Pathway analysis for pituitary adenomas (PitPat analysis)

Sun, 01 Jan 2023 00:00:00 GMT

Title: Pathway analysis for pituitary adenomas (PitPat analysis) Abstract: Investigation of pituitary adenoma development remains one of the most difficult tasks in the medical field. Transcriptome profiling is a useful technology for the discovery of biomarkers and has the potential to subsequently improve the therapeutic approach for this tumour. In this project, bioinformatics analysis will be applied to the available RNA sequencing data derived from 8 tissue samples belonging to Growth Hormone Producing Adenomas (GHPA) and Non-Functioning Pituitary Adenoma (NFPA) subgroups and a healthy pituitary control sample. The main objectives of this project are concerned with the discovery of differentially expressed genes, novel genes involved in the onset and development of Non-Functioning Pituitary Adenoma and Growth Hormone Producing Adenomas as well as identification of associated pathways that connect relative genes. Literature shows extensive evidence of differentially expressed genes (DEGs) and pathway enrichment associated with the respective subgroups, and we hypothesized that our results would concur with existing findings on this point. A bulk RNA-seq analysis bioinformatics pipeline was devised to identify Differential Gene Expression Analysis between the two aforementioned subgroups with the following parameters: p-value <0.05, log2fold change threshold =0.32. Originally, the respective subgroups were to be compared against a healthy control, however certain changes to this portion of the Methodology were made as the Differential Gene Expression Analysis (DESeq2) software requires a minimum of three samples per experimental group. As such, PitNET subsets were compared directly against each other. Gene Set Enrichment Analysis (GSEA) was employed to assess pathway enrichment. Results show a clear pattern of differentially expressed genes, evidenced by the discovery of 1,200 upregulated and 1,116 genes downregulated when analysing NFPA vs GHPA phenotypes, adding up to a total of 2,316 DEGs. Gene Set Enrichment Analysis of Non-Functioning Adenoma Samples showed enrichment of the Pre-Initiation formation pathway network (Network N01470) at False Discovery Rate (FDR) q-value <0.05 and enrichment of 69 pathway networks at a more lenient FDR q-value of 0.25. No results were yielded at FDR q value <0.05 with regard to GHPA phenotype. However, results indicate enrichment of four pathway networks (Network N01662: Interferon RIPK 3 Signalling, Network N00150: Type I Interferon Signalling, Network N01293: Copi Vesicle Formation and Network N01559: Type II Interferon to JAK STAT Signalling) at a more lenient FDR q-value of <0.25 in Growth Hormone Producing Adenomas, and evidence of interplay and interaction between three of these pathways was provided via analysing the Leading Edge subset-that is, the genes contributing the most to the enrichment signal. Our findings show concordance with multiple transcriptomic studies conducted on NFPAs and GHPAs (around 110 genes described in literature). Among the top 10 differentially expressed genes between the subgroups (GALNTI2, BRINP3, GATA2, HACD4, SLC8A2, LINC00672, TFEB, CACNA2D4, UNC13D, MTARC2), CACNA2D4, a calcium metabolism gene, was previously proposed as a marker for pituitary tumours in NFPAs. With regards to the most statistically significant pathway network enrichment, this was Pre-IC formation in NFPA phenotype. Differential expressed genes within the larger Cell Cycle pathway to which the Pre-IC formation pathway network belongs to have been described within literature in relation to Pituitary Adenomas (CDKN21, CDKN2B, GADDA45G, CDKN1A, HDAC1, E2F1, CCND1) except for CDT1, ORC6 and TRIP13. Finding novel genes responsible for the expression of PitNET phenotypes will lead to a better understanding of the aetiology of disease, as well as reflecting on the differential diagnoses and treatments. Description: M.Sc.(Melit.)

Analysis of structural variants in osteoporosis

Sun, 01 Jan 2023 00:00:00 GMT

Title: Analysis of structural variants in osteoporosis Abstract: Osteoporosis is a metabolic bone disorder with a strong genetic influence which has been a subject of study in research aiming to pinpoint crucial genes associated with bone health. This underlying pathophysiology has been linked to both common and rare genetic variations, and to a lesser extent, genomic structural variants (SVs), which are defined as alterations in chromosome structure exceeding 50 base pairs (bp) in size. Despite advances in sequencing, SVs in osteoporosis remain difficult to reliably detect due to the short read length (<300 bp) of 2nd generation sequencing, the diverse spectrum of SVs, and ongoing challenges in achieving high sensitivity and specificity in variant identification. The aim of this study was to conduct a comprehensive analysis of specifically selected SV detection tools using a 2-generation Maltese family having multiple relatives affected with osteoporosis and low bone mineral density (BMD). To achieve this objective, BreakDancer, Pindel, and Lumpy were carefully selected and computationally assessed on six readily available BAM files generated from short-read whole-genome sequencing (WGS). Genotype calling was required for BreakDancer and Lumpy, and additional tools BreakDown and SVtyper were employed. Variants identified by these tools were further annotated using the Variant Effect Predictor (VEP) to assess their impact on genes, transcripts, protein sequences, and regulatory regions. A number of filtering steps were performed to narrow down the list of variants and prioritise SVs located in genes with relevant to bone physiology. Ten shortlisted SVs underwent a computational visualisation using Integrative Genomics Viewer (IGV) and experimental validation by PCR sizing and Sanger sequencing. Among these, three were experimentally confirmed, derived from the Lumpy output: ARHGEF3 g.57003274_57003433del, TBX15 g.119482201_119483619del, and ADAM9 g.38953621_38953881del. Likewise, two others were experimentally confirmed from the Pindel output: SOD2 g.160086257_160088689del and KLF12 g.74284677_74284844dup,. Overall, both Lumpy and Pindel demonstrated effectiveness in detecting SVs, with 5 out of 10 SVs identified as true positives. Around 400 SVs were called by all three tools. No variants were shortlisted by BreakDancer. Lumpy exhibited superiority over Pindel and BreakDancer, showcasing faster runtime, smaller memory footprint for output files, and minimal system requirements. In conclusion, the findings suggest that the SVs detected by Lumpy and Pindel could potentially be contributing to the genetic architecture of osteoporosis and BMD. Description: M.Sc.(Melit.)

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Pathway analysis for pituitary adenomas (PitPat analysis)

Analysis of structural variants in osteoporosis