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In recent years, statistics and machine learning methods have been widely used to analyze the relationship between human gut microbial metagenome and metabolic diseases, which is of great significance for the functional annotation and development of microbial communities. In this study, we proposed a new and scalable framework for image enhancement and deep learning of gut metagenome, which could be used in the classification of human metabolic diseases. Each data sample in three representative human gut metagenome datasets was transformed into image and enhanced, and put into the machine learning models of logistic regression (LR), support vector machine (SVM), Bayesian network (BN) and random forest (RF), and the deep learning models of multilayer perceptron (MLP) and convolutional neural network (CNN). The accuracy performance of the overall evaluation model for disease prediction was verified by accuracy (A), accuracy (P), recall (R), F1 score (F1), area under ROC curve (AUC) and 10 fold cross-validation. The results showed that the overall performance of MLP model was better than that of CNN, LR, SVM, BN, RF and PopPhy-CNN, and the performance of MLP and CNN models was further improved after data enhancement (random rotation and adding salt-and-pepper noise). The accuracy of MLP model in disease prediction was further improved by 4%-11%, F1 by 1%-6% and AUC by 5%-10%. The above results showed that human gut metagenome image enhancement and deep learning could accurately extract microbial characteristics and effectively predict the host disease phenotype. The source code and datasets used in this study can be publicly accessed in https://github.com/HuaXWu/GM_ML_Classification.git.

GULP1 is an engulfment adaptor protein containing a phosphotyrosine-binding (PTB) domain, and existing studies have shown that it can promote glucose uptake in 3T3-L1 adipocytes. To further explore key metabolically related differential genes downstream of GULP1, this study conducted transcriptome analysis on adipocytes and skeletal muscle cells overexpressing GULP1. Subsequently, abnormally expressed genes were subjected to bioinformatic analysis, and real-time fluorescent quantitative PCR (qRT-PCR) was used for mutual validation with transcriptome sequencing. The results indicated that, with a threshold of P < 0.05 and |Log₂FoldChange| ≥ 1 for screening differentially expressed genes, compared with control cells, there were 278 upregulated and 263 downregulated genes in adipocytes overexpressing GULP1. Metabolism-related GO (Gene Ontology) terms included cholesterol biosynthetic process, cholesterol metabolic process, response to lipopolysaccharide, lipid metabolic process, etc. A total of 52 metabolically related differentially expressed genes were enriched in 10 KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways, with lipid metabolism being highly enriched. In skeletal muscle cells overexpressing GULP1, there were 280 upregulated and 302 downregulated genes, with metabolism-related GO terms including hormone metabolic process, response to lipopolysaccharide, one-carbon metabolic process, etc. A total of 86 metabolically related differentially expressed genes were enriched in 10 KEGG pathways, with amino acid metabolism, lipid metabolism, and carbohydrate metabolism being highly enriched. GULP1's biological functions are extensive, including lipid metabolism and oncology. This study, through transcriptomics and bioinformatic analysis, identified key metabolically related differential genes downstream of GULP1, obtained metabolically related differential genes and signaling pathways after GULP1 overexpression, providing important theoretical basis for future research on GULP1 downstream target genes.

With the release of large-scale genomic resources from ancient and modern populations, advancements in computational biology tools, and the enhancement of data mining capabilities, the field of genomics is undergoing a revolutionary transformation. These advancements and changes have not only significantly deepened our understanding of the complex evolutionary processes of human origins, migration, and admixture but have also unveiled the impact of these processes on human health and disease. They have accelerated research into the genetic basis of human health and disease and provided new avenues for uncovering the evolutionary trajectories recorded in the human genome related to population history and disease genetics. The ancestral recombination graph (ARG) reconstructs the evolutionary relationships between genomic segments by analyzing recombination events and coalescence patterns across different regions of the genome. An ARG provides a record of all coalescence and recombination events since the divergence of the sequences under study and specifies a complete genealogy at each genomic position, which is the ideal data structure for genomic analysis. Here, we review the theoretical foundations and research advancements of the ARG, and explore its translational applications and future prospects across various disciplines, including forensic genomics, population genetics, evolutionary medicine, and medical genomics. Our goal is to promote the application of this technique in genomic research, thereby deepening our understanding of the human genome.

