Methodology for discovery of Alzheimer's disease blood-based biomarkers

Biomarker-Based Precision Therapy for Alzheimer's Disease: Multidimensional Evidence Leading a New Breakthrough in Personalized Medicine

(1) Background: Alzheimer's disease (AD) is a worldwide neurodegenerative disorder characterized by the buildup of abnormal proteins in the central nervous system and cognitive decline. Since no radical therapy exists, only symptomatic treatments alleviate symptoms temporarily. In this review, we will explore the latest advancements in precision medicine and biomarkers for AD, including their potential to revolutionize the way we diagnose and treat this devastating condition. (2) Methods: A literature search was performed combining the following Medical Subject Heading (MeSH) terms on PubMed: "Alzheimer's disease", "biomarkers", "APOE", "APP", "GWAS", "cerebrospinal fluid", "polygenic risk score", "Aβ42", "τP-181", " p-tau217", "ptau231", "proteomics", "total tau protein", and "precision medicine" using Boolean operators. (3) Results: Genome-wide association studies (GWAS) have identified numerous genetic variants associated with AD risk, while a transcriptomic analysis has revealed dysregulated gene expression patterns in the brains of individuals with AD. The proteomic and metabolomic profiling of biological fluids, such as blood, urine, and CSF, and neuroimaging biomarkers have also yielded potential biomarkers of AD that could be used for the early diagnosis and monitoring of disease progression. (4) Conclusion: By leveraging a combination of the above biomarkers, novel ultrasensitive immunoassays, mass spectrometry methods, and metabolomics, researchers are making significant strides towards personalized healthcare for individuals with AD.

hdWGCNA and Cellular Communication Identify Active NK Cell Subtypes in Alzheimer's Disease and Screen for Diagnostic Markers through Machine Learning

BACKGROUND

Alzheimer's disease (AD) is a recognized complex and severe neurodegenerative disorder, presenting a significant challenge to global health. Its hallmark pathological features include the deposition of β-amyloid plaques and the formation of neurofibrillary tangles. Given this context, it becomes imperative to develop an early and accurate biomarker model for AD diagnosis, employing machine learning and bioinformatics analysis.

METHODS

In this study, single-cell data analysis was employed to identify cellular subtypes that exhibited significant differences between the diseased and control groups. Following the identification of NK cells, hdWGCNA analysis and cellular communication analysis were conducted to pinpoint NK cell subset with the most robust communication effects. Subsequently, three machine learning algorithms-LASSO, Random Forest, and SVM-RFE-were employed to jointly screen for NK cell subset modular genes highly associated with AD. A logistic regression diagnostic model was then designed based on these characterized genes. Additionally, a protein-protein interaction (PPI) networks of model genes was established. Furthermore, unsupervised cluster analysis was conducted to classify AD subtypes based on the model genes, followed by the analysis of immune infiltration in the different subtypes. Finally, Spearman correlation coefficient analysis was utilized to explore the correlation between model genes and immune cells, as well as inflammatory factors.

RESULTS

We have successfully identified three genes (RPLP2, RPSA, and RPL18A) that exhibit a high association with AD. The nomogram based on these genes provides practical assistance in diagnosing and predicting patients' outcomes. The interconnected genes screened through PPI are intricately linked to ribosome metabolism and the COVID-19 pathway. Utilizing the expression of modular genes, unsupervised cluster analysis unveiled three distinct AD subtypes. Particularly noteworthy is subtype C3, characterized by high expression, which correlates with immune cell infiltration and elevated levels of inflammatory factors. Hence, it can be inferred that the establishment of an immune environment in AD patients is closely intertwined with the heightened expression of model genes.

CONCLUSION

This study has not only established a valuable diagnostic model for AD patients but has also delved deeply into the pivotal role of model genes in shaping the immune environment of individuals with AD. These findings offer crucial insights into early AD diagnosis and patient management strategies.

