Males and females share most of the genome, but many animals show different phenotypes between the sexes, known as sexual dimorphism. Many insect species show extreme sexual dimorphism, including beetles with "weapon traits" represented by extremely developed horns and mandibles. Existing studies of sex-specific development of beetle weapon traits suggest that sex-specific gene expression plays an important role. On the other hand, contributions of the Y-chromosome, which may potentially carry genes necessary for male development, to weapon trait expression have not been examined. In holometabolous insects, including beetles, the feminizing gene transformer (tra) is roughly conserved in its feminizing function. Only females express a functional isoform of Tra, which causes female differentiation. Knocking down tra in females leads to male tissue differentiation, enabling us to analyze male phenotypes in individuals lacking a Y-chromosome (XX-males). In this study, we investigate whether the Y-chromosome is necessary for stag beetles to express male-specific weapon traits by comparing tra-knockdown-induced XX-males with natural XY males. We show that XX-males could express weapons (enlarged mandibles) as in XY-males. These results suggest that the Y-chromosome does not have a major role in weapon trait expression in this species.
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http://dx.doi.org/10.1002/jez.b.23274 | DOI Listing |
Nat Biomed Eng
January 2025
Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, China.
Graph representation learning has been leveraged to identify cancer genes from biological networks. However, its applicability is limited by insufficient interpretability and generalizability under integrative network analysis. Here we report the development of an interpretable and generalizable transformer-based model that accurately predicts cancer genes by leveraging graph representation learning and the integration of multi-omics data with the topologies of homogeneous and heterogeneous networks of biological interactions.
View Article and Find Full Text PDFBioinformatics
December 2024
The Kids Research Institute Australia, University of Western Australia, Nedlands, WA 6009, Australia.
Motivation: Over the last two decades, transcriptomics has become a standard technique in biomedical research. We now have large databases of RNA-seq data, accompanied by valuable metadata detailing scientific objectives and the experimental procedures employed. The metadata is crucial in understanding and replicating published studies, but so far has been underutilised in helping researchers to discover existing datasets.
View Article and Find Full Text PDFAdv Sci (Weinh)
January 2025
State Key Laboratory of Animal Nutrition and Feeding, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, China.
High soluble protein expression in heterologous hosts is crucial for various research and applications. Despite considerable research on the impact of codon usage on expression levels, the relationship between protein sequence and expression is often overlooked. In this study, a novel connection between protein expression and sequence is uncovered, leading to the development of SRAB (Strength of Relative Amino Acid Bias) based on AEI (Amino Acid Expression Index).
View Article and Find Full Text PDFNature
January 2025
Program of Mathematical Genomics, Department of Systems Biology, Columbia University, New York, NY, USA.
Transcriptional regulation, which involves a complex interplay between regulatory sequences and proteins, directs all biological processes. Computational models of transcription lack generalizability to accurately extrapolate to unseen cell types and conditions. Here we introduce GET (general expression transformer), an interpretable foundation model designed to uncover regulatory grammars across 213 human fetal and adult cell types.
View Article and Find Full Text PDFAdv Sci (Weinh)
January 2025
State Key Laboratory of Common Mechanism Research for Major Diseases, Department of Biochemistry and Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100005, China.
The dynamics of chromatin conformation involve continuous and reversible changes within the nucleus of a cell, which participate in regulating processes such as gene expression, DNA replication, and damage repair. Here, SEE is introduced, an artificial intelligence (AI) method that utilizes autoencoder and transformer techniques to analyze chromatin dynamics using single-cell RNA sequencing data and a limited number of single-cell Hi-C maps. SEE is employed to investigate chromatin dynamics across different scales, enabling the detection of (i) rearrangements in topologically associating domains (TADs), and (ii) oscillations in chromatin interactions at gene loci.
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