Illustrating molecular mechanisms of human embryonic development has always been one of the most significant challenges in biology. The scarcity of human embryo samples, the difficulty in dissecting embryo samples, and the complex structures of human organs are the major obstacles in studying human embryogenesis. In recent years, with the rapid advancement of single-cell technology, humans can systematically analyze the dynamic changes in differentiation at various stages of the central dogma and achieve observation and research with spatial information. This has accelerated the progress in constructing a human developmental cell atlas, ultimately allowing us to depict the cell ontology, fate trajectories, and three-dimensional dynamic changes of human development. In this review, we first introduce the single-cell technologies used to construct the atlas, then summarize the latest progress in human developmental cell atlas, followed by identifying the main problems and challenges in this field so far. Finally, we discuss how to utilize the human developmental cell atlas to address key biological and medical issues. This review provides guidance for the optimal use of single-cell omics technology in constructing and applying a human developmental cell atlas.

Expression quantitative trait loci (eQTL) represent genetic variants that regulate gene expression levels. eQTL analysis has become a crucial method for identifying the functional roles of disease-associated genetic variants in the post-genome-wide association study (GWAS) era, yielding numerous significant discoveries. Traditional eQTL analysis relies on whole-genome sequencing combined with bulk RNA-seq, which obscures gene expression differences between cells and thus fails to identify cell type- or state-dependent eQTL. This limitation makes it challenging to elucidate the roles of disease-associated genetic variants under specific conditions. In recent years, with the development and widespread application of single-cell RNA sequencing (scRNA-seq) technology, scRNA-seq-based eQTL (sc-eQTL) research has emerged as a focal point. The advantage of this approach lies in its ability to leverage the resolution and granularity of single-cell sequencing to uncover eQTL that are dependent on cell type, cell state, and cellular dynamics. This significantly enhances our ability to analyze genetic variants associated with gene expression. Consequently, it holds substantial significance for advancing our understanding of the formation of complex organs and the mechanisms underlying disease onset, progression, intervention, and treatment. This review comprehensively examines the recent advancements in sc-eQTL studies, focusing on their development, experimental design strategies, modeling approaches, and current challenges. The aim is to offer researchers novel perspectives for identifying disease-associated loci and elucidating gene regulatory mechanisms.

Targeted precise point editing and knock-in can be achieved by homology-directed repair(HDR) based gene editing strategies in mammalian cells. However, the inefficiency of HDR strategies seriously restricts their application in precision medicine and molecular design breeding. In view of the problem that exogenous donor DNA cannot be efficiently recruited autonomously at double-stranded breaks(DSBs) when using HDR strategies for gene editing, the concept of donor adapting system(DAS) was proposed and the CRISPR/Cas9-Gal4BD DAS was developed previously. Due to the large size of SpCas9 protein, its fusion with the Gal4BD adaptor is inconvenient for protein expression, virus vector packaging and in vivo delivery. In this study, two novel CRISPR/Gal4BD-SlugCas9 and CRISPR/Gal4BD-AsCas12a DASs were further developed, using two miniaturized Cas proteins, namely SlugCas9-HF derived from Staphylococcus lugdunensis and AsCas12a derived from Acidaminococcus sp. Firstly, the SSA reporter assay was used to assess the targeting activity of different Cas-Gal4BD fusions, and the results showed that the fusion of Gal4BD with SlugCas9 and AsCas12a N-terminals had minimal distraction on their activities. Secondly, the HDR efficiency reporter assay was conducted for the functional verification of the two DASs and the corresponding donor patterns were optimized simultaneously. The results demonstrated that the fusion of the Gal4BD adaptor binding sequence at the 5'-end of intent dsDNA template (BS-dsDNA) was better for the CRISPR/Gal4BD-AsCas12a DAS, while for the CRISPR/Gal4BD-SlugCas9 DAS, the dsDNA-BS donor pattern was recommended. Finally, CRISPR/Gal4BD-SlugCas9 DAS was used to achieve gene editing efficiency of 24%, 37% and 31% respectively for EMX1, NUDT5 and AAVS1 gene loci in HEK293T cells, which was significantly increased compared with the controls. In conclusion, this study provides a reference for the subsequent optimization of the donor adapting systems, and expands the gene editing technical toolbox for the researches on animal molecular design breeding.

