La dysprosodie dans les pathologies neurologiques: état des lieux et perspectives

International audienceUn grand nombre d'études a été consacré à l'analyse des dysprosodies dans les pathologies neurologiques. De la masse importante de ces travaux, les résultats sont souvent contradictoires et restent aujourd'hui difficiles à interpréter. Tout d'abord, le manque de consensus pourrait s'expliquer par la définition même donnée à la prosodie et les éléments qui la composent et donc, par là, à la dysprosodie neurologique. Les évaluations, qu'elles soient perceptives ou acoustiques, et les mesures effectuées des traits prosodiques reposant sur des définitions différentes et des critères plus ou moins établis, font qu'il est difficile aujourd'hui d'établir avec précision les indices prosodiques « déviants » d'une pathologie neurologique. Par ailleurs, mis à part quelques rares études (e.g. Jankowski et al, 2004), la plupart sont très souvent établies à partir de populations restreintes (une dizaine voire une vingtaine de sujets) ce qui pose la question de la généralisation des données, d'autant plus que de nombreux facteurs (extra-linguistiques et para-linguistiques) viennent s'ajouter aux troubles neurologiques, expliquant ainsi la variabilité constatée. La difficulté d'une étude de la dysprosodie dans les pathologies neurologiques repose aussi sur le fait que les populations choisies dans ces études s'avèrent être parfois très différentes (e.g. Parkinson; Huntington; SLA) et souvent peu documentées pour pouvoir les comparer. Si dans ces études, les auteurs s'efforcent à renseigner et à apparier les populations en termes d'âge ou sexe, peu ou pas d'attention n'est portée à l'origine géographique ou encore au statut socioprofessionnel, bien que de nombreuses études ont pu démontrer leur influence sur la prosodie de la parole (Byrd et al 1992). De même, les populations divergent en termes d'avancement ou de sévérité de la maladie, de traitement médical, critères parfois établis selon des protocoles cliniques différents. De nombreuses études ont montré que le style de parole pratiqué, selon s'il est préparé ou spontané, formel ou informel, monologal ou dialogal, peut être caractérisé par des patrons prosodiques différents (Ayers, 1994). Or, dans l'étude de la dysprosodie, les consignes demandées aux sujets varient : lecture de phrase (Blonder et al, 1989), de texte (Jankowski et al, 2004) ou encore parole spontanée (Van Lanker Sidtis et al, 2004). De plus, parce que la prosodie exerce des fonctions diverses (i.e. linguistiques et para-linguistiques), la tâche demandée et ainsi les fonctions qu'elle requiert, peuvent entacher les conclusions tirées de la dysprosodie chez un patient atteint d'une maladie neurologique. Nous proposons donc dans ce travail, partie du projet ANR DesPho-APaDy, (1) une définition élargie de la prosodie, notamment par l'abord de ses fonctions, afin de déterminer plus précisément les protocoles expérimentaux qui peuvent être établis pour une étude de la dysprosodie dans les pathologies neurologiques (2) une comparaison des études menées sur la dysprosodie dans les pathologies neurologiques en prenant compte des différences intrinsèques de chaque étude, (3) une proposition de mesures prosodiques qui peuvent être effectuées en s'assurant de leur fiabilité et une présentation d'outils automatiques disponibles pour l'analyse prosodique si l'on souhaite traiter un grand nombre de données (e.g. Hirst, 2007 ; De Looze, 2010)

Additional file 1: Figure S1. of Characterisation of the global transcriptional response to heat shock and the impact of individual genetic variation

PCA plot of ComBat corrected gene expression. PCA plot for gene expression in LCLs following heat shock post microarray processing and QC with individual lines coloured by BeadChIP. (PDF 166 kb

Additional file 3: Table S1. of Characterisation of the global transcriptional response to heat shock and the impact of individual genetic variation

