Biology subjects

Zanin, M.

Publications and source records attributed to Zanin, M..

8 recordsLinked to original sources

bioRxiv2024

A hemagglutinin and neuraminidase biased immunological memory shapes the dynamics of antibody responses to Influenza A virus

Influenza A virus (IAV) infection establishes a more diverse immunological memory to different viral proteins compared to vaccination. We hypothesized that the relative abundance of pre-existing immune memory to different viral antigens could skew post-infection antibody responses. To explore this, we generated mouse models with either an IAV hemagglutinin (HA)- or neuraminidase (NA)-biased immunological memory. We inoculated groups of mice with cocktails of isogenic viruses bearing antigenically-distinct HA (H3v) or NA (N2v) chosen to span the IAV H3N2 human circulation history. We challenged the mice with two H3N2 strains of opposing virulence and antigenic distance (AD) and examined the post-infection antibody landscapes. In both challenges, immune-naive mice seroconverted to both HA and NA whereas in primed mice, antibody response was detected to the antigen for which there is no pre-existing memory. In cases where the homologous antibody response was blunted, there was diversification on the breadth of response to antigenically-related strains with low baseline titers. Our findings clarifies the concept of "original antigenic sin" and demonstrate a mechanism by which the dynamics of antibody responses to HA and NA after infection can be altered by pre-existing immunity.

immunology↗

bioRxiv2022

The episodic resurgence of highly pathogenic avian influenza H5 virus

Highly pathogenic avian influenza (HPAI) H5N1 activity has intensified globally since 2021, replacing the dominant clade 2.3.4.4 H5N8 virus. H5N1 viruses have spread rapidly to four continents, causing increasing reports of mass mortality in wild birds and poultry. The ecological and virological properties required for future mitigation strategies are unclear. Using epidemiological, spatial and genomic approaches, we demonstrate changes in the source of resurgent H5 HPAI and reveal significant shifts in virus ecology and evolution. Outbreak data indicates key resurgent events in 2016/17 and 2020/21 that contributed to the panzootic spread of H5N1 in 2021/22, including an increase in virus diffusion velocity and persistence in wild birds. Genomic analysis reveals that the 2016/17 epizootics originated in Asia, where HPAI H5 reservoirs are documented as persistent. However, in 2020/21, 2.3.4.4b H5N8 viruses emerged in domestic poultry in Africa, featuring several novel mutations altering the HA structure, receptor binding, and antigenicity. The new H5N1 virus emerged from H5N8 through reassortment in wild birds along the Adriatic flyway around the Mediterranean Sea. It was characterized by extensive reassortment with low pathogenic avian influenza in domestic and wild birds as it spread globally. In contrast, earlier outbreaks of H5N8 were caused by a more stable genetic constellation, highlighting dynamic changes in HPAI H5 genomic evolution. These results suggest a shift in the epicenter of HPAI H5 beyond Asia to new regions in Africa, the Middle East, Europe, and North and South America. The persistence of HPAI H5 with resurgence potential in domestic birds indicates that elimination strategies remain a high priority.

microbiology↗

bioRxiv2020

Analysis of new nosological models from disease similarities using clustering

While classical disease nosology is based on phenotypical characteristics, the increasing availability of biological and molecular data is providing new understanding of diseases and their underlying relationships, that could lead to a more comprehensive paradigm for modern medicine. In the present work, similarities between diseases are used to study the generation of new possible disease nosologic models that include both phenotypical and biological information. To this aim, disease similarity is measured in terms of disease feature vectors, that stood for genes, proteins, metabolic pathways and PPIs in the case of biological similarity, and for symptoms in the case of phenotypical similarity. An improvement in similarity computation is proposed, considering weighted instead of Booleans feature vectors. Unsupervised learning methods were applied to these data, specifically, density-based DBSCAN clustering algorithm. As evaluation metric silhouette coefficient was chosen, even though the number of clusters and the number of outliers were also considered. As a results validation, a comparison with randomly distributed data was performed. Results suggest that weighted biological similarities based on proteins, and computed according to cosine index, may provide a good starting point to rearrange disease taxonomy and nosology.

bioinformatics↗

bioRxiv2020

Mitochondria-mitochondria interaction networks show altered topological patterns in Parkinson's disease

