Pitfalls in the interpretation of CFTR variants in the context of incidental findings

International audienceWhole-exome/genome sequencing analyses lead to detect disease-causing variants that are unrelated to the initial clinical question. Irrespective of any actionable gene list, only pathogenic variants should be considered. The pathogenicity of 55 cystic fibrosis transmembrane conductance regulator (CFTR) variants of known various impacts was assessed by a group of experts by comparing data from specialized databases CFTR-France and CFTR2 with those of general clinical databases ClinVar and Human Gene Mutation Database (HGMD®) Professional and data aggregators VarSome and InterVar. The assessment of cystic fibrosis (CF) variants was correct with ClinVar and HGMD® Professional while less reliable with VarSome and InterVar. Conversely, the risk of overclassifying variants as CF-causing was up to 82% with HGMD® Professional. The concordance between data aggregators was only 50%. The use of general databases and aggregators is thus associated with a substantial risk of misclassifying variants. This evaluation may be extrapolated to other disease conditions and incites to remain cautious in interpreting and disclosing incidental findings

Emergence of Mutations in the K13 Propeller Gene of Plasmodium falciparum Isolates from Dakar, Senegal, in 2013-2014

International audienceThe kelch 13 (K13) propeller gene is associated with artemisinin resistance. In a previous work, there were no mutations found in 138 Plasmodium falciparum isolates collected in 2012 and 2013 from patients residing in Dakar, Senegal (M. Torrentino-Madamet et al., Malar J 13: 472, 2014, http://dx.doi.org/10.1186/1475-2875-13-472). However, the N554H, Q613H, and V637I mutations were identified in the propeller region of K13 in 92 (5.5%) isolates in 2013 and 2014. There were five polymorphisms identified in the Plasmodium/Apicomplexa-specific domain (K123R, N137S, N142NN/NNN, T149S, and K189T/N)

Prevalence of anti-malarial resistance genes in Dakar, Senegal from 2013 ă to 2014

International audienceBackground: To determine the impact of the introduction of ă artemisinin-based combination therapy (ACT) on parasite susceptibility, ă a molecular surveillance for antimalarial drug resistance was conducted ă on local isolates from the Hopital Principal de Dakar between November ă 2013 and January 2014 and between August 2014 and December 2014. ă Methods: The prevalence of genetic polymorphisms in antimalarial ă resistance genes (pfcrt, pfmdr1, pfdhfr and pfdhps) was evaluated in 103 ă isolates. ă Results: The chloroquine-resistant haplotypes CVIET and CVMET were ă identified in 31.4 and 3.9 % of the isolates, respectively. The ă frequency of the pfcrt K76T mutation was increased from 29.3 % in ă 2013-2014 to 43.2 % in 2014. The pfmdr1 N86Y and Y184F mutations were ă identified in 6.1 and 53.5 % of the isolates, respectively. The pfdhfr ă triple mutant (S108N, N51I and C59R) was detected in the majority of the ă isolates (82.3 %). The prevalence of quadruple mutants (pfdhfr S108N, ă N51I, C59R and pfdhps A437G) was 40.4 %. One isolate (1.1 %) harboured ă the pfdhps mutations A437G and K540E and the pfdhfr mutations S108N, ă N51I and C59R. ă Conclusions: Despite a decline in the prevalence of chloroquine ă resistance due to the official withdrawal of the drug and to the ă introduction of ACT, the spread of resistance to chloroquine has ă continued. Furthermore, susceptibility to amodiaquine may be decreased ă as a result of cross-resistance. The frequency of the pfmdr1 mutation ă N86Y declined while the Y184F mutation increased in prevalence, ă suggesting that selective pressure is acting on pfmdr1, leading to a ă high prevalence of mutations in these isolates and the lack of specific ă mutations. The 50.5 % prevalence of the pfmdr1 polymorphisms N86Y and ă Y184F suggests a decrease in lumefantrine susceptibility. Based on these ă results, intensive surveillance of ACT partner drugs must be conducted ă regularly in Senegal

Hematopoietic differentiation is characterized by a transient peak of entropy at a single-cell level

International audienceBackground: Mature blood cells arise from hematopoietic stem cells in the bone marrow by a process of differentiation along one of several different lineage trajectories. This is often represented as a series of discrete steps of increasing progenitor cell commitment to a given lineage, but as for differentiation in general, whether the process is instructive or stochastic remains controversial. Here, we examine this question by analyzing single-cell transcriptomic data from human bone marrow cells, assessing cell-to-cell variability along the trajectories of hematopoietic differentiation into four different types of mature blood cells. The instructive model predicts that cells will be following the same sequence of instructions and that there will be minimal variability of gene expression between them throughout the process, while the stochastic model predicts a role for cell-to-cell variability when lineage commitments are being made. Results: Applying Shannon entropy to measure cell-to-cell variability among human hematopoietic bone marrow cells at the same stage of differentiation, we observed a transient peak of gene expression variability occurring at characteristic points in all hematopoietic differentiation pathways. Strikingly, the genes whose cell-to-cell variation of expression fluctuated the most over the course of a given differentiation trajectory are pathway-specific genes, whereas genes which showed the greatest variation of mean expression are common to all pathways. Finally, we showed that the level of cell-to-cell variation is increased in the most immature compartment of hematopoiesis in myelodysplastic syndromes. Conclusions: These data suggest that human hematopoietic differentiation could be better conceptualized as a dynamical stochastic process with a transient stage of cellular indetermination, and strongly support the stochastic view of differentiation. They also highlight the need to consider the role of stochastic gene expression in complex physiological processes and pathologies such as cancers, paving the way for possible noise-based therapies through epigenetic regulation