Source apportionment based on EEM-PARAFAC combined with microbial tracing model and its implication in complex pollution area, Wujin District, China

Further improving the quality of surface water is becoming more difficult after the control of main point-sources, especially in the complex pollution area with mixed industrial and agricultural productions, whereas the pollution source apportionment might be the key to quantify different pollution sources and developing some effective measures. In this study, a technical framework for source apportionment based on three-dimensional fluorescence and microbial traceability model is developed. Based on screening of the main environmental factors and their spatiotemporal characteristics, potential pollution sources have been tentatively identified. Then, the pollution sources are further tested based on the analysis of fluorescence excitation-emission matrix (EEM) and the similarity of fluorescence components in surface water and potential pollution sources. At the same time, the correlation between microbial species and pollution sources is constructed by analyzing the spatiotemporal characteristics of microbial composition and the response of main species to environmental factors. Therefore, pollution source apportionment is quantified using PCA-APCS-MLR, Fast Expectation-maximization for Microbial Source Tracking (FEAST), and Bayesian community-wide culture-independent microbial source tracking (SourceTracker). PCA-APCS-MLR could not effectively distinguish the contributions of different industrial sources in the complex environment of this study, and the contribution of unknown sources was high (average 39.60%). In contrast, the microbial traceability model can accurately identify the contribution of 7 pollution sources and natural sources, effectively reduce the proportion of unknown sources (average of FEAST is 19.81%, SourceTracker is 16.72%), and show better pollution identification and distribution capabilities. FEAST exhibits a more sensitive potential in source apportionment and shorter calculation time than SourceTracker, thus might be used to guide the precise regional pollution control, especially in the complex pollution environments.

Metagenomic analysis of microbial communities and antibiotic resistant genes in the Tijuana river, and potential sources

The Tijuana River is a transborder river that flows northwest across the border from Baja California in Mexico into Southern California before discharging into the Pacific Ocean. The river is frequently contaminated with raw sewage due to inadequate sanitary infrastructure in Tijuana. To assess the type and degree of microbial contamination, water samples were collected monthly from a near-border and an estuarine site from August 2020 until May 2021. A portion of each sample was used for epifluorescent microscopy and DNA was extracted directly from the rest for shotgun metagenomic sequencing. After sequence quality checking and processing, we used the rapid taxonomic identifier tool Kaiju to characterize the microbial diversity of the metagenomes and matched the sequences against the Comprehensive Antibiotic Resistance Database (CARD) to examine antimicrobial resistance genes (ARGs). Bacterial and viral-like particle (VLP) abundance was consistently higher in the near-border samples than in the estuarine samples, while alpha diversity (within sample biodiversity) was higher in estuarine samples. Beta-diversity analysis found clear compositional separation between samples from the two sites, and the near-border samples were more dissimilar to one another than were the estuarine sites. Near-border samples were dominated by fecal-associated bacteria and bacteria associated with sewage sludge, while estuarine sites were dominated by marine bacteria. ARGs were more abundant at the near-border site, but were also readily detectable in the estuarine samples, and the most abundant ARGs had multi-resistance to beta-lactam antibiotics. SourceTracker analysis identified human feces and sewage sludge to be the largest contributors to the near-border samples, while marine waters dominated estuarine samples except for two sewage overflow dates with high fecal contamination. Overall, our research determined human sewage microbes to be common in the Tijuana River, and the prevalence of ARGs confirms the importance of planned infrastructure treatment upgrades for environmental health.

Urban green waste bulking agent is the major source of antimicrobial resistance genes persisted in home compost, not animal manure

Urban green waste and food waste are often used as bulking agents to prepare home compost in combination with animal manure in urban horticulture and community gardening. Although it is known that antimicrobial resistance genes (ARGs) persist in home compost, their origins have not been determined. In addition, the factors contributing to ARGs persistence remain unclear. In this study, we aim to (i) characterize the changes in the microbiome and antimicrobial resistome during the composting process of home compost using metagenomics shotgun sequencing, (ii) identify the source of the ARGs persisted in home compost using SourceTracker, and (iii) elucidate the collective effect of compost microbiome and environmental factors, including the physicochemical properties and antibiotics concentration of home compost, in contributing to ARG persistence using Procrustes analysis, co-occurrence network analysis, variation partitioning analysis, and structural equation modeling. SourceTracker analysis indicated that urban green waste bulking agent was the major source of the persisting ARGs in home compost instead of animal manure. Procrustes analysis and co-occurrence network analysis revealed a strong association between microbiome and antimicrobial resistome. Variation partitioning analysis and structural equation modeling suggested that physicochemical properties shaped the antimicrobial resistome directly and indirectly by influencing the microbiome. Our results indicated that the persistence of ARGs in home compost might be due to the succession of microbial species from the urban green waste bulking agent, and the physicochemical properties might have defined the compost environment to shape the microbiome in the compost, thus, in turn, the persisting antimicrobial resistome.

Assessing impacts of municipal wastewater treatment plant upgrades on bacterial hazard contributions to the receiving urban river using SourceTracker

Upgrading municipal wastewater treatment plants (MWTPs) has been implemented in many megacities of China to reduce the discharges of nutrients and other pollutants and improve water quality of highly urbanized rivers. However, the contribution of MWTP discharge to bacterial hazards in the receiving rivers after upgrades has been largely unknown. In this study, high-throughput sequencing and shotgun metagenomics were applied to investigate the changes in the abundance, composition, potential risks, and contributions of bacteria and antibiotic resistance genes (ARGs) from effluent to receiving river after upgrading the third-largest MWTP in China with denitrification biofilters, ultrafiltration, ozonation, and disinfection processes. The annual loadings of total nitrogen and 27 types of pharmaceuticals were reduced by 42.4% ± 13.2% and 46.2% ± 15.4%, respectively. Bacterial biomass decreased from (3.58 ± 0.49) to (1.23 ± 0.27) × 107 16S rRNA gene copies/mL, and identified biomarkers in effluent and downstream shifted due to the adopted processes. Opportunistic pathogen bacteria downstream were also reduced. Although the relative abundance of total ARGs in MWTP effluent increased from 1.10 ± 0.02 to 2.19 ± 0.03 copies/16S rRNA gene after upgrades, that of total and high-risk ARGs downstream showed no significant difference. More importantly, the Bayesian-based SourceTracker method provided valuable insight by revealing that the contributions of MWTP discharge to downstream bacteria (from 44.2% ± 1.5%-31.4% ± 0.9%) and ARGs (from 61.2% ± 5.3%-47.6% ± 4.1%) were significantly reduced following the upgrades, indicating upgrading MWTP showed integrated benefits to the bacterial hazards in the receiving river. This study provides useful information for better control of bacterial hazard risks and operational strategy for the improvement of the urban aquatic ecosystem.

