Proliferation and anatoxin production of benthic cyanobacteria associated with canine mortalities along a stream-lake continuum.

Proliferations of benthic cyanobacteria are increasingly in the public eye, with rising animal deaths associated with benthic rather than planktonic blooms. In early June 2021, two dogs died after consuming material on the shore of Shubenacadie Grand Lake, Nova Scotia. Preliminary investigations indicated anatoxins produced by benthic cyanobacterial mats were responsible for the deaths. In this study, we monitored the growth of a toxic benthic cyanobacterial species (Microcoleus sp.) along a stream-lake continuum where the canine poisonings occurred. We found that the species was able to proliferate in both lentic and lotic environments, but temporal growth dynamics and the predominant sub-species were influenced by habitat type, and differed with hydrodynamic setting, nutrient and sunlight availability. Toxin concentration was greatest in cyanobacterial mats growing in the oligotrophic lakeshore environment (maximum measured total anatoxins (ATXs) >20 mg·kg-1 wet weight). This corresponded with a shift in the profile of ATX analogues, which also indicated changing sub-species dominance along the stream-lake transition.

From defaults to databases: parameter and database choice dramatically impact the performance of metagenomic taxonomic classification tools.

In metagenomic analyses of microbiomes, one of the first steps is usually the taxonomic classification of reads by comparison to a database of previously taxonomically classified genomes. While different studies comparing metagenomic taxonomic classification methods have determined that different tools are 'best', there are two tools that have been used the most to-date: Kraken (k-mer-based classification against a user-constructed database) and MetaPhlAn (classification by alignment to clade-specific marker genes), the latest versions of which are Kraken2 and MetaPhlAn 3, respectively. We found large discrepancies in both the proportion of reads that were classified as well as the number of species that were identified when we used both Kraken2 and MetaPhlAn 3 to classify reads within metagenomes from human-associated or environmental datasets. We then investigated which of these tools would give classifications closest to the real composition of metagenomic samples using a range of simulated and mock samples and examined the combined impact of tool-parameter-database choice on the taxonomic classifications given. This revealed that there may not be a one-size-fits-all 'best' choice. While Kraken2 can achieve better overall performance, with higher precision, recall and F1 scores, as well as alpha- and beta-diversity measures closer to the known composition than MetaPhlAn 3, the computational resources required for this may be prohibitive for many researchers, and the default database and parameters should not be used. We therefore conclude that the best tool-parameter-database choice for a particular application depends on the scientific question of interest, which performance metric is most important for this question and the limit of available computational resources.

Personalised azithromycin+metronidazole (PAZAZ), in combination with standard induction therapy, to achieve a faecal microbiome community structure and metagenome changes associated with sustained remission in paediatric Crohn's disease (CD): protocol of a pilot study.

INTRODUCTION: Early relapse in Crohn's disease (CD) is associated with a more severe disease course. The microbiome plays a crucial role, yet strategies targeting the microbiome are underrepresented in current guidelines. We hypothesise that early manipulation of the microbiome will improve clinical response to standard-of-care (SOC) induction therapy in patients with a relapse-associated microbiome profile. We describe the protocol of a pilot study assessing feasibility of treatment allocation based on baseline faecal microbiome profiles. METHODS AND ANALYSIS: This is a 52-week, multicentre, randomised, controlled, open-label, add-on pilot study to test the feasibility of a larger multicontinent trial evaluating the efficacy of adjuvant antibiotic therapy in 20 paediatric patients with mild-to-moderate-CD (10

Anthropogenic activities and geographic locations regulate microbial diversity, community assembly and species sorting in Canadian and Indian freshwater lakes.