Gene expression analysis reveals GRIN1, SYT1, and SYN2 as significant therapeutic targets and drug repurposing reveals lorazepam and lorediplon as potent inhibitors to manage Alzheimer's disease

Alzheimer's disease (AD) is a slowly progressive neurodegenerative disease and a leading cause of dementia. We aim to identify key genes for the development of therapeutic targets and biomarkers for potential treatments for AD. Meta-analysis was performed on six microarray datasets and identified the differentially expressed genes between healthy and Alzheimer's disease samples. Thereafter, we filtered out the common genes which were present in at least four microarray datasets for downstream analysis. We have constructed a gene-gene network for the common genes and identified six hub genes. Furthermore, we investigated the regulatory mechanisms of these hub genes by analysing their interaction with miRNAs and transcription factors. The gene ontology analysis results highlighted the enriched terms significantly associated with hub genes. Through an extensive literature survey, we found that three of the hub genes including GRIN1, SYN2, and SYT1 were critically involved in disease development. To leverage existing drugs for potential repurposing, we predicted drug-gene interaction using the drug-gene interaction database, and performed molecular docking studies. The docking results revealed that the drug compounds had strong interactions and favorable binding with selected hub genes. Lorazepam exhibits a binding energy of -7.3 kcal/mol with GRIN1, Lorediplon exhibits binding energies of -7.7 kcal/mol and -6.3 kcal/mol with the SYT1, and SYN2 respectively. In addition, 100 ns molecular dynamics simulations were carried out for the top complexes and apo protein as well. Furthermore, the MM-PBSA free energy calculations also revealed that these complexes are stable and had favorable energies. According to our study, the identified hub gene could serve as a biomarker as well as a therapeutic target for AD, and the proposed repurposed drug molecules appear to have promising efficacy in treating the disease.Communicated by Ramaswamy H. Sarma.

Identification of highly reliable risk genes for Alzheimer's disease through joint-tissue integrative analysis

Numerous genetic variants associated with Alzheimer's disease (AD) have been identified through genome-wide association studies (GWAS), but their interpretation is hindered by the strong linkage disequilibrium (LD) among the variants, making it difficult to identify the causal variants directly. To address this issue, the transcriptome-wide association study (TWAS) was employed to infer the association between gene expression and a trait at the genetic level using expression quantitative trait locus (eQTL) cohorts. In this study, we applied the TWAS theory and utilized the improved Joint-Tissue Imputation (JTI) approach and Mendelian Randomization (MR) framework (MR-JTI) to identify potential AD-associated genes. By integrating LD score, GTEx eQTL data, and GWAS summary statistic data from a large cohort using MR-JTI, a total of 415 AD-associated genes were identified. Then, 2873 differentially expressed genes from 11 AD-related datasets were used for the Fisher test of these AD-associated genes. We finally obtained 36 highly reliable AD-associated genes, including APOC1, CR1, ERBB2, and RIN3. Moreover, the GO and KEGG enrichment analysis revealed that these genes are primarily involved in antigen processing and presentation, amyloid-beta formation, tau protein binding, and response to oxidative stress. The identification of these potential AD-associated genes not only provides insights into the pathogenesis of AD but also offers biomarkers for early diagnosis of the disease.

Revelation of Pivotal Genes Pertinent to Alzheimer's Pathogenesis: A Methodical Evaluation of 32 GEO Datasets

