A Metagenomic Framework for the Study of Airborne Microbial Communities

Understanding the microbial content of the air has important scientific, health, and economic implications. While studies have primarily characterized the taxonomic content of air samples by sequencing the 16S or 18S ribosomal RNA gene, direct analysis of the genomic content of airborne microorganisms has not been possible due to the extremely low density of biological material in airborne environments. We developed sampling and amplification methods to enable adequate DNA recovery to allow metagenomic profiling of air samples collected from indoor and outdoor environments. Air samples were collected from a large urban building, a medical center, a house, and a pier. Analyses of metagenomic data generated from these samples reveal airborne communities with a high degree of diversity and different genera abundance profiles. The identities of many of the taxonomic groups and protein families also allows for the identification of the likely sources of the sampled airborne bacteria.     

Inferring Intra-Community Microbial Interaction Patterns from Metagenomic Datasets Using Associative Rule Mining Techniques

The nature of inter-microbial metabolic interactions defines the stability of microbial communities residing in any ecological niche. Deciphering these interaction patterns is crucial for understanding the mode/mechanism(s) through which an individual microbial community transitions from one state to another (e.g. from a healthy to a diseased state). Statistical correlation techniques have been traditionally employed for mining microbial interaction patterns from taxonomic abundance data corresponding to a given microbial community. In spite of their efficiency, these correlation techniques can capture only ''pair-wise interactions''. Moreover, their emphasis on statistical significance can potentially result in missing out on several interactions that are relevant from a biological standpoint. This study explores the applicability of one of the earliest association rule mining algorithm i.e. the ''Apriori algorithm'' for deriving ''microbial association rules'' from the taxonomic profile of given microbial community. The classical Apriori approach derives association rules by analysing patterns of co-occurrence/co-exclusion between various ''(subsets of) features/items'' across various samples. Using real-world microbiome data, the efficiency/utility of this rule mining approach in deciphering multiple (biologically meaningful) association patterns between ''subsets/subgroups'' of microbes (constituting microbiome samples) is demonstrated. As an example, association rules derived from publicly available gut microbiome datasets indicate an association between a group of microbes (Faecalibacterium, Dorea, and Blautia) that are known to have mutualistic metabolic associations among themselves. Application of the rule mining approach on gut microbiomes (sourced from the Human Microbiome Project) further indicated similar microbial association patterns in gut microbiomes irrespective of the gender of the subjects. A Linux implementation of the Association Rule Mining (ARM) software (customised for deriving ''microbial association rules'' from microbiome data) is freely available for download from the following link: http://metagenomics.atc.tcs.com/arm.     

A Bayesian Approach to Inferring the Phylogenetic Structure of Communities from Metagenomic Data

Metagenomics provides a powerful new tool set for investigating evolutionary interactions with the environment. However, an absence of model-based statistical methods means that researchers are often not able to make full use of this complex information. We present a Bayesian method for inferring the phylogenetic relationship among related organisms found within metagenomic samples. Our approach exploits variation in the frequency of taxa among samples to simultaneously infer each lineage haplotype, the phylogenetic tree connecting them, and their frequency within each sample. Applications of the algorithm to simulated data show that our method can recover a substantial fraction of the phylogenetic structure even in the presence of high rates of migration among sample sites. We provide examples of the method applied to data from green sulfur bacteria recovered from an Antarctic lake, plastids from mixed Plasmodium falciparum infections, and virulent Neisseria meningitidis samples.     

Functional Metagenomic Investigations of Microbial Communities in a Shallow-Sea Hydrothermal System

Little is known about the functional capability of microbial communities in shallow-sea hydrothermal systems (water depth of <200 m). This study analyzed two high-throughput pyrosequencing metagenomic datasets from the vent and the surface water in the shallow-sea hydrothermal system offshore NE Taiwan. This system exhibited distinct geochemical parameters. Metagenomic data revealed that the vent and the surface water were predominated by Epsilonproteobacteria (Nautiliales-like organisms) and Gammaproteobacteria ( Thiomicrospira -like organisms), respectively. A significant difference in microbial carbon fixation and sulfur metabolism was found between the vent and the surface water. The chemoautotrophic microorganisms in the vent and in the surface water might possess the reverse tricarboxylic acid cycle and the Calvin−Bassham−Benson cycle for carbon fixation in response to carbon dioxide highly enriched in the environment, which is possibly fueled by geochemical energy with sulfur and hydrogen. Comparative analyses of metagenomes showed that the shallow-sea metagenomes contained some genes similar to those present in other extreme environments. This study may serve as a basis for deeply understanding the genetic network and functional capability of the microbial members of shallow-sea hydrothermal systems.     

