Frontiers in Genetics最新文献

{"title":"Optimizing purebred selection to improve crossbred performance.","authors":"Somayeh Barani, Sayed Reza Miraie Ashtiani, Ardeshir Nejati Javaremi, Majid Khansefid, Hadi Esfandyari","doi":"10.3389/fgene.2024.1384973","DOIUrl":"https://doi.org/10.3389/fgene.2024.1384973","url":null,"abstract":"<p><p>Crossbreeding is a widely adopted practice in the livestock industry, leveraging the advantages of heterosis and breed complementarity. The prediction of Crossbred Performance (CP) often relies on Purebred Performance (PB) due to limited crossbred data availability. However, the effective selection of purebred parents for enhancing CP depends on non-additive genetic effects and environmental factors. These factors are encapsulated in the genetic correlation between crossbred and purebred populations ( <math> <mrow><msub><mi>r</mi> <mrow><mi>p</mi> <mi>c</mi></mrow> </msub> </mrow> </math> ). In this study, a two-way crossbreeding simulation was employed to investigate various strategies for integrating data from purebred and crossbred populations. The goal was to identify optimal models that maximize CP across different levels of <math> <mrow><msub><mi>r</mi> <mrow><mi>p</mi> <mi>c</mi></mrow> </msub> </mrow> </math> . Different scenarios involving the selection of genotyped individuals from purebred and crossbred populations were explored using ssGBLUP (single-step Genomic Best Linear Unbiased Prediction) and ssGBLUP-MF (ssGBLUP with metafounders) models. The findings revealed an increase in prediction accuracy across all scenarios as <math> <mrow><msub><mi>r</mi> <mrow><mi>p</mi> <mi>c</mi></mrow> </msub> </mrow> </math> values increased. Notably, in the scenario incorporating genotypes from both purebred parent breeds and their crossbreds, both ssGBLUP and ssGBLUP-MF models exhibited nearly identical predictive accuracy. This scenario achieved maximum accuracy when <math> <mrow><msub><mi>r</mi> <mrow><mi>p</mi> <mi>c</mi></mrow> </msub> </mrow> </math> was less than 0.5. However, at <math> <mrow><msub><mi>r</mi> <mrow><mi>p</mi> <mi>c</mi></mrow> </msub> </mrow> </math> = 0.8, ssGBLUP, which exclusively included sire breed genotypes in the training set, achieved the highest overall prediction accuracy at 73.2%. In comparison, the BLUP-UPG (BLUP with unknown parent group) model demonstrated lower accuracy than ssGBLUP and ssGBLUP-MF across all <math> <mrow><msub><mi>r</mi> <mrow><mi>p</mi> <mi>c</mi></mrow> </msub> </mrow> </math> levels. Although ssGBLUP and ssGBLUP-MF did not demonstrate a definitive trend in their respective scenarios, the prediction ability for CP increased when incorporating both crossbred and purebred population genotypes at lower levels of <math> <mrow> <msub><mrow><mtext> </mtext> <mi>r</mi></mrow> <mrow><mi>p</mi> <mi>c</mi></mrow> </msub> </mrow> </math> . Furthermore, when <math> <mrow><msub><mi>r</mi> <mrow><mi>p</mi> <mi>c</mi></mrow> </msub> </mrow> </math> was high, utilizing paternal genotype for CP predictions emerged as the most effective strategy. Predicted dispersion remained relatively similar in all scenarios, indicating a slight underestimation of breeding values. Overall, the <math> <mrow><msub><mi>r</mi> <mrow><mi>p</mi> <mi>c</mi></mrow> </msub> </mrow> </math> value emerged as a critical factor ","PeriodicalId":12750,"journal":{"name":"Frontiers in Genetics","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11458422/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142389834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Phosphatidylcholine's influence on Dysmenorrhea: conclusive insights from Mendelian randomization analysis.","authors":"Yuzheng Li, Shiyao Zhou, Yuchen Huang, Qiuhao Yu, Qibiao Wu","doi":"10.3389/fgene.2024.1404215","DOIUrl":"https://doi.org/10.3389/fgene.2024.1404215","url":null,"abstract":"<p><strong>Introduction: </strong>This study aimed to investigate the causal relationship between phosphatidylcholine (PC) levels and dysmenorrhea using Mendelian randomization (MR) analysis.</p><p><strong>Methods: </strong>We conducted a two-sample MR analysis using GWAS data on PC levels and dysmenorrhea. Single nucleotide polymorphisms (SNPs) associated with PC levels were used as instrumental variables. MR-Egger regression and inverse variance weighting (IVW) were used to estimate the causal effect of PC levels on dysmenorrhea. Sensitivity analyses were performed to assess the robustness of the results.</p><p><strong>Results: </strong>The IVW analysis revealed a significant positive association between higher PC levels and dysmenorrhea (OR: 1.533, 95% CI: 1.039-2.262, <i>P</i> = 0.031). The MR-Egger regression did not detect pleiotropy. Sensitivity analyses confirmed the robustness of the results.