Identification of Key Long Non-Coding RNAs in the Pathology of Alzheimer’s Disease and their Functions Based on Genome-Wide Associations Study, Microarray, and RNA-seq Data

Data Mining of Microarray Datasets in Translational Neuroscience

Data mining involves the computational analysis of a plethora of publicly available datasets to generate new hypotheses that can be further validated by experiments for the improved understanding of the pathogenesis of neurodegenerative diseases. Although the number of sequencing datasets is on the rise, microarray analysis conducted on diverse biological samples represent a large collection of datasets with multiple web-based programs that enable efficient and convenient data analysis. In this review, we first discuss the selection of biological samples associated with neurological disorders, and the possibility of a combination of datasets, from various types of samples, to conduct an integrated analysis in order to achieve a holistic understanding of the alterations in the examined biological system. We then summarize key approaches and studies that have made use of the data mining of microarray datasets to obtain insights into translational neuroscience applications, including biomarker discovery, therapeutic development, and the elucidation of the pathogenic mechanisms of neurodegenerative diseases. We further discuss the gap to be bridged between microarray and sequencing studies to improve the utilization and combination of different types of datasets, together with experimental validation, for more comprehensive analyses. We conclude by providing future perspectives on integrating multi-omics, to advance precision phenotyping and personalized medicine for neurodegenerative diseases.

A Group of Long Non-coding RNAs in Blood Acts as a Specific Biomarker of Alzheimer's Disease

Long non-coding RNAs (lncRNAs) have been identified to be involved in the pathogenesis of Alzheimer's disease (AD). In this study, we evaluated whether lncRNAs can be used to discriminate AD patients from controls and patients with other dementias, such as vascular, Parkinson's disease, behavioral variant frontotemporal, and dementia with Lewy body. In this study, we used three datasets to measure the blood lncRNA levels. A pilot study (dataset 1, n = 40; controls, 20; AD, 20) was used to screen for differentially expressed lncRNAs. Dataset 2 (n = 174; controls, 86; AD, 88) was used to identify a lncRNA panel for the diagnostic model. Dataset 3 (n = 333; control, 60; AD, 54; vascular dementia, 53; Parkinson's disease dementia, 55; behavioral variant frontotemporal dementia, 56; and dementia with Lewy body, 55) was used to validate the diagnostic model. In dataset 1, 12 upregulated and 15 downregulated lncRNAs were identified. In dataset 2, a panel of seven lncRNAs was found to have the ability to differentiate AD patients from controls. Finally, this panel was applied to dataset 3 to successfully distinguish AD from other dementias. This study proposes a panel of seven lncRNAs as specific and promising biomarker for AD diagnosis.

Assessing the Association Between Lead Pollution and Risk of Alzheimer’s Disease by Integrating Multigenomics

Alzheimer’s disease (AD) is a life-threatening neurodegenerative disease of the elderly. In recent observations, exposure to heavy metals environmental may increase the risk of AD. However, there are few studies on the causal relationship between heavy metal exposure and AD. In this study, we integrated two large-scale summaries of AD genome-wide association study (GWAS) datasets and a blood lead level GWAS dataset and performed the two-sample Mendelian randomization analysis to assess the causality of blood lead level and AD risk. The results showed that there is a significantly positive causality between blood lead level and AD risk both in the inverse-variance weighted (IVW) model and the weighted median estimator (WME) model. An independent additional verification also reached a consistent conclusion. These findings further confirm the conclusions of previous studies and improve the understanding of the relationship between AD pathogenesis and the toxicity of lead in environmental pollution.

Blood-Based Biomarkers for Alzheimer's Disease Diagnosis and Progression: An Overview

Alzheimer's Disease (AD) is a progressive neurodegenerative disease characterized by amyloid-β (Aβ) plaque deposition and neurofibrillary tangle accumulation in the brain. Although several studies have been conducted to unravel the complex and interconnected pathophysiology of AD, clinical trial failure rates have been high, and no disease-modifying therapies are presently available. Fluid biomarker discovery for AD is a rapidly expanding field of research aimed at anticipating disease diagnosis and following disease progression over time. Currently, Aβ1-42, phosphorylated tau, and total tau levels in the cerebrospinal fluid are the best-studied fluid biomarkers for AD, but the need for novel, cheap, less-invasive, easily detectable, and more-accessible markers has recently led to the search for new blood-based molecules. However, despite considerable research activity, a comprehensive and up-to-date overview of the main blood-based biomarker candidates is still lacking. In this narrative review, we discuss the role of proteins, lipids, metabolites, oxidative-stress-related molecules, and cytokines as possible disease biomarkers. Furthermore, we highlight the potential of the emerging miRNAs and long non-coding RNAs (lncRNAs) as diagnostic tools, and we briefly present the role of vitamins and gut-microbiome-related molecules as novel candidates for AD detection and monitoring, thus offering new insights into the diagnosis and progression of this devastating disease.

Missing lnc(RNAs) in Alzheimer's Disease?

