Construction of a subgenomic CV-B3 replicon expressing emerald green fluorescent protein to assess viral replication of a cardiotropic enterovirus strain in cultured human cells

Coxsackieviruses B (CV-B) (Picornaviridae) are a common infectious cause of acute myocarditis in children and young adults, a disease, which is a precursor to 10-20% of chronic myocarditis and dilated cardiomyopathy (DCM) cases. The mechanisms involved in the disease progression from acute to chronic myocarditis phase and toward the DCM clinical stage are not fully understood but are influenced by both viral and host factors. Subgenomic replicons of CV-B can be used to assess viral replication mechanisms in human cardiac cells and evaluate the effects of potential antiviral drugs on viral replication activities. Our objectives were to generate a reporter replicon from a cardiotropic prototype CV-B3/28 strain and to characterize its replication properties into human cardiac primary cells. To obtain this replicon, a cDNA plasmid containing the full CV-B3/28 genome flanked by a hammerhead ribozyme sequence and an MluI restriction site was generated and used as a platform for the insertion of sequences encoding emerald green fluorescent protein (EmGFP) in place of those encoding VP3. In vitro transcribed RNA from this plasmid was transfected into HeLa cells and human primary cardiac cells and was able to produce EmGFP and VP1-containing polypeptides. Moreover, non-structural protein biological activity was assessed by the specific cleavage of eIF4G1 by viral 2A(pro). Viral RNA replication was indirectly demonstrated by inhibition assays, fluoxetine was added to cell culture and prevented the EmGFP synthesis. Our results indicated that the EmGFP CV-B3 replicon was able to replicate and translate as well as the CV-B3/28 prototype strain. Our EmGFP CV-B3 replicon will be a valuable tool to readily investigate CV-B3 replication activities in human target cell models

Contribution of MALDI-TOF mass spectrometry in characterization of pathogenic agents in Parasitology-Mycology.

L’utilisation du MALDI-TOF a révolutionné la Microbiologie clinique et l’Entomologie médicale où il s’est imposé comme un outil d’identification fiable, rapide et peu onéreux.Nous avons tout d’abord proposé son utilisation pour l’identification de cercaires de trématodes avec un protocole simple et rapide d’extraction. Notre base de données regroupait 12 espèces autochtones, permettant une identification de 68% des spectres avec une spécificité de 100%.Nous avons ensuite appliqué la technique à l'identification spécifique des cercaires de schistosomes humains et animaux, y compris des hybrides entre S. bovis et S. haematobium. La base de données spectrale développée permettait l'identification des cercaires de Schistosoma avec une grande exactitude (94 % de bonnes identifications) et une bonne spécificité (S. bovis : 99%, S. haematobium 99%, S. mansoni et S. rodhaini : 100 %). L'utilisation de l’apprentissage automatisé (AA) permettait de discriminer les hybrides avec une Sensibilité (Se) et une Sp > 97%.Nous avons également étudié la capacité du MALDI-TOF couplé à des réseaux de neurones convutionnels à detecter un clone épidémique d’Aspergillus flavus, responsable d’aspergilloses cutanées en réanimation pédiatrique. Cette approche a permis de classer 33/34 isolats (97%).Enfin, nous avons évalué le MALDI-TOF dans l’identification des espèces du complexe Ph. perniciosus et de ses morphotypes atypiques. La concordance avec l’identification moléculaire était de 98% et l’utilisation de l’AA permettait de distinguer les formes typiques et atypiques avec une Se et une Sp de 100%. Nos résultats confirment l’intérêt du MALDI-TOF en Parasitologie, Mycologie et Entomologie.Use of MALDI-TOF has revolutionized clinical microbiology and medical entomology where it is recognized as a reliable, rapid and inexpensive identification tool.We first proposed its use for the identification of trematode cercariae with a simple and rapid extraction protocol. Our database included 12 autochthonous species, allowing an identification of 68% of the spectra with 100% of specificity.Then, we applied the technique to the specific identification of cercariae from human and animal schistosomes, including hybrids between S. bovis and S. haematobium. We built a spectral database allowing identification of Schistosoma cercariae with high accuracy (94% good identifications) and good specificity (S. bovis: 99%, S. haematobium 99%, S. mansoni and S. rodhaini : 100%). The use of machine learning (ML) made it possible to discriminate hybrids with sensitivity and specificity > 97%.We also studied the ability of MALDI-TOF coupled with convolutionnal neural networks to detect an epidemic clone of Aspergillus flavus, responsible for cutaneous aspergillosis in pediatric intensive care unit. This approach allowed to classify 33/34 isolates (97%).Finally, we evaluated MALDI-TOF for identification of species of the Ph. perniciosus complex and its atypical morphotypes. The agreement with the molecular identification was 98% and the use of ML made it possible to distinguish between typical and atypical forms with sensitivity and specificity of 100%.Our results confirm the interest of MALDI-TOF in Parasitology, Mycology and Entomology

