Genome-wide analysis of aberrant methylation of enhancer DNA in human osteoarthritis

Epigenetics and alternative splicing in cancer: old enemies, new perspectives

In recent years, significant strides in both conceptual understanding and technological capabilities have bolstered our comprehension of the factors underpinning cancer initiation and progression. While substantial insights have unraveled the molecular mechanisms driving carcinogenesis, there has been an overshadowing of the critical contribution made by epigenetic pathways, which works in concert with genetics. Mounting evidence demonstrates cancer as a complex interplay between genetics and epigenetics. Notably, epigenetic elements play a pivotal role in governing alternative pre-mRNA splicing, a primary contributor to protein diversity. In this review, we have provided detailed insights into the bidirectional communication between epigenetic modifiers and alternative splicing, providing examples of specific genes and isoforms affected. Notably, succinct discussion on targeting epigenetic regulators and the potential of the emerging field of epigenome editing to modulate splicing patterns is also presented. In summary, this review offers valuable insights into the intricate interplay between epigenetics and alternative splicing in cancer, paving the way for novel approaches to understanding and targeting this critical process.

Whole genome methylation sequencing in blood from persons with mild cognitive impairment and dementia due to Alzheimer's disease identifies cognitive status

INTRODUCTION

Whole genome methylation sequencing (WGMS) in blood identifies differential DNA methylation in persons with late-onset dementia due to Alzheimer's disease (AD) but has not been tested in persons with mild cognitive impairment (MCI).

METHODS

We used WGMS to compare DNA methylation levels at 25,244,219 CpG loci in 382 blood samples from 99 persons with MCI, 109 with AD, and 174 who are cognitively unimpaired (CU).

RESULTS

WGMS identified 9,756 differentially methylated positions (DMPs) in persons with MCI, including 1,743 differentially methylated genes encoding proteins in biological pathways related to synapse organization, dendrite development, and ion transport. 447 DMPs exhibit progressively increasing or decreasing DNA methylation levels between CU, MCI, and AD that correspond to cognitive status.

DISCUSSION

WGMS identifies DMPs in known and newly detected genes in blood from persons with MCI and AD that support blood DNA methylation levels as candidate biomarkers of cognitive status.

Feature Paper in Oral Physiology and Pathology

In the realm of life sciences, the journal 'Life' has consistently served as a beacon for groundbreaking research and scientific discovery [...].

Integrated analysis of m6A regulator-mediated RNA methylation modification patterns and immune characteristics in Sjögren's syndrome

The epigenetic modifier N6-methyladenosine (m6A), recognized as the most prevalent internal modification in messenger RNA (mRNA), has recently emerged as a pivotal player in immune regulation. Its dysregulation has been implicated in the pathogenesis of various autoimmune conditions. However, the implications of m6A modification within the immune microenvironment of Sjögren's syndrome (SS), a chronic autoimmune disorder characterized by exocrine gland dysfunction, remain unexplored. Herein, we leverage an integrative analysis combining public database resources and novel sequencing data to investigate the expression profiles of m6A regulatory genes in SS. Our cohort comprised 220 patients diagnosed with SS and 62 healthy individuals, enabling a comprehensive evaluation of peripheral blood at the transcriptomic level. We report a significant association between SS and altered expression of key m6A regulators, with these changes closely tied to the activation of CD4+ T cells. Employing a random forest (RF) algorithm, we identified crucial genes contributing to the disease phenotype, which facilitated the development of a robust diagnostic model via multivariate logistic regression analysis. Further, unsupervised clustering revealed two distinct m6A modification patterns, which were significantly associated with variations in immunocyte infiltration, immune response activity, and biological function enrichment in SS. Subsequently, we proceeded with a screening process aimed at identifying genes that were differentially expressed (DEGs) between the two groups distinguished by m6A modification. Leveraging these DEGs, we employed weight gene co-expression network analysis (WGCNA) to uncover sets of genes that exhibited strong co-variance and hub genes that were closely linked to m6A modification. Through rigorous analysis, we identified three critical m6A regulators - METTL3, ALKBH5, and YTHDF1 - alongside two m6A-related hub genes, COMMD8 and SRP9. These elements collectively underscore a complex but discernible pattern of m6A modification that appears to be integrally linked with SS's pathogenesis. Our findings not only illuminate the significant correlation between m6A modification and the immune microenvironment in SS but also lay the groundwork for a deeper understanding of m6A regulatory mechanisms. More importantly, the identification of these key regulators and hub genes opens new avenues for the diagnosis and treatment of SS, presenting potential targets for therapeutic intervention.

