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/.     

Bacterial genomics: the use of DNA microarrays and bacterial artificial chromosomes

Immense amounts of genetic information are contained within microbial genomes. As the number of completely sequenced microbial genomes is increasing, functional and comparative genomic techniques will be employed for sequence analysis and gene characterization. Sequence comparison and expression profiling by DNA microarrays can determine phylogenetic relationships and identify genes while bacterial artificial chromosomes (BACs) allow the study of entire biochemical pathways and permit the expression of bacterial genes in a foreign host.     

Rational design for over-production of desirable microbial metabolites by precision engineering

Microbes represent a valuable source of commercially significant natural products that have improved our quality of life. Precision engineering can be used to precisely identify and specifically modify genes responsible for production of natural products and improve this production or modify the genes creating products that would not otherwise be produced. There have been several success stories concerning the manipulation of regulatory genes, pathways, and genomes to increase the productivity of industrial microbes. This review will focus on the strategies and integrated approaches for precisely deciphering regulatory genes by various modern techniques. The applications of precision engineering in rational strain improvement also shed light on the biology of natural microbial systems.     

Pollen wall development: the associated enzymes and metabolic pathways

Pollen grains are surrounded by a sculpted wall, which protects male gametophytes from various environmental stresses and microbial attacks, and also facilitates pollination. Pollen wall development requires lipid and polysaccharide metabolism, and some key genes and proteins that participate in these processes have recently been identified. Here, we summarise the genes and describe their functions during pollen wall development via several metabolic pathways. A working model involving substances and catalytic enzyme reactions that occur during pollen development is also presented. This model provides information on the complete process of pollen wall development with respect to metabolic pathways.     

微生物多糖合成关键基因挖掘的研究进展

随着分子生物学技术的快速发展,功能基因的挖掘在微生物高产多糖合成关键途径研究中变得越来越重要,不断发展的基因挖掘方法和基因组分析工具推进了研究的深入进行。本文主要综述了近年来报道的微生物多糖生物合成途径和多糖合成途径中的关键酶,以及利用多种技术手段和分析软件工具对多糖合成关键基因进行挖掘和验证的相关研究,为微生物多糖合成关键基因的验证以及微生物高产多糖菌株的制备提供参考。     

The MORPH Algorithm: Ranking Candidate Genes for Membership in Arabidopsis and Tomato Pathways

Closing gaps in our current knowledge about biological pathways is a fundamental challenge. The development of novel computational methods along with high-throughput experimental data carries the promise to help in the challenge. We present an algorithm called MORPH (for module-guided ranking of candidate pathway genes) for revealing unknown genes in biological pathways. The method receives as input a set of known genes from the target pathway, a collection of expression profiles, and interaction and metabolic networks. Using machine learning techniques, MORPH selects the best combination of data and analysis method and outputs a ranking of candidate genes predicted to belong to the target pathway. We tested MORPH on 230 known pathways in Arabidopsis thaliana and 93 known pathways in tomato (Solanum lycopersicum) and obtained high-quality cross-validation results. In the photosynthesis light reactions, homogalacturonan biosynthesis, and chlorophyll biosynthetic pathways of Arabidopsis, genes ranked highly by MORPH were recently verified to be associated with these pathways. MORPH candidates ranked for the carotenoid pathway from Arabidopsis and tomato are derived from pathways that compete for common precursors or from pathways that are coregulated with or regulate the carotenoid biosynthetic pathway.     

Microbial breakdown of halogenated aromatic pesticides and related compounds.

Considerable progress has been made in the last few years in understanding the mechanisms of microbial degradation of halogenated aromatic compounds. Much is already known about the degradation mechanisms under aerobic conditions, and metabolism under anaerobiosis has lately received increasing attention. The removal of the halogen substituent is a key step in degradation of halogenated aromatics. This may occur as an initial step via reductive, hydrolytic or oxygenolytic mechanisms, or after cleavage of the aromatic ring at a later stage of metabolism. In addition to degradation, several biotransformation reactions, such as methylation and polymerization, may take place and produce more toxic or recalcitrant metabolites. Studies with pure bacterial and fungal cultures have given detailed information on the biodegradation pathways of several halogenated aromatic compounds. Several of the key enzymes have been purified or studied in cell extracts, and there is an increasing understanding of the organization and regulation of the genes involved in haloaromatic degradation. This review will focus on the biodegradation and biotransformation pathways that have been established for halogenated phenols, phenoxyalkanoic acids, benzoic acids, benzenes, anilines and structurally related halogenated aromatic pesticides. There is a growing interest in developing microbiological methods for clean-up of soil and water contaminated with halogenated aromatic compounds.     

Integrated pathway-genome databases and their role in drug discovery.

