Characterization of cell cycle, inflammation, and oxidative stress signaling role in non-communicable diseases: Insights into genetic variants, microRNAs and pathways.

Non-Communicable Diseases (NCDs) significantly impact global health, contributing to over 70% of premature deaths, as reported by the World Health Organization (WHO). These diseases have complex and multifactorial origins, involving genetic, epigenetic, environmental and lifestyle factors. While Genome-Wide Association Study (GWAS) is widely recognized as a valuable tool for identifying variants associated with complex phenotypes; the multifactorial nature of NCDs necessitates a more comprehensive exploration, encompassing not only the genetic but also the epigenetic aspect. For this purpose, we employed a bioinformatics-multiomics approach to examine the genetic and epigenetic characteristics of NCDs (i.e. colorectal cancer, coronary atherosclerosis, squamous cell lung cancer, psoriasis, type 2 diabetes, and multiple sclerosis), aiming to identify novel biomarkers for diagnosis and prognosis. Leveraging GWAS summary statistics, we pinpointed Single Nucleotide Polymorphisms (SNPs) independently associated with each NCD. Subsequently, we identified genes linked to cell cycle, inflammation and oxidative stress mechanisms, revealing shared genes across multiple diseases, suggesting common functional pathways. From an epigenetic perspective, we identified microRNAs (miRNAs) with regulatory functions targeting these genes of interest. Our findings underscore critical genetic pathways implicated in these diseases. In colorectal cancer, the dysregulation of the "Cytokine Signaling in Immune System" pathway, involving LAMA5 and SMAD7, regulated by Hsa-miR-21-5p, Hsa-miR-103a-3p, and Hsa-miR-195-5p, emerged as pivotal. In coronary atherosclerosis, the pathway associated with "binding of TCF/LEF:CTNNB1 to target gene promoters" displayed noteworthy implications, with the MYC factor controlled by Hsa-miR-16-5p as a potential regulatory factor. Squamous cell lung carcinoma analysis revealed significant pathways such as "PTK6 promotes HIF1A stabilization," regulated by Hsa-let-7b-5p. In psoriasis, the "Endosomal/Vacuolar pathway," involving HLA-C and Hsa-miR-148a-3p and Hsa-miR-148b-3p, was identified as crucial. Type 2 Diabetes implicated the "Regulation of TP53 Expression" pathway, controlled by Hsa-miR-106a-5p and Hsa-miR-106b-5p. In conclusion, our study elucidates the genetic framework and molecular mechanisms underlying NCDs, offering crucial insights into potential genetic/epigenetic biomarkers for diagnosis and prognosis. The specificity of pathways and related miRNAs in different pathologies highlights promising candidates for further clinical validation, with the potential to advance personalized treatments and alleviate the global burden of NCDs.

Computational analysis of five neurodegenerative diseases reveals shared and specific genetic loci.

Neurodegenerative diseases (ND) are heterogeneous disorders of the central nervous system that share a chronic and selective process of neuronal cell death. A computational approach to investigate shared genetic and specific loci was applied to 5 different ND: Amyotrophic lateral sclerosis (ALS), Alzheimer's disease (AD), Parkinson's disease (PD), Multiple sclerosis (MS), and Lewy body dementia (LBD). The datasets were analyzed separately, and then we compared the obtained results. For this purpose, we applied a genetic correlation analysis to genome-wide association datasets and revealed different genetic correlations with several human traits and diseases. In addition, a clumping analysis was carried out to identify SNPs genetically associated with each disease. We found 27 SNPs in AD, 6 SNPs in ALS, 10 SNPs in PD, 17 SNPs in MS, and 3 SNPs in LBD. Most of them are located in non-coding regions, with the exception of 5 SNPs on which a protein structure and stability prediction was performed to verify their impact on disease. Furthermore, an analysis of the differentially expressed miRNAs of the 5 examined pathologies was performed to reveal regulatory mechanisms that could involve genes associated with selected SNPs. In conclusion, the results obtained constitute an important step toward the discovery of diagnostic biomarkers and a better understanding of the diseases.

From genetic correlations of Alzheimer's disease to classification with artificial neural network models.

