Alain Malpertuy

Large-scale monitoring of pleiotropic regulation of gene expression by the prokaryotic nucleoid-associated protein, H-NS

Despite many years of intense work investigating the function of nucleoid‐associated proteins in prokaryotes, their role in bacterial physiology remains largely unknown. The two‐dimensional protein patterns were compared and expression profiling was carried out on H‐NS‐deficient and wild‐type strains of Escherichia coli K‐12. The expression of approximately 5% of the genes and/or the accumulation of their protein was directly or indirectly altered in the hns mutant strain. About one‐fifth of these genes encode proteins that are involved in transcription or translation and one‐third are known to or were in silico predicted to encode cell envelope components or proteins that are usually involved in bacterial adaptation to changes in environmental conditions. The increased expression of several genes in the mutant resulted in a better ability of this strain to survive at low pH and high osmolarity than the wild‐type strain. In particular, the putative regulator, YhiX, plays a central role in the H‐NS control of genes required in the glutamate‐dependent acid stress response. These results suggest that there is a strong relationship between the H‐NS regulon and the maintenance of intracellular homeostasis.

Influence of microarrays experiments missing values on the stability of gene groups by hierarchical clustering

BackgroundMicroarray technologies produced large amount of data. The hierarchical clustering is commonly used to identify clusters of co-expressed genes. However, microarray datasets often contain missing values (MVs) representing a major drawback for the use of the clustering methods. Usually the MVs are not treated, or replaced by zero or estimated by the k-Nearest Neighbor (kNN) approach. The topic of the paper is to study the stability of gene clusters, defined by various hierarchical clustering algorithms, of microarrays experiments including or not MVs.ResultsIn this study, we show that the MVs have important effects on the stability of the gene clusters. Moreover, the magnitude of the gene misallocations is depending on the aggregation algorithm. The most appropriate aggregation methods (e.g. complete-linkage and Ward) are highly sensitive to MVs, and surprisingly, for a very tiny proportion of MVs (e.g. 1%). In most of the case, the MVs must be replaced by expected values. The MVs replacement by the kNN approach clearly improves the identification of co-expressed gene clusters. Nevertheless, we observe that kNN approach is less suitable for the extreme values of gene expression.ConclusionThe presence of MVs (even at a low rate) is a major factor of gene cluster instability. In addition, the impact depends on the hierarchical clustering algorithm used. Some methods should be used carefully. Nevertheless, the kNN approach constitutes one efficient method for restoring the missing expression gene values, with a low error level. Our study highlights the need of statistical treatments in microarray data to avoid misinterpretation.

Genomic Exploration of the Hemiascomycetous Yeasts: 4. The genome of Saccharomyces cerevisiae revisited

Since its completion more than 4 years ago, the sequence of Saccharomyces cerevisiae has been extensively used and studied. The original sequence has received a few corrections, and the identification of genes has been completed, thanks in particular to transcriptome analyses and to specialized studies on introns, tRNA genes, transposons or multigene families. In order to undertake the extensive comparative sequence analysis of this program, we have entirely revisited the S. cerevisiae sequence using the same criteria for all 16 chromosomes and taking into account publicly available annotations for genes and elements that cannot be predicted. Comparison with the other yeast species of this program indicates the existence of 50 novel genes in segments previously considered as ‘intergenic’ and suggests extensions for 26 of the previously annotated genes.

Genomic exploration of the hemiascomycetous yeasts: 18. Comparative analysis of chromosome maps and synteny with Saccharomyces cerevisiae.

We have analyzed the evolution of chromosome maps of Hemiascomycetes by comparing gene order and orientation of the 13 yeast species partially sequenced in this program with the genome map of Saccharomyces cerevisiae. From the analysis of nearly 8000 situations in which two distinct genes having homologs in S. cerevisiae could be identified on the sequenced inserts of another yeast species, we have quantified the loss of synteny, the frequency of single gene deletion and the occurrence of gene inversion. Traces of ancestral duplications in the genome of S. cerevisiae could be identified from the comparison with the other species that do not entirely coincide with those identified from the comparison of S. cerevisiae with itself. From such duplications and from the correlation observed between gene inversion and loss of synteny, a model is proposed for the molecular evolution of Hemiascomycetes. This model, which can possibly be extended to other eukaryotes, is based on the reiteration of events of duplication of chromosome segments, creating transient merodiploids that are subsequently resolved by single gene deletion events.

Comparative analysis of missing value imputation methods to improve clustering and interpretation of microarray experiments

BackgroundMicroarray technologies produced large amount of data. In a previous study, we have shown the interest of k-Nearest Neighbour approach for restoring the missing gene expression values, and its positive impact of the gene clustering by hierarchical algorithm. Since, numerous replacement methods have been proposed to impute missing values (MVs) for microarray data. In this study, we have evaluated twelve different usable methods, and their influence on the quality of gene clustering. Interestingly we have used several datasets, both kinetic and non kinetic experiments from yeast and human.ResultsWe underline the excellent efficiency of approaches proposed and implemented by Bo and co-workers and especially one based on expected maximization (EM_array). These improvements have been observed also on the imputation of extreme values, the most difficult predictable values. We showed that the imputed MVs have still important effects on the stability of the gene clusters. The improvement on the clustering obtained by hierarchical clustering remains limited and, not sufficient to restore completely the correct gene associations. However, a common tendency can be found between the quality of the imputation method and the gene cluster stability. Even if the comparison between clustering algorithms is a complex task, we observed that k-means approach is more efficient to conserve gene associations.ConclusionsMore than 6.000.000 independent simulations have assessed the quality of 12 imputation methods on five very different biological datasets. Important improvements have so been done since our last study. The EM_array approach constitutes one efficient method for restoring the missing expression gene values, with a lower estimation error level. Nonetheless, the presence of MVs even at a low rate is a major factor of gene cluster instability. Our study highlights the need for a systematic assessment of imputation methods and so of dedicated benchmarks. A noticeable point is the specific influence of some biological dataset.

