Metabolic flexibility as a major predictor of spatial distribution in microbial communities

A better understand the ecology of microbes and their role in the global ecosystem could be achieved if traditional ecological theories can be applied to microbes. In ecology organisms are defined as specialists or generalists according to the breadth of their niche. Spatial distribution is often used as a proxy measure of niche breadth; generalists have broad niches and a wide spatial distribution and specialists a narrow niche and spatial distribution. Previous studies suggest that microbial distribution patterns are contrary to this idea; a microbial generalist genus (Desulfobulbus) has a limited spatial distribution while a specialist genus (Methanosaeta) has a cosmopolitan distribution. Therefore, we hypothesise that this counter-intuitive distribution within generalist and specialist microbial genera is a common microbial characteristic. Using molecular fingerprinting the distribution of four microbial genera, two generalists, Desulfobulbus and the methanogenic archaea Methanosarcina, and two specialists, Methanosaeta and the sulfate-reducing bacteria Desulfobacter were analysed in sediment samples from along a UK estuary. Detected genotypes of both generalist genera showed a distinct spatial distribution, significantly correlated with geographic distance between sites. Genotypes of both specialist genera showed no significant differential spatial distribution. These data support the hypothesis that the spatial distribution of specialist and generalist microbes does not match that seen with specialist and generalist large organisms. It may be that generalist microbes, while having a wider potential niche, are constrained, possibly by intrageneric competition, to exploit only a small part of that potential niche while specialists, with far fewer constraints to their niche, are more capable of filling their potential niche more effectively, perhaps by avoiding intrageneric competition. We suggest that these counter-intuitive distribution patterns may be a common feature of microbes in general and represent a distinct microbial principle in ecology, which is a real challenge if we are to develop a truly inclusive ecology

Microbiome profiling by Illumina sequencing of combinatorial sequence-tagged PCR products

We developed a low-cost, high-throughput microbiome profiling method that uses combinatorial sequence tags attached to PCR primers that amplify the rRNA V6 region. Amplified PCR products are sequenced using an Illumina paired-end protocol to generate millions of overlapping reads. Combinatorial sequence tagging can be used to examine hundreds of samples with far fewer primers than is required when sequence tags are incorporated at only a single end. The number of reads generated permitted saturating or near-saturating analysis of samples of the vaginal microbiome. The large number of reads al- lowed an in-depth analysis of errors, and we found that PCR-induced errors composed the vast majority of non-organism derived species variants, an ob- servation that has significant implications for sequence clustering of similar high-throughput data. We show that the short reads are sufficient to assign organisms to the genus or species level in most cases. We suggest that this method will be useful for the deep sequencing of any short nucleotide region that is taxonomically informative; these include the V3, V5 regions of the bac- terial 16S rRNA genes and the eukaryotic V9 region that is gaining popularity for sampling protist diversity.Comment: 28 pages, 13 figure

Soil bacterial and fungal communities across a pH gradient in an arable soil

Soils collected across a long-term liming experiment (pH 4.0-8.3), in which variation in factors other than pH have been minimized, were used to investigate the direct influence of pH on the abundance and composition of the two major soil microbial taxa, fungi and bacteria. We hypothesized that bacterial communities would be more strongly influenced by pH than fungal communities. To determine the relative abundance of bacteria and fungi, we used quantitative PCR (qPCR), and to analyze the composition and diversity of the bacterial and fungal communities, we used a bar-coded pyrosequencing technique. Both the relative abundance and diversity of bacteria were positively related to pH, the latter nearly doubling between pH 4 and 8. In contrast, the relative abundance of fungi was unaffected by pH and fungal diversity was only weakly related with pH. The composition of the bacterial communities was closely defined by soil pH; there was as much variability in bacterial community composition across the 180-m distance of this liming experiment as across soils collected from a wide range of biomes in North and South America, emphasizing the dominance of pH in structuring bacterial communities. The apparent direct influence of pH on bacterial community composition is probably due to the narrow pH ranges for optimal growth of bacteria. Fungal community composition was less strongly affected by pH, which is consistent with pure culture studies, demonstrating that fungi generally exhibit wider pH ranges for optimal growth. The ISME Journal (2010) 4, 1340-1351; doi: 10.1038/ismej.2010.58; published online 6 May 2010&nbsp

Chemical Elemental Distribution and Soil DNA Fingerprints Provide the Critical Evidence in Murder Case Investigation

