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This study aims to highlight SARS-COV-2 mutations which are associated with increased or decreased viral virulence. We utilize genetic data from all strains available from GISAID and countries’ regional information, such as deaths and cases per million, as well as COVID-19-related public health austerity measure response times. Initial indications of selective advantage of specific mutations can be obtained from calculating their frequencies across viral strains. By applying modelling approaches, we provide additional information that is not evident from standard statistics or mutation frequencies alone. We therefore, propose a more precise way of selecting informative mutations. We highlight two interesting mutations found in genes N (P13L) and ORF3a (Q57H). The former appears to be significantly associated with decreased deaths and cases per million according to our models, while the latter shows an opposing association with decreased deaths and increased cases per million. Moreover, protein structure prediction tools show that the mutations infer conformational changes to the protein that significantly alter its structure when compared to the reference protein.

Bidirectional cross-talk between commensal microbiota and the immune system is essential for the regulation of immune responses and the formation of immunological memory. Perturbations of microbiome-immune system interactions can lead to dysregulated immune responses against invading pathogens and/or to the loss of self-tolerance, leading to systemic inflammation and genesis of several immune-mediated pathologies, including neurodegeneration. In this paper, we first investigated the contribution of the immunomodulatory effects of microbiota (bacteria and fungi) in shaping immune responses and influencing the formation of immunological memory cells using a network-based bioinformatics approach. In addition, we investigated the possible role of microbiota-host-immune system interactions and of microbiota-virus interactions in a group of neurodegenerative diseases (NDs): Amyotrophic Lateral Sclerosis (ALS), Multiple Sclerosis (MS), Parkinson’s disease (PD) and Alzheimer’s disease (AD). Our analysis highlighted various aspects of the innate and adaptive immune response systems that can be modulated by microbiota, including the activation and maturation of microglia which are implicated in the development of NDs. It also led to the identification of specific microbiota components which might be able to influence immune system processes (ISPs) involved in the pathogenesis of NDs. In addition, it indicated that the impact of microbiota-derived metabolites in influencing disease-associated ISPs, is higher in MS disease, than in AD, PD and ALS suggesting a more important role of microbiota mediated-immune effects in MS.

Prioritization or ranking of different cell types in a single-cell RNA sequencing (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 with respect to a disease under study. Our methodology allows for ranking and prioritization of cell types based on how well their expression profiles relate to the molecular mechanisms and drugs associated with a disease. Molecular mechanisms, as well as drugs, are incorporated as prior knowledge in a standardized, structured manner. Cell types are then ranked/prioritized based on how well results from data-driven analysis of scRNA-seq data match the predefined prior knowledge. In additional cell-cell communication perturbations between disease and control networks are used to further prioritize/rank cell types. 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://github.com/aoulas/scRANK ).

Coronavirus Disease 2019 (COVID-19) is associated with increased incidence of neurological diseases and neuropsychiatric disorders after infection, but how it contributes to their development remains under investigation. Here, we investigate the possible relationship between COVID-19 and the development of ten neurological disorders and three neuropsychiatric disorders by exploring two pathological mechanisms: (i) dysregulation of host biological processes via virus–host protein–protein interactions (PPIs), and (ii) autoreactivity of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epitopes with host “self” proteins via molecular mimicry. We also identify potential genetic risk factors which in combination with SARS-CoV-2 infection might lead to disease development. Our analysis indicated that neurodegenerative diseases (NDs) have a higher number of disease-associated biological processes that can be modulated by SARS-CoV-2 via virus–host PPIs than neuropsychiatric disorders. The sequence similarity analysis indicated the presence of several matching 5-mer and/or 6-mer linear motifs between SARS-CoV-2 epitopes with autoreactive epitopes found in Alzheimer’s Disease (AD), Parkinson’s Disease (PD), Myasthenia Gravis (MG) and Multiple Sclerosis (MS). The results include autoreactive epitopes that recognize amyloid-beta precursor protein (APP), microtubule-associated protein tau (MAPT), acetylcholine receptors, glial fibrillary acidic protein (GFAP), neurofilament light polypeptide (NfL) and major myelin proteins. Altogether, our results suggest that there might be an increased risk for the development of NDs after COVID-19 both via autoreactivity and virus–host PPIs.

