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Parkinson’s disease (PD) is a neurodegenerative disorder with a complex genetic background.
Recent studies have identified genetic variation and group differences in subcortical brain morphometry associated with PD. Further understanding of the genetic pathways underpinning such associations is key to elucidating the aetiology of PD and identify potential therapeutic targets.
For such purpose, we leveraged summary statistics from genome-wide association studies (GWAS) for PD and subcortical brain volumes to examine the genetic correlation between PD risk and the volume of ten brain structures of interest. We conducted pairwise-GWAS analyses between PD risk and brain structures with a significant genetic correlation to identify specific, local regions of the genome with a shared aetiology. Shared regions of the genome were annotated using MAGMA gene-based analysis to identify shared genes and genetic pathways.
A positive significant genetic correlation between PD and intracranial volume, and the volumes of the brainstem, pallidum, putamen, ventral diencephalon, nucleus accumbens, caudate nucleus, brainstem, and thalamus was observed at a whole-genome level. Results showed that genetic variants increasing PD risk are also associated with larger brain volume in such regions. We identified a total of 210 genomic segments shared between PD and at least one of the aforementioned eight brain regions. Furthermore, we identified 129 significantly associated genes in common between PD and at least one brain region. Pathway enrichment analyses suggest potential involvement in biological pathways including chronic inflammation, the hypothalamic-pituitary-adrenal pathway, verbal numerical reasoning, cognitive resilience, mitophagy, and disrupted vesicle-trafficking and autophagic pathways. Overall, these findings contribute to improve our understanding of the aetiology of PD.

Idiopathic pulmonary fibrosis (IPF) is an aging-associated disease characterized by exacerbated extracellular matrix deposition that disrupts oxygen exchange. Hypoxia and its transcription factors (HIF-1α and 2α) influence numerous circuits that could perpetuate fibrosis by increasing myofibroblasts differentiation and by promoting extracellular matrix accumulation. Here, we aimed to elucidate the signature of hypoxia in the transcriptomic circuitry of IPF-derived fibroblasts. To determine this transcriptomic signature, a gene expression analysis with six lines of lung fibroblasts under normoxia or hypoxia was performed: three cell lines were derived from patients with IPF, and three were from healthy donors, a total of 36 replicates. We used the Clariom D platform, which allows us to evaluate a huge number of transcripts, to analyze the response to hypoxia in both controls and IPF. The microarray data were analyzed using R software version 4.1.0, and Bioconductor version 3.13. Quality analysis was performed using affycoretools package version 1.64. We normalized the data using RMA (Robust Multiarray Average) to minimize the non-biological variation in signal intensities. All data were analyzed using the limma package version using a linear model based on Bayes empirical method. Genes were considered statistically significant with higher p-values (adjusted p-value < 0.05), and logFC > 1 or logFC < −1. We found that the control′s response is greater by the number of genes and complexity. In the search for specific genes responsible for the IPF fibroblast phenotype, nineteen dysregulated genes were found in lung fibroblasts from IPF patients in hypoxia (nine upregulated and ten downregulated). In this sense, the signaling pathways revealed to be affected in the pulmonary fibroblasts of patients with IPF may represent an adaptation to chronic hypoxia.

Diabetic kidney disease (DKD) has been identified as frequently occurring as a chronic kidney pathology derived from diabetes comorbidity. This condition has irreversible damage with health implications, and its risk factor increases with Sars-Cov-2 infection. Currently, the prognostic outcome for diabetic patients with COVID-19 is dismal, even with intensive medical treatments. However, there is still scarce information on critical genes involved in the pathophysiological impact of COVID-19 on diabetes kidney disease. Here, we used transcriptomics data analysis to characterize differential gene expression profiles and determine hub genes undergoing transcriptional reprogramming in both disease conditions. Out of a total of 995 differential expressed genes (DEGs), we identified 29 upregulated (UP) genes in the COVID-19 KEGG pathway. Notably, the interrelational pathway enrichment analysis elucidated that they are significantly induced and have implications for immune and inflammatory responses. By performing a PPI network and applying topological analysis methods, we determined among those 29 UP-DEGs, the following five hub genes STAT1, CXCL10, ISG15, MX1, and OAS1. We carried out network deconvolution analysis to have GRN as output using the Corto algorithm and identified their co-expressed gene modules. Moreover, we validate the conservancy of their upregulation being the most statistically overlapped genes using Coronascape database (DB) cohorts. Finally, tissue-specific regulation of the predictive hub genes by Genotype-Tissue Expression DB (GTEx) indicates that CXCL10 and OAS1 expression levels are significantly lower in kidney tissue health conditions. Altogether, our results pointed out that these five hub genes could play an essential role in developing severe outcomes of COVID-19 in DKD patients. The results obtained in this bioinformatics analysis could contribute to establishing future strategies using key genes for clinical decision-making in the medical routine.