Faouzi Jaziri

The Human Gut Chip "HuGChip'', an explorative phylogenetic microarray for determining gut microbiome diversity at family level

Evaluating the composition of the human gut microbiota greatly facilitates studies on its role in human pathophysiology, and is heavily reliant on culture-independent molecular methods. A microarray designated the Human Gut Chip (HuGChip) was developed to analyze and compare human gut microbiota samples. The PhylArray software was used to design specific and sensitive probes. The DNA chip was composed of 4,441 probes (2,442 specific and 1,919 explorative probes) targeting 66 bacterial families. A mock community composed of 16S rRNA gene sequences from intestinal species was used to define the threshold criteria to be used to analyze complex samples. This was then experimentally verified with three human faecal samples and results were compared (i) with pyrosequencing of the V4 hypervariable region of the 16S rRNA gene, (ii) metagenomic data, and (iii) qPCR analysis of three phyla. When compared at both the phylum and the family level, high Pearsons correlation coefficients were obtained between data from all methods. The HuGChip development and validation showed that it is not only able to assess the known human gut microbiota but could also detect unknown species with the explorative probes to reveal the large number of bacterial sequences not yet described in the human gut microbiota, overcoming the main inconvenience encountered when developing microarrays.

Detecting unknown sequences with DNA microarrays: explorative probe design strategies.

Designing environmental DNA microarrays that can be used to survey the extreme diversity of microorganisms existing in nature, represents a stimulating challenge in the field of molecular ecology. Indeed, recent efforts in metagenomics have produced a substantial amount of sequence information from various ecosystems, and will continue to accumulate large amounts of sequence data given the qualitative and quantitative improvements in the next-generation sequencing methods. It is now possible to take advantage of these data to develop comprehensive microarrays by using explorative probe design strategies. Such strategies anticipate genetic variations and thus are able to detect known and unknown sequences in environmental samples. In this review, we provide a detailed overview of the probe design strategies currently available to construct both phylogenetic and functional DNA microarrays, with emphasis on those permitting the selection of such explorative probes. Furthermore, exploration of complex environments requires particular attention on probe sensitivity and specificity criteria. Finally, these innovative probe design approaches require exploiting newly available high-density microarray formats.

In situ TCE degradation mediated by complex dehalorespiring communities during biostimulation processes

The bioremediation of chloroethene contaminants in groundwater polluted systems is still a serious environmental challenge. Many previous studies have shown that cooperation of several dechlorinators is crucial for complete dechlorination of trichloroethene to ethene. In the present study, we used an explorative functional DNA microarray (DechloArray) to examine the composition of specific functional genes in groundwater samples in which chloroethene bioremediation was enhanced by delivery of hydrogen‐releasing compounds. Our results demonstrate for the first time that complete biodegradation occurs through spatial and temporal variations of a wide diversity of dehalorespiring populations involving both Sulfurospirillum, Dehalobacter, Desulfitobacterium, Geobacter and Dehalococcoides genera. Sulfurospirillum appears to be the most active in the highly contaminated source zone, while Geobacter was only detected in the slightly contaminated downstream zone. The concomitant detection of both bvcA and vcrA genes suggests that at least two different Dehalococcoides species are probably responsible for the dechlorination of dichloroethenes and vinyl chloride to ethene. These species were not detected on sites where cis‐dichloroethene accumulation was observed. These results support the notion that monitoring dechlorinators by the presence of specific functional biomarkers using a powerful tool such as DechloArray will be useful for surveying the efficiency of bioremediation strategies.

PhylOPDb: a 16S rRNA oligonucleotide probe database for prokaryotic identification

In recent years, high-throughput molecular tools have led to an exponential growth of available 16S rRNA gene sequences. Incorporating such data, molecular tools based on target-probe hybridization were developed to monitor microbial communities within complex environments. Unfortunately, only a few 16S rRNA gene-targeted probe collections were described. Here, we present PhylOPDb, an online resource for a comprehensive phylogenetic oligonucleotide probe database. PhylOPDb provides a convivial and easy-to-use web interface to browse both regular and explorative 16S rRNA-targeted probes. Such probes set or subset could be used to globally monitor known and unknown prokaryotic communities through various techniques including DNA microarrays, polymerase chain reaction (PCR), fluorescent in situ hybridization (FISH), targeted gene capture or in silico rapid sequence identification. PhylOPDb contains 74 003 25-mer probes targeting 2178 genera including Bacteria and Archaea. Database URL: http://g2im.u-clermont1.fr/phylopdb/

Bacterial Community Composition of Biological Degreasing Systems and Health Risk Assessment for Workers

Biological degreasing system is a new technology based on the degradation capabilities of microorganisms to remove oil, grease, or lubricants from metal parts. No data is available about the potential biological health hazards in such system. Thus, a health risk assessment linked to the bacterial populations present in this new degreasing technology is, therefore, necessary for workers. We performed both cultural and molecular approaches in several biological degreasing systems for various industrial contexts to investigate the composition and dynamics of bacterial populations. These biological degreasing systems did not work with the original bacterial populations. Indeed, they were colonized by a defined and restricted group of bacteria. This group replaced the indigenous bacterial populations known for degrading complex substrates. Klebsiella pneumoniae, Klebsiella oxytoca, Pseudomonas aeruginosa, and Pantoea agglomerans were important members of the microflora found in most of the biological degreasing systems. These bacteria might represent a potential health hazard for workers.

