Application of a Novel Hybrid CNN-GNN for Peptide Ion Encoding.

Almost all state-of-the-art de novo peptide sequencing algorithms now use machine learning models to encode fragment peaks and hence identify amino acids in mass spectrometry (MS) spectra. Previous work has highlighted how the inherent MS challenges of noise and missing peptide peaks detrimentally affect the performance of these models. In the present research we extracted and evaluated the encoding modules from 3 state-of-the-art de novo peptide sequencing algorithms. We also propose a convolutional neural network-graph neural network machine learning model for encoding peptide ions in tandem MS spectra. We compared the proposed encoding module to those used in the state-of-the-art de novo peptide sequencing algorithms by assessing their ability to identify b-ions and y-ions in MS spectra. This included a comprehensive evaluation in both real and artificial data across various levels of noise and missing peptide peaks. The proposed model performed best across all data sets using two different metrics (area under the receiver operating characteristic curve (AUC) and average precision). The work also highlighted the effect of including additional features such as intensity rank in these encoding modules as well as issues with using the AUC as a metric. This work is of significance to those designing future de novo peptide identification algorithms as it is the first step toward a new approach.

Impacts of metal stress on extracellular microbial products, and potential for selective metal recovery.

Harnessing microbial capabilities for metal recovery from secondary waste sources is an eco-friendly and sustainable approach for the management of metal-containing wastes. Soluble microbial products (SMP) and extracellular polymeric substances (EPS) are the two main groups of extracellular compounds produced by microorganisms in response to metal stress that are of great importance for remediation and recovery of metals. These include various high-, and low, molecular weight components, which serve various functional and structural roles. These compounds often contain functional groups with metal binding potential that can attenuate metal stress by sequestering metal ions, making them less bioavailable. Microorganisms can regulate the content and composition of EPS and SMP in response to metal stress in order to increase the compounds specificity and capacity for metal binding. Thus, EPS and SMP represent ideal candidates for developing technologies for selective metal recovery from complex wastes. To discover highly metal-sorptive compounds with specific metal binding affinity for metal recovery applications, it is necessary to investigate the metal binding affinity of these compounds, especially under metal stressed conditions. In this review we critically reviewed microbial EPS and SMP production as a response to metal stress with a particular emphasis on the metal binding properties of these compounds and their role in altering metal bioavailability. Furthermore, for the first time, we compiled the available data on potential application of these compounds for selective metal recovery from waste streams.

Chordomics: a visualization tool for linking function to phylogeny in microbiomes.

SUMMARY: The overarching aim of microbiome analysis is to uncover the links between microbial phylogeny and function in order to access ecosystem functioning. This can be done using several experimental strategies targeting different biomolecules, including DNA (metagenomics), RNA (metatranscriptomics) and proteins (metaproteomics). Despite the importance of linking microbial function to phylogeny there are currently no visualization tools that effectively integrate this information. Chordomics is a Shiny-based application for linked -omics data analysis, allowing users to visualize microbial function and phylogeny on a single plot and compare datasets across time and environments. AVAILABILITY AND IMPLEMENTATION: Chordomics is available on GitHub: https://github.com/kevinmcdonnell6/chordomics; software is coded in R and JavaScript and a demonstration version is available at https://kmcd.shinyapps.io/ChordomicsDemo/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Application of plasma activated water for decontamination of alfalfa and mung bean seeds.

