Profiling Extracellular Long RNA Transcriptome in Human Plasma and Extracellular Vesicles for Biomarker Discovery.

The recent discovery of extracellular RNAs in blood, including RNAs in extracellular vesicles (EVs), combined with low-input RNA-sequencing advances have enabled scientists to investigate their role in human disease. To date, most studies have been focusing on small RNAs, and methodologies to optimize long RNAs measurement are lacking. We used plasma RNA to assess the performance of six long RNA sequencing methods, at two different sites, and we report their differences in reads (%) mapped to the genome/transcriptome, number of genes detected, long RNA transcript diversity, and reproducibility. Using the best performing method, we further compare the profile of long RNAs in the EV- and no-EV-enriched RNA plasma compartments. These results provide insights on the performance and reproducibility of commercially available kits in assessing the landscape of long RNAs in human plasma and different extracellular RNA carriers that may be exploited for biomarker discovery.

HDAC9 complex inhibition improves smooth muscle-dependent stenotic vascular disease.

Patients with heterozygous missense mutations in the ACTA2 or MYH11 gene are known to exhibit thoracic aortic aneurysm and a risk of early-onset aortic dissection. However, less common phenotypes involving arterial obstruction are also observed, including coronary and cerebrovascular stenotic disease. Herein we implicate the HDAC9 complex in transcriptional silencing of contractile protein-associated genes, known to undergo downregulation in stenotic lesions. Furthermore, neointimal formation was inhibited in HDAC9- or MALAT1-deficient mice with preservation of contractile protein expression. Pharmacologic targeting of the HDAC9 complex through either MALAT1 antisense oligonucleotides or inhibition of the methyltransferase EZH2 (catalytic mediator recruited by the HDAC9 complex) reduced neointimal formation. In conclusion, we report the implication of the HDAC9 complex in stenotic disease and demonstrate that pharmacologic therapy targeting epigenetic complexes can ameliorate arterial obstruction in an experimental system.

An HDAC9-MALAT1-BRG1 complex mediates smooth muscle dysfunction in thoracic aortic aneurysm.

Thoracic aortic aneurysm (TAA) has been associated with mutations affecting members of the TGF-ß signaling pathway, or components and regulators of the vascular smooth muscle cell (VSMC) actomyosin cytoskeleton. Although both clinical groups present similar phenotypes, the existence of potential common mechanisms of pathogenesis remain obscure. Here we show that mutations affecting TGF-ß signaling and VSMC cytoskeleton both lead to the formation of a ternary complex comprising the histone deacetylase HDAC9, the chromatin-remodeling enzyme BRG1, and the long noncoding RNA MALAT1. The HDAC9-MALAT1-BRG1 complex binds chromatin and represses contractile protein gene expression in association with gain of histone H3-lysine 27 trimethylation modifications. Disruption of Malat1 or Hdac9 restores contractile protein expression, improves aortic mural architecture, and inhibits experimental aneurysm growth. Thus, we highlight a shared epigenetic pathway responsible for VSMC dysfunction in both forms of TAA, with potential therapeutic implication for other known HDAC9-associated vascular diseases.

Association of Multiorgan Computed Tomographic Phenomap With Adverse Cardiovascular Health Outcomes: The Framingham Heart Study.