High performance computing of oligopeptides complete backtranslation applied to DNA microarray probe design

Complete backtranslation is the step of generating all possible nucleic acid sequences from a protein sequence. This is a time‐consuming task that can provide unreasonable quantities of data. Complete backtranslation was recently used to initiate probe design for functional DNA microarrays from conserved peptidic regions, in order to assess the full microbial gene diversity present in complex environments. In this article, we present an efficient parallelization method to compute a complete backtranslation of short peptides to select probes for functional microarrays. We implemented a software that uses meta‐programming and a model‐driven engineering approach to automatically generate source codes to perform complete backtranslation on different architectures: PCs, Symmetric Multiprocessors servers, computing clusters, or a computing grid. Our software is filtering the generated oligonucleotides with usual selection criteria for the design of microarray probes. It uses load balancing and can be easily integrated in probe design software for functional microarrays. We present its performance on both simulated and real biological datasets. The obtained results show a significant computing speedup on different platforms and an important gain of about 40% of disk space when filtering oligonucleotides. Copyright

MetaExploArrays: A Large-Scale Oligonucleotide Probe Design Software for Explorative DNA Microarrays

The selection of oligonucleotide probes for micro arrays is still very difficult task. With the rapid growth of environmental databases (metagenomics programs coupled to next generation sequencing), the computational capacity requirements of probe design algorithms have hugely increased. The use of parallel and distributed architectures can considerably reduce the complexity and the computational time of these algorithms. In this paper we present a new efficient algorithm of oligonucleotide probe selection for an individual specific nucleic acid sequence or a group of sequences. We used a model driven engineering approach to simultaneous design of thousands of sensitive, specific, isothermal and explorative probes, on both PC, multiprocessor, cluster and grid computing with on the one hand a significant computing speedup and on the other hand an improved quality of the resulting probes when compared to equivalent software.

Large Scale Parallelization Method of 16S rRNA Probe Design Algorithm on Distributed Architecture: Application to Grid Computing

The microbial world represents the most important and diverse group of organisms living on earth. Because of this huge microbial bio complexity, high-throughput molecular tools allowing simultaneous analysis of existing populations are well adapted. Oligonucleotide micro array technologies have been widely used for gene detection and gene expression quantification, and more recently, they have been adapted to profiling environmental communities in a flexible and easy-to-use manner. Designing DNA micro arrays requires special attention to the design of specific and efficient probes in order to obtain an image of the microbial communities close to reality. The rapid growth of datasets, particularly environmental datasets, has led to an important increase in computational capacity requirements coupled with a fundamental change in the way algorithms are designed. Consequently, High Performance, including cluster and grid computing represents a solution to reduce the execution time of probe design algorithms in complex environments. In this paper, we present a method to parallelize probe design program for phylogenetic micro arrays dedicated to microbial ecology on distributed architecture. We implemented a mechanism that generates and monitors jobs over a grid. We obtained a complete design for 3513 genera including fungi and prokaryotes.

A parallel framework for HCC detection in DCE-MRI sequences with wavelet-based description and SVM classification

In this article, we propose a complete framework devoted to detect liver HCC (Hepato-Cellular Carcinoma) tumors within DCE-MRI (Dynamic Contrast Enhanced-MRI) sequences. Our system employs different phases of these hepatic image sequences (depending on time after contrast agent injection) to describe local patches with wavelet-based descriptors. By using a SVM (Support Vector Machine)-based classification, we are able to distinguish healthy patches from pathological ones. Moreover, thanks to a parallel image processing strategy, we are able to reduce significantly the running time so that our system may be utilized as a computer aided diagnosis tool in the future. Our experiments show that our contribution is an accurate system for HCC detection, with a small cohort of patients, but representing a high volume of image data to be processed. This work encourages us to conduct deeper researches for detecting complex HCC cases for larger patients cohorts.

Large scale explorative oligonucleotide probe selection for thousands of genetic groups on a computing grid: application to phylogenetic probe design using a curated small subunit ribosomal RNA gene database.

Phylogenetic Oligonucleotide Arrays (POAs) were recently adapted for studying the huge microbial communities in a flexible and easy-to-use way. POA coupled with the use of explorative probes to detect the unknown part is now one of the most powerful approaches for a better understanding of microbial community functioning. However, the selection of probes remains a very difficult task. The rapid growth of environmental databases has led to an exponential increase of data to be managed for an efficient design. Consequently, the use of high performance computing facilities is mandatory. In this paper, we present an efficient parallelization method to select known and explorative oligonucleotide probes at large scale using computing grids. We implemented a software that generates and monitors thousands of jobs over the European Computing Grid Infrastructure (EGI). We also developed a new algorithm for the construction of a high-quality curated phylogenetic database to avoid erroneous design due to bad sequence affiliation. We present here the performance and statistics of our method on real biological datasets based on a phylogenetic prokaryotic database at the genus level and a complete design of about 20,000 probes for 2,069 genera of prokaryotes.