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Although today many pathogenic species are known and well characterized, species evolutions and selective pressures promote the appearance of new
strains inside species. These strains, called emergents, exhibit different genetic and phenotypic features and may cause problems in the control of biological
hazards. For exemple, some emerging virus strains present stronger virulent or contagious features than common strains. Others may develop a resistance to a vaccine.
In bacteria, some strains are able to acquire antibiotic resistances or new pathogenicity functionalities (eg. haemolytic) through several mechanisms of exchanges of genetic materials
(conjugaison, transfection, transformation).
The observation of emergents in pathogen populations is due to environmental variation and natural selection that will allow some variants proliferate rapidly.
Unlike Pasteur and biochemical methods, DNA technology (PCR, qPCR, ASM, MLST, dna chip, etc.) are very effective methods for pathogen detection.
Fast and accurate, they also have the advantage of identifying species at very low concentrations and therefore meet the problem of pre-symptomatic
identification. To operate, they use molecules, primers, capable of specifically targeting the DNA of a pathogen.
The effectiveness of DNA technology is closely linked to the quality of the primers used. To design these primers, scientists use databases of all
DNA molecules sequenced. If a detection system made at time t has an efficiency optimal, scientific knowledge growing each day should push designers
to redefine their system regularly.
The genome of an emergent have genetic variations from common strains. If the detection system was constructed from conserved genetic regions, it
should be able to target the emerging strain. If the genome of an emergent was sequenced, it will be possible to develop a detection system that
will distinguish it from the rest of the species. This will require that the phylogenetic signal is sufficiently important on a genome region to allow
a discrimination.
In any case, a big data approach is highly recommended for a modeling of the most reliable detection system. In Biomanda, our big data analysis reports
will facilitate the validation steps on benchmark, by refocusing validation experiments on species and strains with a real interest to determining
the specificity and sensitivity of the method.