scholarly journals A Coordinated Approach by Public Domain Bioinformatics Resources to Aid the Fight Against Alzheimer’s Disease Through Expert Curation of Key Protein Targets

2020 ◽  
Vol 77 (1) ◽  
pp. 257-273
Author(s):  
Lionel Breuza ◽  
Cecilia N. Arighi ◽  
Ghislaine Argoud-Puy ◽  
Cristina Casals-Casas ◽  
Anne Estreicher ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Gene Ontology ◽  
Molecular Interaction ◽  
Public Domain ◽  
Research Community ◽  
Biological Data ◽  
Functional Enrichment ◽  
Clinical Samples ◽  
Free Text

Background: The analysis and interpretation of data generated from patient-derived clinical samples relies on access to high-quality bioinformatics resources. These are maintained and updated by expert curators extracting knowledge from unstructured biological data described in free-text journal articles and converting this into more structured, computationally-accessible forms. This enables analyses such as functional enrichment of sets of genes/proteins using the Gene Ontology, and makes the searching of data more productive by managing issues such as gene/protein name synonyms, identifier mapping, and data quality. Objective: To undertake a coordinated annotation update of key public-domain resources to better support Alzheimer’s disease research. Methods: We have systematically identified target proteins critical to disease process, in part by accessing informed input from the clinical research community. Results: Data from 954 papers have been added to the UniProtKB, Gene Ontology, and the International Molecular Exchange Consortium (IMEx) databases, with 299 human proteins and 279 orthologs updated in UniProtKB. 745 binary interactions were added to the IMEx human molecular interaction dataset. Conclusion: This represents a significant enhancement in the expert curated data pertinent to Alzheimer’s disease available in a number of biomedical databases. Relevant protein entries have been updated in UniProtKB and concomitantly in the Gene Ontology. Molecular interaction networks have been significantly extended in the IMEx Consortium dataset and a set of reference protein complexes created. All the resources described are open-source and freely available to the research community and we provide examples of how these data could be exploited by researchers.

scholarly journals Anti-Aggregation Property of Allicin by In Vitro and Molecular Docking Studies

2019 ◽  
Vol 13 ◽  
pp. 117906951986618 ◽  
Author(s):  
Suresh Kumar ◽  
Shivani Kumar ◽  
Heera Ram
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Molecular Docking ◽  
Molecular Interaction ◽  
Fibril Formation ◽  
Amyloid Peptide ◽  
Peptide Aggregation ◽  
Aβ Peptide ◽  
In Silico Studies

Amyloidogenesis is the process in which amyloid beta (Aβ) peptide aggregation results in plaque formation in central nervous system (CNS) are associated with many neurological diseases such as Alzheimer’s disease. The peptide aggregation initiated from peptide monomers results in formation of dimers, tetramers, fibrils, and protofibrils. The ability of allicin, a lipid-soluble volatile organosulfur biological compound, present in freshly crushed garlic ( Allium sativum L.) to inhibit fibril formation by the Aβ peptide in vitro was investigated in the present study. Inhibition of fibrillogenesis was measured by a Thioflavin T (ThT) fluorescence assay and visualized by transmission electron microscopy (TEM). The molecular interaction between allicin and Aβ peptide was also demonstrated by in silico studies. The results show that allicin strongly inhibited Aβ fibrils by 97% at 300 µM, compared with control (Aβ only) ( P < .001). These results were further validated by visual of fibril formation by transmission microscopy and molecular interaction of amyloid peptide with allicin by molecular docking. Aβ forms favourable hydrophobic interaction with Ile32, Met35, Val36, and Val39, and oxygen of allicin forms hydrogen bond with the amino acid residue Lys28. Allicin anti-amyloidogenic property suggests that this naturally occurring compound may have potential to ameliorate and prevent Alzheimer’s disease.

