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Jose V.L.,Indian National Institute of Animal Nutrition and Physiology | Jose V.L.,Bangalore University | More R.P.,Bioinformatics Laboratory | Appoothy T.,Indian National Institute of Animal Nutrition and Physiology | Arun A.S.,Bannerghatta Bear Rescue Center
Systematic and Applied Microbiology | Year: 2017

Rumen houses a plethora of symbiotic microorganisms empowering the host to hydrolyze plant lignocellulose. In this study, NGS based metagenomic approach coupled with bioinformatic analysis was employed to gain an insight into the deconstruction of lignocellulose by carbohydrate-active enzymes (CAZymes) in Indian crossbred Holstein-Friesian cattle. Cattle rumen metagenomic DNA was sequenced using Illumina-MiSeq and 1.9 gigabases of data generated with an average read length of 871 bp. Analysis of the assembled sequences by Pfam-based Carbohydrate-active enzyme Analysis Toolkit identified 17,164 putative protein-encoding CAZymes belonging to different families of glycoside hydrolases (7574), glycosyltransferases (5185), carbohydrate-binding modules (2418), carbohydrate esterases (1516), auxiliary activities (434) and polysaccharide lyases (37). Phylogenetic analysis of putative CAZymes revealed that a significant proportion of CAZymes were contributed by bacteria belonging to the phylum Bacteroidetes (40%), Firmicutes (30%) and Proteobacteria (10%). The comparative analysis of HF cross rumen metagenome with other herbivore metagenomes indicated that Indian crossbred cattle rumen is endowed with a battery of CAZymes that may play a central role in lignocellulose deconstruction. The extensive catalog of enzymes reported in our study that hydrolyzes plant lignocellulose biomass, can be further explored for the better feed utilization in ruminants and also for different industrial applications. © 2017 Elsevier GmbH


News Article | April 27, 2017
Site: www.eurekalert.org

Published in the journal eLife, the study provides a deeper understanding of the structure of this protein, thereby paving the way for the development of more effective inhibitors The protein p38α is a member of a family of molecules that transmit outside signals throughout the cell, thus allowing for an appropriate cell response, such as proliferation, differentiation, senescence, or death. Moreover, the participation of p38α in pathological conditions, like chronic inflammatory diseases and cancer, makes it a promising pharmacological target. In this regard, a complete picture of the activation mechanism of this protein is essential in order to design specific inhibitors that do not affect other processes. The journal eLife has published a study on p38α by Antonija Kuzmanic, an EU Marie Curie COFUND fellow who is undertaking postdoctoral training simultaneously in two IRB Barcelona labs -- the Molecular Modelling and Bioinformatics Laboratory and the Signalling and Cell Cycle Laboratory. Collaborative research between the lab headed by Modesto Orozco and that led by Angel R. Nebreda, the latter an international authority on p38α, has provided an integrative picture of the p38α activation mechanism and new insights into the molecular effects of various molecules that regulate the enzymatic activity of the protein. Using computational techniques, researchers have deciphered the key elements of the complex molecular mechanism underlying p38α activity. This study describes the protein activation mechanism in unprecedented detail and reconciles the apparent contradictory results reported in previous structural studies. "Considering the importance of p38α for pathological processes, we hope the knowledge obtained in this study will help to target the protein with more specificity," stresses Antonija Kuzmanic, first author of the study. p38α has already been targeted for inflammatory diseases and some types of cancer; however, none of the drugs have yet made it to the market. "Our study reveals novel conformations of the protein, which could be used as a starting point in virtual screening studies aimed at uncovering new inhibitors," explains Kuzmanic. And she adds, "We were also able to highlight important electrostatic interactions, which may allow us to explore alternative activation pathways with increased specificity". "We used only computational techniques. Mainly, we employed numerous molecular dynamics simulations combined with an advanced sampling technique called metadynamics," explains Kuzmanic. This combination has an advantage over standard molecular dynamics simulations, as it allows researchers to observe large conformational changes in a reasonable amount of computational time. She goes on to say, "we are able to add statistical significance to the conformations we observed in our simulations". Changes in the free-energy landscape of p38α MAP kinase through its canonical activation and binding events as studied by enhanced molecular dynamics simulations