Mitochondrial dysfunction is linked to pathogenesis of Parkinsons disease (PD). However, individual-mitochondria-based analyses do not show a uniform feature in PD patients. Since mitochondria interact with each other, we hypothesize that PD-related features might exist in topological patterns of mitochondria-mitochondria interaction networks (MINs). Here we showed that MINs form non-classical scale-free supernetworks in colonic ganglia both from healthy controls and PD patients, however, altered topological patterns are observed in PD patients. These patterns highly correlate with PD clinical scores and a machine-learning approach based on the MIN features accurately distinguish between patients and controls with an area-under-curve value of 0.989. The MINs of midbrain dopaminergic neurons (mDANs) derived from several genetic PD patients also display specific changes. CRISPR/CAS9-based genome correction of alpha-synuclein point mutations reverses the changes in MINs of mDANs. Our MIN network analysis opens a new dimension for a deeper characterization of various complex diseases with mitochondrial dysregulation.

neuroscience↗

bioRxiv2019

Evaluation of bacterial proliferation with a microfluidic-based device: Antibiochip.

The measurement of the proliferation (and the relevant inhibition of proliferation) of microbes is used in different settings, from industry to laboratory medicine. Thus, in this study, the capacity of the Antibiochip (ELTEK spa), a microfluidic-based device, to measure the amount of E. coli in certain culture conditions, was evaluated. An Antibiochip is composed of V-shaped microchannels, and the amount of microparticles (such as microbes) is measured by the surface of the pellet after centrifugation. In the present study, different geometries, volumes and times were analyzed. When the best conditions were identified, serial dilutions of microbial cultures were tested to validate the linearity of the results. Then, with the use of wild E. coli strains isolated from medical samples, the relationship between bacterial susceptibility to antibiotics (gentamicin, amikacin and ceftriaxone) measured by standard methods and that measured by the Antibiochip was evaluated. In this report, the good quality performances of the methods, their linearity and the capacity to identify susceptible microbial strains after 60 minutes of incubation are shown. These results represent a novel approach for ultrarapid antibiograms in clinics.

microbiology↗

bioRxiv2019

Completing missing MeSH code mappings in UMLS through alternative expert-curated sources

The increasing availability of biological, clinical and literary sources enables the study of diseases from a more comprehensive approach. However, the interoperability of these sources, particularly of the codes used to identify diseases, poses a major challenge. Because of its role as a hub of multiple medical vocabularies, the Unified Medical Language System (UMLS) has become one of the most widely used resources for mapping diseases from different classification systems. The coverage of these mappings, nevertheless, is still limited, so researchers must resort to other methods to fill these gaps. In this article we analyze the limitations in UMLS mappings for the main disease vocabularies and propose the exploitation of some alternative expert-curated sources to complete it. As a result, we demonstrate that this approach allows resolving more than 50% of the missing mappings for some vocabularies. All the findings are shared for validation and reuse.

bioinformatics↗

bioRxiv2018

DISNET: Disease understanding through complex networks creation and analysis

Within the global endeavour of improving population health, one major challenge is the increasingly high cost associated with drug development. Drug repositioning, i.e. finding new uses for existing drugs, is a promising alternative; yet, its effectiveness has hitherto been hindered by our limited knowledge about diseases and their relationships. In this paper we present DISNET (Drug repositioning and disease understanding through complex networks creation and analysis), a web-based system designed to extract knowledge from signs and symptoms retrieved from medical data bases, and to enable the creation of customisable disease networks. We here present the main functionalities of the DISNET system. We describe how information on diseases and their phenotypic manifestations is extracted from Wikipedia, PubMed and MayoClinic; specifically, texts from these sources are processed through a combination of text mining and natural language processing techniques. We further present a validation of the processing performed by the system; and describe, with some simple use cases, how a user can interact with it and extract information that could be used for subsequent analyses.\n\nDatabase URL: http://disnet.ctb.upm.es

bioinformatics↗

bioRxiv2018

Disease networks and their contribution to disease understanding and drug repurposing. A survey of the state of the art

Over a decade ago, a new discipline called network medicine emerged as an approach to understand human diseases from a network theory point-of-view. Disease networks proved to be an intuitive and powerful way to reveal hidden connections among apparently unconnected biomedical entities such as diseases, physiological processes, signaling pathways, and genes. One of the fields that has benefited most from this improvement is the identification of new opportunities for the use of old drugs, known as drug repurposing. The importance of drug repurposing lies in the high costs and the prolonged time from target selection to regulatory approval of traditional drug development. In this document we analyze the evolution of disease network concept during the last decade and apply a data science pipeline approach to evaluate their functional units. As a result of this analysis, we obtain a list of the most commonly used functional units and the challenges that remain to be solved. This information can be very valuable for the generation of new prediction models based on disease networks.

bioinformatics↗