Alzheimer's Disease Classification Based on Multi-feature Fusion

2019 ◽  
Vol 15 (2) ◽  
pp. 161-169 ◽  
Author(s):  
Nuwan Madusanka ◽  
Heung-Kook Choi ◽  
Jae-Hong So ◽  
Boo-Kyeong Choi
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Feature Fusion ◽  
Biological Data ◽  
Correct Classification ◽  
Mr Images ◽  
Correct Classification Rate ◽  
Classification Rate ◽  
Morphometric Features

Background: In this study, we investigated the fusion of texture and morphometric features as a possible diagnostic biomarker for Alzheimer’s Disease (AD). Methods: In particular, we classified subjects with Alzheimer’s disease, Mild Cognitive Impairment (MCI) and Normal Control (NC) based on texture and morphometric features. Currently, neuropsychiatric categorization provides the ground truth for AD and MCI diagnosis. This can then be supported by biological data such as the results of imaging studies. Cerebral atrophy has been shown to correlate strongly with cognitive symptoms. Hence, Magnetic Resonance (MR) images of the brain are important resources for AD diagnosis. In the proposed method, we used three different types of features identified from structural MR images: Gabor, hippocampus morphometric, and Two Dimensional (2D) and Three Dimensional (3D) Gray Level Co-occurrence Matrix (GLCM). The experimental results, obtained using a 5-fold cross-validated Support Vector Machine (SVM) with 2DGLCM and 3DGLCM multi-feature fusion approaches, indicate that we achieved 81.05% ±1.34, 86.61% ±1.25 correct classification rate with 95% Confidence Interval (CI) falls between (80.75-81.35) and (86.33-86.89) respectively, 83.33%±2.15, 84.21%±1.42 sensitivity and 80.95%±1.52, 85.00%±1.24 specificity in our classification of AD against NC subjects, thus outperforming recent works found in the literature. For the classification of MCI against AD, the SVM achieved a 76.31% ± 2.18, 78.95% ±2.26 correct classification rate, 75.00% ±1.34, 76.19%±1.84 sensitivity and 77.78% ±1.14, 82.35% ±1.34 specificity. Results and Conclusion: The results of the third experiment, with MCI against NC, also showed that the multiclass SVM provided highly accurate classification results. These findings suggest that this approach is efficient and may be a promising strategy for obtaining better AD, MCI and NC classification performance.

scholarly journals Molecular Inverse Comorbidity between Alzheimer’s disease and Lung Cancer: new insights from Matrix Factorization

2019 ◽  
Author(s):  
Alessandro Greco ◽  
Jon Sanchez Valle ◽  
Vera Pancaldi ◽  
Anaïs Baudot ◽  
Emmanuel Barillot ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Matrix Factorization ◽  
Large Scale ◽  
Molecular Mechanisms ◽  
Biological Data ◽  
Biological Data Analysis ◽  
Specific Factors ◽  
Molecular Bases

AbstractMatrix Factorization (MF) is an established paradigm for large-scale biological data analysis with tremendous potential in computational biology.We here challenge MF in depicting the molecular bases of epidemiologically described Disease-Disease (DD) relationships. As use case, we focus on the inverse comorbidity association between Alzheimer’s disease (AD) and lung cancer (LC), described as a lower than expected probability of developing LC in AD patients. To the day, the molecular mechanisms underlying DD relationships remain poorly explained and their better characterization might offer unprecedented clinical opportunities.To this goal, we extend our previously designed MF-based framework for the molecular characterization of DD relationships. Considering AD-LC inverse comorbidity as a case study, we highlight multiple molecular mechanisms, among which the previously identified immune system and mitochondrial metabolism. We then discriminate mechanisms specific to LC from those shared with other cancers through a pancancer analysis. Additionally, new candidate molecular players, such as Estrogen Receptor (ER), CDH1 and HDAC, are pinpointed as factors that might underlie the inverse relationship, opening the way to new investigations. Finally, some lung cancer subtype-specific factors are also detected, suggesting the existence of heterogeneity across patients also in the context of inverse comorbidity.

