Microbial Communities Can Be Described by Metabolic Structure: A General Framework and Application to a Seasonally Variable, Depth-Stratified Microbial Community from the Coastal West Antarctic Peninsula

Comparison of microbial diversity and metabolic activities in organic and conventional rice farms in Thailand

Studies of the soil microbial community have revealed that several factors, both biotic and abiotic, can influence the community structure, diversity, and function. Human agricultural practice has been one of the key factors that affect microbial structure in soil. In this study, we examined the effect of two rice farming practices (i.e., conventional and organic) on soil microbial diversity and their metabolic activities. We proposed that the use of herbicides and chemical fertilizers in the conventional rice farming may decrease the soil microbial diversity and that it may lead to a decline in soil quality. However, our results show that there is no significant difference in the soil chemical properties, microbial diversity, and microbial metabolic activities between the two agricultural management systems. The wetland water cycling regime of rice agriculture in Thailand perhaps prevents the accumulation of chemicals in the soil, thus subdue the effect of these chemicals on soil microbes. Furthermore, our results showed that the key determinant of soil chemical properties as well as microbial community structure and diversity is the soil physical property, while the farming activity influences the microbial metabolic functions. These findings indicate the complex nature of the soil, microbes, and human farming interactions.IMPORTANCERice is a major export commodity in Thailand. As such, an understanding of the effect of conventional or organic farming approaches on soil microbial community could enable a suitable farming management. In this study, microbial communities were surveyed and compared between the two rice farming practices for their diversity and metabolic activities. Results showed no significant differences in microbial community structure and diversity between the two rice farming practices, but significant differences were observed due to the soil type, namely, clay, silty clay, silty clay loam, loam, and silty loam. Interestingly, significant differences in metabolic functions were also observed in different soil farming activities, such as land rest, period of growth, and post-burning, but not due to conventional or organic practices. These findings showed that the soil physical type and the farming activity impact the microbial community more than whether it is conventional or organically farmed.

Changes in microbial community structure of bio-fouled polyolefins over a year-long seawater incubation in Hawai'i

Plastic waste, especially positively buoyant polymers known as polyolefins, are a major component of floating debris in the marine environment. While plastic colonisation by marine microbes is well documented from environmental samples, the succession of marine microbial community structure over longer time scales (> > 1 month) and across different types and shapes of plastic debris is less certain. We analysed 16S rRNA and 18S rRNA amplicon gene sequences from biofilms on polyolefin debris floating in a flow-through seawater tank in Hawai'i to assess differences in microbial succession across the plastic types of polypropylene (PP) and both high-density polyethylene (HDPE) and low-density polyethylene (LDPE) made of different plastic shapes (rod, film and cube) under the same environmental conditions for 1 year. Regardless of type or shape, all plastic debris were dominated by the eukaryotic diatom Nitzschia, and only plastic type was significantly important for bacterial community structure over time (p = 0.005). PE plastics had higher differential abundance when compared to PP for 20 bacterial and eight eukaryotic taxa, including the known plastic degrading bacterial taxon Hyphomonas (p = 0.01). Results from our study provide empirical evidence that plastic type may be more important for bacterial than eukaryotic microbial community succession on polyolefin pollution under similar conditions.

Deep learning for predicting 16S rRNA gene copy number

Culture-independent 16S rRNA gene metabarcoding is a commonly used method for microbiome profiling. To achieve more quantitative cell fraction estimates, it is important to account for the 16S rRNA gene copy number (hereafter 16S GCN) of different community members. Currently, there are several bioinformatic tools available to estimate the 16S GCN values, either based on taxonomy assignment or phylogeny. Here we present a novel approach ANNA16, Artificial Neural Network Approximator for 16S rRNA gene copy number, a deep learning-based method that estimates the 16S GCN values directly from the 16S gene sequence strings. Based on 27,579 16S rRNA gene sequences and gene copy number data from the rrnDB database, we show that ANNA16 outperforms the commonly used 16S GCN prediction algorithms. Interestingly, Shapley Additive exPlanations (SHAP) shows that ANNA16 can identify unexpected informative positions in 16S rRNA gene sequences without any prior phylogenetic knowledge, which suggests potential applications beyond 16S GCN prediction.

Gut microbiota in vaccine naïve Gabonese children with rotavirus A gastroenteritis

Background

While the gut microbiome modulates the pathogenesis of enteric viruses, how infections caused by rotavirus A (RVA), with or without diarrhoea, alter the gut microbiota has been sparsely studied.

Methods

From a cohort of 224 vaccine naïve Gabonese children with and without diarrhoea (n = 177 and n = 67, respectively), 48 stool samples were analysed: (i) RVA with diarrhoea (n = 12); (ii) RVA without diarrhoea (n = 12); (iii) diarrhoea without RVA (n = 12); (iv) healthy controls without diarrhoea and RVA (n = 12). The 16S rRNA metabarcoding using Oxford Nanopore sequencing data was analysed for taxonomic composition, abundance, alpha and beta diversity, and metabolic pathways.

Findings

Alpha diversity showed that children with acute diarrhoea (with and without RVA infection), and children with acute diarrhoea without RVA had low microbial diversity compared to healthy children (p = 0.001 and p = 0.006, respectively). No significant differences observed when comparing children with RVA with or without diarrhoea. Beta diversity revealed high microbial heterogeneity in children without diarrhoea. Proteobacteria (68%) and Firmicutes (69%) were most common in the diarrhoea and non-diarrhoea groups, respectively. Proteobacteria (53%) were most common in children without RVA, while Firmicutes (55%) were most common with RVA. At the genus level, Escherichia (21%), Klebsiella (10%) and Salmonella (4%) were abundant in children with diarrhoea, while Blautia (11%), Clostridium (8%), Lachnoclostridium (6%) and Ruminococcus (5%) were abundant in children without diarrhoea. Metabolites involved in amino acid, carbohydrate, lipid, nucleotide, and vitamin metabolism were quantitatively altered.

Interpretation

Although host physiology dictates the intestinal milieu, diarrhoea per se can alter a balanced gut microbiota, whereas infectious diarrhoea disrupts the gut microbiome and reduces its diversity.

Ecological Trait-Based Digital Categorization of Microbial Genomes for Denitrification Potential

Microorganisms encode proteins that function in the transformations of useful and harmful nitrogenous compounds in the global nitrogen cycle. The major transformations in the nitrogen cycle are nitrogen fixation, nitrification, denitrification, anaerobic ammonium oxidation, and ammonification. The focus of this report is the complex biogeochemical process of denitrification, which, in the complete form, consists of a series of four enzyme-catalyzed reduction reactions that transforms nitrate to nitrogen gas. Denitrification is a microbial strain-level ecological trait (characteristic), and denitrification potential (functional performance) can be inferred from trait rules that rely on the presence or absence of genes for denitrifying enzymes in microbial genomes. Despite the global significance of denitrification and associated large-scale genomic and scholarly data sources, there is lack of datasets and interactive computational tools for investigating microbial genomes according to denitrification trait rules. Therefore, our goal is to categorize archaeal and bacterial genomes by denitrification potential based on denitrification traits defined by rules of enzyme involvement in the denitrification reduction steps. We report the integration of datasets on genome, taxonomic lineage, ecosystem, and denitrifying enzymes to provide data investigations context for the denitrification potential of microbial strains. We constructed an ecosystem and taxonomic annotated denitrification potential dataset of 62,624 microbial genomes (866 archaea and 61,758 bacteria) that encode at least one of the twelve denitrifying enzymes in the four-step canonical denitrification pathway. Our four-digit binary-coding scheme categorized the microbial genomes to one of sixteen denitrification traits including complete denitrification traits assigned to 3280 genomes from 260 bacteria genera. The bacterial strains with complete denitrification potential pattern included Arcobacteraceae strains isolated or detected in diverse ecosystems including aquatic, human, plant, and Mollusca (shellfish). The dataset on microbial denitrification potential and associated interactive data investigations tools can serve as research resources for understanding the biochemical, molecular, and physiological aspects of microbial denitrification, among others. The microbial denitrification data resources produced in our research can also be useful for identifying microbial strains for synthetic denitrifying communities.

