scholarly journals A Computational Exploration of the Molecular Network Associated to Neuroinflammation in Alzheimer’s Disease

2021 ◽  
Vol 12 ◽  
Author(s):  
Fatima El Idrissi ◽  
Bernard Gressier ◽  
David Devos ◽  
Karim Belarbi
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Scientific Literature ◽  
Interaction Network ◽  
Functional Enrichment ◽  
Biological Processes ◽  
Functional Interaction ◽  
Pharmacological Targets ◽  
Computational Exploration ◽  
Pleiotropic Actions

Neuroinflammation, as defined by the presence of classically activated microglia, is thought to play a key role in numerous neurodegenerative disorders such as Alzheimer’s disease. While modulating neuroinflammation could prove beneficial against neurodegeneration, identifying its most relevant biological processes and pharmacological targets remains highly challenging. In the present study, we combined text-mining, functional enrichment and protein-level functional interaction analyses to 1) identify the proteins significantly associated to neuroinflammation in Alzheimer’s disease over the scientific literature, 2) distinguish the key proteins most likely to control the neuroinflammatory processes significantly associated to Alzheimer's disease, 3) identify their regulatory microRNAs among those dysregulated in Alzheimer's disease and 4) assess their pharmacological targetability. 94 proteins were found to be significantly associated to neuroinflammation in Alzheimer’s disease over the scientific literature and IL4, IL10 and IL13 signaling as well as TLR-mediated MyD88- and TRAF6-dependent responses were their most significantly enriched biological processes. IL10, TLR4, IL6, AKT1, CRP, IL4, CXCL8, TNF-alpha, ITGAM, CCL2 and NOS3 were identified as the most potent regulators of the functional interaction network formed by these immune processes. These key proteins were indexed to be regulated by 63 microRNAs dysregulated in Alzheimer's disease, 13 long non-coding RNAs and targetable by 55 small molecules and 8 protein-based therapeutics. In conclusion, our study identifies eleven key proteins with the highest ability to control neuroinflammatory processes significantly associated to Alzheimer’s disease, as well as pharmacological compounds with single or pleiotropic actions acting on them. As such, it may facilitate the prioritization of diagnostic and target-engagement biomarkers as well as the development of effective therapeutic strategies against neuroinflammation in Alzheimer’s disease.

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 Soluble amyloid-beta isoforms predict downstream Alzheimer’s disease pathology

2021 ◽  
Vol 11 (1) ◽  
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 model. 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 Aβ 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.

Identifying Alzheimer’s Disease-related miRNA Based on Semi-clustering

2019 ◽  
Vol 19 (4) ◽  
pp. 216-223 ◽  
Author(s):  
Tianyi Zhao ◽  
Donghua Wang ◽  
Yang Hu ◽  
Ningyi Zhang ◽  
Tianyi Zang ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Drug Targets ◽  
Molecular Mechanisms ◽  
Feature Vector ◽  
Mirna Gene ◽  
Interaction Network ◽  
Gene Interaction ◽  
Proteinprotein Interaction ◽  
Synaptic Structures

Background: More and more scholars are trying to use it as a specific biomarker for Alzheimer’s Disease (AD) and mild cognitive impairment (MCI). Multiple studies have indicated that miRNAs are associated with poor axonal growth and loss of synaptic structures, both of which are early events in AD. The overall loss of miRNA may be associated with aging, increasing the incidence of AD, and may also be involved in the disease through some specific molecular mechanisms. Objective: Identifying Alzheimer’s disease-related miRNA can help us find new drug targets, early diagnosis. Materials and Methods: We used genes as a bridge to connect AD and miRNAs. Firstly, proteinprotein interaction network is used to find more AD-related genes by known AD-related genes. Then, each miRNA’s correlation with these genes is obtained by miRNA-gene interaction. Finally, each miRNA could get a feature vector representing its correlation with AD. Unlike other studies, we do not generate negative samples randomly with using classification method to identify AD-related miRNAs. Here we use a semi-clustering method ‘one-class SVM’. AD-related miRNAs are considered as outliers and our aim is to identify the miRNAs that are similar to known AD-related miRNAs (outliers). Results and Conclusion: We identified 257 novel AD-related miRNAs and compare our method with SVM which is applied by generating negative samples. The AUC of our method is much higher than SVM and we did case studies to prove that our results are reliable.

