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.

Keywords: Genes with housekeeping functions; Kidney transplantation; RNA-sequencing.

Fig. 1

Flow diagram of the steps used to identify and validate HKG genes in this study

Fig. 2

Box plots showing the median, first quartile, third quartile, and range of CV (coefficient of variance) for all 952 HKG defined by nine different normalization algorithms (a) and for the subset of 42 HKG common to all nine normalization methods (b) . a The median values (range) of CV in 952 HKGs defined by RPKM and TC are 0.67 (0.65–0.69) and 0.44 (0.41–0.45), respectively; whereas the mean values of CV defined by UQ, Median, Quantile, TMM, DESeq, TPM and Library size are 0.31 (0.28–0.33), 0.29 (0.27–0.31), 0.29 (0.26–0.31), 0.31 (0.29–0.33), 0.30 (0.27–0.32), 0.29 (0.27–0.31), 0.29 (0.26–0.31), respectively. b The median values (range) of CV in 42 common HKGs defined by RPKM and TC are 0.67 (0.65–0.69) and 0.43 (0.42–0.45), respectively; whereas the mean values of CV defined by UQ, Median, Quantile, TMM, DESeq, TPM and Library size are 0.28 (0.26–0.31), 0.25 (0.23–0.28), 0.25 (0.22–0.29), 0.26 (0.24–0.30), 0.25 (0.22–0.29), 0.25 (0.23–0.28), 0.25 (0.22–0.28), respectively. TC: total counts; UQ: upper quartile; TMM: trimmed mean of M-values; TPM: transcripts per kilobase million; RPKM: reads per kilobase per million mapped reads; e (see Materials and Methods section for details)

Fig. 3

Canonical pathways identified by IPA core analysis as over-represented amongst 42 HKG common to 9 different data normalization methods. Pathways meeting statistical confidence thresholds preset in IPA are identified on the Y-axis (−log₁₀ p = 1.3, right-tailed Fisher’s exact test). The lower X-axis and the line diagram display the proportion of total genes in the specified pathway that meet the cutoff criteria for identification

Fig. 4

Top 20 physiologic functions associated with 42 HKG common to all biopsies and normalization methods. Physiological functions meeting statistical confidence thresholds (−log₁₀ p = 1.3, right-tailed Fisher’s exact test)