Biology subjects

Xiong, Z.

Publications and source records attributed to Xiong, Z..

4 recordsLinked to original sources

bioRxiv2026

A Hymenoptera-restricted gene mediating ant castes co-opts deeply conserved machinery to control organ size

Lineage-specific genes are widespread and have been implicated as phenotypic innovation inducers, but how they acquire complex developmental functions remains poorly understood. Ant queens and workers develop dramatically different organ sizes from identical genomes under juvenile hormone (JH) control, yet the molecular effectors translating JH signalling into caste-specific organ growth remain unknown. Here we identify torch, a Hymenoptera-restricted gene, as the most consistently gyne-biased and JH-responsive gene across 68 ant species. Knockdown of torch in virgin queens of Monomorium pharaonis produces a worker-like, multi-organ growth-restricted phenotype. Mechanistically, torch harbours an E-box-like motif activated by the JH receptor Gce-Tai and acts as a GA-repeat-binding transcription factor that regulates Hippo signalling, the deeply conserved organ-size control pathway in animals. Expressing torch heterologously in mice and a growth-restricted Drosophila background shows that the gene retained its general growth-promoting activity across more than 700 million years of animal evolution in lineages that lack the gene, establishing that its function is mediated through conserved rather than ant-specific machinery. A lineage-specific gene can therefore acquire complex morphogenetic function by co-opting ancient organ-size circuitry, providing a general route by which novel genes can drive phenotypic innovation.

evolutionary biology↗

bioRxiv2018

DeepSeqPan, a novel deep convolutional neural network model for pan-specific class I HLA-peptide binding affinity prediction

Interactions between human leukocyte antigens (HLAs) and peptides play a critical role in the human immune system. Accurate computational prediction of HLA-binding peptides can be used for peptide drug discovery. Currently, the best prediction algorithms are neural network based pan-specific models, which take advantage of the large amount of data across HLA alleles. However, current pan-specific models are all based on the pseudo sequence encoding for modeling the binding context and depend on the available HLA protein-peptide bound structures. In this work, we proposed a novel deep convolutional neural network model (DCNN) for HLA-peptide binding prediction, in which the encoding of the HLA sequence and the binding context are both learned by the network itself without requiring the HLA-peptide bound structure information. Our DCNN model is also characterized by its binding context extraction layer and dual outputs with both binding affinity output and binding probability outputs. Evaluation on public benchmark datasets shows that our DeepSeqPan model without HLA structural information in training achieves state-of-the-art performance on a large number of HLA alleles with good generalization capability. Since our model only needs raw sequences from the HLA-peptide binding pairs, it can be applied to binding predictions of HLAs without structure information and can also be applied to other protein binding problems such as protein-DNA and protein-RNA bindings. The implementation code and trained models are freely available at https://github.com/pcpLiu/DeepSeqPan.

bioinformatics↗

bioRxiv2017

Scalable volumetric imaging for ultrahigh-speed brain mapping at synaptic resolution

We describe a new light-sheet microscopy method for fast, large-scale volumetric imaging. Combining synchronized scanning illumination and oblique imaging over cleared, thick tissue sections in smooth motion, our approach achieves high-speed 3D image acquisition of an entire mouse brain within 2 hours, at a resolution capable of resolving synaptic spines. It is compatible with immunofluorescence labeling, enabling flexible cell-type specific brain mapping, and is readily scalable for large biological samples such as primate brain.

neuroscience↗

bioRxiv2017

Genetics and epigenetic alterations of hexaploid early generation derived from hybrid between Brassica napus and B. oleracea

Good fertility was observed previously in hexaploid derived from hybrid (ACC) between calona Zhongshuang 9(Brassica napus, 2n = 38, AACC) and kale SWU01 (B. oleracea var. acephala, 2n = 18, CC). However, the mechanism to underlying the character is unknown. In the present study, genetic and epigenetic alterations of S0, 6 S1, and 18 of their S2 progenies with hexaploid chromosome conformation (20A + 36C) were selected to compare with ACC and their parental species. 13.08% and 26.45% polymorphism alleles different from two parental species were identified in ACC via 58 SSR (simple sequence repeats) and 14 MSAP (methylation sensitive amplified polymorphism), respectively. 33.74% new alleles in DNA methylation, but not in DNA sequence were detected in S0 after chromosome doubling of ACC. DNA profilling revealed a little genetic but much epigenetic differences among S0, S1 and S2 generations. Genetic alteration was relatively stable, because only 8.09% and 3.21% alleles inheriated from ACC were changed in S2 and S1, respectively. While on average of 52.44 {+/-} 5.32% DNA methylation site inherited from ACC were detected in S1, and 41.52 {+/-} 9.04% in S2 due to dramatic epigenetic variance among early generations. New DNA methylation sites occurred in S0 would inheritated into the successive generations, but the frequency was decreased because some new site might be recovered. It demonstrated that much DNA methylation but a little DNA sequence variance was occurred in hexaploid early generation.

genetics↗