</p><p><strong>Conclusion: </strong>This study provides evidence suggesting a causal link between increased PC levels and dysmenorrhea. Further research is needed to understand the biological mechanisms underlying this relationship and to explore potential therapeutic implications.</p>","PeriodicalId":12750,"journal":{"name":"Frontiers in Genetics","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11456477/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142389836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comprehensive pan-cancer analysis and experiments revealed R3HDM1 as a novel predictive biomarker for prognosis and immune therapy response.","authors":"Jiawei Liu, Zhitong Bing, Junling Wang","doi":"10.3389/fgene.2024.1404348","DOIUrl":"https://doi.org/10.3389/fgene.2024.1404348","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;R3HDM1, an RNA binding protein with one R3H domain, remains uncharacterized in terms of its association with tumor progression, malignant cell regulation, and the tumor immune microenvironment. This paper aims to fill this gap by analyzing the potential of R3HDM1 in diagnosis, prognosis, chemotherapy, and immune function across various cancers.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Data was collected from the Firehost database (http://gdac.broadinstitute.org) to obtain the TCGA pan-cancer queue containing tumor and normal samples. Additional data on miRNA, TCPA, mutations, and clinical information were gathered from the UCSC Xena database (https://xenabrowser.net/datapages/). The mutation frequency and locus of R3HDM1 in the TCGA database were examined using the cBioPortal. External validation through GEO data was conducted to assess the differential expression of R3HDM1 in different cancers. Protein expression levels were evaluated using the Clinical Proteomics Tumor Analysis Alliance (CPTAC). The differential expression of R3HDM1 was verified in lung adenocarcinoma cell lines and normal lung glandular epithelial cells via RT-qPCR. Cell migration and proliferation experiments were conducted by knocking down the expression of R3HDM1 in two lung adenocarcinoma cell lines using small interfering RNA. The biological role of R3HDM1 in pan-cancer was explored using the GSEA method. Multiple immune infiltration algorithms from the TIMER2.0 database was employed to investigate the correlation between R3HDM1 expression and the tumor immune microenvironment. Validation of transcriptome immune infiltration was based on 140 single-cell datasets from the TISCH database. The study also characterized a pan-cancer survival profile and analyzed the differential expression of R3HDM1 in different molecular subtypes. The relationship between R3HDM1 and drug resistance was investigated using four chemotherapy data sources: CellMiner, GDSC, CTRP and PRISM. The impact of chemicals on the expression of R3HDM1 was explored through the CTD database.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Result: &lt;/strong&gt;The study revealed differential expression of R3HDM1 in various tumors, indicating its potential as an early diagnostic marker. Changes in somatic copy number (SCNA) and DNA methylation were identified as factors contributing to abnormal expression levels. Additionally, the study found that R3HDM1 expression is associated with clinical features, metabolic pathways, and important pathways related to metastasis and the immune system. High expression of R3HDM1 was linked to poor prognosis across different tumors and altered drug sensitivity. Furthermore, the expression of R3HDM1 showed significant correlations with immune modulatory molecules and biomarkers of lymphocyte subpopulation infiltration. Finally, the study highlighted four chemicals that could influence the expression of R3HDM1.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusion: &lt;/strong&gt;Overall, this study proposes that R3HDM1 expressi","PeriodicalId":12750,"journal":{"name":"Frontiers in Genetics","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11456529/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142389830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Proficiency testing within Eurotransplant.","authors":"Yvonne M Zoet, Sebastiaan Heidt, Marissa J H van der Linden-van Oevelen, Geert W Haasnoot, Frans H J Claas","doi":"10.3389/fgene.2024.1451748","DOIUrl":"https://doi.org/10.3389/fgene.2024.1451748","url":null,"abstract":"<p><p>Eurotransplant is responsible for the international allocation of organs between eight countries in Europe. All HLA