16S rRNA sequencing reveals likely beneficial core microbes within faecal samples of the EU protected slug Geomalacus maculosus

The EU-protected slug Geomalacus maculosus Allman occurs only in the West of Ireland and in northern Spain and Portugal. We explored the microbial community found within the faeces of Irish specimens with a view to determining whether a core microbiome existed among geographically isolated slugs which could give insight into the adaptations of G. maculosus to the available food resources within its habitat. Faecal samples of 30 wild specimens were collected throughout its Irish range and the V3 region of the bacterial 16S rRNA gene was sequenced using Illumina MiSeq. To investigate the influence of diet on the microbial composition, faecal samples were taken and sequenced from six laboratory reared slugs which were raised on two different foods. We found a widely diverse microbiome dominated by Enterobacteriales with three core OTUs shared between all specimens. While the reared specimens appeared clearly separated by diet in NMDS plots, no significant difference between the slugs fed on the two different diets was found. Our results indicate that while the majority of the faecal microbiome of G. maculosus is probably dependent on the microhabitat of the individual slugs, parts of it are likely selected for by the host

Microbial influencers and cotton leaf curl disease (CLCuD) susceptibility: a network perspective

Biotic stresses, such as plant viruses, e.g., cotton leaf curl virus (CLCuV), can alter root-associated and leaf-associated microbial diversities in plants. There are complex ecological dynamics at play, with each microbe contributing to a multitude of biotic and abiotic interactions, thus deciding the stability of the plant’s ecosystem in response to the disease. Deciphering these networks of interactions is a challenging task. The inferential research in microbiome is also at a nascent stage, often constrained by the underlying analytical assumptions and the limitations with respect to the depth of sequencing. There is also no real consensus on network-wide statistics to identify the influential microbial players in a network. Guided by the latest developments in network science, including recently published metrics such as Integrated View of Influence (IVI) and some other centrality measures, this study provides an exposé of the most influential nodes in the rhizospheric and phyllospheric microbial networks of the cotton leaf curl disease (CLCuD) susceptible, partially tolerant, and resistant cotton varieties. It is evident from our results that the CLCuD-resistant Gossypium arboreum possesses an equal share of keystone species, which helps it to withstand ecological pressures. In the resistant variety, the phyllosphere harbors the most influential nodes, whereas in the susceptible variety, they are present in the rhizosphere. Based on hubness score, spreading score, and IVI, the top 10 occurring keystone species in the FDH-228 (resistant) variety include Actinokineospora, Cohnella, Thermobacillus, Clostridium, Desulfofarcimen, and MDD-D21. Elusimicrobia, Clostridium-sensu-stricto_12, Candidatus woesebacteria, and Dyella were identified as the most influential nodes in the PFV-1 (partially tolerant) variety. In the PFV-2 (susceptible) variety, the keystone species were identified as Georginia, Nesterenkonia, Elusimicrobia MVP-88, Acetivibrio, Tepedisphaerales, Chelatococcus, Nitrosospira, and RCP2-54. This concept deciphers the diseased and healthy plant’s response to viral disease, which may be microbially mediated

The active microbial community more accurately reflects the anaerobic digestion process: 16S rRNA (gene) sequencing as a predictive tool

Background: Amplicon sequencing methods targeting the 16S rRNA gene have been used extensively to investigate microbial community composition and dynamics in anaerobic digestion. These methods successfully characterize amplicons but do not distinguish micro-organisms that are actually responsible for the process. In this research, the archaeal and bacterial community of 48 full-scale anaerobic digestion plants were evaluated on DNA (total community) and RNA (active community) level via 16S rRNA (gene) amplicon sequencing. Results: A significantly higher diversity on DNA compared with the RNA level was observed for archaea, but not for bacteria. Beta diversity analysis showed a significant difference in community composition between the DNA and RNA of both bacteria and archaea. This related with 25.5 and 42.3% of total OTUs for bacteria and archaea, respectively, that showed a significant difference in their DNA and RNA profiles. Similar operational parameters affected the bacterial and archaeal community, yet the differentiating effect between DNA and RNA was much stronger for archaea. Co-occurrence networks and functional prediction profiling confirmed the clear differentiation between DNA and RNA profiles. Conclusions: In conclusion, a clear difference in active (RNA) and total (DNA) community profiles was observed, implying the need for a combined approach to estimate community stability in anaerobic digestion

NanoAmpli-Seq: a workflow for amplicon sequencing for mixed microbial communities on the nanopore sequencing platform

