10 research outputs found
Monitoring soil biodiversity in nature reserves in England - a role for metabarcoding
Tullgren extracts of soil mesofauna are proving challenging to identify using trained volunteers. Could metabarcoding be a rapid, cost-effective approach for monitoring soil mesofauna and characterising their communities
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The pH optimum of soil exoenzymes adapt to long term changes in soil pH
Soil exoenzymes released by microorganisms break down organic matter and are crucial in regulating C, N and P cycling. Soil pH is known to influence enzyme activity, and is also a strong driver of microbial community composition; but little is known about how alterations in soil pH affect enzymatic activity and how this is mediated by microbial communities. To assess long term enzymatic adaptation to soil pH, we conducted enzyme assays at buffered pH levels on two historically managed soils maintained at either pH 5 or 7 from the Rothamsted Park Grass Long-term experiment. The pH optima for a range of exoenzymes involved in C, N, P cycling, differed between the two soils, the direction of the shift being toward the source soil pH, indicating the production of pH adapted isoenzymes by the soil microbial community. Soil bacterial and fungal communities determined by amplicon sequencing were clearly distinct between pH 5 and soil pH 7 soils, possibly explaining differences in enzymatic responses. Furthermore, ?-glucosidase gene sequences extracted from metagenomes revealed an increased abundance of Acidobacterial producers in the pH 5 soils, and Actinobacteria in pH 7 soils. Our findings demonstrate that the pH optimum of soil exoenzymes adapt to long term changes in soil pH, the direction being dependent on the soil pH shift; and we provide further evidence that changes in functional microbial communities may underpin this phenomena, though new research is now needed to directly link change in enzyme activity optima with microbial communities. More generally, our new findings have large implications for modelling the efficiency of different microbial enzymatic processes under changing environmental conditions
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Soil bacterial networks are less stable under drought than fungal networks
Soil microbial communities play a crucial role in ecosystem functioning, but it is unknown how co-occurrence networks within these communities respond to disturbances such as climate extremes. This represents an important knowledge gap because changes in microbial networks could have implications for their functioning and vulnerability to future disturbances. Here, we show in grassland mesocosms that drought promotes destabilising properties in soil bacterial, but not fungal, co-occurrence networks, and that changes in bacterial communities link more strongly to soil functioning during recovery than do changes in fungal communities. Moreover, we reveal that drought has a prolonged effect on bacterial communities and their co-occurrence networks via changes in vegetation composition and resultant reductions in soil moisture. Our results provide new insight in the mechanisms through which drought alters soil microbial communities with potential long-term consequences, including future plant community composition and the ability of aboveground and belowground communities to withstand future disturbances
Population-level faecal metagenomic profiling as a tool to predict antimicrobial resistance in Enterobacterales isolates causing invasive infections: an exploratory study across Cambodia, Kenya, and the UK
Background: Antimicrobial resistance (AMR) in Enterobacterales is a global health threat. Capacity for individual-level surveillance remains limited in many countries, whilst population-level surveillance approaches could inform empiric antibiotic treatment guidelines.
Methods: In this exploratory study, a novel approach to population-level prediction of AMR in Enterobacterales clinical isolates using metagenomic (Illumina) profiling of pooled DNA extracts from human faecal samples was developed and tested. Taxonomic and AMR gene profiles were used to derive taxonomy-adjusted population-level AMR metrics. Bayesian modelling, and model comparison based on cross-validation, were used to evaluate the capacity of each metric to predict the number of resistant Enterobacterales invasive infections at a population-level, using available bloodstream/cerebrospinal fluid infection data.
Findings: Population metagenomes comprised samples from 177, 157, and 156 individuals in Kenya, the UK, and Cambodia, respectively, collected between September 2014 and April 2016. Clinical data from independent populations included 910, 3356 and 197 bacterial isolates from blood/cerebrospinal fluid infections in Kenya, the UK and Cambodia, respectively (samples collected between January 2010 and May 2017). Enterobacterales were common colonisers and pathogens, and faecal taxonomic/AMR gene distributions and proportions of antimicrobial-resistant Enterobacterales infections differed by setting. A model including terms reflecting the metagenomic abundance of the commonest clinical Enterobacterales species, and of AMR genes known to either increase the minimum inhibitory concentration (MIC) or confer clinically-relevant resistance, had a higher predictive performance in determining population-level resistance in clinical Enterobacterales isolates compared to models considering only AMR gene information, only taxonomic information, or an intercept-only baseline model (difference in expected log predictive density compared to best model, estimated using leave-one-out cross-validation: intercept-only model = -223 [95% credible interval (CI): -330,-116]; model considering only AMR gene information = -186 [95% CI: -281,-91]; model considering only taxonomic information = -151 [95% CI: -232,-69]).
