Transcriptome alterations in myotonic dystrophy skeletal muscle and heart.

Myotonic dystrophy (dystrophia myotonica, DM) is a multi-systemic disease caused by expanded CTG or CCTG microsatellite repeats. Characterized by symptoms in muscle, heart and central nervous system, among others, it is one of the most variable diseases known. A major pathogenic event in DM is the sequestration of muscleblind-like proteins by CUG or CCUG repeat-containing RNAs transcribed from expanded repeats, and differences in the extent of MBNL sequestration dependent on repeat length and expression level may account for some portion of the variability. However, many other cellular pathways are reported to be perturbed in DM, and the severity of specific disease symptoms varies among individuals. To help understand this variability and facilitate research into DM, we generated 120 RNASeq transcriptomes from skeletal and heart muscle derived from healthy and DM1 biopsies and autopsies. A limited number of DM2 and Duchenne muscular dystrophy samples were also sequenced. We analyzed splicing and gene expression, identified tissue-specific changes in RNA processing and uncovered transcriptome changes strongly correlating with muscle strength. We created a web resource at http://DMseq.org that hosts raw and processed transcriptome data and provides a lightweight, responsive interface that enables browsing of processed data across the genome.

Antisense transcription of the myotonic dystrophy locus yields low-abundant RNAs with and without (CAG)n repeat.

The unstable (CTG·CAG)n trinucleotide repeat in the myotonic dystrophy type 1 (DM1) locus is bidirectionally transcribed from genes with terminal overlap. By transcription in the sense direction, the DMPK gene produces various alternatively spliced mRNAs with a (CUG)n repeat in their 3' UTR. Expression in opposite orientation reportedly yields (CAG)n-repeat containing RNA, but both structure and biologic significance of this antisense gene (DM1-AS) are largely unknown. Via a combinatorial approach of computational and experimental analyses of RNA from unaffected individuals and DM1 patients we discovered that DM1-AS spans >6 kb, contains alternative transcription start sites and uses alternative polyadenylation sites up- and downstream of the (CAG)n repeat. Moreover, its primary transcripts undergo alternative splicing, whereby the (CAG)n segment is removed as part of an intron. Thus, in patients a mixture of DM1-AS RNAs with and without expanded (CAG)n repeat are produced. DM1-AS expression appears upregulated in patients, but transcript abundance remains very low in all tissues analyzed. Our data suggest that DM1-AS transcripts belong to the class of long non-coding RNAs. These and other biologically relevant implications for how (CAG)n-expanded transcripts may contribute to DM1 pathology can now be explored experimentally.

Predicting transcription factor activity using prior biological information.

Dysregulation of normal transcription factor activity is a common driver of disease. Therefore, the detection of aberrant transcription factor activity is important to understand disease pathogenesis. We have developed Priori, a method to predict transcription factor activity from RNA sequencing data. Priori has two key advantages over existing methods. First, Priori utilizes literature-supported regulatory information to identify transcription factor-target gene relationships. It then applies linear models to determine the impact of transcription factor regulation on the expression of its target genes. Second, results from a third-party benchmarking pipeline reveals that Priori detects aberrant activity from 124 single-gene perturbation experiments with higher sensitivity and specificity than 11 other methods. We applied Priori and other top-performing methods to predict transcription factor activity from two large primary patient datasets. Our work demonstrates that Priori uniquely discovered significant determinants of survival in breast cancer and identified mediators of drug response in leukemia.

Selective enrichment of plasma cell-free messenger RNA in cancer-associated extracellular vesicles.

Extracellular vesicles (EVs) have been shown as key mediators of extracellular small RNA transport. However, carriers of cell-free messenger RNA (cf-mRNA) in human biofluids and their association with cancer remain poorly understood. Here, we performed a transcriptomic analysis of size-fractionated plasma from lung cancer, liver cancer, multiple myeloma, and healthy donors. Morphology and size distribution analysis showed the successful separation of large and medium particles from other soluble plasma protein fractions. We developed a strategy to purify and sequence ultra-low amounts of cf-mRNA from particle and protein enriched subpopulations with the implementation of RNA spike-ins to control for technical variability and to normalize for intrinsic drastic differences in cf-mRNA amount carried in each plasma fraction. We found that the majority of cf-mRNA was enriched and protected in EVs with remarkable stability in RNase-rich environments. We observed specific enrichment patterns of cancer-associated cf-mRNA in each particle and protein enriched subpopulation. The EV-enriched differentiating genes were associated with specific biological pathways, such as immune systems, liver function, and toxic substance regulation in lung cancer, liver cancer, and multiple myeloma, respectively. Our results suggest that dissecting the complexity of EV subpopulations illuminates their biological significance and offers a promising liquid biopsy approach.

