Minimal Change Disease: a proteomics approach
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ABSTRACT:
INSTRUMENT(S): SYNAPT G2-Si
ORGANISM(S): Homo Sapiens (ncbitaxon:9606)
SUBMITTER: 
Iuga Cristina-Adela
PROVIDER: MSV000094293 | MassIVE | Tue Mar 12 01:54:00 GMT 2024
REPOSITORIES: MassIVE
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Project description:Introduction: Minimal change disease (MCD) is a major cause of nephrotic syndrome. With a substantial number of patients requiring long-term immunosuppression leading to significant morbidity, the study aim was to determine MCD glomerular transcriptome to serve as a basis for biomarker discovery and novel drug target identification. Animal work showed podocyte injury induced by IL-7/IL-7R signaling (Zhai S, BBRC, 2018). Methods: Renal biopsies from adult patients representing the following groups were selected from the Norwegian Kidney Biopsy Registry: MCD (n=14), as well as normal tissue (n=8) and primary membranous nephropathy (MN; n=12) as the two reference groups. RNA for 75 base-pair paired-end RNASeq was obtained by dissecting glomeruli via laser capture microdissection (LCM) from FFPE cross-sections. Systematic delineation of condition-specific alteration in transcriptional landscapes was achieved by combining pathway-centered analyses with methodologies derived from network science and integrating multiple bioinformatics resources. Results: Compared to normal glomeruli, glomeuli from MCD displayed an inflammatory signature that appeared to be predominantly governed by the IL1 and IL7 systems. While enrichment of IL1 production and secretion was a shared feature of MCD and MN compared to normal tissue, responses involving IL7 pathway activation were unique to MCD. Indeed, IL7R expressed by glomeruli was the most up-regulated gene of to the interleukin-family in MCD vs normal controls. IL7 pathway activation was paralleled by significant enrichment in adaptive immune system processes and transcriptional regulation and depletion in pathways related to energy metabolism and transcription. Downregulation of these organ function-related themes again occurred predominately in MDC and were significantly less pronounced in MN. Conclusion: Our results demonstrate that archival FFPE-biopsies can be used to generate glomeruli-specific gene expression profiles suitable for systematic delineation of kidney-associated diseases. Here the latter provides a data-driven rationale to experimentally address these MCD-specific features as biomarkers and as novel drug targets. In this context inhibiting activation of the IL7 pathway may be particularly promising.
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Project description:Patients diagnosed with breast cancer were recruited to an important trial, the Tamoxifen Window Trial, in two series, series A during 1988-1989 and series B during 1989-1990. Some patients received tamoxifen for short windows. Clinical samples (biopsies) for this trial were collected before and after treatment. These biopsies were available as formalin-fixed paraffin-embedded (FFPE) tissue blocks. Responding patients and non-responding patients were determined by the change in the Ki67 scores. Responding patients were identified as those that had demonstrated a reduction in their Ki67 scores, and non-responding patients were identified as those in which Ki67 had not changed or had increased. The change in the percentage of Ki67 positive tumour cells was obtained by comparing the pre-treatment and post-treatment scores and calculating the difference in the scores. Laser capture microdissection (LCM) was utilised to microdissect the epithelial and stromal cells from the post-treatment breast cancer FFPE tissue sections of responding and non-responding patients. Samples obtained following LCM underwent RNA extraction and were subsequently used for gene expression profiling.
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Project description:Formalin-fixed, paraffin-embedded (FFPE) tissue samples are an invaluable resource to study the underlying molecular mechanisms of the diseases and when coupled with laser capture microdissection (LCM, isolation of sub histological regions of the tissue sections is readily obtained for further analysis. LCM-based FFPE tissue proteomics is gaining clinicopathological significance particularly in biomarker discovery driven research, beyond its conventional morphology-based application in laboratory diagnosis. Processing of laser capture microdissected tissue sections can be challenging for quantitative proteomic analysis due to lower amount of protein retrieved and losses during the sample processing. A robust, streamlined and automated sample preparation workflow for efficient processing of large cohort of LCM samples which is a primary requisite for biomarker type of studies is needed. Here, we propose a new sample processing workflow for processing of FFPE samples and enable scalable, automated extraction of clean peptides from unprocessed or H&E-stained FFPE tissue sections for deep bottom-up protein profiling and quantification.
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