Alzheimer’s disease (AD), a dreadful neurodegenerative disorder that affects cognitive and behavioral function in geriatric populations, is characterized by the presence of amyloid deposits and neurofibrillary tangles in brain regions. The International D World Alzheimer Report2018 noted a global prevalence of 50 million AD cases and forecasted a threefold rise to 139 million by 2050. Although there exist numerous genetic association studies pertinent to AD in different ethnicities, critical genetic factors and signaling pathways underlying its pathogenesis remain ambiguous. This study was aimed to analyze the genetic data retrieved from 32 Gene Expression Omnibus datasets belonging to diverse ethnic cohorts in order to identify overlapping differentially expressed genes (DEGs). Stringent selection criteria were framed to shortlist appropriate datasets based on false discovery rate (FDR) p-value and log FC, and relevant details of upregulated and downregulated DEGs were retrieved. Among the 32 datasets, only six satisfied the selection criteria. The GEO2R tool was employed to retrieve significant DEGs. Nine common DEGs, i.e., SLC5A3, BDNF, SST, SERPINA3, RTN3, RGS4, NPTX, ENC1 and CRYM were found in more than 60% of the selected datasets. These DEGs were later subjected to protein–protein interaction analysis with 18 AD-specific literature-derived genes. Among the nine common DEGs, BDNF, SST, SERPINA3, RTN3 and RGS4 exhibited significant interactions with crucial proteins including BACE1, GRIN2B, APP, APOE, COMT, PSEN1, INS, NEP and MAPT. Functional enrichment analysis revealed involvement of these genes in trans-synaptic signaling, chemical transmission, PI3K pathway signaling, receptor–ligand activity and G protein signaling. These processes are interlinked with AD pathways.

System biology and bioinformatics pipeline to identify comorbidities risk association: Neurodegenerative disorder case study

Alzheimer's disease (AD) is the commonest progressive neurodegenerative condition in humans, and is currently incurable. A wide spectrum of comorbidities, including other neurodegenerative diseases, are frequently associated with AD. How AD interacts with those comorbidities can be examined by analysing gene expression patterns in affected tissues using bioinformatics tools. We surveyed public data repositories for available gene expression data on tissue from AD subjects and from people affected by neurodegenerative diseases that are often found as comorbidities with AD. We then utilized large set of gene expression data, cell-related data and other public resources through an analytical process to identify functional disease links. This process incorporated gene set enrichment analysis and utilized semantic similarity to give proximity measures. We identified genes with abnormal expressions that were common to AD and its comorbidities, as well as shared gene ontology terms and molecular pathways. Our methodological pipeline was implemented in the R platform as an open-source package and available at the following link: https://github.com/unchowdhury/AD_comorbidity. The pipeline was thus able to identify factors and pathways that may constitute functional links between AD and these common comorbidities by which they affect each others development and progression. This pipeline can also be useful to identify key pathological factors and therapeutic targets for other diseases and disease interactions.

Identification of molecular signatures and pathways to identify novel therapeutic targets in Alzheimer's disease: Insights from a systems biomedicine perspective

Alzheimer's disease (AD) is a progressive neurodegenerative disease characterized by the accumulation of amyloid plaques and neurofibrillary tangles in the brain. However, there are no peripheral biomarkers available that can detect AD onset. This study aimed to identify the molecular signatures in AD through an integrative analysis of blood gene expression data. We used two microarray datasets (GSE4226 and GSE4229) comparing peripheral blood transcriptomes of AD patients and controls to identify differentially expressed genes (DEGs). Gene set and protein overrepresentation analysis, protein-protein interaction (PPI), DEGs-Transcription Factors (TFs) interactions, DEGs-microRNAs (miRNAs) interactions, protein-drug interactions, and protein subcellular localizations analyses were performed on DEGs common to the datasets. We identified 25 common DEGs between the two datasets. Integration of genome scale transcriptome datasets with biomolecular networks revealed hub genes (NOL6, ATF3, TUBB, UQCRC1, CASP2, SND1, VCAM1, BTF3, VPS37B), common transcription factors (FOXC1, GATA2, NFIC, PPARG, USF2, YY1) and miRNAs (mir-20a-5p, mir-93-5p, mir-16-5p, let-7b-5p, mir-708-5p, mir-24-3p, mir-26b-5p, mir-17-5p, mir-193-3p, mir-186-5p). Evaluation of histone modifications revealed that hub genes possess several histone modification sites associated with AD. Protein-drug interactions revealed 10 compounds that affect the identified AD candidate biomolecules, including anti-neoplastic agents (Vinorelbine, Vincristine, Vinblastine, Epothilone D, Epothilone B, CYT997, and ZEN-012), a dermatological (Podofilox) and an immunosuppressive agent (Colchicine). The subcellular localization of molecular signatures varied, including nuclear, plasma membrane and cytosolic proteins. In the present study, it was identified blood-cell derived molecular signatures that might be useful as candidate peripheral biomarkers in AD. It was also identified potential drugs and epigenetic data associated with these molecules that may be useful in designing therapeutic approaches to ameliorate AD.