16S Classifier: A Tool for Fast and Accurate Taxonomic Classification of 16S rRNA Hypervariable Regions in Metagenomic Datasets

The diversity of microbial species in a metagenomic study is commonly assessed using 16S rRNA gene sequencing. With the rapid developments in genome sequencing technologies, the focus has shifted towards the sequencing of hypervariable regions of 16S rRNA gene instead of full length gene sequencing. Therefore, 16S Classifier is developed using a machine learning method, Random Forest, for faster and accurate taxonomic classification of short hypervariable regions of 16S rRNA sequence. It displayed precision values of up to 0.91 on training datasets and the precision values of up to 0.98 on the test dataset. On real metagenomic datasets, it showed up to 99.7% accuracy at the phylum level and up to 99.0% accuracy at the genus level. 16S Classifier is available freely at http://metagenomics.iiserb.ac.in/16Sclassifier and http://metabiosys.iiserb.ac.in/16Sclassifier.     

The Functional Potential of Microbial Communities in Hydraulic Fracturing Source Water and Produced Water from Natural Gas Extraction Characterized by Metagenomic Sequencing

Microbial activity in produced water from hydraulic fracturing operations can lead to undesired environmental impacts and increase gas production costs. However, the metabolic profile of these microbial communities is not well understood. Here, for the first time, we present results from a shotgun metagenome of microbial communities in both hydraulic fracturing source water and wastewater produced by hydraulic fracturing. Taxonomic analyses showed an increase in anaerobic/facultative anaerobic classes related to Clostridia, Gammaproteobacteria, Bacteroidia and Epsilonproteobacteria in produced water as compared to predominantly aerobic Alphaproteobacteria in the fracturing source water. The metabolic profile revealed a relative increase in genes responsible for carbohydrate metabolism, respiration, sporulation and dormancy, iron acquisition and metabolism, stress response and sulfur metabolism in the produced water samples. These results suggest that microbial communities in produced water have an increased genetic ability to handle stress, which has significant implications for produced water management, such as disinfection.     

Probabilistic Inference of Biochemical Reactions in Microbial Communities from Metagenomic Sequences

Shotgun metagenomics has been applied to the studies of the functionality of various microbial communities. As a critical analysis step in these studies, biological pathways are reconstructed based on the genes predicted from metagenomic shotgun sequences. Pathway reconstruction provides insights into the functionality of a microbial community and can be used for comparing multiple microbial communities. The utilization of pathway reconstruction, however, can be jeopardized because of imperfect functional annotation of genes, and ambiguity in the assignment of predicted enzymes to biochemical reactions (e.g., some enzymes are involved in multiple biochemical reactions). Considering that metabolic functions in a microbial community are carried out by many enzymes in a collaborative manner, we present a probabilistic sampling approach to profiling functional content in a metagenomic dataset, by sampling functions of catalytically promiscuous enzymes within the context of the entire metabolic network defined by the annotated metagenome. We test our approach on metagenomic datasets from environmental and human-associated microbial communities. The results show that our approach provides a more accurate representation of the metabolic activities encoded in a metagenome, and thus improves the comparative analysis of multiple microbial communities. In addition, our approach reports likelihood scores of putative reactions, which can be used to identify important reactions and metabolic pathways that reflect the environmental adaptation of the microbial communities. Source code for sampling metabolic networks is available online at http://omics.informatics.indiana.edu/mg/MetaNetSam/.     