On the limits of 16S rRNA gene-based metagenome prediction and functional profiling

Molecular profiling techniques such as metagenomics, metatranscriptomics or metabolomics offer important insights into the functional diversity of the microbiome. In contrast, 16S rRNA gene sequencing, a widespread and cost-effective technique to measure microbial diversity, only allows for indirect estimation of microbial function. To mitigate this, tools such as PICRUSt2, Tax4Fun2, PanFP and MetGEM infer functional profiles from 16S rRNA gene sequencing data using different algorithms. Prior studies have cast doubts on the quality of these predictions, motivating us to systematically evaluate these tools using matched 16S rRNA gene sequencing, metagenomic datasets, and simulated data. Our contribution is threefold: (i) using simulated data, we investigate if technical biases could explain the discordance between inferred and expected results; (ii) considering human cohorts for type two diabetes, colorectal cancer and obesity, we test if health-related differential abundance measures of functional categories are concordant between 16S rRNA gene-inferred and metagenome-derived profiles and; (iii) since 16S rRNA gene copy number is an important confounder in functional profiles inference, we investigate if a customised copy number normalisation with the rrnDB database could improve the results. Our results show that 16S rRNA gene-based functional inference tools generally do not have the necessary sensitivity to delineate health-related functional changes in the microbiome and should thus be used with care. Furthermore, we outline important differences in the individual tools tested and offer recommendations for tool selection.

Community-scale models of microbiomes: Articulating metabolic modelling and metagenome sequencing

Building models is essential for understanding the functions and dynamics of microbial communities. Metabolic models built on genome-scale metabolic network reconstructions (GENREs) are especially relevant as a means to decipher the complex interactions occurring among species. Model reconstruction increasingly relies on metagenomics, which permits direct characterisation of naturally occurring communities that may contain organisms that cannot be isolated or cultured. In this review, we provide an overview of the field of metabolic modelling and its increasing reliance on and synergy with metagenomics and bioinformatics. We survey the means of assigning functions and reconstructing metabolic networks from (meta-)genomes, and present the variety and mathematical fundamentals of metabolic models that foster the understanding of microbial dynamics. We emphasise the characterisation of interactions and the scaling of model construction to large communities, two important bottlenecks in the applicability of these models. We give an overview of the current state of the art in metagenome sequencing and bioinformatics analysis, focusing on the reconstruction of genomes in microbial communities. Metagenomics benefits tremendously from third-generation sequencing, and we discuss the opportunities of long-read sequencing, strain-level characterisation and eukaryotic metagenomics. We aim at providing algorithmic and mathematical support, together with tool and application resources, that permit bridging the gap between metagenomics and metabolic modelling.

A paleoecological investigation of recent cyanobacterial blooms and their drivers in two contrasting lakes

Cyanobacterial blooms are one of the most significant threats to global water security and freshwater biodiversity. Interactions among multiple stressors, including habitat degradation, species invasions, increased nutrient runoff, and climate change, are key drivers. However, assessing the role of anthropogenic activity on the onset of cyanobacterial blooms and exploring response variation amongst lakes of varying size and depth is usually limited by lack of historical records. In the present study we applied molecular, paleolimnological (trace metal, Itrax-µ-XRF and hyperspectral scanning, chronology), paleobotanical (pollen) and historical data to reconstruct cyanobacterial abundance and community composition and anthropogenic impacts in two dune lakes over a period of up to 1200 years. Metabarcoding and droplet digital PCR results showed very low levels of picocyanobacteria present in the lakes prior to about CE 1854 (1839-1870 CE) in the smaller shallow Lake Alice and CE 1970 (1963-1875 CE) in the larger deeper Lake Wiritoa. Hereafter bloom-forming cyanobacteria were detected and increased notably in abundance post CE 1984 (1982-1985 CE) in Lake Alice and CE 1997 (1990-2007 CE) in Lake Wiritoa. Currently, the magnitude of blooms is more pronounced in Lake Wiritoa, potentially attributable to hypoxia-induced release of phosphorus from sediment, introducing an additional source of nutrients. Generalized linear modelling was used to investigate the contribution of nutrients (proxy = bacterial functions), temperature, redox conditions (Mn:Fe), and erosion (Ti:Inc) in driving the abundance of cyanobacteria (ddPCR). In Lake Alice nutrients and erosion had a statistically significant effect, while in Lake Wiritoa nutrients and redox conditions were significant.

Single-cell analysis in hypersaline brines predicts a water-activity limit of microbial anabolic activity

Hypersaline brines provide excellent opportunities to study extreme microbial life. Here, we investigated anabolic activity in nearly 6000 individual cells from solar saltern sites with water activities (aw) ranging from 0.982 to 0.409 (seawater to extreme brine). Average anabolic activity decreased exponentially with aw, with nuanced trends evident at the single-cell level: The proportion of active cells remained high (>50%) even after NaCl saturation, and subsets of cells spiked in activity as aw decreased. Intracommunity heterogeneity in activity increased as seawater transitioned to brine, suggesting increased phenotypic heterogeneity with increased physiological stress. No microbial activity was detected in the 0.409-aw brine (an MgCl2-dominated site) despite the presence of cell-like structures. Extrapolating our data, we predict an aw limit for detectable anabolic activity of 0.540, which is beyond the currently accepted limit of life based on cell division. This work demonstrates the utility of single-cell, metabolism-based techniques for detecting active life and expands the potential habitable space on Earth and beyond.

Land use alters bacterial growth dynamics in soil

Microbial growth and mortality are major determinants of soil carbon cycling. We measured in situ growth dynamics of individual bacterial taxa in cropped and successional soils in response to a resource pulse. We hypothesized that land use imposes selection pressures on growth characteristics. We estimated growth and death for 453 and 73 taxa, respectively. The average generation time was 5.04 ± 6.28 (SD; range 0.7-63.5) days. Lag times were shorter in cultivated than successional soils and resource amendment decreased lag times. Taxa exhibiting the greatest growth response also exhibited the greatest mortality, indicative of boom-and-bust dynamics. We observed a bimodal growth rate distribution, representing fast- and slow-growing clusters. Both clusters grew more rapidly in successional soils, which had more organic matter, than cultivated soils. Resource amendment increased the growth rate of the slower growing but not the faster-growing cluster via a mixture of increased growth rates and species turnover, indicating that competitive dynamics constrain growth rates in situ. These two clusters show that copiotrophic bacteria in soils may be subdivided into different life history groups and that these subgroups respond independently to land use and resource availability.

Critical review of 16S rRNA gene sequencing workflow in microbiome studies: From primer selection to advanced data analysis

The multi-batch reanalysis approach of jointly reevaluating gene/genome sequences from different works has gained particular relevance in the literature in recent years. The large amount of 16S ribosomal ribonucleic acid (rRNA) gene sequence data stored in public repositories and information in taxonomic databases of the same gene far exceeds that related to complete genomes. This review is intended to guide researchers new to studying microbiota, particularly the oral microbiota, using 16S rRNA gene sequencing and those who want to expand and update their knowledge to optimise their decision-making and improve their research results. First, we describe the advantages and disadvantages of using the 16S rRNA gene as a phylogenetic marker and the latest findings on the impact of primer pair selection on diversity and taxonomic assignment outcomes in oral microbiome studies. Strategies for primer selection based on these results are introduced. Second, we identified the key factors to consider in selecting the sequencing technology and platform. The process and particularities of the main steps for processing 16S rRNA gene-derived data are described in detail to enable researchers to choose the most appropriate bioinformatics pipeline and analysis methods based on the available evidence. We then produce an overview of the different types of advanced analyses, both the most widely used in the literature and the most recent approaches. Several indices, metrics and software for studying microbial communities are included, highlighting their advantages and disadvantages. Considering the principles of clinical metagenomics, we conclude that future research should focus on rigorous analytical approaches, such as developing predictive models to identify microbiome-based biomarkers to classify health and disease states. Finally, we address the batch effect concept and the microbiome-specific methods for accounting for or correcting them.

Environmental selection and evolutionary process jointly shape genomic and functional profiles of mangrove rhizosphere microbiomes

Mangrove reforestation with introduced species has been an important strategy to restore mangrove ecosystem functioning. However, how such activities affect microbially driven methane (CH4), nitrogen (N), and sulfur (S) cycling of rhizosphere microbiomes remains unclear. To understand the effect of environmental selection and the evolutionary process on microbially driven biogeochemical cycles in native and introduced mangrove rhizospheres, we analyzed key genomic and functional profiles of rhizosphere microbiomes from native and introduced mangrove species by metagenome sequencing technologies. Compared with the native mangrove (Kandelia obovata, KO), the introduced mangrove (Sonneratia apetala, SA) rhizosphere microbiome had significantly (p < 0.05) higher average genome size (AGS) (5.8 vs. 5.5 Mb), average 16S ribosomal RNA gene copy number (3.5 vs. 3.1), relative abundances of mobile genetic elements, and functional diversity in terms of the Shannon index (7.88 vs. 7.84) but lower functional potentials involved in CH4 cycling (e.g., mcrABCDG and pmoABC), N2 fixation (nifHDK), and inorganic S cycling (dsrAB, dsrC, dsrMKJOP, soxB, sqr, and fccAB). Similar results were also observed from the recovered Proteobacterial metagenome-assembled genomes with a higher AGS and distinct functions in the introduced mangrove rhizosphere. Additionally, salinity and ammonium were identified as the main environmental drivers of functional profiles of mangrove rhizosphere microbiomes through deterministic processes. This study advances our understanding of microbially mediated biogeochemical cycling of CH4, N, and S in the mangrove rhizosphere and provides novel insights into the influence of environmental selection and evolutionary processes on ecosystem functions, which has important implications for future mangrove reforestation.