Freshwater lakes are important reservoirs and sources of drinking water globally. However, the microbiota, which supports the functionality of these ecosystems is threatened by the influx of nutrients, heavy metals and other toxic chemical substances from anthropogenic activities. The influence of these factors on the diversity, assembly mechanisms and co-occurrence patterns of bacterial communities in freshwater lakes is not clearly understood. Hence, samples were collected from six different impacted lakes in Canada and India and examined by 454-pyrosequencing technology. The trophic status of these lakes was determined using specific chemical parameters. Our results revealed that bacterial diversity and community composition was altered by both the lake water chemistry and geographic distance. Anthropogenic activities pervasively influenced species distribution. Dispersal limitation (32.3%), homogenous selection (31.8%) and drift (20%) accounted for the largest proportions of the bacterial community assembly mechanisms. Homogenous selection increased in lakes with higher nutrient concentration, while stochasticity reduced. Community functional profiles revealed that deterministic processes dominated the assembly mechanisms of phylotypes with higher potential for biodegradation, while stochasticity dominated the assembly of phylotypes with potential for antimicrobial resistance. Bacteroidota (44%) and Proteobacteria (34%) were the most abundant phyla. Co-occurrence network analysis revealed that complexity increased in more impacted lakes, while competition and the nature of anthropogenic activity contributed to species sorting. Overall, this study demonstrates that bacterial community changes in freshwater lakes are linked to anthropogenic activities, with corresponding consequences on the distribution of phylotypes of environmental and human health interest.

Microbiome differential abundance methods produce different results across 38 datasets.

Identifying differentially abundant microbes is a common goal of microbiome studies. Multiple methods are used interchangeably for this purpose in the literature. Yet, there are few large-scale studies systematically exploring the appropriateness of using these tools interchangeably, and the scale and significance of the differences between them. Here, we compare the performance of 14 differential abundance testing methods on 38 16S rRNA gene datasets with two sample groups. We test for differences in amplicon sequence variants and operational taxonomic units (ASVs) between these groups. Our findings confirm that these tools identified drastically different numbers and sets of significant ASVs, and that results depend on data pre-processing. For many tools the number of features identified correlate with aspects of the data, such as sample size, sequencing depth, and effect size of community differences. ALDEx2 and ANCOM-II produce the most consistent results across studies and agree best with the intersect of results from different approaches. Nevertheless, we recommend that researchers should use a consensus approach based on multiple differential abundance methods to help ensure robust biological interpretations.

Functional and Taxonomic Effects of Organic Amendments on the Restoration of Semiarid Quarry Soils.

The application of organic amendments to mining soils has been shown to be a successful method of restoration, improving key physicochemical soil properties. However, there is a lack of a clear understanding of the soil bacterial community taxonomic and functional changes that are brought about by these treatments. We present further metagenomic sequencing (MGS) profiling of the effects of different restoration treatments applied to degraded, arid quarry soils in southern Spain which had previously been profiled only with 16S rRNA gene (16S) and physicochemical analyses. Both taxonomic and functional MGS profiles showed clear separation of organic treatment amendments from control samples, and although taxonomic differences were quite clear, functional redundancy was higher than expected and the majority of the latter signal came from the aggregation of minor (<0.1%) community differences. Significant taxonomic differences were seen with the presumably less-biased MGS-for example, the phylum Actinobacteria and the two genera Chloracidobacterium (Acidobacteria) and Paenibacillus (Firmicutes) were determined to be major players by the MGS and this was consistent with their potential functional roles. The former phylum was much less present, and the latter two genera were either minor components or not detected in the 16S data. Mapping of reads to MetaCyc/BioCyc categories showed overall slightly higher biosynthesis and degradation capabilities in all treatments versus control soils, with sewage amendments showing highest values and vegetable-based amendments being at intermediate levels, matching higher nutrient levels, respiration rates, enzyme activities, and bacterial biomass previously observed in the treated soils. IMPORTANCE The restoration of soils impacted by human activities poses specific challenges regarding the reestablishment of functional microbial communities which will further support the reintroduction of plant species. Organic fertilizers, originating from either treated sewage or vegetable wastes, have shown promise in restoration experiments; however, we still do not have a clear understanding of the functional and taxonomic changes that occur during these treatments. We used metagenomics to profile restoration treatments applied to degraded, arid quarry soils in southern Spain. We found that the assortments of individual functions and taxa within each soil could clearly identify treatments, while at the same time they demonstrated high functional redundancy. Functions grouped into higher pathways tended to match physicochemical measurements made on the same soils. In contrast, significant taxonomic differences were seen when the treatments were previously studied with a single marker gene, highlighting the advantage of metagenomic analysis for complex soil communities.