Functional Potential of Soil Microbial Communities in the Maize Rhizosphere

Microbial communities in the rhizosphere make significant contributions to crop health and nutrient cycling. However, their ability to perform important biogeochemical processes remains uncharacterized. Here, we identified important functional genes that characterize the rhizosphere microbial community to understand metabolic capabilities in the maize rhizosphere using the GeoChip-based functional gene array method. Significant differences in functional gene structure were apparent between rhizosphere and bulk soil microbial communities. Approximately half of the detected gene families were significantly (p<0.05) increased in the rhizosphere. Based on the detected gyrB genes, Gammaproteobacteria, Betaproteobacteria, Firmicutes, Bacteroidetes and Cyanobacteria were most enriched in the rhizosphere compared to those in the bulk soil. The rhizosphere niche also supported greater functional diversity in catabolic pathways. The maize rhizosphere had significantly enriched genes involved in carbon fixation and degradation (especially for hemicelluloses, aromatics and lignin), nitrogen fixation, ammonification, denitrification, polyphosphate biosynthesis and degradation, sulfur reduction and oxidation. This research demonstrates that the maize rhizosphere is a hotspot of genes, mostly originating from dominant soil microbial groups such as Proteobacteria, providing functional capacity for the transformation of labile and recalcitrant organic C, N, P and S compounds.     

Analysis of 16S rRNA Primer Systems for Profiling of Thermophilic Microbial Communities

Microbiology - Thermophilic microorganisms are of special interest for phylogenetics and research in prokaryotic evolution, since many of them belong to deep branches of the tree of life. For this...     

16S rRNA Gene Survey of Microbial Communities in Winogradsky Columns

A Winogradsky column is a clear glass or plastic column filled with enriched sediment. Over time, microbial communities in the sediment grow in a stratified ecosystem with an oxic top layer and anoxic sub-surface layers. Winogradsky columns have been used extensively to demonstrate microbial nutrient cycling and metabolic diversity in undergraduate microbiology labs. In this study, we used high-throughput 16s rRNA gene sequencing to investigate the microbial diversity of Winogradsky columns. Specifically, we tested the impact of sediment source, supplemental cellulose source, and depth within the column, on microbial community structure. We found that the Winogradsky columns were highly diverse communities but are dominated by three phyla: Proteobacteria, Bacteroidetes, and Firmicutes. The community is structured by a founding population dependent on the source of sediment used to prepare the columns and is differentiated by depth within the column. Numerous biomarkers were identified distinguishing sample depth, including Cyanobacteria, Alphaproteobacteria, and Betaproteobacteria as biomarkers of the soil-water interface, and Clostridia as a biomarker of the deepest depth. Supplemental cellulose source impacted community structure but less strongly than depth and sediment source. In columns dominated by Firmicutes, the family Peptococcaceae was the most abundant sulfate reducer, while in columns abundant in Proteobacteria, several Deltaproteobacteria families, including Desulfobacteraceae, were found, showing that different taxonomic groups carry out sulfur cycling in different columns. This study brings this historical method for enrichment culture of chemolithotrophs and other soil bacteria into the modern era of microbiology and demonstrates the potential of the Winogradsky column as a model system for investigating the effect of environmental variables on soil microbial communities.     

Comparative Metagenomic Analysis of Coral Microbial Communities Using a Reference-Independent Approach

By comparing the SEED and Pfam functional profiles of metagenomes of two Brazilian coral species with 29 datasets that are publicly available, we were able to identify some functions, such as protein secretion systems, that are overrepresented in the metagenomes of corals and may play a role in the establishment and maintenance of bacteria-coral associations. However, only a small percentage of the reads of these metagenomes could be annotated by these reference databases, which may lead to a strong bias in the comparative studies. For this reason, we have searched for identical sequences (99% of nucleotide identity) among these metagenomes in order to perform a reference-independent comparative analysis, and we were able to identify groups of microbial communities that may be under similar selective pressures. The identification of sequences shared among the metagenomes was found to be even better for the identification of groups of communities with similar niche requirements than the traditional analysis of functional profiles. This approach is not only helpful for the investigation of similarities between microbial communities with high proportion of unknown reads, but also enables an indirect overview of gene exchange between communities.     

Selective Recovery of 16S rRNA Sequences from Natural Microbial Communities in the Form of cDNA

Cloning of cDNA obtained from 16S rRNA (16S rcDNA) selectively retrieves species-specific sequence information useful for analyzing the composition and structure of natural microbial communities. With this technique we obtained recombinant 16S rcDNA libraries from Escherichia coli and from a model hot-spring cyanobacterial-mat community. The recombinant plasmids contained exclusively 16S rRNA-derived inserts. This selective approach is independent of biasing culture techniques and eliminates the laborious screening required to locate 16S rRNA gene-bearing recombinants in genomic DNA libraries obtained from natural communities.     

