Analysis and relevance of genetic variability of ruminant retroviruses (BLV, BFV, SRLVs) in terms of pathogenesis and diagnosis of infection and virus transmission
Ontology highlight
ABSTRACT:
INSTRUMENT(S): ABI 3500 Genetic Analyzer
ORGANISM(S): Capra Hircus
SUBMITTER: National Veterinary Research Institute, 24-100 Pulawy, Poland
PROVIDER: PRJEB42246 | EVA | 2021-01-04
REPOSITORIES: EVA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| merged.accessioned.vcf.gz | Vcf | |||
| merged.vcf.gz | Vcf | |||
| merged.vcf.gz.tbi | Vcf |
Items per page: 5 1 - 3 of 3 |
Similar Datasets
Project description:Transcriptional response of human lung epithelial cells to SARS-CoV-2 infection
2020-04-17 | E-GEOD-147507 | ExpressionAtlas
Project description:Breastfeeding protects against mucosal infections in infants. The underlying mechanisms through which immunity develops in human milk following maternal infection with mucosal pathogens are not well understood. We simulate mucosal influenza infection through live attenuated influenza vaccination (LAIV) and compared milk and blood immune responses to inactivated influenza vaccination (IIV). Transcriptomic analysis was performed on RNA extracted from human milk and whole blood. Differentially expressed genes (DEGs) and gene set enrichment analysis (GSEA) on days 1 and 7 post-vaccination were compared to pre-vaccination.
2022-06-29 | GSE195793 | GEO
Project description:Transcription profiling by array of human bronchial epithelial cells after infection with SARS-CoV and DOHV
2025-01-24 | E-GEOD-17400 | ExpressionAtlas
Project description:Transcription profiling by array of human pharyngeal epithelial cells after infection with various strains of Streptococcus pneumoniae
2025-01-24 | E-GEOD-8527 | ExpressionAtlas
Project description:Transcriptional response of Drosophila cells to FHV infection (infection experiment)
2014-07-03 | E-GEOD-15466 | ExpressionAtlas
Project description:Airway Epithelial Cell Response to Sendai virus infection
2014-07-03 | E-GEOD-10211 | ExpressionAtlas
Project description:Baris Hancioglu, David Swigon & Gilles Clermont. A dynamical model of human immune response to influenza A virus infection. Journal of Theoretical Biology 246, 1 (2007).
We present a simplified dynamical model of immune response to uncomplicated influenza A virus (IAV) infection, which focuses on the control of the infection by the innate and adaptive immunity. Innate immunity is represented by interferon-induced resistance to infection of respiratory epithelial cells and by removal of infected cells by effector cells (cytotoxic T-cells and natural killer cells). Adaptive immunity is represented by virus-specific antibodies. Similar in spirit to the recent model of Bocharov and Romanyukha [1994. Mathematical model of antiviral immune response. III. Influenza A virus infection. J. Theor. Biol. 167, 323-360], the model is constructed as a system of 10 ordinary differential equations with 27 parameters characterizing the rates of various processes contributing to the course of disease. The parameters are derived from published experimental data or estimated so as to reproduce available data about the time course of IAV infection in a naïve host. We explore the effect of initial viral load on the severity and duration of the disease, construct a phase diagram that sheds insight into the dynamics of the disease, and perform sensitivity analysis on the model parameters to explore which ones influence the most the onset, duration and severity of infection. To account for the variability and speed of adaptation of the adaptive response to a particular virus strain, we introduce a variable that quantifies the antigenic compatibility between the virus and the antibodies currently produced by the organism. We find that for small initial viral load the disease progresses through an asymptomatic course, for intermediate value it takes a typical course with constant duration and severity of infection but variable onset, and for large initial viral load the disease becomes severe. This behavior is robust to a wide range of parameter values. The absence of antibody response leads to recurrence of disease and appearance of a chronic state with nontrivial constant viral load.