Predicting variable gene content in Escherichia coli using conserved genes

Having the ability to predict the protein-encoding gene content of an incomplete genome or metagenome-assembled genome is important for a variety of bioinformatic tasks. In this study, as a proof of concept, we built machine learning classifiers for predicting variable gene content in Escherichia coli genomes using only the nucleotide k-mers from a set of 100 conserved genes as features. Protein families were used to define orthologs, and a single classifier was built for predicting the presence or absence of each protein family occurring in 10%-90% of all E. coli genomes. The resulting set of 3,259 extreme gradient boosting classifiers had a per-genome average macro F1 score of 0.944 [0.943-0.945, 95% CI]. We show that the F1 scores are stable across multi-locus sequence types and that the trend can be recapitulated by sampling a smaller number of core genes or diverse input genomes. Surprisingly, the presence or absence of poorly annotated proteins, including "hypothetical proteins" was accurately predicted (F1 = 0.902 [0.898-0.906, 95% CI]). Models for proteins with horizontal gene transfer-related functions had slightly lower F1 scores but were still accurate (F1s = 0.895, 0.872, 0.824, and 0.841 for transposon, phage, plasmid, and antimicrobial resistance-related functions, respectively). Finally, using a holdout set of 419 diverse E. coli genomes that were isolated from freshwater environmental sources, we observed an average per-genome F1 score of 0.880 [0.876-0.883, 95% CI], demonstrating the extensibility of the models. Overall, this study provides a framework for predicting variable gene content using a limited amount of input sequence data. IMPORTANCE Having the ability to predict the protein-encoding gene content of a genome is important for assessing genome quality, binning genomes from shotgun metagenomic assemblies, and assessing risk due to the presence of antimicrobial resistance and other virulence genes. In this study, we built a set of binary classifiers for predicting the presence or absence of variable genes occurring in 10%-90% of all publicly available E. coli genomes. Overall, the results show that a large portion of the E. coli variable gene content can be predicted with high accuracy, including genes with functions relating to horizontal gene transfer. This study offers a strategy for predicting gene content using limited input sequence data.

The impact of diet and ethnicity on gut microbiota variation in irritable bowel syndrome: A multi-center study

BACKGROUND AND AIM

The gut microbiota in irritable bowel syndrome (IBS) is known to vary with diet. We aim to (i) analyze the gut microbiota composition of IBS patients from a multi-ethnic population and (ii) explore the impact of a low FODMAP diet on gastrointestinal symptoms and gut microbiota composition among IBS patients.

METHODS

A multi-center study of multi-ethnic Asian patients with IBS was conducted in two phases: (i) an initial cross-sectional gut microbiota composition study of IBS patients and healthy controls, followed by (ii) a single-arm 6-week dietary interventional study of the IBS patients alone, exploring clinical and gut microbiota changes.

RESULTS

A total of 34 adult IBS patients (IBS sub-types of IBS-D 44.1%, IBS-C 32.4%, and IBS-M 23.5%) and 15 healthy controls were recruited. A greater abundance of Parabacteroides species with lower levels of bacterial fermenters and short-chain fatty acids producers were found among IBS patients compared with healthy controls. Age and ethnicity were found to be associated with gut microbiota composition. Following a low FODMAP dietary intervention, symptom and quality of life improvement were observed in 24 (70.6%) IBS patients. Symptom improvement was associated with adherence to the low FODMAP diet (46.7% poor adherence vs 92.9% good adherence, P = 0.014), and gut microbiota patterns, particularly with a greater abundance of Bifidobacterium longum, Anaerotignum propionicum, and Blautia species post-intervention.

CONCLUSION

Gut microbiota variation in multi-ethnic IBS patients may be related to dietary intake and may be helpful to identify patients who are likely to respond to a low FODMAP diet.

Exploring the Influence of Small-Scale Geographical and Seasonal Variations Over the Microbial Diversity in a Poly-extreme Athalosaline Wetland

Microorganisms are the most diverse life form on the planet and are critical for maintaining the geochemical cycles, especially in extreme environments. Bacterial communities are dynamic and respond directly to changes in abiotic conditions; among these communities, poly-extremophiles are particularly sensitive to perturbations due to their high specialization. Salar de Huasco is a high-altitude wetland located on the Chilean Altiplano exhibiting several conditions considered extreme for life, including negative water balance, extreme variations in temperature and pH values, high UV radiation, and the presence of various toxic metal(oids). However, previous reports have revealed a diverse bacterial community that has adapted to these conditions, here, we aimed to determine whether microbial community diversity and composition changed in response to geographical and seasonal variations. We found that there are significant differences in diversity, abundance, and composition in bacterial taxa that could be attributed to local geographical and seasonal variations, which in turn, can be associated with microbial traits. In conclusion, in this poly-extreme environment, small-scale changes can trigger significant changes in the microbial communities that maintain basic biogeochemical cycles. Further in depth analysis of microbial functionality and geo-ecological dynamics are necessary to better understand the relationships between seasonal changes and bacterial communities.

Comprehensive insights on environmental adaptation strategies in Antarctic bacteria and biotechnological applications of cold adapted molecules

Climate change and the induced environmental disturbances is one of the major threats that have a strong impact on bacterial communities in the Antarctic environment. To cope with the persistent extreme environment and inhospitable conditions, psychrophilic bacteria are thriving and displaying striking adaptive characteristics towards severe external factors including freezing temperature, sea ice, high radiation and salinity which indicates their potential in regulating climate change's environmental impacts. The review illustrates the different adaptation strategies of Antarctic microbes to changing climate factors at the structural, physiological and molecular level. Moreover, we discuss the recent developments in "omics" approaches to reveal polar "blackbox" of psychrophiles in order to gain a comprehensive picture of bacterial communities. The psychrophilic bacteria synthesize distinctive cold-adapted enzymes and molecules that have many more industrial applications than mesophilic ones in biotechnological industries. Hence, the review also emphasizes on the biotechnological potential of psychrophilic enzymes in different sectors and suggests the machine learning approach to study cold-adapted bacteria and engineering the industrially important enzymes for sustainable bioeconomy.

Accounting for 16S rRNA copy number prediction uncertainty and its implications in bacterial diversity analyses

16S rRNA gene copy number (16S GCN) varies among bacterial species and this variation introduces potential biases to microbial diversity analyses using 16S rRNA read counts. To correct the biases, methods have been developed to predict 16S GCN. A recent study suggests that the prediction uncertainty can be so great that copy number correction is not justified in practice. Here we develop RasperGade16S, a novel method and software to better model and capture the inherent uncertainty in 16S GCN prediction. RasperGade16S implements a maximum likelihood framework of pulsed evolution model and explicitly accounts for intraspecific GCN variation and heterogeneous GCN evolution rates among species. Using cross-validation, we show that our method provides robust confidence estimates for the GCN predictions and outperforms other methods in both precision and recall. We have predicted GCN for 592605 OTUs in the SILVA database and tested 113842 bacterial communities that represent an exhaustive and diverse list of engineered and natural environments. We found that the prediction uncertainty is small enough for 99% of the communities that 16S GCN correction should improve their compositional and functional profiles estimated using 16S rRNA reads. On the other hand, we found that GCN variation has limited impacts on beta-diversity analyses such as PCoA, NMDS, PERMANOVA and random-forest test.

