Multi-Omics Approaches to Study Long Non-coding RNA Function in Atherosclerosis

lncRNA Biomarkers of Glioblastoma Multiforme

Long noncoding RNAs (lncRNAs) are RNA molecules of 200 nucleotides or more in length that are not translated into proteins. Their expression is tissue-specific, with the vast majority involved in the regulation of cellular processes and functions. Many human diseases, including cancer, have been shown to be associated with deregulated lncRNAs, rendering them potential therapeutic targets and biomarkers for differential diagnosis. The expression of lncRNAs in the nervous system varies in different cell types, implicated in mechanisms of neurons and glia, with effects on the development and functioning of the brain. Reports have also shown a link between changes in lncRNA molecules and the etiopathogenesis of brain neoplasia, including glioblastoma multiforme (GBM). GBM is an aggressive variant of brain cancer with an unfavourable prognosis and a median survival of 14-16 months. It is considered a brain-specific disease with the highly invasive malignant cells spreading throughout the neural tissue, impeding the complete resection, and leading to post-surgery recurrences, which are the prime cause of mortality. The early diagnosis of GBM could improve the treatment and extend survival, with the lncRNA profiling of biological fluids promising the detection of neoplastic changes at their initial stages and more effective therapeutic interventions. This review presents a systematic overview of GBM-associated deregulation of lncRNAs with a focus on lncRNA fingerprints in patients' blood.

Transcriptomic Analysis of Type 2 Diabetes Mellitus Combined with Lower Extremity Atherosclerotic Occlusive Disease

Background

The pathological damage mechanism of type 2 diabetes (T2D) and macroangiopathy is extremely complex, and T2D and arteriosclerosis obliterans have different biological behaviors and clinical features. To explore the mechanism of lower extremity arteriosclerosis occlusion (LEAOD) in T2D patients, we utilized RNA-seq to identify unique gene expression signatures of T2D and LEAOD through transcriptomic analysis.

Methods

We obtained blood samples and performed RNA sequencing from four patients with T2D, five of whom had LEAOD. Another six age- and gender-matched blood samples from healthy volunteers were used for control. By exploring the general and specific differential expression analysis after transcriptome sequencing, specific gene expression patterns of T2D and LEAOD were verified.

Results

Transcriptome analysis found differentially expressed genes in T2D, and T2D + LEAOD (vs normal) separately, of which 35/486 (T2D/T2D + LEAOD) were up-regulated and 1290/2970 (T2D/T2D + LEAOD) were down-regulated. A strong overlap of 571 genes across T2D, LEAOD, and coexisting conditions was mainly involved in extracellular exosomes and the transcription process. By exploring the sex difference gene expression features between T2D, T2D + LEAOD, and healthy controls, we noticed that sex chromosome-associated genes do not participate in the sexual dimorphism gene expression profiles of T2D and LEAOD. Protein-Protein Interaction Network analysis and drug target prediction provided the drug candidates to treat T2D and LEAOD.

Conclusion

This study provides some evidence at the transcript level to uncover the association of T2D with LEAOD. The screened hub genes and predicted target drugs may be therapeutic targets.

Effect of CASC15 on apoptosis and oxidative stress of cardiomyocytes after hypoxia/reperfusion injury

INTRODUCTION AND OBJECTIVES

The increasing incidence of ischemic heart disease is a serious threat to human health. Increased CASC15, a long non-coding RNA, has been shown to adversely affect cardiac muscle. The objective of this paper was to explore the effect of CASC15 on a cell model of myocardial infarction and its possible mechanism.

METHODS

H9c2 cells were selected to establish the myocardial infarction model through hypoxia/reoxygenation (H/R) treatment. The expression of CASC15 was attenuated by cell transfection in vitro. The level of CASC15 was detected by RT-qPCR. Cell viability was detected by CCK-8 assay, and cell apoptosis was assessed by flow cytometry. The content of MDA and the activity of SOD and GSH-Px were measured by ELISA. Luciferase reporter gene assay was used to determine the relationship between CASC15 and miRNA.

