Identification of Blood Biomarkers for Alzheimer's Disease Through Computational Prediction and Experimental Validation

In a circuit necessary for cognition and emotional affect, Alzheimer's-like pathology associates with neuroinflammation, cognitive and motivational deficits in the young adult TgF344-AD rat

In addition to extracellular amyloid plaques, intracellular neurofibrillary tau tangles, and inflammation, cognitive and emotional affect perturbations are characteristic of Alzheimer's disease (AD). The cognitive and emotional domains impaired by AD include several forms of decision making (such as intertemporal choice), blunted motivation (increased apathy), and impaired executive function (such as working memory and cognitive flexibility). However, the interaction between these domains of the mind and their supporting neurobiological substrates at prodromal stages of AD, or whether these interactions can be predictive of AD severity (individual variability), remain unclear. In this study, we employed a battery of cognitive and emotional tests in the young adult (5-7 mo) transgenic Fisher-344 AD (TgF344-AD; TgAD) rat model of AD. We also assessed whether markers of inflammation or AD-like pathology in the prelimbic cortex (PrL) of the medial prefrontal cortex (mPFC), basolateral amygdala (BLA), or nucleus accumbens (NAc), all structures that directly support the aforementioned behaviors, were predictive of behavioral deficits. We found TgAD rats displayed maladaptive decision making, greater apathy, and impaired working memory that was indeed predicted by AD-like pathology in the relevant brain structures, even at an early age. Moreover, we report that the BLA is an early epicenter of inflammation, and notably, AD-like pathology in the PrL, BLA, and NAc was predictive of BLA inflammation. These results suggest that operant-based battery testing may be sensitive enough to determine pathology trajectories, including neuroinflammation, from early stages of AD.

A multi-ethnic proteomic profiling analysis in Alzheimer's disease identifies the disparities in dysregulation of proteins and pathogenesis

Background

Alzheimer's disease (AD) is the most common type of dementia that affects the elderly population. Lately, blood-based proteomics have been intensively sought in the discovery of AD biomarkers studies due to the capability to link external environmental factors with the development of AD. Demographic differences have been shown to affect the expression of the proteins in different populations which play a vital role in the degeneration of cognitive function.

Method

In this study, a proteomic study focused on Malaysian Chinese and Malay prospects was conducted. Differentially expressed proteins (DEPs) in AD patients and normal controls for Chinese and Malays were identified. Functional enrichment analysis was conducted to further interpret the biological functions and pathways of the DEPs. In addition, a survey investigating behavioural practices among Chinese and Malay participants was conducted to support the results from the proteomic analysis.

Result

The variation of dysregulated proteins identified in Chinese and Malay samples suggested the disparities of pathways involved in this pathological condition for each respective ethnicity. Functional enrichment analysis supported this assumption in understanding the protein-protein interactions of the identified protein signatures and indicate that differentially expressed proteins identified from the Chinese group were significantly enriched with the functional terms related to Aβ/tau protein-related processes, oxidative stress and inflammation whereas neuroinflammation was associated with the Malay group. Besides that, a significant difference in sweet drinks/food intake habits between these two groups implies a relationship between sugar levels and the dysregulation of protein APOA4 in the Malay group. Additional meta-analysis further supported the dysregulation of proteins TF, AHSG, A1BG, APOA4 and C4A among AD groups.

Conclusion

These findings serve as a preliminary understanding in the molecular and demographic studies of AD in a multi-ethnic population.

Exploring key genes and pathways associated with sex differences in autism spectrum disorder: integrated bioinformatic analysis

Autism spectrum disorder (ASD) is a heterogenous neurodevelopmental disorder marked by functional abnormalities in brain that causes social and linguistic difficulties. The incidence of ASD is more prevalent in males compared to females, but the underlying mechanism, as well as molecular indications for identifying sex-specific differences in ASD symptoms remain unknown. Thus, impacting the development of personalized strategy towards pharmacotherapy of ASD. The current study employs an integrated bioinformatic approach to investigate the genes and pathways uniquely associated with sex specific differences in autistic individuals. Based on microarray dataset (GSE6575) extracted from the gene expression omnibus, the dysregulated genes between the autistic and the neurotypical individuals for both sexes were identified. Gene set enrichment analysis was performed to ascertain biological activities linked to the dysregulated genes. Protein-protein interaction network analysis was carried out to identify hub genes. The identified hub genes were examined to determine their functions and involvement in the associated pathways using Enrichr. Additionally, hub genes were validated from autism-associated databases and the potential small molecules targeting the hub genes were identified. The present study utilized whole blood transcriptomic gene expression analysis data and identified 2211 and 958 differentially expressed unique genes in males and females respectively. The functional enrichment analysis revealed that male hub genes were functionally associated with RNA polymerase II mediated transcriptional regulation whereas female hub genes were involved in intracellular signal transduction and cell migration. The top male hub genes exhibited functional enrichment in tyrosine kinase signalling pathway. The pathway enrichment analysis of male hub genes indicates the enrichment of papillomavirus infection. Female hub genes were enriched in androgen receptor signalling pathway and functionally enriched in focal adhesion specific excision repair. Identified drug like candidates targeting these genes may serve as a potential sex specific therapeutics. Wortmannin for males, 5-Fluorouracil for females had the highest scores. Targeted and sex-specific pharmacotherapies may be created for the management of ASD. The current investigation identifies sex-specific molecular signatures derived from whole blood which may serve as a potential peripheral sex-specific biomarkers for ASD. The study also uncovers the possible pharmacological interventions against the selected genes/pathway, providing support in development of therapeutic strategies to mitigate ASD. However, experimental proofs on biological systems are warranted.