Characterization of Bacterial Community Dynamics of the Human Mouth Throughout Decomposition via Metagenomic, Metatranscriptomic, and Culturing Techniques.

The postmortem microbiome has recently moved to the forefront of forensic research, and many studies have focused on the idea that predictable fluctuations in decomposer communities could be used as a "microbial clock" to determine time of death. Commonly, the oral microbiome has been evaluated using 16S rRNA gene sequencing to assess the changes in community composition throughout decomposition. We sampled the hard palates of three human donors over time to identify the prominent members of the microbiome. This study combined 16S rRNA sequencing with whole metagenomic (MetaG) and metatranscriptomic (MetaT) sequencing and culturing methodologies in an attempt to broaden current knowledge about how these postmortem microbiota change and might function throughout decomposition. In all four methods, Proteobacteria, Firmicutes, Actinobacteria, and Bacteroidetes were the dominant phyla, but their distributions were insufficient in separating samples based on decomposition stage or time or by donor. Better resolution was observed at the level of genus, with fresher samples from decomposition clustering away from others via principal components analysis (PCA) of the sequencing data. Key genera in driving these trends included Rothia; Lysinibacillus, Lactobacillus, Staphylococcus, and other Firmicutes; and yeasts including Candida and Yarrowia. The majority of cultures (89%) matched to sequences obtained from at least one of the sequencing methods, while 11 cultures were found in the same samples using all three methods. These included Acinetobacter gerneri, Comamonas terrigena, Morganella morganii, Proteus vulgaris, Pseudomonas koreensis, Pseudomonas moraviensis, Raoutella terrigena, Stenotrophomonas maltophilia, Bacillus cereus, Kurthia zopfii, and Lactobacillus paracasei. MetaG and MetaT data also revealed many novel insects as likely visitors to the donors in this study, opening the door to investigating them as potential vectors of microorganisms during decomposition. The presence of cultures at specific time points in decomposition, including samples for which we have MetaT data, will yield future studies tying specific taxa to metabolic pathways involved in decomposition. Overall, we have shown that our 16S rRNA sequencing results from the human hard palate are consistent with other studies and have expanded on the range of taxa shown to be associated with human decomposition, including eukaryotes, based on additional sequencing technologies.

Identifying biases and their potential solutions in human microbiome studies.

Advances in DNA sequencing technology have vastly improved the ability of researchers to explore the microbial inhabitants of the human body. Unfortunately, while these studies have uncovered the importance of these microbial communities to our health, they often do not result in similar findings. One possible reason for the disagreement in these results is due to the multitude of systemic biases that are introduced during sequence-based microbiome studies. These biases begin with sample collection and continue to be introduced throughout the entire experiment leading to an observed community that is significantly altered from the true underlying microbial composition. In this review, we will highlight the various steps in typical sequence-based human microbiome studies where significant bias can be introduced, and we will review the current efforts within the field that aim to reduce the impact of these biases. Video abstract.

Detection of Helicobacter pylori Microevolution and Multiple Infection from Gastric Biopsies by Housekeeping Gene Amplicon Sequencing.

Despite the great efforts devoted to research on Helicobacter pylori, the prevalence of single-strain infection or H. pylori mixed infection and its implications in the mode of transmission of this bacterium are still controversial. In this study, we explored the usefulness of housekeeping gene amplicon sequencing in the detection of H. pylori microevolution and multiple infections. DNA was extracted from five gastric biopsies from four patients infected with distinct histopathological diagnoses. PCR amplification of six H. pylori-specific housekeeping genes was then assessed on each sample. Optimal results were obtained for the cgt and luxS genes, which were selected for amplicon sequencing. A total of 11,833 cgt and 403 luxS amplicon sequences were obtained, 2042 and 112 of which were unique sequences, respectively. All cgt and luxS sequences were clustered at 97% to 9 and 13 operational taxonomic units (OTUs), respectively. For each sample from a different patient, a single OTU comprised the majority of sequences in both genes, but more than one OTU was detected in all samples. These results suggest that multiple infections with a predominant strain together with other minority strains are the main way by which H. pylori colonizes the human stomach.

