Characterisation of hydrocarbon degradation, biosurfactant production, and biofilm formation in Serratia sp. Tan611: a new strain isolated from industrially contaminated environment in Algeria.

A novel bacterial strain was isolated from industrially contaminated waste water. In the presence of crude oil, this strain was shown to reduce the rate of total petroleum hydrocarbons (TPH) up to 97.10% in 24 h. This bacterium was subsequently identified by 16S rRNA gene sequence analysis and affiliated to the Serratia genus by the RDP classifier. Its genome was sequenced and annotated, and genes coding for catechol 1,2 dioxygenase and naphthalene 1,2-dioxygenase system involved in aromatic hydrocarbon catabolism, and LadA-type monooxygenases involved in alkane degradation, were identified. Gas Chromatography-Mass Spectrometry (GC-MS) analysis of crude oil after biological treatment showed that Serratia sp. Tan611 strain was able to degrade n-alkanes (from C13 to C25). This bacterium was also shown to produce a biosurfactant, the emulsification index (E24) reaching 43.47% and 65.22%, against vegetable and crude oil, respectively. Finally, the formation of a biofilm was increased in the presence of crude oil. These observations make Serratia sp. Tan611 a good candidate for hydrocarbon bioremediation.

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.

Arsenite response in Coccomyxa sp. Carn explored by transcriptomic and non-targeted metabolomic approaches.

Arsenic is a toxic metalloid known to generate an important oxidative stress in cells. In the present study, we focused our attention on an alga related to the genus Coccomyxa, exhibiting an extraordinary capacity to resist high concentrations of arsenite and arsenate. The integrated analysis of high-throughput transcriptomic data and non-targeted metabolomic approaches highlighted multiple levels of protection against arsenite. Indeed, Coccomyxa sp. Carn induced a set of transporters potentially preventing the accumulation of this metalloid in the cells and presented a distinct arsenic metabolism in comparison to another species more sensitive to that compound, i.e. Euglena gracilis, especially in regard to arsenic methylation. Interestingly, Coccomyxa sp. Carn was characterized by a remarkable accumulation of the strong antioxidant glutathione (GSH). Such observation could explain the apparent low oxidative stress in the intracellular compartment, as suggested by the transcriptomic analysis. In particular, the high amount of GSH in the cell could play an important role for the tolerance to arsenate, as suggested by its partial oxidation into oxidized glutathione in presence of this metalloid. Our results therefore reveal that this alga has acquired multiple and original defence mechanisms allowing the colonization of extreme ecosystems such as acid mine drainages.

Arsenic hypertolerance in the protist Euglena mutabilis is mediated by specific transporters and functional integrity maintenance mechanisms.

Arsenic is a toxic metalloid known to cause multiple and severe cellular damages, including lipid peroxidation, protein misfolding, mutagenesis and double and single-stranded DNA breaks. Thus, exposure to this compound is lethal for most organisms but some species such as the photosynthetic protist Euglena mutabilis are able to cope with very high concentrations of this metalloid. Our comparative transcriptomic approaches performed on both an arsenic hypertolerant protist, i.e. E. mutabilis, and a more sensitive one, i.e. E. gracilis, revealed multiple mechanisms involved in arsenic tolerance. Indeed, E. mutabilis prevents efficiently the accumulation of arsenic in the cell through the expression of several transporters. More surprisingly, this protist induced the expression of active DNA reparation and protein turnover mechanisms, which allow E. mutabilis to maintain functional integrity of the cell under challenging conditions. Our observations suggest that this protist has acquired specific functions regarding arsenic and has developed an original metabolism to cope with acid mine drainages-related stresses.

Metagenomic insights into microbial metabolism affecting arsenic dispersion in Mediterranean marine sediments.