laboratories affiliated to Eurotransplant must be EFI or ASHI-accredited and must participate in the Eurotransplant external proficiency testing (EPT) program, organized by the Eurotransplant Reference Laboratory (ETRL). EPT within Eurotransplant has a long tradition, starting in 1978. The current EPT program consists of the following schemes: HLA typing including serology, CDC crossmatching, HLA-specific antibody detection, and identification. Participants enter the results of laboratory tests using a web-based application. Assessed results are visible on the website. An additional component called \"patient-based cases\" runs since 2016. Results are summarized and published on the EPT website. Furthermore, these results are discussed during the annual extramural tissue typers meeting, which is organized by the ETRL. Thanks to this EPT program, the performance of all HLA laboratories affiliated to Eurotransplant can be monitored and corrected, if necessary. Because all affiliated laboratories are assessed in the same EPT program, where these laboratories show to be consistent in most of their results, Eurotransplant EPT has proven to be an efficient tool to create a more uniform level of quality of histocompatibility testing within Eurotransplant.</p>","PeriodicalId":12750,"journal":{"name":"Frontiers in Genetics","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11456461/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142389837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comprehensive in silico analysis and experimental validation of miRNAs capable of discriminating between lung adenocarcinoma and squamous cell carcinoma.","authors":"Zahra Javanmardifard, Saeid Rahmani, Hadi Bayat, Hanifeh Mirtavoos-Mahyari, Mostafa Ghanei, Seyed Javad Mowla","doi":"10.3389/fgene.2024.1419099","DOIUrl":"https://doi.org/10.3389/fgene.2024.1419099","url":null,"abstract":"<p><strong>Background: </strong>Accurate differentiation between lung adenocarcinoma (AC) and lung squamous cell carcinoma (SCC) is crucial owing to their distinct therapeutic approaches. MicroRNAs (miRNAs) exhibit variable expression across subtypes, making them promising biomarkers for discrimination. This study aimed to identify miRNAs with robust discriminatory potential between AC and SCC and elucidate their clinical significance.</p><p><strong>Methods: </strong>MiRNA expression profiles for AC and SCC patients were obtained from The Cancer Genome Atlas (TCGA) database. Differential expression analysis and supervised machine learning methods (Support Vector Machine, Decision trees and Naïve Bayes) were employed. Clinical significance was assessed through receiver operating characteristic (ROC) curve analysis, survival analysis, and correlation with clinicopathological features. Validation was conducted using reverse transcription quantitative polymerase chain reaction (RT-qPCR). Furthermore, signaling pathway and gene ontology enrichment analyses were conducted to unveil biological functions.</p><p><strong>Results: </strong>Five miRNAs (miR-205-3p, miR-205-5p, miR-944, miR-375 and miR-326) emerged as potential discriminative markers. The combination of miR-944 and miR-326 yielded an impressive area under the curve of 0.985. RT-qPCR validation confirmed their biomarker potential. miR-326 and miR-375 were identified as prognostic factors in AC, while miR-326 and miR-944 correlated significantly with survival outcomes in SCC. Additionally, exploration of signaling pathways implicated their involvement in key pathways including PI3K-Akt, MAPK, FoxO, and Ras.</p><p><strong>Conclusion: </strong>This study enhances our understanding of miRNAs as discriminative markers between AC and SCC, shedding light on their role as prognostic indicators and their association with clinicopathological characteristics. Moreover, it highlights their potential involvement in signaling pathways crucial in non-small cell lung cancer pathogenesis.</p>","PeriodicalId":12750,"journal":{"name":"Frontiers in Genetics","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11460580/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142389829","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unveiling the role of IGF1R in autism spectrum disorder: a multi-omics approach to decipher common pathogenic mechanisms in the IGF signaling pathway.","authors":"Kang Yang, Tian Zhang, Ruize Niu, Liyang Zhao, Zhonghe Cheng, Jun Li, Lifang Wang","doi":"10.3389/fgene.2024.1483574","DOIUrl":"https://doi.org/10.3389/fgene.2024.1483574","url":null,"abstract":"<p><p>Autism spectrum disorder (ASD) is a complex neurodevelopmental condition marked by impairments in social