Phylogeographic Dynamics of H9N2 Avian Influenza Viruses in Tunisia.

Avian influenza virus subtype H9N2 is endemic in commercial poultry in Tunisia. This subtype affects poultry and wild birds in Tunisia and poses a potential zoonotic risk. Tunisian H9N2 strains carry, in their hemagglutinins, the human-like marker 226 L that is most influential in avian-to-human viral transmission. For a better understanding of how ecological aspects of the H9N2 virus and its circulation in poultry, migratory birds and environment shapes the spread of the dissemination of H9N2 in Tunisia, herein, we investigate the epidemiological, evolutionary and zoonotic potential of seven H9N2 poultry isolates and sequence their whole genome. Phylogeographic and phylodymanic analysis were used to examine viral spread within and among wild birds, poultry and environment at geographical scales. Genetic evolution results showed that the eight gene sequences of Tunisian H9N2 AIV were characterized by molecular markers involved with virulence and mammalian infections. The geographical distribution of avian influenza virus appears as a network interconnecting countries in Europe, Asia, North Africa and West Africa. The spatiotemporal dynamics analysis showed that the H9N2 virus was transmitted from Tunisia to neighboring countries notably Libya and Algeria. Interestingly, this study also revealed, for the first time, that there was a virus transmission between Tunisia and Morocco. Bayesian analysis showed exchanges between H9N2 strains of Tunisia and those of the Middle Eastern countries, analysis of host traits showed that duck, wild birds and environment were ancestry related to chicken. The subtypes phylodynamic showed that PB1 segment was under multiple inter-subtype reassortment events with H10N7, H12N5, H5N2 and H6N1 and that PB2 was also a subject of inter-subtype reassortment with H10N4.

Functional insights into human macrophage response against Scedosporium apiospermum and Scedosporium dehoogii.

Fungal infections caused by Scedosporium species are rising among immunocompromised and immunocompetent patients. Within the immunocompetent group, patients with cystic fibrosis (pwCF) are at high risk of developing a chronic airway colonization by these molds. While S. apiospermum is one of the major species encountered in the lungs of pwCF, S. dehoogii has rarely been reported. The innate immune response is believed to be critical for host defense against fungal infections. However, its role has only recently been elucidated and the immune mechanisms against Scedosporium species are currently unknown. In this context, we undertook a comparative investigation of macrophage-mediated immune responses toward S. apiospermum and S. dehoogii conidia. Our data showed that S. apiospermum and S. dehoogii conidia strongly stimulated the expression of a set of pro-inflammatory cytokines and chemokines such as IL-1ß, IL-8, IL-6 and TNFα. We demonstrated that S. dehoogii was more potent in stimulating the early release of pro-inflammatory cytokines and chemokines while S. apiospermum induced a late inflammatory response at a higher level. Flow cytometry analysis showed that M1-like macrophages were able to internalize both S. apiospermum and S. dehoogii conidia, with a similar intracellular killing rate for both species. In conclusion, these results suggest that M1-like macrophages can rapidly initiate a strong immune response against both S. apiospermum and S. dehoogii. This response is characterized by a similar killing of internalized conidia, but a different time course of cytokine production.

Retrospective Phylodynamic and Phylogeographic Analysis of the Human Papillomavirus 16 E6 Gene in the Mediterranean Region.

Human papillomavirus 16 (HPV16) is considered to be strongly correlated with the development of cervical cancer. Among the 8 HPV16 genes, the E6 constitutes a remarkable marker to follow the evolutionary history and spatial phylodynamics of HPV16 in the Mediterranean basin. Thus, this work aims to decipher the major evolutionary events and crosstalks in the Mediterranean basin with a focus on Tunisian strains regarding the E6 oncogene. In this study, we first extracted the available and annotated Mediterranean strains of HPV16 E6 gene sequences (n = 155) from the NCBI nucleotide database. These sequences were aligned, edited, and used for the downstream phylogenetic analyses. Finally, a Bayesian Markov Chain Monte Carlo approach was applied to reconstruct the evolutionary history of HPV16 migration. Our results showed that the HPV circulating in Tunisia derived from a Croatian ancestor around the year 1987. This starting point spreads to most European countries to reach northern Africa through the Moroccan gateway in 2004.

Epidemiological and Phylogeographic Study of Equid Herpesviruses in Tunisia.

