Jean-François Gibrat

Complete genome sequence of the fish pathogen Flavobacterium psychrophilum

We report here the complete genome sequence of the virulent strain JIP02/86 (ATCC 49511) of Flavobacterium psychrophilum, a widely distributed pathogen of wild and cultured salmonid fish. The genome consists of a 2,861,988–base pair (bp) circular chromosome with 2,432 predicted protein-coding genes. Among these predicted proteins, stress response mediators, gliding motility proteins, adhesins and many putative secreted proteases are probably involved in colonization, invasion and destruction of the host tissues. The genome sequence provides the basis for explaining the relationships of the pathogen to the host and opens new perspectives for the development of more efficient disease control strategies. It also allows for a better understanding of the physiology and evolution of a significant representative of the family Flavobacteriaceae, whose members are associated with an interesting diversity of lifestyles and habitats.

Complete Genome Sequence of the Probiotic Lactobacillus casei Strain BL23

The entire genome of Lactobacillus casei BL23, a strain with probiotic properties, has been sequenced. The genomes of BL23 and the industrially used probiotic strain Shirota YIT 9029 (Yakult) seem to be very similar.

AGMIAL: implementing an annotation strategy for prokaryote genomes as a distributed system

We have implemented a genome annotation system for prokaryotes called AGMIAL. Our approach embodies a number of key principles. First, expert manual annotators are seen as a critical component of the overall system; user interfaces were cyclically refined to satisfy their needs. Second, the overall process should be orchestrated in terms of a global annotation strategy; this facilitates coordination between a team of annotators and automatic data analysis. Third, the annotation strategy should allow progressive and incremental annotation from a time when only a few draft contigs are available, to when a final finished assembly is produced. The overall architecture employed is modular and extensible, being based on the W3 standard Web services framework. Specialized modules interact with two independent core modules that are used to annotate, respectively, genomic and protein sequences. AGMIAL is currently being used by several INRA laboratories to analyze genomes of bacteria relevant to the food-processing industry, and is distributed under an open source license.

The Arthrobacter arilaitensis Re117 Genome Sequence Reveals Its Genetic Adaptation to the Surface of Cheese

Arthrobacter arilaitensis is one of the major bacterial species found at the surface of cheeses, especially in smear-ripened cheeses, where it contributes to the typical colour, flavour and texture properties of the final product. The A. arilaitensis Re117 genome is composed of a 3,859,257 bp chromosome and two plasmids of 50,407 and 8,528 bp. The chromosome shares large regions of synteny with the chromosomes of three environmental Arthrobacter strains for which genome sequences are available: A. aurescens TC1, A. chlorophenolicus A6 and Arthrobacter sp. FB24. In contrast however, 4.92% of the A. arilaitensis chromosome is composed of ISs elements, a portion that is at least 15 fold higher than for the other Arthrobacter strains. Comparative genomic analyses reveal an extensive loss of genes associated with catabolic activities, presumably as a result of adaptation to the properties of the cheese surface habitat. Like the environmental Arthrobacter strains, A. arilaitensis Re117 is well-equipped with enzymes required for the catabolism of major carbon substrates present at cheese surfaces such as fatty acids, amino acids and lactic acid. However, A. arilaitensis has several specificities which seem to be linked to its adaptation to its particular niche. These include the ability to catabolize D-galactonate, a high number of glycine betaine and related osmolyte transporters, two siderophore biosynthesis gene clusters and a high number of Fe3+/siderophore transport systems. In model cheese experiments, addition of small amounts of iron strongly stimulated the growth of A. arilaitensis, indicating that cheese is a highly iron-restricted medium. We suggest that there is a strong selective pressure at the surface of cheese for strains with efficient iron acquisition and salt-tolerance systems together with abilities to catabolize substrates such as lactic acid, lipids and amino acids.

Mapping Reads on a Genomic Sequence: An Algorithmic Overview and a Practical Comparative Analysis

Mapping short reads against a reference genome is classically the first step of many next-generation sequencing data analyses, and it should be as accurate as possible. Because of the large number of reads to handle, numerous sophisticated algorithms have been developped in the last 3 years to tackle this problem. In this article, we first review the underlying algorithms used in most of the existing mapping tools, and then we compare the performance of nine of these tools on a well controled benchmark built for this purpose. We built a set of reads that exist in single or multiple copies in a reference genome and for which there is no mismatch, and a set of reads with three mismatches. We considered as reference genome both the human genome and a concatenation of all complete bacterial genomes. On each dataset, we quantified the capacity of the different tools to retrieve all the occurrences of the reads in the reference genome. Special attention was paid to reads uniquely reported and to reads with multiple hits.

