Metagenomic annotation networks: construction and applications

Photocatalytic degradation of low-density polythene using protein-coated titania nanoparticles and Lactobacillus plantarum

The biodegradation of low-density Polyethylene (LDPE) is usually time-consuming, In the presence of Titania-nanoparticles, LDPE is photocatalytically degraded in smaller fragments afterward the bacteria can effectively degrade polyethylene. In the current study, potent polyethylene degrading bacteria were screened from the soil of the local dumpsite and identified using 16s rRNA sequencing. The protein-coated titania nanoparticle (TNPs) was synthesized using Sol-gel Method and characterized by XRD, and SAED-HRTEM. The photocatalytic biodegradation of LDPE (30 microns) in presence of 1M NaOH was studied by exposing it to UV irradiation, visible light, and high temperature (50°C) for 21 days separately and photocatalytic biodegradation was assessed by monitoring % weight loss at every 7 days' time interval, tensile strength, and FTIR. After 21 days of photocatalytic biodegradation, LDPE film containing both TNPs and Lactobacillus plantarum along with 1M NaOH in presence of visible light was unveiled oxidation and enumerated via the occurrence of strong absorptions band of the carbonyl group (C=O) and also the breaking and weakening of existing absorptions bonds along with the new carbonyl functional group formation. The decline in tensile strength was measured at 21% after 21 days. Thus, experimental results on LDPE after exposure to visible irradiation along with Lactobacillus plantarum and 5% protein-coated TNP showed improvement in degradation rate and elongation 59 % and 51% within 21 days, respectively in comparison to another study (49 % Weight loss and 12% elongation after 45 days). An excellent application of this research is significantly reduced plastic waste via a maintained procedure.

Metagenomic insights to understand transient influence of Yamuna River on taxonomic and functional aspects of bacterial and archaeal communities of River Ganges

River confluences are interesting ecosystems to investigate for their microbial community structure and functional potentials. River Ganges is one of the most important and holy river of India with great mythological history and religious significance. The Yamuna River meets Ganges at the Prayagraj (formerly known as Allahabad), India to form a unique confluence. The influence of Yamuna River on taxonomic and functional aspects of microbiome at this confluence and its downstream, remains unexplored. To unveil this dearth, whole metagenome sequencing of the microbial (bacterial and archaeal) community from the sediment samples of December 2017 sampling expedition was executed using high throughput MinION technology. Results revealed differences in the relative abundance of bacterial and archaeal communities across the confluence. Grouped by the confluence, a higher abundance of Proteobacteria and lower abundance of Bacteroidetes and Firmicutes was observed for Yamuna River (G15Y) and at immediate downstream of confluence of Ganges (G15DS), as compared to the upstream, confluence, and farther downstream of confluence. A similar trend was observed for archaeal communities with a higher abundance of Euryarchaeota in G15Y and G15DS, indicating Yamuna River's influence. Functional gene(s) analysis revealed the influence of Yamuna River on xenobiotic degradation, resistance to toxic compounds, and antibiotic resistance interceded by the autochthonous microbes at the confluence and succeeding downstream locations. Overall, similar taxonomic and functional profiles of microbial communities before confluence (upstream of Ganges) and farther downstream of confluence, suggested a transient influence of Yamuna River. Our study is significant since it may be foundational basis to understand impact of Yamuna River and also rare event of mass bathing on the microbiome of River Ganges. Further investigation would be required to understand, the underlying cause behind the restoration of microbial profiles post-confluence farther zone, to unravel the rejuvenation aspects of this unique ecosystem.

The evolutionary signal in metagenome phyletic profiles predicts many gene functions

BACKGROUND

The function of many genes is still not known even in model organisms. An increasing availability of microbiome DNA sequencing data provides an opportunity to infer gene function in a systematic manner.

