding patient subgroups.

Observational studies have shown that the composition of the human gut microbiome in children diagnosed with Autism Spectrum Disorder (ASD) differs significantly from that of their neurotypical (NT) counterparts. Thus far, reported ASD-specific microbiome signatures have been inconsistent. To uncover reproducible signatures, we compiled 10 publicly available raw amplicon and metagenomic sequencing datasets alongside new data generated from an internal cohort (the largest ASD cohort to date), unified them with standardized pre-processing methods, and conducted a comprehensive meta-analysis of all taxa and variables detected across multiple studies. By screening metadata to test associations between the microbiome and 52 variables in multiple patient subsets and across multiple datasets, we determined that differentially abundant taxa in ASD versus NT children were dependent upon age, sex, and bowel function, thus marking these variables as potential confounders in case-control ASD studies. Several taxa, including the strains Bacteroides stercoris t__190463 and Clostridium M bolteae t__180407, and the species Granulicatella elegans and Massilioclostridium coli, exhibited differential abundance in ASD compared to NT children only after subjects with bowel dysfunction were removed. Adjusting for age, sex and bowel function resulted in adding or removing significantly differentially abundant taxa in ASD-diagnosed individuals, emphasizing the importance of collecting and controlling for these metadata. We have performed the largest (n = 690) and most comprehensive systematic analysis of ASD gut microbiome data to date. Our study demonstrated the importance of accounting for confounding variables when designing statistical comparative analyses of ASD- and NT-associated gut bacterial profiles. Mitigating these confounders identified robust microbial signatures across cohorts, signifying the importance of accounting for these factors in comparative analyses of ASD and NT-associated gut profiles. Such studies will advance the understanding of different patient groups to deliver appropriate therapeutics by identifying microbiome traits germane to the specific ASD phenotype.

Pneumonia surveillance with culture-independent metatranscriptomics in HIV-positive adults in Uganda: a cross-sectional study.

BACKGROUND: Pneumonia is a leading cause of death worldwide and is a major health-care challenge in people living with HIV. Despite this, the causes of pneumonia in this population remain poorly understood. We aimed to assess the feasibility of metatranscriptomics for epidemiological surveillance of pneumonia in patients with HIV in Uganda. METHODS: We performed a retrospective observational study in patients with HIV who were admitted to Mulago Hospital, Kampala, Uganda between Oct 1, 2009, and Dec 31, 2011. Inclusion criteria were age 18 years or older, HIV-positivity, and clinically diagnosed pneumonia. Exclusion criteria were contraindication to bronchoscopy or an existing diagnosis of tuberculosis. Bronchoalveolar lavage fluid was collected within 72 h of admission and a combination of RNA sequencing and Mycobacterium tuberculosis culture plus PCR were performed. The primary outcome was detection of an established or possible respiratory pathogen in the total study population. FINDINGS: We consecutively enrolled 217 patients during the study period. A potential microbial cause for pneumonia was identified in 211 (97%) patients. At least one microorganism of established respiratory pathogenicity was identified in 113 (52%) patients, and a microbe of possible pathogenicity was identified in an additional 98 (45%). M tuberculosis was the most commonly identified established pathogen (35 [16%] patients; in whom bacterial or viral co-infections were identified in 13 [37%]). Streptococcus mitis, although not previously reported as a cause of pneumonia in patients with HIV, was the most commonly identified bacterial organism (37 [17%] patients). Haemophilus influenzae was the most commonly identified established bacterial pathogen (20 [9%] patients). Pneumocystis jirovecii was only identified in patients with a CD4 count of less than 200 cells per mL. INTERPRETATION: We show the feasibility of using metatranscriptomics for epidemiologic surveillance of pneumonia by describing the spectrum of respiratory pathogens in adults with HIV in Uganda. Applying these methods to a contemporary cohort could enable broad assessment of changes in pneumonia aetiology following the emergence of SARS-CoV-2. FUNDING: US National Institutes of Health, Chan Zuckerberg Biohub.

Comparison of the specificities and efficacies of primers for aromatic dioxygenase gene analysis of environmental samples.

