Frédéric Plewniak

seqMINER: an integrated ChIP-seq data interpretation platform

In a single experiment, chromatin immunoprecipitation combined with high throughput sequencing (ChIP-seq) provides genome-wide information about a given covalent histone modification or transcription factor occupancy. However, time efficient bioinformatics resources for extracting biological meaning out of these gigabyte-scale datasets are often a limiting factor for data interpretation by biologists. We created an integrated portable ChIP-seq data interpretation platform called seqMINER, with optimized performances for efficient handling of multiple genome-wide datasets. seqMINER allows comparison and integration of multiple ChIP-seq datasets and extraction of qualitative as well as quantitative information. seqMINER can handle the biological complexity of most experimental situations and proposes methods to the user for data classification according to the analysed features. In addition, through multiple graphical representations, seqMINER allows visualization and modelling of general as well as specific patterns in a given dataset. To demonstrate the efficiency of seqMINER, we have carried out a comprehensive analysis of genome-wide chromatin modification data in mouse embryonic stem cells to understand the global epigenetic landscape and its change through cellular differentiation.

ADCK3, an Ancestral Kinase, Is Mutated in a Form of Recessive Ataxia Associated with Coenzyme Q10 Deficiency

Muscle coenzyme Q(10) (CoQ(10) or ubiquinone) deficiency has been identified in more than 20 patients with presumed autosomal-recessive ataxia. However, mutations in genes required for CoQ(10) biosynthetic pathway have been identified only in patients with infantile-onset multisystemic diseases or isolated nephropathy. Our SNP-based genome-wide scan in a large consanguineous family revealed a locus for autosomal-recessive ataxia at chromosome 1q41. The causative mutation is a homozygous splice-site mutation in the aarF-domain-containing kinase 3 gene (ADCK3). Five additional mutations in ADCK3 were found in three patients with sporadic ataxia, including one known to have CoQ(10) deficiency in muscle. All of the patients have childhood-onset cerebellar ataxia with slow progression, and three of six have mildly elevated lactate levels. ADCK3 is a mitochondrial protein homologous to the yeast COQ8 and the bacterial UbiB proteins, which are required for CoQ biosynthesis. Three out of four patients tested showed a low endogenous pool of CoQ(10) in their fibroblasts or lymphoblasts, and two out of three patients showed impaired ubiquinone synthesis, strongly suggesting that ADCK3 is also involved in CoQ(10) biosynthesis. The deleterious nature of the three identified missense changes was confirmed by the introduction of them at the corresponding positions of the yeast COQ8 gene. Finally, a phylogenetic analysis shows that ADCK3 belongs to the family of atypical kinases, which includes phosphoinositide and choline kinases, suggesting that ADCK3 plays an indirect regulatory role in ubiquinone biosynthesis possibly as part of a feedback loop that regulates ATP production.

Identification of a Novel BBS Gene (BBS12) Highlights the Major Role of a Vertebrate-Specific Branch of Chaperonin-Related Proteins in Bardet-Biedl Syndrome

Bardet-Biedl syndrome (BBS) is primarily an autosomal recessive ciliopathy characterized by progressive retinal degeneration, obesity, cognitive impairment, polydactyly, and kidney anomalies. The disorder is genetically heterogeneous, with 11 BBS genes identified to date, which account for ~70% of affected families. We have combined single-nucleotide-polymorphism array homozygosity mapping with in silico analysis to identify a new BBS gene, BBS12. Patients from two Gypsy families were homozygous and haploidentical in a 6-Mb region of chromosome 4q27. FLJ35630 was selected as a candidate gene, because it was predicted to encode a protein with similarity to members of the type II chaperonin superfamily, which includes BBS6 and BBS10. We found pathogenic mutations in both Gypsy families, as well as in 14 other families of various ethnic backgrounds, indicating that BBS12 accounts for approximately 5% of all BBS cases. BBS12 is vertebrate specific and, together with BBS6 and BBS10, defines a novel branch of the type II chaperonin superfamily. These three genes are characterized by unusually rapid evolution and are likely to perform ciliary functions specific to vertebrates that are important in the pathophysiology of the syndrome, and together they account for about one-third of the total BBS mutational load. Consistent with this notion, suppression of each family member in zebrafish yielded gastrulation-movement defects characteristic of other BBS morphants, whereas simultaneous suppression of all three members resulted in severely affected embryos, possibly hinting at partial functional redundancy within this protein family.