Bacterial Taxa and Functions Are Predictive of Sustained Remission Following Exclusive Enteral Nutrition in Pediatric Crohn's Disease.

BACKGROUND: The gut microbiome is extensively involved in induction of remission in pediatric Crohn's disease (CD) patients by exclusive enteral nutrition (EEN). In this follow-up study of pediatric CD patients undergoing treatment with EEN, we employ machine learning models trained on baseline gut microbiome data to distinguish patients who achieved and sustained remission (SR) from those who did not achieve remission nor relapse (non-SR) by 24 weeks. METHODS: A total of 139 fecal samples were obtained from 22 patients (8-15 years of age) for up to 96 weeks. Gut microbiome taxonomy was assessed by 16S rRNA gene sequencing, and functional capacity was assessed by metagenomic sequencing. We used standard metrics of diversity and taxonomy to quantify differences between SR and non-SR patients and to associate gut microbial shifts with fecal calprotectin (FCP), and disease severity as defined by weighted Pediatric Crohn's Disease Activity Index. We used microbial data sets in addition to clinical metadata in random forests (RFs) models to classify treatment response and predict FCP levels. RESULTS: Microbial diversity did not change after EEN, but species richness was lower in low-FCP samples (<250 µg/g). An RF model using microbial abundances, species richness, and Paris disease classification was the best at classifying treatment response (area under the curve [AUC] = 0.9). KEGG Pathways also significantly classified treatment response with the addition of the same clinical data (AUC = 0.8). Top features of the RF model are consistent with previously identified IBD taxa, such as Ruminococcaceae and Ruminococcus gnavus. CONCLUSIONS: Our machine learning approach is able to distinguish SR and non-SR samples using baseline microbiome and clinical data.

Nitrate Consumers in Arctic Marine Eukaryotic Communities: Comparative Diversities of 18S rRNA, 18S rRNA Genes, and Nitrate Reductase Genes.

For photosynthetic microbial eukaryotes, the rate-limiting step in NO3- assimilation is its reduction to nitrite (NO2-), which is catalyzed by assimilatory nitrate reductase (NR). Oceanic productivity is primarily limited by available nitrogen and, although nitrate is the most abundant form of available nitrogen in oceanic waters, little is known about the identity of microbial eukaryotes that take up nitrate. This lack of knowledge is especially severe for ice-covered seas that are being profoundly affected by climate change. To address this, we examined the distribution and diversity of NR genes in the Arctic region by way of clone libraries and data mining of available metagenomes (total of 4.24 billion reads). We directly compared NR clone phylogenies with the V4 region of the 18S rRNA gene (DNA pool) and 18S rRNA (RNA pool) at two ice-influenced stations in the Canada Basin (Beaufort Sea). The communities from the two nucleic acid templates were similar at the level of major groups, and species identified by way of NR gene phylogeny and microscopy were a subset of the 18S results. Most NR genes from arctic clone libraries matched diatoms and chromist nanoflagellates, including novel clades, while the NR genes in arctic eukaryote metagenomes were dominated by chlorophyte NR, in keeping with the ubiquitous occurrence of Mamiellophyceae in the Arctic Ocean. Overall, these data suggest that a dynamic and mixed eukaryotic community utilizes nitrate across the Arctic region, and they show the potential utility of NR as a tool to identify ongoing changes in arctic photosynthetic communities.IMPORTANCE To better understand the diversity of primary producers in the Arctic Ocean, we targeted a nitrogen cycle gene, NR, which is required for phytoplankton to assimilate nitrate into organic forms of nitrogen macromolecules. We compared this to the more detailed taxonomy from ice-influenced stations using a general taxonomic gene (18S rRNA). NR genes were ubiquitous and could be classified as belonging to diatoms, dinoflagellates, other flagellates, chlorophytes, and unknown microbial eukaryotes, suggesting novel diversity of both species and metabolism in arctic phytoplankton.

