Gene biomarker discovery at different stages of Alzheimer using gene co-expression network approach

Rare Pathogenic Variants Identified in Whole Exome Sequencing of Monozygotic Twins With Autism Spectrum Disorder

BACKGROUND

Autism spectrum disorder (ASD) is a childhood-onset complex neurodevelopmental disorder characterized by problems with communication and social interaction and restricted, repetitive, stereotyped behavior. The prevalence of ASD is one in 36 children. The genetic architecture of ASD is complex in spite of its high heritability. To identify the potential candidate genes of ASD, we carried out a comprehensive genetic study of monozygotic (MZ) twins concordant or discordant for ASD.

METHODS

Five MZ twins and their parents were recruited for the study. Four of the twins were concordant, whereas one was discordant for ASD. Whole exome sequencing was conducted for the twins and their parents. The exome DNA was enriched using Twist Human Customized Core Exome Kit, and paired-end sequencing was performed on HiSeq system.

RESULTS

We identified several rare and pathogenic variants (homozygous recessive, compound heterozygous, de novo) in ASD-affected individuals.

CONCLUSION

We report novel variants in individuals diagnosed with ASD. Several of these genes are involved in brain-related functions and not previously reported in ASD. Intriguingly, some of the variants were observed in the genes involved in sensory perception (auditory [MYO15A, PLEC, CDH23, UBR3, GPSM2], olfactory [OR9K2], gustatory [TAS2R31], and visual [CDH23, UBR3]). This is the first comprehensive genetic study of MZ twins in an Indian population. Further validation is required to determine whether these variants are associated with ASD.

Phospholipid Acyltransferases: Characterization and Involvement of the Enzymes in Metabolic and Cancer Diseases

This review delves into the enzymatic processes governing the initial stages of glycerophospholipid (phosphatidylcholine, phosphatidylethanolamine, and phosphatidylserine) and triacylglycerol synthesis. The key enzymes under scrutiny include GPAT and AGPAT. Additionally, as most AGPATs exhibit LPLAT activity, enzymes participating in the Lands cycle with similar functions are also covered. The review begins by discussing the properties of these enzymes, emphasizing their specificity in enzymatic reactions, notably the incorporation of polyunsaturated fatty acids (PUFAs) such as arachidonic acid and docosahexaenoic acid (DHA) into phospholipids. The paper sheds light on the intricate involvement of these enzymes in various diseases, including obesity, insulin resistance, and cancer. To underscore the relevance of these enzymes in cancer processes, a bioinformatics analysis was conducted. The expression levels of the described enzymes were correlated with the overall survival of patients across 33 different types of cancer using the GEPIA portal. This review further explores the potential therapeutic implications of inhibiting these enzymes in the treatment of metabolic diseases and cancer. By elucidating the intricate enzymatic pathways involved in lipid synthesis and their impact on various pathological conditions, this paper contributes to a comprehensive understanding of these processes and their potential as therapeutic targets.

Single cell transcriptome analysis of the THY-Tau22 mouse model of Alzheimer's disease reveals sex-dependent dysregulations

Alzheimer's disease (AD) progression and pathology show pronounced sex differences, but the factors driving these remain poorly understood. To gain insights into early AD-associated molecular changes and their sex dependency for tau pathology in the cortex, we performed single-cell RNA-seq in the THY-Tau22 AD mouse model. By examining cell type-specific and cell type-agnostic AD-related gene activity changes and their sex-dimorphism for individual genes, pathways and cellular sub-networks, we identified both statistically significant alterations and interpreted the upstream mechanisms controlling them. Our results confirm several significant sex-dependent alterations in gene activity in the THY-Tau22 model mice compared to controls, with more pronounced alterations in females. Both changes shared across multiple cell types and cell type-specific changes were observed. The differential genes showed significant over-representation of known AD-relevant processes, such as pathways associated with neuronal differentiation, programmed cell death and inflammatory responses. Regulatory network analysis of these genes revealed upstream regulators that modulate many of the downstream targets with sex-dependent changes. Most key regulators have been previously implicated in AD, such as Egr1, Klf4, Chchd2, complement system genes, and myelin-associated glycoproteins. Comparing with similar data from the Tg2576 AD mouse model and human AD patients, we identified multiple genes with consistent, cell type-specific and sex-dependent alterations across all three datasets. These shared changes were particularly evident in the expression of myelin-associated genes such as Mbp and Plp1 in oligodendrocytes. In summary, we observed significant cell type-specific transcriptomic changes in the THY-Tau22 mouse model, with a strong over-representation of known AD-associated genes and processes. These include both sex-neutral and sex-specific patterns, characterized by consistent shifts in upstream master regulators and downstream target genes. Collectively, these findings provide insights into mechanisms influencing sex-specific susceptibility to AD and reveal key regulatory proteins that could be targeted for developing treatments addressing sex-dependent AD pathology.

Extracellular vesicle microRNA-mediated transcriptional regulation may contribute to dementia with Lewy bodies molecular pathology

OBJECTIVE

Dementia with Lewy bodies (DLB) is the second most common dementia. Advancing our limited understanding of its molecular pathogenesis is essential for identifying novel biomarkers and therapeutic targets for DLB. DLB is an α-synucleinopathy, and small extracellular vesicles (SEV) from people with DLB can transmit α-synuclein oligomerisation between cells. Post-mortem DLB brains and serum SEV from those with DLB share common miRNA signatures, and their functional implications are uncertain. Hence, we aimed to investigate potential targets of DLB-associated SEV miRNA and to analyse their functional implications.

METHODS

We identified potential targets of six previously reported differentially expressed miRNA genes in serum SEV of people with DLB (MIR26A1, MIR320C2, MIR320D2, MIR548BA, MIR556, and MIR4722) using miRBase and miRDB databases. We analysed functional implications of these targets using EnrichR gene set enrichment analysis and analysed their protein interactions using Reactome pathway analysis.