Estuarine Sediment Microbiomes from a Chronosequence of Restored Urban Salt Marshes

Salt marshes play an important role in the global nutrient cycle. The sediments in these systems harbor diverse and complex bacterial communities possessing metabolic capacities that provide ecosystem services such as nutrient cycling and removal. On the East Coast of the USA, salt marshes have been experiencing degradation due to anthropogenic stressors. Salt marsh islands within Jamaica Bay, New York City (USA), are surrounded by a large highly urbanized watershed and have declined in area. Restoration efforts have been enacted to reduce further loss, but little is known about how microbial communities develop following restoration activities, or how processes such as nitrogen cycling are impacted. Sediment samples were collected at two sampling depths from five salt marsh islands to characterize the bacterial communities found in marsh sediment including a post-restoration chronosequence of 3-12 years. We used 16s rRNA amplicon sequencing to define alpha and beta diversity, taxonomic composition, and predicted metabolic profile of each sediment sample. We found significant differences in alpha diversity between sampling depths, and significant differences in beta diversity, taxonomic composition, and predicted metabolic capacity among the five sampling locations. The youngest restored site and the degraded natural sampling site exhibited the most distinct communities among the five sites. Our findings suggest that while the salt marsh islands are located in close proximity to each other, they harbor distinct bacterial communities that can be correlated with post-restoration age, marsh health, and other environmental factors such as availability of organic carbon. IMPORTANCE: Salt marshes play a critical role in the global nutrient cycle due to sediment bacteria and their metabolic capacities. Many East Coast salt marshes have experienced significant degradation over recent decades, thought largely to be due to anthropogenic stressors such as nitrogen loading, urban development, and sea-level rise. Salt marsh islands in Jamaica Bay (Queens/Brooklyn NY) are exposed to high water column nitrogen due to wastewater effluent. Several receding marsh islands have been subjected to restoration efforts to mitigate this loss. Little is known about the effect marsh restoration has on bacterial communities, their metabolic capacity, or how they develop post-restoration. Here, we describe the bacterial communities found in marsh islands including a post-restoration chronosequence of 3-12 years and one degraded marsh island that remains unrestored. We found distinct communities at marsh sites, despite their geographic proximity. Differences in diversity and community composition were consistent with changes in organic carbon availability that occur during marsh development, and may result in differences in ecosystem function among sites.

Bacterial diversity in the aquatic system in India based on metagenome analysis-a critical review

Microbial analysis has become one of the most critical areas in aquatic ecology and a crucial component for assessing the contribution of microbes in food web dynamics and biogeochemical processes. Initial research was focused on estimating the abundance and distribution of the microbes using microscopy and culture-based analysis, which are undoubtedly complex tasks. Over the past few decades, microbiologists have endeavored to apply and extend molecular techniques to address pertinent questions related to the function and metabolism of microbes in aquatic ecology. Metagenomics analysis has revolutionized aquatic ecology studies involving the investigation of the genome of a mixed community of organisms in an ecosystem to identify microorganisms, their functionality, and the discovery of novel proteins. This review discusses the metagenomics analysis of bacterial diversity in and around different aquatic systems in India.

Impact of 16S rRNA Gene Redundancy and Primer Pair Selection on the Quantification and Classification of Oral Microbiota in Next-Generation Sequencing

This study aimed to evaluate the number of 16S rRNA genes in the complete genomes of the bacterial and archaeal species inhabiting the human mouth and to assess how the use of different primer pairs would affect the detection and classification of redundant amplicons and matching amplicons (MAs) from different taxa. A total of 518 oral-bacterial and 191 oral-archaeal complete genomes were downloaded from the NCBI database, and their complete 16S rRNA genes were extracted. The numbers of genes and variants per genome were calculated. Next, 39 primer pairs were used to search for matches in the genomes and obtain amplicons. For each primer, we calculated the number of gene amplicons, variants, genomes, and species detected and the percentage of coverage at the species level with no MAs (SC-NMA). The results showed that 94.09% of oral bacteria and 52.59% of oral archaea had more than one intragenomic 16S rRNA gene. From 1.29% to 46.70% of bacterial species and from 4.65% to 38.89% of archaea detected by the primers had MAs. The best primers were the following (SC-NMA; region; position for Escherichia coli [GenBank version no. J01859.1]): KP_F048-OP_R030 for bacteria (93.55%; V3 to V7; 342 to 1079), KP_F018-KP_R063 for archaea (89.63%; V3 to V9; undefined to 1506), and OP_F114-OP_R121 for both domains (92.52%; V3 to V9; 340 to 1405). In addition to 16S rRNA gene redundancy, the presence of MAs must be controlled to ensure an accurate interpretation of microbial diversity data. The SC-NMA is a more useful parameter than the conventional coverage percentage for selecting the best primer pairs. The pairs used the most in the oral microbiome literature were not among the best performers. IMPORTANCE Hundreds of publications have studied the oral microbiome through 16S rRNA gene sequencing. However, none have assessed the number of 16S rRNA genes in the genomes of oral microbes, or how the use of primer pairs targeting different regions affects the detection of MAs from different taxa. Here, we found that almost all oral bacteria and more than half of oral archaea have more than one intragenomic 16S rRNA gene. The performance of the primer pairs in not detecting MAs increases as the length of the amplicon augments. As none of those most employed in the oral literature were among the best performers, we selected a series of primers to detect bacteria and/or archaea based on their percentage of species detected without MAs. The intragenomic 16S rRNA gene redundancy and the presence of MAs between distinct taxa need to be considered to ensure an accurate interpretation of microbial diversity data.

Oral Microbiome in Nonsmoker Patients with Oral Cavity Squamous Cell Carcinoma, Defined by Metagenomic Shotgun Sequencing

Objectives: Smoking is the commonest cause of oral cavity squamous cell carcinoma (OC-SCC), but the etiology of OC-SCC in nonsmokers is unknown. Our primary goal was to use metagenomic shotgun sequencing (MSS) to define the taxonomic composition and functional potential of oral metagenome in nonsmokers with OC-SCC. Methods: We conducted a case-control study with 42 OC-SCC case and 45 control nonsmokers. MSS was performed on DNA extracted from mouthwash samples. Taxonomic analysis and pathway analysis were done using MetaPhlAn2 and HUMAnN2, respectively. Statistical difference was determined using the Mann-Whitney test controlling false discovery rate. Results: There was no significant difference in age, sex, race, or alcohol consumption between OC-SCC and control patients. There was a significant difference in beta diversity between OC-SCC and controls. At the phylum level, Bacteroidetes and Synergistetes were overly represented in OC-SCC while Actinobacteria and Firmicutes were overly represented in controls. At the genus level, Fusobacterium was overly represented in OC-SCC compared with controls, while Corynebacterium, Streptococcus, Actinomyces, Cryptobacterium, and Selenomonas were overly represented in controls. Bacterial pathway analysis identified overrepresentation in OC-SCC of pathways related to metabolism of flavin, biotin, thiamin, heme, sugars, fatty acids, peptidoglycans, and tRNA and overrepresentation of nucleotides and essential amino acids in controls. Conclusions: The oral microbiome in nonsmoker patients with OC-SCC is significantly different from that of nonsmoker control patients in taxonomic compositions and functional potentials. Our study's MSS findings matched with previous 16S-based methods in taxonomic differentiation but varied greatly in functional differentiation of microbiomes in OC-SCC and controls.

Resolving 500 Years of Anthropogenic Impacts in a Mesotrophic Lake: Nutrients Outweigh Other Drivers of Lake Change

Interactions among multiple stressors, legacies of past perturbations, and the lack of historical information make it difficult to determine the influence of individual anthropogenic impacts on lakes and separate them from natural ecosystem variability. In the present study, we coupled paleolimnological approaches, historical data, and ecological experiments to disentangle the impacts of multiple long-term stressors on lake ecosystem structure and function. We found that the lake structure and function remained resistant to the impacts of catchment deforestation and erosion, and the introduction of several exotic fish species. Changes in ecosystem structure and function were consistent, with nutrient enrichment being the primary driver of change. Significant and sustained changes in the lake diatom community structure (and their nutrient requirements), bacterial community function, and paleolimnological proxies of ecosystem function coincided with nitrogen and phosphorus fertilizers in the catchment. The results highlight that the effects of increased nutrient inputs are much stronger than the influence of other, potentially significant, drivers of ecosystem change, and that the degree of nutrient impact can be underestimated by environmental monitoring due to its diffuse and accumulative nature. Delineating the effects of multiple anthropogenic drivers requires long-term records of both impacts and lake ecosystem change across multiple trophic levels.

