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92
datasets available to search
ShareScore release 0.9.0
Dataset results
92 results for “host pathogen interaction”
Malaria Host Pathogen Interaction Center Experiment 03: Host and parasite gene transcript abundance measures from whole blood for Macaca mulatta infected with Plasmodium coatneyi Hackeri strain from 7
GEO Series GSE103259. Macaca mulatta; Plasmodium coatneyi. 35 samples. Type: Expression profiling by high throughput sequencing.
Combined transcriptional profiling during systemic candidiasis reveals organ-specific host-pathogen interactions [mouse tissues 8 h]
GEO Series GSE83681. Mus musculus. 9 samples. Type: Expression profiling by array.
Spaceflight analogue culture enhances the host-pathogen interaction between Salmonella and a 3-D biomimetic intestinal co-culture model
GEO Series GSE146347. Salmonella enterica subsp. enterica serovar Typhimurium; Homo sapiens. 72 samples. Type: Expression profiling by high throughput sequencing.
Host-pathogen genetic interactions underlie tuberculosis susceptibility in genetically diverse mice
GEO Series GSE164156. Mycobacterium tuberculosis H37Rv. 123 samples. Type: Other.
Malaria Host Pathogen Interaction Center Experiment 13: Gene and exon transcript abundances of uninfected Macaca mulatta treated with pyrimethamine over 7 time points in a 100 day study
GEO Series GSE58340. Macaca mulatta. 70 samples. Type: Expression profiling by high throughput sequencing.
MicroRNAs from saliva of anopheline mosquitoes mimic human endogenous miRNAs and may contribute to vector-host-pathogen interactions
GEO Series GSE120658. Anopheles coluzzii. 14 samples. Type: Non-coding RNA profiling by high throughput sequencing.
Dual RNA-Sequencing of Vitis vinifera During Lasiodiplodia theobromae Infection Unveils Host-Pathogen Interactions
GEO Series GSE129109. Lasiodiplodia theobromae; Vitis vinifera. 24 samples. Type: Expression profiling by high throughput sequencing.
Host-Pathogen Interactions in the Plasmodium-Infected Mouse Liver at Spatial and Single-Cell Resolution (Spatial Transcriptomics 2k)
GEO Series GSE268018. Plasmodium berghei ANKA; Mus musculus. 38 samples. Type: Expression profiling by high throughput sequencing.
Expression of the host and pathogen genes during sugarcane - S. scitamineum interaction
GEO Series GSE140801. Saccharum sp. complex hybrid. 24 samples. Type: Expression profiling by array.
Combined transcriptional profiling during systemic candidiasis reveals organ-specific host-pathogen interactions [mouse tissues 12,24,72 h]
GEO Series GSE83680. Mus musculus. 54 samples. Type: Expression profiling by array.
Malaria Host Pathogen Interaction Center Experiment S01: Host and parasite gene transcript abundances, from cultured cells, of Macaca mulatta infected with Plasmodium knowlesi Pk1(A+), Pk1(B+)1+, Pk1(
GEO Series GSE110970. Macaca mulatta; Plasmodium knowlesi. 9 samples. Type: Expression profiling by high throughput sequencing.
Host-pathogen interaction profiling of nontypeable Haemophilus influenzae and Moraxella catarrhalis coinfection of bronchial epithelial cells.
GEO Series GSE283527. Homo sapiens; Moraxella catarrhalis; Haemophilus influenzae. 48 samples. Type: Expression profiling by high throughput sequencing.
Data from: The impact of bottlenecks on microbial survival, adaptation and phenotypic switching in host-pathogen interactions
Microbial pathogens and viruses can often maintain sufficient population diversity to evade a wide range of host immune responses. However, when populations experience bottlenecks, as occurs frequently during initiation of new infections, pathogens require specialized mechanisms to regenerate diversity. We address the evolution of such mechanisms, known as stochastic phenotype switches, which are prevalent in pathogenic bacteria. We analyze a model of pathogen diversification in a changing host environment that accounts for selective bottlenecks, wherein different phenotypes have distinct transmission probabilities between hosts. We show that under stringent bottlenecks, such that only one phenotype can initiate new infections, there exists a threshold stochastic switching rate below which all pathogen lineages go extinct, and above which survival is a near certainty. We determine how quickly stochastic switching rates can evolve by computing a fitness landscape for the evolutionary dynamics of switching rates, and analyzing its dependence on both the stringency of bottlenecks and the duration of within-host growth periods. We show that increasing the stringency of bottlenecks or decreasing the period of growth results in faster adaptation of switching rates. Our model provides strong theoretical evidence that bottlenecks play a critical role in accelerating the evolutionary dynamics of pathogens.
Data from: Phenotypic interactions between tree hosts and invasive forest pathogens in the light of globalization and climate change
Invasive pathogens can cause considerable damage to forest ecosystems. Lack of coevolution is generally thought to enable invasive pathogens to bypass the defence and/or recognition systems in the host. Although mostly true, this argument fails to predict intermittent outcomes in space and time, underlining the need to include the roles of the environment and the phenotype in host–pathogen interactions when predicting disease impacts. We emphasize the need to consider host–tree imbalances from a phenotypic perspective, considering the lack of coevolutionary and evolutionary history with the pathogen and the environment, respectively. We describe how phenotypic plasticity and plastic responses to environmental shifts may become maladaptive when hosts are faced with novel pathogens. The lack of host–pathogen and environmental coevolution are aligned with two global processes currently driving forest damage: globalization and climate change, respectively. We suggest that globalization and climate change act synergistically, increasing the chances of both genotypic and phenotypic imbalances. Short moves on the same continent are more likely to be in balance than if the move is from another part of the world. We use Gremmeniella abietina outbreaks in Sweden to exemplify how host–pathogen phenotypic interactions can help to predict the impacts of specific invasive and emergent diseases. This article is part of the themed issue 'Tackling emerging fungal threats to animal health, food security and ecosystem resilience'.
Data from: Defining host–pathogen interactions employing an artificial intelligence workflow
For image-based infection biology, accurate unbiased quantification of host–pathogen interactions is essential, yet often performed manually or using limited enumeration employing simple image analysis algorithms based on image segmentation. Host protein recruitment to pathogens is often refractory to accurate automated assessment due to its heterogeneous nature. An intuitive intelligent image analysis program to assess host protein recruitment within general cellular pathogen defense is lacking. We present HRMAn (Host Response to Microbe Analysis), an open-source image analysis platform based on machine learning algorithms and deep learning. We show that HRMAn has the capacity to learn phenotypes from the data, without relying on researcher-based assumptions. Using Toxoplasma gondii and Salmonella enterica Typhimurium we demonstrate HRMAn's capacity to recognize, classify and quantify pathogen killing, replication and cellular defense responses. HRMAn thus presents the only intelligent solution operating at human capacity suitable for both single image and high content image analysis.
Host-pathogen Interactions During SARS-CoV-2 Infection
ClinicalTrials.gov study NCT04376476. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Modeling Host-Pathogen Interaction Using Lymphoid Organoids
ClinicalTrials.gov study NCT06479837. IPD Sharing: NO. Countries: 1. Publications: 0.
Host-pathogen Interaction in Otitis Media
ClinicalTrials.gov study NCT00847756. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Environment, Pathogens, and Host Interactions in Melioidosis
ClinicalTrials.gov study NCT07345910. IPD Sharing: YES. Countries: 3. Publications: 0.
Host-pathogen Interactions in Meningococcal Disease: Finding the Key That Fits the Lock
ClinicalTrials.gov study NCT02727465. IPD Sharing: NO. Countries: 1. Publications: 0.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.