scholarly journals Association and Gene–-Gene Interactions Study of Late-Onset Alzheimer’s Disease in the Russian Population

Genes ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1647
Author(s):  
Anna Bocharova ◽  
Kseniya Vagaitseva ◽  
Andrey Marusin ◽  
Natalia Zhukova ◽  
Irina Zhukova ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Neurodegenerative Disorder ◽  
Factor Model ◽  
Late Onset ◽  
Interaction Network ◽  
Russian Population ◽  
Protein Protein Interaction ◽  
Protective Haplotype ◽  
Single Snps

Alzheimer’s disease (AD) is a neurodegenerative disorder, and represents the most common cause of dementia. In this study, we performed several different analyses to detect loci involved in development of the late onset AD in the Russian population. DNA samples from 472 unrelated subjects were genotyped for 63 SNPs using iPLEX Assay and real-time PCR. We identified five genetic loci that were significantly associated with LOAD risk for the Russian population (TOMM40 rs2075650, APOE rs429358 and rs769449, NECTIN rs6857, APOE ε4). The results of the analysis based on comparison of the haplotype frequencies showed two risk haplotypes and one protective haplotype. The GMDR analysis demonstrated three significant models as a result: a one-factor, a two-factor and a three-factor model. A protein–protein interaction network with three subnetworks was formed for the 24 proteins. Eight proteins with a large number of interactions are identified: APOE, SORL1, APOC1, CD33, CLU, TOMM40, CNTNAP2 and CACNA1C. The present study confirms the importance of the APOE-TOMM40 locus as the main risk locus of development and progress of LOAD in the Russian population. Association analysis and bioinformatics approaches detected interactions both at the association level of single SNPs and at the level of genes and proteins.

scholarly journals The function of SIRT3 explored through the substrate interaction network

2020 ◽  
Author(s):  
Jarmila Nahálková
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Interaction Network ◽  
Literature Mining ◽  
Biological Functions ◽  
Alcoholic Fatty Liver ◽  
Pathway Enrichment ◽  
Substrate Interaction ◽  
Lysine Deacetylase ◽  
Brown Adipose

AbstractSIRT3 is the mitochondrial protein lysine deacetylase with a prominent role in the maintenance of mitochondrial integrity vulnerable in the range of diseases. The present study examines the SIRT3 substrate interaction network for the identification of its biological functions in the cellular anti-aging mechanisms. The pathway enrichment, the protein function prediction, and the protein node prioritization analysis were performed based on 407 SIRT3 substrates, which were collected by the data mining. The substrates are interlinked by 1230 direct protein-protein interactions included in the GeneMania database. The analysis of the SIRT3 substrate interaction network highlighted Alzheimer’s disease (AD), Parkinson’s disease (PD), Huntington’s disease (HD), and non-alcoholic fatty liver disease (NAFLD) as the most associated with SIRT3 lysine deacetylase activity. The most important biological functions of SIRT3 substrates are within the respiratory electron transport chain, tricarboxylic acid cycle and fatty acid, triacylglycerol, and ketone body metabolism. In brown adipose tissue, SIRT3 activity contributes to the adaptive thermogenesis by the increase of energy production of the organisms. SIRT3 exhibits several modes of neuroprotective actions in the brain and liver including prevention of the mitochondrial damages due to the respiratory electron transfer chain failure, the quenching of ROS, the inhibition of the mitochondrial membrane potential loss, and the regulation of mitophagy. Related to its role in Alzheimer’s disease, SIRT3 activation performs as a repressor of BACE1 through SIRT3-LKB1-AMPK-CREB-PGC-1α-PPARG-BACE1 (SIRT3-BACE1) pathway, which was created based on the literature mining and by employing Wikipathways application. The pathway enrichment analysis of the extended interaction network of the SIRT3-BACE1 pathway nodes displayed the functional relation to the circadian clock, which also deteriorates during the progress of AD and it is the causative of AD, PD, and HD. The use of SIRT3 activators in combination with the stimulating effect of regular exercise is further discussed as an attractive option for the improvement of cognitive decline during aging and the progressive stages of neurodegeneration.