Anthropogenic activity remains the main contributor to fecal pollution in managed tropical watersheds as unraveled by PhyloChip microarray-based microbial source tracking

The spread of disease by enteric pathogens associated with fecal contamination is a major concern for the management of urban watersheds. So far, the relative contribution of natural and anthropogenic sources to fecal pollution in managed tropical watersheds remains poorly evaluated. In this study, the microbiomes of water samples collected from managed watersheds in Singapore were elicited using the PhyloChip, a dense 16S rRNA gene-based DNA microarray, and fecal impairment was inferred using a machine-learning classification algorithm (SourceTracker). The predicted contribution of wildlife fecal sources to environmental samples was generally negligible (< 0.01 ± 0.01), indicating a low likelihood of fecal impairment from natural sources. However, sewage showed considerably higher contribution (0.09 ± 0.05) to microbial communities in a subset of watershed samples from canals and rivers, suggesting persistent impairment of certain areas by anthropogenic activity although being managed. Interestingly, the contribution of sewage microbial communities showed decreasing trends from canals/rivers to the connected reservoirs, indicating meaningful auto-mitigation of fecal pollution in canals and rivers. Notably, exclusion of locally derived fecal samples and source categories from the training data set impaired the predictive performance of the classification algorithm despite a high degree of similarity in the phylogenetic composition of microbiomes in biologically similar but geographically distinct sources.

Benchmarking MicrobIEM - a user-friendly tool for decontamination of microbiome sequencing data

BACKGROUND

Microbiome analysis is becoming a standard component in many scientific studies, but also requires extensive quality control of the 16S rRNA gene sequencing data prior to analysis. In particular, when investigating low-biomass microbial environments such as human skin, contaminants distort the true microbiome sample composition and need to be removed bioinformatically. We introduce MicrobIEM, a novel tool to bioinformatically remove contaminants using negative controls.

RESULTS

We benchmarked MicrobIEM against five established decontamination approaches in four 16S rRNA amplicon sequencing datasets: three serially diluted mock communities (108-103 cells, 0.4-80% contamination) with even or staggered taxon compositions and a skin microbiome dataset. Results depended strongly on user-selected algorithm parameters. Overall, sample-based algorithms separated mock and contaminant sequences best in the even mock, whereas control-based algorithms performed better in the two staggered mocks, particularly in low-biomass samples (≤ 106 cells). We show that a correct decontamination benchmarking requires realistic staggered mock communities and unbiased evaluation measures such as Youden's index. In the skin dataset, the Decontam prevalence filter and MicrobIEM's ratio filter effectively reduced common contaminants while keeping skin-associated genera.

CONCLUSIONS

MicrobIEM's ratio filter for decontamination performs better or as good as established bioinformatic decontamination tools. In contrast to established tools, MicrobIEM additionally provides interactive plots and supports selecting appropriate filtering parameters via a user-friendly graphical user interface. Therefore, MicrobIEM is the first quality control tool for microbiome experts without coding experience.

Persistence of human- and cattle-associated Bacteroidales and mitochondrial DNA markers in freshwater mesocosms

Accurate identification of the origins of non-point source pollution is essential for the effective control of fecal pollution. Host-associated Bacteroidales and mitochondrial DNA (mtDNA) markers have been developed to identify the sources of human and cattle fecal pollution. However, the differences in persistence between these two types of markers under different environmental conditions are still poorly understood. Here, we conducted mesocosm experiments to investigate the influence of indigenous microbiota and nutrients on the decay of Bacteroidales and mtDNA markers associated with humans and cattle. Raw sewage or cattle feces were inoculated into mesocosms containing natural eutrophic water, sterile eutrophic water or artificial freshwater. The Bacteroidales markers HF183 (human) and CowM3 (cattle) and mtDNA markers HcytB (human) and QMIBo (cattle) were quantified using the quantitative polymerase chain reaction (qPCR) assays. All markers but HF183 decreased the fastest in the presence of indigenous microbiota. Nutrients caused a decrease in the persistence of HF183; however, no significant nutrient effects were observed for HcytB, CowM3, and QMIBo. The time to reach one log reduction (T90) for HF183 and HcytB was similar; CowM3 reached T90 earlier than QMIBo in all the treatments but eutrophic water. E. coli persisted longer than both Bacteroidales and mtDNA markers in the mesocosms regardless of inoculum type. Additionally, 16S rRNA gene amplicon sequencing was used to determine the changes in bacterial communities accompanying the marker decay. Analysis using the SourceTracker software showed that bacterial communities in the mesocosms became more dissimilar to those in the corresponding inoculants over time. Our results indicate that environmental factors are important determinants of genetic markers' persistence, but their impact can vary depending on the genetic markers. The cattle Bacteroidales markers may be more suitable for determining recent fecal contamination than cattle mtDNA.

