Partitioning the human transcriptome using HKera, a novel classifier of housekeeping and tissue-specific genes

Integrative analysis of transcriptomic and epigenomic data reveals distinct patterns for developmental and housekeeping gene regulation

BACKGROUND

Regulation of transcription is central to the emergence of new cell types during development, and it often involves activation of genes via proximal and distal regulatory regions. The activity of regulatory elements is determined by transcription factors (TFs) and epigenetic marks, but despite extensive mapping of such patterns, the extraction of regulatory principles remains challenging.

RESULTS

Here we study differentially and similarly expressed genes along with their associated epigenomic profiles, chromatin accessibility and DNA methylation, during lineage specification at gastrulation in mice. Comparison of the three lineages allows us to identify genomic and epigenomic features that distinguish the two classes of genes. We show that differentially expressed genes are primarily regulated by distal elements, while similarly expressed genes are controlled by proximal housekeeping regulatory programs. Differentially expressed genes are relatively isolated within topologically associated domains, while similarly expressed genes tend to be located in gene clusters. Transcription of differentially expressed genes is associated with differentially open chromatin at distal elements including enhancers, while that of similarly expressed genes is associated with ubiquitously accessible chromatin at promoters.

CONCLUSION

Based on these associations of (linearly) distal genes' transcription start sites (TSSs) and putative enhancers for developmental genes, our findings allow us to link putative enhancers to their target promoters and to infer lineage-specific repertoires of putative driver transcription factors, within which we define subgroups of pioneers and co-operators.

Systematic Identification and Validation of Housekeeping and Tissue-Specific Genes in Allotetraploid Chenopodium quinoa

Quinoa is a gluten-free food crop that contains all the essential amino acids and vitamins. The selection of proper housekeeping and tissue-specific genes is the crucial prerequisite for gene expression analysis using the common approach, real-time quantitative PCR (RT-qPCR). In this study, we identified 40 novel candidate housekeeping genes by the minimum transcript per million (TPM), coefficient of variation (CV) and maximum fold change (MFC) methods and 19 candidate tissue-specific genes by the co-expression network method based on an RNA-seq dataset that included 53 stem, leaf, flower and seed samples, as well as additional shoot and root samples under different stresses. The expression stability of 12 housekeeping and tissue-specific genes, as well as that of another two traditionally used housekeeping genes, was further evaluated using qPCR and ranked using NormFinder, BestKeeper and the comparative delta-Ct method. The results demonstrated that MIF, RGGA, VATE and UBA2B were ranked as the top four most stable candidate housekeeping genes. qPCR analysis also revealed three leaf-specific genes and five root-specific genes, but no stem-specific gene was identified. Gene Ontology (GO) enrichment analysis identified that housekeeping genes were mainly enriched in the small molecule metabolic process, organonitrogen compound metabolic process, NAD binding and ligase activity. In addition, tissue-specific genes are closely associated with the major functions of a specific tissue. Specifically, GO terms “photosynthesis” and “thylakoid” were most significantly overrepresented in candidate leaf-specific genes. The novel housekeeping and tissue-specific genes in our study will enable better normalization and quantification of transcript levels in quinoa.

Systematic identification of human housekeeping genes possibly useful as references in gene expression studies

The ideal reference, or control, gene for the study of gene expression in a given organism should be expressed at a medium-high level for easy detection, should be expressed at a constant/stable level throughout different cell types and within the same cell type undergoing different treatments, and should maintain these features through as many different tissues of the organism. From a biological point of view, these theoretical requirements of an ideal reference gene appear to be best suited to housekeeping (HK) genes. Recent advancements in the quality and completeness of human expression microarray data and in their statistical analysis may provide new clues toward the quantitative standardization of human gene expression studies in biology and medicine, both cross- and within-tissue. The systematic approach used by the present study is based on the Transcriptome Mapper tool and exploits the automated reassignment of probes to corresponding genes, intra- and inter-sample normalization, elaboration and representation of gene expression values in linear form within an indexed and searchable database with a graphical interface recording quantitative levels of expression, expression variability and cross-tissue width of expression for more than 31,000 transcripts. The present study conducted a meta-analysis of a pool of 646 expression profile data sets from 54 different human tissues and identified actin γ 1 as the HK gene that best fits the combination of all the traditional criteria to be used as a reference gene for general use; two ribosomal protein genes, RPS18 and RPS27, and one aquaporin gene, POM121 transmembrane nucleporin C, were also identified. The present study provided a list of tissue- and organ-specific genes that may be most suited for the following individual tissues/organs: Adipose tissue, bone marrow, brain, heart, kidney, liver, lung, ovary, skeletal muscle and testis; and also provides in these cases a representative, quantitative portrait of the relative, typical gene-expression profile in the form of searchable database tables.

Prediction of the gene expression in normal lung tissue by the gene expression in blood

BACKGROUND

Comparative analysis of gene expression in human tissues is important for understanding the molecular mechanisms underlying tissue-specific control of gene expression. It can also open an avenue for using gene expression in blood (which is the most easily accessible human tissue) to predict gene expression in other (less accessible) tissues, which would facilitate the development of novel gene expression based models for assessing disease risk and progression. Until recently, direct comparative analysis across different tissues was not possible due to the scarcity of paired tissue samples from the same individuals.

METHODS

In this study we used paired whole blood/lung gene expression data from the Genotype-Tissue Expression (GTEx) project. We built a generalized linear regression model for each gene using gene expression in lung as the outcome and gene expression in blood, age and gender as predictors.

RESULTS

For ~18 % of the genes, gene expression in blood was a significant predictor of gene expression in lung. We found that the number of single nucleotide polymorphisms (SNPs) influencing expression of a given gene in either blood or lung, also known as the number of quantitative trait loci (eQTLs), was positively associated with efficacy of blood-based prediction of that gene's expression in lung. This association was strongest for shared eQTLs: those influencing gene expression in both blood and lung.

CONCLUSIONS

In conclusion, for a considerable number of human genes, their expression levels in lung can be predicted using observable gene expression in blood. An abundance of shared eQTLs may explain the strong blood/lung correlations in the gene expression.