G3BP orthologs interactional network
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo Sapiens Drosophila Melanogaster Caenorhabditis Elegans
SUBMITTER: 
Peiguo Yang
PROVIDER: PXD054692 | iProX | Thu Aug 08 00:00:00 BST 2024
REPOSITORIES: iProX
Publications
The divergent effects of G3BP orthologs on human stress granule assembly imply a centric role for the core protein interaction network.
Yao Zhiying Z Liu Yi Y Chen Qi Q Chen Xiaoxin X Zhu Zhenshuo Z Song Sha S Ma Xianjue X Yang Peiguo P
Cell reports 20240808 8
Liquid-liquid phase separation (LLPS) mediated by G3BP1/2 proteins and non-translating mRNAs mediates stress granule (SG) assembly. We investigated the phylogenetic evolution of G3BP orthologs from unicellular yeast to mammals and identified both conserved and divergent features. The modular domain organization of G3BP orthologs is generally conserved. However, invertebrate orthologs displayed reduced capacity for SG assembly in human cells compared to vertebrate orthologs. We demonstrated that ...[more]
Similar Datasets
Project description:Translating findings from animal models to human disease is essential for dissecting disease mechanisms, developing and testing precise therapeutic strategies. The coronavirus disease 2019 (COVID-19) pandemic has highlighted this need, particularly for models showing disease severity-dependent immune responses. Single-cell transcriptomics (scRNAseq) is well poised to reveal similarities and differences between species at the molecular and cellular level with unprecedented resolution. However, computational methods enabling detailed matching are still scarce. Here, we provide a structured scRNAseq-based approach that we applied to scRNAseq from blood leukocytes originating from humans and hamsters affected with moderate or severe COVID-19. Integration of COVID-19 patient data with two hamster models that develop moderate (Syrian hamster, Mesocricetus auratus) or severe (Roborovski hamster, Phodopus roborovskii) disease revealed that most cellular states are shared across species. A neural network-based analysis using variational autoencoders quantified the overall transcriptomic similarity across species and severity levels, showing highest similarity between neutrophils of Roborovski hamsters and severe COVID-19 patients, while Syrian hamsters better matched patients with moderate disease, particularly in classical monocytes. We further used transcriptome-wide differential expression analysis to identify which disease stages and cell types display strongest transcriptional changes. Consistently, hamster’s response to COVID-19 was most similar to humans in monocytes and neutrophils. Disease-linked pathways found in all species specifically related to interferon response or inhibition of viral replication. Analysis of candidate genes and signatures supported the results. Our structured neural network-supported workflow could be applied to other diseases, allowing better identification of suitable animal models with similar pathomechanisms across species.
2024-12-13 | GSE253845 | GEO
Project description:In order to best meet the needs of all those affected by the genetic risk of cancer in our region, it is important to identify the factors likely to influence the course leading to the GENEPY surveillance network. The aim of this study is to evaluatie the adhesion to the network of care of people at genetic risk of cancer in Midi-Pyrénées (GENEPY).
| 2307830 | ecrin-mdr-crc
Project description:Species-specific transcriptomic network inference of interspecies interactions
| PRJNA450096 | ENA
Project description:The Marathon of Hope Cancer Centres Network (MOHCCN) is a national network of cancer centres that pursue collaborative cancer research in precision medicine (an emerging approach for disease treatment and prevention that considers individual variability in DNA, environment and lifestyle) to accelerate the discovery of innovations and improve the health outcomes for cancer patients
| 2733054 | ecrin-mdr-crc
Project description:High-throughput measurement technologies produce data sets that have the potential to elucidate the biological impact of disease, drug treatment, and environmental agents on humans. The scientific community faces an ongoing challenge in the analysis of these rich data sources to more accurately characterize biological processes that have been perturbed at the mechanistic level. Here, a new approach is built on previous methodologies in which high-throughput data was interpreted using prior biological knowledge of cause and effect relationships. These relationships are structured into network models that describe specific biological processes, such as inflammatory signaling or cell cycle progression. This enables quantitative assessment of network perturbation in response to a given stimulus.<br>Four complementary methods were devised to quantify treatment-induced activity changes in processes described by network models. In addition, companion statistics were developed to qualify significance and specificity of the results. This approach is called Network Perturbation Amplitude (NPA) scoring because the amplitudes of treatment-induced perturbations are computed for biological network models. The NPA methods were tested on two transcriptomic data sets: normal human bronchial epithelial (NHBE) cells treated with the pro-inflammatory signaling mediator TNFa, and HCT116 colon cancer cells treated with the CDK cell cycle inhibitor R547. Each data set was scored against network models representing different aspects of inflammatory signaling and cell cycle progression, and these scores were compared with independent measures of pathway activity in NHBE cells to verify the approach. The NPA scoring method successfully quantified the amplitude of TNFa-induced perturbation for each network model when compared against NF-kB nuclear localization and cell number. In addition, the degree and specificity to which CDK-inhibition affected cell cycle and inflammatory signaling were meaningfully determined.<br>The NPA scoring method leverages high-throughput measurements and a priori literature-derived knowledge in the form of network models to characterize the activity change for a broad collection of biological processes at high-resolution. Applications of this framework include comparative assessment of the biological impact caused by environmental factors, toxic substances, or drug treatments.