Industrial-scale aerobic composting of livestock manures with the addition of biochar: Variation of bacterial community and antibiotic resistance genes caused by various composting stages

The presence of large amounts of antibiotic resistance genes (ARGs) in livestock manures poses an impending, tough safety risk to ecosystems. To investigate more comprehensively the mechanisms of ARGs removal from industrial-scale composting of livestock manure based on biochar addition, we tracked the dynamics of bacterial community and ARGs at various stages of aerobic composting of livestock manures with 10% biochar. There were no significant effects of biochar on the bacterial community and the profiles of ARGs. During aerobic composting, the relative abundance of ARGs and mobile genetic elements (MGEs) showed overall trends of decreasing and then increasing. The key factor driving the dynamics of ARGs was bacterial community composition, and the potential hosts of ARGs were Caldicoprobacter, Tepidimicrobium, Ignatzschineria, Pseudogracilibacillus, Actinomadura, Flavobacterium and Planifilum. The retention of the thermophilic bacteria and the repopulation of the initial bacteria were the dominant reasons for the increase in ARGs at maturation stage. Additionally, among the MGEs, the relative abundance of transposon gene was substantially removed, while the integron genes remained at high relative abundance. Our results highlighted that the suitability of biochar addition to industrial-scale aerobic composting needs to be further explored and that effective measures are needed to prevent the increase of ARGs content on maturation stage.

Storm promotes the dissemination of antibiotic resistome in an urban lagoon through enhancing bio-interactions

Antibiotic-resistance genes (ARGs) and resistant bacteria (ARB) are abundant in stormwater that could cause serious infections, posing a potential threat to public health. However, there is no inference about how stormwater contributes to ARG profiles as well as the dynamic interplay between ARGs and bacteria via vertical gene transfer (VGT) or horizontal gene transfer (HGT) in urban water ecosystems. In this study, the distribution of ARGs, their host communities, and the source and community assembly process of ARGs were investigated in Yundang Lagoon (China) via high-throughput quantitative PCR, 16S rRNA gene amplicon sequencing, and application of SourceTracker before, after and recovering from an extreme precipitation event (132.1 mm). The abundance of ARGs and mobile genetic elements (MGEs) was the highest one day after precipitation and then decreased 2 days after precipitation and so on. Based on SourceTracker and NMDS analysis, the ARG and bacterial communities in lagoon surface water from one day after precipitation were mainly contributed by the wastewater treatment plant (WWTP) influent and effluent. However, the contribution of WWTP to ARG communities was minor 11 days after the precipitation, suggesting that the storm promoted the ARG levels by introducing the input of ARGs, MGEs, and ARB from point and non-point sources, such as sewer overflow and land-applied manure. Based on a novel microbial network analysis framework, the contribution of positive biological interactions between ARGs and MGEs or bacteria was the highest one day after precipitation, indicating a promoted VGT and HGT for ARG dissemination. The microbial networks deconstructed 11 days after precipitation, suggesting the stormwater practices (e.g., tide gate opening, diversion channels, and pumping) alleviated the spread of ARGs. These results advanced our understanding of the distribution and transport of ARGs associated with their source in urban stormwater runoff.

New insights into identifying sediment phosphorus sources in river-lake coupled system: A framework for optimizing microbial community fingerprints

Identifying sediment phosphorus sources in river-lake coupled system is a question in developing preferential control strategies for phosphorus. As sediments adsorbed phosphorus and microbes would be transported with changing hydrodynamic, the phosphorus source-specific microbial community fingerprints shed light on determining the major sediment phosphorus sources. However, the identification of microbial community fingerprints is a challenge because both microbial succession and hydrological characteristics of river-lake systems would affect the stability of fingerprints. Therefore, this study provided a framework for optimizing phosphorus source-specific microbial community fingerprints, and attempted to identify the major sources of sediment phosphorus in river-lake coupled ecosystem. Meiliang Lake is one of the highly eutrophic area in Taihu Lake, where the sediments, bacterial communities, and phosphorus had a close relationship. Through analyzing the connectivity of microbes along water continuum, a microbial fingerprints candidate database was constructed. The phosphorus-related bacterial communities were screened and optimized by comparing the difference of predicted results between upstream and downstream, forming the stable microbial community fingerprints which consisted of Bacteroidia, Bacilli, Clostridia, and other species at the class level. SourceTracker results that based on the optimized phosphorus source-specific microbial community fingerprints indicated that the major sediment phosphorus sources to Meiliang Lake were Liangxi River, Wujingang River, and Donghuandi River, with the relative standard deviations ranging from 2.59% to 27.56%. The accuracy of phosphorus source apportionments was further confirmed based on the composite pollution index and hydrodynamic condition. This study put forward suggestions on how to improve the stability of microbial community fingerprints, and would help to improve the understanding of applying microbial source tracking method to identify the sources of abiotic pollution like sediment phosphorus.

Tracking microeukaryotic footprint in a peri-urban watershed, China through machine-learning approaches

Microeukaryotes play a significant role in biogeochemical cycling and can serve as bioindicators of water quality in freshwater ecosystems. However, there is a knowledge gap on how freshwater microeukaryotic communities are assembled, especially that how terrestrial microeukaryotes influence freshwater microeukaryotic assemblages. Here, we used a combination of 18S rRNA gene amplicon sequencing and community-based microbial source tracking (MST) approaches (i.e., SourceTracker and FEAST) to assess the contribution of microeukaryotes from surrounding environments (i.e., soils, river sediments, swine wastewater, influents and effluents of decentralized wastewater treatment plants) to planktonic microeukaryotes in the main channel, tributaries and reservoir of a peri-urban watershed, China in wet and dry seasons. The results indicated that SAR (~ 49% of the total communities), Opithokonta (~ 34%), Archaeplastida (~ 9%), and Amoebozoa (~ 2%) were dominant taxa in the watershed. The community-based MST analysis revealed that sewage effluents (7.96 - 21.84%), influents (2.23 - 13.97%), and river sediments (2.56 - 11.71%) were the major exogenous sources of riverine microeukaryotes. At the spatial scale, the downstream of the watershed (i.e., main channel and tributaries) received higher proportions of exogenous microeukaryotic OTUs compared to the upstream reservoirs, while at the seasonal scale, the sewage effluents and influents contributed higher exogenous microeukaryotes to river water in wet season than in dry season. Moreover, the swine and domestic wastewater led to the presence of Apicomplexa in wet season only, implying rainfall runoff may enhance the spread of parasitic microeukaryotes. Taken together, our study provides novel insights into the immigration patterns of microeukaryotes and their dominant supergroups between terrestrial and riverine habitats.