Peripheral transcriptomic biomarkers for early detection of sporadic Alzheimer disease?

Alzheimer disease (AD) is the major epidemic of the 21^st century, its prevalence rising along with improved human longevity. Early AD diagnosis is key to successful treatment, as currently available therapeutics only allow small benefits for diagnosed AD patients. By contrast, future therapeutics, including those already in preclinical or clinical trials, are expected to afford neuroprotection prior to widespread brain damage and dementia. Brain imaging technologies are developing as promising tools for early AD diagnostics, yet their high cost limits their utility for screening at-risk populations. Blood or plasma transcriptomics, proteomics, and/or metabolomics may pave the way for cost-effective AD risk screening in middle-aged individuals years ahead of cognitive decline. This notion is exemplified by data mining of blood transcriptomics from a published dataset. Consortia blood sample collection and analysis from large cohorts with mild cognitive impairment followed longitudinally for their cognitive state would allow the development of a reliable and inexpensive early AD screening tool.

Down-Regulation of Mir-107 Worsen Spatial Memory by Suppressing SYK Expression and Inactivating NF-ΚB Signaling Pathway

Background:

Alzheimer’s Disease (AD) is a chronic progressive neurodegenerative disorder in a central nervous system seen.

Objective:

We aimed to study the miR-107 in Alzheimer's Disease (AD) pathology through regulating SYK and NF-κB signaling pathway. </P><P> Method: Bioinformatics analysis was performed to screen NF-κB signaling pathway and differentially expressed genes. The target relationship between miR-107 and SYK was verified by dual luciferase assay. QRT-PCR and western blot analysis were used to verify the expression level of miR-107, SYK and NF- κB signaling pathway related proteins of hippocampus primary neurons. BAY61-3606 and BAY11-7082 were purchased for functional examination. Morris water maze tests were performed to access spatial memory of AD mice with SYK and NF-κB signaling pathway inhibition. Fluorescence microscope dyeing experiment investigated the neurons nuclear form and apoptosis.

Results:

MiR-107 was lowly expressed while SYK was highly expressed in Tg19959 mouse model. Luciferase Assay confirmed the target relationship in miR-107 and SYK. With the inhibition of miR-107, SYK was up-regulated and the increase of p-p65 and the decrease of p-IκB-α suggested that NF-κB signaling pathway was activated in vitro. Morris water maze test indicated that the spatial memory of Tg19959 mice was increased with the treatment. The result of DAPI staining indicated that the inhibition of SYK or NF-κB signaling pathway reduced the apoptosis of Tg19959 mice neuron cell.

Conclusion:

MiR-107 exerts its effects through suppression of the NF-κB signaling pathway and SYK, the inhibition of SYK and NF-κB signaling pathway can improve spatial memory and suppress cell apoptosis.