Differentially expressed genes following heat shock. Differentially expressed genes for a panel of 43 LCLs exposed to heat shock (42 °C for 1 h, 6 h recovery) and assayed by microarray are shown following limma analysis (FC >1.2, FDR <0.01). Table S2. GO categories enriched for upregulated genes. GO categories for differentially expressed genes upregulated following heat shock in LCLs are shown. Numbers of significant and expected genes shown, together with p values (Fisher’s exact test). Table S3. GO categories enriched for downregulated genes. GO categories for differentially expressed genes downregulated following heat shock in LCLs are shown. Numbers of significant and expected genes shown, together with p values (Fisher’s exact test). Table S4. Network analysis following heat shock. Networks identified on IPA analysis of differentially expressed genes (FC >1.2, FDR <0.01) following heat shock. Table S5. Genes with newly established links to heat shock response. Genes listed together with FC and FDR following heat shock, and p value for presence of the heat shock binding motif. Table S6. Summary of HSF-binding evidence for the promoters of novel and established heat shock response genes. Presence of ChIP-seq peak for HSF1 or HSF2 and HSF1 motif indicated in relation to heat shock genes. Table S7. Differential gene expression between PLS clusters. Differential gene expression between samples assigned to PLS cluster 1 and 2 as assessed by limma analysis is shown for all assayed probes. Table S8. GO categories enriched for genes with increased expression in cluster 2. GO categories for genes differentially expressed between PLS clusters. Categories enriched for genes with increased expression in cluster 2 are shown. Numbers of significant and expected genes shown, together with p values (Fisher’s exact test). Table S9. GO categories enriched for genes with increased expression in cluster 1. GO categories for genes differentially expressed between PLS clusters. Categories enriched for genes with increased expression in cluster 1 are shown. Numbers of significant and expected genes shown, together with p values (Fisher’s exact test). (XLSX 4875 kb

DNA methylation abrogates the activity of both SNP variant TNFα promoters but does not readily explain their functional differences.

<p>(a) Reporter luciferase genes under the control of TNFα promoters carrying either the −237A or G variant were methylated, or demethylated prior to transfection into Jurkat cells. Following 24 hours the cells were stimulated with PMA (for a further 4 hours) prior to the evaluation of luciferase activity. Fold changes (stimulated/non-stimulated) in TNFα production for unmethylated promoters are noted in parentheses (b) Outline of the proximal TNFα promoter adapted from previous reports <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0040100#pone.0040100-Falvo1" target="_blank">[10]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0040100#pone.0040100-Barthel1" target="_blank">[43]</a>, illustrating the various transcription factors known to bind in this region. CpG dinucleotides including those which encompass the −237 and −1030 SNPs are noted as black strips, and their positioning relative to the Transcription Start Site (TSS) is displayed numerically (in red). (c) DNA isolated from −237 AA homozygous, −237GG homozygous and GA heterozygous BCLs was bisulfite converted, PCR amplified and sequenced to assess the methylation status of TNFα promoter sequences. A representative sequencing screen encompassing the −1030 SNP denotes the frequency of methylated cytidines (noted as black circles) within individual templates sequenced for a single donor, with a tabular version of summary data for a selection of BCL lines denoting the percentage of methylation at each CpG dinucleotide motif. (d) Three BCL lines encoding different combinations of the TNFα promoter −237 SNP variants (GG, GA and AA) were either left untreated or pre-incubated for 48 hours with 5-Azacytidine prior to PMA stimulation for 4 hours. sTNFα was subsequently measured by ELISA, and both absolute (bar chart) and sTNFα fold increase compared to their respective non-drug treated, PMA stimulated backgrounds (embedded values) are reported. Two biological and technical replicates were analysed per experiment, and median TNFα production plus SEM is reported.</p

Reduced sTNFα production on a−237A SNP background following LPS-activation of PBMCs.

<p>1 million PBMCs isolated from healthy −237GG (n = 9) homozygous or GA heterozygous (n = 9) donors were either left untreated, or stimulated for 4 hours with LPS, following which TNFα levels were estimated by ELISA. sTNFα datasets were evaluated relative to the number of CD14 positive monocytes present in each sample, and TNFα levels were adjusted to represent TNFα production per 1million CD14+ cells. (a) Absolute sTNFα production (stimulated minus non-stimulated) and (b) fold induction (stimulated/non-stimulated) were compared. Wilcoxon-Mann-Whitney tests were used for statistical comparisons, and two-tailed P values are indicated.</p

−237A homozygosity associates with reduced TNFα production in PMA-activated B cell lines.

