Frontiers in Genetics最新文献

Background: Gene expression is abnormal in disease compared to normal tissue same as the regulatory elements. Regulatory element binding with transcription factors managed transcription of gene, which usually require chromatin accessible.

Methods: To reveal potential epigenetic mechanism during hydronephrosis, we first used RNA-seq to finger out the disfunction genes during hydronephrosis, then combined with ATAC-seq, and BS-seq to reveal the related disfunction regulatory elements.

Results: Finally, we find that 860 differentially genes and 2429 dynamic chromatin open regions between normal and hydronephrosis tissue. Though, most of disfunction genes and regulatory elements significantly enriched in chronic kidney disease GO term, only small part of regulatory element target genes overlapped with truly disfunction genes. And we also find out an important gene OTUD6B, which overexpression in disease tissue is manipulated by distal regulatory element through chromatin loop, and confirm the importance of epigenetic mechanism in disease.

Conclusion: In summary, we found many hub genes and potential therapeutic target during hydronephrosis, and also confirmed that epigenetic play important role in gene expression and relevant in disease progress.

In contemporary breeding programs, typically genomic best linear unbiased prediction (gBLUP) models are employed to drive decisions on artificial selection. Experiments are performed to obtain responses on the units in the breeding program. Due to restrictions on the size of the experiment, an efficient experimental design must usually be found in order to optimize the training population. Classical exchange-type algorithms from optimal design theory can be employed for this purpose. This article suggests several variants for the gBLUP model and compares them to brute-force approaches from the genomics literature for various design criteria. Particular emphasis is placed on evaluating the computational runtime of algorithms along with their respective efficiencies over different sample sizes. We find that adapting classical algorithms from optimal design of experiments can help to decrease runtime, while maintaining efficiency.

Introduction: Lipid accumulation in the liver can negatively impact liver function and health, which is well-described for humans and other mammals, but relatively unexplored in Atlantic salmon. This study investigates the phenotypic, genetic, and transcriptomic variations related to individual differences in liver fat content within a group of slaughter-sized Atlantic salmon reared under the same conditions and fed the same feed. The objective was to increase the knowledge on liver fat deposition in farmed salmon and evaluate the potential for genetic improvement of this trait.

Methods: The study involved measuring liver fat content in a group of slaughter-sized Atlantic salmon. Genetic analysis included estimating heritability and conducting genome-wide association studies (GWAS) to identify quantitative trait loci (QTLs). Transcriptomic analysis was performed to link liver fat content to gene expression, focusing on genes involved in lipid metabolic processes.

Results: There was a large variation in liver fat content, ranging from 3.6% to 18.8%, with frequent occurrences of high liver fat. Livers with higher levels of fat had higher proportions of the fatty acids 16:1 n-7, 18:2 n-6, and 18:1 n-9, and less of the long-chain omega-3 fatty acids. The heritability of liver fat was estimated at 0.38, and the genetic coefficient of variation was 20%, indicating substantial potential for selective breeding to reduce liver fat deposition in Atlantic salmon. Liver fat deposition appears to be a polygenic trait, with no large QTLs detected by GWAS. Gene expression analysis linked liver fat content to numerous genes involved in lipid metabolic processes, including key transcription factors such as LXR, SREBP1, and ChREBP.

Discussion: The results indicated a connection between liver fat and increased cholesterol synthesis in Atlantic salmon, with potentially harmful free cholesterol accumulation. Further, the gene expression results linked liver fat accumulation to reduced peroxisomal β-oxidation, increased conversion of carbohydrates to lipids, altered phospholipid synthesis, and possibly increased de novo lipogenesis. It is undetermined whether these outcomes are due to high fat levels or if they are caused by underlying metabolic differences that result in higher liver fat levels in certain individuals. Nonetheless, the results provide new insights into the metabolic profile of livers in fish with inherent differences in liver fat content.

Introduction: Appendiceal mucinous neoplasms (AMN) are rare tumors of the gastrointestinal tract. They metastasize with widespread abdominal dissemination leading to pseudomyxoma peritonei (PMP), a disease with poor prognosis. There are many unknowns about the cellular features of origin, differentiation and progression of AMN and PMP.