Asxl1 deletion disrupts MYC and RNA polymerase II function in granulocyte progenitors.

Mutations in the gene Additional Sex-Combs Like 1 (ASXL1) are recurrent in myeloid malignancies as well as the pre-malignant condition clonal hematopoiesis, where they are universally associated with poor prognosis. However, the role of ASXL1 in myeloid lineage maturation is incompletely described. To define the role of ASXL1 in myelopoiesis, we employed single cell RNA sequencing and a murine model of hematopoietic-specific Asxl1 deletion. In granulocyte progenitors, Asxl1 deletion leads to hyperactivation of MYC and a quantitative decrease in neutrophil production. This loss of granulocyte production was not accompanied by significant changes in the landscape of covalent histone modifications. However, Asxl1 deletion results in a decrease in RNAPII promoter-proximal pausing in granulocyte progenitors, indicative of a global increase in productive transcription. These results suggest that ASXL1 inhibits productive transcription in granulocyte progenitors, identifying a new role for this epigenetic regulator in myeloid development.

Disruption of the MYC Superenhancer Complex by Dual Targeting of FLT3 and LSD1 in Acute Myeloid Leukemia.

Mutations in Fms-like tyrosine kinase 3 (FLT3) are common drivers in acute myeloid leukemia (AML) yet FLT3 inhibitors only provide modest clinical benefit. Prior work has shown that inhibitors of lysine-specific demethylase 1 (LSD1) enhance kinase inhibitor activity in AML. Here we show that combined LSD1 and FLT3 inhibition induces synergistic cell death in FLT3-mutant AML. Multi-omic profiling revealed that the drug combination disrupts STAT5, LSD1, and GFI1 binding at the MYC blood superenhancer, suppressing superenhancer accessibility as well as MYC expression and activity. The drug combination simultaneously results in the accumulation of repressive H3K9me1 methylation, an LSD1 substrate, at MYC target genes. We validated these findings in 72 primary AML samples with the nearly every sample demonstrating synergistic responses to the drug combination. Collectively, these studies reveal how epigenetic therapies augment the activity of kinase inhibitors in FLT3-ITD (internal tandem duplication) AML. IMPLICATIONS: This work establishes the synergistic efficacy of combined FLT3 and LSD1 inhibition in FLT3-ITD AML by disrupting STAT5 and GFI1 binding at the MYC blood-specific superenhancer complex.

PU.1 and MYC transcriptional network defines synergistic drug responses to KIT and LSD1 inhibition in acute myeloid leukemia.

Responses to kinase-inhibitor therapy in AML are frequently short-lived due to the rapid development of resistance, limiting the clinical efficacy. Combination therapy may improve initial therapeutic responses by targeting pathways used by leukemia cells to escape monotherapy. Here we report that combined inhibition of KIT and lysine-specific demethylase 1 (LSD1) produces synergistic cell death in KIT-mutant AML cell lines and primary patient samples. This drug combination evicts both MYC and PU.1 from chromatin driving cell cycle exit. Using a live cell biosensor for AKT activity, we identify early adaptive changes in kinase signaling following KIT inhibition that are reversed with the addition of LSD1 inhibitor via modulation of the GSK3a/b axis. Multi-omic analyses, including scRNA-seq, ATAC-seq and CUT&Tag, confirm these mechanisms in primary KIT-mutant AML. Collectively, this work provides rational for a clinical trial to assess the efficacy of KIT and LSD1 inhibition in patients with KIT-mutant AML.

An omic and multidimensional spatial atlas from serial biopsies of an evolving metastatic breast cancer.

Mechanisms of therapeutic resistance and vulnerability evolve in metastatic cancers as tumor cells and extrinsic microenvironmental influences change during treatment. To support the development of methods for identifying these mechanisms in individual people, here we present an omic and multidimensional spatial (OMS) atlas generated from four serial biopsies of an individual with metastatic breast cancer during 3.5 years of therapy. This resource links detailed, longitudinal clinical metadata that includes treatment times and doses, anatomic imaging, and blood-based response measurements to clinical and exploratory analyses, which includes comprehensive DNA, RNA, and protein profiles; images of multiplexed immunostaining; and 2- and 3-dimensional scanning electron micrographs. These data report aspects of heterogeneity and evolution of the cancer genome, signaling pathways, immune microenvironment, cellular composition and organization, and ultrastructure. We present illustrative examples of how integrative analyses of these data reveal potential mechanisms of response and resistance and suggest novel therapeutic vulnerabilities.