Epigenetics in Knee Osteoarthritis: A 2020-2023 Update Systematic Review

Osteoarthritis is a leading cause of disability in the world. The scientific literature highlights the critical importance of epigenetic regulatory effects, intertwined with biomechanical and biochemical peculiar conditions within each musculoskeletal district. While the contribution of genetic and epigenetic factors to knee OA is well-recognized, their precise role in disease management remains an area of active research. Such a field is particularly heterogeneous, calling for regular analysis and summarizing of the data that constantly emerge in the scientific literature, often sparse and scant of integration. The aim of this study was to systematically identify and synthesize all new evidence that emerged in human and animal model studies published between 2020 and 2023. This was necessary because, to the best of our knowledge, articles published before 2019 (and partly 2020) had already been included in systematic reviews that allowed to identify the ones concerning the knee joint. The review was carried out in accordance with Preferential Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Only peer-reviewed articles were considered for inclusion. A total of 40 studies were identified, showing promising results in terms either of biomarker identification, new insight in mechanism of action or potential therapeutic targets for knee OA. DNA methylation, histone modification and ncRNA were all mechanisms involved in epigenetic regulation of the knee. Most recent evidence suggests that epigenetics is a most promising field with the long-term goal of improving understanding and management of knee OA, but a variety of research approaches need greater consolidation.

Whole genome methylation sequencing in blood identifies extensive differential DNA methylation in late-onset dementia due to Alzheimer's disease

INTRODUCTION

DNA microarray-based studies report differentially methylated positions (DMPs) in blood between late-onset dementia due to Alzheimer's disease (AD) and cognitively unimpaired individuals, but interrogate < 4% of the genome.

METHODS

We used whole genome methylation sequencing (WGMS) to quantify DNA methylation levels at 25,409,826 CpG loci in 281 blood samples from 108 AD and 173 cognitively unimpaired individuals.

RESULTS

WGMS identified 28,038 DMPs throughout the human methylome, including 2707 differentially methylated genes (e.g., SORCS3, GABA, and PICALM) encoding proteins in biological pathways relevant to AD such as synaptic membrane, cation channel complex, and glutamatergic synapse. One hundred seventy-three differentially methylated blood-specific enhancers interact with the promoters of 95 genes that are differentially expressed in blood from persons with and without AD.

DISCUSSION

WGMS identifies differentially methylated CpGs in known and newly detected genes and enhancers in blood from persons with and without AD.

HIGHLIGHTS

Whole genome DNA methylation levels were quantified in blood from persons with and without Alzheimer's disease (AD). Twenty-eight thousand thirty-eight differentially methylated positions (DMPs) were identified. Two thousand seven hundred seven genes comprise DMPs. Forty-eight of 75 independent genetic risk loci for AD have DMPs. One thousand five hundred sixty-eight blood-specific enhancers comprise DMPs, 173 of which interact with the promoters of 95 genes that are differentially expressed in blood from persons with and without AD.

Multimodal Omics Approaches to Aging and Age-Related Diseases

Aging is associated with a progressive decline in physiological capacities and an increased risk of aging-associated disorders. An increasing body of experimental evidence shows that aging is a complex biological process coordinately regulated by multiple factors at different molecular layers. Thus, it is difficult to delineate the overall systematic aging changes based on single-layer data. Instead, multimodal omics approaches, in which data are acquired and analyzed using complementary omics technologies, such as genomics, transcriptomics, and epigenomics, are needed for gaining insights into the precise molecular regulatory mechanisms that trigger aging. In recent years, multimodal omics sequencing technologies that can reveal complex regulatory networks and specific phenotypic changes have been developed and widely applied to decode aging and age-related diseases. This review summarizes the classification and progress of multimodal omics approaches, as well as the rapidly growing number of articles reporting on their application in the field of aging research, and outlines new developments in the clinical treatment of age-related diseases based on omics technologies.

MLNGCF: circRNA-disease associations prediction with multilayer attention neural graph-based collaborative filtering

MOTIVATION

CircRNAs play a critical regulatory role in physiological processes, and the abnormal expression of circRNAs can mediate the processes of diseases. Therefore, exploring circRNAs-disease associations is gradually becoming an important area of research. Due to the high cost of validating circRNA-disease associations using traditional wet-lab experiments, novel computational methods based on machine learning are gaining more and more attention in this field. However, current computational methods suffer to insufficient consideration of latent features in circRNA-disease interactions.

RESULTS

In this study, a multilayer attention neural graph-based collaborative filtering (MLNGCF) is proposed. MLNGCF first enhances multiple biological information with autoencoder as the initial features of circRNAs and diseases. Then, by constructing a central network of different diseases and circRNAs, a multilayer cooperative attention-based message propagation is performed on the central network to obtain the high-order features of circRNAs and diseases. A neural network-based collaborative filtering is constructed to predict the unknown circRNA-disease associations and update the model parameters. Experiments on the benchmark datasets demonstrate that MLNGCF outperforms state-of-the-art methods, and the prediction results are supported by the literature in the case studies.

AVAILABILITY AND IMPLEMENTATION

The source codes and benchmark datasets of MLNGCF are available at https://github.com/ABard0/MLNGCF.

The Role of Genetics and Epigenetic Regulation in the Pathogenesis of Osteoarthritis

Osteoarthritis (OA) is progressive disease characterised by cartilage degradation, subchondral bone remodelling and inflammation of the synovium. The disease is associated with obesity, mechanical load and age. However, multiple pro-inflammatory immune mediators regulate the expression of metalloproteinases, which take part in cartilage degradation. Furthermore, genetic factors also contribute to OA susceptibility. Recent studies have highlighted that epigenetic mechanisms may regulate the expression of OA-associated genes. This review aims to present the mechanisms of OA pathogenesis and summarise current evidence regarding the role of genetics and epigenetics in this process.