Microbial contamination of fresh produce is a major public health concern, with the number of associated disease outbreaks increasing in recent years. The consumption of sprouted beans and seeds is of particular concern, as these foodstuffs are generally consumed raw, and are produced in conditions favourable for the growth of zoonotic pathogens, if present in seeds prior to sprouting or in irrigation water. This work aimed to evaluate the activity of plasma activated water (PAW) as a disinfecting agent for alfalfa (Medicago sativa) and mung bean (Vigna radiata) seeds, during seed soaking. Each seed type was inoculated with Escherichia coli O157, E. coli O104, Listeria monocytogenes or Salmonella Montevideo, and treated with PAW for different times. A combination of PAW and ultrasound treatment was also evaluated. The germination and growth rate of both seeds were assessed after PAW treatments. PAW was demonstrated to have disinfecting ability on sprouted seeds, with reductions of up to Log10 1.67 cfu/g in alfalfa seeds inoculated with E. coli O104, and a reduction of Log10 1.76 cfu/g for mung bean seeds inoculated with E. coli O157 observed. The germination and growth rate of alfalfa and mung bean sprouts were not affected by the PAW treatments. The combination of a PAW treatment and ultrasound resulted in increased antimicrobial activity, with a reduction of Log10 3.48 cfu/g of S. Montevideo in mung bean seeds observed. These results demonstrate the potential for PAW to be used for the inactivation of pathogenic microorganisms which may be present on sprouted seeds and beans, thereby providing greater assurance of produce safety.

riboSeed: leveraging prokaryotic genomic architecture to assemble across ribosomal regions.

The vast majority of bacterial genome sequencing has been performed using Illumina short reads. Because of the inherent difficulty of resolving repeated regions with short reads alone, only ∼10% of sequencing projects have resulted in a closed genome. The most common repeated regions are those coding for ribosomal operons (rDNAs), which occur in a bacterial genome between 1 and 15 times, and are typically used as sequence markers to classify and identify bacteria. Here, we exploit the genomic context in which rDNAs occur across taxa to improve assembly of these regions relative to de novo sequencing by using the conserved nature of rDNAs across taxa and the uniqueness of their flanking regions within a genome. We describe a method to construct targeted pseudocontigs generated by iteratively assembling reads that map to a reference genome's rDNAs. These pseudocontigs are then used to more accurately assemble the newly sequenced chromosome. We show that this method, implemented as riboSeed, correctly bridges across adjacent contigs in bacterial genome assembly and, when used in conjunction with other genome polishing tools, can assist in closure of a genome.

Microbial Contamination of Fresh Produce: What, Where, and How?

Promotion of healthier lifestyles has led to an increase in consumption of fresh produce. Such foodstuffs may expose consumers to increased risk of foodborne disease, as often they are not subjected to processing steps to ensure effective removal or inactivation of pathogenic microorganisms before consumption. Consequently, reports of ready-to-eat fruit and vegetable related disease outbreak occurrences have increased substantially in recent years, and information regarding these events is often not readily available. Identifying the nature and source of microbial contamination of these foodstuffs is critical for developing appropriate mitigation measures to be implemented by food producers. This review aimed to identify the foodstuffs most susceptible to microbial contamination and the microorganisms responsible for disease outbreaks from information available in peer-reviewed scientific publications. A total of 571 outbreaks were identified from 1980 to 2016, accounting for 72,855 infections and 173 deaths. Contaminated leafy green vegetables were responsible for 51.7% of reported outbreaks. Contaminated soft fruits caused 27.8% of infections. Pathogenic strains of Escherichia coli and Salmonella, norovirus, and hepatitis A accounted for the majority of cases. Large outbreaks resulted in particular biases such as the observation that contaminated sprouted plants caused 31.8% of deaths. Where known, contamination mainly occurred via contaminated seeds, water, and contaminated food handlers. There is a critical need for standardized datasets regarding all aspects of disease outbreaks, including how foodstuffs are contaminated with pathogenic microorganisms. Providing food business operators with this knowledge will allow them to implement better strategies to improve safety and quality of fresh produce.

Stabilisation of spent mushroom substrate for application as a plant growth-promoting organic amendment.