interaction, communication, and repetitive behaviors. Emerging evidence suggests that the insulin-like growth factor (IGF) signaling pathway plays a critical role in ASD pathogenesis; however, the precise pathogenic mechanisms remain elusive. This study utilizes multi-omics approaches to investigate the pathogenic mechanisms of ASD susceptibility genes within the IGF pathway. Whole-exome sequencing (WES) revealed a significant enrichment of rare variants in key IGF signaling components, particularly the IGF receptor 1 (IGF1R), in a cohort of Chinese Han individuals diagnosed with ASD, as well as in ASD patients from the SFARI SPARK WES database. Subsequent single-cell RNA sequencing (scRNA-seq) of cortical tissues from children with ASD demonstrated elevated expression of IGF receptors in parvalbumin (PV) interneurons, suggesting a substantial impact on their development. Notably, IGF1R appears to mediate the effects of IGF2R on these neurons. Additionally, transcriptomic analysis of brain organoids derived from ASD patients indicated a significant association between IGF1R and ASD. Protein-protein interaction (PPI) and gene regulatory network (GRN) analyses further identified ASD susceptibility genes that interact with and regulate IGF1R expression. In conclusion, IGF1R emerges as a central node within the IGF signaling pathway, representing a potential common pathogenic mechanism and therapeutic target for ASD. These findings highlight the need for further investigation into the modulation of this pathway as a strategy for ASD intervention.</p>","PeriodicalId":12750,"journal":{"name":"Frontiers in Genetics","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11456441/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142389840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CSER: a gene regulatory network construction method based on causal strength and ensemble regression.","authors":"Yujia Li, Yang Du, Mingmei Wang, Dongmei Ai","doi":"10.3389/fgene.2024.1481787","DOIUrl":"10.3389/fgene.2024.1481787","url":null,"abstract":"<p><strong>Introduction: </strong>Gene regulatory networks (GRNs) reveal the intricate interactions between and among genes, and understanding these interactions is essential for revealing the molecular mechanisms of cancer. However, existing algorithms for constructing GRNs may confuse regulatory relationships and complicate the determination of network directionality.</p><p><strong>Methods: </strong>We propose a new method to construct GRNs based on causal strength and ensemble regression (CSER) to overcome these issues. CSER uses conditional mutual inclusive information to quantify the causal associations between genes, eliminating indirect regulation and marginal genes. It considers linear and nonlinear features and uses ensemble regression to infer the direction and interaction (activation or regression) from regulatory to target genes.</p><p><strong>Results: </strong>Compared to traditional algorithms, CSER can construct directed networks and infer the type of regulation, thus demonstrating higher accuracy on simulated datasets. Here, using real gene expression data, we applied CSER to construct a colorectal cancer GRN and successfully identified several key regulatory genes closely related to colorectal cancer (CRC), including <i>ADAMDEC1</i>, <i>CLDN8</i>, and <i>GNA11</i>.</p><p><strong>Discussion: </strong>Importantly, by integrating immune cell and microbial data, we revealed the complex interactions between the CRC gene regulatory network and the tumor microenvironment, providing additional new biomarkers and therapeutic targets for the early diagnosis and prognosis of CRC.</p>","PeriodicalId":12750,"journal":{"name":"Frontiers in Genetics","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11449711/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142383299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editorial: Epigenetic mechanisms and their involvement in rare diseases, volume II.","authors":"Mojgan Rastegar","doi":"10.3389/fgene.2024.1483388","DOIUrl":"10.3389/fgene.2024.1483388","url":null,"abstract":"","PeriodicalId":12750,"journal":{"name":"Frontiers in Genetics","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11449842/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142380612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DGDRP: drug-specific gene selection for drug response prediction via re-ranking through propagating and learning biological network.","authors":"Minwoo Pak, Dongmin Bang, Inyoung Sung, Sun Kim, Sunho Lee","doi":"10.3389/fgene.2024.1441558","DOIUrl":"10.3389/fgene.2024.1441558","url":null,"abstract":"<p><p><b>Introduction:</b> Drug