Multifaceted Regulation of ALDH1A1 by Cdk5 in Alzheimer's Disease Pathogenesis

This study revealed multifaceted regulation of ALDH1A1 by Cdk5 in Alzheimer's disease (AD) pathogenesis. ALDH1A1 is a multifunctional enzyme with dehydrogenase, esterase, and anti-oxidant activities. ALDH1A1 is also a major regulator of retinoic acid (RA) signaling, which is critical for normal brain homeostasis. We identified ALDH1A1 as both physiological and pathological target of Cdk5. First, under neurotoxic conditions, Cdk5-induced oxidative stress upregulates ALDH1A1 transcription. Second, Cdk5 increases ALDH1A1 levels by preventing its ubiquitylation via direct phosphorylation. Third, ALDH1A1 phosphorylation increases its dehydrogenase activity by altering its tetrameric state to a highly active monomeric state. Fourth, persistent oxidative stress triggered by deregulated Cdk5 inactivates ALDH1A1. Thus, initially, the good Cdk5 attempts to mitigate ensuing oxidative stress by upregulating ALDH1A1 via phosphorylation and paradoxically by increasing oxidative stress. Later, sustained oxidative stress generated by Cdk5 inhibits ALDH1A1 activity, leading to neurotoxicity. ALDH1A1 upregulation is highly neuroprotective. In human AD tissues, ALDH1A1 levels increase with disease severity. However, ALDH1A1 activity was highest at mild and moderate stages, but declines significantly at severe stage. These findings confirm that during the initial stages, neurons attempt to upregulate and activate ALDH1A1 to protect from accruing oxidative stress-induced damage; however, persistently deleterious conditions inactivate ALDH1A1, further contributing to neurotoxicity. This study thus revealed two faces of Cdk5, good and bad in neuronal function and survival, with a single substrate, ALDH1A1. The bad Cdk5 prevails in the end, overriding the good Cdk5 act, suggesting that Cdk5 is an effective therapeutic target for AD.

Alzheimer's disease and blood-based biomarkers - potential contexts of use

Alzheimer's disease (AD) is an irreversible, incurable, progressive neurodegenerative illness, where dementia symptoms gradually worsen over a number of years. The research of validated biomarkers for AD is essential to improve diagnosis and accelerate the development of new therapies. Biochemical markers including neuroimaging could facilitate diagnosis, predict AD progression from a pre-AD state of mild cognitive impairment, and be used to detect the efficacies of disease-modifying therapies. Established biomarkers of AD from cerebrospinal fluid and neuroimaging are highly accurate, but barriers to clinical implementation exist. The focus on blood-based AD biomarkers has grown exponentially during the past few decades. An ideal diagnostic test for AD should be noninvasive and easily applicable. Clinical cost-effectiveness also needs to be established.

Blood genome-wide transcriptional profiles reflect broad molecular impairments and strong blood-brain links in Alzheimer's disease

To date, little is known regarding the etiology and disease mechanisms of Alzheimer's disease (AD). There is a general urgency for novel approaches to advance AD research. In this study, we analyzed blood RNA from female patients with advanced AD and matched healthy controls using genome-wide gene expression microarrays. Our data showed significant alterations in 3,944 genes (≥2-fold, FDR ≤1%) in AD whole blood, including 2,932 genes that are involved in broad biological functions. Importantly, we observed abnormal transcripts of numerous tissue-specific genes in AD blood involving virtually all tissues, especially the brain. Of altered genes, 157 are known to be essential in neurological functions, such as neuronal plasticity, synaptic transmission and neurogenesis. More importantly, 205 dysregulated genes in AD blood have been linked to neurological disease, including AD/dementia and Parkinson's disease, and 43 are known to be the causative genes of 42 inherited mental retardation and neurodegenerative diseases. The detected transcriptional abnormalities also support robust inflammation, profound extracellular matrix impairments, broad metabolic dysfunction, aberrant oxidative stress, DNA damage, and cell death. While the mechanisms are currently unclear, this study demonstrates strong blood-brain correlations in AD. The blood transcriptional profiles reflect the complex neuropathological status in AD, including neuropathological changes and broad somatic impairments. The majority of genes altered in AD blood have not previously been linked to AD. We believe that blood genome-wide transcriptional profiling may provide a powerful and minimally invasive tool for the identification of novel targets beyond Aβ and tauopathy for AD research.