Equid herpesvirus (EHV) is a contagious viral disease affecting horses, causing illness characterized by respiratory symptoms, abortion and neurological disorders. It is common worldwide and causes severe economic losses to the equine industry. The present study was aimed at investigating the incidence of EHVs, the genetic characterization of Tunisian isolates and a spatiotemporal study, using 298 collected samples from diseased and clinically healthy horses. The global incidence of EHV infection was found to be about 71.81%. EHV2 and EHV5 were detected in 146 (48.99%) and 159 (53.35%) sampled horses, respectively. EHV1 was detected in 11 samples (3.69%); EHV4 was not detected. Co-infections with EHV1-EHV2, EHV1-EHV5 and EHV2-EHV5 were observed in 0.33%, 1.34% and 31.54% of tested horses, respectively. Phylogenetic analyses showed that gB of EHV2 and EHV5 displays high genetic diversity with a nucleotide sequence identity ranging from 88 to 100% for EHV2 and 97.5 to 100% for EHV5. Phylogeography suggested Iceland and USA as the most likely countries of origin of the Tunisian EHV2 and EHV5 isolates. These viruses detected in Tunisia seemed to be introduced in the 2000s. This first epidemiological and phylogeographic study is important for better knowledge of the evolution of equid herpesvirus infections in Tunisia.

Applied Machine Learning Toward Drug Discovery Enhancement: Leishmaniases as a Case Study.

Drug discovery (DD) research is a complex field with a high attrition rate. Machine learning (ML) approaches combined to chemoinformatics are of valuable input to this field. We, herein, focused on implementing multiple ML algorithms that shall learn from different molecular fingerprints (FPs) of 65 057 molecules that have been identified as active or inactive against Leishmania major promastigotes. We sought to build a classifier able to predict whether a given molecule has the potential of being anti-leishmanial or not. Using the RDkit library, we calculated 5 molecular FPs of the molecules. Then, we implemented 4 ML algorithms that we trained and tested for their ability to classify the molecules into active/inactive classes based on their chemical structure, encoded by the molecular FPs. Best performers were random forest (RF) and support vector machine (SVM), while atom-pair and topology torsion FPs were the best embedding functions. Both models were further assessed on different stratification levels of the dataset and showed stable performances. At last, we used them to predict the potential of molecules within the Food and Drug Administration (FDA)-approved drugs collection to present anti-Leishmania effects. We ranked these drugs according to their anti-Leishmanial probability and obtained in total seven anti-Leishmania agents, previously described in the literature, within the top 10 of each model. This validates the robustness of the approach, the algorithms, and FPs choices as well as the importance of the dataset size and content. We further engaged these molecules into reverse docking experiments on 3D crystal structures of seven well-studied Leishmania drug targets and could predict the molecular targets for 4 drugs. The results bring novel insights into anti-Leishmania compounds.

Deep Learning Algorithms Achieved Satisfactory Predictions When Trained on a Novel Collection of Anticoronavirus Molecules.

Drug discovery and repurposing against COVID-19 is a highly relevant topic with huge efforts dedicated to delivering novel therapeutics targeting SARS-CoV-2. In this context, computer-aided drug discovery is of interest in orienting the early high throughput screenings and in optimizing the hit identification rate. We herein propose a pipeline for Ligand-Based Drug Discovery (LBDD) against SARS-CoV-2. Through an extensive search of the literature and multiple steps of filtering, we integrated information on 2,610 molecules having a validated effect against SARS-CoV and/or SARS-CoV-2. The chemical structures of these molecules were encoded through multiple systems to be readily useful as input to conventional machine learning (ML) algorithms or deep learning (DL) architectures. We assessed the performances of seven ML algorithms and four DL algorithms in achieving molecule classification into two classes: active and inactive. The Random Forests (RF), Graph Convolutional Network (GCN), and Directed Acyclic Graph (DAG) models achieved the best performances. These models were further optimized through hyperparameter tuning and achieved ROC-AUC scores through cross-validation of 85, 83, and 79% for RF, GCN, and DAG models, respectively. An external validation step on the FDA-approved drugs collection revealed a superior potential of DL algorithms to achieve drug repurposing against SARS-CoV-2 based on the dataset herein presented. Namely, GCN and DAG achieved more than 50% of the true positive rate assessed on the confirmed hits of a PubChem bioassay.

Differential Gene Expression Patterns in Blood and Cerebrospinal Fluid of Multiple Sclerosis and Neuro-Behçet Disease.

