What are housekeeping genes?

Abstract

The concept of “housekeeping gene” has been used for four decades but remains loosely defined. Housekeeping genes are commonly described as “essential for cellular existence regardless of their specific function in the tissue or organism”, and “stably expressed irrespective of tissue type, developmental stage, cell cycle state, or external signal”. However, experimental support for the tenet that gene essentiality is linked to stable expression across cell types, conditions, and organisms has been limited. Here we use genome-scale functional genomic screens together with bulk and single-cell sequencing technologies to test this link and optimize a quantitative and experimentally validated definition of housekeeping gene. Using the optimized definition, we identify, characterize, and provide as resources, housekeeping gene lists extracted from several human datasets, and 10 other animal species that include primates, chicken, and C. elegans. We find that stably expressed genes are not necessarily essential, and that the individual genes that are essential and stably expressed can considerably differ across organisms; yet the pathways enriched among these genes are conserved. Further, the level of conservation of housekeeping genes across the analyzed organisms captures their taxonomic groups, showing evolutionary relevance for our definition. Therefore, we present a quantitative and experimentally supported definition of housekeeping genes that can contribute to better understanding of their unique biological and evolutionary characteristics.

Whole-body gene expression atlas of an adult metazoan

“…Nevertheless, these 53 genes were enriched in “basal cellular” functions including ribosomal activity, protein translation, and mitochondrial respiration (Fig. 2B), and in essential genes (lethal) as defined in partial 13 and full-genome RNAi screens 14,15 (Fig. 2C), two features in line with these genes being bona fide housekeeping genes.…”

“…As scRNA-Seq is not yet available for C. elegans subject to perturbations (genetic, chemical, or other), we used time, in the form of a different life stage, as the alternative condition. Specifically, we looked at the overlap between genes consistently expressed (Gc <0.3) in young adults and a list of putative housekeeping genes reported for the C. elegans L2 larvae 14 . We found that all genes with high expression equality (Gs ≤0.2) and the majority of genes (319/390) with good expression equality (Gs <0.3) in the young adults were also consistently expressed across cell types in the L2 larvae (Table S2).…”

“…An alternative metric of "housekeeping-ness" that would allow for this possibility, would be consistency of expression across cell types independent of abundance. To identify genes consistently, but not necessarily abundantly, expressed, we applied a metric of inequality called the Gini coefficient or Gc 14 . Genes with lower Gc's are expressed more equally across cell types, and genes with higher Gc's are expressed in a more cell-type-specific manner.…”

“…We found that all genes with high expression equality (Gs ≤0.2) and the majority of genes (319/390) with good expression equality (Gs <0.3) in the young adults were also consistently expressed across cell types in the L2 larvae (Table S2). Furthermore, the majority of these genes (252/319) were experimentally shown to be essential (lethal) in partial 14 and full-genome RNAi screens 15,16 (Fig. 2G).…”

Functional orderly topography of brain networks associated with gene expression heterogeneity

“…Here, we measured gene expression heterogeneity with two metrics, i.e., the proportion of housekeeping genes occupying all expressed genes and topology indices of the gene expression networks. Housekeeping genes (HKGs) are usually expressed at relatively constant rates for the basic molecular and cellular function of neurons 22 , 23 ; thus, the lower proportion of HKGs expressed in brain structures may suggest their higher functional diversity. Topological indices of the gene expression networks in brain structures, including mean path length, clustering coefficient and eigen entropy, offer possibilities to understand how genes work together to perform diverse functions 24 .…”