Drivers of Regional Bacterial Community Structure and Diversity in the Northwest Atlantic Ocean.

The fundamental role of bacteria in global biogeochemical cycles warrants a thorough understanding of the factors controlling bacterial community structure. In this study, the integrated effect of seasonal differences and spatial distribution on bacterial community structure and diversity were investigated at the regional scale. We conducted a comprehensive bacterial survey, with 451 samples of the Scotian Shelf sector of the Northwest Atlantic Ocean during spring and fall of 2014 and 2016, to analyze the effects of physicochemical gradients on bacterial community structure. Throughout the region, Pelagibacteraceae and Rhodobacteraceae were the most common in the free-living fraction, while Flavobacteriia and Deltaproteobacteria were more abundant in the particle-associated fraction. Overall, there was strong covariation of the microbial community diversity from the two size fractions. This relationship existed despite the statistically significant difference in community structure between the free-living and particle-associated size fractions. In both size fractions, distribution patterns of bacterial taxa, and species within taxa, displayed temporal and spatial preferences. Distinct bacterial assemblages specific to season and depth in the water column were identified. These distinct assemblages, consistent for both 2014 and 2016, suggested replicable patterns in microbial communities for spring and fall in this region. Over all sites, temperature and oxygen values were highly correlated with community similarity, and salinity and oxygen values were the most strongly positively- and negatively correlated with alpha diversity, respectively. However, the strengths of these correlations depended on the depth and season sampled. The bathymetry of the Scotian Shelf, the abrupt shelf break to the Scotian Slope and the major ocean currents dominating in the region led to the formation of distinct on-shelf and off-shelf bacterial communities both in spring and fall. The highest species richness was observed at the shelf break, where water masses from the two major currents meet. Our study establishes the baseline for assessing future changes in the bacterial community of the Scotian Shelf waters, a rapidly changing sector of the Atlantic Ocean.

Processing a 16S rRNA Sequencing Dataset with the Microbiome Helper Workflow.

Sequencing microbiome samples has recently become a fast and cost-effective method to taxonomically profile communities. The growing interest in analyzing microbial sequencing data has attracted many new researchers to the field. Here, we present a straightforward bioinformatic pipeline that aims to streamline the processing of 16S rRNA sequencing data. This workflow is part of the larger project called Microbiome Helper (Comeau et al. mSyst 2:e00127-16, 2017), which includes other bioinformatic workflows, tutorials, and scripts available here: https://github.com/mlangill/microbiome_helper/wiki .

The Phosphate Binder Ferric Citrate Alters the Gut Microbiome in Rats with Chronic Kidney Disease.

In chronic kidney disease (CKD), the gut microbiome is altered and bacterial-derived uremic toxins promote systemic inflammation and cardiovascular disease. Ferric citrate complex is a dietary phosphate binder prescribed for patients with end-stage kidney disease to treat hyperphosphatemia and secondary hyperparathyroidism. Iron is an essential nutrient in both microbes and mammals. This study was undertaken to test the hypothesis that the large iron load administered with ferric citrate in CKD may significantly change the gut microbiome. Male Sprague-Dawley rats underwent 5/6 nephrectomy to induce CKD. Normal control and CKD rats were randomized to regular chow or a 4% ferric citrate diet for 6 weeks. Fecal and cecal microbial DNA was analyzed via 16S ribosomal RNA gene sequencing on the Illumina MiSeq system. CKD rats had lower abundances of Firmicutes and Lactobacillus compared with normal rats and had lower overall gut microbial diversity. CKD rats treated with ferric citrate had improved hemoglobin and creatinine clearance and amelioration of hyperphosphatemia and hypertension. Ferric citrate treatment increased bacterial diversity in CKD rats almost to levels observed in control rats. The tryptophanase-possessing families Verrucomicrobia, Clostridiaceae, and Enterobacteriaceae were increased by ferric citrate treatment. The uremic toxins indoxyl sulfate and p-cresyl sulfate were not increased with ferric citrate treatment. Verrucomicrobia was largely represented by Akkermansia muciniphila, which has important roles in mucin degradation and gut barrier integrity. In summary, ferric citrate therapy in CKD rats was associated with significant changes in the gut microbiome and beneficial kidney and blood pressure parameters.

