Plants in their natural habitats adapt to drought stress in the environment through a variety of mechanisms, ranging from transient responses to low soil moisture to major survival mechanisms of escape by early flowering in absence of seasonal rainfall. However, crop plants selected by humans to yield products such as grain, vegetable, or fruit in favorable environments with high inputs of water and fertilizer are expected to yield an economic product in response to inputs. Crop plants selected for their economic yield need to survive drought stress through mechanisms that maintain crop yield. Studies on model plants for their survival under stress do not, therefore, always translate to yield of crop plants under stress, and different aspects of drought stress response need to be emphasized. The crop plant model rice ( Oryza sativa) is used here as an example to highlight mechanisms and genes for adaptation of crop plants to drought stress.

Plant adaptation to drought stress.

Are biological networks different from other large complex networks? Both large biological and non-biological networks exhibit power-law graphs (number of nodes with degree k, N(k) ~ k-b) yet the exponents, b, fall into different ranges. This may be because duplication of the information in the genome is a dominant evolutionary force in shaping biological networks (like gene regulatory networks and protein-protein interaction networks), and is fundamentally different from the mechanisms thought to dominate the growth of most non-biological networks (such as the internet [1-4]). The preferential choice models non-biological networks like web graphs can only produce power-law graphs with exponents greater than 2 [1-4,8]. We use combinatorial probabilistic methods to examine the evolution of graphs by duplication processes and derive exact analytical relationships between the exponent of the power law and the parameters of the model. Both full duplication of nodes (with all their connections) as well as partial duplication (with only some connections) are analyzed. We demonstrate that partial duplication can produce power-law graphs with exponents less than 2, consistent with current data on biological networks. The power-law exponent for large graphs depends only on the growth process, not on the starting graph.

Duplication Models for Biological Networks

Part 1 - Overview of rumen and ruminants .- 1. Rumen Microbiology: An Overview.- 2. Rumen Microbial Ecosystem of Domesticated Ruminants.- 3. Domesticated Rare Animals (Yak, Mithun and Camel): Rumen Microbial Diversity.- 4. Wild Ruminants.- 5. Structure-and-function of a non-ruminant gut: a porcine model.- Part 2 - Rumen microbial diversity.- 6. Rumen bacteria.- 7. Rumen fungi.- 8. Rumen Protozoa.- 9. Ruminal viruses (Bacteriophages, Archaeaphages).- 10. Rumen Methanogens.- Part 3 - Rumen manipulation.- 11. Plant SecondaryMetabolites.- 12. Microbial feed additives.- 13. Utilization of organic acids to manipulate ruminal fermentation and improve ruminant productivity.- 14. Selective inhibition of harmful rumen microbes.- 15. Various 'Omics' approaches to understand and manipulate rumen microbial function.- Part 6 - Exploration and exploitation of rumen microbes.- 16. Rumen Metagenomics.- 17. Rumen: an underutilized niche for industrially important enzymes.- 18. Ruminal Fermentations to Produce Liquid and Gaseous Fuels.- 19. Commercial application of rumen microbial enzymes.- 20. Molecular characterization of Euryarcheal community within an anaerobic digester.- Part 5 - Intestinal disorders and rumen microbes.- 21. Acidosis in cattle.- 22. Urea/ ammonia metabolism in the rumen and toxicity in ruminants.- 23. Nitrate/ nitrite toxicity and possibilities of their use in ruminant diet.- Part 6 - Future prospects of rumen microbiology.- 24. The Revolution in Rumen Microbiology

Rumen microbiology : from evolution to revolution

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. 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. 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.

/pdf/inference-of-phenotype-defining-functional-modules-of-sdhpkbruz0.pdf

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

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

The derivation and comparison of biological interaction networks are vital for understanding the functional capacity and hierarchical organization of integrated microbial communities. In the current work we present metagenomic annotation networks as a novel taxonomy-free approach for understanding the functional architecture of metagenomes. Specifically, metagenomic operon predictions are exploited to derive functional interactions that are translated and categorized according to their associated functional annotations. The result is a collection of discrete networks of weighted annotation linkages. These networks are subsequently examined for the occurrence of annotation modules that portray the functional and organizational characteristics of various microbial communities. A variety of network perspectives and annotation categories are applied to recover a diverse range of modules with different degrees of annotative cohesiveness. Applications to biocatalyst discovery and human health issues are discussed, as well as the limitations of the current implementation.

/pdf/metagenomic-annotation-networks-construction-and-12zc9gfwmu.pdf

Metagenomic Annotation Networks: Construction and Applications

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

/pdf/metaprox-the-database-of-metagenomic-proximons-5gk5dom2cb.pdf

MetaProx: the database of metagenomic proximons.

Gene Coexpression as Hebbian Learning in Prokaryotic Genomes

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.

An analysis of the validity and utility of the proximon proposition

Biology has before it the opportunity to redefine itself, both in terms of addressing its current limitations and harnessing the vast utility that the computational sciences can bestow upon it. Integrative biology has emerged in an effort to capture the greater picture in understanding biological systems and their interactions with one another, all at a myriad of different levels. However, we need to consider our own limitations as biological entities and how these have spawned the disparity between holist and reductionist viewpoints, when attempting to portray the complexity of biological systems. We must reconcile our conceptual limitations with our need for broader vantage points through interface between simpler constructs. Software engineering paradigms illustrate how constituent objects with stochastic properties can interact with each other in ways that capture emergent processes. This approach could permit the specification of a fully integratable concept of biology, assuming that a single and all-encompassing ontology could be developed. Yet, a bona fide ontology requires an earnest and critical rethinking of the foundations of current biology, a proposal that might incur much opposition from conventional biologists. Nevertheless, this is a necessity. Otherwise, biology risks becoming supplanted by its own progeny, fields such as genomics and proteomics.

/pdf/alice-in-wonderland-or-biology-among-the-computational-1fg8lesbjn.pdf

Alice in wonderland or biology among the computational sciences

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