Background: Amplicon sequencing on Illumina sequencing platforms leverages their deep sequencing and multiplexing capacity but is limited in genetic resolution due to short read lengths. While Oxford Nanopore or Pacific Biosciences sequencing platforms overcome this limitation, their application has been limited due to higher error rates or lower data output. Results: In this study, we introduce an amplicon sequencing workflow, i.e., NanoAmpli-Seq, that builds on the intramolecular-ligated nanopore consensus sequencing (INC-Seq) approach and demonstrate its application for full-length 16S rRNA gene sequencing. NanoAmpli-Seq includes vital improvements to the INC-Seq protocol that reduces sample processing time while significantly improving sequence accuracy. The developed protocol includes chopSeq software for fragmentation and read orientation correction of INC-Seq consensus reads while nanoClust algorithm was designed for read partitioning-based de novo clustering and within cluster consensus calling to obtain accurate full-length 16S rRNA gene sequences. Conclusions: NanoAmpli-Seq accurately estimates the diversity of tested mock communities with average consensus sequence accuracy of 99.5% for 2D and 1D2 sequencing on the nanopore sequencing platform. Nearly all residual errors in NanoAmpli-Seq sequences originate from deletions in homopolymer regions, indicating that homopolymer aware base calling or error correction may allow for sequencing accuracy comparable to short-read sequencing platforms

An automated identification and analysis of ontological terms in gastrointestinal diseases and nutrition-related literature provides useful insights

With an unprecedented growth in the biomedical literature, keeping up to date with the new developments presents an immense challenge. Publications are often studied in isolation of the established literature, with interpretation being subjective and often introducing human bias. With ontology-driven annotation of biomedical data gaining popularity in recent years and online databases offering metatags with rich textual information, it is now possible to automatically text-mine ontological terms and complement the laborious task of manual management, interpretation, and analysis of the accumulated literature with downstream statistical analysis. In this paper, we have formulated an automated workflow through which we have identified ontological information, including nutrition-related terms in PubMed abstracts (from 1991 to 2016) for two main types of Inflammatory Bowel Diseases: Crohn’s Disease and Ulcerative Colitis; and two other gastrointestinal (GI) diseases, namely, Coeliac Disease and Irritable Bowel Syndrome. Our analysis reveals unique clustering patterns as well as spatial and temporal trends inherent to the considered GI diseases in terms of literature that has been accumulated so far. Although automated interpretation cannot replace human judgement, the developed workflow shows promising results and can be a useful tool in systematic literature reviews. The workflow is available at https://github.com/KociOrges/pytag

Metagenomic sequencing unravels gene fragments with phylogenetic signatures of O2-tolerant NiFe membrane-bound hydrogenases in lacustrine sediment

Many promising hydrogen technologies utilising hydrogenase enzymes have been slowed by the fact that most hydrogenases are extremely sensitive to O2. Within the group 1 membrane-bound NiFe hydrogenase, naturally occurring tolerant enzymes do exist, and O2 tolerance has been largely attributed to changes in iron–sulphur clusters coordinated by different numbers of cysteine residues in the enzyme’s small subunit. Indeed, previous work has provided a robust phylogenetic signature of O2 tolerance [1], which when combined with new sequencing technologies makes bio prospecting in nature a far more viable endeavour. However, making sense of such a vast diversity is still challenging and could be simplified if known species with O2-tolerant enzymes were annotated with information on metabolism and natural environments. Here, we utilised a bioinformatics approach to compare O2-tolerant and sensitive membrane-bound NiFe hydrogenases from 177 bacterial species with fully sequenced genomes for differences in their taxonomy, O2 requirements, and natural environment. Following this, we interrogated a metagenome from lacustrine surface sediment for novel hydrogenases via high-throughput shotgun DNA sequencing using the Illumina™ MiSeq platform. We found 44 new NiFe group 1 membrane-bound hydrogenase sequence fragments, five of which segregated with the tolerant group on the phylogenetic tree of the enzyme’s small subunit, and four with the large subunit, indicating de novo O2-tolerant protein sequences that could help engineer more efficient hydrogenases

Stormwater quality and microbial ecology in an urban rain garden system

Rain gardens are an alternative to traditional drainage, able to lower flood risk and reduce environmental contamination from stormwater. Removal of contaminants by rain gardens is driven by both physical processes (such as filtration and sedimentation) and biological metabolic processes by soil microorganisms. To better understand rain garden performance, this study explored the impact of rain gardens on pollution removal and microbial composition and function using rain gardens fed real stormwater from a busy road. Each rain garden had different grain size and hydraulic conductivities as these parameters have been argued to impact pollution removal. All four rain gardens were able to reduce the contaminant load in the stormwaters, reducing the concentration of dissolved metals, suspended solids and chemical oxygen demand. Significantly, road salting in the winter did not cause dissolved metals to be released from the rain gardens, suggesting that rain gardens can continue to reduce contaminant loads during winter salting regimes. Some variation in pollutant removal was seen between the soils tested, but overall no clear trend could be identified based on grain size and hydraulic conductivity with all rain gardens performing broadly similarly. The rain garden soil altered the microbial community in the stormwater, resulting in greater taxonomic evenness and functional richness in the effluent water compared to the influent. Functional richness of the soils was also higher than that of the input waters, indicating that the microbes in the rain gardens were able to perform a wider range of functions than those of the influent. Effluent and soil microbiology was more impacted by sampling date than soil grain size, which may be a result of the soil communities maturing and changing over time. As greater numbers of rain gardens are installed to tackle flooding from climate change, it is important to ensure the environment is protected from urban contaminants in the stormwater. The results in this study further highlight the ability of rain gardens to undertake this important task