MALDI-TOF mass spectrometry: a new tool for rapid identification of cercariae (Trematoda, Digenea)

Identification of cercariae was long based on morphological and morphometric features, but these approaches remain difficult to implement and require skills that have now become rare. Molecular tools have become the reference even though they remain relatively time-consuming and expensive. We propose a new approach for the identification of cercariae using MALDI-TOF mass spectrometry. Snails of different genera (Radix, Lymnaea, Stagnicola, Planorbis, and Anisus) were collected in the field to perform emitting tests in the laboratory. The cercariae they emitted (Trichobilharzia anseri, Diplostomum pseudospathaceum, Alaria alata, Echinostoma revolutum, Petasiger phalacrocoracis, Tylodelphys sp., Australapatemon sp., Cotylurus sp., Posthodiplostomum sp., Parastrigea sp., Echinoparyphium sp. and Plagiorchis sp.) were characterized by sequencing the D2, ITS2 and ITS1 domains of rDNA, and by amplification using specific Alaria alata primers. A sample of each specimen, either fresh or stored in ethanol, was subjected to a simple preparation protocol for MALDI-TOF analysis. The main spectral profiles were analyzed by Hierarchical Clustering Analysis. Likewise, the haplotypes were analyzed using the maximum likelihood method. Analytical performance and the log-score value (LSV) cut-off for species identification were then assessed by blind testing. The clusters obtained by both techniques were congruent, allowing identification at a species level. MALDI-TOF enables identification at an LSV cut-off of 1.7 without false-positives; however, it requires more data on closely related species. The development of a “high throughput” identification system for all types of cercariae would be of considerable interest in epidemiological surveys of trematode infections

Artificial Intelligence and Malaria

International audienceMalaria disease is due to the infection with Plasmodium parasites transmitted by a mosquito vector belonging to the genus Anopheles. To combat malaria, effective diagnosis and treatment using artemisinin-based combinations are needed, as well as strategies that are aimed at reducing or stopping transmission by mosquito vectors. Even if the conventional microscopic diagnosis is the gold standard for malaria diagnosis, it is time consuming, and the diagnostic performance depends on techniques and human expertise. In addition, tools for characterizing Anopheles vectors are limited and difficult to establish in the field. The advent of computational biology, information technology infrastructures, and mobile computing power offers the opportunity to use artificial intelligence (AI) approaches to address challenges and technical needs specific to malaria-endemic countries. This chapter illustrates the trends, advances, and future challenges linked to the deployment of AI in malaria. Two innovative AI approaches are described. The first is the image-based automatic classification of malaria parasites and vectors, and the second is the proteomics analysis of vectors. The developed applications are aimed at facilitating malaria diagnosis by performing malaria parasite detection, species identification, and estimation of parasitaemia. In the future, they can lead to efficient and accurate diagnostic tools, revolutionizing the urgent diagnosis of malaria. Other applications focus on the characterization of mosquito vectors by performing species identification, behavior, and biology descriptions. If field-validated, these promising approaches will facilitate the epidemiological monitoring of malaria vectors and saving resources by preventing or reducing malaria transmission

MALDI-TOF: A new tool for the identification of Schistosoma cercariae and detection of hybrids

