Alia Benkahla

Basal DNA repair machinery is subject to positive selection in ionizing-radiation-resistant bacteria

BackgroundIonizing-radiation-resistant bacteria (IRRB) show a surprising capacity for adaptation to ionizing radiation and desiccation. Positive Darwinian selection is expected to play an important role in this trait, but no data are currently available regarding the role of positive adaptive selection in resistance to ionizing-radiation and tolerance of desiccation. We analyzed the four known genome sequences of IRRB (Deinococcus geothermalis, Deinococcus radiodurans, Kineococcus radiotolerans, and Rubrobacter xylanophilus) to determine the role of positive Darwinian selection in the evolution of resistance to ionizing radiation and tolerance of desiccation.ResultsWe used the programs MultiParanoid and DnaSP to deduce the sets of orthologs that potentially evolved due to positive Darwinian selection in IRRB. We find that positive selection targets 689 ortholog sets of IRRB. Among these, 58 ortholog sets are absent in ionizing-radiation-sensitive bacteria (IRSB: Escherichia coli and Thermus thermophilus). The most striking finding is that all basal DNA repair genes in IRRB, unlike many of their orthologs in IRSB, are subject to positive selection.ConclusionOur results provide the first in silico prediction of positively selected genes with potential roles in the molecular basis of resistance to γ-radiation and tolerance of desiccation in IRRB. Identification of these genes provides a basis for future experimental work aimed at understanding the metabolic networks in which they participate.

H3ABioNet, a sustainable Pan-African Bioinformatics Network for Human Heredity and Health in Africa

The application of genomics technologies to medicine and biomedical research is increasing in popularity, made possible by new high-throughput genotyping and sequencing technologies and improved data analysis capabilities. Some of the greatest genetic diversity among humans, animals, plants, and microbiota occurs in Africa, yet genomic research outputs from the continent are limited. The Human Heredity and Health in Africa (H3Africa) initiative was established to drive the development of genomic research for human health in Africa, and through recognition of the critical role of bioinformatics in this process, spurred the establishment of H3ABioNet, a pan-African bioinformatics network for H3Africa. The limitations in bioinformatics capacity on the continent have been a major contributory factor to the lack of notable outputs in high-throughput biology research. Although pockets of high-quality bioinformatics teams have existed previously, the majority of research institutions lack experienced faculty who can train and supervise bioinformatics students. H3ABioNet aims to address this dire need, specifically in the area of human genetics and genomics, but knock-on effects are ensuring this extends to other areas of bioinformatics. Here, we describe the emergence of genomics research and the development of bioinformatics in Africa through H3ABioNet.

Functional integrative levels in the human interactome recapitulate organ organization

Interactome networks represent sets of possible physical interactions between proteins. They lack spatio-temporal information by construction. However, the specialized functions of the differentiated cell types which are assembled into tissues or organs depend on the combinatorial arrangements of proteins and their physical interactions. Is tissue-specificity, therefore, encoded within the interactome? In order to address this question, we combined protein-protein interactions, expression data, functional annotations and interactome topology. We first identified a subnetwork formed exclusively of proteins whose interactions were observed in all tested tissues. These are mainly involved in housekeeping functions and are located at the topological center of the interactome. This ‘Largest Common Interactome Network’ represents a ‘functional interactome core’. Interestingly, two types of tissue-specific interactions are distinguished when considering function and network topology: tissue-specific interactions involved in regulatory and developmental functions are central whereas tissue-specific interactions involved in organ physiological functions are peripheral. Overall, the functional organization of the human interactome reflects several integrative levels of functions with housekeeping and regulatory tissue-specific functions at the center and physiological tissue-specific functions at the periphery. This gradient of functions recapitulates the organization of organs, from cells to organs. Given that several gradients have already been identified across interactomes, we propose that gradients may represent a general principle of protein-protein interaction network organization.