<p>(a) Immortalised BCL lines generated from healthy donors who were −237 AA homozygous (n = 4), −237GG homozygous (n = 11) or GA heterozygous (n = 4) were stimulated for 4 hours with PMA, or left untreated, following which mRNA was isolated for qPCR analysis. TNFα mRNA fold induction (stimulated/non-stimulated) is reported. (b) One million BCLs from −237GG homozygous (n = 11) and GA heterozygous (n = 4) donors were stimulated with PMA for 4 hours, following which soluble TNFα (sTNFα) levels were measured by ELISA. The data is presented as absolute differences in sTNFα secretion in the −237 homozygous versus heterozygous group. (c) Stimulation of TNFα production on a −237AA background was reduced relative to −237GG in the presence of different stimuli. The data is presented as absolute differences in soluble TNFα secretion (pg/ml) for a single −237GG and −237AA homozygous BCL line. Wilcoxon-Mann-Whitney tests were used for statistical comparisons, and two-tailed P values are indicated in (a) and (b).</p

Observed TNFα promoter haplotypes and frequencies in HIV-1 infected patient groups.

<p>Five of the common Caucasian ancestral TNFα promoter SNPs were typed by sequencing and confirmed by RFLP analysis. Statistical analysis was based on the total number of haplotypes (2n) within each group. P values were calculated using the Fisher’s exact test. Statistically significant values are shown in bold. The −237A promoter haplotype was exclusively observed in patients who carried HLA-B*5701.</p

TNFα promoters encoding the −237A SNP display reduced activity following stimulation in a luciferase reporter assay system.

<p>Jurkat cells were transfected with reporter luciferase plasmids under the control of TNFα promoters carrying either the −237A or G variant. Following 24 hours, transfected cells were stimulated for 4 hours with PMA/ionomycin or left untreated following which reporter gene activity was measured. The data is presented as fold change (in relative light units (RLU)), and represents differences in RLU between stimulated and non-stimulated cells for each of the transfected variants. The cat whisker plots illustrate pooled results obtained from 4 independent transfection assays, where each transfection included 5 replicas, and denotes median luciferase induction, standard deviation, the upper and lower quartiles and the data range. Wilcoxon-Mann-Whitney tests were used for statistical comparisons, and two-tailed P values are indicated.</p

Constructing custom-made radiotranscriptomic signatures of vascular inflammation from routine CT angiograms: a prospective outcomes validation study in COVID-19.

BackgroundDirect evaluation of vascular inflammation in patients with COVID-19 would facilitate more efficient trials of new treatments and identify patients at risk of long-term complications who might respond to treatment. We aimed to develop a novel artificial intelligence (AI)-assisted image analysis platform that quantifies cytokine-driven vascular inflammation from routine CT angiograms, and sought to validate its prognostic value in COVID-19.MethodsFor this prospective outcomes validation study, we developed a radiotranscriptomic platform that uses RNA sequencing data from human internal mammary artery biopsies to develop novel radiomic signatures of vascular inflammation from CT angiography images. We then used this platform to train a radiotranscriptomic signature (C19-RS), derived from the perivascular space around the aorta and the internal mammary artery, to best describe cytokine-driven vascular inflammation. The prognostic value of C19-RS was validated externally in 435 patients (331 from study arm 3 and 104 from study arm 4) admitted to hospital with or without COVID-19, undergoing clinically indicated pulmonary CT angiography, in three UK National Health Service (NHS) trusts (Oxford, Leicester, and Bath). We evaluated the diagnostic and prognostic value of C19-RS for death in hospital due to COVID-19, did sensitivity analyses based on dexamethasone treatment, and investigated the correlation of C19-RS with systemic transcriptomic changes.FindingsPatients with COVID-19 had higher C19-RS than those without (adjusted odds ratio [OR] 2·97 [95% CI 1·43-6·27], p=0·0038), and those infected with the B.1.1.7 (alpha) SARS-CoV-2 variant had higher C19-RS values than those infected with the wild-type SARS-CoV-2 variant (adjusted OR 1·89 [95% CI 1·17-3·20] per SD, p=0·012). C19-RS had prognostic value for in-hospital mortality in COVID-19 in two testing cohorts (high [≥6·99] vs low [InterpretationRadiotranscriptomic analysis of CT angiography scans introduces a potentially powerful new platform for the development of non-invasive imaging biomarkers. Application of this platform in routine CT pulmonary angiography scans done in patients with COVID-19 produced the radiotranscriptomic signature C19-RS, a marker of cytokine-driven inflammation driving systemic activation of coagulation and responsible for adverse clinical outcomes, which predicts in-hospital mortality and might allow targeted therapy.FundingEngineering and Physical Sciences Research Council, British Heart Foundation, Oxford BHF Centre of Research Excellence, Innovate UK, NIHR Oxford Biomedical Research Centre, Wellcome Trust, Onassis Foundation