Erythrocyte membrane stability to hydrogen peroxide is decreased in Alzheimer disease

The brain and erythrocytes have similar susceptibility toward free radicals. Therefore, erythrocyte abnormalities might indicate the progression of the oxidative damage in Alzheimer disease (AD). The aim of this study was to investigate erythrocyte membrane stability and plasma antioxidant status in AD. Fasting blood samples (from 17 patients with AD and 14 healthy controls) were obtained and erythrocyte membrane stability against hydrogen peroxide and 2,2'-azobis-(2-amidinopropane) dihydrochloride (AAPH), serum Trolox equivalent antioxidant capacity (TEAC), residual antioxidant activity or gap (GAP), erythrocyte catalase activity (CAT), erythrocyte superoxide dismutase (SOD) activity, erythrocyte nonproteic thiols, and total plasma thiols were determined. A significant decrease in erythrocyte membrane stability to hydrogen peroxide was found in AD patients when compared with controls (P<0.05). On the contrary, CAT activity (P<0.0001) and total plasma thiols (P<0.05) were increased in patients with AD compared with controls. Our results indicate that the most satisfactory measurement of the oxidative stress level in the blood of patients with AD is the erythrocyte membrane stability to hydrogen peroxide. Reduced erythrocyte membrane stability may be further evaluated as a potential peripheral marker for oxidative damage in AD.

Distinctive RNA expression profiles in blood associated with Alzheimer disease after accounting for white matter hyperintensities

BACKGROUND

Defining the RNA transcriptome in Alzheimer Disease (AD) will help understand the disease mechanisms and provide biomarkers. Though the AD blood transcriptome has been studied, effects of white matter hyperintensities (WMH) were not considered. This study investigated the AD blood transcriptome and accounted for WMH.

METHODS

RNA from whole blood was processed on whole-genome microarrays.

RESULTS

A total of 293 probe sets were differentially expressed in AD versus controls, 5 of which were significant for WMH status. The 288 AD-specific probe sets classified subjects with 87.5% sensitivity and 90.5% specificity. They represented 188 genes of which 29 have been reported in prior AD blood and 89 in AD brain studies. Regulated blood genes included MMP9, MME (Neprilysin), TGFβ1, CA4, OCLN, ATM, TGM3, IGFR2, NOV, RNF213, BMX, LRRN1, CAMK2G, INSR, CTSD, SORCS1, SORL1, and TANC2.

CONCLUSIONS

RNA expression is altered in AD blood irrespective of WMH status. Some genes are shared with AD brain.

A strategy focused on MAPT, APP, NCSTN and BACE1 to build blood classifiers for Alzheimer's disease

BACKGROUND

Although Alzheimer's disease (AD) is a brain disorder, a number of peripheral alterations have been found in these patients, including differences in leukocyte gene expression; however, the key genes involved in plaque and tangle formation have shown a relatively small potential as diagnostic markers. We focused on MAPT, APP, NCSTN and BACE1 as the basis to build and compare blood classifiers for AD.

METHODS

We used a combined model to build disease classifiers, using measures of blood pressure and serum glucose, cholesterol and triglyceride levels as well as RT-PCR expression levels of APP, NCSTN and BACE1 in peripheral blood mononuclear cells (PBMCs) from an independent cohort of 36 individuals of cognitively-normal controls, AD and other neuropathologies. Also, a set of genes was carefully selected by molecular interactions with MAPT, APP, NCSTN and BACE1 to test an expression-based classifier in a public microarray dataset of 40 samples (AD and controls). A series of discriminant analyses and classification and regression trees (C&RTs) were used to perform classification tasks.

RESULTS

Using C&RTs, the combined model showed potential to differentially diagnose AD with up to 94.4% accuracy and 100% specificity for our independent sample. Furthermore, a subset of 16 genes showed the best diagnostic potential using a minimum number of expression variables, correctly classifying up to 100% of samples in the public dataset.

CONCLUSIONS

Our unique method of variable selection proves that even elements showing no significant differences between controls and AD, but that have somehow been linked to AD or AD-related elements, still hold a potential to be used in its diagnosis. Sample size and inherent methodological limitations of this study need to be kept in mind. Our classifiers require careful further testing in larger cohorts. Nonetheless, we believe these results provide evidence for the utility of our innovative method, which contributes a different approach to generate promising diagnostic tools for neuropsychiatric disorders.

GSK3β, CREB, and BDNF in peripheral blood of patients with Alzheimer's disease and depression

BACKGROUND

Glycogen synthase kinase-3β (GSK3β), cAMP-response element-binding protein (CREB) and brain-derived neurotrophic factor (BDNF) play critical roles in neuronal survival, synaptic plasticity and memory and participate in the pathophysiology of both depressive disorder and Alzheimer's disease (AD).