Development of Bioinformatics Infrastructure for Genomics Research in H3Africa

BACKGROUND Although pockets of bioinformatics excellence have developed in Africa, generally, large-scale genomic data analysis has been limited by the availability of expertise and infrastructure. H3ABioNet, a pan-African bioinformatics network, was established to build capacity specifically to enable H3Africa (Human Heredity and Health in Africa) researchers to analyze their data in Africa. Since the inception of the H3Africa initiative, H3ABioNets role has evolved in response to changing needs from the consortium and the African bioinformatics community. OBJECTIVES H3ABioNet set out to develop core bioinformatics infrastructure and capacity for genomics research in various aspects of data collection, transfer, storage, and analysis. METHODS AND RESULTS Various resources have been developed to address genomic data management and analysis needs of H3Africa researchers and other scientific communities on the continent. NetMap was developed and used to build an accurate picture of network performance within Africa and between Africa and the rest of the world, and Globus Online has been rolled out to facilitate data transfer. A participant recruitment database was developed to monitor participant enrollment, and data is being harmonized through the use of ontologies and controlled vocabularies. The standardized metadata will be integrated to provide a search facility for H3Africa data and biospecimens. Because H3Africa projects are generating large-scale genomic data, facilities for analysis and interpretation are critical. H3ABioNet is implementing several data analysis platforms that provide a large range of bioinformatics tools or workflows, such as Galaxy, the Job Management System, and eBiokits. A set of reproducible, portable, and cloud-scalable pipelines to support the multiple H3Africa data types are also being developed and dockerized to enable execution on multiple computing infrastructures. In addition, new tools have been developed for analysis of the uniquely divergent African data and for downstream interpretation of prioritized variants. To provide support for these and other bioinformatics queries, an online bioinformatics helpdesk backed by broad consortium expertise has been established. Further support is provided by means of various modes of bioinformatics training. CONCLUSIONS For the past 4 years, the development of infrastructure support and human capacity through H3ABioNet, have significantly contributed to the establishment of African scientific networks, data analysis facilities, and training programs. Here, we describe the infrastructure and how it has affected genomics and bioinformatics research in Africa.

In silico prediction of protein-protein interactions in human macrophages

BackgroundProtein-protein interaction (PPI) network analyses are highly valuable in deciphering and understanding the intricate organisation of cellular functions. Nevertheless, the majority of available protein-protein interaction networks are context-less, i.e. without any reference to the spatial, temporal or physiological conditions in which the interactions may occur. In this work, we are proposing a protocol to infer the most likely protein-protein interaction (PPI) network in human macrophages.ResultsWe integrated the PPI dataset from the Agile Protein Interaction DataAnalyzer (APID) with different meta-data to infer a contextualized macrophage-specific interactome using a combination of statistical methods. The obtained interactome is enriched in experimentally verified interactions and in proteins involved in macrophage-related biological processes (i.e. immune response activation, regulation of apoptosis). As a case study, we used the contextualized interactome to highlight the cellular processes induced upon Mycobacterium tuberculosis infection.ConclusionOur work confirms that contextualizing interactomes improves the biological significance of bioinformatic analyses. More specifically, studying such inferred network rather than focusing at the gene expression level only, is informative on the processes involved in the host response. Indeed, important immune features such as apoptosis are solely highlighted when the spotlight is on the protein interaction level.

The RadioP1 – An Integrative Web Resource for Radioresistant Prokaryotes

The extremely radioresistant eubacterium Deinococcus radiodurans and the phenotyp‐ ically related prokaryotes, whose genomes have been completely sequenced, are presently used as model species in several laboratories to study the lethal effects of DNA-damaging and protein-oxidizing agents, particularly the effects of ionizing radiation (IR). Unfortunately, providing relevant information about radioresistant prokaryotes (RP) in a neatly centralized and organized manner still remains a need. In this study, we designed RadioP1 Web resource (www.radiop.org.tn) to gather information about RP defined by the published literature with specific emphasis on (i) predicted genes that produce and protect against oxidative stress, (ii) predicted proteins involved in DNA repair mechanisms and (iii) potential uses of RP in biotechnology. RadioP1 allows the complete RP proteogenomes to be queried using various patterns in a user-friendly and interactive manner. The output data can be saved in plain text, Excel or HyperText Markup Language (HTML) formats for subsequent analyses. Moreover, RadioP1 provides for users a tool “START ANALY‐ SIS”, including the previously described R-packages “drc” and “lethal”, to generate exponential or sigmoid survival curves with D10 and D50 values. Furthermore, when accessible, links to external databases are provided. Supplementary data will be included in the future when the sequences of other RP genomes will become available.