2012-04-15 | E-MTAB-1027 | biostudies-arrayexpress
Project description:High-throughput measurement technologies produce data sets that have the potential to elucidate the biological impact of disease, drug treatment, and environmental agents on humans. The scientific community faces an ongoing challenge in the analysis of these rich data sources to more accurately characterize biological processes that have been perturbed at the mechanistic level. Here, a new approach is built on previous methodologies in which high-throughput data was interpreted using prior biological knowledge of cause and effect relationships. These relationships are structured into network models that describe specific biological processes, such as inflammatory signaling or cell cycle progression. This enables quantitative assessment of network perturbation in response to a given stimulus. Four complementary methods were devised to quantify treatment-induced activity changes in processes described by network models. In addition, companion statistics were developed to qualify significance and specificity of the results. This approach is called Network Perturbation Amplitude (NPA) scoring because the amplitudes of treatment-induced perturbations are computed for biological network models. The NPA methods were tested on two transcriptomic data sets: normal human bronchial epithelial (NHBE) cells treated with the pro-inflammatory signaling mediator TNFa, and HCT116 colon cancer cells treated with the CDK cell cycle inhibitor R547. Each data set was scored against network models representing different aspects of inflammatory signaling and cell cycle progression, and these scores were compared with independent measures of pathway activity in NHBE cells to verify the approach. The NPA scoring method successfully quantified the amplitude of TNFa-induced perturbation for each network model when compared against NF-kB nuclear localization and cell number. In addition, the degree and specificity to which CDK-inhibition affected cell cycle and inflammatory signaling were meaningfully determined. The NPA scoring method leverages high-throughput measurements and a priori literature-derived knowledge in the form of network models to characterize the activity change for a broad collection of biological processes at high-resolution. Applications of this framework include comparative assessment of the biological impact caused by environmental factors, toxic substances, or drug treatments.
2012-12-15 | E-MTAB-1311 | biostudies-arrayexpress
Project description:It has been reported that Cryptosporidium parvum, a species of a protozoan frequently isolated from humans and animals, is able to induce digestive adenocarcinoma in a rodent model. Consistently, some epidemiological studies have reported an association with cryptosporidiosis in patients with colorectal adenocarcinoma. However, the correlation between cryptosporidiosis and human digestive cancer remains unclear at this time, and it is not known whether this intracellular parasite, considered an opportunistic agent, is able to induce gastrointestinal malignancies in humans. In order to add new arguments for a probable association between cryptosporidiosis and digestive human cancer, the main aim of this study is to determine prevalence and to identify species of Cryptosporidium among a French digestive cancer population.
| 2334959 | ecrin-mdr-crc
Project description:A network of co-hepato/pancreatic stem/progenitors exists in pigs and humans in Brunner’s Glands (BGs) in the submucosa of the duodenum, in peribiliary glands (PBGs) of intrahepatic and extrahepatic biliary trees, and in pancreatic duct glands (PDGs) of intrapancreatic biliary trees, collectively supporting hepatic and pancreatic regeneration postnatally. The network is found in humans postnatally throughout life and, so far, has been demonstrated in pigs postnatally at least through to young adults. These stem/progenitors in vivo in pigs are in highest numbers in BGs and in PDGS nearest the duodenum, and in humans are in BGs and in PBGs in the hepato/pancreatic common duct, a duct missing postnatally in pigs. Elsewhere in PDGs in pigs and in all PDGs in humans are only committed unipotent or bipotent progenitors.
2023-08-02 | GSE229270 | GEO
Project description:Transcriptional plasticity is a major driver of phenotypic differences between species. The lower temperature limit (LTL), namely the lower end of survival temperature, is an important trait delimiting the geographical distribution of a species, however, the genetic mechanisms are poorly understood. We investigated the inter-species transcriptional diversification in cold responses between zebrafish Danio rerio and tilapia Oreochromis niloticus, which were reared at a common temperature (28°C) but have distinct LTLs. We identified significant expressional divergence between the two species in the orthologous genes from gills when the temperature cooled to the LTL of tilapia (8°C). Five KEGG pathways were found sequentially over-represented in the zebrafish/tilapia divergently expressed genes in the duration (12 hour) of 8°C exposure, forming a signaling cascade from metabolic regulation to apoptosis via FoxO signaling. Consistently, we found differential progression of apoptosis in the gills of the two species in which zebrafish manifested a delayed and milder apoptotic phenotype than tilapia, corresponding with a lower LTL of zebrafish. We identified diverged expression in 25 apoptosis-related transcription factors between the two species which forms an interacting network with diverged factors involving the FoxO signaling and metabolic regulation. We propose a genetic network which regulates LTL in fishes. Examination of gene expressional divergence in gill between zebrafish and tilapia
2016-07-03 | E-GEOD-69965 | biostudies-arrayexpress
Project description:C4 photosynthesis was evolved from ancestral C3 photosynthesis by recruited pre-existed genes to perform new functions. Enzymes and transporters required for C4 metabolic pathway has been well documented, however, transcriptional factors (TFs) that regulate those C4 metabolic genes is poorly understood, in particular, how the TF regulatory network of C4 metabolic genes was re-wired, and the involved metabolic functions of those TFs along the evolution of C4 photosynthesis remained unknown. Here, by using RNA-Seq data from growth condition that reported to have effect on C4 photosynthesis, we constructed the TF regulatory network for four evolutionarily closely related species in the genus Flaveria, which represent different stages of the evolution of C4 photosynthesis, namely, C3, type I C3-C4, type II C3-C4 and C4. Our results show that four TFs are conserved along the evolution whose function either relate to stress response or light response. TFs regulating C4 core genes in C3 species involved in functions belong to RNA regulation and nitrogen metabolism, and that in both intermediate species and C4 species involved in photosynthesis and light responsiveness. Moreover, the TF-network of C4 core metabolic genes has the highest network density in type I C3-C4 species and C4 species when consider the fragment of TF-regulatory network that up-regulated under low CO2, suggesting that TFs regulating C4 genes were recruited to photosynthesis at type I C3-C4 both in involved functions and network density. Our results provide a valuable resource for studying molecular regulatory mechanisms underlying C4 metabolic process.
2020-07-28 | GSE137938 | GEO