Next generation sequencing for molecular diagnosis of neuromuscular diseases

Inherited neuromuscular disorders (NMD) are chronic genetic diseases posing a significant burden on patients and the health care system. Despite tremendous research and clinical efforts, the molecular causes remain unknown for nearly half of the patients, due to genetic heterogeneity and conventional molecular diagnosis based on a gene-by-gene approach. We aimed to test next generation sequencing (NGS) as an efficient and cost-effective strategy to accelerate patient diagnosis. We designed a capture library to target the coding and splice site sequences of all known NMD genes and used NGS and DNA multiplexing to retrieve the pathogenic mutations in patients with heterogeneous NMD with or without known mutations. We retrieved all known mutations, including point mutations and small indels, intronic and exonic mutations, and a large deletion in a patient with Duchenne muscular dystrophy, validating the sensitivity and reproducibility of this strategy on a heterogeneous subset of NMD with different genetic inheritance. Most pathogenic mutations were ranked on top in our blind bioinformatic pipeline. Following the same strategy, we characterized probable TTN, RYR1 and COL6A3 mutations in several patients without previous molecular diagnosis. The cost was less than conventional testing for a single large gene. With appropriate adaptations, this strategy could be implemented into a routine genetic diagnosis set-up as a first screening approach to detect most kind of mutations, potentially before the need of more invasive and specific clinical investigations. An earlier genetic diagnosis should provide improved disease management and higher quality genetic counseling, and ease access to therapy or inclusion into therapeutic trials.

Multiple alignment of complete sequences (MACS) in the post-genomic era.

Multiple alignment, since its introduction in the early seventies, has become a cornerstone of modern molecular biology. It has traditionally been used to deduce structure / function by homology, to detect conserved motifs and in phylogenetic studies. There has recently been some renewed interest in the development of multiple alignment techniques, with current opinion moving away from a single all-encompassing algorithm to iterative and / or co-operative strategies. The exploitation of multiple alignments in genome annotation projects represents a qualitative leap in the functional analysis process, opening the way to the study of the co-evolution of validated sets of proteins and to reliable phylogenomic analysis. However, the alignment of the highly complex proteins detected by todays advanced database search methods is a daunting task. In addition, with the explosion of the sequence databases and with the establishment of numerous specialized biological databases, multiple alignment programs must evolve if they are to successfully rise to the new challenges of the post-genomic era. The way forward is clearly an integrated system bringing together sequence data, knowledge-based systems and prediction methods with their inherent unreliability. The incorporation of such heterogeneous, often non-consistent, data will require major changes to the fundamental alignment algorithms used to date. Such an integrated multiple alignment system will provide an ideal workbench for the validation, propagation and presentation of this information in a format that is concise, clear and intuitive.

MACSIMS : multiple alignment of complete sequences information management system

BackgroundIn the post-genomic era, systems-level studies are being performed that seek to explain complex biological systems by integrating diverse resources from fields such as genomics, proteomics or transcriptomics. New information management systems are now needed for the collection, validation and analysis of the vast amount of heterogeneous data available. Multiple alignments of complete sequences provide an ideal environment for the integration of this information in the context of the protein family.ResultsMACSIMS is a multiple alignment-based information management program that combines the advantages of both knowledge-based and ab initio sequence analysis methods. Structural and functional information is retrieved automatically from the public databases. In the multiple alignment, homologous regions are identified and the retrieved data is evaluated and propagated from known to unknown sequences with these reliable regions. In a large-scale evaluation, the specificity of the propagated sequence features is estimated to be >99%, i.e. very few false positive predictions are made. MACSIMS is then used to characterise mutations in a test set of 100 proteins that are known to be involved in human genetic diseases. The number of sequence features associated with these proteins was increased by 60%, compared to the features available in the public databases. An XML format output file allows automatic parsing of the MACSIM results, while a graphical display using the JalView program allows manual analysis.ConclusionMACSIMS is a new information management system that incorporates detailed analyses of protein families at the structural, functional and evolutionary levels. MACSIMS thus provides a unique environment that facilitates knowledge extraction and the presentation of the most pertinent information to the biologist. A web server and the source code are available at http://bips.u-strasbg.fr/MACSIMS/.