Sporadic Alzheimer's disease (AD) is a complex neurological disorder characterized by many risk loci with potential associations with different traits and diseases. AD, characterized by a progressive loss of neuronal functions, manifests with different symptoms such as decline in memory, movement, coordination, and speech. The mechanisms underlying the onset of AD are not always fully understood, but involve a multiplicity of factors. Early diagnosis of AD plays a central role as it can offer the possibility of early treatment, which can slow disease progression. Currently, the methods of diagnosis are cognitive testing, neuroimaging, or cerebrospinal fluid analysis that can be time-consuming, expensive, invasive, and not always accurate. In the present study, we performed a genetic correlation analysis using genome-wide association statistics from a large study of AD and UK Biobank, to examine the association of AD with other human traits and disorders. In addition, since hippocampus, a part of cerebral cortex could play a central role in several traits that are associated with AD; we analyzed the gene expression profiles of hippocampus of AD patients applying 4 different artificial neural network models. We found 65 traits correlated with AD grouped into 9 clusters: medical conditions, fluid intelligence, education, anthropometric measures, employment status, activity, diet, lifestyle, and sexuality. The comparison of different 4 neural network models along with feature selection methods on 5 Alzheimer's gene expression datasets showed that the simple basic neural network model obtains a better performance (66% of accuracy) than other more complex methods with dropout and weight regularization of the network.

Phenome-wide genetic-correlation analysis and genetically informed causal inference of amyotrophic lateral sclerosis.

Leveraging genome-wide association statistics generated from a large study of amyotrophic lateral sclerosis (ALS; 29,612 cases and 122,656 controls) and UK Biobank (UKB; 4,024 phenotypes, up to 361,194 participants), we conducted a phenome-wide analysis of ALS genetic liability and identified 46 genetically correlated traits, such as fluid intelligence score (rg = - 0.21, p = 1.74 × 10-6), "spending time in pub or social club" (rg = 0.24, p = 2.77 × 10-6), non-work related walking (rg = - 0.25, p = 1.95 × 10-6), college education (rg = - 0.15, p = 7.08 × 10-5), "ever diagnosed with panic attacks (rg = 0.39, p = 4.24 × 10-5), and "self-reported other gastritis including duodenitis" (rg = 0.28, p = 1.4 × 10-3). To assess the putative directionality of these genetic correlations, we conducted a latent causal variable analysis, identifying significant genetic causality proportions (gcp) linking ALS genetic liability to seven traits. While the genetic component of "self-reported other gastritis including duodenitis" showed a causal effect on ALS (gcp = 0.50, p = 1.26 × 10-29), the genetic liability to ALS is potentially causal for multiple traits, also including an effect on "ever being diagnosed with panic attacks" (gcp = 0.79, p = 5.011 × 10-15) and inverse effects on "other leisure/social group activities" (gcp = 0.66, p = 1 × 10-4) and prospective memory result (gcp = 0.35, p = 0.005). Our subsequent Mendelian randomization analysis indicated that some of these associations may be due to bidirectional effects. In conclusion, this phenome-wide investigation of ALS polygenic architecture highlights the widespread pleiotropy linking this disorder with several health domains.

Consequences of exposure to pollutants on respiratory health: From genetic correlations to causal relationships.

Modern society grew rapidly over the last few decades and this led to an alarming increase in air pollutants and a worsening of the human health, especially in relation to the respiratory system. Indeed, chronic respiratory diseases were the third main cause of death in 2017, with over 3 million of deaths. Furthermore, the pollution has considerable consequences both for burden medical expenses and environmental. However, the mechanisms linking pollutants to the onset of these diseases remain unclear. Thus, in this study we addressed this problem through the United Kingdom BioBank database, analyzing 170 genome-wide association studies (103 related to respiratory diseases and 67 related to pollutants). We analyzed the genetic correlations and causal relationships of these traits, leveraging the summary statistics and bioinformatics packages such as Linkage Disequilibrium Score Regression and Latent Causal Variable. We obtained 158 significant genetic correlations and subsequently we analyzed them through the Latent Causal Variable analysis, obtaining 20 significant causal relationships. The most significant were between "Workplace full of chemicals or other fumes: Sometimes" and "Condition that has ever been diagnosed by a doctor: Asthma" and between "Workplace very dusty: Sometimes" and "Condition that has ever been diagnosed by a doctor: Emphysema or chronic bronchitis". Finally, we identified single nucleotide polymorphisms independently associated with sveral pollutants to analyze the genes and pathways that could be involved in the onset of the aforementioned respiratory system disorders and that could be useful clinical target. This study highlighted how crucial are the air condition of the working environments and the type of transport used in the onset of respiratory-related morbidity. Based on that, we also suggested some interventions, in order to improve quality life and develop new and eco-friendly society and life style, such as improving indoor air circulation, the use of public transport and urban reforestation.

Amyotrophic Lateral Sclerosis: A Diet Review.

