Defining housekeeping genes suitable for RNA-seq analysis of the human allograft kidney biopsy tissue

Abstract

Background RNA-seq is poised to play a major role in the management of kidney transplant patients. Rigorous definition of housekeeping genes (HKG) is essential for further progress in this field. Using single genes or a limited set HKG is inherently problematic since their expression might be altered by specific diseases in the patients being studied. Methods To generate a HKG set specific for kidney transplantation, we performed RNA-sequencing from renal allograft biopsies collected in a variety of clinical settings. Various normalization methods were applied to identify transcripts that had a coefficient of variation of expression that was below the 2nd percentile across all samples, and the corresponding genes were designated as housekeeping genes. Comparison with transcriptomic data from the Gene Expression Omnibus (GEO) database, pathway analysis and molecular biological functions were utilized to validate the housekeeping genes set. Results We have developed a bioinformatics solution to this problem by using nine different normalization methods to derive large HKG gene sets from a RNA-seq data set of 47,611 transcripts derived from 30 biopsies. These biopsies were collected in a variety of clinical settings, including normal function, acute rejection, interstitial nephritis, interstitial fibrosis/tubular atrophy and polyomavirus nephropathy. Transcripts with coefficient of variation below the 2nd percentile were designated as HKG, and validated by showing their virtual absence in diseased allograft derived transcriptomic data sets available in the GEO. Pathway analysis indicated a role for these genes in maintenance of cell morphology, pyrimidine metabolism, and intracellular protein signaling. Conclusions Utilization of these objectively defined HKG data sets will guard against errors resulting from focusing on individual genes like 18S RNA, actin & tubulin, which do not maintain constant expression across the known spectrum of renal allograft pathology.

Catalogue of stage-specific transcripts in Ixodes ricinus and their potential functions during the tick life-cycle

“…Currently, this is the most recognized methodology to provide reliable gene expression comparisons [105] . It is also a method of choice in cross-sample normalization of read count data or for the evaluation of normalization techniques in studies dealing with transcriptomic NGS data [50,106,107]. All genes tested in our study showed a high degree of stability among tick life stages as indicated by the correlation coefficient (r).…”

“…Our transcriptome assembly was however submitted to a series of quality testing, normalization, and functional annotations. The bioinformatic pipeline that was eventually applied to our sequencing data was used upon thorough consultation of existing studies dealing with stage-specific transcription and with general bioinformatic guidelines [48,50,84,107] . Due to a unique design of our study, we organized our work by a combination of different approaches based on assumptions derived from the type and nature of our data and, at the same time, from biological questions postulated in our project.…”

Technical considerations when designing a gene expression panel for renal transplant diagnosis

“…We observed that the half of the HK genes we tested did not correlate well between FFPE and RNAlater preserved samples (Supplemental Table 3). Others have noted, using RNAseq, that the expression of some HK genes can vary depending on the renal pathology present in the biopsy 26 . HK genes for diagnostic use will need to be www.nature.com/scientificreports/ optimised according to their susceptibility to variation related not only of diagnosis but also to tissue sampling and preservation.…”

Validation of the Nanostring of the Banff Human Organ Transplant Gene Panel in Archival Data from Human Kidney Transplants and its Modelling Complexity

“…Data were renormalized using the housekeeping probes with negligible effect. Coe cients of variation were calculated and the lowest % CV including housekeeping genes were deleted, leaving 667 genes and 764 samples [26]. Data were then log 2 transformed.…”