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Nattokinase (NK) is a serine protease enzyme of the subtilisin family. It exhibits a strong fibrinolytic activity. The fibrinolytic enzymes from Bacillus sp. have attracted interest as thrombolytic agents because of their efficiency in the fibrinolytic process including plasmin activation. In the present study, VIT garden soil was collected and subjected to isolation process in order to screen for the NK production. Screening for NK enzyme was performed by radial caseinolytic assay. The production of NK enzyme was done in two different production medium for comparative studies. The NK enzyme was purified by gel permeation chromatography. The activity of the purified NK was checked by clot lysis and casein digestion assay. To investigate the structural basis of NK and fibrinogen interaction and also to identify the best binding mode, molecular dynamics and docking studies were performed. Based on the morphological and biochemical characterization, the isolate was identified as Bacillus sp. The overall purification fold of NK was about 3 with the specific activity of 664U/mg and 9.9% yield. Homogeneity of the purified enzyme was analyzed and confirmed by the single band obtained in SDS-PAGE. Molecular weight of the purified protease was estimated as 25 kDa. Purified NK enzyme exhibited 97% of effective clot lysis activity. The NK was docked in to the knob region of the fibrinogen at its binding site using Dock server. A total of 26 residues of fibrinogen and 29 residues of NK constitute the interface region. However, 9 residues of fibrinogen (THR238, MET264, LYS266, ARG275, THR277, ALA279, ASN308, MET310, and LYS321) and 8 residues of NK (GLY61, SER63, THR99, PHE189, LEU209, TYR217, ASN218, and MET222) are involved in intact binding. A significant amount of NK enzyme was obtained from Bacillus sp. The docking analysis revealed that the NK and fibrinogen adopt an extended binding pattern and interacts with the crucial residues to exhibit their activity.

The Gibbs sampling method has been widely used for sequence analysis after it was successfully applied to the problem of identifying regulatory motif sequences upstream of genes. Since then, numerous variants of the original idea have emerged: however, in all cases the application has been to finding short motifs in collections of short sequences (typically less than 100 nucleotides long). In this paper, we introduce a Gibbs sampling approach for identifying genes in multiple large genomic sequences up to hundreds of kilobases long. This approach leverages the evolutionary relationships between the sequences to improve the gene predictions, without explicitly aligning the sequences. We have applied our method to the analysis of genomic sequence from 14 genomic regions, totaling roughly 1.8 Mb of sequence in each organism. We show that our approach compares favorably with existing ab initio approaches to gene finding, including pairwise comparison based gene prediction methods which make explicit use of alignments. Furthermore, excellent performance can be obtained with as little as four organisms, and the method overcomes a number of difficulties of previous comparison based gene finding approaches: it is robust with respect to genomic rearrangements, can work with draft sequence, and is fast (linear in the number and length of the sequences). It can also be seamlessly integrated with Gibbs sampling motif detection methods.

Uncertainty in multiple sequence alignments has a large impact on phylogenetic analyses. Little has been done to evaluate the quality of individual positions in protein sequence alignments, which directly impact the accuracy of phylogenetic trees. Here we describe ZORRO, a probabilistic masking program that accounts for alignment uncertainty by assigning confidence scores to each alignment position. Using the BALIBASE database and in simulation studies, we demonstrate that masking by ZORRO significantly reduces the alignment uncertainty and improves the tree accuracy.

A major hindrance to studies of microbial diversity has been that the vast majority of microbes cannot be cultured in the laboratory and thus are not amenable to traditional methods of characterization. Environmental shotgun sequencing (ESS) overcomes this hurdle by sequencing the DNA from the organisms present in a microbial community. The interpretation of this metagenomic data can be greatly facilitated by associating every sequence read with its source organism. We report the development of CompostBin, a DNA composition-based algorithm for analyzing metagenomic sequence reads and distributing them into taxon-specific bins. Unlike previous methods that seek to bin assembled contigs and often require training on known reference genomes, CompostBin has the ability to accurately bin raw sequence reads without need for assembly or training. CompostBin uses a novel weighted PCA algorithm to project the high dimensional DNA composition data into an informative lower-dimensional space, and then uses the normalized cut clustering algorithm on this filtered data set to classify sequences into taxon-specific bins. We demonstrate the algorithm's accuracy on a variety of low to medium complexity data sets.

Affiliations des auteurs : cf page 216 de l'article; International audience; Comparative analysis of multiple genomes in a phylogenetic framework dramatically improves the precision and sensitivity of evolutionary inference, producing more robust results than single-genome analyses can provide. The genomes of 12 Drosophila species, ten of which are presented here for the first time (sechellia, simulans, yakuba, erecta, ananassae, persimilis, willistoni, mojavensis, virilis and grimshawi), illustrate how rates and patterns of sequence divergence across taxa can illuminate evolutionary processes on a genomic scale. These genome sequences augment the formidable genetic tools that have made Drosophila melanogaster a pre-eminent model for animal genetics, and will further catalyse fundamental research on mechanisms of development, cell biology, genetics, disease, neurobiology, behaviour, physiology and evolution. Despite remarkable similarities among these Drosophila species, we identified many putatively non-neutral changes in protein-coding genes, non-coding RNA genes, and cis-regulatory regions. These may prove to underlie differences in the ecology and behaviour of these diverse species.