Over three million tonnes of spent mushroom substrate (SMS) are produced in Europe every year as a by-product of the cultivation of Agaricus bisporus. The management of SMS has become an increasing challenge for the mushroom production industry, and finding environmentally and economically sustainable solutions for this organic residue is, therefore, highly desirable. Due to its physical properties and nutrient content, SMS has great potential to be employed in agricultural and horticultural sectors, and further contribute to reduce the use of non-renewable resources, such as peat. However, SMS is often regarded as not being stable and/or mature, which hampers its wide use for crop production. Here, we demonstrate the stabilisation of SMS and its subsequent use as organic fertiliser and partial peat replacement in horticulture. The stabilisation was performed in a laboratory-scale composting system, with controlled temperature and aeration. Physical and chemical parameters were monitored during composting and provided information on the progress of the process. Water soluble carbohydrates (WSC) content was found to be the most reliable parameter to predict SMS stability. In situ oxygen consumption indicated the main composting phases, reflecting major changes in microbial activity. The structure of the bacterial community was also found to be a potential predictor of stability, as the compositional changes followed the composting progress. By contrast, the fungal community did not present clear successional process along the experiment. Maturity and quality of the stabilised SMS were assessed in a horticultural growing trial. When used as the sole fertiliser source, SMS was able to support Lolium multiflorum (Italian ryegrass) growth and significantly improved grass yield with a concentration-dependent response, increasing grass biomass up to 300%, when compared to the untreated control. In summary, the results indicated that the method employed was efficient in generating a stable and mature product, which has a great potential to be applied in horticulture. This study represents a step forward in the management of SMS residue, and also provides an alternative to reduce the use of peat in horticulture, alleviating environmental impacts to peatland ecosystems.

Agricultural anaerobic digestion power plants in Ireland and Germany: policy and practice.

The process of anaerobic digestion (AD) is valued as a carbon-neutral energy source, while simultaneously treating organic waste, making it safer for disposal or use as a fertilizer on agricultural land. The AD process in many European nations, such as Germany, has grown from use of small, localized digesters to the operation of large-scale treatment facilities, which contribute significantly to national renewable energy quotas. However, these large AD plants are costly to run and demand intensive farming of energy crops for feedstock. Current policy in Germany has transitioned to support funding for smaller digesters, while also limiting the use of energy crops. AD within Ireland, as a new technology, is affected by ambiguous governmental policies concerning waste and energy. A clear governmental strategy supporting on-site AD processing of agricultural waste will significantly reduce Ireland's carbon footprint, improve the safety and bioavailability of agricultural waste, and provide an indigenous renewable energy source. © 2016 Society of Chemical Industry.

Comparative proteomics implicates a role for multiple secretion systems in electrode-respiring Geobacter sulfurreducens biofilms.

Geobacter sulfurreducens is a dissimilatory metal-reducing bacterium capable of forming thick electron-conducting biofilms on solid electrodes. Here, we employ for the first time comparative proteomics to identify key physiological changes involved in G. sulfurreducens adaptation from fumarate-respiring planktonic cells to electron-conducting biofilms. Increased levels of proteins involved in outer membrane biogenesis, cell motility and secretion are expressed in biofilms. Of particular importance to the electron-conducting biofilms are proteins associated with secretion systems of Type I, II, V and Type IV pili. Furthermore, enzymes involved in lipopolysaccharide and peptidoglycan biosynthesis show increased levels of expression in electron-conducting biofilms compared to planktonic cells. These observations point to similarities in long-range electron transfer mechanisms between G. sulfurreducens and Shewanella oneidensis, while highlighting the wider significance of secretion systems beyond that of Type IV pili identified to date in the adaptation of G. sulfurreducens to electrode respiration.

The General Stress Response Is Conserved in Long-Term Soil-Persistent Strains of Escherichia coli.