Microbial Response to Increased Temperatures Within a Lava-Induced Hydrothermal System in Iceland: An Analogue for the Habitability of Volcanic Terrains on Mars

Fossil hydrothermal systems on Mars are important exploration targets because they may have once been habitable and could still preserve evidence of microbial life. We investigated microbial communities within an active lava-induced hydrothermal system associated with the 2014-2015 eruption of Holuhraun in Iceland as a Mars analogue. In 2016, the microbial composition in the lava-heated water differed substantially from that of the glacial river and spring water sources that fed into the system. Several taxonomic and metabolic groups were confined to the water emerging from the lava and some showed the highest sequence similarities to subsurface ecosystems, including to the predicted thermophilic and deeply branching Candidatus Acetothermum autotrophicum. Measurements show that the communities were affected by temperature and other environmental factors. In particular, comparing glacial river water incubated in situ (5.7°C, control) with glacial water incubated within a lava-heated stream (17.5°C, warm) showed that microbial abundance, richness, and diversity increased in the warm treatment compared with the control, with the predicted major metabolism shifting from lithotrophy toward organotrophy and possibly phototrophy. In addition, thermophilic bacteria isolated from the lava-heated water and a nearby acidic hydrothermal system included the known endospore-formers Geobacillus stearothermophilus and Paenibacillus cisolokensis as well as a potentially novel taxon within the order Hyphomicrobiales. Similar lava-water interactions on Mars could therefore have generated habitable environments for microbial communities.

Composition and functionality of bacterioplankton communities in marine coastal zones adjacent to finfish aquaculture

Finfish aquaculture is a fast-growing primary industry and is increasingly common in coastal ecosystems. Bacterioplankton is ubiquitous in marine environment and respond rapidly to environmental changes. Changes in bacterioplankton community are not well understood in semi-enclosed stratified embayments. This study aims to examine aquaculture effects in the composition and functional profiles of the bacterioplankton community using amplicon sequencing along a distance gradient from two finfish leases in a marine embayment. Results revealed natural stratification in bacterioplankton associated to NOx, conductivity, salinity, temperature and PO₄. Among the differentially abundant bacteria in leases, we found members associated with nutrient enrichment and aquaculture activities. Abundant predicted functions near leases were assigned to organic matter degradation, fermentation, and antibiotic resistance. This study provides a first effort to describe changes in the bacterioplankton community composition and function due to finfish aquaculture in a semi-enclosed and highly stratified embayment with a significant freshwater input.

Compare the performance of multiple binary classification models in microbial high-throughput sequencing datasets

The development of machine learning and deep learning provided solutions for predicting microbiota response on environmental change based on microbial high-throughput sequencing. However, there were few studies specifically clarifying the performance and practical of two types of binary classification models to find a better algorithm for the microbiota data analysis. Here, for the first time, we evaluated the performance, accuracy and running time of the binary classification models built by three machine learning methods - random forest (RF), support vector machine (SVM), logistic regression (LR), and one deep learning method - back propagation neural network (BPNN). The built models were based on the microbiota datasets that removed low-quality variables and solved the class imbalance problem. Additionally, we optimized the models by tuning. Our study demonstrated that dataset pre-processing was a necessary process for model construction. Among these 4 binary classification models, BPNN and RF were the most suitable methods for constructing microbiota binary classification models. Using these 4 models to predict multiple microbial datasets, BPNN showed the highest accuracy and the most robust performance, while the RF method was ranked second. We also constructed the optimal models by adjusting the epochs of BPNN and the n_estimators of RF for six times. The evaluation related to performances of models provided a road map for the application of artificial intelligence to assess microbial ecology.

Combination of Whole Genome Sequencing and Metagenomics for Microbiological Diagnostics

Whole genome sequencing (WGS) provides the highest resolution for genome-based species identification and can provide insight into the antimicrobial resistance and virulence potential of a single microbiological isolate during the diagnostic process. In contrast, metagenomic sequencing allows the analysis of DNA segments from multiple microorganisms within a community, either using an amplicon- or shotgun-based approach. However, WGS and shotgun metagenomic data are rarely combined, although such an approach may generate additive or synergistic information, critical for, e.g., patient management, infection control, and pathogen surveillance. To produce a combined workflow with actionable outputs, we need to understand the pre-to-post analytical process of both technologies. This will require specific databases storing interlinked sequencing and metadata, and also involves customized bioinformatic analytical pipelines. This review article will provide an overview of the critical steps and potential clinical application of combining WGS and metagenomics together for microbiological diagnosis.

Methods and Strategies to Uncover Coral-Associated Microbial Dark Matter

The vast majority of environmental microbes have not yet been cultured, and most of the knowledge on coral-associated microbes (CAMs) has been generated from amplicon sequencing and metagenomes. However, exploring cultured CAMs is key for a detailed and comprehensive characterization of the roles of these microbes in shaping coral health and, ultimately, for their biotechnological use as, for example, coral probiotics and other natural products. Here, the strategies and technologies that have been used to access cultured CAMs are presented, while advantages and disadvantages associated with each of these strategies are discussed. We highlight the existing gaps and potential improvements in culture-dependent methodologies, indicating several possible alternatives (including culturomics and in situ diffusion devices) that could be applied to retrieve the CAM "dark matter" (i.e., the currently undescribed CAMs). This study provides the most comprehensive synthesis of the methodologies used to recover the cultured coral microbiome to date and draws suggestions for the development of the next generation of CAM culturomics.

Comparative Analysis of Metagenomics and Metataxonomics for the Characterization of Vermicompost Microbiomes

The study of microbial communities or microbiotas in animals and environments is important because of their impact in a broad range of industrial applications, diseases and ecological roles. High throughput sequencing (HTS) is the best strategy to characterize microbial composition and function. Microbial profiles can be obtained either by shotgun sequencing of genomes, or through amplicon sequencing of target genes (e.g., 16S rRNA for bacteria and ITS for fungi). Here, we compared both HTS approaches at assessing taxonomic and functional diversity of bacterial and fungal communities during vermicomposting of white grape marc. We applied specific HTS workflows to the same 12 microcosms, with and without earthworms, sampled at two distinct phases of the vermicomposting process occurring at 21 and 63 days. Metataxonomic profiles were inferred in DADA2, with bacterial metabolic pathways predicted via PICRUSt2. Metagenomic taxonomic profiles were inferred in PathoScope, while bacterial functional profiles were inferred in Humann2. Microbial profiles inferred by metagenomics and metataxonomics showed similarities and differences in composition, structure, and metabolic function at different taxonomic levels. Microbial composition and abundance estimated by both HTS approaches agreed reasonably well at the phylum level, but larger discrepancies were observed at lower taxonomic ranks. Shotgun HTS identified ~1.8 times more bacterial genera than 16S rRNA HTS, while ITS HTS identified two times more fungal genera than shotgun HTS. This is mainly a consequence of the difference in resolution and reference richness between amplicon and genome sequencing approaches and databases, respectively. Our study also revealed great differences and even opposite trends in alpha- and beta-diversity between amplicon and shotgun HTS. Interestingly, amplicon PICRUSt2-imputed functional repertoires overlapped ~50% with shotgun Humann2 profiles. Finally, both approaches indicated that although bacteria and fungi are the main drivers of biochemical decomposition, earthworms also play a key role in plant vermicomposting. In summary, our study highlights the strengths and weaknesses of metagenomics and metataxonomics and provides new insights on the vermicomposting of white grape marc. Since both approaches may target different biological aspects of the communities, combining them will provide a better understanding of the microbiotas under study.

Abundant and persistent sulfur-oxidizing microbial populations are responsive to hypoxia in the Chesapeake Bay

The number, size and severity of aquatic low‐oxygen dead zones are increasing worldwide. Microbial processes in low‐oxygen environments have important ecosystem‐level consequences, such as denitrification, greenhouse gas production and acidification. To identify key microbial processes occurring in low‐oxygen bottom waters of the Chesapeake Bay, we sequenced both 16S rRNA genes and shotgun metagenomic libraries to determine the identity, functional potential and spatiotemporal distribution of microbial populations in the water column. Unsupervised clustering algorithms grouped samples into three clusters using water chemistry or microbial communities, with extensive overlap of cluster composition between methods. Clusters were strongly differentiated by temperature, salinity and oxygen. Sulfur‐oxidizing microorganisms were found to be enriched in the low‐oxygen bottom water and predictive of hypoxic conditions. Metagenome‐assembled genomes demonstrate that some of these sulfur‐oxidizing populations are capable of partial denitrification and transcriptionally active in a prior study. These results suggest that microorganisms capable of oxidizing reduced sulfur compounds are a previously unidentified microbial indicator of low oxygen in the Chesapeake Bay and reveal ties between the sulfur, nitrogen and oxygen cycles that could be important to capture when predicting the ecosystem response to remediation efforts or climate change.

