Extraction of Natural Fragrance Ingredients: History Overview and Future Trends.

For centuries, perfumes consisted in a combination of natural ingredients, mainly of plant origin. From the 19th century on, the advent of organic synthesis enabled the deployment of multiple synthetic olfactory notes, enriching significantly the perfumers' portfolio. Chemistry is ever since the foundation of modern perfumery. However, sustainable-minded consumers, massively rejecting synthetics for safety and ecological issues, engaged a global return to nature in numerous sectors, and the fragrance industry is not outdone. Sustainable extraction of natural products, making use of innovative technologies, process intensification and agro-based solvents, constitutes the answer to develop eco-conceived fragrant ingredients covering every olfactory family without endangering biodiversity any further. The objective of this review is to draw a clear picture of where those technological advances are today and to assess the ones that may be effectively transposed at the industrial scale tomorrow.

Computational prediction of inter-species relationships through omics data analysis and machine learning.

BACKGROUND: Antibiotic resistance and its rapid dissemination around the world threaten the efficacy of currently-used medical treatments and call for novel, innovative approaches to manage multi-drug resistant infections. Phage therapy, i.e., the use of viruses (phages) to specifically infect and kill bacteria during their life cycle, is one of the most promising alternatives to antibiotics. It is based on the correct matching between a target pathogenic bacteria and the therapeutic phage. Nevertheless, correctly matching them is a major challenge. Currently, there is no systematic method to efficiently predict whether phage-bacterium interactions exist and these pairs must be empirically tested in laboratory. Herein, we present our approach for developing a computational model able to predict whether a given phage-bacterium pair can interact based on their genome. RESULTS: Based on public data from GenBank and phagesDB.org, we collected more than a thousand positive phage-bacterium interactions with their complete genomes. In addition, we generated putative negative (i.e., non-interacting) pairs. We extracted, from the collected genomes, a set of informative features based on the distribution of predictive protein-protein interactions and on their primary structure (e.g. amino-acid frequency, molecular weight and chemical composition of each protein). With these features, we generated multiple candidate datasets to train our algorithms. On this base, we built predictive models exhibiting predictive performance of around 90% in terms of F1-score, sensitivity, specificity, and accuracy, obtained on the test set with 10-fold cross-validation. CONCLUSION: These promising results reinforce the hypothesis that machine learning techniques may produce highly-predictive models accelerating the search of interacting phage-bacteria pairs.

Stereotyped B-cell receptors in one-third of chronic lymphocytic leukemia: a molecular classification with implications for targeted therapies.

Mounting evidence indicates that grouping of chronic lymphocytic leukemia (CLL) into distinct subsets with stereotyped BCRs is functionally and prognostically relevant. However, several issues need revisiting, including the criteria for identification of BCR stereotypy and its actual frequency as well as the identification of "CLL-biased" features in BCR Ig stereotypes. To this end, we examined 7596 Ig VH (IGHV-IGHD-IGHJ) sequences from 7424 CLL patients, 3 times the size of the largest published series, with an updated version of our purpose-built clustering algorithm. We document that CLL may be subdivided into 2 distinct categories: one with stereotyped and the other with nonstereotyped BCRs, at an approximate ratio of 1:2, and provide evidence suggesting a different ontogeny for these 2 categories. We also show that subset-defining sequence patterns in CLL differ from those underlying BCR stereotypy in other B-cell malignancies. Notably, 19 major subsets contained from 20 to 213 sequences each, collectively accounting for 943 sequences or one-eighth of the cohort. Hence, this compartmentalized examination of VH sequences may pave the way toward a molecular classification of CLL with implications for targeted therapeutic interventions, applicable to a significant number of patients assigned to the same subset.

Functional insights into the core-TFIIH from a comparative survey.

TFIIH is a eukaryotic complex composed of two subcomplexes, the CAK (Cdk activating kinase) and the core-TFIIH. The core-TFIIH, composed of seven subunits (XPB, XPD, P62, P52, P44, P34, and P8), plays a crucial role in transcription and repair. Here, we performed an extended sequence analysis to establish the accurate phylogenetic distribution of the core-TFIIH in 63 eukaryotic organisms. In spite of the high conservation of the seven subunits at the sequence and genomic levels, the non-enzymatic P8, P34, P52 and P62 are absent from one or a few unicellular species. To gain insight into their respective roles, we undertook a comparative genomic analysis of the whole proteome to identify the gene sets sharing similar presence/absence patterns. While little information was inferred for P8 and P62, our studies confirm the known role of P52 in repair and suggest for the first time the implication of the core TFIIH in mRNA splicing via P34.

Bacteriophage Genetic Edition Using LSTM.

Bacteriophages are gaining increasing interest as antimicrobial tools, largely due to the emergence of multi-antibiotic-resistant bacteria. Although their huge diversity and virulence make them particularly attractive for targeting a wide range of bacterial pathogens, it is difficult to select suitable phages due to their high specificity which limits their host range. In addition, other challenges remain such as structural fragility under certain environmental conditions, immunogenicity of phage therapy, or development of bacterial resistance. The use of genetically engineered phages may reduce characteristics that hinder prophylactic and therapeutic applications of phages. Nowadays, there is no systematic method to modify a given phage genome conferring its sought characteristics. We explore the use of artificial intelligence for this purpose as it has the potential to both guide and accelerate genome modification to generate phage variants with unique properties that overcome the limitations of natural phages. We propose an original architecture composed of two deep learning-driven components: a phage-bacterium interaction predictor and a phage genome-sequence generator. The former is a multi-branch 1-D convolutional neural network (1D-CNN) that analyses phage and bacterial genomes to predict interactions. The latter is a recurrent neural network, more particularly a long short-term memory (LSTM), that performs genomic modifications to a phage to offer substantial host range improvement. For this component, we developed two different architectures composed of one or two stacked LSTM layers with 256 neurons each. These generators are used to modify, more precisely to rewrite, the genome sequence of 42 selected phages, while the predictor is used to estimate the host range of the modified bacteriophages across 46 strains of Pseudomonas aeruginosa. The proposed generators, trained with an average accuracy of 96.1%, are able to improve the host range for an average of 18 phages among the 42 under study, increasing both their average host range, by 73.0 and 103.7%, and the maximum host ranges from 21 to 24 and 29, respectively. These promising results showed that the use of deep learning methodologies allows genetic modification of phages to extend, for instance, their host range, confirming the potential of these approaches to guide bacteriophage engineering.