UNLABELLED: Although Escherichia coli is generally considered to be predominantly a commensal of the gastrointestinal tract, a number of recent studies suggest that it is also capable of long-term survival and growth in environments outside the host. As the extraintestinal physical and chemical conditions are often different from those within the host, it is possible that distinct genetic adaptations may be required to enable this transition. Several studies have shown a trade-off between growth and stress resistance in nutrient-poor environments, with lesions in the rpoS locus, which encodes the stress sigma factor RpoS (σ(S)). In this study, we investigated a unique collection of long-term soil-persistent E. coli isolates to determine whether the RpoS-controlled general stress response is altered during adaptation to a nutrient-poor extraintestinal environment. The sequence of the rpoS locus was found to be highly conserved in these isolates, and no nonsense or frameshift mutations were detected. Known RpoS-dependent phenotypes, including glycogen synthesis and γ-aminobutyrate production, were found to be conserved in all strains. All strains expressed the full-length RpoS protein, which was fully functional using the RpoS-dependent promoter reporter fusion PgadX::gfp RpoS was shown to be essential for long-term soil survival of E. coli, since mutants lacking rpoS lost viability rapidly in soil survival assays. Thus, despite some phenotypic heterogeneity, the soil-persistent strains all retained a fully functional RpoS-regulated general stress response, which we interpret to indicate that the stresses encountered in soil provide a strong selective pressure for maintaining stress resistance, despite limited nutrient availability. IMPORTANCE: Escherichia coli has been, and continues to be, used as an important indicator species reflecting potential fecal contamination events in the environment. However, recent studies have questioned the validity of this, since E. coli has been found to be capable of long-term colonization of soils. This study investigated whether long-term soil-persistent E. coli strains have evolved altered stress resistance characteristics. In particular, the study investigated whether the main regulator of genes involved in stress protection, the sigma factor RpoS, has been altered in the soil-persistent strains. The results show that RpoS stress protection is fully conserved in soil-persistent strains of E. coli They also show that loss of the rpoS gene dramatically reduces the ability of this organism to survive in a soil environment. Overall, the results indicate that soil represents a stressful environment for E. coli, and their survival in it requires that they deploy a full stress protection response.

An in-depth evaluation of metagenomic classifiers for soil microbiomes.

BACKGROUND: Recent endeavours in metagenomics, exemplified by projects such as the human microbiome project and TARA Oceans, have illuminated the complexities of microbial biomes. A robust bioinformatic pipeline and meticulous evaluation of their methodology have contributed to the success of these projects. The soil environment, however, with its unique challenges, requires a specialized methodological exploration to maximize microbial insights. A notable limitation in soil microbiome studies is the dearth of soil-specific reference databases available to classifiers that emulate the complexity of soil communities. There is also a lack of in-vitro mock communities derived from soil strains that can be assessed for taxonomic classification accuracy. RESULTS: In this study, we generated a custom in-silico mock community containing microbial genomes commonly observed in the soil microbiome. Using this mock community, we simulated shotgun sequencing data to evaluate the performance of three leading metagenomic classifiers: Kraken2 (supplemented with Bracken, using a custom database derived from GTDB-TK genomes along with its own default database), Kaiju, and MetaPhlAn, utilizing their respective default databases for a robust analysis. Our results highlight the importance of optimizing taxonomic classification parameters, database selection, as well as analysing trimmed reads and contigs. Our study showed that classifiers tailored to the specific taxa present in our samples led to fewer errors compared to broader databases including microbial eukaryotes, protozoa, or human genomes, highlighting the effectiveness of targeted taxonomic classification. Notably, an optimal classifier performance was achieved when applying a relative abundance threshold of 0.001% or 0.005%. The Kraken2 supplemented with bracken, with a custom database demonstrated superior precision, sensitivity, F1 score, and overall sequence classification. Using a custom database, this classifier classified 99% of in-silico reads and 58% of real-world soil shotgun reads, with the latter identifying previously overlooked phyla using a custom database. CONCLUSION: This study underscores the potential advantages of in-silico methodological optimization in metagenomic analyses, especially when deciphering the complexities of soil microbiomes. We demonstrate that the choice of classifier and database significantly impacts microbial taxonomic profiling. Our findings suggest that employing Kraken2 with Bracken, coupled with a custom database of GTDB-TK genomes and fungal genomes at a relative abundance threshold of 0.001% provides optimal accuracy in soil shotgun metagenome analysis.

A functional approach to uncover the low-temperature adaptation strategies of the archaeon Methanosarcina barkeri.