RESULTS

CASC15 expression was increased in H/R-treated H9c2 cells. Overexpression of CASC15 adversely affected cell viability and promoted H/R-induced oxidative stress. Inhibition of CASC15 promoted cell viability and suppressed cell apoptosis and oxidative stress damage. Additionally, luciferase reporter gene assay confirmed the targeting relationship between CASC15 and miR-542-3p, and attenuating CASC15 expression enhanced the level of miR-542-3p. Reduction of miR-542-3p weakened the viability of the H/R cell model, increased apoptosis, and enhanced oxidative stress damage.

CONCLUSION

This study suggests that overexpression of CASC15 may inhibit the viability of H9c2 cells, promote apoptosis and induce oxidative stress through targeted regulation of miR-542-3p expression.

Detection of cell-type-enriched long noncoding RNAs in atherosclerosis using single-cell techniques: A brief review

Cardiovascular diseases are the leading causes of mortality and morbidity worldwide. Atherosclerotic plaque underlies the predominant factors and is composed of various cell types, including structure cells, such as endothelial and smooth muscle cells, and immune cells, such as macrophages and T cells. Single-cell RNA sequencing (scRNA-seq) has been extensively applied to decipher these cellular heterogeneities to expand our understanding on the mechanisms of atherosclerosis (AS) and to facilitate identifying cell-type-specific long noncoding RNAs (LncRNAs). LncRNAs have been demonstrated to deeply regulate biological activities at the transcriptional and post-transcriptional levels. A group of well-documented functional lncRNAs in AS have been studied. In our review, we selectively described several lncRNAs involved in the critical process of AS. We highlighted four novel lncRNAs (lncRNA CARMN, LINC00607, PCAT19, LINC01235) detected in scRNA-seq datasets and their functions in AS. We also reviewed open web source and bioinformatic tools, as well as the latest methods to perform an in-depth study of lncRNAs. It is fundamental to annotate functional lncRNAs in the various biological activities of AS, as lncRNAs may represent promising targets in the future for treatment and diagnosis in clinical practice.

Long non-coding RNAs in lung cancer: Unraveling the molecular modulators of MAPK signaling

Lung cancer (LC) continues to pose a significant global medical burden, necessitating a comprehensive understanding of its molecular foundations to establish effective treatment strategies. The mitogen-activated protein kinase (MAPK) signaling system has been scientifically associated with LC growth; however, the intricate regulatory mechanisms governing this system remain unknown. Long non-coding RNAs (lncRNAs) are emerging as crucial regulators of diverse cellular activities, including cancer growth. LncRNAs have been implicated in LC, which can function as oncogenes or tumor suppressors, and their dysregulation has been linked to cancer cell death, metastasis, spread, and proliferation. Due to their involvement in critical pathophysiological processes, lncRNAs are gaining attention as potential candidates for anti-cancer treatments. This article aims to elucidate the regulatory role of lncRNAs in MAPK signaling in LC. We provide a comprehensive review of the key components of the MAPK pathway and their relevance in LC, focusing on aberrant signaling processes associated with disease progression. By examining recent research and experimental findings, this article examines the molecular mechanisms through which lncRNAs influence MAPK signaling in lung cancer, ultimately contributing to tumor development.

Genetic Regulation of SMC Gene Expression and Splicing Predict Causal CAD Genes

BACKGROUND

Coronary artery disease (CAD) is the leading cause of death worldwide. Recent meta-analyses of genome-wide association studies have identified over 175 loci associated with CAD. The majority of these loci are in noncoding regions and are predicted to regulate gene expression. Given that vascular smooth muscle cells (SMCs) play critical roles in the development and progression of CAD, we aimed to identify the subset of the CAD loci associated with the regulation of transcription in distinct SMC phenotypes.

METHODS

We measured gene expression in SMCs isolated from the ascending aortas of 151 heart transplant donors of various genetic ancestries in quiescent or proliferative conditions and calculated the association of their expression and splicing with ~6.3 million imputed single-nucleotide polymorphism markers across the genome.

