Microbial catabolic activities are naturally selected by metabolic energy harvest rate

The fundamental trade-off between yield and rate of energy harvest per unit of substrate has been largely discussed as a main characteristic for microbial established cooperation or competition. In this study, this point is addressed by developing a generalized model that simulates competition between existing and not experimentally reported microbial catabolic activities defined only based on well-known biochemical pathways. No specific microbial physiological adaptations are considered, growth yield is calculated coupled to catabolism energetics and a common maximum biomass-specific catabolism rate (expressed as electron transfer rate) is assumed for all microbial groups. Under this approach, successful microbial metabolisms are predicted in line with experimental observations under the hypothesis of maximum energy harvest rate. Two microbial ecosystems, typically found in wastewater treatment plants, are simulated, namely: (i) the anaerobic fermentation of glucose and (ii) the oxidation and reduction of nitrogen under aerobic autotrophic (nitrification) and anoxic heterotrophic and autotrophic (denitrification) conditions. The experimentally observed cross feeding in glucose fermentation, through multiple intermediate fermentation pathways, towards ultimately methane and carbon dioxide is predicted. Analogously, two-stage nitrification (by ammonium and nitrite oxidizers) is predicted as prevailing over nitrification in one stage. Conversely, denitrification is predicted in one stage (by denitrifiers) as well as anammox (anaerobic ammonium oxidation). The model results suggest that these observations are a direct consequence of the different energy yields per electron transferred at the different steps of the pathways. Overall, our results theoretically support the hypothesis that successful microbial catabolic activities are selected by an overall maximum energy harvest rate

Policing the legume-Rhizobium symbiosis: a critical test of partner choice

In legume-Rhizobium symbioses, specialised soil bacteria fix atmospheric nitrogen in return for carbon. However, ineffective strains can arise, making discrimination essential. Discrimination can occur via partner choice, where legumes prevent ineffective strains from entering, or via sanctioning, where plants provide fewer resources. Several studies have inferred that legumes exercise partner choice, but the rhizobia compared were not otherwise isogenic. To test when and how plants discriminate ineffective strains we developed sets of fixing and non-fixing strains that differed only in the expression of nifH - essential for nitrogen fixation - and could be visualised using marker genes. We show that the plant is unable to select against the non-fixing strain at the point of entry, but that non-fixing nodules are sanctioned. We also used the technique to characterise mixed nodules (containing both a fixing and a non-fixing strain), whose frequency could be predicted using a simple diffusion model. We discuss that sanctioning is likely to evolve in preference to partner choice in any symbiosis where partner quality cannot be adequately assessed until goods or services are actively exchanged

Transformation of PVP coated silver nanoparticles in a simulated wastewater treatment process and the effect on microbial communities

Extent: 18p.Background: Manufactured silver nanoparticles (AgNPs) are one of the most commonly used nanomaterials in consumer goods and consequently their concentrations in wastewater and hence wastewater treatment plants are predicted to increase. We investigated the fate of AgNPs in sludge that was subjected to aerobic and anaerobic treatment and the impact of AgNPs on microbial processes and communities. The initial identification of AgNPs in sludge was carried out using transmission electron microscopy (TEM) with energy dispersive X-ray (EDX) analysis. The solid phase speciation of silver in sludge and wastewater influent was then examined using X-ray absorption spectroscopy (XAS). The effects of transformed AgNPs (mainly Ag-S phases) on nitrification, wastewater microbial populations and, for the first time, methanogenesis was investigated. Results: Sequencing batch reactor experiments and anaerobic batch tests, both demonstrated that nitrification rate and methane production were not affected by the addition of AgNPs [at 2.5 mg Ag L-1 (4.9 g L-1 total suspended solids, TSS) and 183.6 mg Ag kg -1 (2.9 g kg-1 total solids, TS), respectively]. The low toxicity is most likely due to AgNP sulfidation. XAS analysis showed that sulfur bonded Ag was the dominant Ag species in both aerobic (activated sludge) and anaerobic sludge. In AgNP and AgNO3 spiked aerobic sludge, metallic Ag was detected (~15%). However, after anaerobic digestion, Ag(0) was not detected by XAS analysis. Dominant wastewater microbial populations were not affected by AgNPs as determined by DNA extraction and pyrotag sequencing. However, there was a shift in niche populations in both aerobic and anaerobic sludge, with a shift in AgNP treated sludge compared with controls. This is the first time that the impact of transformed AgNPs (mainly Ag-S phases) on anaerobic digestion has been reported. Conclusions: Silver NPs were transformed to Ag-S phases during activated sludge treatment (prior to anaerobic digestion). Transformed AgNPs, at predicted future Ag wastewater concentrations, did not affect nitrification or methanogenesis. Consequently, AgNPs are very unlikely to affect the efficient functioning of wastewater treatment plants. However, AgNPs may negatively affect sub-dominant wastewater microbial communities.Casey L Doolette, Mike J McLaughlin, Jason K Kirby, Damien J Batstone, Hugh H Harris, Huoqing Ge and Geert Corneli

Finding Diagnostically Useful Patterns in Quantitative Phenotypic Data.

Trio-based whole-exome sequence (WES) data have established confident genetic diagnoses in ∼40% of previously undiagnosed individuals recruited to the Deciphering Developmental Disorders (DDD) study. Here we aim to use the breadth of phenotypic information recorded in DDD to augment diagnosis and disease variant discovery in probands. Median Euclidean distances (mEuD) were employed as a simple measure of similarity of quantitative phenotypic data within sets of ≥10 individuals with plausibly causative de novo mutations (DNM) in 28 different developmental disorder genes. 13/28 (46.4%) showed significant similarity for growth or developmental milestone metrics, 10/28 (35.7%) showed similarity in HPO term usage, and 12/28 (43%) showed no phenotypic similarity. Pairwise comparisons of individuals with high-impact inherited variants to the 32 individuals with causative DNM in ANKRD11 using only growth z-scores highlighted 5 likely causative inherited variants and two unrecognized DNM resulting in an 18% diagnostic uplift for this gene. Using an independent approach, naive Bayes classification of growth and developmental data produced reasonably discriminative models for the 24 DNM genes with sufficiently complete data. An unsupervised naive Bayes classification of 6,993 probands with WES data and sufficient phenotypic information defined 23 in silico syndromes (ISSs) and was used to test a "phenotype first" approach to the discovery of causative genotypes using WES variants strictly filtered on allele frequency, mutation consequence, and evidence of constraint in humans. This highlighted heterozygous de novo nonsynonymous variants in SPTBN2 as causative in three DDD probands

Advancing microbial sciences by individual-based modelling

Remarkable technological advances have revealed ever more properties and behaviours of individual microorganisms, but the novel data generated by these techniques have not yet been fully exploited. In this Opinion article, we explain how individual-based models (IBMs) can be constructed based on the findings of such techniques and how they help to explore competitive and cooperative microbial interactions. Furthermore, we describe how IBMs have provided insights into self-organized spatial patterns from biofilms to the oceans of the world, phage-CRISPR dynamics and other emergent phenomena. Finally, we discuss how combining individual-based observations with IBMs can advance our understanding at both the individual and population levels, leading to the new approach of microbial individual-based ecology (μIBE)