Background: The scientific contribution to the solution of crime cases, or throughout the consequent forensic trials, is a crucial aspect of the justice system. The possibility to extract meaningful information from trace amounts of samples, and to match and validate evidences with robust and unambiguous statistical tests, are the key points of such process. The present report is the authorized disclosure of an investigation, carried out by Attorney General appointment, on a murder case in northern Italy, which yielded the critical supporting evidence for the judicial trial. Methodology/Principal Findings: The proportional distribution of 54 chemical elements and the bacterial community DNA fingerprints were used as signature markers to prove the similarity of two soil samples. The first soil was collected on the crime scene, along a corn field, while the second was found in trace amounts on the carpet of a car impounded from the main suspect in a distant location. The matching similarity of the two soils was proven by crossing the results of two independent techniques: a) elemental analysis via inductively coupled plasma mass spectrometry (ICP-MS) and optical emission spectrometry (ICP-OES) approaches, and b) amplified ribosomal DNA restriction analysis by gel electrophoresis (ARDRA). Conclusions: Besides introducing the novel application of these methods to forensic disciplines, the highly accurate level of resolution observed, opens new possibilities also in the fields of soil typing and tracking, historical analyses, geochemical surveys and global land mapping

The impact of different DNA extraction kits and laboratories upon the assessment of human gut microbiota composition by 16S rRNA gene sequencing

Peer reviewedPublisher PD

Pyrosequencing-Based Assessment of Bacterial Community Structure Along Different Management Types in German Forest and Grassland Soils

BACKGROUND: Soil bacteria are important drivers for nearly all biogeochemical cycles in terrestrial ecosystems and participate in most nutrient transformations in soil. In contrast to the importance of soil bacteria for ecosystem functioning, we understand little how different management types affect the soil bacterial community composition. METHODOLOGY/PRINCIPAL FINDINGS: We used pyrosequencing-based analysis of the V2-V3 16S rRNA gene region to identify changes in bacterial diversity and community structure in nine forest and nine grassland soils from the Schwäbische Alb that covered six different management types. The dataset comprised 598,962 sequences that were affiliated to the domain Bacteria. The number of classified sequences per sample ranged from 23,515 to 39,259. Bacterial diversity was more phylum rich in grassland soils than in forest soils. The dominant taxonomic groups across all samples (>1% of all sequences) were Acidobacteria, Alphaproteobacteria, Actinobacteria, Betaproteobacteria, Deltaproteobacteria, Gammaproteobacteria, and Firmicutes. Significant variations in relative abundances of bacterial phyla and proteobacterial classes, including Actinobacteria, Firmicutes, Verrucomicrobia, Cyanobacteria, Gemmatimonadetes and Alphaproteobacteria, between the land use types forest and grassland were observed. At the genus level, significant differences were also recorded for the dominant genera Phenylobacter, Bacillus, Kribbella, Streptomyces, Agromyces, and Defluviicoccus. In addition, soil bacterial community structure showed significant differences between beech and spruce forest soils. The relative abundances of bacterial groups at different taxonomic levels correlated with soil pH, but little or no relationships to management type and other soil properties were found. CONCLUSIONS/SIGNIFICANCE: Soil bacterial community composition and diversity of the six analyzed management types showed significant differences between the land use types grassland and forest. Furthermore, bacterial community structure was largely driven by tree species and soil pH

Microbial Biogeography of Public Restroom Surfaces

We spend the majority of our lives indoors where we are constantly exposed to bacteria residing on surfaces. However, the diversity of these surface-associated communities is largely unknown. We explored the biogeographical patterns exhibited by bacteria across ten surfaces within each of twelve public restrooms. Using high-throughput barcoded pyrosequencing of the 16 S rRNA gene, we identified 19 bacterial phyla across all surfaces. Most sequences belonged to four phyla: Actinobacteria, Bacteriodetes, Firmicutes and Proteobacteria. The communities clustered into three general categories: those found on surfaces associated with toilets, those on the restroom floor, and those found on surfaces routinely touched with hands. On toilet surfaces, gut-associated taxa were more prevalent, suggesting fecal contamination of these surfaces. Floor surfaces were the most diverse of all communities and contained several taxa commonly found in soils. Skin-associated bacteria, especially the Propionibacteriaceae, dominated surfaces routinely touched with our hands. Certain taxa were more common in female than in male restrooms as vagina-associated Lactobacillaceae were widely distributed in female restrooms, likely from urine contamination. Use of the SourceTracker algorithm confirmed many of our taxonomic observations as human skin was the primary source of bacteria on restroom surfaces. Overall, these results demonstrate that restroom surfaces host relatively diverse microbial communities dominated by human-associated bacteria with clear linkages between communities on or in different body sites and those communities found on restroom surfaces. More generally, this work is relevant to the public health field as we show that human-associated microbes are commonly found on restroom surfaces suggesting that bacterial pathogens could readily be transmitted between individuals by the touching of surfaces. Furthermore, we demonstrate that we can use high-throughput analyses of bacterial communities to determine sources of bacteria on indoor surfaces, an approach which could be used to track pathogen transmission and test the efficacy of hygiene practices