Pharmacological evaluation of newly synthesized benzimidazole derivative for anti-Alzheimer potential

Backgound: Alzheimer disease (AD) is a disastrous disease characterized by accretion of amyloid-beta plaques, neurofibrillary tangles inducing oxidative stress, loss of neuronal functions and continuous progression of cognitive impairment leading to severe dementia.

Material and Methods: The newly synthesized benzimidazole derivative 4-chloro-3-(2-phenyl-1H-benzimidazole-1-sulfonyl) benzoic acid (CB) was evaluated for its anti-Alzheimer activity using in silico, in vivo, in vitro and molecular techniques (ELISA, WB & IHC).

Results: In-silico studies revealed that CB has atomic contact energy values of -3.9 to -8.9 kcal/mol against selected targets. In vitro assay showed that CB caused acetylcholinesterase (AChE) and diphenyl-1-picrylhydrazyl inhibition. In-vivo findings revealed improvement in dementia as observed in the morris water maze test and Ymaze test. Amyloid-beta disaggregation, increased level of anti-oxidants, decreased expressions of inflammatory markers and enhanced cellular architecture were found in the cortex and hippocampus of treated rats in the histopathological examination, immunohistochemistry analysis, enzyme-linked immunosorbent assay and western blot analysis.

Conclusions: This study revealed that CB possess different binding affinities with the Alzheimer-related targets and it possess anti-Alzheimer activity, mediated via AChE and amyloid-beta inhibition, anti-oxidant and anti-inflammatory pathways.

Unique transcriptional signatures correlate with behavioral and psychological symptom domains in Alzheimer's disease

Despite the significant burden, cost, and worse prognosis of Alzheimer's disease (AD) with behavioral and psychological symptoms of dementia (BPSD), little is known about the molecular causes of these symptoms. Using antemortem assessments of BPSD in AD, we demonstrate that individual BPSD can be grouped into 4 domain factors in our cohort: affective, apathy, agitation, and psychosis. Then, we performed a transcriptome-wide analysis for each domain utilizing bulk RNA-seq of post-mortem anterior cingulate cortex (ACC) tissues. Though all 4 domains are associated with a predominantly downregulated pattern of hundreds of differentially expressed genes (DEGs), most DEGs are unique to each domain, with only 22 DEGs being common to all BPSD domains, including TIMP1. Weighted gene co-expression network analysis (WGCNA) yielded multiple transcriptional modules that were shared between BPSD domains or unique to each domain, and NetDecoder was used to analyze context-dependent information flow through the biological network. For the agitation domain, we found that all DEGs and a highly associated transcriptional module were functionally enriched for ECM-related genes including TIMP1, TAGLN, and FLNA. Another unique transcriptional module also associated with the agitation domain was enriched with genes involved in post-synaptic signaling, including DRD1, PDE1B, CAMK4, and GABRA4. By comparing context-dependent changes in DEGs between cases and control networks, ESR1 and PARK2 were implicated as two high-impact genes associated with agitation that mediated significant information flow through the biological network. Overall, our work establishes unique targets for future study of the biological mechanisms of BPSD and resultant drug development.

Dissecting the functional significance of HSP90AB1 and other heat shock proteins in countering glioblastomas and ependymomas using omics analysis and drug prediction using virtual screening

Heat shock proteins (HSPs) are the evolutionary family of proteins that are highly conserved and present widely in various organisms and play an array of important roles and cellular functions. Currently, very few or no studies are based on the systematic analysis of the HSPs in Glioblastoma (GBMs) and ependymomas. We performed an integrated omics analysis to predict the mutual regulatory differential HSP signatures that were associated with both glioblastoma and ependymomas. Further, we explored the various common dysregulated biological processes operating in both the tumors, and were analyzed using functional enrichment, gene ontology along with the pathway analysis of the predicted HSPs. We established an interactome network of protein-protein interaction (PPIN) to identify the hub HSPs that were commonly associated with GBMs and ependymoma. To understand the mutual molecular mechanism of the HSPs in both malignancies, transcription factors, and miRNAs overlapping with both diseases were explored. Moreover, a transcription factor-miRNAs-HSPs coregulatory network was constructed along with the prediction of potential candidate drugs that were based on perturbation-induced gene expression analysis. Based on the RNA-sequencing data, HSP90AB1 was identified as the most promising target among other predicted HSPs. Finally, the ranking of the drugs was arranged based on various drug scores. In conclusion, this study gave a spotlight on the mutual targetable HSPs, biological pathways, and regulatory signatures associated with GBMs and ependymoma with an improved understanding of crosstalk involved. Additionally, the role of therapeutics was also explored against HSP90AB1. These findings could potentially be able to explain the interplay of HSP90AB1 and other HSPs within these two malignancies.

Blood-Based Transcriptomic Biomarkers Are Predictive of Neurodegeneration Rather Than Alzheimer's Disease

Alzheimer's disease (AD) is a growing global health crisis affecting millions and incurring substantial economic costs. However, clinical diagnosis remains challenging, with misdiagnoses and underdiagnoses being prevalent. There is an increased focus on putative, blood-based biomarkers that may be useful for the diagnosis as well as early detection of AD. In the present study, we used an unbiased combination of machine learning and functional network analyses to identify blood gene biomarker candidates in AD. Using supervised machine learning, we also determined whether these candidates were indeed unique to AD or whether they were indicative of other neurodegenerative diseases, such as Parkinson's disease (PD) and amyotrophic lateral sclerosis (ALS). Our analyses showed that genes involved in spliceosome assembly, RNA binding, transcription, protein synthesis, mitoribosomes, and NADH dehydrogenase were the best-performing genes for identifying AD patients relative to cognitively healthy controls. This transcriptomic signature, however, was not unique to AD, and subsequent machine learning showed that this signature could also predict PD and ALS relative to controls without neurodegenerative disease. Combined, our results suggest that mRNA from whole blood can indeed be used to screen for patients with neurodegeneration but may be less effective in diagnosing the specific neurodegenerative disease.