Machine Learning Predicts Biogeochemistry from Microbial Community Structure in a Complex Model System

Microbial community structure is influenced by the environment and in turn exerts control on many environmental parameters. We applied this concept in a bioreactor study to test whether microbial community structure contains information sufficient to predict the concentration of H₂S as the product of sulfate reduction. Microbial sulfate reduction is a major source of H₂S in many industrial and environmental systems and is often influenced by the existing physicochemical conditions. Production of H₂S in industrial systems leads to occupational hazards and adversely affects the quality of products. A long-term (148 days) experiment was conducted in upflow bioreactors to mimic sulfidogenesis, followed by inhibition with nitrate salts and a resumption of H₂S generation when inhibition was released. We determined microbial community structure in 731 samples across 20 bioreactors using 16S rRNA gene sequencing and applied a random forest algorithm to successfully predict different phases of sulfidogenesis and mitigation (accuracy = 93.17%) and sessile and effluent microbial communities (accuracy = 100%). Similarly derived regression models that also included cell abundances were able to predict H₂S concentration with remarkably high fidelity (R² > 0.82). Metabolic profiles based on microbial community structure were also found to be reliable predictors for H₂S concentration (R² = 0.78). These results suggest that microbial community structure contains information sufficient to predict sulfidogenesis in a closed system, with anticipated applications to microbially driven processes in open environments.

IMPORTANCE Microbial communities control many biogeochemical processes. Many of these processes are impractical or expensive to measure directly. Because the taxonomic structure of the microbial community is indicative of its function, it encodes information that can be used to predict biogeochemistry. Here, we demonstrate how a machine learning technique can be used to predict sulfidogenesis, a key biogeochemical process in a model system. A distinction of this research was the ability to predict H₂S production in a bioreactor from the effluent bacterial community structure without direct observations of the sessile community or other environmental conditions. This study establishes the ability to use machine learning approaches in predicting sulfide concentrations in a closed system, which can be further developed as a valuable tool for predicting biogeochemical processes in open environments. As machine learning algorithms continue to improve, we anticipate increased applications of microbial community structure to predict key environmental and industrial processes.

Inferring microbiota functions from taxonomic genes: a review

Deciphering microbiota functions is crucial to predict ecosystem sustainability in response to global change. High-throughput sequencing at the individual or community level has revolutionized our understanding of microbial ecology, leading to the big data era and improving our ability to link microbial diversity with microbial functions. Recent advances in bioinformatics have been key for developing functional prediction tools based on DNA metabarcoding data and using taxonomic gene information. This cheaper approach in every aspect serves as an alternative to shotgun sequencing. Although these tools are increasingly used by ecologists, an objective evaluation of their modularity, portability, and robustness is lacking. Here, we reviewed 100 scientific papers on functional inference and ecological trait assignment to rank the advantages, specificities, and drawbacks of these tools, using a scientific benchmarking. To date, inference tools have been mainly devoted to bacterial functions, and ecological trait assignment tools, to fungal functions. A major limitation is the lack of reference genomes-compared with the human microbiota-especially for complex ecosystems such as soils. Finally, we explore applied research prospects. These tools are promising and already provide relevant information on ecosystem functioning, but standardized indicators and corresponding repositories are still lacking that would enable them to be used for operational diagnosis.

From microbial community structure to metabolic inference using paprica

Microbial taxonomic marker gene studies using 16S rRNA gene amplicon sequencing provide an understanding of microbial community structure and diversity; however, it can be difficult to infer the functionality of microbes in the ecosystem from these data. Here, we show how to predict metabolism from phylogeny using the paprica pipeline. This approach allows resolution at the strain and species level for select regions on the prokaryotic phylogenetic tree and provides an estimate of gene and metabolic pathway abundance. For complete details on the use and execution of this protocol, please refer to Erazo and Bowman (2021).

New approaches for the characterization of plastic-associated microbial communities and the discovery of plastic-degrading microorganisms and enzymes

Plastics in the environment represent new substrates for microbial colonization, and recent methodological advances allow for in-depth characterization of plastic-associated microbial communities (PAMCs). Over the past several decades, discovery of plastic degrading enzymes (PDEs) and plastic degrading microorganisms (PDMs) has been driven by efforts to understand microbially-mediated plastic degradation in the environment and to discover biocatalysts for plastic processing. In this review, we discuss the evolution of methodology in plastic microbiology and highlight major advancements in the field stemming from computational microbiology. Initial research relied largely on culture-based approaches like clear-zone assays to screen for PDMs and microscopy to characterize PAMCs. New computational tools and sequencing technologies are accelerating discoveries in the field through culture-independent and multi-omic approaches, rapidly generating targets for protein engineering and improving the potential for plastic-waste management.

Gut microbiome in Parkinson's disease: New insights from meta-analysis

BACKGROUND

Gut microbiome alterations have been reported in Parkinson's disease (PD), but with heterogenous findings, likely due to differences in study methodology and population. We investigated the main microbiome alterations in PD, their correlations with disease severity, and the impact of study and geographical differences.

METHODS

After systematic screening, raw 16S rRNA gene sequences were obtained from ten case-control studies totaling 1703 subjects (969 PD, 734 non-PD controls; seven predominantly Caucasian and three predominantly non-Caucasian cohorts). Quality-filtered gene sequences were analyzed using a phylogenetic placement approach, which precludes the need for the sequences to be sourced from similar regions in the 16S rRNA gene, thus allowing a direct comparison between studies. Differences in microbiome composition and correlations with clinical variables were analyzed using multivariate statistics.

RESULTS

Study and geography accounted for the largest variations in gut microbiome composition. Microbiome composition was more similar for subjects from the same study than those from different studies with the same disease status. Microbiome composition significantly differed between Caucasian and non-Caucasian populations. After accounting for study differences, microbiome composition was significantly different in PD vs. controls (albeit with a marginal effect size), with several distinctive features including increased abundances of Megasphaera and Akkermansia, and reduced Roseburia. Several bacterial genera correlated with PD motor severity, motor response complications and cognitive function.

CONCLUSION

Consistent microbial features in PD merit further investigation. The large variations in microbiome findings of PD patients underscore the need for greater harmonization of future research, and personalized approaches in designing microbial-directed therapeutics.

Detection of Sulfate-Reducing Bacteria as an Indicator for Successful Mitigation of Sulfide Production

Sulfate-reducing bacteria (SRBs) are one of the main sources of biogenic H₂S generation in oil reservoirs. Excess H₂S production in these systems leads to oil biosouring, which causes operational risks and health hazards and can increase the cost of refining crude oil. Nitrate salts are often added to the system to suppress sulfidogenesis. Because SRB populations can persist in biofilms even after nitrate treatment, identifying shifts in the sessile community is crucial for successful mitigation. However, sampling the sessile community is hampered by its inaccessibility. Here, we use the results of a long-term (148 days) ex situ experiment to identify particular sessile community members from observations of the sample waste stream. Microbial community structure was determined for 731 samples across 20 bioreactors using 16S rRNA gene sequencing. By associating microbial community structure with specific steps in the mitigation process, we could distinguish between taxa associated with H₂S production and mitigation. After initiation of nitrate treatment, certain SRB populations increased in the planktonic community during critical time points, indicating the dissociation of SRBs from the biofilm. Predicted relative abundances of the dissimilatory sulfate reduction pathway also increased during the critical time points. Here, by analyzing the planktonic community structure, we describe a general method that uses high-throughput amplicon sequencing, metabolic inferences, and cell abundance data to identify successful biofilm mitigation. We anticipate that our approach is also applicable to other systems where biofilms must be mitigated but cannot be sampled easily.

IMPORTANCE Microbial biofilms are commonly present in many industrial processes and can negatively impact performance and safety. Within the oil industry, subterranean biofilms cause biosouring with implications for oil quality, cost, occupational health, and the environment. Because these biofilms cannot be sampled directly, methods are needed to indirectly assess the success of mitigation measures. This study demonstrates how the planktonic microbial community can be used to assess the dissociation of sulfate-reducing bacterium (SRB)-containing biofilms. We found that an increase in the abundance of a specific SRB population in the effluent after nitrate treatment can be used as a potential indicator for the successful mitigation of biofilm-forming SRBs. Moreover, a method for determining critical time points for detecting potential indicators is suggested. This study expands our knowledge of improving mitigation strategies for biosouring and could have broader implications in other systems where biofilms lead to adverse consequences.