RESULTS

These SEV miRNA may regulate 4278 genes that were significantly enriched among the genes involved in neuronal development, cell-to-cell communication, vesicle-mediated transport, apoptosis, regulation of cell cycle, post-translational protein modifications, and autophagy lysosomal pathway, after Benjamini-Hochberg false discovery rate correction at 5%. The miRNA target genes and their protein interactions were significantly associated with several neuropsychiatric disorders and with multiple signal transduction, transcriptional regulation, and cytokine signalling pathways.

CONCLUSION

Our findings provide in-silico evidence that potential targets of DLB-associated SEV miRNAs may contribute to Lewy pathology by transcriptional regulation. Experimental validation of these dysfunctional pathways is warranted and could lead to novel therapeutic avenues for DLB.

Artificial intelligence for dementia genetics and omics

Genetics and omics studies of Alzheimer's disease and other dementia subtypes enhance our understanding of underlying mechanisms and pathways that can be targeted. We identified key remaining challenges: First, can we enhance genetic studies to address missing heritability? Can we identify reproducible omics signatures that differentiate between dementia subtypes? Can high-dimensional omics data identify improved biomarkers? How can genetics inform our understanding of causal status of dementia risk factors? And which biological processes are altered by dementia-related genetic variation? Artificial intelligence (AI) and machine learning approaches give us powerful new tools in helping us to tackle these challenges, and we review possible solutions and examples of best practice. However, their limitations also need to be considered, as well as the need for coordinated multidisciplinary research and diverse deeply phenotyped cohorts. Ultimately AI approaches improve our ability to interrogate genetics and omics data for precision dementia medicine. HIGHLIGHTS: We have identified five key challenges in dementia genetics and omics studies. AI can enable detection of undiscovered patterns in dementia genetics and omics data. Enhanced and more diverse genetics and omics datasets are still needed. Multidisciplinary collaborative efforts using AI can boost dementia research.

Identification of gene-level methylation for disease prediction

DNA methylation is an epigenetic alteration that plays a fundamental part in governing gene regulatory processes. The DNA methylation mechanism affixes methyl groups to distinct cytosine residues, influencing chromatin architectures. Multiple studies have demonstrated that DNA methylation's regulatory effect on genes is linked to the beginning and progression of several disorders. Researchers have recently uncovered thousands of phenotype-related methylation sites through the epigenome-wide association study (EWAS). However, combining the methylation levels of several sites within a gene and determining the gene-level DNA methylation remains challenging. In this study, we proposed the supervised UMAP Assisted Gene-level Methylation method (sUAGM) for disease prediction based on supervised UMAP (Uniform Manifold Approximation and Projection), a manifold learning-based method for reducing dimensionality. The methylation values at the gene level generated using the proposed method are evaluated by employing various feature selection and classification algorithms on three distinct DNA methylation datasets derived from blood samples. The performance has been assessed employing classification accuracy, F-1 score, Mathews Correlation Coefficient (MCC), Kappa, Classification Success Index (CSI) and Jaccard Index. The Support Vector Machine with the linear kernel (SVML) classifier with Recursive Feature Elimination (RFE) performs best across all three datasets. From comparative analysis, our method outperformed existing gene-level and site-level approaches by achieving 100% accuracy and F1-score with fewer genes. The functional analysis of the top 28 genes selected from the Parkinson's disease dataset revealed a significant association with the disease.

Artificial intelligence for biomarker discovery in Alzheimer's disease and dementia

With the increase in large multimodal cohorts and high-throughput technologies, the potential for discovering novel biomarkers is no longer limited by data set size. Artificial intelligence (AI) and machine learning approaches have been developed to detect novel biomarkers and interactions in complex data sets. We discuss exemplar uses and evaluate current applications and limitations of AI to discover novel biomarkers. Remaining challenges include a lack of diversity in the data sets available, the sheer complexity of investigating interactions, the invasiveness and cost of some biomarkers, and poor reporting in some studies. Overcoming these challenges will involve collecting data from underrepresented populations, developing more powerful AI approaches, validating the use of noninvasive biomarkers, and adhering to reporting guidelines. By harnessing rich multimodal data through AI approaches and international collaborative innovation, we are well positioned to identify clinically useful biomarkers that are accurate, generalizable, unbiased, and acceptable in clinical practice. HIGHLIGHTS: Artificial intelligence and machine learning approaches may accelerate dementia biomarker discovery. Remaining challenges include data set suitability due to size and bias in cohort selection. Multimodal data, diverse data sets, improved machine learning approaches, real-world validation, and interdisciplinary collaboration are required.

Joint triplet loss with semi-hard constraint for data augmentation and disease prediction using gene expression data

The accurate prediction of patients with complex diseases, such as Alzheimer's disease (AD), as well as disease stages, including early- and late-stage cancer, is challenging owing to substantial variability among patients and limited availability of clinical data. Deep metric learning has emerged as a promising approach for addressing these challenges by improving data representation. In this study, we propose a joint triplet loss model with a semi-hard constraint (JTSC) to represent data in a small number of samples. JTSC strictly selects semi-hard samples by switching anchors and positive samples during the learning process in triplet embedding and combines a triplet loss function with an angular loss function. Our results indicate that JTSC significantly improves the number of appropriately represented samples during training when applied to the gene expression data of AD and to cancer stage prediction tasks. Furthermore, we demonstrate that using an embedding vector from JTSC as an input to the classifiers for AD and cancer stage prediction significantly improves classification performance by extracting more accurate features. In conclusion, we show that feature embedding through JTSC can aid in classification when there are a small number of samples compared to a larger number of features.

