The molecular basis, genetic control and pleiotropic effects of local gene co-expression

Ribeiro, Diogo M; Rubinacci, Simone; Ramisch, Anna; Hofmeister, Robin J.; Dermitzakis, Emmanouil T.; Delaneau, Olivier

doi:10.1038/s41467-021-25129-x

Cited by 24 publications

(41 citation statements)

References 67 publications

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“…To identify locally co-expressed gene pairs (COPs) using single-cell data, we adapted a method developed previously 10 to handle gene expression measurements across thousands of single cells (Fig. 1 ).…”

Section: Resultsmentioning

confidence: 99%

“…To achieve a tight control and robust expression level, genes are usually regulated by multiple enhancers, eventually with redundant action 7 , as well as multiple target genes 8 , 9 . Indeed, neighbouring genes frequently exhibit similar behaviour in terms of expression level 10 – 12 . This local gene co-expression is more pronounced in the immediate vicinity of a gene (e.g., <100 kb) but can occur at longer distances and regardless of the transcriptional orientation or shared functionality 13 , 14 .…”

Section: Introductionmentioning

confidence: 99%

“…This local gene co-expression is more pronounced in the immediate vicinity of a gene (e.g., <100 kb) but can occur at longer distances and regardless of the transcriptional orientation or shared functionality 13 , 14 . In particular, we have previously shown that as many as 59% genes are co-expressed with a nearby gene (within 1 Mb) across 49 GTEx tissues 10 . However, these observations came from studies measuring bulk gene expression in tissues, which entails several limitations.…”

Section: Introductionmentioning

confidence: 99%

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Shared regulation and functional relevance of local gene co-expression revealed by single cell analysis

2022

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Most human genes are co-expressed with a nearby gene. Previous studies have revealed this local gene co-expression to be widespread across chromosomes and across dozens of tissues. Yet, so far these studies used bulk RNA-seq, averaging gene expression measurements across millions of cells, thus being unclear if this co-expression stems from transcription events in single cells. Here, we leverage single cell datasets in >85 individuals to identify gene co-expression across cells, unbiased by cell-type heterogeneity and benefiting from the co-occurrence of transcription events in single cells. We discover >3800 co-expressed gene pairs in two human cell types, induced pluripotent stem cells (iPSCs) and lymphoblastoid cell lines (LCLs) and (i) compare single cell to bulk RNA-seq in identifying local gene co-expression, (ii) show that many co-expressed genes – but not the majority – are composed of functionally related genes and (iii) using proteomics data, provide evidence that their co-expression is maintained up to the protein level. Finally, using single cell RNA-sequencing (scRNA-seq) and single cell ATAC-sequencing (scATAC-seq) data for the same single cells, we identify gene-enhancer associations and reveal that >95% of co-expressed gene pairs share regulatory elements. These results elucidate the potential reasons for co-expression in single cell gene regulatory networks and warrant a deeper study of shared regulatory elements, in view of explaining disease comorbidity due to affecting several genes. Our in-depth view of local gene co-expression and regulatory element co-activity advances our understanding of the shared regulatory architecture between genes.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Shared regulation and functional relevance of local gene co-expression revealed by single cell analysis

2022

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“…There is a second reason. Over a few millions of years, cis -regulatory sequences in noncoding regions (i.e., introns, promoters, enhancers, usually within 10 to 200 kb of the original regulated gene) might control expression of some, or many, parologous genes located nearby on the same chromosomal segment [ 42 , 43 ]. In contrast, single gene-duplication events, taking place over much longer periods of evolutionary time, more likely have established their own distinct cis -regulatory noncoding regions—thereby not needing to remain as a cluster at one chromosomal segment; examples would include the type III, IV, V and VI IntFil genes.…”

Section: Main Textmentioning

confidence: 99%

Update of the keratin gene family: evolution, tissue-specific expression patterns, and relevance to clinical disorders