Aromatic dioxygenase genes have long been of interest for bioremediation and aromatic carbon cycling studies. To date, 115 primers and probes have been designed and used to analyze dioxygenase gene diversities in environmental samples. Here we analyze those primers' specificities, coverages, and PCR product lengths compared to known aromatic dioxygenase genes based on in silico analysis as well as summarize their differing advantages or effectiveness from over 50 reported experimental studies. We also provide some guidance for primer use in future studies.

Novel biphenyl-oxidizing bacteria and dioxygenase genes from a korean tidal mudflat.

Gene-targeted FLX titanium pyrosequencing integrated with stable isotope probing (SIP) using [(13)C]biphenyl substrate revealed that tidal mudflat sediments harbor novel aromatic ring hydroxylating dioxygenases (ARHD). More than 80% of the detected ARHD genes comprise four clades (0.5 distance) with 49 to 70% amino acid identity to sequences in public databases. The 16S rRNA sequences enriched in the (13)C fraction were from the Betaproteobacteria, bacilli (primarily Paenibacillus-like), and unclassified phyla.

Prevalence of depressive symptoms in Japanese male patients with chronic obstructive pulmonary disease.

AIM: The objective of this study was to utilize commonly applied tools, the Hospital Anxiety and Depression Scale - Depression subscale (HADS-D) and the Center for Epidemiological Studies Depression Scale (CES-D), to screen for depressive symptoms in patients with stable chronic obstructive pulmonary disease (COPD). Furthermore, we sought to identify whether differences existed in the prevalence of depressive symptoms as assessed by CES-D and HADS-D, and predictors of depressive symptoms. METHODS: The presence of depressive symptoms in 80 outpatients and 51 inpatients with stable COPD was assessed using the CES-D and HADS-D. Data regarding sex, educational level, body mass index, smoking index and pulmonary function were obtained to evaluate their independent contribution as predictors of depressive symptoms. RESULTS: The prevalence of depressive symptoms was 29.8% based on CES-D and 40.5% based on HADS-D. A MacNemar test of COPD severity and analysis of the results of depressive symptoms based on CES-D and HADS-D revealed significant differences. Logistic regression analysis suggested that 'severity' is a predictor of depressive symptoms as assessed by CES-D, whereas 'body mass index', 'education level' and 'setting' were predictors of depressive symptoms as assessed by HADS-D. CONCLUSIONS: The prevalence of depressive symptoms differed when assessed with CES-D and HADS-D. The reasons behind this difference include the fact that HADS-D frequently detected depressive symptoms in patients with mild COPD as well as a tendency for HADS-D to be strongly influenced by education levels. In contrast, the severity of COPD was reflected in CES-D. It is possible that prevalence of depressive symptoms differs in accordance with the applied screening tool.

Longitudinal study of stool-associated microbial taxa in sibling pairs with and without autism spectrum disorder.

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder influenced by both genetic and environmental factors. Recently, gut dysbiosis has emerged as a powerful contributor to ASD symptoms. In this study, we recruited over 100 age-matched sibling pairs (between 2 and 8 years old) where one had an Autism ASD diagnosis and the other was developing typically (TD) (432 samples total). We collected stool samples over four weeks, tracked over 100 lifestyle and dietary variables, and surveyed behavior measures related to ASD symptoms. We identified 117 amplicon sequencing variants (ASVs) that were significantly different in abundance between sibling pairs across all three timepoints, 11 of which were supported by at least two contrast methods. We additionally identified dietary and lifestyle variables that differ significantly between cohorts, and further linked those variables to the ASVs they statistically relate to. Overall, dietary and lifestyle features were explanatory of ASD phenotype using logistic regression, however, global compositional microbiome features were not. Leveraging our longitudinal behavior questionnaires, we additionally identified 11 ASVs associated with changes in reported anxiety over time within and across all individuals. Lastly, we find that overall microbiome composition (beta-diversity) is associated with specific ASD-related behavioral characteristics.

A Comparative Evaluation of Tools to Predict Metabolite Profiles From Microbiome Sequencing Data.