Influence of polycyclic aromatic hydrocarbon pollution on the diversity and function of bacterial communities in urban wetlands

Human disturbance has become the primary driving factor behind declining urban wetland ecological health due to rapid urbanization. Sediment microbial communities are critical for wetland ecosystem functioning but experience a range of natural and anthropogenic stressors due to rapid urbanization and land use changes, especially in developing countries. Polycyclic aromatic hydrocarbons (PAHs) released into the environment primarily come from anthropogenic sources like industrial activities and traffic emissions. Environmental PAH contamination is accelerating due to rapid urbanization, which also increases potential PAH-related dangers to human health. However, PAHs are widely distributed and not easy to centrally control. Microorganisms are the primary mediators of wetland purification, with most PAH-degrading microorganisms being bacteria. To better understand the influence of PAH contamination on urban wetland microbial communities, bacterial community compositions within sediments of urban wetlands in three land use types were investigated using high-throughput DNA sequencing and bioinformatics analyses. Statistical analyses revealed significant differences in overall microbial compositions among the three land use types, although γ-proteobacteria was the dominant phyla across all samples. Among the potential PAH-degrading bacterial taxa in sediments, Sphingomonas was the most prevalent. The distributions of PAH-degrading taxa were primarily affected by variance in organic compound abundances in addition to various physico-chemical variables, among which high-ring PAH content was a key parameter associated with bacterial distributions, except in the riverine wetlands. Functional inference via phylogenetic investigation of communities by reconstruction of unobserved states (PICRUSt) indicated that 30 of the 43 genes related to PAH metabolism were predicted to be present within the genomes of bacteria among the three land use type. In particular, dioxygenase and dehydrogenase genes involved in PAH degradation were inferred to be prevalent, indicating that the host urban wetlands exhibited strong potential for organic pollutant degradation.

Making Sense of a Scent-Sensing Metaphor for Microbes and Environmental Predictions

Microbes serve as sensitive indicators of ecosystem change due to their vast diversity and tendency to change in abundance in response to environmental conditions. Although we most frequently observe these changes to study the microbial community itself, it is increasingly common to use them to understand the surrounding environment. In this way microbial communities can be thought of as powerful sensors capable of reporting shifts in chemical or physical conditions with high fidelity. In this commentary, I further explore this idea by drawing a comparison to the olfactory system, where populations of sensory neurons respond to the presence of specific odorants. The possible combinations of sensory neurons that can transduce a signal are virtually limitless. Yet, the brain can deconvolute the signal into recognizable and actionable data. The further development of machine learning techniques and its application hold great promise for our ability to interpret microbes to detect environmental change.

NetCom: A Network-Based Tool for Predicting Metabolic Activities of Microbial Communities Based on Interpretation of Metagenomics Data

The study of microbial activity can be viewed as a triangle with three sides: environment (dominant resources in a specific habitat), community (species dictating a repertoire of metabolic conversions) and function (production and/or utilization of resources and compounds). Advances in metagenomics enable a high-resolution description of complex microbial communities in their natural environments and support a systematic study of environment-community-function associations. NetCom is a web-tool for predicting metabolic activities of microbial communities based on network-based interpretation of assembled and annotated metagenomics data. The algorithm takes as an input, lists of differentially abundant enzymatic reactions and generates the following outputs: (i) pathway associations of differently abundant enzymes; (ii) prediction of environmental resources that are unique to each treatment, and their pathway associations; (iii) prediction of compounds that are produced by the microbial community, and pathway association of compounds that are treatment-specific; (iv) network visualization of enzymes, environmental resources and produced compounds, that are treatment specific (2 and 3D). The tool is demonstrated on metagenomic data from rhizosphere and bulk soil samples. By predicting root-specific activities, we illustrate the relevance of our framework for forecasting the impact of soil amendments on the corresponding microbial communities. NetCom is available online.

Compositional profile of mucosal bacteriome of smokers and smokeless tobacco users

INTRODUCTION

Smoked, and especially smokeless, tobacco are major causes of oral cancer globally. Here, we examine the oral bacteriome of smokers and of smokeless tobacco users, in comparison to healthy controls, using 16S rRNA gene sequencing.

METHODS

Oral swab samples were collected from smokers, smokeless tobacco users, and healthy controls (n = 44). Microbial DNA was extracted and the 16S rRNA gene profiled using the Illumina MiSeq platform. Sequencing reads were processed using DADA2, and taxonomical classification was performed using the phylogenetic placement method. Differentially abundant taxa were identified using DESeq2, while functional metagenomes based on KEGG orthology abundance were inferred using LIMMA.

RESULTS

A significantly higher microbial diversity was observed in smokeless tobacco users and smokers relative to controls (P < 0.05). Compositional differences in microbial communities were observed in all comparisons with healthy controls (PERMANOVA P < 0.05) but not between smokers and smokeless tobacco users. Levels of Fusobacterium spp., Saccharibacterium spp., and members of Shuttleworthia were elevated in smokers when compared to controls (BH adj P < 0.01). In addition, the relative abundance of three bacterial taxa belonging to genera Fusobacterium spp., Catonella, and Fretibacterium spp. was significantly increased in smokeless tobacco users relative to controls (BH adj P < 0.01). Major functional pathways significantly increased in smokeless tobacco users relative to both controls, and smokers were similar and involved amino acid metabolism including glutamate and aspartate biosynthesis and degradation (log FC > 1.5; BH adj P < 0.01).

CONCLUSIONS

A distinct taxonomic and functional profile of oral microbiome in smokers and smokeless tobacco users as compared to healthy controls implicates a significant role of microbes and their metabolites in diseases associated with tobacco use including oral cancer.

CLINICAL RELEVANCE

Future efforts in preventive, diagnostic, curative, and prognostic strategies for diseases associated with tobacco use in smokers and smokeless tobacco users could incorporate the oral microbiome.

MicFunPred: A conserved approach to predict functional profiles from 16S rRNA gene sequence data

The 16S rRNA gene amplicon sequencing is a popular technique that provides accurate characterization of microbial taxonomic abundances but does not provide any functional information. Several tools are available to predict functional profiles based on 16S rRNA gene sequence data that use different genome databases and approaches. As variable regions of partially-sequenced 16S rRNA gene cannot resolve taxonomy accurately beyond the genus level, these tools may give inflated results. Here, we developed 'MicFunPred', which uses a novel approach to derive imputed metagenomes based on a set of core genes only, thereby minimizing false-positive predictions. On simulated datasets, MicFunPred showed the lowest False Positive Rate (FPR) with mean Spearman's correlation of 0.89 (SD = 0.03), while on seven real datasets the mean correlation was 0.75 (SD = 0.08). MicFunPred was found to be faster with low computational requirements and performed better or comparable when compared with other tools.

Lake microbial communities are not resistant or resilient to repeated large-scale natural pulse disturbances

Opportunities to study community level responses to extreme natural pulse disturbances in unaltered ecosystems are rare. Lake sediment records that span thousands of years can contain well resolved sediment pulses, triggered by earthquakes. These paleo-records provide a means to study repeated pulse disturbance and processes of resistance (insensitivity to disturbance) and ecological resilience (capacity to regain structure, function and process). In this study, sedimentary DNA was extracted from a sediment core from Lake Paringa (New Zealand) that is situated in a near natural catchment. Metabarcoding and inferred functions were used to assess the lake microbial community over the past 1,100 years - a period that included four major earthquakes. Microbial community composition and function differed significantly between highly perturbed (postseismic, c. 50 yrs) phases directly after the earthquakes and more stable (interseismic, c. 250 yr) phases, indicating a lack of community resistance. Although community structure differed significantly in successive postseismic phases, function did not, suggesting potential functional redundancy. Significant differences in composition and function in successive interseismic phases demonstrates communities are not resilient to large-scale natural pulse disturbances. The clear difference in structure and function, and high number of indicator taxa (responsible for driving differences in communities between phases) in the fourth interseismic phase likely represents a regime shift, possibly due to the two-fold increase in sediment and terrestrial biospheric organic carbon fluxes recorded following the fourth earthquake. Large pulse disturbances that enhance sediment inputs into lake systems may produce an underappreciated mechanism that destabilises lake ecosystem processes.

Identification of Biomolecules Involved in the Adaptation to the Environment of Cold-Loving Microorganisms and Metabolic Pathways for Their Production

Cold-loving microorganisms of all three domains of life have unique and special abilities that allow them to live in harsh environments. They have acquired structural and molecular mechanisms of adaptation to the cold that include the production of anti-freeze proteins, carbohydrate-based extracellular polymeric substances and lipids which serve as cryo- and osmoprotectants by maintaining the fluidity of their membranes. They also produce a wide diversity of pigmented molecules to obtain energy, carry out photosynthesis, increase their resistance to stress and provide them with ultraviolet light protection. Recently developed analytical techniques have been applied as high-throughoutput technologies for function discovery and for reconstructing functional networks in psychrophiles. Among them, omics deserve special mention, such as genomics, transcriptomics, proteomics, glycomics, lipidomics and metabolomics. These techniques have allowed the identification of microorganisms and the study of their biogeochemical activities. They have also made it possible to infer their metabolic capacities and identify the biomolecules that are parts of their structures or that they secrete into the environment, which can be useful in various fields of biotechnology. This Review summarizes current knowledge on psychrophiles as sources of biomolecules and the metabolic pathways for their production. New strategies and next-generation approaches are needed to increase the chances of discovering new biomolecules.

