Comparative proteomics of artificially infected Dermacentor reticulatus tick vector by human pathogenic bacterium Rickettsia slovaca
Ontology highlight
ABSTRACT:
INSTRUMENT(S): Q-Tof Premier
ORGANISM(S): Arachnida Dermacentor Reticulatus
TISSUE(S): Whole Body
DISEASE(S): Tick Borne
SUBMITTER: 
Maksym Danchenko
LAB HEAD: Eva Spitalska
PROVIDER: PXD011442 | Pride | 2019-08-14
REPOSITORIES: Pride
Dataset's files
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| 1482.mzid.gz | Mzid | |||
| 1482.pride.mztab.gz | Mztab | |||
| 1482raw.zip | Other | |||
| 1489.mzid.gz | Mzid | |||
| 1489.pride.mztab.gz | Mztab |
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Publications
Comparative proteomics of the vector Dermacentor reticulatus revealed differentially regulated proteins associated with pathogen transmission in response to laboratory infection with Rickettsia slovaca.
Flores-Ramirez Gabriela G Sallay Balázs B Danchenko Maksym M Lakhneko Olha O Špitalská Eva E Skultety Ludovit L
Parasites & vectors 20190624 1
<h4>Background</h4>Tick-borne rickettsial diseases are caused by pathogens acquired from hard ticks. In particular, Rickettsia slovaca, a zoonotic infectious bacterium causing tick-borne lymphadenopathy (TIBOLA), is transmitted by the vectors Dermacentor spp. that can be found all over Europe. Although recent studies point out the extreme complexity of bacteria-induced effects in these blood-feeding vectors, the knowledge of individual molecules involved in the preservation and transmission of t ...[more]
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Subsequent knockdown experiments utilising 196bp and 109bp dsRNAs, and a cocktail of short hairpin RNAs targeting Ubiquitin-63E demonstrated similar phenotypes for the dsRNAs but nil effect following shRNA treatment. Quantitative real time PCR analysis confirmed TC6372 and predicted off target differential expression. Our study demonstrates the effectiveness and specificity for utilising ~100bp dsRNA products for targeted tick gene knockdown with nil identified off targets. The microarray was conducted by NimbleGen Systems Inc following the method reported by Saldivar [Saldivar L et al., Insect Mol Biol 2008, 17(6):597-606]. Briefly, Custom high-density single channel oligonucleotide arrays were constructed by NimbleGen Systems Inc. using 13 601 of the 13 643 members of BmiGI Version 2 with 14 perfect match 50-mer probes per BmiGI target. No mismatched probes were included on the arrays, although probes with randomly generated sequences were included. These random sequence probes were designed to match the melting temperature (Tm) of the other probes on the array and to reflect the distribution of non-specific signal intensities for binding events to probes with approximately the same composition as the perfect match probes but with random sequences. The associated GEO dataset can be found online at http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GPL6373. Each microarray chip includes two in-slide replicates (spot replicates). In this experiment two biological replicates for each sample were hybridized to assess variability between individual samples. Each pooled sample consisted of 30 female adult ticks (N strain) as the source of RNA. Four technical replicates were processed, i.e. repeated measurements of the same pooled R. microplus mRNA, consisting of two chip replicates with two spot replicates. The chip replicates were hybridized on different dates to different chips, while spot replicates indicate the same probe was spotted on different locations on the chip. Four R. microplus samples were hybridized to separate microarrays at NimbleGen Systems Inc. (Madison, WI, USA). Data Processing: Statistical analysis Following array hybridization the raw intensity values were background corrected using convolution and normalized using quantile normalization to adjust for technical sources of variation. Final log2 expression intensities were generated using the Robust Multichip Average (RMA) algorithm [Irizarry et al.,Biostatistics 2003, 4:249-264]; [Groeneveld E, et al.,Proceedings 6th World Cong Genet Appl Livest Prod,: 1998; Armidale, NSW, Australia; 455-458] implemented in R. Differential expression was tested on the RMA normalized intensities using a mixed model of the form yijkr=?+BSrk+Gi+GTij+Eijkr where yijkr are the log2 RMA normalized signal intensities; i =gene, j=treatment, r=block, k=slide/array, the main fixed effect is BSrk (block by slide interaction), Gi is the main random effect of the gene; GTij is the random interaction term of the gene by treatment; E is just the error term; and normal assumptions for the random effects – iid are assumed. The model was fitted using VCE4.0 [Groeneveld E, et al., 1998]. Differentially expressed (DE) probes were considered as those which were three or more standard deviations away from the mean (p-value <0.001). The contrast of interest was the 600pb dsRNA microinjected ticks (R. microplus Ubiquitin-63E homologue as per Kurscheid et al 2009, BMC Molecular Biology 10:26) compared to normal untreated controls in order to examine the knockdown effect associated with phenotype. Kurscheid et al 2009 reported a strong knockdown phenotype following dsRNA knockdown and the subsequent differentially expressed ESTs were examined in this array experiment. The putative identities of the lists of up and down regulated tick sequences (DE probes) were then subsequently analysed using bioinformatic tools to predict function (described below). Bioinfomatics: Nimblegen tick microarray differentially expressed probes with fold changes >1.5 and p-value <0.001 were screened against the following databases on the CCG [Centre for Comparative Genomics: http://ccg.murdoch.edu.au], Grendel HPC system [Hunter A, et al.,Australasian Workshop on Grid Computing and e-Research 2005:2-3]; NCBI protein (nr and patent) [National Centre for Biotechnology Information: http://www.ncbi.nlm.nih.gov], String [Mering von C., et al.,Nucleic Acids Research 2006:00: D01-D05], COG [Tatusov R, et al., BMC Bioinformatics 2003, 4], tigr_bmigi.062608 [Wang et al.,BMC Genomics 8:368], NCBI Conserved Domain database [Goonesekere N, et al., Proteins 2008, 71(2):910-919], and KEGG (Kanehisa & Goto 2000 Nucleic Acids Res. 28, 27-30; Kanehisa et al 2006 Nucleic Acids Research 34, D354-D357; Kanehisa et al 2010 Nucleic Acids Research 38, D355-D360). Further analysis of differentially expressed transcripts identified in the microarray experiments was undertaken using the BLAST program suite (Altschul et al., 1997) for similarity searches in the UniProt database (TheUniProtConsortium, 2010 Nucleic Acids Res 38, D142-D148.) and in the protein sequence database of Ixodes scapularis tick (available from http://www.vectorbase.org/Ixodes_scapularis/Info/Index; last accessed 2010-11-21). The results of the blastx searches against the I. scapularis protein sequences where used to map the R. microplus ESTs to unique identifiers in the DAVID Bioinformatics Resource and perform a subsequent functional annotation to Gene Ontology terms in the categories “Biological Process (BP)”, “Cellular Component (CC)”, and “Molecular Functions (MF)” (Dennis et al 2003 Genome Biology 4: P3; Huang et al 2009 Nature Protoc. 4: 44-57.).
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