Inflammatory demyelinating disorders of the central nervous system are debilitating conditions of the young adult, here we focus on multiple sclerosis (MS) and neuro-Behçet disease (NBD). MS is an autoimmune disorder of the central nervous system. NBD, a neurological manifestation of an idiopathic chronic relapsing multisystem inflammatory disease, the behçet disease. The diagnosis of MS and NBD relies on clinical symptoms, magnetic resonance imaging and laboratory tests. At first onset, clinical and imaging similarities between the two disorders may occur, making differential diagnosis challenging and delaying appropriate management. Aiming to identify additional discriminating biomarker patterns, we measured and compared gene expression of a broad panel of selected genes in blood and cerebrospinal fluid (CSF) cells of patients suffering from NBD, MS and non inflammatory neurological disorders (NIND). To reach this aim, bivariate and multivariate analysis were applied. The Principal Analysis Component (PCA) highlighted distinct profiles between NBD, MS, and controls. Transcription factors foxp3 in the blood along with IL-4, IL-10, and IL-17 expressions were the parameters that are the main contributor to the segregation between MS and NBD clustering. Moreover, parameters related to cellular activation and inflammatory cytokines within the CSF clearly differentiate between the two inflammatory diseases and the controls. We proceeded to ROC analysis in order to identify the most distinctive parameters between both inflammatory neurological disorders. The latter analysis suggested that IL-17, CD73 in the blood as well as IL-1ß and IL-10 in the CSF were the most discriminating parameters between MS and NBD. We conclude that combined multi-dimensional analysis in blood and CSF suggests distinct mechanisms governing the pathophysiology of these two neuro-inflammatory disorders.

Genomic Characteristics of Invasive Streptococcus pneumoniae Serotype 1 in New Caledonia Prior to the Introduction of PCV13.

Streptococcus pneumoniae serotype 1 is a common cause of global invasive pneumococcal disease. In New Caledonia, serotype 1 is the most prevalent serotype and led to two major outbreaks reported in the 2000s. The pneumococcal conjugate vaccine 13 (PCV13) was introduced into the vaccination routine, intending to prevent the expansion of serotype 1 in New Caledonia. Aiming to provide a baseline for monitoring the post-PCV13 changes, we performed a whole-genome sequence analysis on 67 serotype 1 isolates collected prior to the PCV13 introduction. To highlight the S. pneumoniae serotype 1 population structure, we performed a multilocus sequence typing (MLST) analysis revealing that NC serotype 1 consisted of 2 sequence types: ST3717 and the highly dominant ST306. Both sequence types harbored the same resistance genes to beta-lactams, macrolide, streptogramin B, fluoroquinolone, and lincosamide antibiotics. We have also identified 36 virulence genes that were ubiquitous to all the isolates. Among these virulence genes, the pneumolysin sequence presented an allelic profile associated with disease outbreaks and reduced hemolytic activity. Moreover, recombination hotspots were identified in 4 virulence genes and more notably in the cps locus (cps2L), potentially leading to capsular switching, a major mechanism of the emergence of nonvaccine types. In summary, this study represents the first overview of the genomic characteristics of S. pneumoniae serotype 1 in New Caledonia prior to the introduction of PCV13. This preliminary description represents a baseline to assess the impact of PCV13 on serotype 1 population structure and genomic diversity.

Nuclear and mitochondrial genome sequencing of North-African Leishmania infantum isolates from cured and relapsed visceral leishmaniasis patients reveals variations correlating with geography and phenotype.

Although several studies have investigated genetic diversity of Leishmania infantum in North Africa, genome-wide analyses are lacking. Here, we conducted comparative analyses of nuclear and mitochondrial genomes of seven L. infantum isolates from Tunisia with the aim to gain insight into factors that drive genomic and phenotypic adaptation. Isolates were from cured (n=4) and recurrent (n=3) visceral leishmaniasis (VL) cases, originating from northern (n=2) and central (n=5) Tunisia, where respectively stable and emerging VL foci are observed. All isolates from relapsed patients were from Kairouan governorate (Centre); one showing resistance to the anti-leishmanial drug Meglumine antimoniate. Nuclear genome diversity of the isolates was analysed by comparison to the L. infantum JPCM5 reference genome. Kinetoplast maxi and minicircle sequences (1 and 59, respectively) were extracted from unmapped reads and identified by blast analysis against public data sets. The genome variation analysis grouped together isolates from the same geographical origins. Strains from the North were very different from the reference showing more than 34 587 specific single nucleotide variants, with one isolate representing a full genetic hybrid as judged by variant frequency. Composition of minicircle classes within isolates corroborated this geographical population structure. Read depth analysis revealed several significant gene copy number variations correlating with either geographical origin (amastin and Hsp33 genes) or relapse (CLN3 gene). However, no specific gene copy number variation was found in the drug-resistant isolate. In contrast, resistance was associated with a specific minicircle pattern suggesting Leishmania mitochondrial DNA as a potential novel source for biomarker discovery.

Development of Bioinformatics Infrastructure for Genomics Research.

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, H3ABioNet's 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.