Application of Protein-Protein Interaction Network Analysis in Order to Identify Cervical Cancer miRNA and mRNA Biomarkers

Cervical cancer (CC) is one of the world's most common and severe cancers. This cancer includes two histological types: squamous cell carcinoma (SCC) and adenocarcinoma (ADC). The current study aims at identifying novel potential candidate mRNA and miRNA biomarkers for SCC based on a protein-protein interaction (PPI) and miRNA-mRNA network analysis. The current project utilized a transcriptome profile for normal and SCC samples. First, the PPI network was constructed for the 1335 DEGs, and then, a significant gene module was extracted from the PPI network. Next, a list of miRNAs targeting module's genes was collected from the experimentally validated databases, and a miRNA-mRNA regulatory network was formed. After network analysis, four driver genes were selected from the module's genes including MCM2, MCM10, POLA1, and TONSL and introduced as potential candidate biomarkers for SCC. In addition, two hub miRNAs, including miR-193b-3p and miR-615-3p, were selected from the miRNA-mRNA regulatory network and reported as possible candidate biomarkers. In summary, six potential candidate RNA-based biomarkers consist of four genes containing MCM2, MCM10, POLA1, and TONSL, and two miRNAs containing miR-193b-3p and miR-615-3p are opposed as potential candidate biomarkers for CC.

Eye-tracking paradigms for the assessment of mild cognitive impairment: a systematic review

Mild cognitive impairment (MCI), representing the 'transitional zone' between normal cognition and dementia, has become a novel topic in clinical research. Although early detection is crucial, it remains logistically challenging at the same time. While traditional pen-and-paper tests require in-depth training to ensure standardized administration and accurate interpretation of findings, significant technological advancements are leading to the development of procedures for the early detection of Alzheimer's disease (AD) and facilitating the diagnostic process. Some of the diagnostic protocols, however, show significant limitations that hamper their widespread adoption. Concerns about the social and economic implications of the increasing incidence of AD underline the need for reliable, non-invasive, cost-effective, and timely cognitive scoring methodologies. For instance, modern clinical studies report significant oculomotor impairments among patients with MCI, who perform poorly in visual paired-comparison tasks by ascribing less attentional resources to novel stimuli. To accelerate the Global Action Plan on the Public Health Response to Dementia 2017-2025, this work provides an overview of research on saccadic and exploratory eye-movement deficits among older adults with MCI. The review protocol was drafted based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Electronic databases were systematically searched to identify peer-reviewed articles published between 2017 and 2022 that examined visual processing in older adults with MCI and reported gaze parameters as potential biomarkers. Moreover, following the contemporary trend for remote healthcare technologies, we reviewed studies that implemented non-commercial eye-tracking instrumentation in order to detect information processing impairments among the MCI population. Based on the gathered literature, eye-tracking-based paradigms may ameliorate the screening limitations of traditional cognitive assessments and contribute to early AD detection. However, in order to translate the findings pertaining to abnormal gaze behavior into clinical applications, it is imperative to conduct longitudinal investigations in both laboratory-based and ecologically valid settings.

The Ragulator complex: delving its multifunctional impact on metabolism and beyond

Our understanding of lysosomes has undergone a significant transformation in recent years, from the view that they are static organelles primarily responsible for the disposal and recycling of cellular waste to their recognition as highly dynamic structures. Current research posits that lysosomes function as a signaling hub that integrates both extracellular and intracellular stimuli, thereby regulating cellular homeostasis. The dysregulation of lysosomal function has been linked to a wide range of diseases. Of note, lysosomes contribute to the activation of mammalian target of rapamycin complex 1 (mTORC1), a key regulator of cellular metabolism. The Ragulator complex, a protein complex anchored on the lysosomal membrane, was initially shown to tether the mTORC1 complex to lysosomes. Recent research has substantially expanded our understanding of the roles of the Ragulator complex in lysosomes, including roles in the regulation of metabolism, inflammation, cell death, cell migration, and the maintenance of homeostasis, via interactions with various proteins. This review summarizes our current knowledge on the diverse functions of the Ragulator complex, highlighting important protein interactions.

State of the Art of microRNAs Signatures as Biomarkers and Therapeutic Targets in Parkinson's and Alzheimer's Diseases: A Systematic Review and Meta-Analysis

Identifying target microRNAs (miRNAs) might serve as a basis for developing advanced therapies for Parkinson's disease (PD) and Alzheimer's disease. This review aims to identify the main therapeutic targets of miRNAs that can potentially act in Parkinson's and Alzheimer's diseases. The publication research was conducted from May 2021 to March 2022, selected from Scopus, PubMed, Embase, OVID, Science Direct, LILACS, and EBSCO. A total of 25 studies were selected from 1549 studies evaluated. The total number of miRNAs as therapeutic targets evidenced was 90 for AD and 54 for PD. An average detection accuracy of above 84% for the miRNAs was observed in the selected studies of AD and PD. The major signatures were miR-26b-5p, miR-615-3p, miR-4722-5p, miR23a-3p, and miR-27b-3p for AD and miR-374a-5p for PD. Six miRNAs of intersection were found between AD and PD. This article identified the main microRNAs as selective biomarkers for diagnosing PD and AD and therapeutic targets through a systematic review and meta-analysis. This article can act as a microRNA guideline for laboratory research and pharmaceutical industries for treating Alzheimer's and Parkinson's diseases and offers the opportunity to evaluate therapeutic interventions earlier in the disease process.

Development and analysis of a comprehensive diagnostic model for aortic valve calcification using machine learning methods and artificial neural networks

BACKGROUND

Although advanced surgical and interventional treatments are available for advanced aortic valve calcification (AVC) with severe clinical symptoms, early diagnosis, and intervention is critical in order to reduce calcification progression and improve patient prognosis. The aim of this study was to develop therapeutic targets for improving outcomes for patients with AVC.