Rapeseed is one important oil crop in China. However, its planting benefit is frequently affected by environmental stresses such as drought in the northwest region of China. The abscisic acid(ABA) signaling pathway plays an important role in plant abiotic stress response and tolerance, and ABFs/AREBs(ABA-responsive element binding factors/ABA-responsive element binding proteins) are the core transcription factors that regulate the expression of ABA-responsive genes. To dissect the key transcription factors mediated abiotic stress, we mainly characterized abscisic acid insensitive 5(BnaABI5) in rapeseed, including its subcellular localization, expression pattern in response to various stress and tissue-specific expression analysis, transcriptional activity analysis as well as interaction screening with BnaMPKs(mitogen-activated protein kinases). Our results showed that the BnaABI5-GFP fusion protein was localized in the nucleus, and its transcript level is induced by drought stress and was mainly expressed in the roots of rapeseed. Furthermore, BnaABI5 showed transcriptional activation activity through a yeast transactivation assay and it also activated the promoter activity of EM6 target gene in the transient expression system in tobacco leaves. Moreover, BnaABI5 interacted with BnaMPK6 and BnaMPK13 through BiFC and Y2H analysis. This study preliminarily explored the expression characteristics of transcription factor BnaABI5 and its interaction with BnaMPKs, which might help us for further understanding the function of BnaABI5.

Gene editing is a kind of genetic engineering technology that can modify the genome. In recent years, with the rapid development of molecular biotechnology, the clustered regularly interspaced short palindromic repeats associated protein system has been widely used as a powerful gene editing tool due to its high efficiency, accuracy and flexibility. The CRISPR-Cas system makes a significant contribution to different aspects of livestock production by introducing site-specific modifications such as insertions, deletions or single base replacements at specific genomic sites. In terms of sheep production applications, by establishing animal models that improve production economic traits and disease resistance, the function of key genes can be studied to accelerate the improvement of traits, thereby accelerating the improvement of traits. In this review, we summarize the mechanism and function of CRISPR-Cas system and its application in the production of reproductive traits, meat use traits, wool production traits, lactation traits and disease resistance traits of sheep and the establishment of sheep animal models.

Split-hand/foot malformation is a serious congenital limb malformation characterized by syndactyly and underdevelopment of the phalanges and metatarsals. In this study, we reported a case of a fetus with hand-foot cleft deformity. Whole exome and Sanger sequencing were used to filter out candidate gene mutation sites and provide pre-implantation genetic testing(PGT) for family members. Genetic testing results showed that there was a homozygous mutation c.786G>A (p.Trp262*) in the fetal WNT10B, and both parents were carriers of heterozygous mutations. PGT results showed that out of the two blastocysts, one was a heterozygous mutant and the other was a homozygous mutant. All the embryos had diploid chromosomes. The heterozygous embryo was transferred, and a singleton pregnancy was successfully achieved. This study suggests that homozygous mutations in WNT10B are the likely cause of hand-foot clefts in this family. For families with monogenic diseases, preimplantation genetic testing can effectively prevent the birth of an affected child only after identifying the pathogenic mutation.

With the rapid growth of data driven by high-throughput sequencing technologies, genomics has entered an era characterized by big data, which presents significant challenges for traditional bioinformatics methods in handling complex data patterns. At this critical juncture of technological progress, deep learning-an advanced artificial intelligence technology-offers powerful capabilities for data analysis and pattern recognition, revitalizing genomic research. In this review, we focus on four major deep learning models: Convolutional Neural Network(CNN), Recurrent Neural Network(RNN), Long Short-Term Memory(LSTM), and Generative Adversarial Network(GAN). We outline their core principles and provide a comprehensive review of their applications in DNA, RNA, and protein research over the past five years. Additionally, we also explore the use of deep learning in livestock genomics, highlighting its potential benefits and challenges in genetic trait analysis, disease prevention, and genetic enhancement. By delivering a thorough analysis, we aim to enhance precision and efficiency in genomic research through deep learning and offer a framework for developing and applying livestock genomic strategies, thereby advancing precision livestock farming and genetic breeding technologies.