Microorganisms dwelling in sediments have a crucial role in biogeochemical cycles and are expected to have a strong influence on the cycle of arsenic, a metalloid responsible for severe water pollution and presenting major health risks for human populations. We present here a metagenomic study of the sediment from two harbours on the Mediterranean French coast, l'Estaque and St Mandrier. The first site is highly polluted with arsenic and heavy metals, while the arsenic concentration in the second site is below toxicity levels. The goal of this study was to elucidate the potential impact of the microbial community on the chemical parameters observed in complementary geochemical studies performed on the same sites. The metagenomic sequences, along with those from four publicly available metagenomes used as control data sets, were analysed with the RAMMCAP workflow. The resulting functional profiles were compared to determine the over-represented Gene Ontology categories in the metagenomes of interest. Categories related to arsenic resistance and dissimilatory sulphate reduction were over-represented in l'Estaque. More importantly, despite very similar profiles, the identification of specific sequence markers for sulphate-reducing bacteria and sulphur-oxidizing bacteria showed that sulphate reduction was significantly more associated with l'Estaque than with St Mandrier. We propose that biotic sulphate reduction, arsenate reduction and fermentation may together explain the higher mobility of arsenic observed in l'Estaque in previous physico-chemical studies of this site. This study also demonstrates that it is possible to draw sound conclusions from comparing complex and similar unassembled metagenomes at the functional level, even with very low sequence coverage.

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.

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.

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.

Complete Genome Sequence of Stenotrophomonas maltophilia 1800, a New Bacterial Strain with Potential for Bioremediation of Oil-Contaminated Environments.

Stenotrophomonas maltophilia strain 1800 was isolated from the effluent of an industrial oil refinery in Algeria. Its genome was sequenced using Illumina MiSeq (2 × 150-bp read pairs) and Oxford Nanopore (long reads) technologies and assembled using Unicycler. It is composed of one chromosome of 4.83 Mb.

Water and soil contaminated by arsenic: the use of microorganisms and plants in bioremediation.

Owing to their roles in the arsenic (As) biogeochemical cycle, microorganisms and plants offer significant potential for developing innovative biotechnological applications able to remediate As pollutions. This possible use in bioremediation processes and phytomanagement is based on their ability to catalyse various biotransformation reactions leading to, e.g. the precipitation, dissolution, and sequestration of As, stabilisation in the root zone and shoot As removal. On the one hand, genomic studies of microorganisms and their communities are useful in understanding their metabolic activities and their interaction with As. On the other hand, our knowledge of molecular mechanisms and fate of As in plants has been improved by laboratory and field experiments. Such studies pave new avenues for developing environmentally friendly bioprocessing options targeting As, which worldwide represents a major risk to many ecosystems and human health.

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.

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.

Metatranscriptomic outlook on green and brown food webs in acid mine drainage.

Acid mine drainages (AMDs), metal-rich acidic effluents generated by mining activities, are colonized by prokaryotic and eukaryotic microorganisms widely distributed among different phyla. We compared metatranscriptomic data from two sampling stations in the Carnoulès AMD and from a third station in the nearby Amous River, focussing on processes involved in primary production and litter decomposition. A synergistic relationship between the green and brown food webs was favoured in the AMD sediments by the low carbon content and the availability of mineral nutrients: primary production of organic matter would benefit C-limited decomposers whose activity of organic matter mineralization would in turn profit primary producers. This balance could be locally disturbed by heterogeneous factors such as an input of plant debris from the riparian vegetation, strongly boosting the growth of Tremellales which would then outcompete primary producers. In the unpolluted Amous River on the contrary, the competition for limited mineral nutrients was dominated by the green food web, fish and bacterivorous protists having a positive effect on phytoplankton. These results suggest that in addition to direct effects of low pH and metal contamination, trophic conditions like carbon or mineral nutrient limitations also have a strong impact on assembly and activities of AMDs' microbial communities.

In situ metabolic activities of uncultivated Ferrovum sp. CARN8 evidenced by metatranscriptomic analysis.

Amongst iron-oxidizing bacteria playing a key role in the natural attenuation of arsenic in acid mine drainages (AMDs), members of the Ferrovum genus were identified in mine effluent or water treatment plants, and were shown to dominate biogenic precipitates in field pilot experiments. In order to address the question of the in situ activity of the uncultivated Ferrovum sp. CARN8 strain in the Carnoulès AMD, we assembled its genome using metagenomic and metatranscriptomic sequences and we determined standardized expression values for protein-encoding genes. Our results showed that this microorganism was indeed metabolically active and allowed us to sketch out its metabolic activity in its natural environment. Expression of genes related to the respiratory chain and carbon fixation suggests aerobic energy production coupled to ferrous iron oxidation and chemolithoautotrophic growth. Notwithstanding the presence of nitrogenase genes in its genome, expression data also indicated that Ferrovum sp. CARN8 relied on ammonium import rather than nitrogen fixation. The expression of flagellum and chemotaxis genes hints that at least a proportion of this strain population was motile. Finally, apart from some genes related to metal resistance showing surprisingly low expression values, genes involved in stress response were well expressed as expected in AMDs.