Amyotrophic lateral sclerosis (ALS) is a fatal disease related to upper and lower motor neurons degeneration. Although the environmental and genetic causes of this disease are still unclear, some factors involved in ALS onset such as oxidative stress may be influenced by diet. A higher risk of ALS has been correlated with a high fat and glutamate intake and ß-methylamino-L-alanine. On the contrary, a diet based on antioxidant and anti-inflammatory compounds, such as curcumin, creatine, coenzyme Q10, vitamin E, vitamin A, vitamin C, and phytochemicals could reduce the risk of ALS. However, data are controversial as there is a discrepancy among different studies due to a limited number of samples and the many variables that are involved. In addition, an improper diet could lead to an altered microbiota and consequently to an altered metabolism that could predispose to the ALS onset. In this review we summarized some research that involve aspects related to ALS such as the epidemiology, the diet, the eating behaviour, the microbiota, and the metabolic diseases. Further research is needed to better comprehend the role of diet and the metabolic diseases in the mechanisms leading to ALS onset and progression.

Minor Allele Frequencies and Molecular Pathways Differences for SNPs Associated with Amyotrophic Lateral Sclerosis in Subjects Participating in the UKBB and 1000 Genomes Project.

Amyotrophic lateral sclerosis (ALS) is a complex disease with a late onset and is characterized by the progressive loss of muscular and respiratory functions. Although recent studies have partially elucidated ALS's mechanisms, many questions remain such as what the most important molecular pathways involved in ALS are and why there is such a large difference in ALS onset among different populations. In this study, we addressed this issue with a bioinformatics approach, using the United Kingdom Biobank (UKBB) and the European 1000 Genomes Project (1KG) in order to analyze the most ALS-representative single nucleotide polymorphisms (SNPs) that differ for minor allele frequency (MAF) between the United Kingdom population and some European populations including Finnish in Finland, Iberian population in Spain, and Tuscans in Italy. We found 84 SNPs associated with 46 genes that are involved in different pathways including: "Ca2+ activated K+ channels", "cGMP effects", "Nitric oxide stimulates guanylate cyclase", "Proton/oligopeptide cotransporters", and "Signaling by MAPK mutants". In addition, we revealed that 83% of the 84 SNPs can alter transcription factor-motives binding sites of 224 genes implicated in "Regulation of beta-cell development", "Transcription-al regulation by RUNX3", "Transcriptional regulation of pluripotent stem cells", and "FOXO-mediated transcription of cell death genes". In conclusion, the genes and pathways analyzed could explain the cause of the difference of ALS onset.

Cross-ancestry genome-wide association studies identified heterogeneous loci associated with differences of allele frequency and regulome tagging between participants of European descent and other ancestry groups from the UK Biobank.

To investigate cross-ancestry genetics of complex traits, we conducted a phenome-wide analysis of loci with heterogeneous effects across African, Admixed-American, Central/South Asian, East Asian, European and Middle Eastern participants of the UK Biobank (N = 441 331). Testing 843 phenotypes, we identified 82 independent genomic regions mapping variants showing genome-wide significant (GWS) associations (P < 5 × 10-8) in the trans-ancestry meta-analysis and GWS heterogeneity among the ancestry-specific effects. These included (i) loci with GWS association in one ancestry and concordant but heterogeneous effects among the other ancestries and (ii) loci with a GWS association in one ancestry group and an experiment-wide significant discordant effect (P < 6.1 × 10-4) in at least another ancestry. Since the trans-ancestry GWS associations were mostly driven by the European ancestry sample size, we investigated the differences of the allele frequency (ΔAF) and linkage disequilibrium regulome tagging (ΔLD) between European populations and the other ancestries. Within loci with concordant effects, the degree of heterogeneity was associated with European-Middle Eastern ΔAF (P = 9.04 × 10-6) and ΔLD of European populations with respect to African, Admixed-American and Central/South Asian groups (P = 8.21 × 10-4, P = 7.17 × 10-4 and P = 2.16 × 10-3, respectively). Within loci with discordant effects, ΔAF and ΔLD of European populations with respect to African and Central/South Asian ancestries were associated with the degree of heterogeneity (ΔAF: P = 7.69 × 10-3 and P = 5.31 × 10-3, ΔLD: P = 0.016 and P = 2.65 × 10-4, respectively). Considering the traits associated with cross-ancestry heterogeneous loci, we observed enrichments for blood biomarkers (P = 5.7 × 10-35) and physical appearance (P = 1.38 × 10-4). This suggests that these specific phenotypic classes may present considerable cross-ancestry heterogeneity owing to large allele frequency and LD variation among worldwide populations.