Ask about this productRelated genes to: GTPBP5 antibody
- Gene:
- MTG2 NIH gene
- Name:
- mitochondrial ribosome associated GTPase 2
- Previous symbol:
- GTPBP5
- Synonyms:
- FLJ10741, dJ1005F21.2, ObgH1
- Chromosome:
- 20q13.33
- Locus Type:
- gene with protein product
- Date approved:
- 2001-07-17
- Date modifiied:
- 2016-04-05
Related products to: GTPBP5 antibody
Related articles to: GTPBP5 antibody
- Mitochondrial dysfunction is increasingly recognized as a pivotal factor in cancer pathogenesis. We thus explored the causal role of mitochondrial-related genes (MRGs) in uveal melanoma (UM) and the underlying mechanisms. - Source: PubMed
Zhang YanMin DaliuWang YonggangYu WenxiCui BowenSu YuyingShen ZanFeng Changzhou - The Saccharomyces cerevisiae mitoribosome synthesizes eight mitochondrial DNA-encoded proteins essential for oxidative phosphorylation. Mitoribosome large subunit (mtLSU) biogenesis involves the conserved DEAD-box helicase Mrh4 and the GTPases Mtg1/GTPBP7 and Mtg2/GTPBP5. Here, we have employed genetic, biochemical, in vitro reconstitution, and cryo-EM approaches to elucidate their hierarchical action during the late stages of mtLSU assembly. We show that Mrh4-mediated bL33m incorporation precedes Mtg1 recruitment to the 21S rRNA. Cryo-EM structures of mitoribosome assembly intermediates accumulating in the absence of Mtg1 or uL16m reveal that Mtg1 restructures the 21S rRNA H73-75 and H93 domains to their mature fold. This subsequently allows the structuring of neighboring peptidyl transfer center region helices and the incorporation of uL6m, uL16m, bL35m, and bL36m during late mtLSU maturation. Unexpectedly, monosomes containing immature mtLSU assemble in Mrh4-, bL33m-, uL16m-, Mtg1-, and Mtg2-depleted mitochondria, at levels that increase with the maturation state of the mtLSU particle. Our data have shed light on the rRNA folding events and the structuring of the MRPs that occur during the late stages of assembly. They have provided insight into the roles of assembly factors Mrh4, Mtg1, and Mtg2 during the process and revealed evolutionarily conserved mechanisms underlying mitochondrial ribosome assembly. - Source: PubMed
Publication date: 2025/08/25
Rathore SorbhiConrad JulianDe Silva DasmanthieFerrari AlbertoBouquio DanielleKim Hyung-JunSalvatori RogerLinden AndreasDybkov OlexandrUrlaub HenningOtt MartinBarrientos Antoni - Esophageal cancer ranks as the 11th most diagnosed cancer worldwide and the 7th leading cause of cancer-related deaths, mainly due to late-stage diagnosis. Identifying novel biomarkers is essential for enhancing prognostic evaluations and targeting patients for immunotherapy. - Source: PubMed
Publication date: 2025/06/22
Yao XiangrongHe JunyanXiao WentaoChen LimouXiao Fangzhu - This study estimated the heritabilities (h) and genetic and phenotypic correlations between reproductive traits, including calving interval (CI), age at first calving (AFC), gestation length (GL), number of artificial inseminations per conception (NAIPC), and carcass traits, including carcass weight (CWT), eye muscle area (EMA), backfat thickness (BF), and marbling score (MS) in Korean Hanwoo cows. In addition, the accuracy of genomic predictions of breeding values was evaluated by applying the genomic best linear unbiased prediction (GBLUP) and the weighted GBLUP (WGBLUP) method. The phenotypic data for reproductive and carcass traits were collected from 1,544 Hanwoo cows, and all animals were genotyped using Illumina Bovine 50K single nucleotide polymorphism (SNP) chip. The genetic parameters were estimated using a multi-trait animal model using the MTG2 program. The estimated h for CI, AFC, GL, NAIPC, CWT, EMA, BF, and MS were 0.10, 0.13, 0.17, 0.11, 0.37, 0.35, 0.27, and 0.45, respectively, according to the GBLUP model. The GBLUP accuracy estimates ranged from 0.51 to 0.74, while the WGBLUP accuracy estimates for the traits under study ranged from 0.51 to 0.79. Strong and favorable genetic correlations were observed between GL and NAIPC (0.61), CWT and EMA (0.60), NAIPC and CWT (0.49), AFC and CWT (0.48), CI and GL (0.36), BF and MS (0.35), NAIPC and EMA (0.35), CI and BF (0.30), EMA and MS (0.28), CI and AFC (0.26), AFC and EMA (0.24), and AFC and BF (0.21). The present study identified low to moderate positive genetic correlations between reproductive and CWT traits, suggesting that a heavier body weight may lead to a longer CI, AFC, GL, and NAIPC. The moderately positive genetic correlation between CWT and AFC, and NAIPC, with a phenotypic correlation of nearly zero, suggesting that the genotype-environment interactions are more likely to be responsible for the phenotypic manifestation of these traits. As a result, the inclusion of these traits by breeders as selection criteria may present a good opportunity for developing a selection index to increase the response to the selection and identification of candidate animals, which can result in significantly increased profitability of production systems. - Source: PubMed
Publication date: 2024/07/31
Haque Md AzizulIqbal AsifAlam Mohammad ZahangirLee Yun-MiHa Jae-JungKim Jong-Joo - Artificial insemination plays a crucial role in pig production, particularly in enhancing the genetic potential of elite boars. To accelerate genetic progress for semen traits in pigs, it is vital to understand and identify the underlying genetic markers associated with desirable traits. Herein, we genotyped 1238 Landrace boars with GeneSeek Porcine SNP50 K Bead chip and conducted genome-wide association studies to identify genetic regions and candidate genes associated with 12 semen traits. Our study identified 38 SNPs associated with the analyzed 12 semen traits. Furthermore, we identified several promising candidate genes, including , , , , , , and . These candidate genes have the potential function to facilitate the breeding of boars with improved semen traits. By further investigating and understanding the roles of these genes, we can develop more effective breeding strategies that contribute to the overall enhancement of pig production. The results of our study provide valuable insights for the pig-breeding industry and support ongoing research efforts to optimize genetic selection for superior semen traits. - Source: PubMed
Publication date: 2024/06/21
Zhuang ZhanweiLi KebiaoYang KaiGao GuangxiongLi ZhiliZhu XiaopingZhao Yunxiang