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.

GSN gene frameshift mutations in Alzheimer's disease

BACKGROUND

The pathogenic missense mutations of the gelsolin (GSN) gene lead to familial amyloidosis of the Finnish type (FAF); however, our previous study identified GSN frameshift mutations existed in patients with Alzheimer's disease (AD). The GSN genotype-phenotype heterogeneity and the role of GSN frameshift mutations in patients with AD are unclear.

METHOD

In total, 1192 patients with AD and 1403 controls were screened through whole genome sequencing, and 884 patients with AD were enrolled for validation. Effects of GSN mutations were evaluated in vitro. GSN, Aβ42, Aβ40 and Aβ42/40 were detected in both plasma and cerebrospinal fluid (CSF).

RESULTS

Six patients with AD with GSN P3fs and K346fs mutations (0.50%, 6/1192) were identified, who were diagnosed with AD but not FAF. In addition, 13 patients with AD with GSN frameshift mutations were found in the validation cohort (1.47%, 13/884). Further in vitro experiments showed that both K346fs and P3fs mutations led to the GSN loss of function in inhibiting Aβ-induced toxicity. Moreover, a higher level of plasma (p=0.001) and CSF (p=0.005) GSN was observed in AD cases than controls, and a positive correlation was found between the CSF GSN and CSF Aβ42 (r=0.289, p=0.009). Besides, the GSN level was initially increasing and then decreasing with the disease course and cognitive decline.

CONCLUSIONS

GSN frameshift mutations may be associated with AD. An increase in plasma GSN is probably a compensatory reaction in AD, which is a potential biomarker for early AD.

Unique Transcriptional Signatures Correlate with Behavioral and Psychological Symptom Domains in Alzheimer's Disease

Despite the significant burden, cost, and worse prognosis of Alzheimer's disease (AD) with behavioral and psychological symptoms of dementia (BPSD), little is known about the molecular causes of these symptoms. Using antemortem assessments of BPSD in AD, we demonstrate that individual BPSD can be grouped into 4 domain factors in our sample: affective, apathy, agitation, and psychosis. Then, we performed a transcriptome-wide analysis for each domain utilizing bulk RNA-seq of post-mortem anterior cingulate cortex (ACC) tissue. Though all 4 domains are associated with a predominantly downregulated pattern of hundreds of differentially expressed genes (DEGs), most DEGs are unique to each domain, with only 22 DEGs being common to all BPSD domains, including TIMP1. Weighted gene co-expression network analysis (WGCNA) yielded multiple transcriptional modules that were shared between BPSD domains or unique to each domain, and NetDecoder was used to analyze context-dependent information flow through the biological network. For the agitation domain, we found that all DEGs and a highly correlated transcriptional module were functionally enriched for ECM-related genes including TIMP1, TAGLN, and FLNA. Another unique transcriptional module also associated with the agitation domain was enriched with genes involved in post-synaptic signaling, including DRD1, PDE1B, CAMK4, and GABRA4. By comparing context-dependent changes in DEGs between cases and control networks, ESR1 and PARK2 were implicated as two high impact genes associated with agitation that mediated significant information flow through the biological network. Overall, our work establishes unique targets for future study of the biological mechanisms of BPSD and resultant drug development.

Investigation of Whole and Glandular Saliva as a Biomarker for Alzheimer’s Disease Diagnosis

Salivary Aβ40, Aβ42, t-tau, and p-tau 181 are commonly employed in Alzheimer’s disease (AD) investigations. However, the collection method of these biomarkers can affect their levels. To assess the impact of saliva collection methods on biomarkers in this study, 15 healthy people were employed in the morning with six saliva collection methods. The chosen methods were then applied in 30 AD patients and 30 non-AD controls. The levels of salivary biomarkers were calculated by a specific enzyme-linked immunosorbent assay. The receiver operating characteristic was utilized to assess salivary biomarkers in AD patients. The results demonstrated that the highest levels of salivary Aβ40, Aβ42, t-tau, and p-tau were in different saliva collection methods. The correlations between different saliva biomarkers in the same collection method were different. Salivary Aβ40, Aβ42, t-tau, and p-tau had no significant association. Salivary Aβ42 was higher in AD than in non-AD controls. However, p-tau/t-tau and Aβ42/Aβ40 had some relevance. The area under the curve for four biomarkers combined in AD diagnosis was 92.11%. An alternate saliva collection method (e.g., USS in Aβ40, UPS in Aβ42, t-tau, SSS in p-tau 181) was demonstrated in this study. Moreover, combining numerous biomarkers improves AD diagnosis.

Potential Protein Blood-Based Biomarkers in Different Types of Dementia: A Therapeutic Overview

Abstract:

Biomarkers capable of identifying and distinguishing types of dementia such as Alzheimer's disease (AD), Parkinson's disease dementia (PDD), Lewy body dementia (LBD), and frontotemporal dementia (FTD) have been become increasingly relentless. Studies of possible biomarker proteins in the blood that can help formulate new diagnostic proposals and therapeutic visions of different types of dementia are needed. However, due to several limitations of these biomarkers, especially in discerning dementia, their clinical applications are still undetermined. Thus, the updating of biomarker blood proteins that can help in the diagnosis and discrimination of these main dementia conditions is essential to enable new pharmacological and clinical management strategies, with specificities for each type of dementia. To review the literature concerning protein blood-based AD and non-AD biomarkers as new pharmacological targets and/or therapeutic strategies. Recent findings for protein-based AD, PDD, LBD, and FTD biomarkers are focused on in this review. Protein biomarkers were classified according to the pathophysiology of the dementia types. The diagnosis and distinction of dementia through protein biomarkers is still a challenge. The lack of exclusive biomarkers for each type of dementia highlights the need for further studies in this field. Only after this, blood biomarkers may have a valid use in clinical practice as they are promising to help in diagnosis and in the differentiation of diseases.