Identification of key candidate genes and pathways in rheumatoid arthritis and osteoarthritis by integrated bioinformatical analysis

Rheumatoid arthritis (RA) and osteoarthritis (OA) are the most common joint disorders. Although they have shown analogous clinical manifestations, the pathogenesis of RA and OA are different. In this study, we used the online Gene Expression Omnibus (GEO) microarray expression profiling dataset GSE153015 to identify gene signatures between RA and OA joints. The relevant data on 8 subjects obtained from large joints of RA patients (RA-LJ), 8 subjects obtained from small joints of RA patients (RA-SJ), and 4 subjects with OA were investigated. Differentially expressed genes (DEGs) were screened. Functional enrichment analysis of DEGs including the Gene Ontology terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were identified, which were mainly associated with T cell activation or chemokine activity. Besides, protein-protein interaction (PPI) network analysis was performed, and key modules were identified. Hub genes of RA-LJ and OA groups were screened, they were CD8A, GZMB, CCL5, CD2, and CXCL9, whereas CD8A, CD2, IL7R, CD27, and GZMB were hub genes of RA-SJ and OA group. The novel DEGs and functional pathways between RA and OA identified in this study may provide new insight into the underlying molecular mechanisms and therapeutic strategies of RA and OA.

A mechanistic mathematical model of initiation and malignant transformation in sporadic vestibular schwannoma

BACKGROUND

A vestibular schwannoma (VS) is a relatively rare, benign tumour of the eighth cranial nerve, often involving alterations to the gene NF2. Previous mathematical models of schwannoma incidence have not attempted to account for alterations in specific genes, and could not distinguish between nonsense mutations and loss of heterozygosity (LOH).

METHODS

Here, we present a mechanistic approach to modelling initiation and malignant transformation in schwannoma. Each parameter is associated with a specific gene or mechanism operative in Schwann cells, and can be determined by combining incidence data with empirical frequencies of pathogenic variants and LOH.

RESULTS

This results in new estimates for the base-pair mutation rate u = 4.48 × 10-10 and the rate of LOH = 2.03 × 10-6/yr in Schwann cells. In addition to new parameter estimates, we extend the approach to estimate the risk of both spontaneous and radiation-induced malignant transformation.

DISCUSSION

We conclude that radiotherapy is likely to have a negligible excess risk of malignancy for sporadic VS, with a possible exception of rapidly growing tumours.

Establishment and evaluation of module-based immune-associated gene signature to predict overall survival in patients of colon adenocarcinoma

BACKGROUND

Patients with colon adenocarcinoma (COAD) exhibit significant heterogeneity in overall survival. The current tumor-node-metastasis staging system is insufficient to provide a precise prediction for prognosis. Identification and evaluation of new risk models by using big cancer data may provide a good way to identify prognosis-related signature.

METHODS

We integrated different datasets and applied bioinformatic and statistical methods to construct a robust immune-associated risk model for COAD prognosis. Furthermore, a nomogram was constructed based on the gene signature and clinicopathological features to improve risk stratification and quantify risk assessment for individual patients.

RESULTS

The immune-associated risk model discriminated high-risk patients in our investigated and validated cohorts. Survival analyses demonstrated that our gene signature served as an independent risk factor for overall survival and the nomogram exhibited high accuracy. Functional analysis interpreted the correlation between our risk model and its role in prognosis by classifying groups with different immune activities. Remarkably, patients in the low-risk group showed higher immune activity, while those in the high-risk group displayed a lower immune activity.

CONCLUSIONS

Our study provides a novel tool that may contribute to the optimization of risk stratification for survival and personalized management of COAD.

Current understanding of osteoarthritis pathogenesis and relevant new approaches

Osteoarthritis (OA) is the most common degenerative joint disease that causes painful swelling and permanent damage to the joints in the body. The molecular mechanisms of OA are currently unknown. OA is a heterogeneous disease that affects the entire joint, and multiple tissues are altered during OA development. To better understand the pathological mechanisms of OA, new approaches, methods, and techniques need to be used to understand OA pathogenesis. In this review, we first focus on the epigenetic regulation of OA, with a particular focus on DNA methylation, histone modification, and microRNA regulation, followed by a summary of several key mediators in OA-associated pain. We then introduce several innovative techniques that have been and will continue to be used in the fields of OA and OA-associated pain, such as CRISPR, scRNA sequencing, and lineage tracing. Next, we discuss the timely updates concerning cell death regulation in OA pathology, including pyroptosis, ferroptosis, and autophagy, as well as their individual roles in OA and potential molecular targets in treating OA. Finally, our review highlights new directions on the role of the synovial lymphatic system in OA. An improved understanding of OA pathogenesis will aid in the development of more specific and effective therapeutic interventions for OA.