RESULTS

We identified 4910 expression and 4412 splicing quantitative trait loci (sQTLs) representing regions of the genome associated with transcript abundance and splicing. A total of 3660 expression quantitative trait loci (eQTLs) had not been observed in the publicly available Genotype-Tissue Expression dataset. Further, 29 and 880 eQTLs were SMC-specific and sex-biased, respectively. We made these results available for public query on a user-friendly website. To identify the effector transcript(s) regulated by CAD loci, we used 4 distinct colocalization approaches. We identified 84 eQTL and 164 sQTL that colocalized with CAD loci, highlighting the importance of genetic regulation of mRNA splicing as a molecular mechanism for CAD genetic risk. Notably, 20% and 35% of the eQTLs were unique to quiescent or proliferative SMCs, respectively. One CAD locus colocalized with a sex-specific eQTL (TERF2IP), and another locus colocalized with SMC-specific eQTL (ALKBH8). The most significantly associated CAD locus, 9p21, was an sQTL for the long noncoding RNA CDKN2B-AS1, also known as ANRIL, in proliferative SMCs.

CONCLUSIONS

Collectively, our results provide evidence for the molecular mechanisms of genetic susceptibility to CAD in distinct SMC phenotypes.

From 'Omics to Multi-omics Technologies: the Discovery of Novel Causal Mediators

PURPOSE OF REVIEW

'Omics studies provide a comprehensive characterisation of a biological entity, such as the genome, epigenome, transcriptome, proteome, metabolome, or microbiome. This review covers the unique properties of these types of 'omics and their roles as causal mediators in cardiovascular disease. Moreover, applications and challenges of integrating multiple types of 'omics data to increase predictive power, improve causal inference, and elucidate biological mechanisms are discussed.

RECENT FINDINGS

Multi-omics approaches are growing in adoption as they provide orthogonal evidence and overcome the limitations of individual types of 'omics data. Studies with multiple types of 'omics data have improved the diagnosis and prediction of disease states and afforded a deeper understanding of underlying pathophysiological mechanisms, beyond any single type of 'omics data. For instance, disease-associated loci in the genome can be supplemented with other 'omics to prioritise causal genes and understand the function of non-coding variants. Alternatively, techniques, such as Mendelian randomisation, can leverage genetics to provide evidence supporting a causal role for disease-associated molecules, and elucidate their role in disease pathogenesis. As technologies improve, costs for 'omics studies will continue to fall and datasets will become increasingly accessible to researchers. The intrinsically unbiased nature of 'omics data is well-suited to exploratory analyses that discover causal mediators of disease, and multi-omics is an emerging discipline that leverages the strengths of each type of 'omics data to provide insights greater than the sum of its parts.

Emerging biology of noncoding RNAs in malaria parasites

In eukaryotic organisms, noncoding RNAs (ncRNAs) have been implicated as important regulators of multifaceted biological processes, including transcriptional, posttranscriptional, and epigenetic regulation of gene expression. In recent years, it is becoming clear that protozoan parasites encode diverse ncRNA transcripts; however, little is known about their cellular functions. Recent advances in high-throughput “omic” studies identified many novel long ncRNAs (lncRNAs) in apicomplexan parasites, some of which undergo splicing, polyadenylation, and encode small proteins. To date, only a few of them are characterized, leaving a big gap in our understanding regarding their origin, mode of action, and functions in parasite biology. In this review, we focus on lncRNAs of the human malaria parasite Plasmodium falciparum and highlight their cellular functions and possible mechanisms of action.

Artificial intelligence and machine learning in precision and genomic medicine

The advancement of precision medicine in medical care has led behind the conventional symptom-driven treatment process by allowing early risk prediction of disease through improved diagnostics and customization of more effective treatments. It is necessary to scrutinize overall patient data alongside broad factors to observe and differentiate between ill and relatively healthy people to take the most appropriate path toward precision medicine, resulting in an improved vision of biological indicators that can signal health changes. Precision and genomic medicine combined with artificial intelligence have the potential to improve patient healthcare. Patients with less common therapeutic responses or unique healthcare demands are using genomic medicine technologies. AI provides insights through advanced computation and inference, enabling the system to reason and learn while enhancing physician decision making. Many cell characteristics, including gene up-regulation, proteins binding to nucleic acids, and splicing, can be measured at high throughput and used as training objectives for predictive models. Researchers can create a new era of effective genomic medicine with the improved availability of a broad range of datasets and modern computer techniques such as machine learning. This review article has elucidated the contributions of ML algorithms in precision and genome medicine.

