The MLL3/4 complexes and MiDAC act antagonistically as genome-wide regulators of H4K20ac to control a specific gene expression program

The mitotic deacetylase complex MiDAC has recently been shown to play a vital physiological role in embryonic development and neurite outgrowth. However, how MiDAC functionally intersects with other chromatin-modifying regulators is poorly understood. Here, we describe a physical interaction between the histone H3K27 demethylase UTX, a complex-specific subunit of the enhancer-associated MLL3/4 complexes, and MiDAC. We demonstrate that UTX bridges the association of the MLL3/4 complexes and MiDAC by interacting with ELMSAN1, a scaffolding subunit of MiDAC. Our data shows that MiDAC constitutes a negative genome-wide regulator of H4K20ac, an activity which is counteracted by the MLL3/4 complexes. MiDAC and the MLL3/4 complexes co-localize at many genomic regions, that are enriched for H4K20ac and the enhancer marks H3K4me1, H3K4me2 and H3K27ac. We find that MiDAC antagonizes the recruitment of the MLL3/4 complexes to negatively regulate H4K20ac, H3K4me2 and H3K27ac resulting in transcriptional attenuation of associated genes. In summary, our findings provide a paradigm how the opposing roles of chromatin-modifying components, such as MiDAC and the MLL3/4 complexes, balance the transcriptional output of specific gene expression programs.

m6A Regulators in Human Adipose Tissue - Depot-Specificity and Correlation With Obesity

BackgroundN6-methyladenosine (m6A) is one of the most abundant post-transcriptional modifications on mRNA influencing mRNA metabolism. There is emerging evidence for its implication in metabolic disease. No comprehensive analyses on gene expression of m6A regulators in human adipose tissue, especially in paired adipose tissue depots, and its correlation with clinical variables were reported so far. We hypothesized that inter-depot specific gene expression of m6A regulators may differentially correlate with clinical variables related to obesity and fat distribution.MethodsWe extracted intra-individually paired gene expression data (omental visceral adipose tissue (OVAT) N=48; subcutaneous adipose tissue (SAT) N=56) of m6A regulators from an existing microarray dataset. We also measured gene expression in another sample set of paired OVAT and SAT (N=46) using RT-qPCR. Finally, we extracted existing gene expression data from peripheral mononuclear blood cells (PBMCs) and single nucleotide polymorphisms (SNPs) in METTL3 and YTHDF3 from genome wide data from the Sorbs population (N=1049). The data were analysed for differential gene expression between OVAT and SAT; and for association with obesity and clinical variables. We further tested for association of SNP markers with gene expression and clinical traits.ResultsIn adipose tissue we observed that several m6A regulators (WTAP, VIRMA, YTHDC1 and ALKBH5) correlate with obesity and clinical variables. Moreover, we found adipose tissue depot specific gene expression for METTL3, WTAP, VIRMA, FTO and YTHDC1. In PBMCs, we identified ALKBH5 and YTHDF3 correlated with obesity. Genetic markers in METTL3 associate with BMI whilst SNPs in YTHDF3 are associated with its gene expression.ConclusionsOur data show that expression of m6A regulators correlates with obesity, is adipose tissue depot-specific and related to clinical traits. Genetic variation in m6A regulators adds an additional layer of variability to the functional consequences.

Neighbor-specific gene expression revealed from physically interacting cells during mouse embryonic development

ABSTRACTCells continuously communicate with the neighboring cells during development. Direct interaction of different cell types can induce molecular signals dictating lineage specification and cell fate decisions. The current single cell RNAseq (scRNAseq) technology cannot study cell contact dependent gene expression due to the loss of spatial information. To overcome this issue and determine cell contact specific gene expression during embryogenesis, we performed RNA sequencing of physically interacting cells (PICseq) and assessed alongside our single cell transcriptomes (scRNAseq) derived from developing mouse embryos between embryonic day (E) 7.5 and E9.5. Analysis of PICseq data identifies an interesting suite of gene expression signatures depending on neighboring cell types. For instance, neural progenitor (NP) cells expressed Nkx2-1 when interacting with definitive endoderm (DE) and DE cells expressed Gsc when interacting with NP. Based on the identified cell contact specific genes, we devised a means to predict the neighboring cell types from individual cell transcriptome. We further developed spatial-tSNE to show the pseudo-spatial distribution of cells in a 2-dimensional space. In sum, we suggest an approach to study contact specific gene regulation during embryogenesis.

A human fetal liver-derived infant MLL-AF4 acute lymphoblastic leukemia model reveals a distinct fetal gene expression program

Although 90% of children with acute lymphoblastic leukemia (ALL) are now cured, the prognosis for infant-ALL remains dismal. Infant-ALL is usually caused by a single genetic hit that arises in utero: an MLL/KMT2A gene rearrangement (MLL-r). This is sufficient to induce a uniquely aggressive and treatment-refractory leukemia compared to older children. The reasons for disparate outcomes in patients of different ages with identical driver mutations are unknown. Using the most common MLL-r in infant-ALL, MLL-AF4, as a disease model, we show that fetal-specific gene expression programs are maintained in MLL-AF4 infant-ALL but not in MLL-AF4 childhood-ALL. We use CRISPR-Cas9 gene editing of primary human fetal liver hematopoietic cells to produce a t(4;11)/MLL-AF4 translocation, which replicates the clinical features of infant-ALL and drives infant-ALL-specific and fetal-specific gene expression programs. These data support the hypothesis that fetal-specific gene expression programs cooperate with MLL-AF4 to initiate and maintain the distinct biology of infant-ALL.

Identification of Potential Genes in Pathogenesis and Diagnostic Value Analysis of Partial Androgen Insensitivity Syndrome Using Bioinformatics Analysis

BackgroundAndrogen insensitivity syndrome (AIS) is a rare X-linked genetic disease and one of the causes of 46,XY disorder of sexual development. The unstraightforward diagnosis of AIS and the gender assignment dilemma still make a plague for this disorder due to the overlapping clinical phenotypes.MethodsPeripheral blood mononuclear cells (PBMCs) of partial AIS (PAIS) patients and healthy controls were separated, and RNA-seq was performed to investigate transcriptome variance. Then, tissue-specific gene expression, functional enrichment, and protein–protein interaction (PPI) network analyses were performed; and the key modules were identified. Finally, the RNA expression of differentially expressed genes (DEGs) of interest was validated by quantitative real-time PCR (qRT-PCR).ResultsIn our dataset, a total of 725 DEGs were captured, with functionally enriched reproduction and immune-related pathways and Gene Ontology (GO) functions. The most highly specific systems centered on hematologic/immune and reproductive/endocrine systems. We finally filtered out CCR1, PPBP, PF4, CLU, KMT2D, GP6, and SPARC by the key gene clusters of the PPI network and manual screening of tissue-specific gene expression. These genes provide novel insight into the pathogenesis of AIS in the immune system or metabolism and bring forward possible molecular markers for clinical screening. The qRT-PCR results showed a consistent trend in the expression levels of related genes between PAIS patients and healthy controls.ConclusionThe present study sheds light on the molecular mechanisms underlying the pathogenesis and progression of AIS, providing potential targets for diagnosis and future investigation.