With the ongoing demographic shift towards increasingly elderly populations, it is estimated that approximately 150 million people will live with Alzheimer's disease (AD) by 2050. By then, AD will be one of the most burdensome diseases of this and potentially next centuries. Although its exact etiology remains elusive, both environmental and genetic factors play crucial roles in the mechanisms underlying AD neuropathology. Genome-wide association studies (GWAS) identified genetic variants associated with AD susceptibility in more than 40 different genomic loci. Most of these disease-associated variants reside in non-coding regions of the genome. In recent years, it has become clear that functionally active transcripts arise from these non-coding loci. One type of non-coding transcript, referred to as long non-coding RNAs (lncRNAs), gained significant attention due to their multiple roles in neurodevelopment, brain homeostasis, aging, and their dysregulation or dysfunction in neurological diseases including in AD. Here, we will summarize the current knowledge regarding genetic variations, expression profiles, as well as potential functions, diagnostic or therapeutic roles of lncRNAs in AD. We postulate that lncRNAs may represent the missing link in AD pathology and that unraveling their role may open avenues to better AD treatments.

Aberrant hypermethylation induced downregulation of antisense lncRNA STXBP5-AS1 and its sense gene STXBP5 correlate with tumorigenesis of glioma

AIMS

The expression of antisense lncRNA STXBP5-AS1 and its sense gene STXBP5 were found to be downregulated in glioma by RNA sequencing; however, the function and mechanism of both two genes in the development of glioma have not been studied.

MATERIALS AND METHODS

QRT-PCR and western blot were used to determine the transcriptional and translational levels of moleculars. MSP and BSP assays were used to evaluate the methylation status of promoter CpG island. MTT, EdU, flow cytometry, and transwell assays were used to reveal biological effects. The in vivo mice model was used to validate the role of target genes in tumorigenesis.

KEY FINDINGS

The mRNA and protein expression of STXBP5 was significantly downregulated in glioma tissues and positively correlated with prognosis. STXBP5-AS1 was downregulated in glioma cells and tissues, and associated with tumor size and clinical stages. Both of two genes were significantly restored in cells treatment with 5-Aza. The promoter CpG island of STXBP5/STXBP5-AS1 was hypermethylated in glioma cells, but partially methylated in NHA cells. We found that promoter methylation frequency was significantly higher in glioma tissues. Functionally, overexpression of STXBP5 and STXBP5-AS1 inhibited cell proliferation, migration, and invasion and promoted apoptosis in vitro, whereas depletion of STXBP5 and STXBP5-AS1 showed opposite effects. Both the mRNA and protein expression of STXBP5 were positively regulated by STXBP5-AS1. Ectopic expression of STXBP5 and STXBP5-AS1 suppressed tumor formation in vivo.

SIGNIFICANCE

Our findings suggested that epigenetically silenced STXBP5-AS1 and STXBP5 might act as novel tumor suppressors of glioma.

The Implications of ncRNAs in the Development of Human Diseases

The mammalian genome comprehends a small minority of genes that encode for proteins (barely 2% of the total genome in humans) and an immense majority of genes that are transcribed into RNA but not encoded for proteins (ncRNAs). These non-coding genes are intimately related to the expression regulation of protein-coding genes. The ncRNAs subtypes differ in their size, so there are long non-coding genes (lncRNAs) and other smaller ones, like microRNAs (miRNAs) and piwi-interacting RNAs (piRNAs). Due to their important role in the maintenance of cellular functioning, any deregulation of the expression profiles of these ncRNAs can dissemble in the development of different types of diseases. Among them, we can highlight some of high incidence in the population, such as cancer, neurodegenerative, or cardiovascular disorders. In addition, thanks to the enormous advances in the field of medical genomics, these same ncRNAs are starting to be used as possible drugs, approved by the FDA, as an effective treatment for diseases.

Advances in Current Diabetes Proteomics: From the Perspectives of Label- free Quantification and Biomarker Selection

BACKGROUND

Due to its prevalence and negative impacts on both the economy and society, the diabetes mellitus (DM) has emerged as a worldwide concern. In light of this, the label-free quantification (LFQ) proteomics and diabetic marker selection methods have been applied to elucidate the underlying mechanisms associated with insulin resistance, explore novel protein biomarkers, and discover innovative therapeutic protein targets.

OBJECTIVE

The purpose of this manuscript is to review and analyze the recent computational advances and development of label-free quantification and diabetic marker selection in diabetes proteomics.

METHODS

Web of Science database, PubMed database and Google Scholar were utilized for searching label-free quantification, computational advances, feature selection and diabetes proteomics.

RESULTS

In this study, we systematically review the computational advances of label-free quantification and diabetic marker selection methods which were applied to get the understanding of DM pathological mechanisms. Firstly, different popular quantification measurements and proteomic quantification software tools which have been applied to the diabetes studies are comprehensively discussed. Secondly, a number of popular manipulation methods including transformation, pretreatment (centering, scaling, and normalization), missing value imputation methods and a variety of popular feature selection techniques applied to diabetes proteomic data are overviewed with objective evaluation on their advantages and disadvantages. Finally, the guidelines for the efficient use of the computationbased LFQ technology and feature selection methods in diabetes proteomics are proposed.