Quantifying the Importance of Abiotic and Biotic Factors Governing the Succession of Gut Microbiota Over Shrimp Ontogeny

Intensive studies have evaluated abiotic factors in shaping host gut microbiota. In contrast, little is known on how and to what extent abiotic (geochemical variables) and biotic (i.e., surrounding microbes, younger shrimp, and age) factors assemble the gut microbiota over shrimp ontogeny. Considering the functional importance of gut microbiota in improving host fitness, this knowledge is fundamental to sustain a desirable gut microbiota for a healthy aquaculture. Here, we characterized the successional rules of both the shrimp gut and rearing water bacterial communities over the entire shrimp farming. Both the gut and rearing water bacterial communities exhibited the time decay of similarity relationship, with significantly lower temporal turnover rate for the gut microbiota, which were primarily governed by shrimp age (days postlarval inoculation) and water pH. Gut commensals were primary sourced (averaged 60.3%) from their younger host, rather than surrounding bacterioplankton (19.1%). A structural equation model revealed that water salinity, pH, total phosphorus, and dissolve oxygen directly governed bacterioplankton communities but not for the gut microbiota. In addition, shrimp gut microbiota did not simply mirror the rearing bacterioplankton communities. The gut microbiota tended to be governed by variable selection over shrimp ontogeny, while the rearing bacterioplankton community was shaped by homogeneous selection. However, the determinism of rare and stochasticity of abundant subcommunities were consistent between shrimp gut and rearing water. These findings highlight the importance of independently interpreting host-associated and free-living communities, as well as their rare and abundant subcommunities for a comprehensive understanding of the ecological processes that govern microbial successions.

Analysis of Cow-Calf Microbiome Transfer Routes and Microbiome Diversity in the Newborn Holstein Dairy Calf Hindgut

Hindgut microorganisms in newborn calves play an important role in the development of immunity and metabolism, and optimization of performance. However, knowledge of the extent to which microbiome colonization of the calf intestine is dependent on maternal characteristics is limited. In this study, placenta, umbilical cord, amniotic fluid, colostrum, cow feces, and calf meconium samples were collected from 6 Holstein cow-calf pairs. Microbial composition was analyzed by 16S rRNA gene high-throughput sequencing, and maternal transfer characteristics assessed using SourceTracker based on Gibbs sampling to fit the joint distribution using the mean proportions of each sample with meconium as the "sink" and other sample types as different "sources." Alpha and beta diversity analyses revealed sample type-specific microbiome features: microbial composition of the placenta, umbilical cord, amniotic fluid, colostrum, and calf feces were similar, but differed from cow feces (p < 0.05). Compared with profiles of meconium vs. placenta, meconium vs. umbilical cord, and meconium vs. colostrum, differences between the meconium and amniotic fluid were most obvious. SourceTracker analysis revealed that 23.8 ± 2.21% of the meconium OTUs matched those of umbilical cord samples, followed by the meconium-placenta pair (15.57 ± 2.2%), meconium-colostrum pair (14.4 ± 1.9%), and meconium-amniotic fluid pair (11.2 ± 1.7%). The matching ratio between meconium and cow feces was the smallest (10.5 ± 1%). Overall, our data indicated that the composition of the meconium microflora was similar compared with multiple maternal sites including umbilical cord, placenta, colostrum, and amniotic fluid. The umbilical cord microflora seemed to contribute the most to colonization of the fecal microflora of calves. Bacteria with digestive functions such as cellulose decomposition and rumen fermentation were mainly transmitted during the maternal transfer process.

Effect of temperature on the persistence of fecal bacteria in ambient anaerobic digestion systems treating swine manure

This study aimed to explore the effect of temperature on the persistence of fecal bacteria by multiple approaches in ambient anaerobic digestion systems treating swine manure. Both lab-scale (15 °C, 20 °C, and 25 °C) and field (26 °C on average) studies were conducted by high-throughput sequencing and culture-based methods. A community-wide Bayesian SourceTracker method was used to identify and estimate the fecal bacterial proportion in anaerobic effluent. High proportional contributions of fecal bacteria were observed in effluent at 15 °C (73%) and 20 °C (75%), while less was found at 25 °C (19%). This was further verified by a field study (23%) and an anaerobic reactor study at 37 °C (0.01%). To explore the potential reasons for differences in fecal bacterial proportions, bacterial taxa were divided into "lost" and "survivor" taxa in manure waste by LEfSe. The "survivor" taxa abundance was positively correlated with SourceTracker proportion (r = 0.913, P = 0.001), but negatively correlated with temperature (r = -0.826, P = 0.006). In addition, biomarkers in effluent were divided into "enriched" and "de novo" taxa. "Enriched" taxa, including acidogenic and acetogenic bacteria, were found at all temperatures, whereas taxa related to organic degradation were multiplied "de novo" at 25 °C. Variation partition analysis showed that temperature could explain 30% of variations in effluent bacterial community. Moreover, coliforms isolated from the manure and effluents at 15 °C and 20 °C were also phylogenetically related. This study provided comprehensive insight into the impact of temperature on the persistence of fecal bacteria in anaerobic effluent, with temperatures over 25 °C recommended to reduce fecal pollution.

Deciphering the Shifts in Microbial Community Diversity From Material Pretreatment to Saccharification Process of Fuyu-Flavor Baijiu

The microbiota of the pretreatment phase is crucial to the assembly of the microbial community in the saccharification of fuyu-flavor baijiu. This study investigates the shifts in microbial community diversity from the pretreatment of raw materials to the end of saccharification. High-throughput sequencing reveals that Lactobacillus, Weissella, and Bacillus in the bacterial community and Rhizopus, Candida, Pichia, and Aspergillus in the fungal community are predominant during raw material pretreatment and saccharification processes. Also, 11 bacterial genera, including Bacillus, Lactobacillus, Leuconostoc, Weissella, Lactococcus, and Acetobacter, and eight yeast genera, including Candida, Pichia, Saccharomyces, and Wickerhamomyces, were isolated from the initial saccharification stage by culture-dependent approaches. Sourcetracker analysis indicates that the cooling grains and rice husks were the main contributors to the bacterial community composition of the saccharification process, and Qu was the main contributor to the shaping of the fungal community structure during the saccharification process. Abundance variation of the predictive functional profiles of microbial communities encoding for key enzymes involved in pyruvate metabolism, starch and sucrose metabolism, and glycolysis/gluconeogenesis during the pretreatment and saccharification phases were inferred by PICRUSt2 analysis. The results of this study will be utilized to produce consistently high-quality fuyu-flavor baijiu via better controlling the shaping of microbial community structures during the pretreatment and fermentation processes.