Thompson²,

Fisk

et al. 2022

Hum Genomics

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Intermediate filament (IntFil) genes arose during early metazoan evolution, to provide mechanical support for plasma membranes contacting/interacting with other cells and the extracellular matrix. Keratin genes comprise the largest subset of IntFil genes. Whereas the first keratin gene appeared in sponge, and three genes in arthropods, more rapid increases in keratin genes occurred in lungfish and amphibian genomes, concomitant with land animal-sea animal divergence (~ 440 to 410 million years ago). Human, mouse and zebrafish genomes contain 18, 17 and 24 non-keratin IntFil genes, respectively. Human has 27 of 28 type I “acidic” keratin genes clustered at chromosome (Chr) 17q21.2, and all 26 type II “basic” keratin genes clustered at Chr 12q13.13. Mouse has 27 of 28 type I keratin genes clustered on Chr 11, and all 26 type II clustered on Chr 15. Zebrafish has 18 type I keratin genes scattered on five chromosomes, and 3 type II keratin genes on two chromosomes. Types I and II keratin clusters—reflecting evolutionary blooms of keratin genes along one chromosomal segment—are found in all land animal genomes examined, but not fishes; such rapid gene expansions likely reflect sudden requirements for many novel paralogous proteins having divergent functions to enhance species survival following sea-to-land transition. Using data from the Genotype-Tissue Expression (GTEx) project, tissue-specific keratin expression throughout the human body was reconstructed. Clustering of gene expression patterns revealed similarities in tissue-specific expression patterns for previously described “keratin pairs” (i.e., KRT1/KRT10, KRT8/KRT18, KRT5/KRT14, KRT6/KRT16 and KRT6/KRT17 proteins). The ClinVar database currently lists 26 human disease-causing variants within the various domains of keratin proteins.

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“…As Ribeiro et al [24] integrated ChIP-seq and Hi-C to interpret the molecular mechanisms driving gene co-expression, we further characterized the impact of CRDs on the complexity of gene regulation, and aimed to quantify how much of gene co-expression is driven by shared CRD regulation. We looked into the 29940, 46146 and 13737 cis co-expressed gene pairs we found (i.e.…”

Section: Regulatory Interaction Dynamics Across Immune Cell Typesmentioning

confidence: 99%

Genetic variation in correlated regulatory regions of immunity

Avalos

Rey

Ribeiro

et al. 2022

Preprint

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Studying the interplay between genetic variation, epigenetic changes and regulation of gene expression in immune cells is important to understand the modification of cellular states in various conditions, including immune diseases. Here, we built cis maps of regulatory regions with coordinated activity – Cis Regulatory Domains (CRDs) – in neutrophils, monocytes and T cells. For this, we leveraged (i) whole-genome sequencing (WGS), (ii) chromatin immunoprecipitation sequencing (ChIP-seq), (iii) DNA methylation (450k arrays), and (iv) transcriptional profiles (RNA-seq) from the BLUEPRINT consortium, for up to 200 individuals. Our study uncovers 9287, 7666 and 5480 histone CRDs (hCRDs) and 6053, 6112, 5701 methyl CRDs (mCRDs) in monocytes, neutrophils and T-cells, respectively. We discovered 15294 hCRD-gene and 6185 mCRD-gene associations (5% FDR). Only 33% of hCRD-gene associations and 37% of mCRD-gene associations were shared between cell-types, revealing the dynamic nature of regulatory interactions and how similarly located regulatory regions modulate the activity of different genes on different cell types. We mapped Quantitative Trait Loci associated with CRD activity (CRD-QTLs) and found that 89% and 70% of these hCRDs and mCRDs are under genetic control highlighting the importance of genetic variation to study the coordination of cellular regulatory programs. We found CRD-QTLs to be enriched in cell-type-specific transcription factor binding sites, such as SPI1, STAT3, RFX1, SOX4, ATF3 for neutrophils and monocytes and TCF4 and BCL11A for T-cells, in line with the Human protein Atlas. We integrated PCHi-C data, which showed that most significant associations discovered within gene-CRD associations and co-expressed genes associated with the same CRD, involving large genomic distances, tend to happen between genomic regions in close spatial proximity. Finally, we mapped trans regulatory associations between CRDs, which enabled the discovery of 207 trans-eQTLs across cell types. Overlapping our hits with trans eQTLs from eQTLGen Consortium meta-analysis in whole blood revealed 81 trans-eQTLs shared between the two studies. Overall, we show that mapping functional regulatory units using population genomics data allows discovering important mechanisms in the regulation of gene expression in immune cells and gain a greater understanding of cell-type specific regulatory mechanisms of immunity.

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The molecular basis, genetic control and pleiotropic effects of local gene co-expression

Cited by 24 publications

References 67 publications

Shared regulation and functional relevance of local gene co-expression revealed by single cell analysis

Shared regulation and functional relevance of local gene co-expression revealed by single cell analysis

Update of the keratin gene family: evolution, tissue-specific expression patterns, and relevance to clinical disorders

Genetic variation in correlated regulatory regions of immunity

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