Metabolomic analyses of human gut microbiome samples can unveil the metabolic potential of host tissues and the numerous microorganisms they support, concurrently. As such, metabolomic information bears immense potential to improve disease diagnosis and therapeutic drug discovery. Unfortunately, as cohort sizes increase, comprehensive metabolomic profiling becomes costly and logistically difficult to perform at a large scale. To address these difficulties, we tested the feasibility of predicting the metabolites of a microbial community based solely on microbiome sequencing data. Paired microbiome sequencing (16S rRNA gene amplicons, shotgun metagenomics, and metatranscriptomics) and metabolome (mass spectrometry and nuclear magnetic resonance spectroscopy) datasets were collected from six independent studies spanning multiple diseases. We used these datasets to evaluate two reference-based gene-to-metabolite prediction pipelines and a machine-learning (ML) based metabolic profile prediction approach. With the pre-trained model on over 900 microbiome-metabolome paired samples, the ML approach yielded the most accurate predictions (i.e., highest F1 scores) of metabolite occurrences in the human gut and outperformed reference-based pipelines in predicting differential metabolites between case and control subjects. Our findings demonstrate the possibility of predicting metabolites from microbiome sequencing data, while highlighting certain limitations in detecting differential metabolites, and provide a framework to evaluate metabolite prediction pipelines, which will ultimately facilitate future investigations on microbial metabolites and human health.

Development of an oligonucleotide microarray to detect di- and monooxygenase genes for benzene degradation in soil.

Diverse environmental genes have been identified recently. To characterize their functions, it is necessary to understand which genes and what combinations of those genes are responsible for the biodegradation of soil contaminants. In this article, a 60-mer oligonucleotide microarray was constructed to simultaneously detect di- and monooxygenase genes for benzene and related compounds. In total, 148 probes were designed and validated by pure-culture hybridizations using the following criteria to discriminate between highly homologous genes: < or =53-bp identities and < or =25-bp continuous stretch to nontarget sequences. Microarray hybridizations were performed using PCR products amplified from five benzene-amended soils and two oil-contaminated soils. Six of the probes gave a positive signal for more than six soils; thus, they may represent key sequences for benzene degradation in the environment. The microarray developed in this study will be a powerful tool for the screening of key genes involved in benzene degradation and for the rapid profiling of benzene oxygenase gene diversity in contaminated soils.

Piphillin: Improved Prediction of Metagenomic Content by Direct Inference from Human Microbiomes.

Functional analysis of a clinical microbiome facilitates the elucidation of mechanisms by which microbiome perturbation can cause a phenotypic change in the patient. The direct approach for the analysis of the functional capacity of the microbiome is via shotgun metagenomics. An inexpensive method to estimate the functional capacity of a microbial community is through collecting 16S rRNA gene profiles then indirectly inferring the abundance of functional genes. This inference approach has been implemented in the PICRUSt and Tax4Fun software tools. However, those tools have important limitations since they rely on outdated functional databases and uncertain phylogenetic trees and require very specific data pre-processing protocols. Here we introduce Piphillin, a straightforward algorithm independent of any proposed phylogenetic tree, leveraging contemporary functional databases and not obliged to any singular data pre-processing protocol. When all three inference tools were evaluated against actual shotgun metagenomics, Piphillin was superior in predicting gene composition in human clinical samples compared to both PICRUSt and Tax4Fun (p<0.01 and p<0.001, respectively) and Piphillin's ability to predict disease associations with specific gene orthologs exhibited a 15% increase in balanced accuracy compared to PICRUSt. From laboratory animal samples, no performance advantage was observed for any one of the tools over the others and for environmental samples all produced unsatisfactory predictions. Our results demonstrate that functional inference using the direct method implemented in Piphillin is preferable for clinical biospecimens. Piphillin is publicly available for academic use at http://secondgenome.com/Piphillin.

Sphingomonas wittichii Strain RW1 Genome-Wide Gene Expression Shifts in Response to Dioxins and Clay.