Effects of shading levels on the composition and co-occurrence patterns of bacterioplankton and epibiotic bacterial communities of Cabomba caroliniana

Epibiotic bacterial community colonized on the plant leaf plays important roles in promoting plant growth and nutrient absorption, but is sensitive to environmental changes. As one of the most important environmental factors affecting the growth of plants and photosynthetic microorganisms, light may affect the diversity, composition, and interactions of the epibiotic bacterial community. Submerged plants in the aquatic ecosystem may be more sensitive to light intensity variations compared to the terrestrial plants since they usually receive less light. However, the effects of light on the interactions between the submerged plants and their epibiotic microbial communities remain uncertain. Here we used the 16S rRNA gene high-throughput sequencing to investigate the diversity and composition of the bacterioplankton and epibiotic bacterial communities of the Cabomba caroliniana under four different shading levels. A total of 24 water and leaf samples were collected from the experimental microcosms near Lake Taihu. We found the epibiotic bacterial community possessed a higher diversity than that of the bacterioplankton community, although the alpha diversity of the bacterioplankton community was more susceptible to different levels of shading. SourceTracker analysis revealed that with the increase of shading, the colonization of bacterioplankton to epibiotic bacteria decreased. Network analysis showed that the bacterial community network at 50% shading level had the lowest modularity and highest clustering coefficient compared to the bacterial community networks of other shading levels. Our findings provided new understandings of the effects of different light intensities on the epibiotic bacterial communities of submerged macrophytes.

Use of metabarcoding and source tracking to identify desirable or spoilage autochthonous microorganism sources during black olive fermentations

This study aimed at investigating the influence of the process environment and raw materials as sources of microorganisms during Nyons black table olive fermentations. Fermented olives and/or brine from spoiled fermentation tanks were analyzed and compared to good quality samples from fermentations collected during 3 consecutive harvest years. Fresh olives, salt and different process environment samples were also analyzed. Microbial diversity of all samples was analyzed using 16S and ITS2 amplicon sequencing and SourceTracker tool was used to investigate links between environment, raw materials and fermentation samples. First, comparison of microbial diversity in control and most spoiled fermentations revealed striking differences in bacterial composition with an overall higher diversity in spoiled fermentations especially for lactic acid bacteria with Lentilactobacillus buchneri, Lentilactobacillus parafarraginis dominating in brine and Pediococcus parvulus, Pediococcus ethanolidurans dominating in olive fruits. Fungal communities were similar in composition although higher abundances of Pichia membranifaciens and Penicillium carneum/roqueforti were observed in spoiled samples. Secondly, process environment samples were characterized by high bacterial and fungal diversity, especially compared to fresh olive fruits. Overall, dominant fungal species in control fermentations were also found in most environmental samples revealing a "house mycobiota". SourceTracker analysis further highlighted the contribution of brine and water from the optical sorter as a source of fungi. Most interestingly, spoilage fungi and most bacteria were retrieved in brine and environmental samples while others such as P. ethanolidurans were only found in environmental samples indicating that the studied spoilage originated from a fermentation deviation rather than a punctual contamination. Taken altogether, these results highlighted the positive and negative influence of the process environment and emphasized the relevance of studying it to better understand microbial vectors occurring during food fermentations, especially natural ones.

Shared bacterial communities between soil, stored drinking water, and hands in rural Bangladeshi households

Understanding household-level transmission pathways of fecal pathogens can provide insight for developing effective strategies to reduce diarrheal illness in low- and middle-income countries. We applied whole bacterial community analysis to investigate pathways of bacterial transmission in 50 rural Bangladeshi households. SourceTracker was used to quantify the shared microbial community in household reservoirs (stored drinking water, soil, and hands) and estimate the percentage of fecal-associated bacteria from child and mothers' feces in these reservoirs. Among the reservoirs studied, most bacterial transfer occurred between mothers' and children's hands and between mothers' hands and stored water. The relative percentage of human fecal-associated bacteria in all household reservoirs was low. We also quantified the number of identical amplicon sequence variants within and between individual households to assess bacterial community exchange in the domestic environment. Intra-household sharing of bacteria between mothers' and children's hands and between hands and soil was significantly greater than inter-household sharing.

Homogeneous selection drives antibiotic resistome in two adjacent sub-watersheds, China

Rivers are a significant reservoir of antibiotic resistance genes (ARGs), yet the biogeographic pattern of riverine ARGs and its underlying driving forces remain poorly understood. Here, we used metagenomic approach to investigate the spatio-temporal variation of ARGs in two adjacent sub-watersheds viz. North River (NR) and West River (WR), China. The results demonstrated that Bacitracin (22.8 % of the total ARGs), multidrug (20.7 %), sulfonamide (15.2 %) and tetracycline (10.9 %) were the dominant ARG types. SourceTracker analysis indicated that sewage treatment plants as the main source of ARGs, while animal feces mainly contributed in spreading the ARGs in the upstream of NR. Random forest and network analyses confirmed that NR was under the influence of fecal pollution. PCoA analysis demonstrated that the composition of ARGs changed along with the anthropogenic gradients, while the Raup-Crick null model showed that homogenizing selection mediated by class 1 integron intI1 resulted in stable ARG communities at whole watershed scale. Structural equation models revealed that microbial community, grassland and several non-antibiotic micropollutants may also play certain roles in influencing the distribution of ARGs. Overall, the observed deterministic formation of ARGs in riverine systems calls effective management strategies to mitigate the risks of antibiotic resistance on public health.