News Article | April 27, 2017
Site: phys.org

Researchers revealed details of the p38 activation mechanism. The image represents the structural changes from the inactive state (purple) to the active one (green) proposed by X-ray crystallography. Credit: Antonija Kuzmanic. The protein p38α is a member of a family of molecules that transmit outside signals throughout the cell, thus allowing for an appropriate cell response, such as proliferation, differentiation, senescence, or death. Moreover, the participation of p38α in pathological conditions, like chronic inflammatory diseases and cancer, makes it a promising pharmacological target. In this regard, a complete picture of the activation mechanism of this protein is essential in order to design specific inhibitors that do not affect other processes. The journal eLife has published a study on p38α by Antonija Kuzmanic, an EU Marie Curie COFUND fellow who is undertaking postdoctoral training simultaneously in two IRB Barcelona labs—the Molecular Modelling and Bioinformatics Laboratory and the Signalling and Cell Cycle Laboratory. Collaborative research between the lab headed by Modesto Orozco and that led by Angel R. Nebreda, the latter an international authority on p38α, has provided an integrative picture of the p38α activation mechanism and new insights into the molecular effects of various molecules that regulate the enzymatic activity of the protein. Using computational techniques, researchers have deciphered the key elements of the complex molecular mechanism underlying p38α activity. This study describes the protein activation mechanism in unprecedented detail and reconciles the apparent contradictory results reported in previous structural studies. "Considering the importance of p38α for pathological processes, we hope the knowledge obtained in this study will help to target the protein with more specificity," stresses Antonija Kuzmanic, first author of the study. p38α has already been targeted for inflammatory diseases and some types of cancer; however, none of the drugs have yet made it to the market. "Our study reveals novel conformations of the protein, which could be used as a starting point in virtual screening studies aimed at uncovering new inhibitors," explains Kuzmanic. And she adds, "We were also able to highlight important electrostatic interactions, which may allow us to explore alternative activation pathways with increased specificity". "We used only computational techniques. Mainly, we employed numerous molecular dynamics simulations combined with an advanced sampling technique called metadynamics," explains Kuzmanic. This combination has an advantage over standard molecular dynamics simulations, as it allows researchers to observe large conformational changes in a reasonable amount of computational time. She goes on to say, "we are able to add statistical significance to the conformations we observed in our simulations". More information: Antonija Kuzmanic et al, Changes in the free-energy landscape of p38α MAP kinase through its canonical activation and binding events as studied by enhanced molecular dynamics simulations, eLife (2017). DOI: 10.7554/eLife.22175


T'Hoen P.A.C.,Leiden University | T'Hoen P.A.C.,Netherlands Bioinformatics Center | Friedlander M.R.,Center for Genomic Regulation | Friedlander M.R.,University Pompeu Fabra | And 29 more authors.
Nature Biotechnology | Year: 2013

RNA sequencing is an increasingly popular technology for genome-wide analysis of transcript sequence and abundance. However, understanding of the sources of technical and interlaboratory variation is still limited. To address this, the GEUVADIS consortium sequenced mRNAs and small RNAs of lymphoblastoid cell lines of 465 individuals in seven sequencing centers, with a large number of replicates. The variation between laboratories appeared to be considerably smaller than the already limited biological variation. Laboratory effects were mainly seen in differences in insert size and GC content and could be adequately corrected for. In small-RNA sequencing, the microRNA (miRNA) content differed widely between samples owing to competitive sequencing of rRNA fragments. This did not affect relative quantification of miRNAs. We conclude that distributing RNA sequencing among different laboratories is feasible, given proper standardization and randomization procedures. We provide a set of quality measures and guidelines for assessing technical biases in RNA-seq data. © 2013 Nature America, Inc.