Artificial intelligence and machine learning in precision and genomic medicine

The advancement of precision medicine in medical care has led behind the conventional symptom-driven treatment process by allowing early risk prediction of disease through improved diagnostics and customization of more effective treatments. It is necessary to scrutinize overall patient data alongside broad factors to observe and differentiate between ill and relatively healthy people to take the most appropriate path toward precision medicine, resulting in an improved vision of biological indicators that can signal health changes. Precision and genomic medicine combined with artificial intelligence have the potential to improve patient healthcare. Patients with less common therapeutic responses or unique healthcare demands are using genomic medicine technologies. AI provides insights through advanced computation and inference, enabling the system to reason and learn while enhancing physician decision making. Many cell characteristics, including gene up-regulation, proteins binding to nucleic acids, and splicing, can be measured at high throughput and used as training objectives for predictive models. Researchers can create a new era of effective genomic medicine with the improved availability of a broad range of datasets and modern computer techniques such as machine learning. This review article has elucidated the contributions of ML algorithms in precision and genome medicine.

A network-based matrix factorization framework for ceRNA co-modules recognition of cancer genomic data

With the development of high-throughput technologies, the accumulation of large amounts of multidimensional genomic data provides an excellent opportunity to study the multilevel biological regulatory relationships in cancer. Based on the hypothesis of competitive endogenous ribonucleic acid (RNA) (ceRNA) network, lncRNAs can eliminate the inhibition of microRNAs (miRNAs) on their target genes by binding to intracellular miRNA sites so as to improve the expression level of these target genes. However, previous studies on cancer expression mechanism are mostly based on individual or two-dimensional data, and lack of integration and analysis of various RNA-seq data, making it difficult to verify the complex biological relationships involved. To explore RNA expression patterns and potential molecular mechanisms of cancer, a network-regularized sparse orthogonal-regularized joint non-negative matrix factorization (NSOJNMF) algorithm is proposed, which combines the interaction relations among RNA-seq data in the way of network regularization and effectively prevents multicollinearity through sparse constraints and orthogonal regularization constraints to generate good modular sparse solutions. NSOJNMF algorithm is performed on the datasets of liver cancer and colon cancer, then ceRNA co-modules of them are recognized. The enrichment analysis of these modules shows that >90% of them are closely related to the occurrence and development of cancer. In addition, the ceRNA networks constructed by the ceRNA co-modules not only accurately mine the known correlations of the three RNA molecules but also further discover their potential biological associations, which may contribute to the exploration of the competitive relationships among multiple RNAs and the molecular mechanisms affecting tumor development.

Cis-regulated expression of non-conserved lincRNAs associates with cardiometabolic related traits

Many complex disease risk loci map to intergenic regions containing long intergenic noncoding RNAs (lincRNAs). The majority of these is not conserved outside humans, raising the question whether genetically regulated expression of non-conserved and conserved lincRNAs has similar rates of association with complex traits. Here we leveraged data from the Genotype-Tissue Expression (GTEx) project and multiple public genome-wide association study (GWAS) resources. Using an established transcriptome-wide association study (TWAS) tool, FUSION, we interrogated the associations between cis-regulated expression of lincRNAs and multiple cardiometabolic traits. We found that cis-regulated expression of non-conserved lincRNAs had a strikingly similar trend of association with complex cardiometabolic traits as conserved lincRNAs. This finding challenges the conventional notion of conservation that has led to prioritization of conserved loci for functional studies and calls attention to the need to develop comprehensive strategies to study the large number of non-conserved human lincRNAs that may contribute to human disease.

Evaluation of Early Biomarkers of Atherosclerosis Associated with Polychlorinated Biphenyl Exposure: An in Vitro and in Vivo Study

BACKGROUND

Miscellaneous cardiovascular risk factors have been defined, but the contribution of environmental pollutants exposure on cardiovascular disease (CVD) remains underappreciated.

OBJECTIVE

We investigated the potential impact of typical environmental pollutant exposure on atherogenesis and its underlying mechanisms.

METHODS

We used human umbilical vein endothelial cells (HUVECs) and apolipoprotein E knockout (ApoE-/-) mice to investigate how 2,3,5-trichloro-6-phenyl-[1,4]-benzoquinone (PCB29-pQ, a toxic polychlorinated biphenyl metabolite) affects atherogenesis and identified early biomarkers of CVD associated with PCB29-pQ exposures. Then, we used long noncoding RNAs (lncRNAs) HDAC7-AS1-overexpressing ApoE-/- mice and apolipoprotein E/caveolin 1 double-knockout (ApoE-/-/CAV1-/-) mice to address the role of these early biomarkers in PCB29-pQ-induced atherogenesis. Plasma samples from patients with coronary heart disease (CHD) were also used to confirm our findings.