Source identification of phosphorus in the river-lake interconnected system using microbial community fingerprints

Phosphorus, one of the primary limiting factors for eutrophication, plays a crucial role in the ecology health of aquatic ecosystems. However, understanding phosphorus bioavailability and source contributions in contaminated lake sediments which could help develop effective eutrophication management plans is limited largely due to the lack of appropriate methods in large catchments with a complex arrangement of sources. Based on the significant relationships between sediment, phosphorus and microbial community, source-specific microbial community fingerprints formed by machine-learning classification SourceTracker might shed light on determining dominant phosphorus sources in the river-lake systems in the era of high-throughput sequencing. This study was conducted in Dongting Lake that suffered accelerated eutrophication due to considerable phosphorus input from the inflow-rivers. The results of phosphorus fractionation according to the Standards, Measurements and Testing harmonized procedure indicated that sediments in the central lake had a higher concentration of non-apatite inorganic phosphorus (Mann-Whitney U test), which deserves greater attention on the risk of phosphorus release. The significant relationships between phosphorus fractionations, sediment and bacterial community were established with the spearman correlation and network analysis. SourceTracker analysis indicated that the major inflow-rivers of phosphorus sources to Dongting Lake were the Songzi, Miluo, and Xinqiang Rivers. The effects of sediment diffusion distance on phosphorus source apportionment were further confirmed. Taken together, our results contribute to an improved understanding of phosphorus fractionations and source contributions in the river-lake systems and its potential impact to eutrophication management plans.

Exploring the diversity and role of microbiota during material pretreatment of light-flavor Baijiu

Baijiu (Chinese liquor) is a type of traditional distilled alcoholic beverage produced through spontaneous solid-state fermentation with sorghum as the primary material. Material processing, including sorghum soaking, steaming and cooling which is carried out in an open environment, is an integral part of Baijiu manufacturing. However, the microbiota involved in material pretreatment and its associate with the alcoholic fermentation is unclear. This research is aimed to exploring the diversity and role of microbiota during material pretreatment of light-flavor Baijiu. Results showed that Cyanobacteria, Epicoccum, and Cladosporium predominated in the sorghum at the beginning of soaking. Lactobacillus and Pichia became the predominant bacterial and fungal genera by the end of soaking. With the dynamics of microbiota, the pH declined sharply and the categories and concentration of volatile flavors such as alcohols, esters, acids, phenols, ketones, and aldehydes increased. Correlation analysis indicated that Lactobacillus and Pichia showed positive correlation with various flavors during soaking. Furthermore, SourceTracker analysis revealed that the microbiota involved during cooling processing was an important source of the Lactobacillus during fermentation of light-flavor Baijiu. This study illustrates the role of microbiota during material pretreatment and the association with alcoholic fermentation, which contributes to reveal the mechanism of Baijiu manufacturing.

Assessing the impact of source water on tap water bacterial communities in 46 drinking water supply systems in China

Drinking water purification and distribution processes shape the bacterial community in tap water; however, this study calls attention to the impact of source water on tap water bacterial community. Herein, paired source and tap water samples were collected from 46 drinking water supply systems in different watersheds across China, and high-throughput sequencing delineated that bacterial richness and diversity decreased and biogeographical distribution pattern weakened in tap water compared to source water. Despite the great changes of bacteria from source to tap, Bayesian-based SourceTracker analysis still verified that the proportional contributions of source water to shaping the tap water bacterial community ranged from 0% to 92.8% (49.73% ± 30.22% on average). This indelible contribution was further confirmed by the Mantel test (P < 0.001), Procrustes test (P < 0.001) and variance partition analysis, which showed that the source water together with geographical location explained 40.11% of tap water bacterial community variation. To explore the potential reasons that explain the great differences in the impact of source water among different samples, the universal shift pattern of bacterial communities from source to tap was summarized as a classification of dominant bacterial taxa: "sensitive taxa" versus "resistant taxa". The taxa including Planctomycetes, Verrucomicrobia, Acidobacteria, Chloroflexi, and δ-Proteobacteria could act as biomarkers to distinguish samples between source and tap water, and were classified as sensitive taxa. In contrast, α-Proteobacteria, β-Proteobacteria, γ-Proteobacteria, Actinobacteria, Firmicutes, Bacteroidetes, and Cyanobacteria were classified as resistant taxa. The abundance of sensitive taxa was negatively correlated with the SourceTracker proportion, while the abundance of resistant taxa was positively correlated with the SourceTracker proportion (P < 0.01). Thus, variation in source water bacterial community could be responsible for the degree of impact on tap water. Our findings give notice that the impact of source water microbiomes must be taken more seriously, and sufficient source water protection and engineering control strategies should be implemented to prevent the biological consequences of source water on tap water.

Source identification of antibiotic resistance genes in a peri-urban river using novel crAssphage marker genes and metagenomic signatures

Antimicrobial resistance is a growing public health concern, and environment is regarded as an important reservoir and dissemination route for antibiotic resistance genes (ARGs). To prevent and control ARG pollution, it is essential to correctly disentangle source-sink relationship of ARGs in the environment. However, accurately apportioning sources of ARGs is still a big challenge due to the complex interaction of multiple sources and contaminants in the environment with changing dynamics. In this study, we addressed this problem and focused on identifying the potential sources of ARGs in a peri-urban river by jointly utilizing two novel microbial source tracking methods. To attain the objective, sediment/water samples were collected from the peri-urban river and four ARG-associated ecotypes including effluents of sewage treatment plants (STPs), STP influent, chicken manures and pig manures. The high-throughput profilings of ARGs and microbial taxa in the river sediments and the four ecotypes were comprehensively characterized in combination of shotgun sequencing and metagenomic assembly analysis. CrAssphage, a recently-discovered DNA bacteriophage, was employed to track the impact of human fecal pollution on ARGs in the river sediments. Further, SourceTracker, a machine-learning classification tool, was used for quantifying the contributions of potential sources to ARGs in the river sediments based on the metagenomic signatures of ARGs and microbial taxa. In total, 888 ARG subtypes belonging to 29 ARG types were detected across all samples, including mcr-1 and a range of carbapenemases types. Statistical analyses suggested different ecotypes generally had distinct profiles of both ARGs and microbial taxa, while the ARG compositions were significantly correlated with the microbial community. Source tracking with crAssphage showed the presence of ARGs in the river sediments might be largely impacted by the extent of human fecal pollution, which was also confirmed by the analyses of SourceTracker that the discharge from STPs was the largest contributor of ARGs (81.6-92.1%) and microbes (49.3-68.1%) in the river sediments. Results of the study can help us to better understand the characterization of ARGs in the peri-urban ecosystem and to design effective prevention and control strategies for reducing ARG dissemination.