METHODS

This study was designed to determine the association of GSK3β activity, CREB activity and BDNF concentration in peripheral blood of patients with AD with or without depressive symptoms and in depressive patients without AD. GSK3β activity in platelets, CREB activity in lymphocytes and BDNF concentration in plasma, platelet-rich plasma or platelets were measured in 85 AD patients (36 of whom displayed co-morbid depressive symptoms), 65 non-AD patients with depressive disorder and 96 healthy controls. AD patients were clinically assessed for stage of dementia, cognitive impairment and severity of depressive symptoms. Depressive patients were clinically assessed for severity of depression.

RESULTS

We observed increased CREB activity and GSK3β activity in AD with depressive symptoms or in AD at mild stage of dementia. Decreased BDNF concentration was found in platelet-rich plasma of AD patients at moderate to severe stages of dementia or in AD without depressive symptoms. An association was revealed of the severity of cognitive impairment with the increase of GSK3β in the platelets of AD patients with mild dementia. In depressive patients, a lower concentration of phosphorylated GSK3β was associated with a higher severity of depression. Association was confirmed between severity of depression, CREB activation, and BDNF concentration in drug-naïve depressive patients.

CONCLUSION

Our data demonstrated that AD is accompanied by increased CREB activity in lymphocytes and a decreased concentration of BDNF in platelet-rich plasma. The decreased BDNF concentration appears to correlate with moderate to severe stages of dementia in AD. Observation of decreased phosphorylation of GSK3β in platelets of both AD patients with depressive symptoms and depressive patients after treatment confirms the role of increased GSK3β activity in the pathophysiology of both AD and depressive disorder. Associations were confirmed between AD and platelet GSK3β activity, lymphocyte CREB activity and plasma BDNF. CREB activity and platelet BDNF concentration seems to be related to depressive disorder.

In-depth profiling of the peripheral blood mononuclear cells proteome for clinical blood proteomics

Peripheral blood mononuclear cells (PBMCs) are an easy accessible cellular part of the blood organ and, along with platelets, represent the only site of active gene expression in blood. These cells undergo immunophenotypic changes in various diseases and represent a peripheral source of monitoring gene expression and posttranslational modifications relevant to many diseases. Little is known about the source of many blood proteins and we hypothesise that release from PBMCs through active and passive mechanisms may account for a substantial part of the plasma proteome. The use of state-of-the-art proteomic profiling methods in PBMCs will enable minimally invasive monitoring of disease progression or response to treatment and discovery of biomarkers. To achieve this goal, detailed mapping of the PBMC proteome using a sensitive, robust, and quantitative methodological setup is required. We have applied an indepth gel-free proteomics approach using tandem mass tags (TMT), unfractionated and SCX fractionated PBMC samples, and LC-MS/MS with various modulations. This study represents a benchmark in deciphering the PBMC proteome as we provide a deep insight by identifying 4129 proteins and 25503 peptides. The identified proteome defines the scope that enables PBMCs to be characterised as cellular major biomarker pool within the blood organ.

Decreased level of olfactory receptors in blood cells following traumatic brain injury and potential association with tauopathy

