Blood mRNA Expression in Alzheimer's Disease and Dementia With Lewy Bodies

Exosomal mRNA Signatures as Predictive Biomarkers for Risk and Age of Onset in Alzheimer's Disease

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by progressive cognitive decline and memory loss. While the precise causes of AD remain unclear, emerging evidence suggests that messenger RNA (mRNA) dysregulation contributes to AD pathology and risk. This study examined exosomal mRNA expression profiles of 15 individuals diagnosed with AD and 15 healthy controls from Barranquilla, Colombia. Utilizing advanced bioinformatics and machine learning (ML) techniques, we identified differentially expressed mRNAs and assessed their predictive power for AD diagnosis and AD age of onset (ADAOO). Our results showed that ENST00000331581 (CADM1) and ENST00000382258 (TNFRSF19) were significantly upregulated in AD patients. Key predictors for AD diagnosis included ENST00000311550 (GABRB3), ENST00000278765 (GGTLC1), ENST00000331581 (CADM1), ENST00000372572 (FOXJ3), and ENST00000636358 (ACY1), achieving > 90% accuracy in both training and testing datasets. For ADAOO, ENST00000340552 (LIMK2) expression correlated with a delay of ~12.6 years, while ENST00000304677 (RNASE6), ENST00000640218 (HNRNPU), ENST00000602017 (PPP5D1), ENST00000224950 (STN1), and ENST00000322088 (PPP2R1A) emerged as the most important predictors. ENST00000304677 (RNASE6) and ENST00000602017 (PPP5D1) showed promising predictive accuracy in unseen data. These findings suggest that mRNA expression profiles may serve as effective biomarkers for AD diagnosis and ADAOO, providing a cost-efficient and minimally invasive tool for early detection and monitoring. Further research is needed to validate these results in larger, diverse cohorts and explore the biological roles of the identified mRNAs in AD pathogenesis.

A review and analysis of key biomarkers in Alzheimer's disease

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that affects over 50 million elderly individuals worldwide. Although the pathogenesis of AD is not fully understood, based on current research, researchers are able to identify potential biomarker genes and proteins that may serve as effective targets against AD. This article aims to present a comprehensive overview of recent advances in AD biomarker identification, with highlights on the use of various algorithms, the exploration of relevant biological processes, and the investigation of shared biomarkers with co-occurring diseases. Additionally, this article includes a statistical analysis of key genes reported in the research literature, and identifies the intersection with AD-related gene sets from databases such as AlzGen, GeneCard, and DisGeNet. For these gene sets, besides enrichment analysis, protein-protein interaction (PPI) networks utilized to identify central genes among the overlapping genes. Enrichment analysis, protein interaction network analysis, and tissue-specific connectedness analysis based on GTEx database performed on multiple groups of overlapping genes. Our work has laid the foundation for a better understanding of the molecular mechanisms of AD and more accurate identification of key AD markers.

A multiclass extreme gradient boosting model for evaluation of transcriptomic biomarkers in Alzheimer's disease prediction

BACKGROUND

Patients with young-onset Alzheimer's disease (AD) (before the age of 50 years old) often lack obvious imaging changes and amyloid protein deposition, which can lead to misdiagnosis with other cognitive impairments. Considering the association between immunological dysfunction and progression of neurodegenerative disease, recent research has focused on identifying blood transcriptomic signatures for precise prediction of AD.

METHODS

In this study, we extracted blood biomarkers from large-scale transcriptomics to construct multiclass eXtreme Gradient Boosting models (XGBoost), and evaluated their performance in distinguishing AD from cognitive normal (CN) and mild cognitive impairment (MCI).

RESULTS

Independent testing with external dataset revealed that the combination of blood transcriptomic signatures achieved an area under the receiver operating characteristic curve (AUC of ROC) of 0.81 for multiclass classification (sensitivity = 0.81; specificity = 0.63), 0.83 for classification of AD vs. CN (sensitivity = 0.72; specificity = 0.73), and 0.85 for classification of AD vs. MCI (sensitivity = 0.77; specificity = 0.73). These candidate signatures were significantly enriched in 62 chromosome regions, such as Chr.19p12-19p13.3, Chr.1p22.1-1p31.1, and Chr.1q21.2-1p23.1 (adjusted p < 0.05), and significantly overrepresented by 26 transcription factors, including E2F2, FOXO3, and GATA1 (adjusted p < 0.05). Biological analysis of these signatures pointed to systemic dysregulation of immune responses, hematopoiesis, exocytosis, and neuronal support in neurodegenerative disease (adjusted p < 0.05).

CONCLUSIONS

Blood transcriptomic biomarkers hold great promise in clinical use for the accurate assessment and prediction of AD.