Secure human action recognition by encrypted neural network inference

Advanced computer vision technology can provide near real-time home monitoring to support "aging in place" by detecting falls and symptoms related to seizures and stroke. Affordable webcams, together with cloud computing services (to run machine learning algorithms), can potentially bring significant social benefits. However, it has not been deployed in practice because of privacy concerns. In this paper, we propose a strategy that uses homomorphic encryption to resolve this dilemma, which guarantees information confidentiality while retaining action detection. Our protocol for secure inference can distinguish falls from activities of daily living with 86.21% sensitivity and 99.14% specificity, with an average inference latency of 1.2 seconds and 2.4 seconds on real-world test datasets using small and large neural nets, respectively. We show that our method enables a 613x speedup over the latency-optimized LoLa and achieves an average of 3.1x throughput increase in secure inference compared to the throughput-optimized nGraph-HE2.

Identification of TMEM129, encoding a ubiquitin-protein ligase, as an effector gene of osteoarthritis genetic risk

BACKGROUND

Osteoarthritis is highly heritable and genome-wide studies have identified single nucleotide polymorphisms (SNPs) associated with the disease. One such locus is marked by SNP rs11732213 (T > C). Genotype at rs11732213 correlates with the methylation levels of nearby CpG dinucleotides (CpGs), forming a methylation quantitative trait locus (mQTL). This study investigated the regulatory activity of the CpGs to identify a target gene of the locus.

METHODS

Nucleic acids were extracted from the articular cartilage of osteoarthritis patients. Samples were genotyped, and DNA methylation was quantified by pyrosequencing at 14 CpGs within a 259-bp interval. CpGs were tested for enhancer effects in immortalised chondrocytes using a reporter gene assay. DNA methylation at the locus was altered using targeted epigenome editing, with the impact on gene expression determined using quantitative polymerase chain reaction.

RESULTS

rs11732213 genotype correlated with DNA methylation at nine CpGs, which formed a differentially methylated region (DMR), with the osteoarthritis risk allele T corresponding to reduced levels of methylation. The DMR acted as an enhancer and demethylation of the CpGs altered expression of TMEM129. Allelic imbalance in TMEM129 expression was identified in cartilage, with under-expression of the risk allele.

CONCLUSIONS

TMEM129 is a target of osteoarthritis genetic risk at this locus. Genotype at rs11732213 impacts DNA methylation at the enhancer, which, in turn, modulates TMEM129 expression. TMEM129 encodes an enzyme involved in protein degradation within the endoplasmic reticulum, a process previously implicated in osteoarthritis. TMEM129 is a compelling osteoarthritis susceptibility target.

A blockchain-based framework to support pharmacogenetic data sharing

The successful implementation of pharmacogenetics (PGx) into clinical practice requires patient genomic data to be shared between stakeholders in multiple settings. This creates a number of barriers to widespread adoption of PGx, including privacy concerns related to the storage and movement of identifiable genomic data. Informatic solutions that support secure and equitable data access for genomic data are therefore important to PGx. Here we propose a methodology that uses smart contracts implemented on a blockchain-based framework, PGxChain, to address this issue. The design requirements for PGxChain were identified through a systematic literature review, identifying technical challenges and barriers impeding the clinical implementation of pharmacogenomics. These requirements included security and privacy, accessibility, interoperability, traceability and legal compliance. A proof-of-concept implementation based on Ethereum was then developed that met the design requirements. PGxChain's performance was examined using Hyperledger Caliper for latency, throughput, and transaction success rate. The findings clearly indicate that blockchain technology offers considerable potential to advance pharmacogenetic data sharing, particularly with regard to PGx data security and privacy, large-scale accessibility of PGx data, PGx data interoperability between multiple health care providers and compliance with data-sharing laws and regulations.

SARS-CoV-2 potential drugs, drug targets, and biomarkers: a viral-host interaction network-based analysis

COVID-19 is a global pandemic impacting the daily living of millions. As variants of the virus evolve, a complete comprehension of the disease and drug targets becomes a decisive duty. The Omicron variant, for example, has a notably high transmission rate verified in 155 countries. We performed integrative transcriptomic and network analyses to identify drug targets and diagnostic biomarkers and repurpose FDA-approved drugs for SARS-CoV-2. Upon the enrichment of 464 differentially expressed genes, pathways regulating the host cell cycle were significant. Regulatory and interaction networks featured hsa-mir-93-5p and hsa-mir-17-5p as blood biomarkers while hsa-mir-15b-5p as an antiviral agent. MYB, RRM2, ERG, CENPF, CIT, and TOP2A are potential drug targets for treatment. HMOX1 is suggested as a prognostic biomarker. Enhancing HMOX1 expression by neem plant extract might be a therapeutic alternative. We constructed a drug-gene network for FDA-approved drugs to be repurposed against the infection. The key drugs retrieved were members of anthracyclines, mitotic inhibitors, anti-tumor antibiotics, and CDK1 inhibitors. Additionally, hydroxyquinone and digitoxin are potent TOP2A inhibitors. Hydroxyurea, cytarabine, gemcitabine, sotalol, and amiodarone can also be redirected against COVID-19. The analysis enforced the repositioning of fluorouracil and doxorubicin, especially that they have multiple drug targets, hence less probability of resistance.