Integrated pathway-genome databases describe the genes and genome of an organism, as well as its predicted pathways, reactions, enzymes and metabolites. In conjunction with visualization and analysis software, these databases provide a framework for improved understanding of microbial physiology and for antimicrobial drug discovery. We describe pathway-based analyses of the genomes of a number of medically relevant microorganisms and a novel software tool that visualizes gene-expression data on a diagram showing the whole metabolic network of the microorganism.     

Tools and strategies for constructing cell-free enzyme pathways

Single enzyme systems or engineered microbial hosts have been used for decades but the notion of assembling multiple enzymes into cell-free synthetic pathways is a relatively new development. The extensive possibilities that stem from this synthetic concept makes it a fast growing and potentially high impact field for biomanufacturing fine and platform chemicals, pharmaceuticals and biofuels. However, the translation of individual single enzymatic reactions into cell-free multi-enzyme pathways is not trivial. In reality, the kinetics of an enzyme pathway can be very inadequate and the production of multiple enzymes can impose a great burden on the economics of the process. We examine here strategies for designing synthetic pathways and draw attention to the requirements of substrates, enzymes and cofactor regeneration systems for improving the effectiveness and sustainability of cell-free biocatalysis. In addition, we comment on methods for the immobilisation of members of a multi-enzyme pathway to enhance the viability of the system. Finally, we focus on the recent development of integrative tools such as in silico pathway modelling and high throughput flux analysis with the aim of reinforcing their indispensable role in the future of cell-free biocatalytic pathways for biomanufacturing.     

Designing synthetic microbial communities for effectual bioremediation: A review

Abstract

Bioremediation is a better alternative and widely accepted approach used for efficient degradation of environmental pollutants released from industries, urban and agricultural activities due to its eco-friendly nature. Systems biology helps in the identification of new genes, proteins, metabolites, and metabolic pathways involved in bioremediation. Such information can be used for designing synthetic microbial communities that can degrade multiple recalcitrant pollutants simultaneously. This review gives a brief insight into various systems biology tools towards providing a greater understanding of microbial behaviour and improving the way of bioremediation. These techniques alone or in combination, provide a way to understand and improve the genetic potential of microorganisms to remediate various environmental contaminants efficiently. Further, this review also describes the successful employment of synthetic microbial consortium in the bioremediation. Moreover, In-silico tools are also described to analyse the data obtained through different laboratory experiments as well to predict the behaviour of microbial consortium towards the pollutants using different databases.     

Linking plant functional genes to rhizosphere microbes: a review

The importance of rhizomicrobiome in plant development, nutrition acquisition and stress tolerance is unquestionable. Relevant plant genes corresponding to the above functions also regulate rhizomicrobiome construction. Deciphering the molecular regulatory network of plant-microbe interactions could substantially contribute to improving crop yield and quality. Here, the plant gene-related nutrient uptake, biotic and abiotic stress resistance, which may influence the composition and function of microbial communities, are discussed in this review. In turn, the influence of microbes on the expression of functional plant genes, and thereby plant growth and immunity, is also reviewed. Moreover, we have specifically paid attention to techniques and methods used to link plant functional genes and rhizomicrobiome. Finally, we propose to further explore the molecular mechanisms and signalling pathways of microbe-host gene interactions, which could potentially be used for managing plant health in agricultural systems.     

Bioinformatics in microbial biotechnology – a mini review

The revolutionary growth in the computation speed and memory storage capability has fueled a new era in the analysis of biological data. Hundreds of microbial genomes and many eukaryotic genomes including a cleaner draft of human genome have been sequenced raising the expectation of better control of microorganisms. The goals are as lofty as the development of rational drugs and antimicrobial agents, development of new enhanced bacterial strains for bioremediation and pollution control, development of better and easy to administer vaccines, the development of protein biomarkers for various bacterial diseases, and better understanding of host-bacteria interaction to prevent bacterial infections. In the last decade the development of many new bioinformatics techniques and integrated databases has facilitated the realization of these goals. Current research in bioinformatics can be classified into: (i) genomics – sequencing and comparative study of genomes to identify gene and genome functionality, (ii) proteomics – identification and characterization of protein related properties and reconstruction of metabolic and regulatory pathways, (iii) cell visualization and simulation to study and model cell behavior, and (iv) application to the development of drugs and anti-microbial agents. In this article, we will focus on the techniques and their limitations in genomics and proteomics. Bioinformatics research can be classified under three major approaches: (1) analysis based upon the available experimental wet-lab data, (2) the use of mathematical modeling to derive new information, and (3) an integrated approach that integrates search techniques with mathematical modeling. The major impact of bioinformatics research has been to automate the genome sequencing, automated development of integrated genomics and proteomics databases, automated genome comparisons to identify the genome function, automated derivation of metabolic pathways, gene expression analysis to derive regulatory pathways, the development of statistical techniques, clustering techniques and data mining techniques to derive protein-protein and protein-DNA interactions, and modeling of 3D structure of proteins and 3D docking between proteins and biochemicals for rational drug design, difference analysis between pathogenic and non-pathogenic strains to identify candidate genes for vaccines and anti-microbial agents, and the whole genome comparison to understand the microbial evolution. The development of bioinformatics techniques has enhanced the pace of biological discovery by automated analysis of large number of microbial genomes. We are on the verge of using all this knowledge to understand cellular mechanisms at the systemic level. The developed bioinformatics techniques have potential to facilitate (i) the discovery of causes of diseases, (ii) vaccine and rational drug design, and (iii) improved cost effective agents for bioremediation by pruning out the dead ends. Despite the fast paced global effort, the current analysis is limited by the lack of available gene-functionality from the wet-lab data, the lack of computer algorithms to explore vast amount of data with unknown functionality, limited availability of protein-protein and protein-DNA interactions, and the lack of knowledge of temporal and transient behavior of genes and pathways.     

