Alice Gerrits

Genetical genomics: Spotlight on QTL hotspots

Genetical genomics aims at identifying quantitative trait loci (QTLs) for molecular traits such as gene expression or protein levels (eQTL and pQTL, respectively). One of the central concepts in genetical genomics is the existence of hotspots [1], where a single polymorphism leads to widespread downstream changes in the expression of distant genes, which are all mapping to the same genomic locus. Several groups have hypothesized that many genetic polymorphisms—e.g., in major regulators or transcription factors—would lead to large and consistent biological effects that would be visible as eQTL hotspots.

Expression quantitative trait loci are highly sensitive to cellular differentiation state

Genetical genomics is a strategy for mapping gene expression variation to expression quantitative trait loci (eQTLs). We performed a genetical genomics experiment in four functionally distinct but developmentally closely related hematopoietic cell populations isolated from the BXD panel of recombinant inbred mouse strains. This analysis allowed us to analyze eQTL robustness/sensitivity across different cellular differentiation states. Although we identified a large number (365) of “static” eQTLs that were consistently active in all four cell types, we found a much larger number (1,283) of “dynamic” eQTLs showing cell-type–dependence. Of these, 140, 45, 531, and 295 were preferentially active in stem, progenitor, erythroid, and myeloid cells, respectively. A detailed investigation of those dynamic eQTLs showed that in many cases the eQTL specificity was associated with expression changes in the target gene. We found no evidence for target genes that were regulated by distinct eQTLs in different cell types, suggesting that large-scale changes within functional regulatory networks are uncommon. Our results demonstrate that heritable differences in gene expression are highly sensitive to the developmental stage of the cell population under study. Therefore, future genetical genomics studies should aim at studying multiple well-defined and highly purified cell types in order to construct as comprehensive a picture of the changing functional regulatory relationships as possible.

Genetic screen identifies microRNA cluster 99b/let-7e/125a as a regulator of primitive hematopoietic cells

Hematopoietic stem/progenitor cell (HSPC) traits differ between genetically distinct mouse strains. For example, DBA/2 mice have a higher HSPC frequency compared with C57BL/6 mice. We performed a genetic screen for micro-RNAs that are differentially expressed between LSK, LS(-)K(+), erythroid and myeloid cells isolated from C57BL/6 and DBA/2 mice. This analysis identified 131 micro-RNAs that were differentially expressed between cell types and 15 that were differentially expressed between mouse strains. Of special interest was an evolutionary conserved miR cluster located on chromosome 17 consisting of miR-99b, let-7e, and miR-125a. All cluster members were most highly expressed in LSKs and down-regulated upon differentiation. In addition, these microRNAs were higher expressed in DBA/2 cells compared with C57BL/6 cells, and thus correlated with HSPC frequency. To functionally characterize these microRNAs, we overexpressed the entire miR-cluster 99b/let-7e/125a and miR-125a alone in BM cells from C57BL/6 mice. Overexpression of the miR-cluster or miR-125a dramatically increased day-35 CAFC activity and caused severe hematopoietic phenotypes upon transplantation. We showed that a single member of the miR-cluster, namely miR-125a, is responsible for the majority of the observed miR-cluster overexpression effects. Finally, we performed genome-wide gene expression arrays and identified candidate target genes through which miR-125a may modulate HSPC fate.

Epigenetic Control of Hematopoietic Stem Cell Aging The Case of Ezh2

Abstract:  Hematopoietic stem cells have potent, but not unlimited, selfrenewal potential. The mechanisms that restrict selfrenewal are likely to play a role during aging. Recent data suggest that the regulation of histone modifications by Polycomb group genes may be of crucial relevance to balance selfrenewal and aging. We provide evidence for the involvement of one of these Polycomb group genes, Ezh2, in aging of the hematopoietic stem cell system.

Modern genome-wide genetic approaches to reveal intrinsic properties of stem cells

Purpose of reviewThe clinical use of hematopoietic stem cells, which produce all mature blood cell lineages in the circulation, is continuously increasing. Identification of genes and gene networks specifying either stemness or commitment will not only be of major relevance for a fundamental understanding of developmental biology, but also for the emerging fields of tissue engineering and regenerative medicine. Our appreciation of the transcriptional machinery that distinguishes stem cells from their nonstem cell progeny is, however, rudimentary. State-of-the art genome-wide tools are now becoming available to elucidate intrinsic properties of stem cells. Here, we review recent progress that has been made in this field. Recent findingsApproaches to study stem cell-specific genes and gene networks include genetical genomics, mRNA and microRNA expression profiling of carefully selected cells, proteomics, chromatin studies using ‘CHIP-on-chip’ tools, genome-wide binding site analyses for transcription factors and chromatin-remodeling proteins, and tools to study the three-dimensional organization of gene loci. It is promising to see that the combined application of these tools has resulted in the identification of multiple novel genes that regulate stem cell self-renewal. SummaryExploitation of the available technology and integrating the data by translation into a dynamic model of networks, operating in all four dimensions, will be essential to fully comprehend the elusive concept of ‘stemness’. It is time to harvest.

Inferring combinatorial association logic networks in multimodal genome-wide screens

Motivation: We propose an efficient method to infer combinatorial association logic networks from multiple genome-wide measurements from the same sample. We demonstrate our method on a genetical genomics dataset, in which we search for Boolean combinations of multiple genetic loci that associate with transcript levels. Results: Our method provably finds the global solution and is very efficient with runtimes of up to four orders of magnitude faster than the exhaustive search. This enables permutation procedures for determining accurate false positive rates and allows selection of the most parsimonious model. When applied to transcript levels measured in myeloid cells from 24 genotyped recombinant inbred mouse strains, we discovered that nine gene clusters are putatively modulated by a logical combination of trait loci rather than a single locus. A literature survey supports and further elucidates one of these findings. Due to our approach, optimal solutions for multi-locus logic models and accurate estimates of the associated false discovery rates become feasible. Our algorithm, therefore, offers a valuable alternative to approaches employing complex, albeit suboptimal optimization strategies to identify complex models. Availability: The MATLAB code of the prototype implementation is available on: http://bioinformatics.tudelft.nl/ or http://bioinformatics.nki.nl/ Contact: m.j.t.reinders@tudelft.nl; l.wessels@nki.nl