Mild cognitive impairment (MCI) and Alzheimer’s Disease (AD) are complex diseases with their molecular architecture not elucidated. APOE, Amyloid Beta Precursor Protein (APP), and Presenilin-1 (PSEN1) are well-known genes associated with both MCI and AD. Recently, epigenetic alterations and dysregulated regulatory elements, such as microRNAs (miRNAs), have been reported associated with neurodegeneration. In this study, differential expression analysis (DEA) was performed for genes and miRNAs based on microarray and RNA-Seq data. Global gene profile of healthy individuals, early and late mild cognitive impairment (EMCI and LMCI, respectively), and AD was obtained from ADNI Cohort. miRNA global profile of healthy individuals and AD patients was extracted from public RNA-Seq data. DEA performed with limma package on ADNI Cohort data highlighted eight differential expressed (DE) genes (AGER, LINC00483, MMP19, CATSPER1, ARFGAP1, GPER1, PHLPP2, TRPM2) (false discovery rate (FDR) p-value < 0.05) between EMCI and LMCI patients. Previous molecular studies showed associations between these genes with dementia and neurological-related pathways. Five dysregulated miRNAs were identified by DEA performed with RNA-Seq data and edgeR (FDR p-value < 0.002). All reported miRNAs in AD interact with the aforementioned genes. Our integrative transcriptomic analysis was able to identify a set of miRNA–gene interactions that may be involved in cognitive and neurodegeneration processes.
Nuclear DNA has been the main source of genome-wide loci association in neurodegenerative diseases, only partially accounting for the heritability of Alzheimer’s Disease (AD). In this context, mitochondrial DNA (mtDNA) is gaining more attention. Here, we investigated mitochondrial genes and genetic variants that may influence mild cognitive impairment and AD, through an integrative analysis including differential gene expression and mitochondrial genome-wide epistasis. We assessed the expression of mitochondrial genes in different brain tissues from two public RNA-Seq databases (GEO and GTEx). Then, we analyzed mtDNA from the ADNI Cohort and investigated epistasis regarding mitochondrial variants and levels of Aβ1−42, TAU, and Phosphorylated TAU (PTAU) from cognitively healthy controls, and both mild cognitive impairment (MCI) and AD cases. We identified multiple differentially expressed mitochondrial genes in the comparisons between cognitively healthy individuals and AD patients. We also found increased protein levels in MCI and AD patients when compared to healthy controls, as well as novel candidate networks of mtDNA epistasis, which included variants in all mitochondrially-encoded oxidative phosphorylation complexes, 12S rRNA and MT-DLOOP. Our results highlight layers of potential interactions involving mitochondrial genetics and suggest specific molecular alterations as potential biomarkers for AD.
Aim: Circular RNAs (circRNAs) are dysregulated in complex diseases, so we investigated their global expression profile in stroke. Material & methods: Public RNA-Seq data of human ischemic stroke lesion tissues and controls were used to perform the global expression analysis. Target RNA binding proteins and microRNAs were predicted in silico. Functional enrichment analysis was performed to infer the circRNAs’ potential roles. Results: We found that circRNAs are potentially involved in synaptic components and transmission, inflammation and ataxia. An integrative analysis revealed that hsa_circ_0078299 and FXN may be major players in the molecular stroke-context. Conclusion: Our results suggest a broad involvement of circRNAs in some stroke-related processes, indicating their potential as therapeutic targets to allow neuroprotection and brain recovery.
Background Next generation sequencing (NGS) has been a handy tool in clinical practice, mainly due to its efficiency and cost-effectiveness. It has been widely used in genetic diagnosis of several inherited diseases, and, in clinical oncology, it may enhance the discovery of new susceptibility genes and enable individualized care of cancer patients. In this context, we explored a pan-cancer panel in the investigation of germline variants in Brazilian patients presenting clinical criteria for hereditary cancer syndromes or familial history. Methods Seventy-one individuals diagnosed or with familial history of hereditary cancer syndromes were submitted to custom pan-cancer panel including 16 high and moderate penetrance genes previously associated with hereditary cancer syndromes (APC, BRCA1, BRCA2, CDH1, CDKN2A, CHEK2, MSH2, MSH6, MUTYH, PTEN, RB1, RET, TP53, VHL, XPA and XPC). All pathogenic variants were validated by Sanger sequencing. Results We identified a total of eight pathogenic variants among 12 of 71 individuals (16.9%). Among the mutation-positive subjects, 50% were diagnosed with breast cancer and had mutations in BRCA1, CDH1 and MUTYH. Notably, 33.3% were individuals diagnosed with polyposis or who had family cases and harbored pathogenic mutations in APC and MUTYH. The remaining individuals (16.7%) were gastric cancer patients with pathogenic variants in CDH1 and MSH2. Overall, 54 (76.05%) individuals presented at least one variant uncertain significance (VUS), totalizing 81 VUS. Of these, seven were predicted to have disease-causing potential. Conclusion Overall, analysis of all these genes in NGS-panel allowed the identification not only of pathogenic variants related to hereditary cancer syndromes but also of some VUS that need further clinical and molecular investigations. The results obtained in this study had a significant impact on patients and their relatives since it allowed genetic counselling and personalized management decisions.
ClinVar is a web platform that stores ∼789,000 genetic associations with complex diseases. A partial set of these cataloged genetic associations has challenged clinicians and geneticists, often leading to conflicting interpretations or uncertain clinical impact significance. In this study, we addressed the (re)classification of genetic variants by AmazonForest, which is a random-forest-based pathogenicity metaprediction model that works by combining functional impact data from eight prediction tools. We evaluated the performance of representation learning algorithms such as autoencoders to propose a better strategy. All metaprediction models were trained with ClinVar data, and genetic variants were annotated with eight functional impact predictors cataloged with SnpEff/SnpSift. AmazonForest implements the best random forest model with a one hot data-encoding strategy, which shows an Area Under ROC Curve of ≥0.93. AmazonForest was employed for pathogenicity prediction of a set of ∼101,000 genetic variants of uncertain significance or conflict of interpretation. Our findings revealed ∼24,000 variants with high pathogenic probability (RFprob≥0.9). In addition, we show results for Alzheimer’s Disease as a demonstration of its application in clinical interpretation of genetic variants in complex diseases. Lastly, AmazonForest is available as a web tool and R object that can be loaded to perform pathogenicity predictions.
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