MATERIALS AND METHODS

We used the public expression profiles of individuals with AVC (GSE12644 and GSE51472) to identify potential diagnostic markers. First, the R software was used to identify differentially expressed genes (DEGs) and perform functional enrichment analysis. Next, we combined bioinformatics techniques with machine learning methodologies such as random forest algorithms and support vector machines to screen for and identify diagnostic markers of AVC. Subsequently, artificial neural networks were employed to filter and model the diagnostic characteristics for AVC incidence. The diagnostic values were determined using the receiver operating characteristic (ROC) curves. Furthermore, CIBERSORT immune infiltration analysis was used to determine the expression of different immune cells in the AVC. Finally, the CMap database was used to predict candidate small compounds as prospective AVC therapeutics.

RESULTS

A total of 78 strong DEGs were identified. The leukocyte migration and pid integrin 1 pathways were highly enriched for AVC-specific DEGs. CXCL16, GPM6A, BEX2, S100A9, and SCARA5 genes were all regarded diagnostic markers for AVC. The model was effectively constructed using a molecular diagnostic score system with significant diagnostic value (AUC = 0.987) and verified using the independent dataset GSE83453 (AUC = 0.986). Immune cell infiltration research revealed that B cell naive, B cell memory, plasma cells, NK cell activated, monocytes, and macrophage M0 may be involved in the development of AVC. Additionally, all diagnostic characteristics may have varying degrees of correlation with immune cells. The most promising small molecule medicines for reversing AVC gene expression are Doxazosin and Terfenadine.

CONCLUSION

It was identified that CXCL16, GPM6A, BEX2, S100A9, and SCARA5 are potentially beneficial for diagnosing and treating AVC. A diagnostic model was constructed based on a molecular prognostic score system using machine learning. The aforementioned immune cell infiltration may have a significant influence on the development and incidence of AVC.

Comparative analysis of transcriptome remodeling in plaque-associated and plaque-distant microglia during amyloid-β pathology progression in mice

BACKGROUND

Research in recent years firmly established that microglial cells play an important role in the pathogenesis of Alzheimer's disease (AD). In parallel, a series of studies showed that, under both homeostatic and pathological conditions, microglia are a heterogeneous cell population. In AD, amyloid-β (Aβ) plaque-associated microglia (PAM) display a clearly distinct phenotype compared to plaque-distant microglia (PCM), suggesting that these two microglia subtypes likely differently contribute to disease progression. So far, molecular characterization of PAM was performed indirectly using single cell RNA sequencing (scRNA-seq) approaches or based on markers that are supposedly up-regulated in this microglia subpopulation.

METHODS

In this study based on a well-characterized AD mouse model, we combined cell-specific laser capture microdissection and RNA-seq analysis to i) identify, without preconceived notions of the molecular and/or functional changes that would affect these cells, the genes and gene networks that are dysregulated in PAM or PCM at three critical stages of the disease, and ii) to investigate the potential contribution of both plaque-associated and plaque-distant microglia.

RESULTS

First, we established that our approach allows selective isolation of microglia, while preserving spatial information and preventing transcriptome changes induced by classical purification approaches. Then, we identified, in PAM and PCM subpopulations, networks of co-deregulated genes and analyzed their potential functional roles in AD. Finally, we investigated the dynamics of microglia transcriptomic remodeling at early, intermediate and late stages of the disease and validated select findings in postmortem human AD brain.

CONCLUSIONS

Our comprehensive study provides useful transcriptomic information regarding the respective contribution of PAM and PCM across the Aβ pathology progression. It highlights specific pathways that would require further study to decipher their roles across disease progression. It demonstrates that the proximity of microglia to Aβ-plaques dramatically alters the microglial transcriptome and reveals that these changes can have both positive and negative impacts on the surrounding cells. These opposing effects may be driven by local microglia heterogeneity also demonstrated by this study. Our approach leads to molecularly define the less well studied plaque-distant microglia. We show that plaque-distant microglia are not bystanders of the disease, although the transcriptomic changes are far less striking compared to what is observed in plaque-associated microglia. In particular, our results suggest they may be involved in Aβ oligomer detection and in Aβ-plaque initiation, with increased contribution as the disease progresses.

Identification of genetic molecular markers and immune infiltration characteristics of Alzheimer's disease through weighted gene co-expression network analysis

Background

Alzheimer's disease (AD) is a progressive neurodegenerative disease that leads to cognitive impairment and memory loss. Currently, the pathogenesis and underlying causative genes of AD remain unclear, and there exists no effective treatment for this disease. This study explored AD-related diagnostic and therapeutic biomarkers from the perspective of immune infiltration by analyzing public data from the NCBI Gene Expression Omnibus database.

Method

In this study, weighted gene co-expression network analysis (WGCNA) was conducted to identify modules and hub genes contributing to AD development. A protein–protein interaction network was constructed when the genes in the modules were enriched and examined by Gene Ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Furthermore, a gene network was established using topological WGCNA, from which five hub genes were selected. Logistic regression analysis and receiver operating characteristic curve analysis were performed to explore the clinical value of genes in AD diagnosis. The genes in the core module intersected with the hub genes, and four intersection genes (ATP2A2, ATP6V1D, CAP2, and SYNJ1) were selected. These four genes were enriched by gene set enrichment analysis (GSEA). Finally, an immune infiltration analysis was performed.

Results

The GO/KEGG analysis suggested that genes in the core module played a role in the differentiation and growth of neural cells and in the transmission of neurotransmitters. The GSEA of core genes showed that these four genes were mainly enriched in immune/infection pathways (e.g., cholera infection and Helicobacter pylori infection pathways) and other metabolic pathways. An investigation of immune infiltration characteristics revealed that activated mast cells, regulatory T cells, plasma cells, neutrophils, T follicular helper cells, CD8 T cells, resting memory CD4 T cells, and M1 macrophages were the core immune cells contributing to AD progression. qRT-PCR analysis showed that the ATP6V1D is upregulated in AD.