The impact of chemerin or chemokine-like receptor 1 loss on the mouse gut microbiome.

Chemerin is an adipocyte derived signalling molecule (adipokine) that serves as a ligand activator of Chemokine-like receptor 1(CMKLR1). Chemerin/CMKLR1 signalling is well established to regulate fundamental processes in metabolism and inflammation. The composition and function of gut microbiota has also been shown to impact the development of metabolic and inflammatory diseases such as obesity, diabetes and inflammatory bowel disease. In this study, we assessed the microbiome composition of fecal samples isolated from wildtype, chemerin, or CMKLR1 knockout mice using Illumina-based sequencing. Moreover, the knockout mice and respective wildtype mice used in this study were housed at different universities allowing us to compare facility-dependent effects on microbiome composition. While there was no difference in alpha diversity within samples when compared by either facility or genotype, we observed a dramatic difference in the presence and abundance of numerous taxa between facilities. There were minor differences in bacterial abundance between wildtype and chemerin knockout mice, but significantly more differences in taxa abundance between wildtype and CMKLR1 knockout mice. Specifically, CMKLR1 knockout mice exhibited decreased abundance of Akkermansia and Prevotella, which correlated with body weight in CMKLR1 knockout, but not wildtype mice. This is the first study to investigate a linkage between chemerin/CMKLR1 signaling and microbiome composition. The results of our study suggest that chemerin/CMKLR1 signaling influences metabolic processes through effects on the gut microbiome. Furthermore, the dramatic difference in microbiome composition between facilities might contribute to discrepancies in the metabolic phenotype of CMKLR1 knockout mice reported by independent groups. Considered altogether, these findings establish a foundation for future studies to investigate the relationship between chemerin signaling and the gut microbiome on the development and progression of metabolic and inflammatory disease.

Denoising the Denoisers: an independent evaluation of microbiome sequence error-correction approaches.

High-depth sequencing of universal marker genes such as the 16S rRNA gene is a common strategy to profile microbial communities. Traditionally, sequence reads are clustered into operational taxonomic units (OTUs) at a defined identity threshold to avoid sequencing errors generating spurious taxonomic units. However, there have been numerous bioinformatic packages recently released that attempt to correct sequencing errors to determine real biological sequences at single nucleotide resolution by generating amplicon sequence variants (ASVs). As more researchers begin to use high resolution ASVs, there is a need for an in-depth and unbiased comparison of these novel "denoising" pipelines. In this study, we conduct a thorough comparison of three of the most widely-used denoising packages (DADA2, UNOISE3, and Deblur) as well as an open-reference 97% OTU clustering pipeline on mock, soil, and host-associated communities. We found from the mock community analyses that although they produced similar microbial compositions based on relative abundance, the approaches identified vastly different numbers of ASVs that significantly impact alpha diversity metrics. Our analysis on real datasets using recommended settings for each denoising pipeline also showed that the three packages were consistent in their per-sample compositions, resulting in only minor differences based on weighted UniFrac and Bray-Curtis dissimilarity. DADA2 tended to find more ASVs than the other two denoising pipelines when analyzing both the real soil data and two other host-associated datasets, suggesting that it could be better at finding rare organisms, but at the expense of possible false positives. The open-reference OTU clustering approach identified considerably more OTUs in comparison to the number of ASVs from the denoising pipelines in all datasets tested. The three denoising approaches were significantly different in their run times, with UNOISE3 running greater than 1,200 and 15 times faster than DADA2 and Deblur, respectively. Our findings indicate that, although all pipelines result in similar general community structure, the number of ASVs/OTUs and resulting alpha-diversity metrics varies considerably and should be considered when attempting to identify rare organisms from possible background noise.

Adaptive Mutations in Influenza A/California/07/2009 Enhance Polymerase Activity and Infectious Virion Production.

