Meta-Omics-Supervised Characterization of Respiration Activities Associated with Microbial Immigrants in Anaerobic Sludge Digesters

Immigration has been recently recognized as an important ecological process that affects the microbial community structure in diverse ecosystems. However, the fate of microbial immigrants in the new environment and their involvement in the local biochemical network remain unclear. In this study, we performed meta-omics-supervised characterization of immigrants’ activities in anaerobic sludge digesters. Metagenomic analyses revealed that immigrants from the feed sludge accounted for the majority of populations capable of anaerobic respiration in a digester. Electron acceptors that were predicted to be respired, including nitrate, nitrite, sulfate, and elemental sulfur, were added to digester sludge in batch tests. Consumption of up to 91% of the added electron acceptors was observed within the experiment period. 16S rRNA sequencing detected populations that were stimulated by the electron acceptors, largely overlapping with respiration-capable immigrants identified by metagenomic analysis. Metatranscriptomic analysis of the batch tests provided additional evidence for upregulated expression of respiration genes and concomitant suppressed expression of methanogenesis. Anaerobic respiration activity was further evaluated in full-scale digesters in nine wastewater treatment plants. Although nitrate and sulfate respiration were ubiquitous, the expression level of respiration genes was generally 2–3 orders of magnitude lower than the expression of methanogenesis in most digesters, suggesting marginal ecological roles by immigrants in full-scale digester ecosystems

Community Structure Analysis of Reverse Osmosis Membrane Biofilms and the Significance of <i>Rhizobiales</i> Bacteria in Biofouling

The biofilm community structure of a biofouled reverse osmosis (RO) membrane was examined using a polyphasic approach, and the dominant phylotypes retrieved were related to the order Rhizobiales, a group of bacteria that is hitherto not implicated in membrane biofouling. A comparison with two other membrane biofilms using T-RFLP fingerprinting also revealed the dominance of Rhizobiales organisms. When pure culture RO biofilm isolates were cultivated aerobically in BIOLOG microplates, most Rhizobiales were metabolically versatile in their choice of carbon substrates. Nitrate reduction was observed in five RO isolates related to Castellaniella, Ochrobactrum, Stenotrophomonas, and Xanthobacter. Many of the key Rhizobiales genera including Bosea, Ochrobactrum, Shinella, and Rhodopseudomonas were detected by PCR to contain the nirK gene responsible for nitrite reductase activity. These findings suggest that Rhizobiales organisms are ecologically significant in membrane biofilm communities under both aerobic and anoxic conditions and may be responsible for biofouling in membrane separation systems

Membrane biofouling characterization: effects of sample preparation procedures on biofilm structure and the microbial community

<div><p>Ensuring the quality and reproducibility of results from biofilm structure and microbial community analysis is essential to membrane biofouling studies. This study evaluated the impacts of three sample preparation factors (ie number of buffer rinses, storage time at 4°C, and DNA extraction method) on the downstream analysis of nitrifying biofilms grown on ultrafiltration membranes. Both rinse and storage affected biofilm structure, as suggested by their strong correlation with total biovolume, biofilm thickness, roughness and the spatial distribution of EPS. Significant variations in DNA yields and microbial community diversity were also observed among samples treated by different rinses, storage and DNA extraction methods. For the tested biofilms, two rinses, no storage and DNA extraction with both mechanical and chemical cell lysis from attached biofilm were the optimal sample preparation procedures for obtaining accurate information about biofilm structure, EPS distribution and the microbial community.</p></div

Data_Sheet_1_Nexus of Stochastic and Deterministic Processes on Microbial Community Assembly in Biological Systems.docx

Microbial community assembly in engineered biological systems is often simultaneously influenced by stochastic and deterministic processes, and the nexus of these two mechanisms remains to be further investigated. Here, three lab-scale activated sludge reactors were seeded with identical inoculum and operated in parallel under eight different sludge retention time (SRT) by sequentially reducing the SRT from 15 days to 1 day. Using 16S rRNA gene amplicon sequencing data, the microbial populations at the start-up (15-day SRT) and SRT-driven (≤10-day SRT) phases were observed to be noticeably different. Clustering results demonstrated ecological succession at the start-up phase with no consistent successional steps among the three reactors, suggesting that stochastic processes played an important role in the community assembly during primary succession. At the SRT-driven phase, the three reactors shared 31 core operational taxonomic units (OTUs). Putative primary acetate utilizers and secondary metabolizers were proposed based on K-means clustering, network and synchrony analysis. The shared core populations accounted for 65% of the total abundance, indicating that the microbial communities at the SRT-driven phase were shaped predominantly by deterministic processes. Sloan’s Neutral model and a null model analysis were performed to disentangle and quantify the relative influence of stochastic and deterministic processes on community assembly. The increased estimated migration rate in the neutral community model and the higher percentage of stochasticity in the null model implied that stochastic community assembly was intensified by strong deterministic factors. This was confirmed by the significantly different α- and β-diversity indices at SRTs shorter than 2 days and the observation that over half of the core OTUs were unshared or unsynchronized. Overall, this study provided quantitative insights into the nexus of stochastic and deterministic processes on microbial community assembly in a biological process.</p

Toward Characterizing Environmental Sources of Non-tuberculous Mycobacteria (NTM) at the Species Level: A Tutorial Review of NTM Phylogeny and Phylogenetic Classification