CONCLUSION

In summary, this review provides guidelines for researchers who will engage in proteomics biomarker discovery and by properly applying these proteomic computational advances, more reliable therapeutic targets will be found in the field of diabetes mellitus.

Protein functional annotation of simultaneously improved stability, accuracy and false discovery rate achieved by a sequence-based deep learning

Functional annotation of protein sequence with high accuracy has become one of the most important issues in modern biomedical studies, and computational approaches of significantly accelerated analysis process and enhanced accuracy are greatly desired. Although a variety of methods have been developed to elevate protein annotation accuracy, their ability in controlling false annotation rates remains either limited or not systematically evaluated. In this study, a protein encoding strategy, together with a deep learning algorithm, was proposed to control the false discovery rate in protein function annotation, and its performances were systematically compared with that of the traditional similarity-based and de novo approaches. Based on a comprehensive assessment from multiple perspectives, the proposed strategy and algorithm were found to perform better in both prediction stability and annotation accuracy compared with other de novo methods. Moreover, an in-depth assessment revealed that it possessed an improved capacity of controlling the false discovery rate compared with traditional methods. All in all, this study not only provided a comprehensive analysis on the performances of the newly proposed strategy but also provided a tool for the researcher in the fields of protein function annotation.

Identification of the gene signature reflecting schizophrenia's etiology by constructing artificial intelligence-based method of enhanced reproducibility

Aims

As one of the most fundamental questions in modern science, “what causes schizophrenia (SZ)” remains a profound mystery due to the absence of objective gene markers. The reproducibility of the gene signatures identified by independent studies is found to be extremely low due to the incapability of available feature selection methods and the lack of measurement on validating signatures’ robustness. These irreproducible results have significantly limited our understanding of the etiology of SZ.

Methods

In this study, a new feature selection strategy was developed, and a comprehensive analysis was then conducted to ensure a reliable signature discovery. Particularly, the new strategy (a) combined multiple randomized sampling with consensus scoring and (b) assessed gene ranking consistency among different datasets, and a comprehensive analysis among nine independent studies was conducted.

Results

Based on a first‐ever evaluation of methods’ reproducibility that was cross‐validated by nine independent studies, the newly developed strategy was found to be superior to the traditional ones. As a result, 33 genes were consistently identified from multiple datasets by the new strategy as differentially expressed, which might facilitate our understanding of the mechanism underlying the etiology of SZ.

Conclusion

A new strategy capable of enhancing the reproducibility of feature selection in current SZ research was successfully constructed and validated. A group of candidate genes identified in this study should be considered as great potential for revealing the etiology of SZ.

A critical assessment of the feature selection methods used for biomarker discovery in current metaproteomics studies

Microbial community (MC) has great impact on mediating complex disease indications, biogeochemical cycling and agricultural productivities, which makes metaproteomics powerful technique for quantifying diverse and dynamic composition of proteins or peptides. The key role of biostatistical strategies in MC study is reported to be underestimated, especially the appropriate application of feature selection method (FSM) is largely ignored. Although extensive efforts have been devoted to assessing the performance of FSMs, previous studies focused only on their classification accuracy without considering their ability to correctly and comprehensively identify the spiked proteins. In this study, the performances of 14 FSMs were comprehensively assessed based on two key criteria (both sample classification and spiked protein discovery) using a variety of metaproteomics benchmarks. First, the classification accuracies of those 14 FSMs were evaluated. Then, their abilities in identifying the proteins of different spiked concentrations were assessed. Finally, seven FSMs (FC, LMEB, OPLS-DA, PLS-DA, SAM, SVM-RFE and T-Test) were identified as performing consistently superior or good under both criteria with the PLS-DA performing consistently superior. In summary, this study served as comprehensive analysis on the performances of current FSMs and could provide a valuable guideline for researchers in metaproteomics.

Consistent gene signature of schizophrenia identified by a novel feature selection strategy from comprehensive sets of transcriptomic data

The etiology of schizophrenia (SCZ) is regarded as one of the most fundamental puzzles in current medical research, and its diagnosis is limited by the lack of objective molecular criteria. Although plenty of studies were conducted, SCZ gene signatures identified by these independent studies are found highly inconsistent. As one of the most important factors contributing to this inconsistency, the feature selection methods used currently do not fully consider the reproducibility among the signatures discovered from different datasets. Therefore, it is crucial to develop new bioinformatics tools of novel strategy for ensuring a stable discovery of gene signature for SCZ. In this study, a novel feature selection strategy (1) integrating repeated random sampling with consensus scoring and (2) evaluating the consistency of gene rank among different datasets was constructed. By systematically assessing the identified SCZ signature comprising 135 differentially expressed genes, this newly constructed strategy demonstrated significantly enhanced stability and better differentiating ability compared with the feature selection methods popular in current SCZ research. Based on a first-ever assessment on methods’ reproducibility cross-validated by independent datasets from three representative studies, the new strategy stood out among the popular methods by showing superior stability and differentiating ability. Finally, 2 novel and 17 previously reported transcription factors were identified and showed great potential in revealing the etiology of SCZ. In sum, the SCZ signature identified in this study would provide valuable clues for discovering diagnostic molecules and potential targets for SCZ.