Activity limitation and participation restriction in Osteoarthritis and Rheumatoid arthritis: findings based on the National Health and Nutritional Examination Survey

Background

Osteoarthritis (OA) and Rheumatoid arthritis (RA) are the most common joint diseases leading to chronic pain and disability. Given the chronicity and disabling nature of OA and RA, they are likely to influence full participation of individuals in the society. An activity limitation occurs when a person has difficulty executing an activity; a participation restriction is experienced when a person has difficulty participating in a real-life situation. The aim of this study was to examine the associations between OA and RA and the domains of activity limitation and participation restriction.

Methods

A cross-sectional study design comprised 3604 adults from the 2009 to 2018 National Health and Nutrition Examination Survey (NHANES). All participants aged ≥ 20 years with complete data were included. Activity limitation and participation restriction were assessed by reported difficulty in performing 14 tasks selected from Physical Functioning Questionnaire. Data on OA and RA were obtained from Medical Conditions Questionnaire. Weighted logistic regression model was used to examine the associations between OA and RA and the selected tasks.

Results

Over 36% of participants had limitations. Both OA (OR = 2.11) and RA (OR = 2.36) were positively associated with activity limitation and participation restriction (p < 0.001). Poor or fair health was associated with difficulty in physical functioning, with highest odds observed in leisure activities (OR = 2.05), followed by difficulty in attending social events (OR = 1.99), walking for a quarter mile (OR = 1.97), preparing meals (OR = 1.93) and walking up ten steps (OR = 1.92).

Conclusion

Adults with OA and RA had nearly similar odds of having activity limitations and participation restrictions. Difficulty in executing most activities of daily living (ADLs) has significant association with poor or fair health. Holistic interdisciplinary care to individuals with OA or RA focusing on ADLs and environmental factors may improve health status.

Oncogenic role and potential regulatory mechanism of topoisomerase IIα in a pan-cancer analysis

Topoisomerase IIα (TOP2A) plays an oncogenic role in multiple tumor types. However, no pan-cancer analysis about the function and the upstream molecular mechanism of TOP2A is available. For the first time, we analyzed potential oncogenic roles of TOP2A in 33 cancer types via The Cancer Genome Atlas (TCGA) database. Overexpression of TOP2A was existed in almost all cancer types, and related to poor prognosis and advanced pathological stages in most cases. Besides, the high frequency of TOP2A genetic alterations was observed in several cancer types, and related to prognosis in some cases. Moreover, we conduct upstream miRNAs and lncRNAs of TOP2A to establish ceRNA networks in kidney renal clear cell carcinoma (SNHG3-miR-139-5p), kidney renal papillary cell carcinoma (TMEM147-AS1/N4BP2L2-IT2/THUMPD3-AS1/ERICD/TTN-AS1/SH3BP5-AS1/THRB-IT1/SNHG3/NEAT1-miR-139-5p), liver hepatocellular carcinoma (SNHG3/THUMPD3-AS1/NUTM2B-AS1/NUTM2A-AS1-miR-139-5p and SNHG6/GSEC/SNHG1/SNHG14/LINC00265/MIR3142HG-miR-101-3p) and lung adenocarcinoma (TYMSOS/HELLPAR/SNHG1/GSEC/SNHG6-miR-101-3p). TOP2A expression was generally positively correlated with cancer associated fibroblasts, M0 and M1 macrophages in most cancer types. Furthermore, TOP2A was positively associated with expression of immune checkpoints (CD274, CTLA4, HAVCR2, LAG3, PDCD1 and TIGIT) in most cancer types. Our first TOP2A pan-cancer study contributes to understanding the prognostic roles, immunological roles and potential upstream molecular mechanism of TOP2A in different cancers.

A survey on the interpretability of deep learning in medical diagnosis

Deep learning has demonstrated remarkable performance in the medical domain, with accuracy that rivals or even exceeds that of human experts. However, it has a significant problem that these models are "black-box" structures, which means they are opaque, non-intuitive, and difficult for people to understand. This creates a barrier to the application of deep learning models in clinical practice due to lack of interpretability, trust, and transparency. To overcome this problem, several studies on interpretability have been proposed. Therefore, in this paper, we comprehensively review the interpretability of deep learning in medical diagnosis based on the current literature, including some common interpretability methods used in the medical domain, various applications with interpretability for disease diagnosis, prevalent evaluation metrics, and several disease datasets. In addition, the challenges of interpretability and future research directions are also discussed here. To the best of our knowledge, this is the first time that various applications of interpretability methods for disease diagnosis have been summarized.

DNA methylation and noncoding RNA in OA: Recent findings and methodological advances

Introduction:

Osteoarthritis (OA) is a chronic musculoskeletal disease characterized by progressive loss of joint function. Historically, it has been characterized as a disease caused by mechanical trauma, so-called ‘wear and tear’. Over the past two decades, it has come to be understood as a complex systemic disorder involving gene-environmental interactions. Epigenetic changes have been increasingly implicated. Recent improvements in microarray and next-generation sequencing (NGS) technologies have allowed for ever more complex evaluations of epigenetic aberrations associated with the development and progression of OA.

Methods:

A systematic review was conducted in the Pubmed database. We curated studies that presented the results of DNA methylation and noncoding RNA research in human OA and OA animal models since 1985.