Mice are not natural hosts for influenza A viruses (IAVs), but they are useful models for studying antiviral immune responses and pathogenesis. Serial passage of IAV in mice invariably causes the emergence of adaptive mutations and increased virulence. Here, we report the adaptation of IAV reference strain A/California/07/2009(H1N1) (also known as CA/07) in outbred Swiss Webster mice. Serial passage led to increased virulence and lung titers, and dissemination of the virus to brains. We adapted a deep-sequencing protocol to identify and enumerate adaptive mutations across all genome segments. Among mutations that emerged during mouse-adaptation, we focused on amino acid substitutions in polymerase subunits: polymerase basic-1 (PB1) T156A and F740L and polymerase acidic (PA) E349G. These mutations were evaluated singly and in combination in minigenome replicon assays, which revealed that PA E349G increased polymerase activity. By selectively engineering three PB1 and PA mutations into the parental CA/07 strain, we demonstrated that these mutations in polymerase subunits decreased the production of defective viral genome segments with internal deletions and dramatically increased the release of infectious virions from mouse cells. Together, these findings increase our understanding of the contribution of polymerase subunits to successful host adaptation.

Exacerbation induces a microbiota shift in sputa of COPD patients.

Little is known about the microbiota shift induced by exacerbation in chronic obstructive pulmonary disease (COPD) patients. The sputa microbiota of COPD patients was evaluated when clinically stable and during acute exacerbations of the disease. Sputa microbiota was analyzed using 16S ribosomal RNA gene pyrosequencing and quantitative polymerase chain reaction-based pathogen detection. Nine COPD patients were enrolled. Pyrosequencing of 16S rRNA genes identified 2,267 unique bacterial operational taxonomic units. Principal microbiota shifts during exacerbation were in either Proteobacteria, Firmicutes or Bacteroidetes. Streptococcus and Moraxella levels were detected during exacerbation in severe (Global Initiative for Chronic Obstructive Lung Disease 3) COPD patients. Most of the clinically-important genera found in the sputum with the pyrosequencing of 16S rRNA gene correlated with specific quantitative polymerase chain reactions for bacteria while respiratory viruses were nearly absent. Sputum microbiotas of exacerbated COPD patients are complex. This pilot study shows a clear shift in the microbiota of patients during exacerbation. The nature of this shift varies from patient to patient in such a way that the treatment should be patient-specific. Further studies are needed to establish the impact of microbial exacerbations on the pulmonary microbiota.

Multi-omics differentially classify disease state and treatment outcome in pediatric Crohn's disease.

BACKGROUND: Crohn's disease (CD) has an unclear etiology, but there is growing evidence of a direct link with a dysbiotic microbiome. Many gut microbes have previously been associated with CD, but these have mainly been confounded with patients' ongoing treatments. Additionally, most analyses of CD patients' microbiomes have focused on microbes in stool samples, which yield different insights than profiling biopsy samples. RESULTS: We sequenced the 16S rRNA gene (16S) and carried out shotgun metagenomics (MGS) from the intestinal biopsies of 20 treatment-naïve CD and 20 control pediatric patients. We identified the abundances of microbial taxa and inferred functional categories within each dataset. We also identified known human genetic variants from the MGS data. We then used a machine learning approach to determine the classification accuracy when these datasets, collapsed to different hierarchical groupings, were used independently to classify patients by disease state and by CD patients' response to treatment. We found that 16S-identified microbes could classify patients with higher accuracy in both cases. Based on follow-ups with these patients, we identified which microbes and functions were best for predicting disease state and response to treatment, including several previously identified markers. By combining the top features from all significant models into a single model, we could compare the relative importance of these predictive features. We found that 16S-identified microbes are the best predictors of CD state whereas MGS-identified markers perform best for classifying treatment response. CONCLUSIONS: We demonstrate for the first time that useful predictors of CD treatment response can be produced from shotgun MGS sequencing of biopsy samples despite the complications related to large proportions of host DNA. The top predictive features that we identified in this study could be useful for building an improved classifier for CD and treatment response based on sufferers' microbiome in the future. The BISCUIT project is funded by a Clinical Academic Fellowship from the Chief Scientist Office (Scotland)-CAF/08/01.