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1.
PLoS Comput Biol ; 20(6): e1012208, 2024 Jun.
Article in English | MEDLINE | ID: mdl-38900844

ABSTRACT

The apicomplexan intracellular parasite Toxoplasma gondii is a major food borne pathogen that is highly prevalent in the global population. The majority of the T. gondii proteome remains uncharacterized and the organization of proteins into complexes is unclear. To overcome this knowledge gap, we used a biochemical fractionation strategy to predict interactions by correlation profiling. To overcome the deficit of high-quality training data in non-model organisms, we complemented a supervised machine learning strategy, with an unsupervised approach, based on similarity network fusion. The resulting combined high confidence network, ToxoNet, comprises 2,063 interactions connecting 652 proteins. Clustering identifies 93 protein complexes. We identified clusters enriched in mitochondrial machinery that include previously uncharacterized proteins that likely represent novel adaptations to oxidative phosphorylation. Furthermore, complexes enriched in proteins localized to secretory organelles and the inner membrane complex, predict additional novel components representing novel targets for detailed functional characterization. We present ToxoNet as a publicly available resource with the expectation that it will help drive future hypotheses within the research community.


Subject(s)
Protein Interaction Maps , Protozoan Proteins , Toxoplasma , Toxoplasma/metabolism , Protozoan Proteins/metabolism , Protozoan Proteins/chemistry , Protein Interaction Maps/physiology , Computational Biology , Protein Interaction Mapping/methods , Proteome/metabolism , Databases, Protein , Machine Learning , Cluster Analysis
2.
Mol Biol Evol ; 39(11)2022 11 03.
Article in English | MEDLINE | ID: mdl-36223483

ABSTRACT

Geographically distinct populations can adapt to the temperature conditions of their local environment, leading to temperature-dependent fitness differences between populations. Consistent with local adaptation, phylogeographically distinct Caenorhabditis briggsae nematodes show distinct fitness responses to temperature. The genetic mechanisms underlying local adaptation, however, remain unresolved. To investigate the potential role of small noncoding RNAs in genotype-specific responses to temperature, we quantified small RNA expression using high-throughput sequencing of C. briggsae nematodes from tropical and temperate strain genotypes reared under three temperature conditions (14 °C, 20 °C, and 30 C). Strains representing both tropical and temperate regions showed significantly lower expression of PIWI-interacting RNAs (piRNAs) at high temperatures, primarily mapping to a large ∼7 Mb long piRNA cluster on chromosome IV. We also documented decreased expression of 22G-RNAs antisense to protein-coding genes and other genomic features at high rearing temperatures for the thermally-intolerant temperate strain genotype, but not for the tropical strain genotype. Reduced 22G-RNA expression was widespread along chromosomes and among feature types, indicative of a genome-wide response. Targets of the EGO-1/CSR-1 22G-RNA pathway were most strongly impacted compared with other 22G-RNA pathways, implicating the CSR-1 Argonaute and its RNA-dependent RNA polymerase EGO-1 in the genotype-dependent modulation of C. briggsae 22G-RNAs under chronic thermal stress. Our work suggests that gene regulation via small RNAs may be an important contributor to the evolution of local adaptations.


Subject(s)
Caenorhabditis elegans Proteins , Caenorhabditis , Animals , Caenorhabditis/genetics , Caenorhabditis/metabolism , Caenorhabditis elegans Proteins/genetics , Caenorhabditis elegans/genetics , Temperature , RNA, Small Interfering/genetics , Genetic Background , RNA-Dependent RNA Polymerase
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