The exon–intron structure of eukaryotic genes allows for phenomena such as alternative splicing, nonsense-mediated decay, and regulation through untranslated regions. However, the evolution of the exon structure of genes is not well elucidated because of limited and phylogenetically sparse data sets. In this study, we use the phylogenetically diverse sequencing of the ENCODE regions to study gene structure evolution in mammalian genomes. This first phylogenetically diverse study of gene structure changes offers insights into the mode and tempo of mammalian gene structure evolution. The genes undergoing structure changes appear to be moderately to highly expressed in germline cells and show levels of selection similar to those of other ENCODE genes. Patterns of gene duplication of the affected genes are more complex than expected. The number of sampled genomes is sufficiently dense to infer that certain gene duplications happened after intron loss. Thus, although gene duplication is highly correlated with intron loss, we conclude that structural changes in genes are not necessarily due to a loss of constraint following gene duplication as previously suggested.

The Gibbs sampling method has been widely used for sequence analysis after it was successfully applied to the problem of identifying regulatory motif sequences upstream of genes. Since then numerous variants of the original idea have emerged, however in all cases the application has been to finding short motifs in collections of short sequences (typically less than 100 nucleotides long). In this paper we introduce a Gibbs sampling approach for identifying genes in multiple large genomic sequences up to hundreds of kilobases long. This approach leverages the evolutionary relationships between the sequences to improve the gene predictions, without explicitly aligning the sequences. We have applied our method to the analysis of genomic sequence from 14 genomic regions, totaling roughly 1.8Mb of sequence in each organism. We show that our approach compares favorably with existing ab-initio approaches to gene finding, including pairwise comparison based gene prediction methods which make explicit use of alignments. Furthermore, excellent performance can be obtained with as little as 4 organisms, and the method overcomes a number of difficulties of previous comparison based gene finding approaches: it is robust with respect to genomic rearrangements, can work with draft sequence, and is fast (linear in the number and length of the sequences). It can also be seamlessly integrated with Gibbs sampling motif detection methods.

The laboratory rat (Rattus norvegicus) is an indispensable tool in experimental medicine and drug development, having made inestimable contributions to human health. We report here the genome sequence of the Brown Norway (BN) rat strain. The sequence represents a high-quality 'draft' covering over 90% of the genome. The BN rat sequence is the third complete mammalian genome to be deciphered, and three-way comparisons with the human and mouse genomes resolve details of mammalian evolution. This first comprehensive analysis includes genes and proteins and their relation to human disease, repeated sequences, comparative genome-wide studies of mammalian orthologous chromosomal regions and rearrangement breakpoints, reconstruction of ancestral karyotypes and the events leading to existing species, rates of variation, and lineage-specific and lineage-independent evolutionary events such as expansion of gene families, orthology relations and protein evolution.

This study presents the first algorithm, Oriscan, to predict eukaryotic replication origins and examines the structure of the origins and evolutionary conservation of origin sequences at the nucleotide level in several Saccharomyces species., Background Autonomously replicating sequences (ARSs) function as replication origins in Saccharomyces cerevisiae. ARSs contain the 17 bp ARS consensus sequence (ACS), which binds the origin recognition complex. The yeast genome contains more than 10,000 ACS matches, but there are only a few hundred origins, and little flanking sequence similarity has been found. Thus, identification of origins by sequence alone has not been possible. Results We developed an algorithm, Oriscan, to predict yeast origins using similarity to 26 characterized origins. Oriscan used 268 bp of sequence, including the T-rich ACS and a 3' A-rich region. The predictions identified the exact location of the ACS. A total of 84 of the top 100 Oriscan predictions, and 56% of the top 350, matched known ARSs or replication protein binding sites. The true accuracy was even higher because we tested 25 discrepancies, and 15 were in fact ARSs. Thus, 94% of the top 100 predictions and an estimated 70% of the top 350 were correct. We compared the predictions to corresponding sequences in related Saccharomyces species and found that the ACSs of experimentally supported predictions show significant conservation. Conclusions The high accuracy of the predictions indicates that we have defined near-sufficient conditions for ARS activity, the A-rich region is a recognizable feature of ARS elements with a probable role in replication initiation, and nucleotide sequence is a reliable predictor of yeast origins. Oriscan detected most origins in the genome, demonstrating previously unrecognized generality in yeast replication origins and significant discriminatory power in the algorithm.

