遗传最新文献

As the second important staple crop next to rice in China, common wheat (Triticum aestivum) plays a decisive role in national food security. Wild and semi-wild relatives of wheat provide abundant genetic resources for wheat genetic improvement. In China, wheat wide hybridization and chromosome engineering breeding initiated in the 1950s and developed into a well-defined theoretical and technical system over the next three decades through learning, exploration and practice. Subsequently, the technological innovation in alien chromatin identification and the isolation and analysis of alien resistance genes sponsored by continuous national projects have significantly enhanced China's impact on the world in this field. Eminent scientists such as Professor Li Zhensheng, who was awarded the Medal of the Republic before the National Day in 2024, have made outstanding contributions to the establishment and development of the research in this area in China. This article reviews the history of wheat wide hybridization and chromosome engineering breeding in China, aiming to honor the senior scientists and inspire future researchers to work hard in germplasm innovation and alien gene transfer, cloning and utilization in breeding.

Follicle development is a crucial step in mammalian reproductive processes, the specific role of Mfn2 in regulating mitochondrial function and endoplasmic reticulum stress in this process is still unclear, this study aimed to investigate the role of Mfn2 in the follicular development of adult sheep. Large, medium, and small follicles were collected, and granulosa cells (GCs) were isolated from large follicles. The expression levels of Mfn2 in different follicles were detected using qRT-PCR and Western blot, and the localization of Mfn2 in follicles was determined through immunofluorescence. Additionally, the expression levels of the mitochondrial autophagy-related protein Pink1, endoplasmic reticulum stress proteins (Grp78, Perk, Chop), and apoptosis-related proteins (Bcl2 and BAX) were detected. Furthermore, siRNAs were transfected into GCs to knock down Mfn2 expression, and changes in intracellular Ca²⁺ accumulation and mitochondrial membrane potential were evaluated, along with the expression levels of the aforementioned proteins. The results showed that Mfn2 expression was significantly higher in large follicles compared to small follicles and was primarily localized in GCs. Compared to small follicles, the expression levels of Pink1, Grp78, Perk, Chop, and BAX were significantly lower in large follicles, while Bcl2 expression was significantly increased (P<0.01). After Mfn2 knockdown, intracellular Ca²⁺ levels and mitochondrial membrane potential were significantly reduced, while the expression levels of Pink1, Grp78, Perk, Chop, and BAX were significantly increased, and Bcl2 expression was significantly decreased (P<0.01). Mfn2 may influence cell apoptosis during sheep follicular development by regulating mitochondrial function and endoplasmic reticulum stress.

Single-cell transcriptome sequencing (scRNA-seq) is widely used in the fields of animal and plant developmental biology and important trait analysis by obtaining single-cell transcript abundance data in high throughput, which can deeply reveal cell types, subtype composition, specific gene markers and functional differences. However, scRNA-seq data are often accompanied by problems such as high noise, high dimensionality and batch effect, resulting in a large number of low-expressed genes and variants, which seriously affect the accuracy and reliability of data analysis. This not only increases the complexity of data processing, but also limits the effectiveness of feature selection and downstream analysis. Although several statistical inference and machine learning methods have been used to address these challenges, the existing methods still have limitations in cell type identification, feature selection, and batch effect correction, which are difficult to meet the needs of complex biological research. In this study, we proposes an innovative single-cell classification method, scIC (single-cell image classification), which converts scRNA-seq data into image form and combines it with deep learning techniques for cell classification. Through this image conversion, we are able to capture complex patterns in the data more efficiently, and then construct efficient classification models using convolutional neural networks (CNN) and residual networks (ResNet). After testing scRNA-seq data from four cell types (mouse skin basal cells, mouse lymphocytes, human neuronal cells, and mouse spinal cord cells), the accuracy of the classification models exceeded 94%, with the mouse skin basal cell dataset achieving a classification accuracy of 99.8% when using the ResNet50 model. These results indicate that image transformation of scRNA-seq data and combining it with deep learning techniques can significantly improve the classification accuracy, providing new ideas and effective tools for solving key challenges in single-cell data analysis. The code for this study is publicly available at: https://github.com/Bingxi-Gao/SCImageClassify.

In the early 1990s, based on China's basic national conditions, Li Zhensheng put forward the concept of sustainable agricultural development that took the path of resource-conserving and high-yield agriculture. He carried out breeding explorations on the efficient use of mineral nutrients by crops and pioneered a new direction for wheat breeding with the goals of "less input, more output, environmental protection, and sustainable development", mainly focusing on improving the absorption and utilization efficiency of phosphorus and nitrogen. He has led and greatly promoted "the Second Green Revolution" in China's agriculture. In September 2024, Academician Li Zhensheng was awarded the "Medal of the Republic". This review summarizes Academician Li Zhensheng's strategic considerations in advocating the new direction of breeding and how he arranged to conduct research on the physiological and genetic basis of phosphorus efficient use in wheat. By doing so, we pay tribute to the outstanding work done by Academician Li Zhensheng in the research field of nutrient-efficient use by crops, and it is expected to further demonstrate Li Zhensheng's academic approaches and spirit so as to provide references for those who come after him.