Microbial diversity and function in crystalline basement beneath the Deccan Traps explored in a 3 km borehole at Koyna,western India

Deep terrestrial subsurface represents a huge repository of global prokaryotic biomass. Given its vastness and importance, microbial life within the deep subsurface continental crust remains under-represented in global studies. We characterize the microbial communities of deep, extreme and oligotrophic realm hosted by crystalline Archaean granitic rocks underneath the Deccan Traps, through sampling via 3000 m deep scientific borehole at Koyna, India through metagenomics, amplicon sequencing and cultivation-based analyses. Gene sequences 16S rRNA (7.37 × 10⁶) show considerable bacterial diversity and the existence of a core microbiome (5724 operational taxonomic units conserved out of a total 118,064 OTUs) across the depths. Relative abundance of different taxa of core microbiome varies with depth in response to prevailing lithology and geochemistry. Co-occurrence network analysis and cultivation attempt to elucidate close interactions among autotrophic and organotrophic bacteria. Shotgun metagenomics reveals a major role of autotrophic carbon fixation via the Wood–Ljungdahl pathway and genes responsible for energy and carbon metabolism. Deeper analysis suggests the existence of an ‘acetate switch’, coordinating biosynthesis and cellular homeostasis. We conclude that the microbial life in the nutrient- and energy-limited deep granitic crust is constrained by the depth and managed by a few core members via a close interplay between autotrophy and organotrophy.     

Nitrogen Cycling Potential of a Grassland Litter Microbial Community

Because microorganisms have different abilities to utilize nitrogen (N) through various assimilatory and dissimilatory pathways, microbial composition and diversity likely influence N cycling in an ecosystem. Terrestrial plant litter decomposition is often limited by N availability; however, little is known about the microorganisms involved in litter N cycling. In this study, we used metagenomics to characterize the potential N utilization of microbial communities in grassland plant litter. The frequencies of sequences associated with eight N cycling pathways differed by several orders of magnitude. Within a pathway, the distributions of these sequences among bacterial orders differed greatly. Many orders within the Actinobacteria and Proteobacteria appeared to be N cycling generalists, carrying genes from most (five or six) of the pathways. In contrast, orders from the Bacteroidetes were more specialized and carried genes for fewer (two or three) pathways. We also investigated how the abundance and composition of microbial N cycling genes differed over time and in response to two global change manipulations (drought and N addition). For many pathways, the abundance and composition of N cycling taxa differed over time, apparently reflecting precipitation patterns. In contrast to temporal variability, simulated global change had minor effects on N cycling potential. Overall, this study provides a blueprint for the genetic potential of N cycle processes in plant litter and a baseline for comparisons to other ecosystems.     

MRAD: Metabolic reaction analysis database--an entity-relationship approach

The Metabolic Reaction Analysis Database (MRAD) is a relational database based on the Entity-Relationship (ER) model which combines information about organisms, biochemical pathways, reactions, enzymes, substrates, products and genes. It describes 244,596 genes in 79 organisms, 6,552 enzymes, and 3,552 reactions, 3,100 substrates, 2,866 products and 118 metabolic pathways. The MRAD graphical user interface allows for the identification of metabolic reactions which are similar and dissimilar in multiple organisms, reactions in a pathway which are missing in an organism and using any combination between one to six of the biological entities of organisms, genes, pathways, enzymes, substrates and products to determine metabolic reactions. MRAD provides a powerful and efficient tool for the construction of flux balance models for metabolic engineering applications.     

Databases for Microbiologists

Databases play an increasingly important role in biology. They archive, store, maintain, and share information on genes, genomes, expression data, protein sequences and structures, metabolites and reactions, interactions, and pathways. All these data are critically important to microbiologists. Furthermore, microbiology has its own databases that deal with model microorganisms, microbial diversity, physiology, and pathogenesis. Thousands of biological databases are currently available, and it becomes increasingly difficult to keep up with their development. The purpose of this minireview is to provide a brief survey of current databases that are of interest to microbiologists.