Mendes N.D.,CNRS Biometry and Evolutionary Biology Laboratory | Mendes N.D.,University of Lisbon | Mendes N.D.,French Institute for Research in Computer Science and Automation | Freitas A.T.,University of Lisbon | And 3 more authors.
BMC Genomics | Year: 2010

Background: Efforts using computational algorithms towards the enumeration of the full set of miRNAs of an organism have been limited by strong reliance on arguments of precursor conservation and feature similarity. However, miRNA precursors may arise anew or be lost across the evolutionary history of a species and a newly sequenced genome may be evolutionarily too distant from other genomes for an adequate comparative analysis. In addition, the learning of intricate classification rules based purely on features shared by miRNA precursors that are currently known may reflect a perpetuating identification bias rather than a sound means to tell true miRNAs from other genomic stem-loops.Results: We show that there is a strong bias amongst annotated pre-miRNAs towards robust stem-loops in the genomes of Drosophila melanogaster and Anopheles gambiae and we propose a scoring scheme for precursor candidates which combines four robustness measures. Additionally, we identify several known pre-miRNA homologs in the newly sequenced Anopheles darlingi and show that most are found amongst the top-scoring precursor candidates. Furthermore, a comparison of the performance of our approach is made against two single-genome pre-miRNA classification methods.Conclusions: In this paper we present a strategy to sieve through the vast amount of stem-loops found in metazoan genomes in search of pre-miRNAs, significantly reducing the set of candidates while retaining most known miRNA precursors. This approach makes no use of conservation data and relies solely on properties derived from our knowledge of miRNA biogenesis. © 2010 Mendes et al; licensee BioMed Central Ltd.


PubMed | Bannerghatta Bear Rescue Center, Indian National Institute of Animal Nutrition and Physiology and Bioinformatics Laboratory
Type: Journal Article | Journal: AMB Express | Year: 2017

The rumen is a unique natural habitat, exhibiting an unparalleled genetic resource of fibrolytic enzymes of microbial origin that degrade plant polysaccharides. The objectives of this study were to identify the principal plant cell wall-degrading enzymes and the taxonomic profile of rumen microbial communities that are associated with it. The cattle rumen microflora and the carbohydrate-active enzymes were functionally classified through a whole metagenomic sequencing approach. Analysis of the assembled sequences by the Carbohydrate-active enzyme analysis Toolkit identified the candidate genes encoding fibrolytic enzymes belonging to different classes of glycoside hydrolases(11,010 contigs), glycosyltransferases (6366 contigs), carbohydrate esterases (4945 contigs), carbohydrate-binding modules (1975 contigs), polysaccharide lyases (480 contigs), and auxiliary activities (115 contigs). Phylogenetic analysis of CAZyme encoding contigs revealed that a significant proportion of CAZymes were contributed by bacteria belonging to genera Prevotella, Bacteroides, Fibrobacter, Clostridium, and Ruminococcus. The results indicated that the cattle rumen microbiome and the CAZymes are highly complex, structurally similar but compositionally distinct from other ruminants. The unique characteristics of rumen microbiota and the enzymes produced by resident microbes provide opportunities to improve the feed conversion efficiency in ruminants and serve as a reservoir of industrially important enzymes for cellulosic biofuel production.


Hernandez L.G.,Mass Spectrometry Group | Hernandez L.G.,University of Brasilia | Lu B.,Scripps Research Institute | Da Cruz G.C.N.,University of Brasilia | And 7 more authors.
Journal of Proteome Research | Year: 2012

A large-scale mapping of the worker honeybee brain proteome was achieved by MudPIT. We identified 2742 proteins from forager and nurse honeybee brain samples; 17% of the total proteins were found to be differentially expressed by spectral count sampling statistics and a G-test. Sequences were compared with the EuKaryotic Orthologous Groups (KOG) catalog set using BLASTX and then categorized into the major KOG categories of most similar sequences. According to this categorization, nurse brain showed increased expression of proteins implicated in translation, ribosomal structure, and biogenesis (14.5%) compared with forager (1.8%). Experienced foragers overexpressed proteins involved in energy production and conversion, showing an extensive difference in this set of proteins (17%) in relation to the nurse subcaste (0.6%). Examples of proteins selectively expressed in each subcaste were analyzed. A comparison between these MudPIT experiments and previous 2-DE experiments revealed nine coincident proteins differentially expressed in both methodologies. © 2011 American Chemical Society.


