MHC class I-related antigen-processing machinery component defects in feline mammary carcinoma.

Defects in HLA class I antigen-processing machinery (APM) component expression and/or function are frequent in human tumors. These defects may provide tumor cells with a mechanism to escape from recognition and destruction by HLA class I antigen-restricted, tumor antigen-specific cytotoxic T cells. However, expression and functional properties of MHC class I antigens and APM components in malignant cells in other animal species have been investigated to a limited extent. However, this information can contribute to our understanding of the mechanisms underlying the association of MHC class I antigen and APM component defects with malignant transformation of cells and to identify animal models to validate targeted therapies to correct these defects. To overcome this limitation in the present study, we have investigated the expression of the catalytic subunits of proteasome (Y, X, and Z) and of immunoproteasome (LMP2, LMP7, and LMP10) as well as of MHC class I heavy chain (HC) in 25 primary feline mammary carcinomas (FMCs) and in 23 matched healthy mammary tissues. We found a reduced expression of MHC class I HC and of LMP2 and LMP7 in tumors compared with normal tissues. Concordantly, proteasomal cleavage specificities in extracts from FMCs were different from those in healthy tissues. In addition, correlation analysis showed that LMP2 and LMP7 were concordantly expressed in FMCs, and their expression was significantly correlated with that of MHC class I HC. The abnormalities we have found in the APM in FMCs may cause a defective processing of some tumor antigens

The Relative Composition of the Inflammatory Infiltrate as an Additional Tool for Synovial Tissue Classification

10.1371/journal.pone.0072494PLoS ONE88-POLN

Evaluation of glyceraldehyde-3-phosphate, prolylpeptidyl isomerase A, and a set of stably expressed genes as reference mRNAs in urate crystal inflammation

ABSTRACT

Optimizing a Massive Parallel Sequencing Workflow for Quantitative miRNA Expression Analysis

BACKGROUND: Massive Parallel Sequencing methods (MPS) can extend and improve the knowledge obtained by conventional microarray technology, both for mRNAs and short non-coding RNAs, e.g. miRNAs. The processing methods used to extract and interpret the information are an important aspect of dealing with the vast amounts of data generated from short read sequencing. Although the number of computational tools for MPS data analysis is constantly growing, their strengths and weaknesses as part of a complex analytical pipe-line have not yet been well investigated. PRIMARY FINDINGS: A benchmark MPS miRNA dataset, resembling a situation in which miRNAs are spiked in biological replication experiments was assembled by merging a publicly available MPS spike-in miRNAs data set with MPS data derived from healthy donor peripheral blood mononuclear cells. Using this data set we observed that short reads counts estimation is strongly under estimated in case of duplicates miRNAs, if whole genome is used as reference. Furthermore, the sensitivity of miRNAs detection is strongly dependent by the primary tool used in the analysis. Within the six aligners tested, specifically devoted to miRNA detection, SHRiMP and MicroRazerS show the highest sensitivity. Differential expression estimation is quite efficient. Within the five tools investigated, two of them (DESseq, baySeq) show a very good specificity and sensitivity in the detection of differential expression. CONCLUSIONS: The results provided by our analysis allow the definition of a clear and simple analytical optimized workflow for miRNAs digital quantitative analysis

Convergent Evidence from Mouse and Human Studies Suggests the Involvement of Zinc Finger Protein 326 Gene in Antidepressant Treatment Response

OBJECTIVES: The forced swim test (FST) is a commonly used model to predict antidepressant efficacy. Uncovering the genetic basis of the model may unravel the mechanism of antidepressant treatment. METHODS: FVB/NJ (FVB) and C57BL/6J (B6) were first identified as the response and non-response strains to fluoxetine (a serotonin-specific reuptake inhibitor antidepressant) treatment in the mouse FST. Simple-interval (SIM) and composite-interval (CIM) mappings were applied to map the quantitative trait loci (QTLs) of the anti-immobility effect of fluoxetine in FST (FST(FLX)) in 865 male B6×FVB-F2 mice. The brain mRNA expressions of the gene with the maximum QTL-linkage signal for FST(FLX) after the FST were compared between B6 and FVB mice and also compared between fluoxetine and saline treatment. The association of the variants in the human homologue of the mouse FST(FLX)-QTL gene with major depressive disorder (MDD) and antidepressant response were investigated in 1080 human subjects (MDD/control = 582/498). RESULTS: One linkage signal for FST(FLX)-QTL was detected at an intronic SNP (rs6215396) of the mouse Zfp326 gene (maximal CIM-LOD = 9.36). The Zfp326 mRNA expression in the FVB thalamus was significantly down-regulated by fluoxetine in the FST, and the higher FVB-to-B6 Zfp326 mRNA expressions in the frontal cortex, striatum and hypothalamus diminished after fluoxetine treatment. Two coding-synonymous SNPs (rs2816881 and rs10922744) in the human homologue of Zfp326, ZNF326, were significantly associated with the 8-week antidepressant treatment response in the MDD patients (Bonferroni-corrected p = 0.004-0.028). CONCLUSIONS: The findings suggest the involvement of the Zfp326 and ZNF326 genes in antidepressant treatment response

Alternative splicing and transcriptome profiling of experimental autoimmune encephalomyelitis using genome-wide exon arrays

BACKGROUND: Multiple Sclerosis (MS) is a chronic inflammatory disease causing demyelination and nerve loss in the central nervous system. Experimental autoimmune encephalomyelitis (EAE) is an animal model of MS that is widely used to investigate complex pathogenic mechanisms. Transcriptional control through isoform selection and mRNA levels determines pathway activation and ultimately susceptibility to disease. METHODOLOGY/PRINCIPAL FINDINGS: We have studied the role of alternative splicing and differential expression in lymph node cells from EAE-susceptible Dark Agouti (DA) and EAE-resistant Piebald Virol Glaxo.AV1 (PVG) inbred rat strains using Affymetrix Gene Chip Rat Exon 1.0 ST Arrays. Comparing the two strains, we identified 11 differentially spliced and 206 differentially expressed genes at day 7 post-immunization, as well as 9 differentially spliced and 144 differentially expressed genes upon autoantigen re-stimulation. Functional clustering and pathway analysis implicate genes for glycosylation, lymphocyte activation, potassium channel activity and cellular differentiation in EAE susceptibility. CONCLUSIONS/SIGNIFICANCE: Our results demonstrate that alternative splicing occurs during complex disease and may govern EAE susceptibility. Additionally, transcriptome analysis not only identified previously defined EAE pathways regulating the immune system, but also novel mechanisms. Furthermore, several identified genes overlap known quantitative trait loci, providing novel causative candidate targets governing EAE

HUM calculator and HUM package for R: Easy-to-use software tools for multicategory receiver operating characteristic analysis

10.1093/bioinformatics/btu086Bioinformatics30111635-1636BOIN