Conclusion

The results of enrichment and immuno-osmotic analyses indicated that immune pathways and immune cells played an important role in the occurrence and development of AD. The selected key genes were used as biomarkers related to the pathogenesis of AD to further explore the pathways and cells, which provided new perspectives on therapeutic targets in AD.

A retrotransposon storm marks clinical phenoconversion to late-onset Alzheimer's disease

Recent reports have suggested that the reactivation of otherwise transcriptionally silent transposable elements (TEs) might induce brain degeneration, either by dysregulating the expression of genes and pathways implicated in cognitive decline and dementia or through the induction of immune-mediated neuroinflammation resulting in the elimination of neural and glial cells. In the work we present here, we test the hypothesis that differentially expressed TEs in blood could be used as biomarkers of cognitive decline and development of AD. To this aim, we used a sample of aging subjects (age > 70) that developed late-onset Alzheimer’s disease (LOAD) over a relatively short period of time (12–48 months), for which blood was available before and after their phenoconversion, and a group of cognitive stable subjects as controls. We applied our developed and validated customized pipeline that allows the identification, characterization, and quantification of the differentially expressed (DE) TEs before and after the onset of manifest LOAD, through analyses of RNA-Seq data. We compared the level of DE TEs within more than 600,000 TE-mapping RNA transcripts from 25 individuals, whose specimens we obtained before and after their phenotypic conversion (phenoconversion) to LOAD, and discovered that 1790 TE transcripts showed significant expression differences between these two timepoints (logFC ± 1.5, logCMP > 5.3, nominal p value < 0.01). These DE transcripts mapped both over- and under-expressed TE elements. Occurring before the clinical phenoconversion, this TE storm features significant increases in DE transcripts of LINEs, LTRs, and SVAs, while those for SINEs are significantly depleted. These dysregulations end with signs of manifest LOAD. This set of highly DE transcripts generates a TE transcriptional profile that accurately discriminates the before and after phenoconversion states of these subjects. Our findings suggest that a storm of DE TEs occurs before phenoconversion from normal cognition to manifest LOAD in risk individuals compared to controls, and may provide useful blood-based biomarkers for heralding such a clinical transition, also suggesting that TEs can indeed participate in the complex process of neurodegeneration.

MicroRNA-4722-5p and microRNA-615-3p serve as potential biomarkers for Alzheimer's disease

The aim of the present study was to investigate the expression levels of microRNA(miR)-4722-5p and miR-615-3p in Alzheimer's disease (AD) and their diagnostic value. Blood samples were collected from 33 patients with AD and 33 healthy controls, and an β-amyloid (Aβ)25-35-induced PC12 cell model was also established. The relative mRNA expression levels of miR-4722-5p and miR-615-3p were detected using reverse transcription-quantitative PCR. The correlations between the mRNA expression levels of the two miRNAs and the mini-mental state examination (MMSE) scores were analyzed, and the receiver operating characteristic curve was used to assess the diagnostic value of miR-4722-5p and miR-615-3p in AD. Functional enrichment analysis of the miRNA target genes was performed using The Database for Annotation, Visualization and Integrated Discovery database and the R language analysis package. The mRNA expression levels of miR-4722-5p and miR-615-3p were increased in patients with AD and the Aβ25-35-induced PC12 cell model. The mRNA expression levels of miR-4722-5p and miR-615-3p were negatively correlated with MMSE scores, and the combination of the two miRNAs for AD had an improved diagnostic value than that of each miRNA alone. The results of Gene Ontology (GO) enrichment analysis showed that the target genes of miR-4722-5p were found in the cytoplasm and cytosol, and were mainly involved in protein folding and cell division. The molecular functions included protein binding and GTPase activator activity. The results of Kyoto Encyclopedia of Genes and Genomes analysis showed that miR-4722-5p was associated with the regulation of dopaminergic synapses and mTOR signaling pathways. GO enrichment analysis also revealed that the target genes of miR-615-3p were located in the nucleus and cytoplasm, were involved in the regulation of transcription and protein phosphorylation, and were associated with protein binding, metal ion binding and transcription factor activity. The target genes of miR-615-3p played important roles in the regulation of the Ras and FoxO signaling pathways. In conclusion, miR-4722-5p and miR-615-3p may be potential biomarkers in the early diagnosis of AD.

Identification of condition-specific biomarker systems in uterine cancer

Uterine cancer is the fourth most common cancer among women, projected to affect 66,000 US women in 2021. Uterine cancer often arises in the inner lining of the uterus, known as the endometrium, but can present as several different types of cancer, including endometrioid cancer, serous adenocarcinoma, and uterine carcinosarcoma. Previous studies have analyzed the genetic changes between normal and cancerous uterine tissue to identify specific genes of interest, including TP53 and PTEN. Here we used Gaussian Mixture Models to build condition-specific gene coexpression networks for endometrial cancer, uterine carcinosarcoma, and normal uterine tissue. We then incorporated uterine regulatory edges and investigated potential coregulation relationships. These networks were further validated using differential expression analysis, functional enrichment, and a statistical analysis comparing the expression of transcription factors and their target genes across cancerous and normal uterine samples. These networks allow for a more comprehensive look into the biological networks and pathways affected in uterine cancer compared with previous singular gene analyses. We hope this study can be incorporated into existing knowledge surrounding the genetics of uterine cancer and soon become clinical biomarkers as a tool for better prognosis and treatment.