The difficulty associated with the cultivation of most microorganisms and the complexity of natural microbial assemblages, such as marine plankton or human microbiome, hinder genome reconstruction of representative taxa using cultivation or metagenomic approaches. Here we used an alternative, single cell sequencing approach to obtain high-quality genome assemblies of two uncultured, numerically significant marine microorganisms. We employed fluorescence-activated cell sorting and multiple displacement amplification to obtain hundreds of micrograms of genomic DNA from individual, uncultured cells of two marine flavobacteria from the Gulf of Maine that were phylogenetically distant from existing cultured strains. Shotgun sequencing and genome finishing yielded 1.9 Mbp in 17 contigs and 1.5 Mbp in 21 contigs for the two flavobacteria, with estimated genome recoveries of about 91% and 78%, respectively. Only 0.24% of the assembling sequences were contaminants and were removed from further analysis using rigorous quality control. In contrast to all cultured strains of marine flavobacteria, the two single cell genomes were excellent Global Ocean Sampling (GOS) metagenome fragment recruiters, demonstrating their numerical significance in the ocean. The geographic distribution of GOS recruits along the Northwest Atlantic coast coincided with ocean surface currents. Metabolic reconstruction indicated diverse potential energy sources, including biopolymer degradation, proteorhodopsin photometabolism, and hydrogen oxidation. Compared to cultured relatives, the two uncultured flavobacteria have small genome sizes, few non-coding nucleotides, and few paralogous genes, suggesting adaptations to narrow ecological niches. These features may have contributed to the abundance of the two taxa in specific regions of the ocean, and may have hindered their cultivation. We demonstrate the power of single cell DNA sequencing to generate reference genomes of uncultured taxa from a complex microbial community of marine bacterioplankton. A combination of single cell genomics and metagenomics enabled us to analyze the genome content, metabolic adaptations, and biogeography of these taxa.

In order to enrich the phylogenetic diversity represented in the available sequenced bacterial genomes and as part of an “Assembling the Tree of Life” project, we determined the genome sequence of Thermomicrobium roseum DSM 5159. T. roseum DSM 5159 is a red-pigmented, rod-shaped, Gram-negative extreme thermophile isolated from a hot spring that possesses both an atypical cell wall composition and an unusual cell membrane that is composed entirely of long-chain 1,2-diols. Its genome is composed of two circular DNA elements, one of 2,006,217 bp (referred to as the chromosome) and one of 919,596 bp (referred to as the megaplasmid). Strikingly, though few standard housekeeping genes are found on the megaplasmid, it does encode a complete system for chemotaxis including both chemosensory components and an entire flagellar apparatus. This is the first known example of a complete flagellar system being encoded on a plasmid and suggests a straightforward means for lateral transfer of flagellum-based motility. Phylogenomic analyses support the recent rRNA-based analyses that led to T. roseum being removed from the phylum Thermomicrobia and assigned to the phylum Chloroflexi. Because T. roseum is a deep-branching member of this phylum, analysis of its genome provides insights into the evolution of the Chloroflexi. In addition, even though this species is not photosynthetic, analysis of the genome provides some insight into the origins of photosynthesis in the Chloroflexi. Metabolic pathway reconstructions and experimental studies revealed new aspects of the biology of this species. For example, we present evidence that T. roseum oxidizes CO aerobically, making it the first thermophile known to do so. In addition, we propose that glycosylation of its carotenoids plays a crucial role in the adaptation of the cell membrane to this bacterium's thermophilic lifestyle. Analyses of published metagenomic sequences from two hot springs similar to the one from which this strain was isolated, show that close relatives of T. roseum DSM 5159 are present but have some key differences from the strain sequenced.

We introduce GeneMapper, a program for transferring annotations from a well annotated genome to other genomes. Drawing on high quality curated annotations, GeneMapper enables rapid and accurate annotation of newly sequenced genomes and is suitable for both finished and draft genomes. GeneMapper uses a profile based approach for mapping genes into multiple species, improving upon the standard pairwise approach. GeneMapper is freely available for academic use.

Autonomously replicating sequences (ARSs) function as replication origins in Saccharomyces cerevisiae. ARSs contain the 17 bp ARS consensus sequence (ACS), which binds the origin recognition complex. The yeast genome contains more than 10,000 ACS matches, but there are only a few hundred origins, and little flanking sequence similarity has been found. Thus, identification of origins by sequence alone has not been possible. We developed an algorithm, Oriscan, to predict yeast origins using similarity to 26 characterized origins. Oriscan used 268 bp of sequence, including the T-rich ACS and a 3' A-rich region. The predictions identified the exact location of the ACS. A total of 84 of the top 100 Oriscan predictions, and 56% of the top 350, matched known ARSs or replication protein binding sites. The true accuracy was even higher because we tested 25 discrepancies, and 15 were in fact ARSs. Thus, 94% of the top 100 predictions and an estimated 70% of the top 350 were correct. We compared the predictions to corresponding sequences in related Saccharomyces species and found that the ACSs of experimentally supported predictions show significant conservation. The high accuracy of the predictions indicates that we have defined near-sufficient conditions for ARS activity, the A-rich region is a recognizable feature of ARS elements with a probable role in replication initiation, and nucleotide sequence is a reliable predictor of yeast origins. Oriscan detected most origins in the genome, demonstrating previously unrecognized generality in yeast replication origins and significant discriminatory power in the algorithm.