RESULTS

We evaluated if the evolutionary signal contained in metagenome phyletic profiles (MPP) is predictive of a broad array of gene functions. The MPPs are an encoding of environmental DNA sequencing data that consists of relative abundances of gene families across metagenomes. We find that such MPPs can accurately predict 826 Gene Ontology functional categories, while drawing on human gut microbiomes, ocean metagenomes, and DNA sequences from various other engineered and natural environments. Overall, in this task, the MPPs are highly accurate, and moreover they provide coverage for a set of Gene Ontology terms largely complementary to standard phylogenetic profiles, derived from fully sequenced genomes. We also find that metagenomes approximated from taxon relative abundance obtained via 16S rRNA gene sequencing may provide surprisingly useful predictive models. Crucially, the MPPs derived from different types of environments can infer distinct, non-overlapping sets of gene functions and therefore complement each other. Consistently, simulations on > 5000 metagenomes indicate that the amount of data is not in itself critical for maximizing predictive accuracy, while the diversity of sampled environments appears to be the critical factor for obtaining robust models.

CONCLUSIONS

In past work, metagenomics has provided invaluable insight into ecology of various habitats, into diversity of microbial life and also into human health and disease mechanisms. We propose that environmental DNA sequencing additionally constitutes a useful tool to predict biological roles of genes, yielding inferences out of reach for existing comparative genomics approaches.

Molecular Detection and Environment-Specific Diversity of Glycosyl Hydrolase Family 1 β-Glucosidase in Different Habitats

β-glucosidase is a crucial element of the microbial cellulose multienzyme complex since it is responsible for the regulation of the entire cellulose hydrolysis process. Therefore, the aim of the present work was to explore the diversity and distribution of glycosyl hydrolase family 1 β-glucosidase genes in three different environmental niches including, Himalayan soil, cow dung and compost by metagenomic approach. Preliminary evaluation through metabolic profiling using BIOLOG based utilization patterns of carbon, nitrogen, phosphorus and sulfur revealed the environment and substrate specific nature of the indigenous microbial population. Furthermore, clonal library selection, screening and sequence analysis revealed that most of the GH1 β-glucosidase proteins had low identities with the available database. Analysis of the distribution of GH1 β-glucosidase gene fragments and β-glucosidase producing microbial community revealed the environment specific nature. The OTUs obtained from Himalayan soil and compost metagenomic libraries were grouped into 19 different genera comprising 6 groups. The cow dung sample displayed the least diversity of GH1 β-glucosidase sequences, with only 14 genera, distributed among three groups- Bacteroidetes, Firmicutes, and Actinobacteria. The metagenomic study coupled with metabolic profiling of GH1 β-glucosidase illustrated the existence of intricate relationship between the geochemical environmental factors and inherent microbial community.

An analysis of the validity and utility of the proximon proposition

The utilization of metagenomic functional interactions represents a key technique for metagenomic functional annotation efforts. By definition, metagenomic operons represent such interactions, but many operon predictions protocols rely on information about orthology and/or gene function that is frequently unavailable for metagenomic genes. Recently, the concept of the metagenomic proximon was proposed for use in metagenomic scenarios where supplemental information is sparse. In this paper, we examine the validity and utility of the proximon proposition by measuring the extent to which proximons emulate actual operons. Using the Escherichia coli K-12 genome, we compare proximons and operons from the same genome and observe the configurations and cardinalities among their corresponding mappings. The results demonstrate that the vast majority of proximons map discretely to a single operon in a conservative fashion where a typical proximon is synonymous to an equivalent or truncated operon. However, a large proportion of operons had no corresponding mappings to any proximon. Various perspectives of operon and proximon intersection are discussed, along with the potential limitations for proximon detection and usage.

MetaProx: the database of metagenomic proximons

MetaProx is the database of metagenomic proximons: a searchable repository of proximon objects conceived with two specific goals. The first objective is to accelerate research involving metagenomic functional interactions by providing a database of metagenomic operon candidates. Proximons represent a special subset of directons (series of contiguous co-directional genes) where each member gene is in close proximity to its neighbours with respect to intergenic distance. As a result, proximons represent significant operon candidates where some subset of proximons is the set of true metagenomic operons. Proximons are well suited for the inference of metagenomic functional networks because predicted functional linkages do not rely on homology-dependent information that is frequently unavailable in metagenomic scenarios. The second objective is to explore representations for semistructured biological data that can offer an alternative to the traditional relational database approach. In particular, we use a serialized object implementation and advocate a Data as Data policy where the same serialized objects can be used at all levels (database, search tool and saved user file) without conversion or the use of human-readable markups. MetaProx currently includes 4 210 818 proximons consisting of 8 926 993 total member genes.