Microbiome Aggregated Traits and Assembly Are More Sensitive to Soil Management than Diversity

How soil is managed, particularly for agriculture, exerts stresses upon soil microbiomes, resulting in altered community structures and functional states. Understanding how soil microbiomes respond to combined stresses is important for predicting system performance under different land use scenarios, aids in identification of the most environmentally benign managements, and provides insight into how system function can be recovered in degraded soils. We use a long-established field experiment to study the effects of combined chronic (press) disturbance of the magnitude of organic carbon inputs with acute (pulse) effects of physical disturbance by tillage and chemical disturbance due to inorganic fertilization and pesticide application. We show that because of the variety of ways it can be assessed, biodiversity-here based on microbial small subunit rRNA gene phylotypes-does not provide a consistent view of community change. In contrast, aggregated traits associated with soil microbiomes indicate general loss of function, measured as a reduction of average genome lengths, associated with chronic reduction of organic inputs in arable or bare fallow soils and altered growth strategies associated with rRNA operon copy number in prokaryotes, as well as a switch to pathogenicity in fungal communities. In addition, pulse disturbance by soil tillage is associated with an increased influence of stochastic processes upon prokaryote community assembly, but fungicide used in arable soils results in niche assembly of fungal communities compared to untilled grassland. Overall, bacteria, archaea, and fungi do not share a common response to land management change, and estimates of biodiversity do not capture important facets of community adaptation to stresses adequately. IMPORTANCE Changes in soil microbiome diversity and function brought about by land management are predicted to influence a range of environmental services provided by soil, including provision of food and clean water. However, opportunities to compare the long-term effects of combinations of stresses imposed by different management approaches are limited. We exploit a globally unique 50-year field experiment, demonstrating that soil management practices alter microbiome diversity, community traits, and assembly. Grassland soil microbiomes are dominated by fewer-but phylogenetically more diverse-prokaryote phylotypes which sustain larger genomes than microbiomes in arable or bare fallow soil maintained free of plants. Dominant fungi in grassland soils are less phylogenetically diverse than those in arable or fallow soils. Soil tillage increases stochastic processes in microbiome assembly: this, combined with reduced plant biomass, presents opportunities for organisms with a capacity for pathogenesis to become established in stressed soils.

Metagenomics and Other Omics Approaches to Bacterial Communities and Antimicrobial Resistance Assessment in Aquacultures

The shortage of wild fishery resources and the rising demand for human nutrition has driven a great expansion in aquaculture during the last decades in terms of production and economic value. As such, sustainable aquaculture production is one of the main priorities of the European Union's 2030 agenda. However, the intensification of seafood farming has resulted in higher risks of disease outbreaks and in the increased use of antimicrobials to control them. The selective pressure exerted by these drugs provides the ideal conditions for the emergence of antimicrobial resistance hotspots in aquaculture facilities. Omics technology is an umbrella term for modern technologies such as genomics, metagenomics, transcriptomics, proteomics, culturomics, and metabolomics. These techniques have received increasing recognition because of their potential to unravel novel mechanisms in biological science. Metagenomics allows the study of genomes in microbial communities contained within a certain environment. The potential uses of metagenomics in aquaculture environments include the study of microbial diversity, microbial functions, and antibiotic resistance genes. A snapshot of these high throughput technologies applied to microbial diversity and antimicrobial resistance studies in aquacultures will be presented in this review.

Endogenous bacteria inhabiting the Ophiocordyceps highlandensis during fruiting body development

Background

The genus Ophiocordyceps, which includes Ophiocordyceps sinensis, has been demonstrated to be one of the most valuable medicinal taxa. The low rate of larval infection and slow development that characterize the cultivation of this genus should be urgently addressed. To identify potential bioinoculants that stimulate the growth of Ophiocordyceps, O. highlandensis was selected as a model system, and a total of 72 samples were collected to systematically compare the microbial communities present during fruiting body development. By applying high-throughput 16S and ITS2 amplicon sequencing technology, the bacterial and fungal communities were identified in O. highlandensis and its surrounding soil, and the functional dynamics of the bacteria were explored.

Results

The results indicate that the most abundant bacteria across all the samples from O. highlandensis were Proteobacteria, Firmicutes and Bacteroidetes, while members of Ascomycota were detected among the fungi. The pathways enriched in the developmental stages were associated with carbohydrate degradation, nucleotides and pyridoxal biosynthesis, and the TCA cycle. Compared with that in the fungal community, an unexpectedly high taxonomic and functional fluctuation was discovered in the bacterial community during the maturation of O. highlandensis. Furthermore, bipartite network analysis identified four potential supercore OTUs associated with O. highlandensis growth.

Conclusions

All the findings of this study suggest unexpectedly high taxonomic and functional fluctuations in the bacterial community of O. highlandensis during its maturation. O. highlandensis may recruit different endogenous bacteria across its life cycle to enhance growth and support rapid infection. These results may facilitate Ophiocordyceps cultivation and improve the development of strategies for the identification of potential bioinoculant resources.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12866-021-02227-w.

Identifying biases and their potential solutions in human microbiome studies

Advances in DNA sequencing technology have vastly improved the ability of researchers to explore the microbial inhabitants of the human body. Unfortunately, while these studies have uncovered the importance of these microbial communities to our health, they often do not result in similar findings. One possible reason for the disagreement in these results is due to the multitude of systemic biases that are introduced during sequence-based microbiome studies. These biases begin with sample collection and continue to be introduced throughout the entire experiment leading to an observed community that is significantly altered from the true underlying microbial composition. In this review, we will highlight the various steps in typical sequence-based human microbiome studies where significant bias can be introduced, and we will review the current efforts within the field that aim to reduce the impact of these biases. Video abstract.

Recurrent microbial community types driven by nearshore and seasonal processes in coastal Southern California

A multitude of concurrent biological and physical processes contribute to microbial community turnover, especially in highly dynamic coastal environments. Characterizing what factors contribute most to shifts in microbial community structure and the specific organisms that correlate with changes in the products of photosynthesis improves our understanding of nearshore microbial ecosystem functions. We conducted high frequency sampling in nearshore Southern California in order to capture sub-weekly microbial community dynamics. Microbial communities were characterized by flow cytometry and 16S rRNA gene sequencing, and placed in the context of physicochemical parameters. Within our time-series, season and nutrient availability corresponded to changes in dominant microbial community members. Concurrent aseasonal drivers with overlapping scales of variability were also apparent when we used network analysis to assess the microbial community as subsets of the whole. Our analyses revealed the microbial community as a mosaic, with overlapping groups of taxa that varied on different timescales and correlated with unique abiotic and biotic factors. Specifically, a subnetwork associated with chlorophyll a exhibited rapid turnover, indicating that ecologically important subsets of the microbial community can change on timescales different than and in response to factors other than those that govern turnover of most members of the assemblage.

Beyond taxonomy: Validating functional inference approaches in the context of fish-farm impact assessments

Characterization of microbial assemblages via environmental DNA metabarcoding is increasingly being used in routine monitoring programs due to its sensitivity and cost-effectiveness. Several programs have recently been developed which infer functional profiles from 16S rRNA gene data using hidden-state prediction (HSP) algorithms. These might offer an economic and scalable alternative to shotgun metagenomics. To date, HSP-based methods have seen limited use for benthic marine surveys and their performance in these environments remains unevaluated. In this study, 16S rRNA metabarcoding was applied to sediment samples collected at 0 and ≥1,200 m from Norwegian salmon farms, and three metabolic inference approaches (Paprica, Picrust2 and Tax4Fun2) evaluated against metagenomics and environmental data. While metabarcoding and metagenomics recovered a comparable functional diversity, the taxonomic composition differed between approaches, with genera richness up to 20× higher for metabarcoding. Comparisons between the sensitivity (highest true positive rates) and specificity (lowest true negative rates) of HSP-based programs in detecting functions found in metagenomic data ranged from 0.52 and 0.60 to 0.76 and 0.79, respectively. However, little correlation was observed between the relative abundance of their specific functions. Functional beta-diversity of HSP-based data was strongly associated with that of metagenomics (r ≥ 0.86 for Paprica and Tax4Fun2) and responded similarly to the impact of fish farm activities. Our results demonstrate that although HSP-based metabarcoding approaches provide a slightly different functional profile than metagenomics, partly due to recovering a distinct community, they represent a cost-effective and valuable tool for characterizing and assessing the effects of fish farming on benthic ecosystems.