Compositional and temporal stability of fecal taxon libraries for use with SourceTracker in sub-tropical catchments

Characterization of microbial communities using high-throughput amplicon sequencing is an emerging approach for microbial source tracking of fecal pollution. This study used SourceTracker software to examine temporal and geographical variability of fecal bacterial community profiles to identify pollutant sources in three freshwater catchments in sub-tropical Australia. Fecal bacterial communities from 10 animal species, humans, and composite wastewater samples from six sewage treatment plants were characterized and compared to freshwater samples using Illumina amplicon sequencing of the V5-V6 regions of the 16S rRNA gene. Source contributions were calculated in SourceTracker using new fecal taxon libraries as well as previously generated libraries to determine the effects of geographic and temporal variability on source assignments. SourceTracker determined 16S rRNA bacterial communites within freshwater samples, shared taxonomic similarities to that of wastewater at low levels (typically <3%). SourceTraker also predicted occasional fecal detection of deer and flying fox sources in the water samples. No significant differences in source contributions were observed within sequences from current and previously characterized fecal samples (P ≥ 0.107). However, significant differences were observed between previously characterized and newly characterized source communities (ANOSIM P ≤ 0.001), which shared <15% community composition. Results suggest temporal instability of fecal taxon libraries among tested sources and highlight continual evaluation of community-based MST using confirmatory qPCR analyses of marker genes.

Controlling for Contaminants in Low-Biomass 16S rRNA Gene Sequencing Experiments

The relative scarcity of microbes in low-microbial-biomass environments makes accurate determination of community composition challenging. Identifying and controlling for contaminant bacterial DNA are critical steps in understanding microbial communities from these low-biomass environments. Our study introduces the use of a mock community dilution series as a positive control and evaluates four computational strategies that can identify contaminants in 16S rRNA gene sequencing experiments in order to remove them from downstream analyses. The appropriate computational approach for removing contaminant sequences from an experiment depends on prior knowledge about the microbial environment under investigation and can be evaluated with a dilution series of a mock microbial community.

Microbial communities are commonly studied using culture-independent methods, such as 16S rRNA gene sequencing. However, one challenge in accurately characterizing microbial communities is exogenous bacterial DNA contamination, particularly in low-microbial-biomass niches. Computational approaches to identify contaminant sequences have been proposed, but their performance has not been independently evaluated. To identify the impact of decreasing microbial biomass on polymicrobial 16S rRNA gene sequencing experiments, we created a mock microbial community dilution series. We evaluated four computational approaches to identify and remove contaminants, as follows: (i) filtering sequences present in a negative control, (ii) filtering sequences based on relative abundance, (iii) identifying sequences that have an inverse correlation with DNA concentration implemented in Decontam, and (iv) predicting the sequence proportion arising from defined contaminant sources implemented in SourceTracker. As expected, the proportion of contaminant bacterial DNA increased with decreasing starting microbial biomass, with 80.1% of the most diluted sample arising from contaminant sequences. Inclusion of contaminant sequences led to overinflated diversity estimates and distorted microbiome composition. All methods for contaminant identification successfully identified some contaminant sequences, which varied depending on the method parameters used and contaminant prevalence. Notably, removing sequences present in a negative control erroneously removed >20% of expected sequences. SourceTracker successfully removed over 98% of contaminants when the experimental environments were well defined. However, SourceTracker misclassified expected sequences and performed poorly when the experimental environment was unknown, failing to remove >97% of contaminants. In contrast, the Decontam frequency method did not remove expected sequences and successfully removed 70 to 90% of the contaminants.

IMPORTANCE The relative scarcity of microbes in low-microbial-biomass environments makes accurate determination of community composition challenging. Identifying and controlling for contaminant bacterial DNA are critical steps in understanding microbial communities from these low-biomass environments. Our study introduces the use of a mock community dilution series as a positive control and evaluates four computational strategies that can identify contaminants in 16S rRNA gene sequencing experiments in order to remove them from downstream analyses. The appropriate computational approach for removing contaminant sequences from an experiment depends on prior knowledge about the microbial environment under investigation and can be evaluated with a dilution series of a mock microbial community.

Assessing the origin of bacteria in tap water and distribution system in an unchlorinated drinking water system by SourceTracker using microbial community fingerprints

The general consensus is that the abundance of tap water bacteria is greatly influenced by water purification and distribution. Those bacteria that are released from biofilm in the distribution system are especially considered as the major potential risk for drinking water bio-safety. For the first time, this full-scale study has captured and identified the proportional contribution of the source water, treated water, and distribution system in shaping the tap water bacterial community based on their microbial community fingerprints using the Bayesian "SourceTracker" method. The bacterial community profiles and diversity analyses illustrated that the water purification process shaped the community of planktonic and suspended particle-associated bacteria in treated water. The bacterial communities associated with suspended particles, loose deposits, and biofilm were similar to each other, while the community of tap water planktonic bacteria varied across different locations in distribution system. The microbial source tracking results showed that there was not a detectable contribution of source water to bacterial community in the tap water and distribution system. The planktonic bacteria in the treated water was the major contributor to planktonic bacteria in the tap water (17.7-54.1%). The particle-associated bacterial community in the treated water seeded the bacterial community associated with loose deposits (24.9-32.7%) and biofilm (37.8-43.8%) in the distribution system. In return, the loose deposits and biofilm showed a significant influence on tap water planktonic and particle-associated bacteria, which were location dependent and influenced by hydraulic changes. This was revealed by the increased contribution of loose deposits to tap water planktonic bacteria (from 2.5% to 38.0%) and an increased contribution of biofilm to tap water particle-associated bacteria (from 5.9% to 19.7%) caused by possible hydraulic disturbance from proximal to distal regions. Therefore, our findings indicate that the tap water bacteria could possibly be managed by selecting and operating the purification process properly and cleaning the distribution system effectively.