Complete Genome Sequence of Microbacterium sp. Strain Nx66, Isolated from Waters Contaminated with Petrochemicals in El Saf-Saf Valley, Algeria.

Microbacterium sp. strain Nx66 was isolated from waters contaminated by petrochemical effluents collected in Algeria. Its genome was sequenced using Illumina MiSeq (2 × 150-bp read pairs) and Oxford Nanopore (long reads) technologies and was assembled using Unicycler. It is composed of one chromosome of 3.42 Mb and one plasmid of 34.22 kb.

SAGETTARIUS: a program to reduce the number of tags mapped to multiple transcripts and to plan SAGE sequencing stages.

SAGE (Serial Analysis of Gene Expression) experiments generate short nucleotide sequences called 'tags' which are assumed to map unambiguously to their original transcripts (1 tag to 1 transcript mapping). Nevertheless, many tags are generated that do not map to any transcript or map to multiple transcripts. Current bioinformatics resources, such as SAGEmap and TAGmapper, have focused on reducing the number of unmapped tags. Here, we describe SAGETTARIUS, a new high-throughput program that performs successive precise Nla3 and Sau3A tag to transcript mapping, based on specifically designed Virtual Tag (VT) libraries. First, SAGETTARIUS decreases the number of tags mapped to multiple transcripts. Among the various mapping resources compared, SAGETTARIUS performed the best in this respect by decreasing up to 11% the number of multiply mapped tags. Second, SAGETTARIUS allows the establishment of a guideline for SAGE experiment sequencing efforts through efficient mapping of the CRT (Cytoplasmic Ribosomal protein Transcripts)-specific tags. Using all publicly available human and mouse Nla3 SAGE experiments, we show that sequencing 100,000 tags is sufficient to map almost all CRT-specific tags and that four sequencing stages can be identified when carrying out a human or mouse SAGE project. SAGETTARIUS is web interfaced and freely accessible to academic users.

Database similarity searches.

With genome sequencing projects producing huge amounts of sequence data, database sequence similarity search has become a central tool in bioinformatics to identify potentially homologous sequences. It is thus widely used as an initial step for sequence characterization and annotation, phylogeny, genomics, transcriptomics, and proteomics studies. Database similarity search is based upon sequence alignment methods also used in pairwise sequence comparison. Sequence alignment can be global (whole sequence alignment) or local (partial sequence alignment) and there are algorithms to find the optimal alignment given particular comparison criteria. However, as database searches require the comparison of the query sequence with every single sequence in the database, heuristic algorithms have been designed to reduce the time required to build an alignment that has a reasonable chance to be the best one. Such algorithms have been implemented as fast and efficient programs (Blast, FastA) available in different types to address different kinds of problems. After searching the appropriate database, similarity search programs produce a list of similar sequences and local alignments. These results should be carefully examined before coming to any conclusion, as many traps await the similarity seeker: paralogues, multidomain proteins, pseudogenes, etc. This chapter presents points that should always be kept in mind when performing database similarity searches for various goals. It ends with a practical example of sequence characterization from a single protein database search using Blast.

A Genomic Outlook on Bioremediation: The Case of Arsenic Removal.

Microorganisms play a major role in biogeochemical cycles. As such they are attractive candidates for developing new or improving existing biotechnological applications, in order to deal with the accumulation and pollution of organic and inorganic compounds. Their ability to participate in bioremediation processes mainly depends on their capacity to metabolize toxic elements and catalyze reactions resulting in, for example, precipitation, biotransformation, dissolution, or sequestration. The contribution of genomics may be of prime importance to a thorough understanding of these metabolisms and the interactions of microorganisms with pollutants at the level of both single species and microbial communities. Such approaches should pave the way for the utilization of microorganisms to design new, efficient and environmentally sound remediation strategies, as exemplified by the case of arsenic contamination, which has been declared as a major risk for human health in various parts of the world.

MACSIMS: multiple alignment of complete sequences information management system.

BACKGROUND: In 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. RESULTS: MACSIMS 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. CONCLUSION: MACSIMS 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/.