Low-temperature anaerobic digestion (LTAD) technology is underpinned by a diverse microbial community. The methanogenic archaea represent a key functional group in these consortia, undertaking CO2 reduction as well as acetate and methylated C1 metabolism with subsequent biogas (40 to 60% CH4 and 30 to 50% CO2) formation. However, the cold adaptation strategies, which allow methanogens to function efficiently in LTAD, remain unclear. Here, a pure-culture proteomic approach was employed to study the functional characteristics of Methanosarcina barkeri (optimum growth temperature, 37°C), which has been detected in LTAD bioreactors. Two experimental approaches were undertaken. The first approach aimed to characterize a low-temperature shock response (LTSR) of M. barkeri DSMZ 800(T) grown at 37°C with a temperature drop to 15°C, while the second experimental approach aimed to examine the low-temperature adaptation strategies (LTAS) of the same strain when it was grown at 15°C. The latter experiment employed cell viability and growth measurements (optical density at 600 nm [OD600]), which directly compared M. barkeri cells grown at 15°C with those grown at 37°C. During the LTSR experiment, a total of 127 proteins were detected in 37°C and 15°C samples, with 20 proteins differentially expressed with respect to temperature, while in the LTAS experiment 39% of proteins identified were differentially expressed between phases of growth. Functional categories included methanogenesis, cellular information processing, and chaperones. By applying a polyphasic approach (proteomics and growth studies), insights into the low-temperature adaptation capacity of this mesophilically characterized methanogen were obtained which suggest that the metabolically diverse Methanosarcinaceae could be functionally relevant for LTAD systems.

Critical evaluation of the use of artificial data for machine learning based de novo peptide identification.

Proteins are essential components of all living cells and so the study of their in situ expression, proteomics, has wide reaching applications. Peptide identification in proteomics typically relies on matching high resolution tandem mass spectra to a protein database but can also be performed de novo. While artificial spectra have been successfully incorporated into database search pipelines to increase peptide identification rates, little work has been done to investigate the utility of artificial spectra in the context of de novo peptide identification. Here, we perform a critical analysis of the use of artificial data for the training and evaluation of de novo peptide identification algorithms. First, we classify the different fragment ion types present in real spectra and then estimate the number of spurious matches using random peptides. We then categorise the different types of noise present in real spectra. Finally, we transfer this knowledge to artificial data and test the performance of a state-of-the-art de novo peptide identification algorithm trained using artificial spectra with and without relevant noise addition. Noise supplementation increased artificial training data performance from 30% to 77% of real training data peptide recall. While real data performance was not fully replicated, this work provides the first steps towards an artificial spectrum framework for the training and evaluation of de novo peptide identification algorithms. Further enhanced artificial spectra may allow for more in depth analysis of de novo algorithms as well as alleviating the reliance on database searches for training data.

Impact of trichloroethylene exposure on the microbial diversity and protein expression in anaerobic granular biomass at 37°C and 15°C.

Granular biomass from a laboratory-scale anaerobic bioreactor trial was analysed to identify changes in microbial community structure and function in response to temperature and trichloroethylene (TCE). Two bioreactors were operated at 37°C, while two were operated at 15°C. At the time of sampling, one of each temperature pair of bioreactors was exposed to process failure-inducing concentrations of TCE (60 mg L(-1)) while the other served as a TCE-free control. Bacterial community structure was investigated using denaturing gradient gel electrophoresis (DGGE) and 16S rRNA gene clone library analysis. Temperature was identified as an important factor for bacterial community composition, while minor differences were associated with trichloroethylene supplementation. Proteobacteria was the dominant phylum in all bioreactors, while clone library analysis revealed a higher proportion of Bacteroidetes-, Chloroflexi-, and Firmicutes-like clones at 15°C than at 37°C. Comparative metaproteomics in the presence and absence of TCE was carried out by two-dimensional gel electrophoresis (2-DGE), and 28 protein spots were identified, with putative functions related to cellular processes, including methanogenesis, glycolysis, the glyoxylate cycle, and the methyl malonyl pathway. A good agreement between metaproteomic species assignment and phylogenetic information was observed, with 10 of the identified proteins associated with members of the phylum Proteobacteria.

The impact of noise and missing fragmentation cleavages on de novo peptide identification algorithms.

