Biology subjects

Savva, K.

Publications and source records attributed to Savva, K..

3 recordsLinked to original sources

bioRxiv2024

Stage-specific gene ratios highlight genes and mechanisms related to presymptomatic and symptomatic Multiple Myeloma

Background/aimMultiple Myeloma is the second most common blood cancer, characterised by the accumulation of malignant plasma cells and the production of large amounts of a monoclonal immunoglobulin protein, in the bone marrow. The identification and progression/behaviour of molecular markers across stages remains a scientific challenge. This work aims to provide a holistic approach to the understanding of the disease progression, providing specific methodologies and candidate biomarkers, able to characterise and distinguish the disease state across stages. Materials and methodsTwo large bulk RNA datasets were used to collect and integrate stage-specific information at the gene level by means of: (a) differential expression analysis to obtain differential expressed genes (DEGs), (b) a recently introduced computational methodology able to detect monotonically expressed genes (MEGs), (c) a proposed computational methodology that uses pairs of MEGs at sample level, to classify and discriminate different stages of Multiple Myeloma. Additional numerical metrics were applied to rank the performance of these pairs across samples, facilitating the characterization and differentiation of disease stages. Validation was conducted using five additional external datasets, which were then utilised to enrich the final selection of top-rated genes identified from the two bulk RNA datasets under study. The final top-ranked genes were further used for pathway enrichment analysis in order to provide candidate pathways per stage. ResultsWe first show that MEGs provide better statistics than DEGs, both at gene and pathway level analysis. Secondly, we show that the proposed computational methodology by means of MEGs reveals short lists of high discriminative genes across stages, which in turn highlight significant groups of pathways. ConclusionWe integrated traditional analysis of DEGs with a recently introduced methodology for identifying MEGs, creating a novel computational approach capable of identifying highly discriminative genes and pathways that can serve as candidate markers for stage identification in a single sample. HighlightsO_LIA novel computational approach was used to identify Monotonically Expressed Genes (MEGs) whose expression was constantly increasing or decreasing. Genes such as RB1, CD27, TP53, and MCL1, previously highlighted in Multiple Myeloma, showed a consistent monotonic pattern, providing potential indicators for tracking the progression from the pre-malignant stages to active Multiple Myeloma. C_LIO_LIThe study calculated gene pair ratios using MEGs characterised by normal distribution and low dispersion. These ratios effectively distinguished healthy from disease samples, although the discrimination between disease stages (MGUS, SMM, MM) was less clear due to their overlapping molecular profiles. C_LIO_LIEnrichment analysis of significant gene pairs identified critical pathways affected during Multiple Myeloma progression, such as bone disease-related calcium pathways, glucocorticoid-regulated functions, and cardiac and neurological systems. These findings align with known clinical manifestations in MM patients, such as bone disease and amyloid cardiomyopathy. C_LIO_LIThe gene lists generated from our computational approach were validated against internal and external datasets, confirming their applicability. The methodology showed promise in identifying candidate genes for disease progression and could be applied to other diseases to uncover novel gene pairs not highlighted by traditional analyses. C_LI

bioinformatics↗

bioRxiv2023

Ranking of cell clusters in a single-cell RNA-sequencing analysis framework using prior knowledge

Prioritization or ranking of different cell types in a scRNA-Seq framework can be performed in a variety of ways, some of these include: i) obtaining an indication of the proportion of cell types between the different conditions under study, ii) counting the number of differentially expressed genes (DEGs) between cell types and conditions in the experiment or, iii) prioritizing cell types based on prior knowledge about the conditions under study (i.e., a specific disease). These methods have drawbacks and limitations thus novel methods for improving cell ranking are required. Here we present a novel methodology that exploits prior knowledge in combination with expert-user information to accentuate cell types from a scRNA-seq analysis that yield the most biologically meaningful results. Prior knowledge is incorporated in a standardized, structured manner, whereby a checklist is attained by querying MalaCards human disease database with a disease of interest. The checklist is comprised of pathways and drugs and, optionally, drug mode of actions (MOAs), associated with the disease. The user is prompted to "edit" this checklist by removing or adding terms (in the form of keywords) from the list of predefined terms. Our methodology has substantial advantages to more traditional cell ranking techniques and provides an informative complementary methodology that utilizes prior knowledge in a rapid and automated manner, that has previously not been attempted by other studies. The current methodology is also implemented as an R package entitled Single Cell Ranking Analysis Toolkit (scRANK) and is available for download and installation via GitHub (https://#hub.com/aoulas/scRANK) Author SummarySingle-cell RNA Sequencing (scRNA-Seq) provides an additive resolution down to the cellular level that was previously not available from traditional "Bulk" RNA sequencing experiments. However, it is often difficult to prioritize the specific cell-types which are primarily responsible for the cause of a disease. This work presents a novel methodology that exploits prior knowledge for a disease in combination with expert-user information to accentuate cell types from a scRNA-seq analysis that are most closely related to the molecular mechanism of a disease of interest. Prior knowledge is incorporated in a standardized, structured manner, whereby a checklist is attained by querying a human disease database called MalaCards. This checklist comes in the form of pathways and drugs and, optionally, drug mode of actions (MOAs), associated with the disease. The expert-user is prompted to "edit" the prior knowledge checklist by removing or adding terms from the list of predefined terms. This methodology has substantial advantages to more traditional cell prioritizing or ranking techniques and provides an informative complementary methodology that utilizes prior knowledge in a rapid and automated manner.

bioinformatics↗

bioRxiv2023

DReAmocracy: A Method to Capitalize on Prior Drug Discovery Efforts to Highlight Candidate Drugs for Repurposing

In the area of drug research, several computational drug repurposing studies have highlighted candidate repurposed drugs, as well as drugs from clinical trial studies in different phases. To our knowledge, the aggregation of the proposed lists of drugs by previous studies has not been extensively exploited towards the generation of a dynamic reference matrix regarding the disease-related frequencies of drug features such as the modes of action, the initial indications and the targeted pathways. To fill this knowledge gap, we performed a weight-modulated majority voting of the modes of action, initial indications and targeted pathways of the drugs in a well-known repository, namely the Drug Repurposing Hub. Our method, DReAmocracy, exploits this pile of information and creates frequency tables and finally a disease suitability score for each drug. As a testbed, we applied this method to a group of neurodegenerative diseases (Alzheimers, Parkinsons, Huntingtons and Multiple Sclerosis). A super-reference table with drug suitability scores has been created for all four neurodegenerative diseases and can be queried for any drug candidate against them. Top-scored drugs for Alzheimers disease include agomelatine and mirtazapine, for Parkinsons disease apomorphine and pramipexole, for Huntingtons fluphezine and perphezine, and for Multiple Sclerosis zonisamide and disopyramide. Overall, DReAmocracy is a methodology that leverages the existing experimental and/or computational knowledge (1) to reveal trends in selected tracks of drug discovery research that includes modes of action, targeted pathways and initial indications for the investigated drugs and (2) to score new candidate drugs for repurposing against a selected disease.

bioinformatics↗