Sphingomonas wittichii strain RW1 (RW1) is one of the few strains that can grow on dibenzo-p-dioxin (DD). We conducted a transcriptomic study of RW1 using RNA-Seq to outline transcriptional responses to DD, dibenzofuran (DF), and the smectite clay mineral saponite with succinate as carbon source. The ability to grow on DD is rare compared to growth on the chemically similar DF even though the same initial dioxygenase may be involved in oxidation of both substrates. Therefore, we hypothesized the reason for this lies beyond catabolic pathways and may concern genes involved in processes for cell-substrate interactions such as substrate recognition, transport, and detoxification. Compared to succinate (SUC) as control carbon source, DF caused over 240 protein-coding genes to be differentially expressed, whereas more than 300 were differentially expressed with DD. Stress response genes were up-regulated in response to both DD and DF. This effect was stronger with DD than DF, suggesting a higher toxicity of DD compared to DF. Both DD and DF caused changes in expression of genes involved in active cross-membrane transport such as TonB-dependent receptor proteins, but the patterns of change differed between the two substrates. Multiple transcription factor genes also displayed expression patterns distinct to DD and DF growth. DD and DF induced the catechol ortho- and the salicylate/gentisate pathways, respectively. Both DD and DF induced the shared down-stream aliphatic intermediate compound pathway. Clay caused category-wide down-regulation of genes for cell motility and chemotaxis, particularly those involved in the synthesis, assembly and functioning of flagella. This is an environmentally important finding because clay is a major component of soil microbes' microenvironment influencing local chemistry and may serve as a geosorbent for toxic pollutants. Similar to clay, DD and DF also affected expression of genes involved in motility and chemotaxis.

The lung microbiome of Ugandan HIV-infected pneumonia patients is compositionally and functionally distinct from that of San Franciscan patients.

Sub-Saharan Africa represents 69% of the total number of individuals living with HIV infection worldwide and 72% of AIDS deaths globally. Pulmonary infection is a common and frequently fatal complication, though little is known regarding the lower airway microbiome composition of this population. Our objectives were to characterize the lower airway microbiome of Ugandan HIV-infected patients with pneumonia, to determine relationships with demographic, clinical, immunological, and microbiological variables and to compare the composition and predicted metagenome of these communities to a comparable cohort of patients in the US (San Francisco). Bronchoalveolar lavage samples from a cohort of 60 Ugandan HIV-infected patients with acute pneumonia were collected. Amplified 16S ribosomal RNA was profiled and aforementioned relationships examined. Ugandan airway microbiome composition and predicted metagenomic function were compared to US HIV-infected pneumonia patients. Among the most common bacterial pulmonary pathogens, Pseudomonas aeruginosa was most prevalent in the Ugandan cohort. Patients with a richer and more diverse airway microbiome exhibited lower bacterial burden, enrichment of members of the Lachnospiraceae and sulfur-reducing bacteria and reduced expression of TNF-alpha and matrix metalloproteinase-9. Compared to San Franciscan patients, Ugandan airway microbiome was significantly richer, and compositionally distinct with predicted metagenomes that encoded a multitude of distinct pathogenic pathways e.g secretion systems. Ugandan pneumonia-associated airway microbiome is compositionally and functionally distinct from those detected in comparable patients in developed countries, a feature which may contribute to adverse outcomes in this population.

Relationship between depression in patients with COPD and the percent of predicted FEV(1), BODE index, and health-related quality of life.

BACKGROUND: We investigated the prevalence of depression among patients with COPD treated in long-term in-patient rehabilitation facilities, using the Center for Epidemiologic Studies Depression scale (CES-D). Furthermore, the relationship between the severity of air-flow obstruction (the percent of predicted FEV(1)), BODE (body mass index, degree of air-flow obstruction, dyspnea, exercise capacity) index, health-related quality of life (St George's Respiratory Questionnaire [SGRQ]), and depression were investigated. METHODS: We recruited 74 in-patients (64 males, 10 females) with COPD. The mean age of the subjects was 72.7 years (range 52-85 y). Subjects completed the CES-D, and measurements were made of pulmonary function, body mass index, Modified Medical Research Council dyspnea scale, 6-min walk test (6MWT), and SGRQ. RESULTS: Depression was evident in 48.6% (n = 36) of the subjects. A weak correlation was found between the CES-D scores and the percent of predicted FEV(1). The prevalence of depression showed a significant association with BODE stage. Scores for the SGRQ activity and impacts domains, and total SGRQ score were significantly worse in the subjects who were depressed. CONCLUSIONS: We found a high prevalence of depression among patients with stable COPD treated in long-term in-patient rehabilitation facilities. Depression among these patients, as measured by the CES-D, was associated with greater impairment in respiratory function and with poorer Modified Medical Research Council dyspnea scale and SGRQ scores. The prevalence of depression increased with BODE stage.