scholarly journals FINANCIAL DECISION MAKING SELF-EFFICACY IN COGNITIVELY AND FINANCIALLY VULNERABLE OLDER ADULTS

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S477-S478
Author(s):  
Evan Z Gross ◽  
Rebecca J Campbell ◽  
LaToya Hall ◽  
Peter Lichtenberg
Keyword(s):  
Alzheimer’S Disease ◽  
Older Adults ◽  
Alzheimer's Disease ◽  
Decision Making ◽  
Cognitive Impairment ◽  
Self Efficacy ◽  
Clinical Samples ◽  
Financial Decisions ◽  
Financial Decision Making ◽  
Financial Decision

Abstract Financial decision making self-efficacy (FDMSE) is a novel construct that may influence how older adults make financial decisions. Our previous research with a community sample of older adults demonstrated that cognitive functioning and suspected history of financial exploitation were both associated with low FDMSE. We sought to replicate these findings in two clinical samples of older adults: people with mild cognitive impairment (MCI) or probable Alzheimer’s disease (PAD) and current victims of scams or exploitation as determined by a financial coach. Samples were obtained from the Michigan Alzheimer’s Disease Center and a financial coaching intervention study. All participants completed a 4-item FDMSE measure. One-way ANOVAs, t-tests and chi-square tests were conducted to test for group differences with controls on demographics. There was a main effect of cognitive status on FDMSE, F(2,138) = 8.10, p &lt; .001, which was driven by higher FDMSE in the healthy group (N = 63) than the MCI (N = 76) or PAD (N = 28) groups. Similarly, scam victims (N = 25) had significantly lower FDMSE than non-exploited (N = 25) peers, t(48)=2.33, p &lt; 05. Cognitive impairment and current financial scams are both associated with low FDMSE levels. Low FDMSE may exacerbate cognitive and psychosocial vulnerabilities that contribute to risk for poor financial decisions among older adults. Future interventions to enhance FDMSE may help older adults make better decisions despite changes in thinking abilities or previous negative financial experiences.

scholarly journals Soluble Amyloid-Beta Isoforms Predict Downstream Alzheimer’s Disease Pathology

2021 ◽  
Author(s):  
Guilherme Povala ◽  
Bruna Bellaver ◽  
Marco Antônio De Bastiani ◽  
Wagner S. Brum ◽  
Pamela C. L. Ferreira ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Amyloid Beta ◽  
Area Under The Curve ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Biological Processes ◽  
Proteomics Data ◽  
Proteomics Analysis ◽  
Significant Enrichment

Abstract Background: Changes in soluble amyloid-beta (Aβ) levels in cerebrospinal fluid (CSF) are detectable at early preclinical stages of Alzheimer's disease (AD). However, whether Aβ levels can predict downstream AD pathological features in cognitively unimpaired (CU) individuals remains unclear. With this in mind, we aimed at investigating whether a combination of soluble Aβ isoforms can predict tau pathology (T+) and neurodegeneration (N+) positivity. Methods: We used CSF measurements of three soluble Aβ peptides (Aβ1‑38, Aβ1‑40 and Aβ1‑42) in CU individuals (n = 318) as input features in machine learning (ML) models aiming at predicting T+ and N+. Input data was used for building 2046 tuned predictive ML models with a nested cross-validation technique. Additionally, proteomics data was employed to investigate the functional enrichment of biological processes altered in T+ and N+ individuals. Results: Our findings indicate that Aβ isoforms can predict T+ and N+ with an area under the curve (AUC) of 0.929 and 0.936, respectively. Additionally, proteomics analysis identified 17 differentially expressed proteins (DEPs) in individuals wrongly classified by our ML algorithm. More specifically, enrichment analysis of gene ontology biological processes revealed an upregulation in myelinization and glucose metabolism-related processes in CU individuals wrongly predicted as T+. A significant enrichment of DEPs in pathways including biosynthesis of amino acids, glycolysis/gluconeogenesis, carbon metabolism, cell adhesion molecules and prion disease was also observed. Conclusions: Our results demonstrate that, by applying a refined ML analysis, a combination of Ab isoforms can predict T+ and N+ with a high AUC. CSF proteomics analysis highlighted a promising group of proteins that can be further explored for improving T+ and N+ prediction.

