Allergens and Wolbachia proteins in Tyrophagus putrescentiae
Ontology highlight
ABSTRACT:
INSTRUMENT(S): Orbitrap Fusion
ORGANISM(S): Tyrophagus Putrescentiae (ncbitaxon:59818)
SUBMITTER: 
Tomas Erban
PROVIDER: MSV000088016 | MassIVE | Tue Aug 24 06:09:00 BST 2021
REPOSITORIES: MassIVE
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All the honeys in our dataset are denoted as we originally denoted them after addition to our collection. The honey samples include authentic honeys from verified sources and honeys from markets in Czechia or foreign countries, some of which were declared as single-origin honeys.
Compressed (zipped) combined_txt folders of database searches in MaxQuant are included. In addition, used databases (*.fasta) are provided.
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Project description:The following strains of Paenibacillus larvae classified as ERIC I-IV genotypes (pl1-pl4) were used in the study: ERIC I - DSM-7030; ERIC II - DSM-25430; ERIC III - LMG-16252; and ERIC IV - DSM-3615.
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Project description:Background: The ability to predict the spatial frequency of relapses in multiple sclerosis (MS) would enable treating physicians to decide when to intervene more aggressively and to plan clinical trials more accurately. Methods: In the current study our objective was to determine if subsets of genes can predict the time to the next acute relapse in patients with MS. Data-mining and predictive modeling tools were utilized to analyze a gene-expression dataset of 94 non-treated patients; 62 patients with definite MS and 32 patients with clinically isolated syndrome (CIS). The dataset included the expression levels of 10,594 genes and annotated sequences corresponding to 22,215 gene-transcripts that appear in the microarray. Results: We designed a two stage predictor. The first stage predictor was based on the expression level of 10 genes, and predicted the time to next relapse with a resolution of 500 days (error rate 0.079, p< 0.001). If the predicted relapse was to occur in less than 500 days, a second stage predictor based on an additional different set of 9 genes was used, resulting in a prediction with a resolution of 50 days as to the timing of the next relapse. The error rate of this predictor was 2.3 fold lower than the error rate of random predictions (error rate = 0.35, p<0.001). The predictors were further evaluated and found effective not only in untreated patients but were also valid for MS patients which subsequently received immunomodulatory treatments after the initial testing (the error rate of the first level predictor was < 0.18 with p<0.001 for all the patient groups). Conclusions: We conclude that gene expression analysis is a valuable tool that can be used in clinical practice to predict future MS disease activity. Similar approach can be also useful for dealing with other autoimmune diseases that characterized by relapsing-remitting nature Keywords: Disease prediction Data-mining and predictive modeling tools were utilized to analyze a gene-expression dataset of 94 non-treated patients; 62 patients with definite MS and 32 patients with clinically isolated syndrome (CIS). The dataset included the expression levels of 10,594 genes and annotated sequences corresponding to 22,215 gene-transcripts that appear in the microarray. We designed a two stage predictor. The first stage predictor was based on the expression level of 10 genes, and predicted the time to next relapse with a resolution of 500 days. If the predicted relapse was to occur in less than 500 days, a second stage predictor based on an additional different set of 9 genes was used, resulting in a prediction with a resolution of 50 days as to the timing of the next relapse. The predictors were further evaluated and found effective not only in untreated patients but were also valid for MS patients which subsequently received immunomodulatory treatments after the initial testing.
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