Integrated analysis of differential gene expression profiles in hippocampi to identify candidate genes involved in Alzheimer's disease

Differential Expression of Proteins Associated with Bipolar Disorder as Identified Using the PeptideShaker Software

The prevalence of bipolar disorder (BD) in modern society is growing rapidly, but due to the lack of paraclinical criteria, its differential diagnosis with other mental disorders is somewhat challenging. In this regard, the relevance of proteomic studies is increasing due to the development of methods for processing large data arrays; this contributes to the discovery of protein patterns of pathological processes and the creation of new methods of diagnosis and treatment. It seems promising to search for proteins involved in the pathogenesis of BD in an easily accessible material-blood serum. Sera from BD patients and healthy individuals were purified via affinity chromatography to isolate 14 major proteins and separated using 1D SDS-PAGE. After trypsinolysis, the proteins in the samples were identified via HPLC/mass spectrometry. Mass spectrometric data were processed using the OMSSA and X!Tandem search algorithms using the UniProtKB database, and the results were analyzed using PeptideShaker. Differences in proteomes were assessed via an unlabeled NSAF-based analysis using a two-tailed Bonferroni-adjusted t-test. When comparing the blood serum proteomes of BD patients and healthy individuals, 10 proteins showed significant differences in NSAF values. Of these, four proteins were predominantly present in BD patients with the maximum NSAF value: 14-3-3 protein zeta/delta; ectonucleoside triphosphate diphosphohydrolase 7; transforming growth factor-beta-induced protein ig-h3; and B-cell CLL/lymphoma 9 protein. Further exploration of the role of these proteins in BD is warranted; conducting such studies will help develop new paraclinical criteria and discover new targets for BD drug therapy.

Cancer and Alzheimer's Inverse Correlation: an Immunogenetic Analysis

Numerous studies have demonstrated an inverse link between cancer and Alzheimer's disease (AD), with data suggesting that people with Alzheimer's have a decreased risk of cancer and vice versa. Although other studies have investigated mechanisms to explain this relationship, the connection between these two diseases remains largely unexplained. Processes seen in cancer, such as decreased apoptosis and increased cell proliferation, seem to be reversed in AD. Given the need for effective therapeutic strategies for AD, comparisons with cancer could yield valuable insights into the disease process and perhaps result in new treatments. Here, through a review of existing literature, we compared the expressions of genes involved in cell proliferation and apoptosis to establish a genetic basis for the reciprocal association between AD and cancer. We discuss an array of genes involved in the aforementioned processes, their relevance to both diseases, and how changes in those genes produce varying effects in either disease.

Validating a Genomic Convergence and Network Analysis Approach Using Association Analysis of Identified Candidate Genes in Alzheimer's Disease

Previously, we demonstrated an integrated genomic convergence and network analysis approach to identify the candidate genes associated with the complex neurodegenerative disorder, Alzheimer's disease (AD). Here, we performed a pilot study to validate the in silico approach by studying the association of genetic variants from three identified critical genes, APOE, EGFR, and ACTB, with AD. A total of 103 patients with AD and 146 healthy controls were recruited. A total of 46 single-nucleotide polymorphisms (SNPs) spanning the three genes were genotyped, of which only 19 SNPs were included in the final analyses after excluding non-polymorphic and Hardy-Weinberg equilibrium-violating SNPs. Apart from our previously reported APOE ε4, four other SNPs in APOE (rs405509, rs7259620, -rs769449, and rs7256173), one in EGFR (rs6970262), and one in ACTB (rs852423) showed a significant association with AD (p < 0.05). Our results validate the reliability of genomic convergence and network analysis approach in identifying the AD-associated candidate genes.

Synaptic proteins associated with cognitive performance and neuropathology in older humans revealed by multiplexed fractionated proteomics

Alzheimer's disease (AD) is defined by the presence of abundant amyloid-β (Aβ) and tau neuropathology. While this neuropathology is necessary for AD diagnosis, it is not sufficient for causing cognitive impairment. Up to one third of community dwelling older adults harbor intermediate to high levels of AD neuropathology at death yet demonstrate no significant cognitive impairment. Conversely, there are individuals who exhibit dementia with no gross explanatory neuropathology. In prior studies, synapse loss correlated with cognitive impairment. To understand how synaptic composition changes in relation to neuropathology and cognition, multiplexed liquid chromatography mass-spectrometry was used to quantify enriched synaptic proteins from the parietal association cortex of 100 subjects with contrasting levels of AD pathology and cognitive performance. 123 unique proteins were significantly associated with diagnostic category. Functional analysis showed enrichment of serotonin release and oxidative phosphorylation categories in normal (cognitively unimpaired, low neuropathology) and "resilient" (unimpaired despite AD pathology) individuals. In contrast, frail individuals, (low pathology, impaired cognition) showed a metabolic shift towards glycolysis and increased presence of proteasome subunits.