RESULTS

Our data indicate that lncRNA HDAC7-AS1 bound to MIR-7-5p via argonaute 2 in PCB29-pQ-challenged HUVECs. Our mRNA sequencing assay identified transforming growth factor-β2 (TGF-β2) as a possible target gene of MIR-7-5p; HDAC7-AS1 sponged MIR-7-5p and inhibited the binding of TGF-β2 to MIR-7-5p. The effect of PCB29-pQ-induced endothelial injury, vascular inflammation, development of plaques, and atherogenesis in ApoE-/- mice was greater with MIR-7-5p-mediated TGF-β2 inhibition, whereas HDAC7-AS1-overexpressing ApoE-/- mice and ApoE-/-/CAV1-/- mice showed the opposite effect. Consistently, plasma levels of HDAC7-AS1 and MIR-7-5p were found to be significantly associated individuals diagnosed with CHD.

DISCUSSIONS

These findings demonstrated that a mechanism-based, integrated-omics approach enabled the identification of potentially clinically relevant diagnostic indicators and therapeutic targets of CHD mediated by environmental contaminants using in vitro and in vivo models of HUVECs and ApoE-/- and ApoE-/-/CAV1-/- mice. https://doi.org/10.1289/EHP9833.

Beyond the List: Bioagent-Agnostic Signatures Could Enable a More Flexible and Resilient Biodefense Posture Than an Approach Based on Priority Agent Lists Alone

As of 2021, the biothreat policy and research communities organize their efforts around lists of priority agents, which elides consideration of novel pathogens and biotoxins. For example, the Select Agents and Toxins list is composed of agents that historic biological warfare programs had weaponized or that have previously caused great harm during natural outbreaks. Similarly, lists of priority agents promulgated by the World Health Organization and the National Institute of Allergy and Infectious Diseases are composed of previously known pathogens and biotoxins. To fill this gap, we argue that the research/scientific and biodefense/biosecurity communities should categorize agents based on how they impact their hosts to augment current list-based paradigms. Specifically, we propose integrating the results of multi-omics studies to identify bioagent-agnostic signatures (BASs) of disease-namely, patterns of biomarkers that accurately and reproducibly predict the impacts of infection or intoxication without prior knowledge of the causative agent. Here, we highlight three pathways that investigators might exploit as sources of signals to construct BASs and their applicability to this framework. The research community will need to forge robust interdisciplinary teams to surmount substantial experimental, technical, and data analytic challenges that stand in the way of our long-term vision. However, if successful, our functionality-based BAS model could present a means to more effectively surveil for and treat known and novel agents alike.

Long non-coding RNAs in metabolic disorders: pathogenetic relevance and potential biomarkers and therapeutic targets

BACKGROUND

It has been suggested that dysregulation of long non-coding RNAs (lncRNAs) could be associated with the incidence and development of metabolic disorders.

AIM

Accordingly, this narrative review described the molecular mechanisms of lncRNAs in the development of metabolic diseases including insulin resistance, diabetes, obesity, non-alcoholic fatty liver disease (NAFLD), cirrhosis, and coronary artery diseases (CAD). Furthermore, we investigated the up-to-date findings on the association of deregulated lncRNAs in the metabolic disorders, and potential use of lncRNAs as biomarkers and therapeutic targets.

CONCLUSION

LncRNAs/miRNA/regulatory proteins axis plays a crucial role in progression of metabolic disorders and may be used in development of therapeutic and diagnostic approaches.