Decay of sewage-associated bacterial communities in fresh and marine environmental waters and sediment

Understanding the microbial quality of recreational waters is critical to effectively managing human health risks. In recent years, the development of new molecular methods has provided scientists with alternatives to the use of culture-based fecal indicator methods for investigating sewage contamination in recreational waters. Before these methods can be formalized into guidelines, however, we must investigate their utility, including strengths and weaknesses in different environmental media. In this study, we investigated the decay of sewage-associated bacterial communities in water and sediment from three recreational areas in Southeast Queensland, Australia. Outdoor mesocosms with water and sediment samples from two marine and one freshwater sites were inoculated with untreated sewage and sampled on days 0, 1, 4, 8, 14, 28, and 50. Amplicon sequencing was performed on the DNA extracted from water and sediment samples, and SourceTracker was used to determine the decay of sewage-associated bacterial communities and how they change following a contamination event. No sewage-associated operational taxonomic units (OTUs) were detected in water and sediment samples after day 4; however, the bacterial communities remained changed from their background measures, prior to sewage amendment. Following untreated sewage inoculation, the mesocosm that had the most diverse starting bacterial community recovered to about 60% of its initial community composition, whereas the least diverse bacterial community only recovered to about 30% of its initial community composition. This suggests that a more diverse bacterial community may play an important role in water quality outcomes after sewage contamination events. Further investigation into potential links between bacterial communities and measures of fecal indicators, pathogens, and microbial source tracking (MST) markers is warranted and may provide insight for recreational water decision-makers.

Source tracking using microbial community fingerprints: Method comparison with hydrodynamic modelling

Urban estuaries around the world are experiencing contamination from diffuse and point sources, which increases risks to public health. To mitigate and manage risks posed by elevated levels of contamination in urban waterways, it is critical to identify the primary water sources of contamination within catchments. Source tracking using microbial community fingerprints is one tool that can be used to identify sources. However, results derived from this approach have not yet been evaluated using independent datasets. As such, the key objectives of this investigation were: (1) to identify the major sources of water responsible for bacterial loadings within an urban estuary using microbial source tracking (MST) using microbial communities; and (2) to evaluate this method using a 3-dimensional hydrodynamic model. The Yarra River estuary, which flows through the city of Melbourne in South-East Australia was the focus of this study. We found that the water sources contributing to the bacterial community in the Yarra River estuary varied temporally depending on the estuary's hydrodynamic conditions. The water source apportionment determined using microbial community MST correlated to those determined using a 3-dimensional hydrodynamic model of the transport and mixing of a tracer in the estuary. While there were some discrepancies between the two methods, this investigation demonstrated that MST using bacterial community fingerprints can identify the primary water sources of microorganisms in an estuarine environment. As such, with further optimization and improvements, microbial community MST has the potential to become a powerful tool that could be practically applied in the mitigation of contaminated aquatic systems.

The Oral and Skin Microbiomes of Captive Komodo Dragons Are Significantly Shared with Their Habitat

Examining the way in which animals, including those in captivity, interact with their environment is extremely important for studying ecological processes and developing sophisticated animal husbandry. Here we use the Komodo dragon (Varanus komodoensis) to quantify the degree of sharing of salivary, skin, and fecal microbiota with their environment in captivity. Both species richness and microbial community composition of most surfaces in the Komodo dragon's environment are similar to the Komodo dragon's salivary and skin microbiota but less similar to the stool-associated microbiota. We additionally compared host-environment microbiome sharing between captive Komodo dragons and their enclosures, humans and pets and their homes, and wild amphibians and their environments. We observed similar host-environment microbiome sharing patterns among humans and their pets and Komodo dragons, with high levels of human/pet- and Komodo dragon-associated microbes on home and enclosure surfaces. In contrast, only small amounts of amphibian-associated microbes were detected in the animals' environments. We suggest that the degree of sharing between the Komodo dragon microbiota and its enclosure surfaces has important implications for animal health. These animals evolved in the context of constant exposure to a complex environmental microbiota, which likely shaped their physiological development; in captivity, these animals will not receive significant exposure to microbes not already in their enclosure, with unknown consequences for their health. IMPORTANCE Animals, including humans, have evolved in the context of exposure to a variety of microbial organisms present in the environment. Only recently have humans, and some animals, begun to spend a significant amount of time in enclosed artificial environments, rather than in the more natural spaces in which most of evolution took place. The consequences of this radical change in lifestyle likely extend to the microbes residing in and on our bodies and may have important implications for health and disease. A full characterization of host-microbe sharing in both closed and open environments will provide crucial information that may enable the improvement of health in humans and in captive animals, both of which experience a greater incidence of disease (including chronic illness) than counterparts living under more ecologically natural conditions.

Into the deep: Evaluation of SourceTracker for assessment of faecal contamination of coastal waters

Faecal contamination of recreational waters is an increasing global health concern. Tracing the source of the contaminant is a vital step towards mitigation and disease prevention. Total 16S rRNA amplicon data for a specific environment (faeces, water, soil) and computational tools such as the Markov-Chain Monte Carlo based SourceTracker can be applied to microbial source tracking (MST) and attribution studies. The current study applied artificial and in-laboratory derived bacterial communities to define the potential and limitations associated with the use of SourceTracker, prior to its application for faecal source tracking at three recreational beaches near Port Phillip Bay (Victoria, Australia). The results demonstrated that at minimum multiple model runs of the SourceTracker modelling tool (i.e. technical replicates) were required to identify potential false positive predictions. The calculation of relative standard deviations (RSDs) for each attributed source improved overall predictive confidence in the results. In general, default parameter settings provided high sensitivity, specificity, accuracy and precision. Application of SourceTracker to recreational beach samples identified treated effluent as major source of human-derived faecal contamination, present in 69% of samples. Site-specific sources, such as raw sewage, stormwater and bacterial populations associated with the Yarra River estuary were also identified. Rainfall and associated sand resuspension at each location correlated with observed human faecal indicators. The results of the optimised SourceTracker analysis suggests that local sources of contamination have the greatest effect on recreational coastal water quality.