Results:

Herein, we discuss recent findings and methodological advancements in OA epigenetics, including a discussion of DNA methylation, including microarray and NGS studies, and noncoding RNAs. Beyond cartilage, we also highlight studies in subchondral bone and peripheral blood mononuclear cells, which highlight widespread and potentially clinically important alterations in epigenetic patterns seen in OA patients. Finally, we discuss epigenetic editing approaches in the context of OA.

Conclusions:

Although a substantial body of literature has already been published in OA, much is still unknown. Future OA epigenetics studies will no doubt continue to broaden our understanding of underlying pathophysiology and perhaps offer novel diagnostics and/or treatments for human OA.

Data-independent acquisition mass spectrometry in severe rheumatic heart disease (RHD) identifies a proteomic signature showing ongoing inflammation and effectively classifying RHD cases

BACKGROUND

Rheumatic heart disease (RHD) remains a major source of morbidity and mortality in developing countries. A deeper insight into the pathogenetic mechanisms underlying RHD could provide opportunities for drug repurposing, guide recommendations for secondary penicillin prophylaxis, and/or inform development of near-patient diagnostics.

METHODS

We performed quantitative proteomics using Sequential Windowed Acquisition of All Theoretical Fragment Ion Mass Spectrometry (SWATH-MS) to screen protein expression in 215 African patients with severe RHD, and 230 controls. We applied a machine learning (ML) approach to feature selection among the 366 proteins quantifiable in at least 40% of samples, using the Boruta wrapper algorithm. The case-control differences and contribution to Area Under the Receiver Operating Curve (AUC) for each of the 56 proteins identified by the Boruta algorithm were calculated by Logistic Regression adjusted for age, sex and BMI. Biological pathways and functions enriched for proteins were identified using ClueGo pathway analyses.

RESULTS

Adiponectin, complement component C7 and fibulin-1, a component of heart valve matrix, were significantly higher in cases when compared with controls. Ficolin-3, a protein with calcium-independent lectin activity that activates the complement pathway, was lower in cases than controls. The top six biomarkers from the Boruta analyses conferred an AUC of 0.90 indicating excellent discriminatory capacity between RHD cases and controls.

CONCLUSIONS

These results support the presence of an ongoing inflammatory response in RHD, at a time when severe valve disease has developed, and distant from previous episodes of acute rheumatic fever. This biomarker signature could have potential utility in recognizing different degrees of ongoing inflammation in RHD patients, which may, in turn, be related to prognostic severity.

Computational identification of host genomic biomarkers highlighting their functions, pathways and regulators that influence SARS-CoV-2 infections and drug repurposing

The pandemic threat of COVID-19 has severely destroyed human life as well as the economy around the world. Although, the vaccination has reduced the outspread, but people are still suffering due to the unstable RNA sequence patterns of SARS-CoV-2 which demands supplementary drugs. To explore novel drug target proteins, in this study, a transcriptomics RNA-Seq data generated from SARS-CoV-2 infection and control samples were analyzed. We identified 109 differentially expressed genes (DEGs) that were utilized to identify 10 hub-genes/proteins (TLR2, USP53, GUCY1A2, SNRPD2, NEDD9, IGF2, CXCL2, KLF6, PAG1 and ZFP36) by the protein–protein interaction (PPI) network analysis. The GO functional and KEGG pathway enrichment analyses of hub-DEGs revealed some important functions and signaling pathways that are significantly associated with SARS-CoV-2 infections. The interaction network analysis identified 5 TFs proteins and 6 miRNAs as the key regulators of hub-DEGs. Considering 10 hub-proteins and 5 key TFs-proteins as drug target receptors, we performed their docking analysis with the SARS-CoV-2 3CL protease-guided top listed 90 FDA approved drugs. We found Torin-2, Rapamycin, Radotinib, Ivermectin, Thiostrepton, Tacrolimus and Daclatasvir as the top ranked seven candidate drugs. We investigated their resistance performance against the already published COVID-19 causing top-ranked 11 independent and 8 protonated receptor proteins by molecular docking analysis and found their strong binding affinities, which indicates that the proposed drugs are effective against the state-of-the-arts alternatives independent receptor proteins also. Finally, we investigated the stability of top three drugs (Torin-2, Rapamycin and Radotinib) by using 100 ns MD-based MM-PBSA simulations with the two top-ranked proposed receptors (TLR2, USP53) and independent receptors (IRF7, STAT1), and observed their stable performance. Therefore, the proposed drugs might play a vital role for the treatment against different variants of SARS-CoV-2 infections.

ECNano: A cost-effective workflow for target enrichment sequencing and accurate variant calling on 4800 clinically significant genes using a single MinION flowcell

BACKGROUND

The application of long-read sequencing using the Oxford Nanopore Technologies (ONT) MinION sequencer is getting more diverse in the medical field. Having a high sequencing error of ONT and limited throughput from a single MinION flowcell, however, limits its applicability for accurate variant detection. Medical exome sequencing (MES) targets clinically significant exon regions, allowing rapid and comprehensive screening of pathogenic variants. By applying MES with MinION sequencing, the technology can achieve a more uniform capture of the target regions, shorter turnaround time, and lower sequencing cost per sample.