Indigenous Populations of Three Closely Related Lysobacter spp. in Agricultural Soils Using Real-Time PCR

Previous research had shown that three closely related species of Lysobacter, i.e., Lysobacter antibioticus, Lysobacter capsici, and Lysobacter gummosus, were present in different Rhizoctonia-suppressive soils. However, the population dynamics of these three Lysobacter spp. in different habitats remains unknown. Therefore, a specific primer–probe combination was designed for the combined quantification of these three Lysobacter spp. using TaqMan. Strains of the three target species were efficiently detected with TaqMan, whereas related non-target strains of Lysobacter enzymogenes and Xanthomonas campestris were not or only weakly amplified. Indigenous Lysobacter populations were analyzed in soils of 10 organic farms in the Netherlands during three subsequent years with TaqMan. These soils differed in soil characteristics and crop rotation. Additionally, Lysobacter populations in rhizosphere and bulk soil of different crops on one of these farms were studied. In acid sandy soils low Lysobacter populations were present, whereas pH neutral clay soils contained high populations (respectively, <4.0–5.87 and 6.22–6.95 log gene copy numbers g−1 soil). Clay content, pH and C/N ratio, but not organic matter content in soil, correlated with higher Lysobacter populations. Unexpectedly, different crops did not significantly influence population size of the three Lysobacter spp. and their populations were barely higher in rhizosphere than in bulk soil

A pilot randomized controlled study of the mental health first aid elearning course with UK medical students

Background: Medical students face many barriers to seeking out professional help for their mental health, including stigma relating to mental illness, and often prefer to seek support and advice from fellow students. Improving medical students’ mental health literacy and abilities to support someone experiencing a mental health problem could reduce barriers to help seeking and improve mental health in this population. Mental Health First Aid (MHFA) is an evidence-based intervention designed to improve mental health literacy and ability to respond to someone with a mental health problem. This pilot randomised controlled trial aims to evaluate the MHFA eLearning course in UK medical students. Methods: Fifty-five medical students were randomised to receive six weeks access to the MHFA eLearning course (n = 27) or to a no-access control group (n = 28). Both groups completed baseline (pre-randomisation) and follow-up (six weeks post-randomisation) online questionnaires measuring recognition of a mental health problem, mental health first aid intentions, confidence to help a friend experiencing a mental health problem, and stigmatising attitudes. Course feedback was gathered at follow-up. Results: More participants were lost follow-up in the MHFA group (51.9%) compared to control (21.4%). Both intention-to-treat (ITT) and non-ITT analyses showed that the MHFA intervention improved mental health first aid intentions (p = <.001) and decreased stigmatising attitudes towards people with mental health problems (p = .04). While ITT analysis found no significant Group x Time interaction for confidence to help a friend, the non-ITT analysis did show the intervention improved confidence to help a friend with mental health problems (p =<.001), and improved mental health knowledge (p = .003). Medical students in the intervention group reported a greater number of actual mental health first aid actions at follow-up (p = .006). Feedback about the MHFA course was generally positive, with participants stating it helped improve their knowledge and confidence to help someone. Conclusion: This pilot study demonstrated the potential for the MHFA eLearning course to improve UK medical students’ mental health first aid skills, confidence to help a friend and stigmatising attitudes. It could be useful in supporting their own and others’ mental health while studying and in their future healthcare careers

Boolean analysis reveals systematic interactions among low-abundance species in the human gut microbiome

The analysis of microbiome compositions in the human gut has gained increasing interest due to the broader availability of data and functional databases and substantial progress in data analysis methods, but also due to the high relevance of the microbiome in human health and disease. While most analyses infer interactions among highly abundant species, the large number of low-abundance species has received less attention. Here we present a novel analysis method based on Boolean operations applied to microbial co-occurrence patterns. We calibrate our approach with simulated data based on a dynamical Boolean network model from which we interpret the statistics of attractor states as a theoretical proxy for microbiome composition. We show that for given fractions of synergistic and competitive interactions in the model our Boolean abundance analysis can reliably detect these interactions. Analyzing a novel data set of 822 microbiome compositions of the human gut, we find a large number of highly significant synergistic interactions among these low-abundance species, forming a connected network, and a few isolated competitive interactions