Dementia with Lewy Bodies: Genomics, Transcriptomics, and Its Future with Data Science

Dementia with Lewy bodies (DLB) is a significant public health issue. It is the second most common neurodegenerative dementia and presents with severe neuropsychiatric symptoms. Genomic and transcriptomic analyses have provided some insight into disease pathology. Variants within SNCA, GBA, APOE, SNCB, and MAPT have been shown to be associated with DLB in repeated genomic studies. Transcriptomic analysis, conducted predominantly on candidate genes, has identified signatures of synuclein aggregation, protein degradation, amyloid deposition, neuroinflammation, mitochondrial dysfunction, and the upregulation of heat-shock proteins in DLB. Yet, the understanding of DLB molecular pathology is incomplete. This precipitates the current clinical position whereby there are no available disease-modifying treatments or blood-based diagnostic biomarkers. Data science methods have the potential to improve disease understanding, optimising therapeutic intervention and drug development, to reduce disease burden. Genomic prediction will facilitate the early identification of cases and the timely application of future disease-modifying treatments. Transcript-level analyses across the entire transcriptome and machine learning analysis of multi-omic data will uncover novel signatures that may provide clues to DLB pathology and improve drug development. This review will discuss the current genomic and transcriptomic understanding of DLB, highlight gaps in the literature, and describe data science methods that may advance the field.

Elucidating distinct molecular signatures of Lewy body dementias

Dementia with Lewy bodies and Parkinson's disease dementia are common neurodegenerative diseases that share similar neuropathological profiles and spectra of clinical symptoms but are primarily differentiated by the order in which symptoms manifest. The question of whether a distinct molecular pathological profile could distinguish these disorders is yet to be answered. However, in recent years, studies have begun to investigate genomic, epigenomic, transcriptomic and proteomic differences that may differentiate these disorders, providing novel insights in to disease etiology. In this review, we present an overview of the clinical and pathological hallmarks of Lewy body dementias before summarizing relevant research into genetic, epigenetic, transcriptional and protein signatures in these diseases, with a particular interest in those resolving "omic" level changes. We conclude by suggesting future research directions to address current gaps and questions present within the field.

Orchestration of a blood-derived and ADARB1-centred network in Alzheimer's and Parkinson's disease

The peripheral immune system is thought to influence the pathogenesis of the central nervous system in Alzheimer's disease (AD) and Parkinson's disease (PD). This study aimed to investigate the characteristics of peripheral leukocytes in AD and PD by comprehensively analyzing the transcriptomic and metabolic features in the blood (NCONTROL = 15; NAD = 11; NPD = 13). The study found an ADARB1-centered module that was associated with diagnosis, phenethylamine, and glutamate. The module consisted of ADARB1, a vital RNA-editing enzyme, LINC02960, and 109 miRNAs. The study also predicted that the ADARB1 and involved regulators were targeted by miRNAs in the ADARB1 module. The integrated analysis of transcriptome and metabolic panel revealed a central role of ADARB1, miR-199b-5p, miR-26a, miR-450b-5p, miR-34c-5p, glutamate and phenethylamine in the regulatory relationships. The study highlights a set of synergetic non-coding RNA related to ADARB1 in the blood ecosystem of AD and PD with dynamic glutamate and phenethylamine, providing new insights into the pathogenesis of these diseases.

Comprehensive analysis of mRNAs in the cerebral cortex in APP/PS1 double-transgenic mice with Alzheimer's disease based on high-throughput sequencing of N4-acetylcytidine

N4-acetylcytidine (ac4C), a significant modified nucleoside, participates in the development of many diseases. Messenger RNAs (mRNAs) contain most of the information of the genome and are the molecules that transmit information from genes to proteins. Alzheimer's disease (AD) is a progressive neurodegenerative disease in which fibrillar amyloid plaques are present. However, it remains unknown how mRNA ac4C modification affects the development of AD. In the current study, ac4C-modified mRNAs were comprehensively analyzed in AD mice by ac4C-RIP-seq and RNA-seq. Next, a protein-protein interaction (PPI) network was constructed to examine the relationships between the genes with differential ac4C modification levels and their RNA expression levels. The differentially expressed genes (DEGs) acquired above were subjected to Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis to further analyze the molecular mechanisms in AD. In total, 3312 significant ac4C peaks were found on 2512 mRNAs, 1241 of which were hyperacetylated and 1271 of which were hypoacetylated. In addition, 956 mRNAs with differential expression were found, including 520 upregulated mRNAs and 436 downregulated mRNAs. Overall, 134 mRNAs with simultaneous changes at the ac4C levels as well as RNA expression levels were identified via joint analysis. Then, through PPI network construction and functional enrichment analysis, 37 key mRNAs were screened, which were predominantly enriched in GABAergic synapses and the PI3K/AKT signaling pathway. The significant difference in the abundance of mRNA ac4C modification indicates that this modification is associated with AD progression, which may provide insight for more investigations of the potential mechanisms.