METHOD

We introduced a cost-effective optimized workflow, ECNano, comprising a wet-lab protocol and bioinformatics analysis, for accurate variant detection at 4800 clinically important genes and regions using a single MinION flowcell. The ECNano wet-lab protocol was optimized to perform long-read target enrichment and ONT library preparation to stably generate high-quality MES data with adequate coverage. The subsequent variant-calling workflow, Clair-ensemble, adopted a fast RNN-based variant caller, Clair, and was optimized for target enrichment data. To evaluate its performance and practicality, ECNano was tested on both reference DNA samples and patient samples.

RESULTS

ECNano achieved deep on-target depth of coverage (DoC) at average > 100× and > 98% uniformity using one MinION flowcell. For accurate ONT variant calling, the generated reads sufficiently covered 98.9% of pathogenic positions listed in ClinVar, with 98.96% having at least 30× DoC. ECNano obtained an average read length of 1000 bp. The long reads of ECNano also covered the adjacent splice sites well, with 98.5% of positions having ≥ 30× DoC. Clair-ensemble achieved > 99% recall and accuracy for SNV calling. The whole workflow from wet-lab protocol to variant detection was completed within three days.

CONCLUSION

We presented ECNano, an out-of-the-box workflow comprising (1) a wet-lab protocol for ONT target enrichment sequencing and (2) a downstream variant detection workflow, Clair-ensemble. The workflow is cost-effective, with a short turnaround time for high accuracy variant calling in 4800 clinically significant genes and regions using a single MinION flowcell. The long-read exon captured data has potential for further development, promoting the application of long-read sequencing in personalized disease treatment and risk prediction.

Genetic biomarkers in osteoarthritis: a quick overview

Osteoarthritis (OA) is a chronic musculoskeletal disease with a polygenic and heterogeneous nature. In addition, when clinical manifestations appear, the evolution of the disease is usually already irreversible. Therefore, the efforts on OA research are focused mainly on the discovery of therapeutic targets and reliable biomarkers that permit the early identification of different OA-related parameters such as diagnosis, prognosis, or phenotype identification. To date, potential candidate protein biomarkers have been associated with different aspects of the disease; however, there is currently no gold standard. In this sense, genomic data could act as complementary biomarkers of diagnosis and prognosis or even help to identify therapeutic targets of the disease. In this review, we will describe the most recent advances in genetic biomarkers in OA over the past three years.

Evaluation of exosomal non-coding RNAs in cancer using high-throughput sequencing

Clinical oncologists need more reliable and non-invasive diagnostic and prognostic biomarkers to follow-up cancer patients. However, the existing biomarkers are often invasive and costly, emphasizing the need for the development of biomarkers to provide convenient and precise detection. Extracellular vesicles especially exosomes have recently been the focus of translational research to develop non-invasive and reliable biomarkers for several diseases such as cancers, suggesting as a valuable source of tumor markers. Exosomes are nano-sized extracellular vesicles secreted by various living cells that can be found in all body fluids including serum, urine, saliva, cerebrospinal fluid, and ascites. Different molecular and genetic contents of their origin such as nucleic acids, proteins, lipids, and glycans in a stable form make exosomes a promising approach for various cancers' diagnoses, prediction, and follow-up in a minimally invasive manner. Since exosomes are used by cancer cells for intercellular communication, they play a critical role in the disease process, highlighting the importance of their use as clinically relevant biomarkers. However, regardless of the advantages that exosome-based diagnostics have, they suffer from problems regarding their isolation, detection, and characterization of their contents. This study reviews the history and biogenesis of exosomes and discusses non-coding RNAs (ncRNAs) and their potential as tumor markers in different types of cancer, with a focus on next generation sequencing (NGS) as a detection method. Moreover, the advantages and challenges associated with exosome-based diagnostics are also presented.

XDeathDB: a visualization platform for cell death molecular interactions

Lots of cell death initiator and effector molecules, signalling pathways and subcellular sites have been identified as key mediators in both cell death processes in cancer. The XDeathDB visualization platform provides a comprehensive cell death and their crosstalk resource for deciphering the signaling network organization of interactions among different cell death modes associated with 1461 cancer types and COVID-19, with an aim to understand the molecular mechanisms of physiological cell death in disease and facilitate systems-oriented novel drug discovery in inducing cell deaths properly. Apoptosis, autosis, efferocytosis, ferroptosis, immunogenic cell death, intrinsic apoptosis, lysosomal cell death, mitotic cell death, mitochondrial permeability transition, necroptosis, parthanatos, and pyroptosis related to 12 cell deaths and their crosstalk can be observed systematically by the platform. Big data for cell death gene-disease associations, gene-cell death pathway associations, pathway-cell death mode associations, and cell death-cell death associations is collected by literature review articles and public database from iRefIndex, STRING, BioGRID, Reactom, Pathway's commons, DisGeNET, DrugBank, and Therapeutic Target Database (TTD). An interactive webtool, XDeathDB, is built by web applications with R-Shiny, JavaScript (JS) and Shiny Server Iso. With this platform, users can search specific interactions from vast interdependent networks that occur in the realm of cell death. A multilayer spectral graph clustering method that performs convex layer aggregation to identify crosstalk function among cell death modes for a specific cancer. 147 hallmark genes of cell death could be observed in detail in these networks. These potential druggable targets are displayed systematically and tailoring networks to visualize specified relations is available to fulfil user-specific needs. Users can access XDeathDB for free at https://pcm2019.shinyapps.io/XDeathDB/ .