Life in an arsenic-containing gold mine: genome and physiology of the autotrophic arsenite-oxidizing bacterium Rhizobium sp. NT-26

Arsenic is widespread in the environment and its presence is a result of natural or anthropogenic activities. Microbes have developed different mechanisms to deal with toxic compounds such as arsenic and this is to resist or metabolize the compound. Here, we present the first reference set of genomic, transcriptomic and proteomic data of an Alphaproteobacterium isolated from an arsenic-containing goldmine: Rhizobium sp. NT-26. Although phylogenetically related to the plant-associated bacteria, this organism has lost the major colonizing capabilities needed for symbiosis with legumes. In contrast, the genome of Rhizobium sp. NT-26 comprises a megaplasmid containing the various genes, which enable it to metabolize arsenite. Remarkably, although the genes required for arsenite oxidation and flagellar motility/biofilm formation are carried by the megaplasmid and the chromosome, respectively, a coordinate regulation of these two mechanisms was observed. Taken together, these processes illustrate the impact environmental pressure can have on the evolution of bacterial genomes, improving the fitness of bacterial strains by the acquisition of novel functions.

Surface properties and intracellular speciation revealed an original adaptive mechanism to arsenic in the acid mine drainage bio-indicator Euglena mutabilis

Euglena mutabilis is a protist ubiquitously found in extreme environments such as acid mine drainages which are often rich in arsenic. The response of E. mutabilis to this metalloid was compared to that of Euglena gracilis, a protist not found in such environments. Membrane fatty acid composition, cell surface properties, arsenic accumulation kinetics, and intracellular arsenic speciation were determined. The results revealed a modification in fatty acid composition leading to an increased membrane fluidity in both Euglena species under sublethal arsenic concentrations exposure. This increased membrane fluidity correlated to an induced gliding motility observed in E. mutabilis in the presence of this metalloid but did not affect the flagellar dependent motility of E. gracilis. Moreover, when compared to E. gracilis, E. mutabilis showed highly hydrophobic cell surface properties and a higher tolerance to water-soluble arsenical compounds but not to hydrophobic ones. Finally, E. mutabilis showed a lower accumulation of total arsenic in the intracellular compartment and an absence of arsenic methylated species in contrast to E. gracilis. Taken together, our results revealed the existence of a specific arsenical response of E. mutabilis that may play a role in its hypertolerance to this toxic metalloid.

Knowledge-based expert systems and a proof-of-concept case study for multiple sequence alignment construction and analysis

The traditional approach to bioinformatics analyses relies on independent task-specific services and applications, using different input and output formats, often idiosyncratic, and frequently not designed to inter-operate. In general, such analyses were performed by experts who manually verified the results obtained at each step in the process. Today, the amount of bioinformatics information continuously being produced means that handling the various applications used to study this information presents a major data management and analysis challenge to researchers. It is now impossible to manually analyse all this information and new approaches are needed that are capable of processing the large-scale heterogeneous data in order to extract the pertinent information. We review the recent use of integrated expert systems aimed at providing more efficient knowledge extraction for bioinformatics research. A general methodology for building knowledge-based expert systems is described, focusing on the unstructured information management architecture, UIMA, which provides facilities for both data and process management. A case study involving a multiple alignment expert system prototype called AlexSys is also presented.

Meneco, a Topology-Based Gap-Filling Tool Applicable to Degraded Genome-Wide Metabolic Networks

Increasing amounts of sequence data are becoming available for a wide range of non-model organisms. Investigating and modelling the metabolic behaviour of those organisms is highly relevant to understand their biology and ecology. As sequences are often incomplete and poorly annotated, draft networks of their metabolism largely suffer from incompleteness. Appropriate gap-filling methods to identify and add missing reactions are therefore required to address this issue. However, current tools rely on phenotypic or taxonomic information, or are very sensitive to the stoichiometric balance of metabolic reactions, especially concerning the co-factors. This type of information is often not available or at least prone to errors for newly-explored organisms. Here we introduce Meneco, a tool dedicated to the topological gap-filling of genome-scale draft metabolic networks. Meneco reformulates gap-filling as a qualitative combinatorial optimization problem, omitting constraints raised by the stoichiometry of a metabolic network considered in other methods, and solves this problem using Answer Set Programming. Run on several artificial test sets gathering 10,800 degraded Escherichia coli networks Meneco was able to efficiently identify essential reactions missing in networks at high degradation rates, outperforming the stoichiometry-based tools in scalability. To demonstrate the utility of Meneco we applied it to two case studies. Its application to recent metabolic networks reconstructed for the brown algal model Ectocarpus siliculosus and an associated bacterium Candidatus Phaeomarinobacter ectocarpi revealed several candidate metabolic pathways for algal-bacterial interactions. Then Meneco was used to reconstruct, from transcriptomic and metabolomic data, the first metabolic network for the microalga Euglena mutabilis. These two case studies show that Meneco is a versatile tool to complete draft genome-scale metabolic networks produced from heterogeneous data, and to suggest relevant reactions that explain the metabolic capacity of a biological system.