Bacterial protein signals are associated with Crohn’s disease

Objective No Crohn’s disease (CD) molecular maker has advanced to clinical use, and independent lines of evidence support a central role of the gut microbial community in CD. Here we explore the feasibility of extracting bacterial protein signals relevant to CD, by interrogating myriads of intestinal bacterial proteomes from a small number of patients and healthy controls. Design We first developed and validated a workflow—including extraction of microbial communities, two-dimensional difference gel electrophoresis (2D-DIGE), and LC-MS/MS—to discover protein signals from CD-associated gut microbial communities. Then we used selected reaction monitoring (SRM) to confirm a set of candidates. In parallel, we used 16S rRNA gene sequencing for an integrated analysis of gut ecosystem structure and functions. Results Our 2D-DIGE-based discovery approach revealed an imbalance of intestinal bacterial functions in CD. Many proteins, largely derived from Bacteroides species, were over-represented, while under-represented proteins were mostly from Firmicutes and some Prevotella members. Most overabundant proteins could be confirmed using SRM. They correspond to functions allowing opportunistic pathogens to colonise the mucus layers, breach the host barriers and invade the mucosae, which could still be aggravated by decreased host-derived pancreatic zymogen granule membrane protein GP2 in CD patients. Moreover, although the abundance of most protein groups reflected that of related bacterial populations, we found a specific independent regulation of bacteria-derived cell envelope proteins. Conclusions This study provides the first evidence that quantifiable bacterial protein signals are associated with CD, which can have a profound impact on future molecular diagnosis.

Overview of a surface-ripened cheese community functioning by meta-omics analyses

Cheese ripening is a complex biochemical process driven by microbial communities composed of both eukaryotes and prokaryotes. Surface-ripened cheeses are widely consumed all over the world and are appreciated for their characteristic flavor. Microbial community composition has been studied for a long time on surface-ripened cheeses, but only limited knowledge has been acquired about its in situ metabolic activities. We applied metagenomic, metatranscriptomic and biochemical analyses to an experimental surface-ripened cheese composed of nine microbial species during four weeks of ripening. By combining all of the data, we were able to obtain an overview of the cheese maturation process and to better understand the metabolic activities of the different community members and their possible interactions. Furthermore, differential expression analysis was used to select a set of biomarker genes, providing a valuable tool that can be used to monitor the cheese-making process.

Automatic modeling of mammalian olfactory receptors and docking of odorants

We present a procedure that (i) automates the homology modeling of mammalian olfactory receptors (ORs) based on the six three-dimensional (3D) structures of G protein-coupled receptors (GPCRs) available so far and (ii) performs the docking of odorants on these models, using the concept of colony energy to score the complexes. ORs exhibit low-sequence similarities with other GPCR and current alignment methods often fail to provide a reliable alignment. Here, we use a fold recognition technique to obtain a robust initial alignment. We then apply our procedure to a human OR that we have previously functionally characterized. The analysis of the resulting in silico complexes, supported by receptor mutagenesis and functional assays in a heterologous expression system, suggests that antagonists dock in the upper part of the binding pocket whereas agonists dock in the narrow lower part. We propose that the potency of agonists in activating receptors depends on their ability to establish tight interactions with the floor of the binding pocket. We developed a web site that allows the user to upload a GPCR sequence, choose a ligand in a library and obtain the 3D structure of the free receptor and ligand-receptor complex (http://genome.jouy.inra.fr/GPCRautomodel).

Analysis of an optimal hidden Markov model for secondary structure prediction

BackgroundSecondary structure prediction is a useful first step toward 3D structure prediction. A number of successful secondary structure prediction methods use neural networks, but unfortunately, neural networks are not intuitively interpretable. On the contrary, hidden Markov models are graphical interpretable models. Moreover, they have been successfully used in many bioinformatic applications. Because they offer a strong statistical background and allow model interpretation, we propose a method based on hidden Markov models.ResultsOur HMM is designed without prior knowledge. It is chosen within a collection of models of increasing size, using statistical and accuracy criteria. The resulting model has 36 hidden states: 15 that model α-helices, 12 that model coil and 9 that model β-strands. Connections between hidden states and state emission probabilities reflect the organization of protein structures into secondary structure segments. We start by analyzing the model features and see how it offers a new vision of local structures. We then use it for secondary structure prediction. Our model appears to be very efficient on single sequences, with a Q3 score of 68.8%, more than one point above PSIPRED prediction on single sequences. A straightforward extension of the method allows the use of multiple sequence alignments, rising the Q3 score to 75.5%.ConclusionThe hidden Markov model presented here achieves valuable prediction results using only a limited number of parameters. It provides an interpretable framework for protein secondary structure architecture. Furthermore, it can be used as a tool for generating protein sequences with a given secondary structure content.

FROST: A filter‐based fold recognition method

To assess the reliability of fold assignments to protein sequences, we developed a fold recognition method called FROST (Fold Recognition‐Oriented Search Tool) based on a series of filters and a database specifically designed as a benchmark for this new method under realistic conditions. This benchmark database consists of proteins for which there exists, at least, another protein with an extensively similar 3D structure in a database of representative 3D structures (i.e., more than 65% of the residues in both proteins can be structurally aligned). Because the testing of our method must be carried out under conditions similar to those of real fold recognition experiments, no protein pair with sequence similarity detectable using standard sequence comparison methods such as FASTA is included in the benchmark database. While using FROST, we achieved a coverage of 60% for a rate of error of 1%. To obtain a baseline for our method, we used PSI‐BLAST and 3D‐PSSM. Under the same conditions, for a 1% error rate, coverages for PSI‐BLAST and 3D‐PSSM were 33 and 56%, respectively. Proteins 2002;49:493–509.