Structural and functional analysis of somatic coding and UTR indels in breast and lung cancer genomes

Insertions and deletions (Indels) represent one of the major variation types in the human genome and have been implicated in diseases including cancer. To study the features of somatic indels in different cancer genomes, we investigated the indels from two large samples of cancer types: invasive breast carcinoma (BRCA) and lung adenocarcinoma (LUAD). Besides mapping somatic indels in both coding and untranslated regions (UTRs) from the cancer whole exome sequences, we investigated the overlap between these indels and transcription factor binding sites (TFBSs), the key elements for regulation of gene expression that have been found in both coding and non-coding sequences. Compared to the germline indels in healthy genomes, somatic indels contain more coding indels with higher than expected frame-shift (FS) indels in cancer genomes. LUAD has a higher ratio of deletions and higher coding and FS indel rates than BRCA. More importantly, these somatic indels in cancer genomes tend to locate in sequences with important functions, which can affect the core secondary structures of proteins and have a bigger overlap with predicted TFBSs in coding regions than the germline indels. The somatic CDS indels are also enriched in highly conserved nucleotides when compared with germline CDS indels.

The regulatory pattern of target gene expression by aberrant enhancer methylation in glioblastoma

Background

Glioblastoma multiforme (GBM) is the most common and aggressive primary malignant brain tumor with grim prognosis. Aberrant DNA methylation is an epigenetic mechanism that promotes GBM carcinogenesis, while the function of DNA methylation at enhancer regions in GBM remains poorly described.

Results

We integrated multi-omics data to identify differential methylation enhancer region (DMER)-genes and revealed global enhancer hypomethylation in GBM. In addition, a DMER-mediated target genes regulatory network and functional enrichment analysis of target genes that might be regulated by hypomethylation enhancer regions showed that aberrant enhancer regions could contribute to tumorigenesis and progression in GBM. Further, we identified 22 modules in which lncRNAs and mRNAs synergistically competed with each other. Finally, through the construction of drug-target association networks, our study identified potential small-molecule drugs for GBM treatment.

Conclusions

Our study provides novel insights for understanding the regulation of aberrant enhancer region methylation and developing methylation-based biomarkers for the diagnosis and treatment of GBM.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04345-8.

DNA Methylation Signatures of Bone Metabolism in Osteoporosis and Osteoarthritis Aging-Related Diseases: An Updated Review

DNA methylation is one of the most studied epigenetic mechanisms that play a pivotal role in regulating gene expression. The epigenetic component is strongly involved in aging-bone diseases, such as osteoporosis and osteoarthritis. Both are complex multi-factorial late-onset disorders that represent a globally widespread health problem, highlighting a crucial point of investigations in many scientific studies. In recent years, new findings on the role of DNA methylation in the pathogenesis of aging-bone diseases have emerged. The aim of this systematic review is to update knowledge in the field of DNA methylation associated with osteoporosis and osteoarthritis, focusing on the specific tissues involved in both pathological conditions.

SUSCC: Secondary Construction of Feature Space based on UMAP for Rapid and Accurate Clustering Large-scale Single Cell RNA-seq Data

Clustering is a common method to identify cell types in single cell analysis, but the increasing size of scRNA-seq datasets brings challenges to single cell clustering. Therefore, it is an urgent need to design a faster and more accurate clustering method for large-scale scRNA-seq data. In this paper, we proposed a new method for single cell clustering. First, a count matrix is constructed through normalization and gene filtration. Second, the raw data of gene expression matrix are projected to feature space constructed by secondary construction of feature space based on UMAP (Uniform Manifold Approximation and Projection). Third, the low-dimensional matrix on the feature space is randomly divided into two sub-matrices according to a certain proportion for clustering and classifying, respectively. Finally, one subset is clustered by k-means algorithm and then the other subset is classified by k-nearest neighbor algorithm based on clustering results. Experimental results show that our method can cluster the scRNA-seq datasets effectively.

Differential ESR1 Promoter Methylation in the Peripheral Blood-Findings from the Women 40+ Healthy Aging Study

Background Estrogen receptor α (ERα) contributes to maintaining biological processes preserving health during aging. DNA methylation changes of ERα gene (ESR1) were established as playing a direct role in the regulation of ERα levels. In this study, we hypothesized decreased DNA methylation of ESR1 associated with postmenopause, lower estradiol (E2) levels, and increased age among healthy middle-aged and older women. Methods We assessed DNA methylation of ESR1 promoter region from dried blood spots (DBSs) and E2 from saliva samples in 130 healthy women aged 40-73 years. Results We found that postmenopause and lower E2 levels were associated with lower DNA methylation of a distal regulatory region, but not with DNA methylation of proximal promoters. Conclusion Our results indicate that decreased methylation of ESR1 cytosine-phosphate-guanine island (CpGI) shore may be associated with conditions of lower E2 in older healthy women.