To reveal the differences in transcript levels of Bartonella spp. from different species and hosts and their impacts on phylogenetic relationships, we focus on 27 strains from four Bartonella species (B. henselae, B. koehlerae, B. clarridgeiae and B. quintana) and three hosts (Felis catus, Homo sapiens and Macaca mulatta) to conduct the transcriptome sequencing using Illumina high-throughput sequencing technology. Gene expression differences between strains from different species and hosts are analyzed, and the results of phylogenetic analysis at the transcriptome and genome levels are compared. The results show significant differences in gene transcription between strains from different species and hosts. Twelve genes are screened, including virB10, bepC and virB4, which may facilitate host-specific recognition. Furthermore, phylogenetic analysis based on SNPs within the core genes of the transcriptome demonstrate species-specific clustering patterns among strains. Further analysis indicate that host factors influence the genetic divergence of strains, while geographic factors exert a small impact on this process. These findings are congruent with the phylogenetic analysis of SNPs in the core genes of the genome. Our study uses differential transcriptome analysis to reveal the genetic divergence and phylogenetic relationships of Bartonella species. And the observed regular differences between strains from different species and hosts are found to correspond with the results of traditional genome analysis. Thus, our results indicate the utility of transcriptome data in efficiently investigating the genetic divergence between species.

Seed longevity is the period over which seeds remain viable and capable of gemination, and is an important trait of seed quality. Longevity changes in seed directly affect the germination rate, seedling morphology, and storage time. Therefore, the identification of seed longevity genes has significant value for cultivating seeds that are storage-resistant and have long lifespan. The study found that NJ9108 seeds are a type of rice that is resistant to aging; Using transcriptomic technology, the annotated genes were subjected to mfuzz fuzzy clustering and divided into 6 subtypes, with a total of 8,384 genes upregulated/downregulated by aging induction. These differentially expressed genes are enriched into biological processes (BP), cellular components (CC), and molecular functions (MF), with 42 genes enriched in phenylpropanoid biosynthesis, 31 genes enriched in sugar signaling, and 42 genes enriched in plant hormone signaling pathways. They are the most important pathways involved in the aging resistance process of NJ9108. qRT-PCR results showed that compared with ZH11, 4CL5, CAD5, PRX3 and PRX86 in the phenylpropanoid biosynthesis pathway were significantly upregulated in NJ9108 after aging; BGLU18, BGLU22 and TPP3 in the sugar signaling pathway were significantly upregulated in NJ9108; RR12 and SAPK5 involved in the plant hormone signaling pathway were significantly upregulated after aging, while IAA12 and IAA20 were significantly downregulated in NJ9108 seeds. The expression trends of these genes are consistent with transcriptomic results, suggesting that these genes regulating rice seed longevity. BGLU18, BGLU22, OsRR12, and TPP3, as the new identified seed longevity genes, can be further studied in the future. Above all, the experimental results provide a theoretical basis for understanding the regulatory network of rice seed longevity and for breeding rice varieties that are resistant to aging.

The large-scale data generated by various omics technologies pose significant scientific challenges about how to rapidly and accurately analyze these data. It is essential to develop convenient tools that allow users to efficiently and precisely handle massive biological data. Based on new theories and mathematical models, as well as software engineering, this field is becoming an important research direction in bioinformatics and computational biology. In this review, we briefly review the development history of bioinformatics-related software. We also summarize the recent progress, focus on their application on evolutionary biology, and discuss three major ways of computer running mode and three paradigms of software programming. We also introduce the eGPS, a self-developed multi-functional evolutionary and omics analysis software platform, including the application of eGPS along with Conda and R for data analysis on individual genes, pathways, or genomes. We then propose new ideas for software development, use, and maintenance tailored to different users with varying scientific objectives. It posits that using a personal computer for evolutionary and multi-omics analysis is not only a necessity but also playing an important role.

Chromosomes, as the fundamental unit of genetic material located within the cell nucleus, have undergone extensive and complex changes throughout the evolutionary history of eukaryotes. Many of these patterns and mechanisms of change share commonalities across various diseases, including cancer. For a long time, biologists were limited to research methods with relatively low resolution, such as fluorescence in situ hybridization (FISH). However, the rapid advancement of high-throughput sequencing technologies is revolutionizing our understanding of chromosomal variations across different species, among individuals of the same species, and even at the cellular level within a single individual. In this review, we focus on the chromosomal evolution in vertebrates, and provide an overview of the role of chromosom rearrangements in speciation, the molecular mechanisms of chromosomal rearrangements, the evolutionary patterns from ancestral chromosomes to extant chromosomes, and the significance of sex chromosomes as a general paradigm for studying chromosomal evolution. Finally, we discuss the new opportunities and challenges that synthetic biology brings to the field of chromosomal evolution research, with the aim of offering new insights and references for understanding and studying vertebrate chromosomal evolution.

The COVID-19 pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has significantly impacted human life safety and the global economy. The rapid mutation of the SARS-CoV-2 genome has attracted widespread attention, with almost every site in the genome experiencing single nucleotide variants (SNVs). Among these, the mutations in the spike (S) protein are of particular importance, as they play a more critical role in the virus's adaptive evolution and transmission. In this review, we summarize the phylogenetic relationships between SARS-CoV-2 and related coronaviruses in non-human animals, and delves into the lineage classification of SARS-CoV-2 and the impact of key amino acid variations on viral biological characteristics. Furthermore, it outlines the current challenges and looks forward to the promising application of deep mutational scanning (DMS) combined with artificial intelligence methods in predicting the prevalence trends of SARS-CoV-2 variants.

Gene duplication is the process of a gene copied via specific molecular mechanisms to form more duplicate genes. As an important approach to the origination of new genes, gene duplication contributes to around half of the genes in eukaryotic genomes, facilitating the adaptive evolution of species. Over the past fifty years, especially since entering the genomics era in the last two decades, there have been extensive and profound discussions on the mechanisms, evolutionary processes and forces behind the emergence of duplicate genes. Sequence similarity of duplicate genes often leads to functional redundancy, enhancing organismal robustness. Conversely, functional divergence can create novel functions and improve evolvability. In this review, we summarize the mechanism of gene duplication, the fate and the evolutionary models of duplicate genes. This article concludes by outlining how long-read sequencing technologies, gene editing, and various other high-throughput techniques will further advance our understanding of the role of duplicate genes in the genetics-development-evolution network.