International audienceSchistosomiasis is a neglected water-born parasitic disease caused by Schistosoma affecting more than 200 million people. Introgressive hybridization is common among these parasites and raises issues concerning their zoonotic transmission. Morphological identification of Schistosoma cercariae is difficult and does not permit hybrids detection. Our objective was to assess the performance of MALDI-TOF (Matrix Assistated Laser Desorption-Ionization-Time Of Flight) mass spectrometry for the specific identification of cercariae in human and non-human Schistosoma and for the detection of hybridization between S. bovis and S. haematobium. Spectra were collected from laboratory reared molluscs infested with strains of S. haematobium, S. mansoni, S. bovis, S. rodhaini and S. bovis x S. haematobium natural (Corsican hybrid) and artificial hybrids. Cluster analysis showed a clear separation between S. haematobium, S. bovis, S. mansoni and S. rodhaini. Corsican hybrids are classified with those of the parental strain of S. haematobium whereas other hybrids formed a distinct cluster. In blind test analysis the developed MALDI-TOF spectral database permits identification of Schistosoma cercariae with high accuracy (94%) and good specificity (S. bovis: 99.59%, S. haematobium 99.56%, S. mansoni and S. rodhaini: 100%). Most misidentifications were between S. haematobium and the Corsican hybrids. The use of machine learning permits to improve the discrimination between these last two taxa, with accuracy, F1 score and Sensitivity/Specificity > 97%. In multivariate analysis the factors associated with obtaining a valid identification score (> 1.7) were absence of ethanol preservation (p < 0.001) and a number of 2-3 cercariae deposited per well (p < 0.001). Also, spectra acquired from S. mansoni cercariae are more likely to obtain a valid identification score than those acquired from S. haematobium (p<0.001). MALDI-TOF is a reliable technique for high-throughput identification of Schistosoma cercariae of medical and veterinary importance and could be useful for field survey in endemic areas.Author summary:Schistosomiases are neglected tropical diseases, affecting approximately 200 million people worldwide. They are transmitted during contact with water contaminated with the infesting stage of the parasite (the cercaria stage). Species-level recognition of cercariae present in water has important implications for field campaigns aimed at eradicating schistosomiasis. In addition, Schistosomes are able to hybridize between different species. Identification of Schistosomes cercariae on microscopy is difficult because of their similarity, and it does not allow hybrids to be distinguished. Molecular biology techniques allow a reliable diagnosis but are expensive. MALDI-TOF is a recent technique that permits an inexpensive identification of micro-organisms in a few minutes. In this paper, we evaluate MALDI-TOF identification of Schistosomes cercariae. We have implemented a database of MALDI-TOF cercariae spectra obtained from parental strains and hybrids of species of medical or veterinary interest, allowing reliable identification with an accuracy of 94%. The identification errors mainly come from confusion between the natural Corsican hybrid (S. haematobium x S. bovis) and S. haematobium. The use of machine learning algorithms permits to obtain an accuracy of more than 97% in the recognition of these two parasites. In conclusion, MALDI-TOF is a promising tool for the identification of Schistosome cercariae

Pancytopénie sévère secondaire à un déficit en folates en dépit d’un dosage de folatesérythrocytaires normal

International audienceWe report the case of an alcoholic patient with severe pancytopenia with lowplasma folate level but normal erythrocyte folates and cobalamin levels. The bone marrow smear concluded to a pancytopenia due to folates and/or cobalamin deficiency. Severe pancytopenia due to acute plasma folate deficiency can be observed despite normal erythrocyte folates level which reflects the organism’s folates store.Nous rapportons un cas de pancytopénie sévère avec dosage en folates sériques isolément bas contrastant avec des folates érythrocytaires et vitamine B12 normaux, chez un patient alcoolique. Le myélogramme montrait un aspect de moelle carentielle en ces vitamines. Ce cas met en lumière la possibilité de survenue d’une pancytopénie sévère secondaire à une carence en folates, en dépit d’un dosage normal de folates érythrocytaires, qui est un indicateur des apports en folates des 3 derniers mois (durée de vie du globule rouge) et donc des réserves de l’organisme

MALDI-TOF MS Limits for the Identification of Mediterranean Sandflies of the Subgenus Larroussius, with a Special Focus on the Phlebotomus perniciosus Complex

International audienceLeishmania infantum is the agent of visceral leishmaniasis in the Mediterranean basin. It is transmitted by sandflies of the subgenus Larroussius. Although Phlebotomus perniciosus is the most important vector in this area, an atypical Ph. perniciosus easily confused with Ph. longicuspis has been observed in North Africa. MALDI-TOF MS, an important tool for vector identification, has recently been applied for the identification of sandflies. Spectral databases presented in the literature, however, include only a limited number of Larroussius species. Our objective was to create an in-house database to identify Mediterranean sandflies and to evaluate the ability of MALDI-TOF MS to discriminate close species or atypical forms within the Larroussius subgenus. Field-caught specimens (n = 94) were identified morphologically as typical Ph. perniciosus (PN; n = 55), atypical Ph. perniciosus (PNA; n = 9), Ph. longicuspis (n = 9), Ph. ariasi (n = 9), Ph. mascittii (n = 3), Ph. neglectus (n = 5), Ph. perfiliewi (n = 1), Ph. similis (n = 9) and Ph. papatasi (n = 2). Identifications were confirmed by sequencing of the mtDNA CytB region and sixteen specimens were included in the in-house database. Blind assessment on 73 specimens (representing 1073 good quality spectra) showed a good agreement (98.5%) between MALDI-TOF MS and molecular identification. Discrepancies concerned confusions between Ph. perfiliewi and Ph. perniciosus. Hierarchical clustering did not allow classification of PN and PNA. The use of machine learning, however, allowed discernment between PN and PNA and between the lcus and lcx haplotypes of Ph. longicuspis (accuracy: 0.8938 with partial-least-square regression and random forest models). MALDI-TOF MS is a promising tool for the rapid and accurate identification of field-caught sandflies. The use of machine learning could allow to discriminate similar species

Analysis of transcription and translation viral activities of wild type (WT) and 5’ terminally deleted enteroviruses in a model of cultured primary human cardiomyocytes

International audienc