Golbert D.C.F.,Bioinformatics Laboratory | Golbert D.C.F.,Oswaldo Cruz Foundation | Santana-Van-Vliet E.,Oswaldo Cruz Foundation | Mundstein A.S.,Bioinformatics Laboratory | And 3 more authors.
Nucleic Acids Research | Year: 2014

The laminin (LM)-database, hosted at http://www.lm.lncc.br, was published in the NAR database 2011 edition. It was the first database that provided comprehensive information concerning a non-collagenous family of extracellular matrix proteins, the LMs. In its first version, this database contained a large amount of information concerning LMs related to health and disease, with particular emphasis on the haemopoietic system. Users can easily access several tabs for LMs and LM-related molecules, as well as LM nomenclatures and direct links to PubMed.The LM-database version 2.0 integrates data from several publications to achieve a more comprehensive knowledge of LMs in health and disease. The novel features include the addition of two new tabs, 'Neuromuscular Disorders' and 'miRNA - LM Relationship'. More specifically, in this updated version, an expanding set of data has been displayed concerning the role of LMs in neuromuscular and neurodegenerative diseases, as well as the putative involvement of microRNAs. Given the importance of LMs in several biological processes, such as cell adhesion, proliferation, differentiation, migration and cell death, this upgraded version expands for users a panoply of information, regarding complex molecular circuitries that involve LMs in health and disease, including neuromuscular and neurodegenerative disorders. © 2013 The Author(s). Published by Oxford University Press.


Efimov D.,Moscow State University | Zaki N.,Bioinformatics Laboratory | Berengueres J.,Media Laboratory
Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining | Year: 2012

High-throughput experimental techniques have made available large datasets of experimentally detected protein-protein interactions. However, experimentally determined protein complexes datasets are not exhaustive nor reliable. A protein complex plays a key role in disease development. Therefore, the identification and characterization of protein complexes involved is crucial to the understanding of the molecular events under normal and abnormal physiological conditions. In this paper, we propose a novel graph mining algorithm to identify protein complexes. The algorithm first checks the quality of the interaction data, then predicts protein complexes based on the concept of weighted clustering coefficient. To demonstrate the effectiveness of our proposed method, we present experimental results on yeast protein interaction data. The level of accuracy achieved is a strong argument in favor of the proposed method. Novel protein complexes were also predicted to assist biologists in their search for protein complexes. The datasets and programs are freely available from http://faculty.uaeu.ac.ae/nzaki/PE-WCC. htm. Copyright 2012 ACM.


Shen W.,Chongqing Medical University | Chen M.,Chongqing Medical University | Wei G.,Chongqing Medical University | Li Y.,Chongqing Medical University | Li Y.,Bioinformatics Laboratory
PLoS ONE | Year: 2012

Predicting miRNAs is an arduous task, due to the diversity of the precursors and complexity of enzyme processes. Although several prediction approaches have reached impressive performances, few of them could achieve a full-function recognition of mature miRNA directly from the candidate hairpins across species. Therefore, researchers continue to seek a more powerful model close to biological recognition to miRNA structure. In this report, we describe a novel miRNA prediction algorithm, known as FOMmiR, using a fixed-order Markov model based on the secondary structural pattern. For a training dataset containing 809 human pre-miRNAs and 6441 human pseudo-miRNA hairpins, the model's parameters were defined and evaluated. The results showed that FOMmiR reached 91% accuracy on the human dataset through 5-fold cross-validation. Moreover, for the independent test datasets, the FOMmiR presented an outstanding prediction in human and other species including vertebrates, Drosophila, worms and viruses, even plants, in contrast to the well-known algorithms and models. Especially, the FOMmiR was not only able to distinguish the miRNA precursors from the hairpins, but also locate the position and strand of the mature miRNA. Therefore, this study provides a new generation of miRNA prediction algorithm, which successfully realizes a full-function recognition of the mature miRNAs directly from the hairpin sequences. And it presents a new understanding of the biological recognition based on the strongest signal's location detected by FOMmiR, which might be closely associated with the enzyme cleavage mechanism during the miRNA maturation. © 2012 Shen et al.

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