Importance: Increased ability to quantify anatomical phenotypes across multiple organs provides the opportunity to assess their cumulative ability to identify individuals at greatest susceptibility for adverse outcomes. Objective: To apply unsupervised machine learning to define the distribution and prognostic importance of computed tomography-based multiorgan phenotypes associated with adverse health outcomes. Design, Setting, and Participants: This asymptomatic community-based cohort study included 2924 Framingham Heart Study participants between July 2002 and April 2005 undergoing computed tomographic imaging of the chest and abdomen. Participants are from the offspring and third-generation cohorts. Exposures: Eleven computed tomography-based measures of valvular/vascular calcification, adiposity, and muscle attenuation. Main Outcomes and Measures: All-cause mortality and cardiovascular disease (myocardial infarction, stroke, or cardiovascular death). Results: The median age of the participants was 50 years (interquartile range, 43-60 years), and 1422 (48.6%) were men. Principal component analysis identified 3 major anatomic axes: (1) global calcification (defined by aortic, thoracic, coronary, and valvular calcification); (2) adiposity (defined by pericardial, visceral, hepatic, and intrathoracic fat); and (3) muscle attenuation that explained 65.7% of the population variation. Principal components showed different evolution with age (continuous increase in global calcification, decrease in muscle attenuation, and U-shaped association with adiposity) but similar patterns in men and women. Using unsupervised clustering approaches in the offspring cohort (n = 1150), we identified a cohort (n = 232; 20.2%) with an unfavorable multiorgan phenotype across all 3 anatomic axes as compared with a favorable multiorgan phenotype. Membership in the unfavorable phenotypic cluster was associated with a greater prevalence of cardiovascular disease risk factors and with increased all-cause mortality (hazard ratio, 2.61; 95% CI, 1.74-3.92; P < .001), independent of coronary artery calcium score, visceral adipose tissue, and 10-year global cardiovascular disease Framingham risk, and it provided improvement in metrics of discrimination and reclassification. Conclusions and Relevance: This proof-of-concept analysis demonstrates that unsupervised machine learning, in an asymptomatic community cohort, identifies an unfavorable multiorgan phenotype associated with adverse health outcomes, especially in elderly American adults. Future investigations in larger populations are required not only to validate the present results, but also to harness clinical, biochemical, imaging, and genetic markers to increase our understanding of healthy cardiovascular aging.

Analysis of extracellular RNA in cerebrospinal fluid.

We examined the extracellular vesicle (EV) and RNA composition of pooled normal cerebrospinal fluid (CSF) samples and CSF from five major neurological disorders: Alzheimer's disease (AD), Parkinson's disease (PD), low-grade glioma (LGG), glioblastoma multiforme (GBM), and subarachnoid haemorrhage (SAH), representing neurodegenerative disease, cancer, and severe acute brain injury. We evaluated: (I) size and quantity of EVs by nanoparticle tracking analysis (NTA) and vesicle flow cytometry (VFC), (II) RNA yield and purity using four RNA isolation kits, (III) replication of RNA yields within and between laboratories, and (IV) composition of total and EV RNAs by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and RNA sequencing (RNASeq). The CSF contained ~106 EVs/µL by NTA and VFC. Brain tumour and SAH CSF contained more EVs and RNA relative to normal, AD, and PD. RT-qPCR and RNASeq identified disease-related populations of microRNAs and messenger RNAs (mRNAs) relative to normal CSF, in both total and EV fractions. This work presents relevant measures selected to inform the design of subsequent replicative CSF studies. The range of neurological diseases highlights variations in total and EV RNA content due to disease or collection site, revealing critical considerations guiding the selection of appropriate approaches and controls for CSF studies.

Isolation, identification, and characterization of novel nanovesicles.

Extracellular microvesicles (EVs) have been recognized for many potential clinical applications including biomarkers for disease diagnosis. In this study, we identified a major population of EVs by simply screening fluid samples with a nanosizer. Unlike other EVs, this extracellular nanovesicle (named HG-NV, HG-NV stands for HomoGenous nanovesicle as well as for Huang-Ge- nanovesicle) can be detected with a nanosizer with minimal in vitro manipulation and are much more homogenous in size (8-12 nm) than other EVs. A simple filtration platform is capable of separating HG-NVs from peripheral blood or cell culture supernatants. In comparison with corresponding exosome profiles, HG-NVs released from both mouse and human breast tumor cells are enriched with RNAs. Tumor derived HG-NVs are more potent in promoting tumor progression than exosomes. In summary, we identified a major subset of EVs as a previously unrecognized nanovesicle. Tumor cell derived HG-NVs promote tumor progression. Molecules predominantly present in breast tumor HG-NVs have been identified and characterized. This discovery may have implications in advancing both microvesicle biology research and clinical management including potential used as a biomarker.

Mutation screening based on the mechanical properties of DNA molecules tethered to a solid surface.

We report a rapid gene mutation screening method by making use of the mechanical properties of single-strand DNA (ssDNA) tethered to a solid surface. With proper temperature control, ssDNA in solution undergoes intrabase pairing and forms a specific complex 3D structure. By tethering such ssDNA strands to a solid surface, a DNA film can be formed. The mechanical properties of such DNA films, probed by devices such as a quartz crystal resonator, are directly related to the specific structure of the ssDNA which is characteristic of its base sequence and thus can be used as the basis for mutation screening. Using this approach, we have detected a single base mutation among 545 bases in the P53 gene. This result suggests promising potential of employing the mechanical properties of DNA strands for rapid screening of mutations.