Methods: We characterized AMNs, PMPs and matched normal tissues using single-cell RNA-sequencing. We validated our findings with immunohistochemistry, mass spectrometry on malignant ascites from PMP patients and gene expression data from an independent set of PMP tumors.

Results: We identified previously undescribed cellular features and heterogeneity in AMN and PMP tumors. There were gene expression signatures specific to the tumor epithelial cells among AMN and PMP. These signatures included genes indicative of goblet cell differentiation and elevated mucin gene expression. Metastatic PMP cells had a distinct gene expression signature with increased lipid metabolism, inflammatory, JAK-STAT and RAS signaling pathway among others. We observed clonal heterogeneity in a single PMP tumor as well as PMP metastases from the same patient.

Discussion: Our study defined tumor cell gene signatures of AMN and PMP, successfully overcoming challenges of low cellularity and mucinous composition of these tumors. These gene expression signatures provide insights on tumor origin and differentiation, together with the identification of novel treatment targets. The heterogeneity observed within an individual tumor and between different tumors from the same patient, represents a potential source of treatment resistance.

In complex and diverse environments, plants face constant challenges from various pathogens, including fungi, bacteria, and viruses, which can severely impact their growth, development, and survival. Mosses, representing early divergent lineages of land plants, lack traditional vascular systems yet demonstrate remarkable adaptability across diverse habitats. While sharing the fundamental innate immune systems common to all land plants, mosses have evolved distinct chemical and physical defense mechanisms. Notably, they exhibit resistance to many pathogens that typically affect vascular plants. Their evolutionary significance, relatively simple morphology, and well-conserved defense mechanisms make mosses excellent model organisms for studying plant-pathogen interactions. This article reviews current research on moss-pathogen interactions, examining host-pathogen specificity, characterizing infection phenotypes and physiological responses, and comparing pathogen susceptibility and defense mechanisms between mosses and angiosperms. Through this analysis, we aim to deepen our understanding of plant immune system evolution and potentially inform innovative approaches to enhancing crop disease resistance.

Recent studies indicate that microorganisms are crucial for maintaining human health. Dysbiosis, or an imbalance in these microbial communities, is strongly linked to a variety of human diseases. Therefore, understanding the impact of microbes on disease is essential. The DuGEL model leverages the strengths of graph convolutional neural network (GCN) and graph attention network (GAT), ensuring that both local and global relationships within the microbe-disease association network are captured. The integration of the Long Short-Term Memory Network (LSTM) further enhances the model's ability to understand sequential dependencies in the feature representations. This comprehensive approach allows DuGEL to achieve a high level of accuracy in predicting potential microbe-disease associations, making it a valuable tool for biomedical research and the discovery of new therapeutic targets. By combining advanced graph-based and sequence-based learning techniques, DuGEL addresses the limitations of existing methods and provides a robust framework for the prediction of microbe-disease associations. To evaluate the performance of DuGEL, we conducted comprehensive comparative experiments and case studies based on two databases, HMDAD, and Disbiome to demonstrate that DuGEL can effectively predict potential microbe-disease associations.

Stochastic simulation software, ADAM, has been developed for the purpose of breeding optimization in animals and plants, and for validation of statistical models used in genetic evaluations. Just like other common simulation programs, ADAM assumed the bi-allelic state of quantitative trait locus (QTL). While the bi-allelic state of marker loci is due to the common choice of genotyping technology of single nucleotide polymorphism (SNP) chip, the assumption may not hold for the linked QTL. In the version of ADAM-Multi, we employ a novel simulation model capable of simulating additive, dominance, and epistatic genotypic effects for species with different levels of ploidy, providing with a more realistic assumption of multiple allelism for QTL variants. When assuming bi-allelic QTL, our proposed model becomes identical to the model assumption in common simulation programs, and in genetic textbooks. Along with the description of the updated simulation model in ADAM-Multi, this paper shows two small-scale studies that investigate the effects of multi-allelic versus bi-allelic assumptions in simulation and the use of different prediction models in a single-population breeding program for potatoes. We found that genomic models using dense bi-allelic markers could effectively predicted breeding values of individuals in a well-structure population despite the presence of multi-allelic QTL. Additionally, the small-scale study indicated that including non-additive genetic effects in the prediction model for selection did not lead to an improvement in the rate of genetic gains of the breeding program.