Investigation of Inflammation in Lewy Body Dementia: A Systematic Scoping Review

Inflammatory mechanisms are increasingly recognized as important contributors to the pathogenesis of neurodegenerative diseases, including Lewy body dementia (LBD). Our objectives were to, firstly, review inflammation investigation methods in LBD (dementia with Lewy bodies and Parkinson's disease dementia) and, secondly, identify alterations in inflammatory signals in LBD compared to people without neurodegenerative disease and other neurodegenerative diseases. A systematic scoping review was performed by searching major electronic databases (MEDLINE, Embase, Web of Science, and PSYCHInfo) to identify relevant human studies. Of the 2509 results screened, 80 studies were included. Thirty-six studies analyzed postmortem brain tissue, and 44 investigated living subjects with cerebrospinal fluid, blood, and/or brain imaging assessments. Largely cross-sectional data were available, although two longitudinal clinical studies investigated prodromal Lewy body disease. Investigations were focused on inflammatory immune cell activity (microglia, astrocytes, and lymphocytes) and inflammatory molecules (cytokines, etc.). Results of the included studies identified innate and adaptive immune system contributions to inflammation associated with Lewy body pathology and clinical disease features. Different signals in early and late-stage disease, with possible late immune senescence and dystrophic glial cell populations, were identified. The strength of these associations is limited by the varying methodologies, small study sizes, and cross-sectional nature of the data. Longitudinal studies investigating associations with clinical and other biomarker outcomes are needed to improve understanding of inflammatory activity over the course of LBD. This could identify markers of disease activity and support therapeutic development.

Glaucoma-Associated CDR1 Peptide Promotes RGC Survival in Retinal Explants through Molecular Interaction with Acidic Leucine Rich Nuclear Phosphoprotein 32A (ANP32A)

Glaucoma is a complex, multifactorial optic neuropathy mainly characterized by the progressive loss of retinal ganglion cells (RGCs) and their axons, resulting in a decline of visual function. The pathogenic molecular mechanism of glaucoma is still not well understood, and therapeutic strategies specifically addressing the neurodegenerative component of this ocular disease are urgently needed. Novel immunotherapeutics might overcome this problem by targeting specific molecular structures in the retina and providing direct neuroprotection via different modes of action. Within the scope of this research, the present study showed for the first time beneficial effects of the synthetic CDR1 peptide SCTGTSSDVGGYNYVSWYQ on the viability of RGCs ex vivo in a concentration-dependent manner compared to untreated control explants (CTRL, 50 µg/mL: p < 0.05 and 100 µg/mL: p < 0.001). Thereby, this specific peptide was identified first as a potential biomarker candidate in the serum of glaucoma patients and was significantly lower expressed in systemic IgG molecules compared to healthy control subjects. Furthermore, MS-based co-immunoprecipitation experiments confirmed the specific interaction of synthetic CDR1 with retinal acidic leucine-rich nuclear phosphoprotein 32A (ANP32A; p < 0.001 and log2 fold change > 3), which is a highly expressed protein in neurological tissues with multifactorial biological functions. In silico binding prediction analysis revealed the N-terminal leucine-rich repeat (LRR) domain of ANP32A as a significant binding site for synthetic CDR1, which was previously reported as an important docking site for protein-protein interactions (PPI). In accordance with these findings, quantitative proteomic analysis of the retinae ± CDR1 treatment resulted in the identification of 25 protein markers, which were significantly differentially distributed between both experimental groups (CTRL and CDR1, p < 0.05). Particularly, acetyl-CoA biosynthesis I-related enzymes (e.g., DLAT and PDHA1), as well as cytoskeleton-regulating proteins (e.g., MSN), were highly expressed by synthetic CDR1 treatment in the retina; on the contrary, direct ANP32A-interacting proteins (e.g., NME1 and PPP2R4), as well as neurodegenerative-related markers (e.g., CEND1), were identified with significant lower abundancy in the CDR1-treated retinae compared to CTRL. Furthermore, retinal protein phosphorylation and histone acetylation were also affected by synthetic CDR1, which are both partially controlled by ANP32A. In conclusion, the synthetic CDR1 peptide provides a great translational potential for the treatment of glaucoma in the future by eliciting its neuroprotective mechanism via specific interaction with ANP32A's N terminal LRR domain.

Overview of the blood biomarkers in Alzheimer's disease: Promises and challenges

The increasing number of people with advanced Alzheimer's disease (AD) represents a significant psychological and financial cost to the world population. Accurate detection of the earliest phase of preclinical AD is of major importance for the success of preventive and therapeutic strategies (Cullen et al., 2021). Advances in analytical techniques have been essential for the development of sensitive, specific and reliable diagnostic tests for AD biomarkers in biological fluids (cerebrospinal fluid and blood). Blood biomarkers hold promising potential for early and minimally invasive detection of AD, but also for differential diagnosis of dementia and for monitoring the course of the disease. The aim of this review is to provide an overview of current blood biomarkers of AD, from tau proteins and amyloid peptides to biomarkers of neuronal degeneration and inflammation, reactive and metabolic factors. We thus discuss the informative value of currently candidate blood biomarkers and their potential to be integrated into clinical practice for the management of AD in the near future.