Traumatic brain injury (TBI) is a leading cause of death and disability among children and young adults in the United States. In this study, we explored whether changes in the gene expression profile of peripheral blood mononuclear cells (PBMC) may provide a clinically assessable "window" into the brain, reflecting molecular alterations following TBI that might contribute to the onset and progression of TBI clinical complications. We identified three olfactory receptor (OR) TBI biomarkers that are aberrantly down-regulated in PBMC specimens from TBI subjects. Down-regulation of these OR biomarkers in PBMC was correlated with the severity of brain injury and TBI-specific symptoms. A two- biomarker panel comprised of OR11H1 and OR4M1 provided the best criterion for segregating the TBI and control cases with 90% accuracy, 83.3% sensitivity, and 100% specificity. We found that the OR biomarkers are ectopically expressed in multiple brain regions, including the entorhinal-hippocampus system known to play an important role in memory formation and consolidation. Activation of OR4M1 led to attenuation of abnormal tau phosphorylation, possibly through JNK signaling pathway. Our results suggested that addition of the two-OR biomarker model to current diagnostic criteria may lead to improved TBI detection for clinical trials, and decreased expression of OR TBI biomarkers might be associated with TBI-induced tauopathy. Future studies exploring the physiological relevance of OR TBI biomarkers in the normal brain and in the brain following TBI will provide a better understanding of the biological mechanisms underlying TBI and insights into novel therapeutic targets for TBI.

A diagnostic methodology for Alzheimer's disease

Background

Like all other neurodegenerative diseases, Alzheimer’s disease (AD) remains a very challenging and difficult problem for diagnosis and therapy. For many years, only historical, behavioral and psychiatric measures have been available to AD cases. Recently, a definitive diagnostic framework, using biomarkers and imaging, has been proposed. In this paper, we propose a promising diagnostic methodology for the framework.

Methods

In a previous paper, we developed an efficient SVM (Support Vector Machine) based method, which we have now applied to discover important biomarkers and target networks which provide strategies for AD therapy.

Results

The methodology selects a number of blood-based biomarkers (fewer than 10% of initial numbers on three AD datasets from NCBI), and the results are statistically verified by cross-validation. The resulting SVM is a classifier of AD vs. normal subjects. We construct target networks of AD based on MI (mutual information). In addition, a hierarchical clustering is applied on the initial data and clustered genes are visualized in a heatmap. The proposed method also performs gender analysis by classifying subjects based on gender.

Conclusions

Unlike other traditional statistical analyses, our method uses a machine learning-based algorithm. Our method selects a small set of important biomarkers for AD, differentiates noisy (irrelevant) from relevant biomarkers and also provides the target networks of the selected biomarkers, which will be useful for diagnosis and therapeutic design. Finally, based on the gender analysis, we observe that gender could play a role in AD diagnosis.

Circulatory miR34a as an RNAbased, noninvasive biomarker for brain aging

MicroRNAs in blood samples have been identified as an important class of biomarkers, which can reflect physiological changes from cancer to brain dysfunction. In this report we identify concordant increases in levels of expression of miR-34a in brain and two components of mouse blood samples, peripheral blood mononuclear cells (PBMCs) and plasma, from 2 day old neonates through young adulthood and mid-life to old age at 25 months. Levels of this microRNA's prime target, silent information regulator 1 (SIRT1), in brain and the two blood-derived specimens decrease with age inversely to miR-34a, starting as early as 4 months old, when appreciable tissue aging has not yet begun. Our results suggest that: 1. Increased miR-34a and the reciprocal decrease of its target, SIRT1, in blood specimens are the accessible biomarkers for age-dependent changes in brain; and 2. these changes are predictors of impending decline in brain function, as early as in young adult mice.

MicroRNA: Implications for Alzheimer Disease and other Human CNS Disorders

Understanding complex diseases such as sporadic Alzheimer disease (AD) has been a major challenge. Unlike the familial forms of AD, the genetic and environmental risks factors identified for sporadic AD are extensive. MicroRNAs are one of the major noncoding RNAs that function as negative regulators to silence or suppress gene expression via translational inhibition or message degradation. Their discovery has evoked great excitement in biomedical research for their promise as potential disease biomarkers and therapeutic targets. Key microRNAs have been identified as essential for a variety of cellular events including cell lineage determination, proliferation, apoptosis, DNA repair, and cytoskeletal organization; most, if not all, acting to fine-tune gene expression at the post-transcriptional level in a host of cellular signaling networks. Dysfunctional microRNA-mediated regulation has been implicated in the pathogenesis of many disease states. Here, the current understanding of the role of miRNAs in the central nervous system is reviewed with emphasis on their impact on the etiopathogenesis of sporadic AD.