Background The health benefits of the Mediterranean diet are partially attributed to the polyphenols present in extra virgin olive oil (EVOO), which have been shown to have anti-cancer properties. However, the possible effect that EVOO could have on Chronic Lymphocytic Leukemia (CLL) has not been fully explored. Methods  This study investigates the anti-CLL activity of EVOO through a computational multi-level data analysis procedure, focusing on the identification of shared biological functions between them. Specifically, publicly available data from genomics, transcriptomics and proteomics related to EVOO consumption and CLL were collected from several resources and analyzed through a computational pipeline, highlighting common molecular mechanisms and biological processes. Computational verification of a number of the highlighted functional terms associating CLL and EVOO has been performed as well. Results Our investigation revealed four molecular pathways and three biological processes that overlap between mechanisms associated with CLL and those impacted by the consumption of EVOO. To further investigate the common biological functions, we focused on AKT1 -related terms, aiming to investigate the potential importance of AKT1 in the anti- CLL effects associated with EVOO. Conclusions Overall, the results provide valuable insights into the potential beneficial effect of EVOO in CLL and highlight EVOO’s bioactive compounds as promising candidates for future investigations. Graphical Abstract

Fibrotic diseases cover a spectrum of systemic and organ-specific maladies that affect a large portion of the population, currently without cure. The shared characteristic these diseases feature is their uncontrollable fibrogenesis deemed responsible for the accumulated damage in the susceptible tissues. Idiopathic Pulmonary Fibrosis , an interstitial lung disease, is one of the most common and studied fibrotic diseases and still remains an active research target. In this study we highlight unique and common (i) genes, (ii) biological pathways and (iii) candidate repurposed drugs among 9 fibrotic diseases. We identify 7 biological pathways involved in all 9 fibrotic diseases as well as pathways unique to some of these diseases. Based on our Drug Repurposing results, we suggest captopril and ibuprofen that both appear to slow the progression of fibrotic diseases according to existing bibliography. We also recommend nafcillin and memantine, which haven’t been studied against fibrosis yet, for further wet-lab experimentation. We also observe a group of cardiomyopathy-related pathways that are exclusively highlighted for Oral Submucous Fibrosis . We suggest digoxin to be tested against Oral Submucous Fibrosis , since we observe cardiomyopathy-related pathways implicated in Oral Submucous Fibrosis and there is bibliographic evidence that digoxin may potentially clear myocardial fibrosis. Finally, we establish that Idiopathic Pulmonary Fibrosis shares several involved genes, biological pathways and candidate inhibiting-drugs with Dupuytren’s Disease , IgG4-related Disease , Systemic Sclerosis and Cystic Fibrosis . We propose that treatments for these fibrotic diseases should be jointly pursued.

[This corrects the article DOI: 10.1371/journal.pone.0274356.].