Custom long non-coding RNA capture enhances detection sensitivity in different human sample types

Long non-coding RNAs (lncRNAs) are a heterogeneous group of transcripts that lack protein coding potential and display regulatory functions in various cellular processes. As a result of their cell- and cancer-specific expression patterns, lncRNAs have emerged as potential diagnostic and therapeutic targets. The accurate characterization of lncRNAs in bulk transcriptome data remains challenging due to their low abundance compared to protein coding genes. To tackle this issue, we describe a unique short-read custom lncRNA capture sequencing approach that relies on a comprehensive set of 565,878 capture probes for 49,372 human lncRNA genes. This custom lncRNA capture approach was evaluated on various sample types ranging from artificial high-quality RNA mixtures to more challenging formalin-fixed paraffin-embedded tissue and biofluid material. The custom enrichment approach allows the detection of a more diverse repertoire of lncRNAs, with better reproducibility and higher coverage compared to classic total RNA-sequencing.

Multi-omics approaches to improve malaria therapy

Malaria contributes to the most widespread infectious diseases worldwide. Even though current drugs are commercially available, the ever-increasing drug resistance problem by malaria parasites poses new challenges in malaria therapy. Hence, searching for efficient therapeutic strategies is of high priority in malaria control. In recent years, multi-omics technologies have been extensively applied to provide a more holistic view of functional principles and dynamics of biological mechanisms. We briefly review multi-omics technologies and focus on recent malaria progress conducted with the help of various omics methods. Then, we present up-to-date advances for multi-omics approaches in malaria. Next, we describe resistance phenomena to established antimalarial drugs and underlying mechanisms. Finally, we provide insight into novel multi-omics approaches, new drugs and vaccine developments and analyze current gaps in multi-omics research. Although multi-omics approaches have been successfully used in malaria studies, they are still limited. Many gaps need to be filled to bridge the gap between basic research and treatment of malaria patients. Multi-omics approaches will foster a better understanding of the molecular mechanisms of Plasmodium that are essential for the development of novel drugs and vaccines to fight this disastrous disease.

Skeleton-vasculature chain reaction: a novel insight into the mystery of homeostasis

Angiogenesis and osteogenesis are coupled. However, the cellular and molecular regulation of these processes remains to be further investigated. Both tissues have recently been recognized as endocrine organs, which has stimulated research interest in the screening and functional identification of novel paracrine factors from both tissues. This review aims to elaborate on the novelty and significance of endocrine regulatory loops between bone and the vasculature. In addition, research progress related to the bone vasculature, vessel-related skeletal diseases, pathological conditions, and angiogenesis-targeted therapeutic strategies are also summarized. With respect to future perspectives, new techniques such as single-cell sequencing, which can be used to show the cellular diversity and plasticity of both tissues, are facilitating progress in this field. Moreover, extracellular vesicle-mediated nuclear acid communication deserves further investigation. In conclusion, a deeper understanding of the cellular and molecular regulation of angiogenesis and osteogenesis coupling may offer an opportunity to identify new therapeutic targets.

Colorectal cancer screening and diagnosis: omics-based technologies for development of a non-invasive blood-based method

Introduction: Colorectal cancer (CRC) is one of the most important health problems in the Western world. In order to reduce the burden of the disease, two strategies are proposed: screening and prompt detection in symptomatic patients. Although diagnosis and prevention are mainly based on colonoscopy, fecal hemoglobin detection has been widely implemented as a noninvasive strategy. Various studies aiming to discover blood-based biomarkers have recently emerged.Areas covered: The burgeoning omics field provides diverse high-throughput approaches for CRC blood-based biomarker discovery. In this review, we appraise the most robust and commonly used technologies within the fields of genomics, transcriptomics, epigenomics, proteomics, and metabolomics, together with their targeted validation approaches. We summarize the transference process from the discovery phase until clinical translation. Finally, we review the best candidate biomarkers and their potential clinical applicability.Expert opinion: Some available biomarkers are promising, especially in the field of epigenomics: DNA methylation and microRNA. Transference requires the joint collaboration of basic researchers, intellectual property experts, technology transfer officers and clinicians. Blood-based biomarkers will be selected not only based on their diagnostic accuracy and cost but also on their reliability, applicability to clinical analysis laboratories and their acceptance by the population.