Proteomics aims to characterise system-wide protein expression and typically relies on mass-spectrometry and peptide fragmentation, followed by a database search for protein identification. It has wide ranging applications from clinical to environmental settings and virtually impacts on every area of biology. In that context, de novo peptide sequencing is becoming increasingly popular. Historically its performance lagged behind database search methods but with the integration of machine learning, this field of research is gaining momentum. To enable de novo peptide sequencing to realise its full potential, it is critical to explore the mass spectrometry data underpinning peptide identification. In this research we investigate the characteristics of tandem mass spectra using 8 published datasets. We then evaluate two state of the art de novo peptide sequencing algorithms, Novor and DeepNovo, with a particular focus on their performance with regard to missing fragmentation cleavage sites and noise. DeepNovo was found to perform better than Novor overall. However, Novor recalled more correct amino acids when 6 or more cleavage sites were missing. Furthermore, less than 11% of each algorithms' correct peptide predictions emanate from data with more than one missing cleavage site, highlighting the issues missing cleavages pose. We further investigate how the algorithms manage to correctly identify peptides with many of these missing fragmentation cleavages. We show how noise negatively impacts the performance of both algorithms, when high intensity peaks are considered. Finally, we provide recommendations regarding further algorithms' improvements and offer potential avenues to overcome current inherent data limitations.

Simultaneous adsorption and biodegradation of trichloroethylene occurs in a biochar packed column treating contaminated landfill leachate.

Trichloroethylene (TCE) is a human carcinogen that is commonly found in landfill leachate. Contaminated leachate plumes may be intercepted prior to reaching groundwater and treated in situ using permeable reactive barriers (PRB). This study used a packed column system containing herbal pomace and spruce biochar, previously shown to have TCE adsorptive capabilities. Influent containing raw or autoclaved landfill leachate was used to investigate the potential for environmental micro-organisms to establish a TCE-dechlorinating biofilm on the biochar, in order to prolong the operational life span of the system. TCE removal ≥ 99.7 % was observed by both biochars. No dichloroethylene (DCE) isomers were present in the column effluents, but cis-1,2 DCE was adsorbed to the biochar treating raw landfill leachate, indicating that dechlorination was occurring biologically in these columns. Known microbial species that are individually capable of complete dechlorination of TCE to ethene were not detected by 16S rRNA gene sequencing, but several species capable of partial TCE dechlorination (Desulfitobacterium spp., Sulfurospirillium spp. and Desulfuromonas spp) were present in the biofilms of the columns treating raw landfill leachate. These data demonstrate that biochar from waste material may be capable of supporting a dechlorinating biofilm to promote bioremediation of TCE.

Evolutionary trade-offs between growth and survival: The delicate balance between reproductive success and longevity in bacteria.

All living cells strive to allocate cellular resources in a way that promotes maximal evolutionary fitness. While there are many competing demands for resources the main decision making process centres on whether to proceed with growth and reproduction or to "hunker down" and invest in protection and survival (or to strike an optimal balance between these two processes). The transcriptional programme active at any given time largely determines which of these competing processes is dominant. At the top of the regulatory hierarchy are the sigma factors that commandeer the transcriptional machinery and determine which set of promoters are active at any given time. The regulatory inputs controlling their activity are therefore often highly complex, with multiple layers of regulation, allowing relevant environmental information to produce the most beneficial response. The tension between growth and survival is also evident in the developmental programme necessary to promote biofilm formation, which is typically associated with low growth rates and enhanced long-term survival. Nucleotide second messengers and energy pools (ATP/ADP levels) play critical roles in determining the fate of individual cells. Regulatory small RNAs frequently play important roles in the decision making processes too. In this review we discuss the trade-off that exists between reproduction and persistence in bacteria and discuss some of the recent advances in this fascinating field.

Survival of Escherichia coli and Listeria innocua on Lettuce after Irrigation with Contaminated Water in a Temperate Climate.