Double hit viral parasitism, polymicrobial CNS residency and perturbed proteostasis in Alzheimer's disease: A data driven, in silico analysis of gene expression data

The aim of this study was to determine the interaction of peripheral immunity vs. the CNS in the setting of AD pathogenesis at the transcriptomic level in a data driven manner. For this purpose, publicly available gene expression data from the GEO Datasets repository. We performed differential gene expression and functional enrichment analyses were performed on the five retrieved studies: (a) three hippocampal cortex (HC) studies (b) one study of peripheral blood mononuclear cells (PBMC) and (c) one involving neurofibrillary tangle - containing neurons of the entorhinal cortex (NFT EC). Subsequently, BLAST was used to determine protein conservation between human proteins vs. microbial, whereas putative protein / oligopeptide antigenicity were determined via RANKPep. Gene ontology and pathway analyses revealed significantly enriched viral parasitism pathways in both PBMC and NFT - EC datasets, mediated by ribosomal protein families and epigenetic regulators. Among these, a salient viral pathway referred to Influenza A infection. NFT - EC annotations included leukocyte chemotaxis and immune response pathways. All datasets were significantly enriched for infectious pathways, as well as pathways involved in impaired proteostasis and non - phagocytic cell phagosomal cascades. In conclusion, our in silico analysis outlined an ad hoc model of AD pathophysiology in which double hit (PBMC and NFT-EC) viral parasitism is mediated by eukaryotic translational hijacking, and may be further implicated by impaired immune responses. Overall, our results overlap with the antimicrobial protection hypothesis of AD pathogenesis and support the notion of a pathogen - driven etiology.

Predicting Protein Functions Based on Differential Co-expression and Neighborhood Analysis

Proteins are polypeptides essential in biological processes. Protein physical interactions are complemented by other types of functional relationship data including genetic interactions, knowledge about co-expression, and evolutionary pathways. Existing algorithms integrate protein interaction and gene expression data to retrieve context-specific subnetworks composed of genes/proteins with known and unknown functions. However, most protein function prediction algorithms fail to exploit diverse intrinsic information in feature and label spaces. We develop a novel integrative method based on differential Co-expression analysis and Neighbor-voting algorithm for Protein Function Prediction, namely CNPFP. The method integrates heterogeneous data and exploits intrinsic and latent linkages via global iterative approach and genomic features. CNPFP performs three tasks: clustering, differential co-expression analysis, and predicts protein functions. Our aim is to identify yeast cell cycle-specific proteins linked to differentially expressed proteins in the protein-protein interaction network. To capture intrinsic information, CNPFP selects the most relevant feature subset based on global iterative neighbor-voting algorithm. We identify eight condition-specific modules. The most relevant subnetwork has 87 genes highly enriched with cyclin-dependent kinases, a protein kinase relevant for cell cycle regulation. We present comprehensive annotations for 3538 Saccharomyces cerevisiae proteins. Our method achieves an AUROC of 0.9862, accuracy of 0.9710, and F-score of 0.9691. From the results, we can summarize that exploiting intrinsic nature of protein relationships improves the quality of function prediction. Thus, the proposed method is useful in functional genomics studies.

Heparan Sulfate Proteoglycans Undergo Differential Expression Alterations in Alzheimer Disease Brains

Previous studies have reported that heparan sulfate proteoglycans (HSPGs) promote amyloid-beta peptide and tau fibrillization in Alzheimer disease (AD) and provide resistance against proteolytic breakdown. We compared the expression levels of 17 HSPG core proteins in 18 AD cases and 6 controls. RT-PCR was used to analyze transcription levels. Immunohistochemistry was performed to localize HSPGs in the brain tissue. We detected expression of all HSPG genes investigated. SDC1, GPC3, and CD44v3 showed the lowest levels of expression, while SDC3 and GPC1 showed the highest. Remarkably, SDC4 and SRGN were overexpressed in most of the areas analyzed. Immunohistochemistry revealed the presence of both SDC4 and SRGN mostly associated with tau and amyloid-β pathology throughout the AD brains. In conclusion, in view of the involvement of HSPGs in AD pathology, especially SDC4 and SRGN, there would seem to be a relationship between the regulation of core protein expression and the pathological features suggesting HSPGs are potential inducers of the disease.

Network Medicine Approach for Analysis of Alzheimer's Disease Gene Expression Data

Alzheimer’s disease (AD) is the most widespread diagnosed cause of dementia in the elderly. It is a progressive neurodegenerative disease that causes memory loss as well as other detrimental symptoms that are ultimately fatal. Due to the urgent nature of this disease, and the current lack of success in treatment and prevention, it is vital that different methods and approaches are applied to its study in order to better understand its underlying mechanisms. To this end, we have conducted network-based gene co-expression analysis on data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. By processing and filtering gene expression data taken from the blood samples of subjects with varying disease states and constructing networks based on that data to evaluate gene relationships, we have been able to learn about gene expression correlated with the disease, and we have identified several areas of potential research interest.

Missing-Values Imputation Algorithms for Microarray Gene Expression Data

In gene expression studies, missing values are a common problem with important consequences for the interpretation of the final data (Satija et al., Nat Biotechnol 33(5):495, 2015). Numerous bioinformatics examination tools are used for cancer prediction, including the data set matrix (Bailey et al., Cell 173(2):371-385, 2018); thus, it is necessary to resolve the problem of missing-values imputation. This chapter presents a review of the research on missing-values imputation approaches for gene expression data. By using local and global correlation of the data, we were able to focus mostly on the differences between the algorithms. We classified the algorithms as global, hybrid, local, or knowledge-based techniques. Additionally, this chapter presents suitable assessments of the different approaches. The purpose of this review is to focus on developments in the current techniques for scientists rather than applying different or newly developed algorithms with identical functional goals. The aim was to adapt the algorithms to the characteristics of the data.