Nontuberculous mycobacteria (NTM) are any mycobacteria that do not cause tuberculosis or leprosy. While the majority of NTM are harmless and some of them are considered probiotic, a growing number of people are being diagnosed with NTM infections. Therefore, their detection in the environment is of interest to clinicians, environmental microbiologists, and water quality researchers alike. This review provides a tutorial on the foundational approaches for taxonomic classifications, with a focus on the phylogenetic relationships among NTM revealed by the 16S rRNA gene, rpoB gene, and hsp65 gene, and by genome-based approaches. Recent updates on the Mycobacterium genus taxonomy are also provided. A synthesis on the habitats of 189 mycobacterial species in a genome-based taxonomy framework was performed, with attention paid to environmental sources (e.g., drinking water, aquatic environments, and soil). The 16S rRNA gene-based classification accuracy for various regions was evaluated (V3, V3–V4, V3–V5, V4, V4–V5, and V1–V9), revealing overall excellent genus-level classification (up to 100% accuracy) yet only modest performance (up to 63.5% accuracy) at the species level. Future research quantifying NTM species in water systems, determining the effects of water treatment and plumbing conditions on their variations, developing high throughput species-level characterization tools for use in the environment, and incorporating the characterization of functions in a phylogenetic framework will likely fill critical knowledge gaps. We believe this tutorial will be useful for researchers new to the field of molecular or genome-based taxonomic profiling of environmental microbiomes. Experts may also find this review useful in terms of the selected key findings of the past 30 years, recent updates on phylogenomic analyses, as well as a synthesis of the ecology of NTM in a phylogenetic framework

Additional file 1: of Coupling growth kinetics modeling with machine learning reveals microbial immigration impacts and identifies key environmental parameters in a biological wastewater treatment process

Supplementary figures and tables. This file contains supplementary Figures S1–S8. and Tables S1–S2. (PDF 4054 kb

Additional file 1 of Machine learning-aided analyses of thousands of draft genomes reveal specific features of activated sludge processes

Additional file 1: Table S1. Information about the WWTPs and activate sludge samples analyzed in this study. Table S2. Accession numbers of the metagenomic datasets used in this study. Table S3. Abundance of AS MAGs assigned to each phylum. Table S4. Prediction report of the random forest model. Table S5. Importance values and descriptions of the top 20 COGs identified by random forest model to differentiate the AS and non-AS MAGs. Figure S1. Geographical locations of the WWTPs where activated sludge samples were collected by us and other researchers. Figure S2. Associations between MAG completeness and number of contigs (a), and associations between MAG completeness and number of contigs (b). Figure S3. Venn diagram showing the shared and unique MAGs of WWTP1, WWTP2, WWTP3 and WWTP4. Figure S4. Profile of protein sequences identity between different WWTPs. The protein sequences predicted from all assembly contigs of each WWTP were compared each other with Diamond and then the best hits of the protein sequences were counted and summarized. Figure S5. Random forest parameter tuning and optimization. (a) Number of trees (n_estimators); (b) Tree depth; (c) Maximum features. Figure S6. Phylogeny of the erroneously predicted MAGs. The topology of this tree is exactly same with Fig. 1b. Extended lines were added to show positions of the erroneously predicted MAGs

Cross-section illustration of the anaerobic packed-bed (AP) and hybrid packed-bed (HP) reactors.

<p>The reactors were equipped with water jacket and heated by water heater to kept at 35°C. The numbers in italics indicate size (mm).</p

Additional file 1 of Machine learning-aided analyses of thousands of draft genomes reveal specific features of activated sludge processes

Additional file 1: Table S1. Information about the WWTPs and activate sludge samples analyzed in this study. Table S2. Accession numbers of the metagenomic datasets used in this study. Table S3. Abundance of AS MAGs assigned to each phylum. Table S4. Prediction report of the random forest model. Table S5. Importance values and descriptions of the top 20 COGs identified by random forest model to differentiate the AS and non-AS MAGs. Figure S1. Geographical locations of the WWTPs where activated sludge samples were collected by us and other researchers. Figure S2. Associations between MAG completeness and number of contigs (a), and associations between MAG completeness and number of contigs (b). Figure S3. Venn diagram showing the shared and unique MAGs of WWTP1, WWTP2, WWTP3 and WWTP4. Figure S4. Profile of protein sequences identity between different WWTPs. The protein sequences predicted from all assembly contigs of each WWTP were compared each other with Diamond and then the best hits of the protein sequences were counted and summarized. Figure S5. Random forest parameter tuning and optimization. (a) Number of trees (n_estimators); (b) Tree depth; (c) Maximum features. Figure S6. Phylogeny of the erroneously predicted MAGs. The topology of this tree is exactly same with Fig. 1b. Extended lines were added to show positions of the erroneously predicted MAGs

FOREWARD: Pro Se Prisoner Litigation: Looking for Needles in Haystacks

<p>AP, anaerobic packed-bed reactor; HP, hybrid packed-bed reactor; COD: chemical oxygen demand; HRT, hydraulic retention time; OLR, organic loading rate; N.D. not determined.</p><p>Operational parameter of anaerobic packed-bed (AP) and hybrid packed-bed (HP) reactors.</p