scholarly journals Characterization of Human Genes Modulated by Porphyromonas gingivalis Highlights the Ribosome, Hypothalamus, and Cholinergic Neurons

2021 ◽  
Vol 12 ◽  
Author(s):  
Sejal Patel ◽  
Derek Howard ◽  
Nityananda Chowdhury ◽  
Casey Derieux ◽  
Bridgette Wellslager ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Porphyromonas Gingivalis ◽  
Protein Targeting ◽  
Expression Patterns ◽  
Cholinergic Neurons ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Human Genes ◽  
Lysine Residues

Porphyromonas gingivalis, a bacterium associated with periodontal disease, is a suspected cause of Alzheimer’s disease. This bacterium is reliant on gingipain proteases, which cleave host proteins after arginine and lysine residues. To characterize gingipain susceptibility, we performed enrichment analyses of arginine and lysine proportion proteome-wide. Genes differentially expressed in brain samples with detected P. gingivalis reads were also examined. Genes from these analyses were tested for functional enrichment and specific neuroanatomical expression patterns. Proteins in the SRP-dependent cotranslational protein targeting to membrane pathway were enriched for these residues and previously associated with periodontal and Alzheimer’s disease. These ribosomal genes are up-regulated in prefrontal cortex samples with detected P. gingivalis sequences. Other differentially expressed genes have been previously associated with dementia (ITM2B, MAPT, ZNF267, and DHX37). For an anatomical perspective, we characterized the expression of the P. gingivalis associated genes in the mouse and human brain. This analysis highlighted the hypothalamus, cholinergic neurons, and the basal forebrain. Our results suggest markers of neural P. gingivalis infection and link the cholinergic and gingipain hypotheses of Alzheimer’s disease.

scholarly journals Designing dual inhibitors for the treatment of Alzheimer’s disease as well as Type 2 diabetes mellitus via pharmacoinformatics approach: A step towards better medication for diabetes-associated neurological disorder

2020 ◽  
Vol 19 (6) ◽  
pp. 1233-1242
Author(s):  
Talib Hussain ◽  
Syed Mohd Danish Rizvi ◽  
Gehad M. Subaiea ◽  
Abulrahman Sattam Alanazi ◽  
Afrasim Moin
Keyword(s):  
Diabetes Mellitus ◽  
Alzheimer’S Disease ◽  
Type 2 Diabetes ◽  
Alzheimer's Disease ◽  
Type 2 Diabetes Mellitus ◽  
Molecular Interaction ◽  
Drug Candidate ◽  
Dual Inhibitors ◽  
Dual Drug

Purpose: To design dual inhibitors against Alzheimer’s disease (AD) and type 2 diabetes mellitus (T2DM) via pharmacoinformatics approach.Methods: Dual Drug Candidates (DDC) were designed and explored for their molecular interaction with several AD and T2DM targets. Pterostilbene, a natural anti-T2DM compound was coupled with different cholinesterase inhibitors to design DDC. Orisis Datawarrior online property calculator  tools, Autock 4.2 and Hex 5.1 were used to investigate the potency of all DDC relative to positive controls.Results: The study found that DDC2 (pterostilbene - methylene linker -octa hydro amino phenothiazine), DDC3 (pterostilbene - ethylene linker - N-phthalimide) and DDC5 (pterostilbene - carbonyl linker - 2-methyl-4-aminoquinoline) were the most promising out of all the DDCs. DDC2 showed strong molecular interaction with most of the AD and T2DM targets, including acetylcholinesterase, butrylcholinesterase, β-secretase, receptor for advanced glycation end products and ATP sensitive potassium channel, dipeptidyl peptidase IV and sodium glucose transport protien 2. The findings also revealed the amyloid anti-aggregation potential of DDC.Conclusion: The results show that DDC3 and DDC5 significantly interfer with the primary nucleation process of β amyloid. Thus, DDC2, DDC3 and DDC5 have strong anti-T2DM and anti-AD potential. Keywords: Type 2 Diabetes Mellitus, Alzheimer’s disease, Dual drug candidate, Amyloid-beta, Pterostilbene

Sign in / Sign up

Export Citation Format

Share Document