Exploring the Genetic Associations Between the Use of Statins and Alzheimer's Disease

Objective

Alzheimer's disease (AD) is the most common cause of dementia. The statins have shown beneficial effects on cognitive functions and reduced the risk of dementia development. However, the exact mechanisms of statin effects in AD are not yet fully understood. In this study, we aimed to explore the underlying mechanisms of statin on AD.

Methods

We downloaded AD blood dataset (GSE63060) and statin-related blood gene expression dataset (GSE86216). Then we performed gene expression analysis of each dataset and compared blood gene expressions between AD patients and statin-treated patients. Then, we downloaded mouse embryonic neural stem cell dataset (GSE111945) and performed gene expression analysis.

Results

From the human blood dataset, we identified upregulated/downregulated genes in AD patients and statin-treated patients. Some of the upregulated genes (AEN, MBTPS1, ABCG1) in the blood of AD patients are downregulated in statin-treated patients. Several downregulated genes (FGL2, HMGCS1, PSME2, SRSF3, and ATG3) are upregulated in statin-treated patients. Gene set enrichment analysis using mouse stem cell dataset revealed a significant relationship of Kyoto Encyclopedia of Genes and Genomes-defined pathway of AD in statin-treated neural stem cells compared to vehicle-treated neural stem cells (normalized enrichment score: −2.24 in male and −1.6 in female).

Conclusion

These gene expression analyses from human blood and mouse neural stem cell demonstrate the important clues on the molecular mechanisms of impacts of statin on AD disease. Further studies are needed to investigate the exact role of candidate genes and pathways suggested in our AD pathogenesis study.

Deep transfer learning of structural magnetic resonance imaging fused with blood parameters improves brain age prediction

Machine learning has been applied to neuroimaging data for estimating brain age and capturing early cognitive impairment in neurodegenerative diseases. Blood parameters like neurofilament light chain are associated with aging. In order to improve brain age predictive accuracy, we constructed a model based on both brain structural magnetic resonance imaging (sMRI) and blood parameters. Healthy subjects (n = 93; 37 males; aged 50–85 years) were recruited. A deep learning network was firstly pretrained on a large set of MRI scans (n = 1,481; 659 males; aged 50–85 years) downloaded from multiple open‐source datasets, to provide weights on our recruited dataset. Evaluating the network on the recruited dataset resulted in mean absolute error (MAE) of 4.91 years and a high correlation (r = .67, p <.001) against chronological age. The sMRI data were then combined with five blood biochemical indicators including GLU, TG, TC, ApoA1 and ApoB, and 9 dementia‐associated biomarkers including ApoE genotype, HCY, NFL, TREM2, Aβ40, Aβ42, T‐tau, TIMP1, and VLDLR to construct a bilinear fusion model, which achieved a more accurate prediction of brain age (MAE, 3.96 years; r = .76, p <.001). Notably, the fusion model achieved better improvement in the group of older subjects (70–85 years). Extracted attention maps of the network showed that amygdala, pallidum, and olfactory were effective for age estimation. Mediation analysis further showed that brain structural features and blood parameters provided independent and significant impact. The constructed age prediction model may have promising potential in evaluation of brain health based on MRI and blood parameters.

Identification of tear-based protein and non-protein biomarkers: Its application in diagnosis of human diseases using biosensors

Discovery of robust, selective and specific biomarkers are important for early diagnosis and monitor progression of human diseases. Eye being a common target for several human diseases, vision impediment and complications are often associated with systemic and ocular diseases. Tears are bodily fluids that are closest to eye and are rich in protein content and other metabolites. As a biomarker repository, it advantages over other bodily fluids due to the ability to collect it non-invasively. In this review, we highlight some recent advancements in identification of tear-based protein biomarkers like lacryglobin and cystatin SA for cancer; interleukin-6 and immunoglobulin-A antibody for COVID-19; tau, amyloid-β-42 and lysozyme-C for Alzheimer's disease; peroxiredoxin-6 and α-synuclein for Parkinson's disease; kallikrein, angiotensin converting enzyme and lipocalin-1 for glaucoma; lactotransferrin and lipophilin-A for diabetic retinopathy and zinc-alpha-2 glycoprotein-1, prolactin and calcium binding protein-A4 for eye thyroid disease. We also discussed identification of tear based non-protein biomarkers like lysophospholipids and acetylcarnitine for glaucoma, 8-hydroxy-2'-deoxyquanosine and malondialdehyde for thyroid eye disease. We elucidate technological advancement in developing tear-based biosensors for diagnosis and monitoring diseases such as diabetes, diabetic retinopathy and Alzheimer's disease. Altogether, the study of tears as potential biomarkers for early diagnosis of human diseases is promising.