Comparative Metagenomic Analysis of Soil Microbial Communities across Three Hexachlorocyclohexane Contamination Levels

This paper presents the characterization of the microbial community responsible for the in-situ bioremediation of hexachlorocyclohexane (HCH). Microbial community structure and function was analyzed using 16S rRNA amplicon and shotgun metagenomic sequencing methods for three sets of soil samples. The three samples were collected from a HCH-dumpsite (450 mg HCH/g soil) and comprised of a HCH/soil ratio of 0.45, 0.0007, and 0.00003, respectively. Certain bacterial; (Chromohalobacter, Marinimicrobium, Idiomarina, Salinosphaera, Halomonas, Sphingopyxis, Novosphingobium, Sphingomonas and Pseudomonas), archaeal; (Halobacterium, Haloarcula and Halorhabdus) and fungal (Fusarium) genera were found to be more abundant in the soil sample from the HCH-dumpsite. Consistent with the phylogenetic shift, the dumpsite also exhibited a relatively higher abundance of genes coding for chemotaxis/motility, chloroaromatic and HCH degradation (lin genes). Reassembly of a draft pangenome of Chromohalobacter salaxigenes sp. (∼8X coverage) and 3 plasmids (pISP3, pISP4 and pLB1; 13X coverage) containing lin genes/clusters also provides an evidence for the horizontal transfer of HCH catabolism genes.     

Explorative Multivariate Analyses of 16S rRNA Gene Data from Microbial Communities in Modified-Atmosphere-Packed Salmon and Coalfish

Modified-atmosphere packaging (MAP) of foods in combination with low-temperature storage extends product shelf life by limiting microbial growth. We investigated the microbial biodiversity of MAP salmon and coalfish by using an explorative approach and analyzing both the total amounts of bacteria and the microbial group composition (both aerobic and anaerobic bacteria). Real-time PCR analyses revealed a surprisingly large difference in the microbial loads for the different fish samples. The microbial composition was determined by examining partial 16S rRNA gene sequences from 180 bacterial isolates, as well as by performing terminal restriction fragment length polymorphism analysis and cloning 92 sequences from PCR products of DNA directly retrieved from the fish matrix. Twenty different bacterial groups were identified. Partial least-squares (PLS) regression was used to relate the major groups of bacteria identified to the fish matrix and storage time. A strong association of coalfish with Photobacterium phosphoreum was observed. Brochothrix spp. and Carnobacterium spp., on the other hand, were associated with salmon. These bacteria dominated the fish matrixes after a storage period. Twelve Carnobacterium isolates were identified as either Carnobacterium piscicola (five isolates) or Carnobacterium divergens (seven isolates), while the eight Brochothrix isolates were identified as Brochothrix thermosphacta by full-length 16S rRNA gene sequencing. Principal-component analyses and PLS analysis of the growth characteristics (with 49 different substrates) showed that C. piscicola had distinct substrate requirements, while the requirements of B. thermosphacta and C. piscicola were quite divergent. In conclusion, our explorative multivariate approach gave a picture of the total microbial biodiversity in MAP fish that was more comprehensive than the picture that could be obtained previously. Such information is crucial in controlled food production when, for example, the hazard analysis of critical control points principle is used.     

Modular Spectral Imaging System for Discrimination of Pigments in Cells and Microbial Communities

Here we describe a spectral imaging system for minimally invasive identification, localization, and relative quantification of pigments in cells and microbial communities. The modularity of the system allows pigment detection on spatial scales ranging from the single-cell level to regions whose areas are several tens of square centimeters. For pigment identification in vivo absorption and/or autofluorescence spectra are used as the analytical signals. Along with the hardware, which is easy to transport and simple to assemble and allows rapid measurement, we describe newly developed software that allows highly sensitive and pigment-specific analyses of the hyperspectral data. We also propose and describe a number of applications of the system for microbial ecology, including identification of pigments in living cells and high-spatial-resolution imaging of pigments and the associated phototrophic groups in complex microbial communities, such as photosynthetic endolithic biofilms, microbial mats, and intertidal sediments. This system provides new possibilities for studying the role of spatial organization of microorganisms in the ecological functioning of complex benthic microbial communities or for noninvasively monitoring changes in the spatial organization and/or composition of a microbial community in response to changing environmental factors.