2024-09-02 | BIOMD0000000711 | BioModels
Project description:The molecular processes underlying human milk production and the effects of mastitic infection are largely unknown because of limitations in obtaining tissue samples. Determination of gene expression in normal lactating women would be a significant step towards understanding why some women display poor lactation outcomes. Here we demonstrate the utility of RNA obtained directly from human milk cells to detect mammary epithelial cell (MEC)-specific gene expression. Milk cell RNA was collected from 5 time points (24 hours pre-partum during the colostrum period, mid lactation, two involution, and during a bout of mastitis) in addition to an involution series comprising three time points. Gene expression profiles were determined by use of human Affymetrix arrays. Milk cells collected during milk production showed that the most highly expressed genes were involved in milk synthesis (eg. CEL, OLAH, FOLR1, BTN1A1, ARG2), while milk cells collected during involution showed a significant down regulation of milk synthesis genes and activation of involution associated genes (eg. STAT3, NF-kB, IRF5, IRF7). Milk cells collected during mastitic infection revealed regulation of a unique set of genes specific to this disease state, whilst maintaining regulation of milk synthesis genes. Use of conventional epithelial cell markers was used to determine the population of MECâ??s within each sample. This paper is the first to describe the milk cell transcriptome across the human lactation cycle and during mastitic infection, providing valuable insight into gene expression of the human mammary gland. Human milk sampling throughout lactation cycle and during mastitic infection.
2016-04-01 | E-GEOD-77978 | biostudies-arrayexpress
Project description:Transcription profiling by array of cultured bronchial epithelial cells from patients with asthma and healthy individuals after infection with human rhinovirus
2025-01-24 | E-GEOD-13396 | ExpressionAtlas
Project description:Leber2015 - Mucosal immunity and gut
microbiome interaction during C. difficile infection
This model is described in the article:
Systems Modeling of
Interactions between Mucosal Immunity and the Gut Microbiome
during Clostridium difficile Infection.
Leber A, Viladomiu M, Hontecillas R,
Abedi V, Philipson C, Hoops S, Howard B, Bassaganya-Riera
J.
PLoS ONE 2015; 10(7): e0134849
Abstract:
Clostridium difficile infections are associated with the use
of broad-spectrum antibiotics and result in an exuberant
inflammatory response, leading to nosocomial diarrhea, colitis
and even death. To better understand the dynamics of mucosal
immunity during C. difficile infection from initiation through
expansion to resolution, we built a computational model of the
mucosal immune response to the bacterium. The model was
calibrated using data from a mouse model of C. difficile
infection. The model demonstrates a crucial role of T helper 17
(Th17) effector responses in the colonic lamina propria and
luminal commensal bacteria populations in the clearance of C.
difficile and colonic pathology, whereas regulatory T (Treg)
cells responses are associated with the recovery phase. In
addition, the production of anti-microbial peptides by inflamed
epithelial cells and activated neutrophils in response to C.
difficile infection inhibit the re-growth of beneficial
commensal bacterial species. Computational simulations suggest
that the removal of neutrophil and epithelial cell derived
anti-microbial inhibitions, separately and together, on
commensal bacterial regrowth promote recovery and minimize
colonic inflammatory pathology. Simulation results predict a
decrease in colonic inflammatory markers, such as neutrophilic
influx and Th17 cells in the colonic lamina propria, and length
of infection with accelerated commensal bacteria re-growth
through altered anti-microbial inhibition. Computational
modeling provides novel insights on the therapeutic value of
repopulating the colonic microbiome and inducing regulatory
mucosal immune responses during C. difficile infection. Thus,
modeling mucosal immunity-gut microbiota interactions has the
potential to guide the development of targeted fecal
transplantation therapies in the context of precision medicine
interventions.
This model is hosted on
BioModels Database
and identified by:
BIOMD0000000583.
To cite BioModels Database, please use:
BioModels Database:
An enhanced, curated and annotated resource for published
quantitative kinetic models.
To the extent possible under law, all copyright and related or
neighbouring rights to this encoded model have been dedicated to
the public domain worldwide. Please refer to
CC0
Public Domain Dedication for more information.
2024-09-02 | BIOMD0000000583 | BioModels