scholarly journals Systems Pharmacological Approach to Investigate the Mechanism of Acori Tatarinowii Rhizoma for Alzheimer’s Disease

2018 ◽  
Vol 2018 ◽  
pp. 1-20 ◽  
Author(s):  
Zhenyan Song ◽  
Fang Yin ◽  
Biao Xiang ◽  
Bin Lan ◽  
Shaowu Cheng
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Therapeutic Effects ◽  
Biological Processes ◽  
Pathway Enrichment ◽  
Study Results ◽  
Target Network ◽  
Anti Inflammatory

In traditional Chinese medicine (TCM), Acori Tatarinowii Rhizoma (ATR) is widely used to treat memory and cognition dysfunction. This study aimed to confirm evidence regarding the potential therapeutic effect of ATR on Alzheimer’s disease (AD) using a system network level based in silico approach. Study results showed that the compounds in ATR are highly connected to AD-related signaling pathways, biological processes, and organs. These findings were confirmed by compound-target network, target-organ location network, gene ontology analysis, and KEGG pathway enrichment analysis. Most compounds in ATR have been reported to have antifibrillar amyloid plaques, anti-tau phosphorylation, and anti-inflammatory effects. Our results indicated that compounds in ATR interact with multiple targets in a synergetic way. Furthermore, the mRNA expressions of genes targeted by ATR are elevated significantly in heart, brain, and liver. Our results suggest that the anti-inflammatory and immune system enhancing effects of ATR might contribute to its major therapeutic effects on Alzheimer’s disease.

scholarly journals KGen: a knowledge graph generator from biomedical scientific literature

2020 ◽  
Vol 20 (S4) ◽  
Author(s):  
Anderson Rossanez ◽  
Julio Cesar dos Reis ◽  
Ricardo da Silva Torres ◽  
Hélène de Ribaupierre
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Language Processing ◽  
Scientific Community ◽  
Scientific Literature ◽  
Software Tool ◽  
Scientific Texts ◽  
New Knowledge ◽  
Scientific Papers ◽  
Biomedical Texts

Abstract Background Knowledge is often produced from data generated in scientific investigations. An ever-growing number of scientific studies in several domains result into a massive amount of data, from which obtaining new knowledge requires computational help. For example, Alzheimer’s Disease, a life-threatening degenerative disease that is not yet curable. As the scientific community strives to better understand it and find a cure, great amounts of data have been generated, and new knowledge can be produced. A proper representation of such knowledge brings great benefits to researchers, to the scientific community, and consequently, to society. Methods In this article, we study and evaluate a semi-automatic method that generates knowledge graphs (KGs) from biomedical texts in the scientific literature. Our solution explores natural language processing techniques with the aim of extracting and representing scientific literature knowledge encoded in KGs. Our method links entities and relations represented in KGs to concepts from existing biomedical ontologies available on the Web. We demonstrate the effectiveness of our method by generating KGs from unstructured texts obtained from a set of abstracts taken from scientific papers on the Alzheimer’s Disease. We involve physicians to compare our extracted triples from their manual extraction via their analysis of the abstracts. The evaluation further concerned a qualitative analysis by the physicians of the generated KGs with our software tool. Results The experimental results indicate the quality of the generated KGs. The proposed method extracts a great amount of triples, showing the effectiveness of our rule-based method employed in the identification of relations in texts. In addition, ontology links are successfully obtained, which demonstrates the effectiveness of the ontology linking method proposed in this investigation. Conclusions We demonstrate that our proposal is effective on building ontology-linked KGs representing the knowledge obtained from biomedical scientific texts. Such representation can add value to the research in various domains, enabling researchers to compare the occurrence of concepts from different studies. The KGs generated may pave the way to potential proposal of new theories based on data analysis to advance the state of the art in their research domains.

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