Genomic Exploration of the Hemiascomycetous Yeasts: 19. Ascomycetes-specific genes

Comparisons of the 6213 predicted Saccharomyces cerevisiae open reading frame (ORF) products with sequences from organisms of other biological phyla differentiate genes commonly conserved in evolution from ‘maverick’ genes which have no homologue in phyla other than the Ascomycetes. We show that a majority of the ‘maverick’ genes have homologues among other yeast species and thus define a set of 1892 genes that, from sequence comparisons, appear ‘Ascomycetes‐specific’. We estimate, retrospectively, that the S. cerevisiae genome contains 5651 actual protein‐coding genes, 50 of which were identified for the first time in this work, and that the present public databases contain 612 predicted ORFs that are not real genes. Interestingly, the sequences of the ‘Ascomycetes‐specific’ genes tend to diverge more rapidly in evolution than that of other genes. Half of the ‘Ascomycetes‐specific’ genes are functionally characterized in S. cerevisiae, and a few functional categories are over‐represented in them.

Genomic Exploration of the Hemiascomycetous Yeasts: 3. Methods and strategies used for sequence analysis and annotation

The primary analysis of the sequences for our Hemiascomycete random sequence tag (RST) project was performed using a combination of classical methods for sequence comparison and contig assembly, and of specifically written scripts and computer visualization routines. Comparisons were performed first against DNA and protein sequences from Saccharomyces cerevisiae, then against protein sequences from other completely sequenced organisms and, finally, against protein sequences from all other organisms. Blast alignments were individually inspected to help recognize genes within our random genomic sequences despite the fact that only parts of them were available. For each yeast species, validated alignments were used to infer the proper genetic code, to determine codon usage preferences and to calculate their degree of sequence divergence with S. cerevisiae. The quality of each genomic library was monitored from contig analysis of the DNA sequences. Annotated sequences were submitted to the EMBL database, and the general annotation tables produced served as a basis for our comparative description of the evolution, redundancy and function of the Hemiascomycete genomes described in other articles of this issue.

Genomic Exploration of the Hemiascomycetous Yeasts: 13. Pichia angusta

As part of a comparative genomics project on 13 hemiascomycetous yeasts, the Pichia angusta type strain was studied using a partial random sequencing strategy. With coverage of 0.5 genome equivalents, about 2500 novel protein‐coding genes were identified, probably corresponding to more than half of the P. angusta protein‐coding genes, 6% of which do not have homologs in Saccharomyces cerevisiae. Some of them contain one or two introns, on average three times shorter than those in S. cerevisiae. We also identified 28 tRNA genes, a few retrotransposons similar to Ty5 of S. cerevisiae, solo long terminal repeats, the whole ribosomal DNA cluster, and segments of mitochondrial DNA. The P. angusta sequences were deposited in EMBL under the accession numbers AL430961 to AL436044.

Trends in IT Innovation to Build a Next Generation Bioinformatics Solution to Manage and Analyse Biological Big Data Produced by NGS Technologies

Sequencing the human genome began in 1994, and 10 years of work were necessary in order to provide a nearly complete sequence. Nowadays, NGS technologies allow sequencing of a whole human genome in a few days. This deluge of data challenges scientists in many ways, as they are faced with data management issues and analysis and visualization drawbacks due to the limitations of current bioinformatics tools. In this paper, we describe how the NGS Big Data revolution changes the way of managing and analysing data. We present how biologists are confronted with abundance of methods, tools, and data formats. To overcome these problems, focus on Big Data Information Technology innovations from web and business intelligence. We underline the interest of NoSQL databases, which are much more efficient than relational databases. Since Big Data leads to the loss of interactivity with data during analysis due to high processing time, we describe solutions from the Business Intelligence that allow one to regain interactivity whatever the volume of data is. We illustrate this point with a focus on the Amadea platform. Finally, we discuss visualization challenges posed by Big Data and present the latest innovations with JavaScript graphic libraries.

Global analysis of VHHs framework regions with a structural alphabet

The VHHs are antigen-binding region/domain of camelid heavy chain antibodies (HCAb). They have many interesting biotechnological and biomedical properties due to their small size, high solubility and stability, and high affinity and specificity for their antigens. HCAb and classical IgGs are evolutionary related and share a common fold. VHHs are composed of regions considered as constant, called the frameworks (FRs) connected by Complementarity Determining Regions (CDRs), a highly variable region that provide interaction with the epitope. Actually, no systematic structural analyses had been performed on VHH structures despite a significant number of structures. This work is the first study to analyse the structural diversity of FRs of VHHs. Using a structural alphabet that allows approximating the local conformation, we show that each of the four FRs do not have a unique structure but exhibit many structural variant patterns. Moreover, no direct simple link between the local conformational change and amino acid composition can be detected. These results indicate that long-range interactions affect the local conformation of FRs and impact the building of structural models.