Risk Prediction of Cardiovascular Events by Exploration of Molecular Data with Explainable Artificial Intelligence

Cardiovascular diseases (CVD) annually take almost 18 million lives worldwide. Most lethal events occur months or years after the initial presentation. Indeed, many patients experience repeated complications or require multiple interventions (recurrent events). Apart from affecting the individual, this leads to high medical costs for society. Personalized treatment strategies aiming at prediction and prevention of recurrent events rely on early diagnosis and precise prognosis. Complementing the traditional environmental and clinical risk factors, multi-omics data provide a holistic view of the patient and disease progression, enabling studies to probe novel angles in risk stratification. Specifically, predictive molecular markers allow insights into regulatory networks, pathways, and mechanisms underlying disease. Moreover, artificial intelligence (AI) represents a powerful, yet adaptive, framework able to recognize complex patterns in large-scale clinical and molecular data with the potential to improve risk prediction. Here, we review the most recent advances in risk prediction of recurrent cardiovascular events, and discuss the value of molecular data and biomarkers for understanding patient risk in a systems biology context. Finally, we introduce explainable AI which may improve clinical decision systems by making predictions transparent to the medical practitioner.

A Concise Overview of Biosensing Technologies for the Detection of Alzheimer's Disease Biomarkers

Alzheimer&#039;s disease (AD) is a brain-linked pathophysiological condition with neuronal degeneration, cognition dysfunctions, and other debilitations. Due to the growing prevalence of AD, there is a highly commended tendency to accelerate and develop analytical technologies for easy, cost-effective, and sensitive detection of AD biomarkers. In the last decade, remarkable advancements have been achieved on the gate to the progression of biosensors, predominantly optical and electrochemical, to detect AD biomarkers. Biosensors are commanding analytical devices that can conduct biological responses on transducers into measurable signals. These analytical devices can assist the case finding and management of AD. This review focuses on up-to-date developments, contests, and tendencies regarding AD biosensing principally, emphasizing the exclusive possessions of nanomaterials.

Exploring the Key Genes and Identification of Potential Diagnosis Biomarkers in Alzheimer's Disease Using Bioinformatics Analysis

Background

Alzheimer’s disease (AD) is one of the major threats of the twenty-first century and lacks available therapy. Identification of novel molecular markers for diagnosis and treatment of AD is urgently demanded, and genetic biomarkers show potential prospects.

Method

We identify and intersected differentially expressed genes (DEGs) from five microarray datasets to detect consensus DEGs. Based on these DEGs, we conducted Gene Ontology (GO), performed the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, constructed a protein—protein interaction (PPI) network, and utilized Cytoscape to identify hub genes. The least absolute shrinkage and selection operator (LASSO) logistic regression was applied to identify potential diagnostic biomarkers. Gene set enrichment analysis (GSEA) was performed to investigate the biological functions of the key genes.

Result

We identified 608 consensus DEGs, several dysregulated pathways, and 18 hub genes. Sixteen hub genes dysregulated as AD progressed. The diagnostic model of 35 genes was constructed, which has a high area under the curve (AUC) value in both the validation dataset and combined dataset (AUC = 0.992 and AUC = 0.985, respectively). The model can also differentiate mild cognitive impairment and AD patients from controls in two blood datasets. Brain-derived neurotrophic factor (BDNF) and WW domain-containing transcription regulator protein 1 (WWTR1), which are associated with the Braak stage, Aβ 42 levels, and β-secretase activity, were identified as critical genes of AD.

Conclusion

Our study identified 16 hub genes correlated to the neuropathological stage and 35 potential biomarkers for the diagnosis of AD. WWTR1 were identified as candidate genes for future studies. This study deepens our understanding of the transcriptomic and functional features and provides new potential diagnostic biomarkers and therapeutic targets for AD.

Construction of Unified Human Antimicrobial and Immunomodulatory Peptide Database and Examination of Antimicrobial and Immunomodulatory Peptides in Alzheimer's Disease Using Network Analysis of Proteomics Datasets

The reanalysis of genomics and proteomics datasets by bioinformatics approaches is an appealing way to examine large amounts of reliable data. This can be especially true in cases such as Alzheimer's disease, where the access to biological samples, along with well-defined patient information can be challenging. Considering the inflammatory part of Alzheimer's disease, our aim was to examine the presence of antimicrobial and immunomodulatory peptides in human proteomic datasets deposited in the publicly available proteomics database ProteomeXchange (http://www.proteomexchange.org/). First, a unified, comprehensive human antimicrobial and immunomodulatory peptide database, containing all known human antimicrobial and immunomodulatory peptides was constructed and used along with the datasets containing high-quality proteomics data originating from the examination of Alzheimer's disease and control groups. A throughout network analysis was carried out, and the enriched GO functions were examined. Less than 1% of all identified proteins in the brain were antimicrobial and immunomodulatory peptides, but the alterations characteristic of Alzheimer's disease could be recapitulated with their analysis. Our data emphasize the key role of the innate immune system and blood clotting in the development of Alzheimer's disease. The central role of antimicrobial and immunomodulatory peptides suggests their utilization as potential targets for mechanistic studies and future therapies.

Exosomal Aβ-Binding Proteins Identified by "In Silico" Analysis Represent Putative Blood-Derived Biomarker Candidates for Alzheimer´s Disease

The potential of exosomes as biomarker resources for diagnostics and even for therapeutics has intensified research in the field, including in the context of Alzheimer´s disease (AD). The search for disease biomarkers in peripheral biofluids is advancing mainly due to the easy access it offers. In the study presented here, emphasis was given to the bioinformatic identification of putative exosomal candidates for AD. The exosomal proteomes of cerebrospinal fluid (CSF), serum and plasma, were obtained from three databases (ExoCarta, EVpedia and Vesiclepedia), and complemented with additional exosomal proteins already associated with AD but not found in the databases. The final biofluids’ proteomes were submitted to gene ontology (GO) enrichment analysis and the exosomal Aβ-binding proteins that can constitute putative candidates were identified. Among these candidates, gelsolin, a protein known to be involved in inhibiting Abeta fibril formation, was identified, and it was tested in human samples. The levels of this Aβ-binding protein, with anti-amyloidogenic properties, were assessed in serum-derived exosomes isolated from controls and individuals with dementia, including AD cases, and revealed altered expression patterns. Identification of potential peripheral biomarker candidates for AD may be useful, not only for early disease diagnosis but also in drug trials and to monitor disease progression, allowing for a timely therapeutic intervention, which will positively impact the patient’s quality of life.