Microbial disease outbreaks related to fresh produce consumption, including leafy green vegetables, have increased in recent years. Where contamination occurs, pathogen persistence may represent a risk for consumers' health. This study analysed the survival of E. coli and L. innocua on lettuce plants watered with contaminated irrigation water via a single irrigation event and within stored irrigation water. Separate lettuce plants (Lactuca sativa var. capitata) were irrigated with water spiked with Log10 7 cfu/mL of each of the two strains and survival assessed via direct enumeration, enrichment and qPCR. In parallel, individual 20 L water microcosms were spiked with Log10 7 cfu/mL of the individual strains and sampled at similar time points. Both strains were observed to survive on lettuce plants up to 28 days after inoculation. Direct quantification by culture methods showed a Log10 4 decrease in the concentration of E. coli 14 days after inoculation, and a Log10 3 decrease in the concentration of L. innocua 10 days after inoculation. E. coli was detected in water samples up to 7 days after inoculation and L. innocua was detected up to 28 days by direct enumeration. Both strains were recovered from enriched samples up to 28 days after inoculation. These results demonstrate that E. coli and L. innocua strains are able to persist on lettuce after a single contamination event up until the plants reach a harvestable state. Furthermore, the persistence of E. coli and L. innocua in water for up to 28 days after inoculation illustrates the potential for multiple plant contamination events from stored irrigation water, emphasising the importance of ensuring that irrigation water is of a high quality.

Combined Stochastic and Deterministic Processes Drive Community Assembly of Anaerobic Microbiomes During Granule Flotation.

Advances in null-model approaches have resulted in a deeper understanding of community assembly mechanisms for a variety of complex microbiomes. One under-explored application is assembly of communities from the built-environment, especially during process disturbances. Anaerobic digestion for biological wastewater treatment is often underpinned by retaining millions of active granular biofilm aggregates. Flotation of granules is a major problem, resulting in process failure. Anaerobic aggregates were sampled from three identical bioreactors treating dairy wastewater. Microbiome structure was analysed using qPCR and 16S rRNA gene amplicon sequencing from DNA and cDNA. A comprehensive null-model approach quantified assembly mechanisms of floating and settled communities. Significant differences in diversity were observed between floating and settled granules, in particular, we highlight the changing abundances of Methanosaeta and Lactococcus. Both stochastic and deterministic processes were important for community assembly. Homogeneous selection was the primary mechanism for all categories, but dispersal processes also contributed. The lottery model was used to identify clade-level competition driving community assembly. Lottery "winners" were identified with different winners between floating and settled groups. Some groups changed their winner status when flotation occurred. Spirochaetaceae, for example, was only a winner in settled biomass (cDNA-level) and lost its winner status during flotation. Alternatively, Arcobacter butzerli gained winner status during flotation. This analysis provides a deeper understanding of changes that occur during process instabilities and identified groups which may be washed out-an important consideration for process control.

Characterization of environmentally persistent Escherichia coli isolates leached from an Irish soil.

Soils are typically considered to be suboptimal environments for enteric organisms, but there is increasing evidence that Escherichia coli populations can become resident in soil under favorable conditions. Previous work reported the growth of autochthonous E. coli in a maritime temperate Luvic Stagnosol soil, and this study aimed to characterize, by molecular and physiological means, the genetic diversity and physiology of environmentally persistent E. coli isolates leached from the soil. Molecular analysis (16S rRNA sequencing, enterobacterial repetitive intergenic consensus PCR, pulsed-field gel electrophoresis, and a multiplex PCR method) established the genetic diversity of the isolates (n = 7), while physiological methods determined the metabolic capability and environmental fitness of the isolates, relative to those of laboratory strains, under the conditions tested. Genotypic analysis indicated that the leached isolates do not form a single genetic grouping but that multiple genotypic groups are capable of surviving and proliferating in this environment. In physiological studies, environmental isolates grew well across a broad range of temperatures and media, in comparison with the growth of laboratory strains. These findings suggest that certain E. coli strains may have the ability to colonize and adapt to soil conditions. The resulting lack of fecal specificity has implications for the use of E. coli as an indicator of fecal pollution in the environment.