ARMC Subfamily: Structures, Functions, Evolutions, Interactions, and Diseases

Armadillo repeat-containing proteins (ARMCs) are widely distributed in eukaryotes and have important influences on cell adhesion, signal transduction, mitochondrial function regulation, tumorigenesis, and other processes. These proteins share a similar domain consisting of tandem repeats approximately 42 amino acids in length, and this domain constitutes a substantial platform for the binding between ARMCs and other proteins. An ARMC subfamily, including ARMC1∼10, ARMC12, and ARMCX1∼6, has received increasing attention. These proteins may have many terminal regions and play a critical role in various diseases. On the one hand, based on their similar central domain of tandem repeats, this ARMC subfamily may function similarly to other ARMCs. On the other hand, the unique domains on their terminals may cause these proteins to have different functions. Here, we focus on the ARMC subfamily (ARMC1∼10, ARMC12, and ARMCX1∼6), which is relatively conserved in vertebrates and highly conserved in mammals, particularly primates. We review the structures, biological functions, evolutions, interactions, and related diseases of the ARMC subfamily, which involve more than 30 diseases and 40 bypasses, including interactions and relationships between more than 100 proteins and signaling molecules. We look forward to obtaining a clearer understanding of the ARMC subfamily to facilitate further in-depth research and treatment of related diseases.

MiR-574-5P, miR-1827, and miR-4429 as Potential Biomarkers for Schizophrenia

Schizophrenia is a severe chronic debilitating disorder with millions of affected individuals. Diagnosis is based on clinical presentations, which are made when the progressive disease has appeared. Early diagnosis may help improve the clinical outcomes and response to treatments. Lack of a reliable molecular diagnostic invokes the identification of novel biomarkers. To elucidate the molecular basis of the disease, in this study we used two mRNA expression arrays, including GSE93987 and GSE38485, and one miRNA array, GSE54914, and meta-analysis was conducted for evaluation of mRNA expression arrays via metaDE package. Using WGCNA package, we performed network analysis for both mRNA expression arrays separately. Then, we constructed protein-protein interaction network for significant modules. Limma package was employed to analyze the miRNA array for identification of dysregulated miRNAs (DEMs). Using genes of significant modules and DEMs, a mRNA-miRNA network was constructed and hub genes and miRNAs were identified. To confirm the dysregulated genes, expression values were evaluated through available datasets including GSE62333, GSE93987, and GSE38485. The ability of the detected hub miRNAs to discriminate schizophrenia from healthy controls was evaluated by assessing the receiver-operating curve. Finally, the expression levels of genes and miRNAs were evaluated in 40 schizophrenia patients compared with healthy controls via Real-Time PCR. The results confirmed dysregulation of hsa-miR-574-5P, hsa-miR-1827, hsa-miR-4429, CREBRF, ARPP19, TGFBR2, and YWHAZ in blood samples of schizophrenia patients. In conclusion, three miRNAs including hsa-miR-574-5P, hsa-miR-1827, and hsa-miR-4429 are suggested as potential biomarkers for diagnosis of schizophrenia.

Drug repurposing for coronavirus (SARS-CoV-2) based on gene co-expression network analysis

Severe acute respiratory syndrome (SARS) is a highly contagious viral respiratory illness. This illness is spurred on by a coronavirus known as SARS-associated coronavirus (SARS-CoV). SARS was first detected in Asia in late February 2003. The genome of this virus is very similar to the SARS-CoV-2. Therefore, the study of SARS-CoV disease and the identification of effective drugs to treat this disease can be new clues for the treatment of SARS-Cov-2. This study aimed to discover novel potential drugs for SARS-CoV disease in order to treating SARS-Cov-2 disease based on a novel systems biology approach. To this end, gene co-expression network analysis was applied. First, the gene co-expression network was reconstructed for 1441 genes, and then two gene modules were discovered as significant modules. Next, a list of miRNAs and transcription factors that target gene co-expression modules' genes were gathered from the valid databases, and two sub-networks formed of transcription factors and miRNAs were established. Afterward, the list of the drugs targeting obtained sub-networks' genes was retrieved from the DGIDb database, and two drug-gene and drug-TF interaction networks were reconstructed. Finally, after conducting different network analyses, we proposed five drugs, including FLUOROURACIL, CISPLATIN, SIROLIMUS, CYCLOPHOSPHAMIDE, and METHYLDOPA, as candidate drugs for SARS-CoV-2 coronavirus treatment. Moreover, ten miRNAs including miR-193b, miR-192, miR-215, miR-34a, miR-16, miR-16, miR-92a, miR-30a, miR-7, and miR-26b were found to be significant miRNAs in treating SARS-CoV-2 coronavirus.

Drug Repurposing for Alzheimer's Disease Based on Protein-Protein Interaction Network

Alzheimer's disease (AD) is known as a critical neurodegenerative disorder. It worsens as symptoms concerning dementia grow severe over the years. Due to the globalization of Alzheimer's disease, its prevention and treatment are vital. This study proposes a method to extract substantial gene complexes and then introduces potential drugs in Alzheimer's disease. To this end, a protein-protein interaction (PPI) network was utilized to extract five meaningful gene complexes functionally interconnected. An enrichment analysis to introduce the most important biological processes and pathways was accomplished on the obtained genes. The next step is extracting the drugs related to AD and introducing some new drugs which may be helpful for this disease. Finally, a complete network including all the genes associated with each gene complex group and genes' target drug was illustrated. For validating the proposed potential drugs, Connectivity Map (CMAP) analysis was accomplished to determine target genes that are up- or downregulated by proposed drugs. Medical studies and publications were analyzed thoroughly to introduce AD-related drugs. This analysis proves the accuracy of the proposed method in this study. Then, new drugs were introduced that can be experimentally examined as future work. Raloxifene and gentian violet are two new drugs, which have not been introduced as AD-related drugs in previous scientific and medical studies, recommended by the method of this study. Besides the primary goal, five bipartite networks representing the genes of each group and their target miRNAs were constructed to introduce target miRNAs.