Background Mild Cognitive Impairment (MCI), considered the prodromal stage of Alzheimer’s disease (AD), is a heterogeneous condition characterised by mild but measurable cognitive decline. However, not all individuals with MCI follow the same trajectory—some remain stable, while others progress rapidly to AD. Understanding variation in clinical, molecular, and imaging features is crucial for reducing disease heterogeneity, improving prognosis, and developing targeted interventions. This study aims to increase MCI subtyping resolution by generating enriched individual-level profiles through the integration of imaging and omics data, facilitating precision medicine approaches for AD prevention and treatment. Methods We used data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), including structural MRI, CSF peptidomics/proteomics, and clinical indices. Using a multi-modal integration and clustering framework, we identified distinct MCI subgroups, characterised by clinical and neuropsychological scores, AD biomarkers, biological pathway enrichments, and imaging patterns. We further employed supervised multi-modal integration and correlation analyses to explore the links between imaging, peptidomic/proteomic and clinical features within each subgroup. Additionally, we labelled individuals by future conversion to AD and analysed longitudinal cognitive function (CDRSB and MMSE scores). Finally, we performed in silico drug repurposing to identify candidate drugs targeting each subgroup’s molecular profile. Results (1) Multi-modal integration revealed two distinct MCI subgroups. (2) The Resilient Neuronal Hyperplasticity subgroup was characterised by elevated markers of neuronal plasticity, minimal brain atrophy and cortical thinning, better clinical scores, and upregulated peptide/protein markers associated with less severe structural changes. In contrast, the Vulnerable Neurodegenerative subgroup exhibited AD-like disturbances, pronounced atrophy and cortical thinning, primarily affecting executive functions, and downregulation of peptide/protein markers linked to significant structural changes. (3) Future conversion analysis revealed the second subgroup predominantly comprised fast converters, while the first predominantly consisted of stable individuals. (4) Longitudinal cognitive analysis showed a more pronounced decline in the second subgroup compared to the first. (5) Drug repurposing identified both shared and subgroup-specific candidate compounds aligned with the underlying pathologies. Conclusions This study delineates two MCI subgroups, using multi-modal integration, offering insights into disease heterogeneity and laying the foundation for precision medicine and AI-driven strategies in MCI and AD research and clinical care.

Females are underrepresented in the science, technology, engineering, mathematics and medicine (STEMM) disciplines globally and although progress has been made, the gender gap persists. Our aim was to explore gender parity in the context of gender representation and internal collaboration at the Cyprus Institute of Neurology and Genetics (CING), a leading national biomedical organisation accredited as an equal opportunity employer. Towards this aim we (1) explored trends in gender parity within the different departments, positions and qualifications and in student representation in the CING’s postgraduate school and, (2) investigated the degree of collaboration between male and female researchers within the Institute and the degree of influence within its co-authorship network. We recorded an over-representation of females both in the CING employees and the postgraduate students. The observed female over-representation in pooled CING employees was consistent with a similar over-representation in less senior positions and was contrasted with an observed male over-representation in only one middle rank and culminated in gender equality in the top rank in employee hierarchy. In terms of collaboration, both males and females tended to collaborate with each other without any significant preference to either inter-group or intra-group collaboration. Further comparison of the two groups with respect to their influence in the network in terms of occupying the positions of highest centrality scores, indicated that both gender and seniority level (head vs non-head) were significant in shaping the authors’ influence, with no significant difference in those belonging in the same seniority level with respect to their gender. To conclude, our study has validated the formal recognition of the CING’s policies and procedures pertinent to its egalitarian culture through the majority of the metrics of gender equality assessed in this study and has provided an extendable paradigm for evaluating gender parity in academic organizations.

Whole genome sequencing of viral specimens following molecular diagnosis is a powerful analytical tool of molecular epidemiology that can critically assist in resolving chains of transmission, identifying of new variants or assessing pathogen evolution and allows a real-time view into the dynamics of a pandemic. In Cyprus, the first two cases of COVID-19 were identified on March 9, 2020 and since then 33,567 confirmed cases and 230 deaths were documented. In this study, viral whole genome sequencing was performed on 133 SARS-CoV-2 positive samples collected between March 2020 and January 2021. Phylogenetic analysis was conducted to evaluate the genomic diversity of circulating SARS-CoV-2 lineages in Cyprus. 15 different lineages were identified that clustered into three groups associated with the spring, summer and autumn/winter wave of SARS-CoV-2 incidence in Cyprus, respectively. The majority of the Cypriot samples belonged to the B.1.258 lineage first detected in September that spread rapidly and largely dominated the autumn/winter wave with a peak prevalence of 86% during the months of November and December. The B.1.1.7 UK variant (VOC-202012/01) was identified for the first time at the end of December and spread rapidly reaching 37% prevalence within one month. Overall, we describe the changing pattern of circulating SARS-CoV-2 lineages in Cyprus since the beginning of the pandemic until the end of January 2021. These findings highlight the role of importation of new variants through travel towards the emergence of successive waves of incidence in Cyprus and demonstrate the importance of genomic surveillance in determining viral genetic diversity and the timely identification of new variants for guiding public health intervention measures.