Database URL:http://metaprox.uwaterloo.ca

Inference of phenotype-defining functional modules of protein families for microbial plant biomass degraders

BACKGROUND

Efficient industrial processes for converting plant lignocellulosic materials into biofuels are a key to global efforts to come up with alternative energy sources to fossil fuels. Novel cellulolytic enzymes have been discovered in microbial genomes and metagenomes of microbial communities. However, the identification of relevant genes without known homologs, and the elucidation of the lignocellulolytic pathways and protein complexes for different microorganisms remain challenging.

RESULTS

We describe a new computational method for the targeted discovery of functional modules of plant biomass-degrading protein families, based on their co-occurrence patterns across genomes and metagenome datasets, and the strength of association of these modules with the genomes of known degraders. From approximately 6.4 million family annotations for 2,884 microbial genomes, and 332 taxonomic bins from 18 metagenomes, we identified 5 functional modules that are distinctive for plant biomass degraders, which we term "plant biomass degradation modules" (PDMs). These modules incorporate protein families involved in the degradation of cellulose, hemicelluloses, and pectins, structural components of the cellulosome, and additional families with potential functions in plant biomass degradation. The PDMs were linked to 81 gene clusters in genomes of known lignocellulose degraders, including previously described clusters of lignocellulolytic genes. On average, 70% of the families of each PDM were found to map to gene clusters in known degraders, which served as an additional confirmation of their functional relationships. The presence of a PDM in a genome or taxonomic metagenome bin furthermore allowed us to accurately predict the ability of any particular organism to degrade plant biomass. For 15 draft genomes of a cow rumen metagenome, we used cross-referencing to confirmed cellulolytic enzymes to validate that the PDMs identified plant biomass degraders within a complex microbial community.

CONCLUSIONS

Functional modules of protein families that are involved in different aspects of plant cell wall degradation can be inferred from co-occurrence patterns across (meta-)genomes with a probabilistic topic model. PDMs represent a new resource of protein families and candidate genes implicated in microbial plant biomass degradation. They can also be used to predict the plant biomass degradation ability for a genome or taxonomic bin. The method is also suitable for characterizing other microbial phenotypes.

Metagenomic guilt by association: an operonic perspective

Next-generation sequencing projects continue to drive a vast accumulation of metagenomic sequence data. Given the growth rate of this data, automated approaches to functional annotation are indispensable and a cornerstone heuristic of many computational protocols is the concept of guilt by association. The guilt by association paradigm has been heavily exploited by genomic context methods that offer functional predictions that are complementary to homology-based annotations, thereby offering a means to extend functional annotation. In particular, operon methods that exploit co-directional intergenic distances can provide homology-free functional annotation through the transfer of functions among co-operonic genes, under the assumption that guilt by association is indeed applicable. Although guilt by association is a well-accepted annotative device, its applicability to metagenomic functional annotation has not been definitively demonstrated. Here a large-scale assessment of metagenomic guilt by association is undertaken where functional associations are predicted on the basis of co-directional intergenic distances. Specifically, functional annotations are compared within pairs of adjacent co-directional genes, as well as operons of various lengths (i.e. number of member genes), in order to reveal new information about annotative cohesion versus operon length. The results suggests that co-directional gene pairs offer reduced confidence for metagenomic guilt by association due to difficulty in resolving the existence of functional associations when intergenic distance is the sole predictor of pairwise gene interactions. However, metagenomic operons, particularly those with substantial lengths, appear to be capable of providing a superior basis for metagenomic guilt by association due to increased annotative stability. The need for improved recognition of metagenomic operons is discussed, as well as the limitations of the present work.