Identification of key mechanisms controlling gene expression in Leishmania infected macrophages using genome-wide promoter analysis

The present study describes the in silico prediction of the regulatory network of Leishmania infected human macrophages. The construction of the gene regulatory network requires the identification of Transcription Factor Binding Sites (TFBSs) in the regulatory regions (promoters, enhancers) of genes that are regulated upon Leishmania infection. The promoters of human, mouse, rat, dog and chimpanzee genes were first identified in the whole genomes using available experimental data on full length cDNA sequences or deep CAGE tag data (DBTSS, FANTOM3, FANTOM4), mRNA models (ENSEMBL), or using hand annotated data (EPD, TRANSFAC). A phylogenetic footprinting analysis and a MATCH analysis of the promoter sequences were then performed to predict TFBS. Then, an SQL database that integrates all results of promoter analysis as well as other genome annotation information obtained from ENSEMBL, TRANSFAC, TRED (Transcription Regulatory Element Database), ORegAnno and the ENCODE project, was established. Finally publicly available expression data from human Leishmania infected macrophages were analyzed using the genome-wide information on predicted TFBS with the computer system ExPlain™. The gene regulatory network was constructed and activated signal transduction pathways were revealed. The Irak1 pathway was identified as a key pathway regulating gene expression changes in Leishmania infected macrophages.

A common molecular signature of patients with sickle cell disease revealed by microarray meta-analysis and a genome-wide association study

A chronic inflammatory state to a large extent explains sickle cell disease (SCD) pathophysiology. Nonetheless, the principal dysregulated factors affecting this major pathway and their mechanisms of action still have to be fully identified and elucidated. Integrating gene expression and genome-wide association study (GWAS) data analysis represents a novel approach to refining the identification of key mediators and functions in complex diseases. Here, we performed gene expression meta-analysis of five independent publicly available microarray datasets related to homozygous SS patients with SCD to identify a consensus SCD transcriptomic profile. The meta-analysis conducted using the MetaDE R package based on combining p values (maxP approach) identified 335 differentially expressed genes (DEGs; 224 upregulated and 111 downregulated). Functional gene set enrichment revealed the importance of several metabolic pathways, of innate immune responses, erythrocyte development, and hemostasis pathways. Advanced analyses of GWAS data generated within the framework of this study by means of the atSNP R package and SIFT tool identified 60 regulatory single-nucleotide polymorphisms (rSNPs) occurring in the promoter of 20 DEGs and a deleterious SNP, affecting CAMKK2 protein function. This novel database of candidate genes, transcription factors, and rSNPs associated with SCD provides new markers that may help to identify new therapeutic targets.

Homology modeling and docking of AahII-Nanobody complexes reveal the epitope binding site on AahII scorpion toxin

Scorpion envenoming and its treatment is a public health problem in many parts of the world due to highly toxic venom polypeptides diffusing rapidly within the body of severely envenomed victims. Recently, 38 AahII-specific Nanobody sequences (Nbs) were retrieved from which the performance of NbAahII10 nanobody candidate, to neutralize the most poisonous venom compound namely AahII acting on sodium channels, was established. Herein, structural computational approach is conducted to elucidate the Nb-AahII interactions that support the biological characteristics, using Nb multiple sequence alignment (MSA) followed by modeling and molecular docking investigations (RosettaAntibody, ZDOCK software tools). Sequence and structural analysis showed two dissimilar residues of NbAahII10 CDR1 (Tyr27 and Tyr29) and an inserted polar residue Ser30 that appear to play an important role. Indeed, CDR3 region of NbAahII10 is characterized by a specific Met104 and two negatively charged residues Asp115 and Asp117. Complex dockings reveal that NbAahII17 and NbAahII38 share one common binding site on the surface of the AahII toxin divergent from the NbAahII10 ones. At least, a couple of NbAahII10 - AahII residue interactions (Gln38 - Asn44 and Arg62, His64, respectively) are mainly involved in the toxic AahII binding site. Altogether, this study gives valuable insights in the design and development of next generation of antivenom.