Diagnosis of Alzheimer's Disease in Developed and Developing Countries: Systematic Review and Meta-Analysis of Diagnostic Test Accuracy

BACKGROUND

The present systematic review and meta-analysis of diagnostic test accuracy summarizes the last three decades in advances on diagnosis of Alzheimer's disease (AD) in developed and developing countries.

OBJECTIVE

To determine the accuracy of biomarkers in diagnostic tools in AD, for example, cerebrospinal fluid, positron emission tomography (PET), and magnetic resonance imaging (MRI), etc.

METHODS

The authors searched PubMed for published studies from 1990 to April 2020 on AD diagnostic biomarkers. 84 published studies were pooled and analyzed in this meta-analysis and diagnostic accuracy was compared by summary receiver operating characteristic statistics.

RESULTS

Overall, 84 studies met the criteria and were included in a meta-analysis. For EEG, the sensitivity ranged from 67 to 98%, with a median of 80%, 95% CI [75, 91], tau-PET diagnosis sensitivity ranged from 76 to 97%, with a median of 94%, 95% CI [76, 97]; and MRI sensitivity ranged from 41 to 99%, with a median of 84%, 95% CI [81, 87]. Our results showed that tau-PET diagnosis had higher performance as compared to other diagnostic methods in this meta-analysis.

CONCLUSION

Our findings showed an important discrepancy in diagnostic data for AD between developed and developing countries, which can impact global prevalence estimation and management of AD. Also, our analysis found a better performance for the tau-PET diagnostic over other methods to diagnose AD patients, but the expense of tau-PET scan seems to be the limiting factor in the diagnosis of AD in developing countries such as those found in Asia, Africa, and Latin America.

Protein Biomarkers of Autism Spectrum Disorder Identified by Computational and Experimental Methods

Background: Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder that affects millions of people worldwide. However, there are currently no reliable biomarkers for ASD diagnosis. Materials and Methods: The strategy of computational prediction combined with experimental verification was used to identify blood protein biomarkers for ASD. First, brain tissue-based transcriptome data of ASD were collected from Gene Expression Omnibus database and analyzed to find ASD-related genes by bioinformatics method of significance analysis of microarrays. Then, a prediction program of blood-secretory proteins was applied on these genes to predict ASD-related proteins in blood. Furthermore, ELISA was used to verify these proteins in plasma samples of ASD patients. Results: A total of 364 genes were identified differentially expressed in brain tissue of ASD, among which 59 genes were predicted to encode ASD-related blood-secretory proteins. After functional analysis and literature survey, six proteins were chosen for experimental verification and five were successfully validated. Receiver operating characteristic curve analyses showed that the area under the curve of SLC25A12, LIMK1, and RARS was larger than 0.85, indicating that they are more powerful in discriminating ASD cases from controls. Conclusion: SLC25A12, LIMK1, and RARS might serve as new potential blood protein biomarkers for ASD. Our findings provide new insights into the pathogenesis and diagnosis of ASD.

Blood-based Biomarkers of Alzheimer's Disease: The Long and Winding Road

BACKGROUND

Blood-based biomarkers can be very useful in formulating new diagnostic and treatment proposals in the field of dementia, especially in Alzheimer's disease (AD). However, due to the influence of several factors on the reproducibility and reliability of these markers, their clinical use is still very uncertain. Thus, up-to-date knowledge about the main blood biomarkers that are currently being studied is extremely important in order to discover clinically useful and applicable tools, which could also be used as novel pharmacological strategies for the AD treatment.

METHODS

A narrative review was performed based on the current candidates of blood-based biomarkers for AD to show the main results from different studies, focusing on their clinical applicability and association with AD pathogenesis.

OBJECTIVE

The aim of this paper was to carry out a literature review on the major blood-based biomarkers for AD, connecting them with the pathophysiology of the disease.

RESULTS

Recent advances in the search of blood-based AD biomarkers were summarized in this review. The biomarkers were classified according to the topics related to the main hallmarks of the disease such as inflammation, amyloid, and tau deposition, synaptic degeneration and oxidative stress. Moreover, molecules involved in the regulation of proteins related to these hallmarks were described, such as non-coding RNAs, neurotrophins, growth factors and metabolites. Cells or cellular components with the potential to be considered as blood-based AD biomarkers were described in a separate topic.

CONCLUSION

A series of limitations undermine new discoveries on blood-based AD biomarkers. The lack of reproducibility of findings due to the small size and heterogeneity of the study population, different analytical methods and other assay conditions make longitudinal studies necessary in this field to validate these structures, especially when considering a clinical evaluation that includes a broad panel of these potential and promising blood-based biomarkers.

Body Fluid Biomarkers for Alzheimer's Disease-An Up-To-Date Overview

Neurodegeneration is a highly complex process which is associated with a variety of molecular mechanisms related to ageing. Among neurodegenerative disorders, Alzheimer's disease (AD) is the most common, affecting more than 45 million individuals. The underlying mechanisms involve amyloid plaques and neurofibrillary tangles (NFTs) deposition, which will subsequently lead to oxidative stress, chronic neuroinflammation, neuron dysfunction, and neurodegeneration. The current diagnosis methods are still limited in regard to the possibility of the accurate and early detection of the diseases. Therefore, research has shifted towards the identification of novel biomarkers and matrices as biomarker sources, beyond amyloid-β and tau protein levels within the cerebrospinal fluid (CSF), that could improve AD diagnosis. In this context, the aim of this paper is to provide an overview of both conventional and novel biomarkers for AD found within body fluids, including CSF, blood, saliva, urine, tears, and olfactory fluids.