Antisense long non‑coding RNA WEE2‑AS1 regulates human vascular endothelial cell viability via cell cycle G2/M transition in arteriosclerosis obliterans

Long non-coding RNAs (lncRNAs) affect atherosclerosis by regulating the physiological and pathological processes of endothelial cells; however, the role of lncRNA WEE2 antisense RNA 1 (WEE2-AS1) in arteriosclerosis obliterans (ASO) is not completely understood. The present study aimed to explore the function of lncRNA WEE2-AS1 in human vascular endothelial cells. The results indicated that lncRNA WEE2-AS1 was significantly elevated in plasma and artery tissue samples of patients with ASO compared with healthy controls. The fluorescence in situ hybridization results suggested that lncRNA WEE2-AS1 was expressed in the cytoplasm and nuclei of primary human umbilical vein endothelial cells (HUVECs). The Cell Counting Kit-8 assay results suggested that lncRNA WEE2-AS1 knockdown significantly promoted HUVEC viability, whereas lncRNA WEE2-AS1 overexpression inhibited HUVEC viability compared with the negative control groups. Furthermore, analysis of the cell cycle by flow cytometry indicated that lncRNA WEE2-AS1 knockdown significantly decreased the proportion of cells in the G₀/G₁ phase and significantly increased the proportion of cells in the G₂/M phase compared with the negative control group. However, lncRNA WEE2-AS1 overexpression had no significant effect on cell cycle distribution compared with the negative control group. The western blotting results indicated that lncRNA WEE2-AS1 knockdown significantly reduced the expression levels of phosphorylated cyclin dependent kinase 1, WEE1 homolog 2 and myelin transcription factor 1, but increased the expression level of cell division cycle 25B compared with the negative control group. lncRNA WEE2-AS1 overexpression displayed the opposite effect on protein expression. Collectively, the present study suggested that lncRNA WEE2-AS1 was significantly upregulated in ASO and may serve a role in regulating human vascular endothelial cell viability. Further investigation into lncRNA WEE2-AS1 may broaden the current understanding of the molecular mechanism underlying ASO, and aid with the identification of specific probes and precise targeted drugs for the diagnosis and treatment of ASO.

Network medicine in Cardiovascular Research

The ability to generate multi-omics data coupled with deeply characterizing the clinical phenotype of individual patients promises to improve understanding of complex cardiovascular pathobiology. There remains an important disconnection between the magnitude and granularity of these data and our ability to improve phenotype-genotype correlations for complex cardiovascular diseases. This shortcoming may be due to limitations associated with traditional reductionist analytical methods, which tend to emphasize a single molecular event in the pathogenesis of diseases more aptly characterized by crosstalk between overlapping molecular pathways. Network medicine is a rapidly growing discipline that considers diseases as the consequences of perturbed interactions between multiple interconnected biological components. This powerful integrative approach has enabled a number of important discoveries in complex disease mechanisms. In this review, we introduce the basic concepts of network medicine and highlight specific examples by which this approach has accelerated cardiovascular research. We also review how network medicine is well-positioned to promote rational drug design for patients with cardiovascular diseases, with particular emphasis on advancing precision medicine.

Epigenetic-sensitive pathways in personalized therapy of major cardiovascular diseases

The complex pathobiology underlying cardiovascular diseases (CVDs) has yet to be explained. Aberrant epigenetic changes may result from alterations in enzymatic activities, which are responsible for putting in and/or out the covalent groups, altering the epigenome and then modulating gene expression. The identification of novel individual epigenetic-sensitive trajectories at single cell level might provide additional opportunities to establish predictive, diagnostic and prognostic biomarkers as well as drug targets in CVDs. To date, most of studies investigated DNA methylation mechanism and miRNA regulation as epigenetics marks. During atherogenesis, big epigenetic changes in DNA methylation and different ncRNAs, such as miR-93, miR-340, miR-433, miR-765, CHROME, were identified into endothelial cells, smooth muscle cells, and macrophages. During man development, lipid metabolism, inflammation and homocysteine homeostasis, alter vascular transcriptional mechanism of fundamental genes such as ABCA1, SREBP2, NOS, HIF1. At histone level, increased HDAC9 was associated with matrix metalloproteinase 1 (MMP1) and MMP2 expression in pro-inflammatory macrophages of human carotid plaque other than to have a positive effect on toll like receptor signaling and innate immunity. HDAC9 deficiency promoted inflammation resolution and reverse cholesterol transport, which might block atherosclerosis progression and promote lesion regression. Here, we describe main human epigenetic mechanisms involved in atherosclerosis, coronary heart disease, ischemic stroke, peripheral artery disease; cardiomyopathy and heart failure. Different epigenetics mechanisms are activated, such as regulation by circular RNAs, as MICRA, and epitranscriptomics at RNA level. Moreover, in order to open new frontiers for precision medicine and personalized therapy, we offer a panoramic view on the most innovative bioinformatic tools designed to identify putative genes and molecular networks underlying CVDs in man.