response prediction, especially in terms of cell viability prediction, is a well-studied research problem with significant implications for personalized medicine. It enables the identification of the most effective drugs based on individual genetic profiles, aids in selecting potential drug candidates, and helps identify biomarkers that predict drug efficacy and toxicity.A deeper investigation on drug response prediction reveals that drugs exert their effects by targeting specific proteins, which in turn perturb related genes in cascading ways. This perturbation affects cellular pathways and regulatory networks, ultimately influencing the cellular response to the drug. Identifying which genes are perturbed and how they interact can provide critical insights into the mechanisms of drug action. Hence, the problem of predicting drug response can be framed as a dual problem involving both the prediction of drug efficacy and the selection of drug-specific genes. Identifying these drug-specific genes (biomarkers) is crucial because they serve as indicators of how the drug will affect the biological system, thereby facilitating both drug response prediction and biomarker discovery.<b>Methods:</b> In this study, we propose DGDRP (Drug-specific Gene selection for Drug Response Prediction), a graph neural network (GNN)-based model that uses a novel rank-and-re-rank process for drug-specific gene selection. DGDRP first ranks genes using a pathway knowledge-enhanced network propagation algorithm based on drug target information, ensuring biological relevance. It then re-ranks genes based on the similarity between gene and drug target embeddings learned from the GNN, incorporating semantic relationships. Thus, our model adaptively learns to select drug mechanism-associated genes that contribute to drug response prediction. This integrated approach not only improves drug response predictions compared to other gene selection methods but also allows for effective biomarker discovery.<b>Discussion:</b> As a result, our approach demonstrates improved drug response predictions compared to other gene selection methods and demonstrates comparability with state-of-the-art deep learning models. Case studies further support our method by showing alignment of selected gene sets with the mechanisms of action of input drugs.<b>Conclusion:</b> Overall, DGDRP represents a deep learning based re-ranking strategy, offering a robust gene selection framework for more accurate drug response prediction. The source code for DGDRP can be found at: https://github.com/minwoopak/heteronet.</p>","PeriodicalId":12750,"journal":{"name":"Frontiers in Genetics","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11450864/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142380611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Genomic analysis and mechanisms exploration of a stress tolerance and high-yield pullulan producing strain.","authors":"Jing Yang, Ning Sun, Wenru Wang, Ruihua Zhang, Siqi Sun, Biqi Li, Yue Shi, Junfeng Zeng, Shulei Jia","doi":"10.3389/fgene.2024.1469600","DOIUrl":"10.3389/fgene.2024.1469600","url":null,"abstract":"<p><p>Pullulan is a kind of natural polymer, which is widely used in medicine and food because of its solubility, plasticity, edible, non-toxicity and good biocompatibility. It is of great significance to improve the yield of pullulan by genetic modification of microorganisms. It was previously reported that <i>Aureobasidium melanogenum</i> TN3-1 isolated from honey-comb could produce high-yield of pullulan, but the molecular mechanisms of its production of pullulan had not been completely solved. In this study, the reported strains of <i>Aureobasidium</i> spp. were further compared and analyzed at genome level. It was found that genome duplication and genome genetic variations might be the crucial factors for the high yield of pullulan and stress resistance. This particular phenotype may be the result of adaptive evolution, which can adapt to its environment through genetic variation and adaptive selection. In addition, the TN3-1 strain has a large genome, and the special regulatory sequences of its specific genes and promoters may ensure a unique characteristics. This study is a supplement of the previous studies, and provides basic data for the research of microbial genome modification in food and healthcare applications.</p>","PeriodicalId":12750,"journal":{"name":"Frontiers in Genetics","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11449735/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142380613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}