Preservation Analysis on Spatiotemporal Specific Co-expression Networks Suggests the Immunopathogenesis of Alzheimer's Disease

The occurrence and development of Alzheimer’s disease (AD) is a continuous clinical and pathophysiological process, molecular biological, and brain functional change often appear before clinical symptoms, but the detailed underlying mechanism is still unclear. The expression profiling of postmortem brain tissue from AD patients and controls provides evidence about AD etiopathogenesis. In the current study, we used published AD expression profiling data to construct spatiotemporal specific coexpression networks in AD and analyzed the network preservation features of each brain region in different disease stages to identify the most dramatically changed coexpression modules and obtained AD-related biological pathways, brain regions and circuits, cell types and key genes based on these modules. As result, we constructed 57 spatiotemporal specific networks (19 brain regions by three disease stages) in AD and observed universal expression changes in all 19 brain regions. The eight most dramatically changed coexpression modules were identified in seven brain regions. Genes in these modules are mostly involved in immune response-related pathways and non-neuron cells, and this supports the immune pathology of AD and suggests the role of blood brain barrier (BBB) injuries. Differentially expressed genes (DEGs) meta-analysis and protein–protein interaction (PPI) network analysis suggested potential key genes involved in AD development that might be therapeutic targets. In conclusion, our systematical network analysis on published AD expression profiling data suggests the immunopathogenesis of AD and identifies key brain regions and genes.

A murine model of the human CREBRFR457Q obesity-risk variant does not influence energy or glucose homeostasis in response to nutritional stress

Obesity and diabetes have strong heritable components, yet the genetic contributions to these diseases remain largely unexplained. In humans, a missense variant in Creb3 regulatory factor (CREBRF) [rs373863828 (p.Arg457Gln); CREBRFR457Q] is strongly associated with increased odds of obesity but decreased odds of diabetes. Although virtually nothing is known about CREBRF's mechanism of action, emerging evidence implicates it in the adaptive transcriptional response to nutritional stress downstream of TORC1. The objectives of this study were to generate a murine model with knockin of the orthologous variant in mice (CREBRFR458Q) and to test the hypothesis that this CREBRF variant promotes obesity and protects against diabetes by regulating energy and glucose homeostasis downstream of TORC1. To test this hypothesis, we performed extensive phenotypic analysis of CREBRFR458Q knockin mice at baseline and in response to acute (fasting/refeeding), chronic (low- and high-fat diet feeding), and extreme (prolonged fasting) nutritional stress as well as with pharmacological TORC1 inhibition, and aging to 52 weeks. The results demonstrate that the murine CREBRFR458Q model of the human CREBRFR457Q variant does not influence energy/glucose homeostasis in response to these interventions, with the exception of possible greater loss of fat relative to lean mass with age. Alternative preclinical models and/or studies in humans will be required to decipher the mechanisms linking this variant to human health and disease.

Molecular characterization of mouse CREB3 regulatory factor in Neuro2a cells

We performed expression and functional analysis of mouse CREB3 regulatory factor (CREBRF) in Neuro2a cells by constructing several expression vectors. Overexpressed full-length (FL) CREBRF protein was stabilized by MG132; however, the intrinsic CREBRF expression in Neuro2a cells was negligible under all conditions. On the other hand, N- or C-terminal deletion of CREBRF influenced its stability. Cotransfection of CREBRF together with GAL4-tagged FL CREB3 increased luciferase reporter activity, and only the N-terminal region of CREBRF was sufficient to potentiate luciferase activity. Furthermore, this positive effect of CREBRF was also observed in cells expressing GAL4-tagged cleaved CREB3, although CREBRF hardly influenced the protein stability of NanoLuc-tagged cleaved CREB3 or intracellular localization of EGFP-tagged one. In conclusion, this study suggests that CREBRF, a quite unstable proteasome substrate, positively regulates the CREB3 pathway, which is distinct from the canonical ER stress pathway in Neuro2a cells.

Identification of potential biomarkers for pathogenesis of Alzheimer's disease

BACKGROUND

Alzheimer's disease (AD) is an extremely complicated neurodegenerative disorder, which accounts for almost 80 % of all dementia diagnoses. Due to the limited treatment efficacy, it is imperative for AD patients to take reliable prevention and diagnosis measures. This study aimed to explore potential biomarkers for AD.

METHODS

GSE63060 and GSE140829 datasets were downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEG) between AD and control groups in GSE63060 were analyzed using the limma software package. The mRNA expression data in GSE140829 was analyzed using weighted gene co-expression network analysis (WGCNA) function package. Protein functional connections and interactions were analyzed using STRING and key genes were screened based on the degree and Maximal Clique Centrality (MCC) algorithm. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed on the key genes.

RESULTS

There were 65 DEGs in GSE63060 dataset between AD patients and healthy controls. In GSE140829 dataset, the turquoise module was related to the pathogenesis of AD, among which, 42 genes were also differentially expressed in GSE63060 dataset. Then 8 genes, RPS17, RPL26, RPS3A, RPS25, EEF1B2, COX7C, HINT1 and SNRPG, were finally screened. Additionally, these 42 genes were significantly enriched in 12 KEGG pathways and 119 GO terms.

CONCLUSIONS

In conclusion, RPS17, RPL26, RPS3A, RPS25, EEF1B2, COX7C, HINT1 and SNRPG, were potential biomarkers for pathogenesis of AD, which should be further explored in AD in the future.

Discovery and Validation of Key Biomarkers Based on Immune Infiltrates in Alzheimer's Disease

BACKGROUND

As the most common neurodegenerative disease, Alzheimer's disease (AD) leads to progressive loss of cognition and memory. Presently, the underlying pathogenic genes of AD patients remain elusive, and effective disease-modifying therapy is not available. This study explored novel biomarkers that can affect diagnosis and treatment in AD based on immune infiltration.