Analyses Mutations in GSN, CST3, TTR, and ITM2B Genes in Chinese Patients With Alzheimer's Disease

Amyloid protein deposition is a common mechanism of hereditary amyloidosis (HA) and Alzheimer’s disease (AD). Mutations of gelsolin (GSN), cystatin C (CST3), transthyretin (TTR), and integral membrane protein 2B (ITM2B) genes can lead to HA. But the relationship is unclear between these genes and AD. Genes targeted sequencing (GTS), including GSN, CST3, TTR, and ITM2B, was performed in a total of 636 patients with clinical AD and 365 normal controls from China. As a result, according to American College of Medical Genetics and Genomics (ACMG) guidelines, two novel likely pathogenic frame-shift mutations (GSN:c.1036delA:p.K346fs and GSN:c.8_35del:p.P3fs) were detected in five patients with AD, whose initial symptom was memory decline, accompanied with psychological and behavioral abnormalities later. Interestingly, the patient with K346fs mutation, presented cerebral β-amyloid protein deposition, had an early onset (48 years) and experienced rapid progression, while the other four patients with P3fs mutation had a late onset [(Mean ± SD): 69.50 ± 5.20 years] and a long course of illness [(Mean ± SD): 9.24 ± 4.86 years]. Besides, we also discovered 17 variants of uncertain significance (VUS) in these four genes. To our knowledge, we are the first to report AD phenotype with GSN mutations in patients with AD in the Chinese cohort. Although mutations in the GSN gene are rare, it may explain a small portion of clinically diagnosed AD.

Advantages and Pitfalls in Fluid Biomarkers for Diagnosis of Alzheimer's Disease

Alzheimer’s disease (AD) is a commonly occurring neurodegenerative disease in the advanced-age population, with a doubling of prevalence for each 5 years of age above 60 years. In the past two decades, there has been a sustained effort to find suitable biomarkers that may not only aide with the diagnosis of AD early in the disease process but also predict the onset of the disease in asymptomatic individuals. Current diagnostic evidence is supportive of some biomarker candidates isolated from cerebrospinal fluid (CSF), including amyloid beta peptide (Aβ), total tau (t-tau), and phosphorylated tau (p-tau) as being involved in the pathophysiology of AD. However, there are a few biomarkers that have been shown to be helpful, such as proteomic, inflammatory, oral, ocular and olfactory in the early detection of AD, especially in the individuals with mild cognitive impairment (MCI). To date, biomarkers are collected through invasive techniques, especially CSF from lumbar puncture; however, non-invasive (radio imaging) methods are used in practice to diagnose AD. In order to reduce invasive testing on the patients, present literature has highlighted the potential importance of biomarkers in blood to assist with diagnosing AD.

Alzheimer's Disease, and Breast and Prostate Cancer Research: Translational Failures and the Importance to Monitor Outputs and Impact of Funded Research

Dementia and cancer are becoming increasingly prevalent in Western countries. In the last two decades, research focused on Alzheimer's disease (AD) and cancer, in particular, breast cancer (BC) and prostate cancer (PC), has been substantially funded both in Europe and worldwide. While scientific research outcomes have contributed to increase our understanding of the disease etiopathology, still the prevalence of these chronic degenerative conditions remains very high across the globe. By definition, no model is perfect. In particular, animal models of AD, BC, and PC have been and still are traditionally used in basic/fundamental, translational, and preclinical research to study human disease mechanisms, identify new therapeutic targets, and develop new drugs. However, animals do not adequately model some essential features of human disease; therefore, they are often unable to pave the way to the development of drugs effective in human patients. The rise of new technological tools and models in life science, and the increasing need for multidisciplinary approaches have encouraged many interdisciplinary research initiatives. With considerable funds being invested in biomedical research, it is becoming pivotal to define and apply indicators to monitor the contribution to innovation and impact of funded research. Here, we discuss some of the issues underlying translational failure in AD, BC, and PC research, and describe how indicators could be applied to retrospectively measure outputs and impact of funded biomedical research.

Integrated analysis and network pharmacology approaches to explore key genes of Xingnaojing for treatment of Alzheimer's disease

BACKGROUND

Alzheimer's disease (AD), as a neurodegenerative condition, is one of the leading causes of dementia. Our study aims to explore the key genes of Xingnaojing (XNJ) for treatment of AD by integrated microarray analysis and network pharmacology.

METHODS

The differentially expressed genes (DEGs) were identified in AD compared with normal control. According to these DEGs, we performed the functional annotation, protein-protein interaction (PPI) network construction. The network pharmacology was used to explore the potential targets of XNJ in the treatment of AD. The expression level of selected candidate genes was validated by quantitative real-time polymerase chain reaction (qRT-PCR).

RESULTS

A total of 1,424 DEGs (620 genes were upregulated and 804 genes were downregulated) between AD and normal control were obtained. The functional annotation results displayed that neuroactive ligand-receptor interaction, regulation of actin cytoskeleton, Estrogen signaling pathway and notch signaling pathway were significantly enriched pathways in AD. Comparing the target genes of four active ingredients, a total of 16 shared genes were found. Among which, HTR2A and ADRA2A were also enriched in pathway of neuroactive ligand-receptor interaction. The expression of 4 DEGs (SORCS3, HTR2A, NEFL, and TAC1) was validated by qRT-PCR. Except for TAC1, the other 3 DEGs in AD were consistent with our integrated analysis.