Unveiling ncRNA regulatory axes in atherosclerosis progression

Completion of the human genome sequencing project highlighted the richness of the cellular RNA world, and opened the door to the discovery of a plethora of short and long non-coding RNAs (the dark transcriptome) with regulatory or structural potential, which shifted the balance of pathological gene alterations from coding to non-coding RNAs. Thus, disease risk assessment currently has to also evaluate the expression of new RNAs such as small micro RNAs (miRNAs), long non-coding RNAs (lncRNAs), circular RNAs (circRNAs), competing endogenous RNAs (ceRNAs), retrogressed elements, 3'UTRs of mRNAs, etc. We are interested in the pathogenic mechanisms of atherosclerosis (ATH) progression in patients suffering Chronic Kidney Disease, and in this review, we will focus in the role of the dark transcriptome (non-coding RNAs) in ATH progression. We will focus in miRNAs and in the formation of regulatory axes or networks with their mRNA targets and with the lncRNAs that function as miRNA sponges or competitive inhibitors of miRNA activity. In this sense, we will pay special attention to retrogressed genomic elements, such as processed pseudogenes and Alu repeated elements, that have been recently seen to also function as miRNA sponges, as well as to the use or miRNA derivatives in gene silencing, anti-ATH therapies. Along the review, we will discuss technical developments associated to research in lncRNAs, from sequencing technologies to databases, repositories and algorithms to predict miRNA targets, as well as new approaches to miRNA function, such as integrative or enrichment analysis and their potential to unveil RNA regulatory networks.

Transcriptional control of a novel long noncoding RNA Mymsl in smooth muscle cells by a single Cis-element and its initial functional characterization in vessels

Differentiated vascular smooth muscle cells (VSMCs) are crucial in maintaining vascular homeostasis. While the coding transcriptome of the differentiated VSMC phenotype has been defined, we know little about its noncoding signature. Herein, we identified a Myocardin-induced muscle specific long noncoding RNA (lncRNA) (Mymsl) downregulated upon VSMC phenotypic modulation. We demonstrated an essential role of a proximal consensus CArG element in response to MYOCD/SRF in vitro. To validate the in vivo role of this CArG element, we generated CArG mutant mice via CRISPR-Cas9 genome editing. While the CArG mutation had no impact on the expression of surrounding genes, it abolished Mymsl expression in SMCs, but not skeletal and cardiac muscle. Chromatin immunoprecipitation assays (ChIPs) showed decreased SRF binding to CArG region in mutants whereas the enrichment of H3K79Me2 remained the same. RNA-seq analysis showed a downregulation of matrix genes in aortas from Mymsl knockout mice, which was further validated in injured carotid arteries. Our study defined the transcriptional control of a novel lncRNA in SMCs via a single transcription factor binding site, which may offer a new strategy for generating SMC-specific knockout mouse models. We also provided in vivo evidence supporting the potential importance of Mymsl in vascular pathophysiology.

Current applications of big data and machine learning in cardiology

Machine learning (ML) is a software solution with the ability of making predictions without prior explicit programming, aiding in the analysis of large amounts of data. These algorithms can be trained through supervised or unsupervised learning. Cardiology is one of the fields of medicine with the highest interest in its applications. They can facilitate every step of patient care, reducing the margin of error and contributing to precision medicine. In particular, ML has been proposed for cardiac imaging applications such as automated computation of scores, differentiation of prognostic phenotypes, quantification of heart function and segmentation of the heart. These tools have also demonstrated the capability of performing early and accurate detection of anomalies in electrocardiographic exams. ML algorithms can also contribute to cardiovascular risk assessment in different settings and perform predictions of cardiovascular events. Another interesting research avenue in this field is represented by genomic assessment of cardiovascular diseases. Therefore, ML could aid in making earlier diagnosis of disease, develop patient-tailored therapies and identify predictive characteristics in different pathologic conditions, leading to precision cardiology.