Bacterial community analysis in upflow multilayer anaerobic reactor (UMAR) treating high-solids organic wastes

A novel anaerobic digestion configuration, the upflow multi-layer anaerobic reactor (UMAR), was developed to treat high-solids organic wastes. The UMAR was hypothesized to form multi-layer along depth due to the upflow plug flow; use of a recirculation system and a rotating distributor and baffles aimed to assist treating high-solids influent. The chemical oxygen demand (COD) removal efficiency and methane (CH4) production rate were 89% and 2.10 L CH4/L/day, respectively, at the peak influent COD concentration (110.4 g/L) and organic loading rate (7.5 g COD/L/day). The 454 pyrosequencing results clearly indicated heterogeneous distribution of bacterial communities at different vertical locations (upper, middle, and bottom) of the UMAR. Firmicutes was the dominant (>70%) phylum at the middle and bottom parts, while Deltaproteobacteria and Chloroflexi were only found in the upper part. Potential functions of the bacteria were discussed to speculate on their roles in the anaerobic performance of the UMAR system

Molecular insights informing factors affecting low temperature anaerobic applications: diversity, collated core microbiomes and complexity stability relationships in LCFA-fed systems

Fats, oil and grease, and their hydrolyzed counterparts-long chain fatty acids (LCFA) make up a large fraction of numerous wastewaters and are challenging to degrade anaerobically, more so, in low temperature anaerobic digestion (LtAD) systems. Herein, we perform a comparative analysis of publicly available Illumina 16S rRNA datasets generated from LCFA-degrading anaerobic microbiomes at low temperatures (10 and 20 °C) to comprehend the factors affecting microbial community dynamics. The various factors considered were the inoculum, substrate and operational characteristics, the reactor operation mode and reactor configuration, and the type of nucleic acid sequenced. We found that LCFA-degrading anaerobic microbiomes were differentiated primarily by inoculum characteristics (inoculum source and morphology) in comparison to the other factors tested. Inoculum characteristics prominently shaped the species richness, species evenness and beta-diversity patterns in the microbiomes even after long term operation of continuous reactors up to 150 days, implying the choice of inoculum needs careful consideration. The generalised additive models represented through beta diversity contour plots revealed that psychrophilic bacteria RBG-13-54-9 from family Anaerolineae, and taxa WCHB1–41 and Williamwhitmania were highly abundant in LCFA-fed microbial niches, suggesting their role in anaerobic treatment of LCFAs at low temperatures of 10–20 °C. Overall, we showed that the following bacterial genera: uncultured Propionibacteriaceae, Longilinea, Christensenellaceae R7 group, Lactivibrio, candidatus Caldatribacterium, Aminicenantales, Syntrophus, Syntrophomonas, Smithella, RBG-13-54-9, WCHB1–41, Trichococcus, Proteiniclasticum, SBR1031, Lutibacter and Lentimicrobium have prominent roles in LtAD of LCFA-rich wastewaters at 10–20 °C. This study provides molecular insights of anaerobic LCFA degradation under low temperatures from collated datasets and will aid in improving LtAD systems for treating LCFA-rich wastewaters

Biokinetics Of microbial consortia using biogenic sulfur as a novel electron donor for sustainable denitrification

In this study, the biokinetics of autotrophic denitrification with biogenic S0 (ADBIOS) for the treatment of nitrogen pollution in wastewaters were investigated. The used biogenic S0, a by-product of gas desulfurization, was an elemental microcrystalline orthorhombic sulfur with a median size of 4.69 µm and a specific surface area of 3.38 m2/g, which made S0 particularly reactive and bioavailable. During denitritation, the biomass enriched on nitrite (NO2–) was capable of degrading up to 240 mg/l NO2–-N with a denitritation activity of 339.5 mg NO2–-N/g VSS·d. The use of biogenic S0 induced a low NO2–-N accumulation, hindering the NO2–-N negative impact on the denitrifying consortia and resulting in a specific denitrification activity of 223.0 mg NO3–-N/g VSS·d. Besides Thiobacillus being the most abundant genus, Moheibacter and Thermomonas were predominantly selected for denitrification and denitritation, respectively