Interpretation: Whilst our findings are exploratory and require validation, intermittent metagenomics of pooled samples could represent an effective approach for AMR surveillance and to predict population-level AMR in clinical isolates, complementary to ongoing development of laboratory infrastructures processing individual samples
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Land use driven change in soil pH affects microbial carbon cycling processes
Soil microorganisms act as gatekeepers for soil–atmosphere carbon exchange by balancing the accumulation and release of soil organic matter. However, poor understanding of the mechanisms responsible hinders the development of effective land management strategies to enhance soil carbon storage. Here we empirically test the link between microbial ecophysiological traits and topsoil carbon content across geographically distributed soils and land use contrasts. We discovered distinct pH controls on microbial mechanisms of carbon accumulation. Land use intensification in low-pH soils that increased the pH above a threshold (~6.2) leads to carbon loss through increased decomposition, following alleviation of acid retardation of microbial growth. However, loss of carbon with intensification in near-neutral pH soils was linked to decreased microbial biomass and reduced growth efficiency that was, in turn, related to trade-offs with stress alleviation and resource acquisition. Thus, less-intensive management practices in near-neutral pH soils have more potential for carbon storage through increased microbial growth efficiency, whereas in acidic soils, microbial growth is a bigger constraint on decomposition rates
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Bio-Linux as a tool for bioinformatics training
Because of the ever-increasing application of next-generation sequencing (NGS) in research, and the expectation of faster experiment turn-around, it is becoming unfeasible and unscalable for analysis to be done exclusively by existing trained bioinformaticians. Instead, researchers and bench biologists are performing at least parts of most analyses. In order for this to be realized, two conditions must be satisfied: (1) well designed and accessible tools need to be made available, and (2) researchers and biologists need to be trained to use such tools in order to confidently handle high volumes of NGS data. Bio-Linux is a fully featured, powerful, configurable and easy to maintain bioinformatics workstation and helps on both counts by offering well over one hundred bioinformatics tools packaged into a single distribution, easily accessible and readily usable. Bio-Linux is also accessible in the form of virtual images or on the cloud, thus providing researchers with immediate access to scalable compute infrastructure required to run the analysis. Furthermore this paper discusses how bioinformatics training on Bio-Linux is helping to bridge the data production and analysis gap
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Nutrients influence the dynamics of Klebsiella pneumoniae carbapenemase producing enterobacterales in transplanted hospital sinks
Antimicrobial resistance has been recognized as a threat to human health. The role of hospital sinks acting as a reservoir for some of the most concerning antibiotic resistant organisms, carbapenemase producing Enterobacterales (CPE) is evident but not well understood. Strategies to prevent establishment, interventions to eliminate these reservoirs and factors which drive persistence of CPE are not well established. We use a uniquely designed sink lab to transplant CPE colonized hospital sink plumbing with an aim to understand CPE dynamics in a controlled setting, notably exploiting both molecular and culture techniques. After ex situ installation the CPE population in the sink plumbing drop from previously detectable to undetectable levels. The addition of nutrients is followed by a quick rebound in CPE detection in the sinks after as many as 37 days. We did not however detect a significant shift in microbial community structure or the overall resistance gene carriage in longitudinal samples from a subset of these transplanted sinks using whole shotgun metagenomic sequencing. Comparing nutrient types in a benchtop culture study model, protein rich nutrients appear to be the most supportive for CPE growth and biofilm formation ability. The role of nutrients exposure is determining factor for maintaining a high bioburden of CPE in the sink drains and P-traps. Therefore, limiting nutrient disposal into sinks has reasonable potential with regard to decreasing the CPE wastewater burden, especially in hospitals seeking to control an environmental reservoir
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The pH optimum of soil exoenzymes adapt to long term changes in soil pH
Soil exoenzymes released by microorganisms break down organic matter and are crucial in regulating C, N and P cycling. Soil pH is known to influence enzyme activity, and is also a strong driver of microbial community composition; but little is known about how alterations in soil pH affect enzymatic activity and how this is mediated by microbial communities. To assess long term enzymatic adaptation to soil pH, we conducted enzyme assays at buffered pH levels on two historically managed soils maintained at either pH 5 or 7 from the Rothamsted Park Grass Long-term experiment. The pH optima for a range of exoenzymes involved in C, N, P cycling, differed between the two soils, the direction of the shift being toward the source soil pH, indicating the production of pH adapted isoenzymes by the soil microbial community. Soil bacterial and fungal communities determined by amplicon sequencing were clearly distinct between pH 5 and soil pH 7 soils, possibly explaining differences in enzymatic responses. Furthermore, β-glucosidase gene sequences extracted from metagenomes revealed an increased abundance of Acidobacterial producers in the pH 5 soils, and Actinobacteria in pH 7 soils. Our findings demonstrate that the pH optimum of soil exoenzymes adapt to long term changes in soil pH, the direction being dependent on the soil pH shift; and we provide further evidence that changes in functional microbial communities may underpin this phenomena, though new research is now needed to directly link change in enzyme activity optima with microbial communities. More generally, our new findings have large implications for modelling the efficiency of different microbial enzymatic processes under changing environmental conditions
OTU tables and metadata belonging to De Vries et al. 2018
OTU tables and metadata for De Vries et al. 2018. See the paper for detailed description of experimental set up. See Readme.txt for a description of all variables