CONCLUSIONS

The results of this study may provide novel insights into the molecular mechanisms of AD and indicate potential therapeutic targets for AD.

Real-time, personalized medicine through wearable sensors and dynamic predictive modeling: a new paradigm for clinical medicine

Accurately predicting the onset and course of a disease in an individual is a major unmet challenge in medicine due to the complex and dynamic nature of disease progression. Continuous data from wearable technologies and biomarker data with a fine time resolution provide a unique opportunity to learn more about disease evolution and to usher in a new era of personalized and real-time medicine. Herein, we propose the potential of real-time, continuously measured physiological data as a noninvasive biomarker approach for detecting disease transitions, using allogeneic hematopoietic stem cell transplant (HCT) patient care as an example. Additionally, we review a recent computational technique, the landscape dynamic network biomarker method, that uses biomarker data to identify transition states in disease progression and explore how to use it with both biomarker and physiological data for earlier detection of graft-versus-host disease specifically. Throughout, we argue that increased collaboration across multiple fields is essential to realizing the full potential of wearable and biomarker data in a new paradigm of personalized and real-time medicine.

Therapeutic Study of Phytochemicals Against Cancer and Alzheimer's Disease Management

BACKGROUND

Phytochemicals are a significant piece of conventional prescription and have been researched in detail for conceivable consideration in current drug discovery. Medications and plants are firmly identified for traditional prescriptions and ethnomedicines that are basically arranged from plants. Recognizing the medical advantages of phytochemicals is of fundamental advancement in medication and useful sustenance improvement. Secondary metabolites of different plants have been customarily used for the improvement of human wellbeing. The phytochemicals are diets rich, which can upgrade neuroplasticity and protection from neurodegeneration.

RESULTS

Phytochemicals keep on entering clinical preliminaries or provide leads for the synthesis of medicinal agents. Phytochemicals are a great extent cancer prevention agents in nature at lower concentrations and under favorable cell conditions that adequately avoid the oxidation of different molecules that have an ability to produce free radicals and thus protect the body.

CONCLUSION

The purpose of this review is to describe the use of phytochemicals against cancer and Alzheimer's disease treatment.

Spermidine in dementia : Relation to age and memory performance

Previous studies have highlighted that spermidine has the ability to trigger the important process of dissolving amyloid-beta plaques by autophagy. This manuscript focuses on the correlation of serum spermidine levels between age and between performance in mini-mental state examinations. It will serve as a premise for an ongoing multicentric placebo-controlled study, which focuses on the effect of oral spermidine supplementation on memory performance. Memory tests were carried out on 80 subjects aged 60-96 years old in 6 nursing homes in Styria. Blood samples were taken for the determination of spermidine concentration. The results showed a significant correlation between the spermidine concentration and the mini-mental state examination score (p = 0.025). On the basis of the dependence demonstrated it can be concluded that spermidine might be suitable as a biomarker for the diagnosis of neurocognitive changes (senile dementia or Alzheimer's disease).

Oxidative Damage of DNA as Early Marker of Alzheimer's Disease

Alzheimer’s Disease (AD) is the most common cause of dementia, and its characteristic histopathological hallmarks are neurofibrillary tangles and senile plaques. Among involved mechanisms, oxidative stress plays an important role in damaging cell components (e.g., proteins, nucleic acids). In this study, different oxidized products of proteins and DNA were determined in the urine samples from mild cognitive impairment due to AD patients (n = 53) and healthy controls (n = 27) by means of ultra-performance liquid chromatography-tandem mass spectrometry analysis. A multivariate model developed by partial least squares generated a diagnostic model for AD with an AUC-ROC (area under the curve-receiver operating characteristic) of 0.843. From the studied analytes, 8-OHdG (8-hydroxy-2’-deoxyguanosine) and the ratio 8-OHdG/2dG (2’-deoxyguanosine) were able to distinguish between AD and healthy participants, showing statistically significant differences between groups, postulating DNA oxidation as a molecular pathway involved in early AD.

Blood-brain barrier dysfunction underlying Alzheimer's disease is induced by an SSAO/VAP-1-dependent cerebrovascular activation with enhanced Aβ deposition

Dysfunctions of the vascular system directly contribute to the onset and progression of Alzheimer's disease (AD). The blood-brain barrier (BBB) shows signs of malfunction at early stages of the disease. When Abeta peptide (Aβ) is deposited on brain vessels, it induces vascular degeneration by producing reactive oxygen species and promoting inflammation. These molecular processes are also related to an excessive SSAO/VAP-1 (semicarbazide-sensitive amine oxidase) enzymatic activity, observed in plasma and in cerebrovascular tissue of AD patients. We studied the contribution of vascular SSAO/VAP-1 to the BBB dysfunction in AD using in vitro BBB models. Our results show that SSAO/VAP-1 expression is associated to endothelial activation by altering the release of pro-inflammatory and pro-angiogenic angioneurins, most highly IL-6, IL-8 and VEGF. It is also related to a BBB structure alteration, with a decrease in tight-junction proteins such as zona occludens or claudin-5. Moreover, the BBB function reveals increased permeability and leukocyte adhesion in cells expressing SSAO/VAP-1, as well as an enhancement of the vascular Aβ deposition induced by mechanisms both dependent and independent of the enzymatic activity of SSAO/VAP-1. These results reveal an interesting role of vascular SSAO/VAP-1 in BBB dysfunction related to AD progression, opening a new window in the search of alternative therapeutic targets for fighting AD.