IMGT, the international ImMunoGeneTics information system.

IMGT, the international ImMunoGeneTics information system (http://www.imgt.org), was created in 1989 by Marie-Paule Lefranc, Laboratoire d'ImmunoGénétique Moléculaire LIGM (Université Montpellier 2 and CNRS) at Montpellier, France, in order to standardize and manage the complexity of immunogenetics data. The building of a unique ontology, IMGT-ONTOLOGY, has made IMGT the global reference in immunogenetics and immunoinformatics. IMGT is a high-quality integrated knowledge resource specialized in the immunoglobulins or antibodies, T cell receptors, major histocompatibility complex, of human and other vertebrate species, proteins of the IgSF and MhcSF, and related proteins of the immune systems of any species. IMGT provides a common access to standardized data from genome, proteome, genetics and 3D structures. IMGT consists of five databases (IMGT/LIGM-DB, IMGT/GENE-DB, IMGT/3Dstructure-DB, etc.), fifteen interactive online tools for sequence, genome and 3D structure analysis, and more than 10,000 HTML pages of synthesis and knowledge. IMGT is used in medical research (autoimmune diseases, infectious diseases, AIDS, leukemias, lymphomas and myelomas), veterinary research, biotechnology related to antibody engineering (phage displays, combinatorial libraries, chimeric, humanized and human antibodies), diagnostics (clonalities, detection and follow-up of residual diseases) and therapeutical approaches (graft, immunotherapy, vaccinology). IMGT is freely available at http://www.imgt.org.

IMGT/V-QUEST: the highly customized and integrated system for IG and TR standardized V-J and V-D-J sequence analysis.

IMGT/V-QUEST is the highly customized and integrated system for the standardized analysis of the immunoglobulin (IG) and T cell receptor (TR) rearranged nucleotide sequences. IMGT/V-QUEST identifies the variable (V), diversity (D) and joining (J) genes and alleles by alignment with the germline IG and TR gene and allele sequences of the IMGT reference directory. New functionalities were added through a complete rewrite in Java. IMGT/V-QUEST analyses batches of sequences (up to 50) in a single run. IMGT/V-QUEST describes the V-REGION mutations and identifies the hot spot positions in the closest germline V gene. IMGT/V-QUEST can detect insertions and deletions in the submitted sequences by reference to the IMGT unique numbering. IMGT/V-QUEST integrates IMGT/JunctionAnalysis for a detailed analysis of the V-J and V-D-J junctions, and IMGT/Automat for a full V-J- and V-D-J-REGION annotation. IMGT/V-QUEST displays, in 'Detailed view', the results and alignments for each submitted sequence individually and, in 'Synthesis view', the alignments of the sequences that, in a given run, express the same V gene and allele. The 'Advanced parameters' allow to modify default parameters used by IMGT/V-QUEST and IMGT/JunctionAnalysis according to the users' interest. IMGT/V-QUEST is freely available for academic research at http://imgt.cines.fr.

IMGT-Kaleidoscope, the formal IMGT-ONTOLOGY paradigm.

IMGT, the international ImMunoGeneTics information system (http://imgt.cines.fr), is the reference in immunogenetics and immunoinformatics. IMGT standardizes and manages the complex immunogenetic data which include the immunoglobulins (IG) or antibodies, the T cell receptors (TR), the major histocompatibility complex (MHC) and the related proteins of the immune system (RPI) which belong to the immunoglobulin superfamily (IgSF) and the MHC superfamily (MhcSF). The accuracy and consistency of IMGT data and the coherence between the different IMGT components (databases, tools and Web resources) are based on IMGT-ONTOLOGY, the first ontology for immunogenetics and immunoinformatics. IMGT-ONTOLOGY manages the immunogenetics knowledge through diverse facets relying on seven axioms, "IDENTIFICATION", "DESCRIPTION", "CLASSIFICATION", "NUMEROTATION", "LOCALIZATION", "ORIENTATION" and "OBTENTION", that postulate that objects, processes and relations have to be identified, described, classified, numerotated, localized, orientated, and that the way they are obtained has to be determined. These axioms constitute the Formal IMGT-ONTOLOGY, also designated as IMGT-Kaleidoscope. Through the example of the IG molecular synthesis, the concepts generated from the "IDENTIFICATION", "DESCRIPTION", "CLASSIFICATION" and "NUMEROTATION" axioms are detailed with their main instances and semantic relations. The axioms have been essential for the conceptualization of the molecular immunogenetics knowledge and can be used to generate concepts for multi scale approaches at the molecule, cell, tissue, organ, organism or population level, emphasizing the generalization of the application domain. In that way the Formal IMGT-ONTOLOGY represents a paradigm for the elaboration of ontologies in system biology.