METHODS

The gene expression profiles of 139 AD cases and 134 normal controls were obtained from the NCBI GEO public database. We applied the computational method CIBERSORT to bulk gene expression profiles of AD to quantify 22 subsets of immune cells. Besides, based on the use of the Least Absolute Shrinkage Selection Operator (LASSO), this study also applied SVM-RFE analysis to screen key genes. GO-based semantic similarity and logistic regression model analyses were applied to explore hub genes further.

RESULTS

There was a remarkable significance in the infiltration of immune cells between the subgroups. The proportions for monocytes, M0 macrophages, and dendritic cells in the AD group were significantly higher than those in the normal group, while the proportion of some cells was lower than that of the normal group, such as NK cell resting, T-cell CD4 naive, T-cell CD4 memory activation, and eosinophils. Additionally, seven genes (ABCA2, CREBRF, CD72, CETN2, KCNG1, NDUFA2, and RPL36AL) were identified as hub genes. Then we performed the analysis of immune factor correlation, gene set enrichment analysis (GSEA), and GO based on seven hub genes. The AUC of ROC prediction model in test and validation sets were 0.845 and 0.839, respectively. Eventually, the mRNA expression analysis of ABCA2, NDUFA2, CREBRF, and CD72 revealed significant differences among the seven hub genes and then was confirmed by RT-PCR.

CONCLUSION

A model based on immune cell infiltration might be used to forecast AD patients' diagnosis, and it provided a new perspective for AD treatment targets.

Association between structural brain features and gene expression by weighted gene co-expression network analysis in conversion from MCI to AD

Alzheimer's disease (AD) is a neurodegenerative disease. Mild cognitive impairment (MCI) represents a state of cognitive function between normal cognition and dementia. Longitudinal studies showed that some MCI patients remained in a state of MCI, and some developed AD. The reason for these different conversions from MCI remains to be investigated. 180 MCI participants were followed for eight years. 143 MCI patients maintained the MCI state (MCI_S), and the remaining thirty-seven MCI patients were re-evaluated as having AD (MCI_AD). We obtained 1,036 structural brain characteristics and 15,481 gene expression values from the 180 MCI participants and applied weighted gene co-expression network analysis (WGCNA) to explore the relationship between structural brain features and gene expression. Regulating mediator effect analysis was employed to explore the relationships among gene expression, brain region measurements and clinical phenotypes. We found that 60 genes from the MCI_S group and 18 genes from the MCI_AD group respectively had the most significant correlations with left paracentral lobule and sulcus (L.PTS) and right subparietal sulcus (R.SubPS) thickness; CTCF, UQCR11 and WDR5B were the mutual genes between the two groups. The expression of CTCF gene and clinical score are completely mediated by L.PTS thickness, and the UQCR11 and WDR5B gene expression levels significantly regulate the mediating effect pathway. In conclusion, the factors affecting the different conversions from MCI are closely related to L.PTS thickness and the CTCF, UQCR11 and WDR5B gene expression levels. Our results add a theoretical foundation of imaging genetics for conversion from MCI to AD.

Assessment of Weighted Gene Co-Expression Network Analysis to Explore Key Pathways and Novel Biomarkers in Muscular Dystrophy

Purpose

This study aimed to explore the key molecular pathways involved in Duchenne muscular dystrophy (DMD) and Becker muscular dystrophy (BMD) and thereby identify hub genes to be potentially used as novel biomarkers using a bioinformatics approach.

Methods

Raw GSE109178 data were collected from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was conducted on the top 50% of altered genes. The key modules associated with the clinical features of DMD and BMD were identified. Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed using the DAVID website. A protein-protein interaction (PPI) network was constructed using the STRING website. MCODE, together with the Cytohubba plug-ins of Cytoscape, screened out the potential hub genes, which were subsequently verified via receiver operating characteristic (ROC) curves in other datasets.

Results

Among the 11 modules obtained, the black module was predominantly associated with pathology and DMD, whereas the light-green module was primarily related to age and BMD. Functional enrichment assessments indicated that the genes in the black module were primarily clustered in “immune response” and “phagosome,” whereas the ones in the light-green module were chiefly enriched in “protein polyubiquitination”. Eleven essential genes were eventually identified, including VCAM1, TYROBP, CD44, ITGB2, CSF1R, LCP2, C3AR1, CCL2, and ITGAM for DMD, along with UBA5 and UBR2 for BMD.

Conclusion

Overall, our findings may be useful for investigating the mechanisms underlying DMD and BMD. In addition, the hub genes discovered might serve as novel molecular markers correlated with dystrophinopathies.

Co-expression Network Analysis Reveals Novel Genes Underlying Alzheimer's Disease Pathogenesis

Background: The pathogenesis of Alzheimer’s disease (AD) remains to be elucidated. This study aimed to identify the hub genes in AD pathogenesis and determine their functions and pathways.

Methods: A co-expression network for an AD gene dataset with 401 samples was constructed, and the AD status-related genes were screened. The hub genes of the network were identified and validated by an independent cohort. The functional pathways of hub genes were analyzed.

Results: The co-expression network revealed a module that related to the AD status, and 101 status-related genes were screened from the trait-related module. Gene enrichment analysis indicated that these status-related genes are involved in synaptic processes and pathways. Four hub genes (ENO2, ELAVL4, SNAP91, and NEFM) were identified from the module, and these hub genes all participated in AD-related pathways, but the associations of each gene with clinical features were variable. An independent dataset confirmed the different expression of hub genes between AD and controls.

Conclusions: Four novel genes associated with AD pathogenesis were identified and validated, which provided novel therapeutic targets for AD.