Biomedical subjects

Pengfei Xu

Publications and source records attributed to Pengfei Xu.

4 recordsLinked to original sources

PubMed2026

Enhancer-promoter interaction maps in pig implantation tissue identify candidate cis-regulatory elements and regulatory variants.

Due to the polygenic nature of pig reproductive traits, most variants accounting for the phenotypic variation remain poorly characterized. It is well established that trait-associated variants are often enriched in regulatory elements of relevant tissues, and coordinated endometrial-conceptus crosstalk is critical for implantation success. Accordingly, this study aimed to investigate the chromatin landscape in pig endometrium-conceptus tissues and identify regulatory variants associated with reproductive traits. Combining RNA-seq and ChIP-seq, we uncovered cis-regulatory elements whose histone modification patterns correlate with gene expression. H3K27ac-based chromatin interaction profiling further resolved 2137 cis-regulatory elements involved in enhancer-promoter (E-P) interactions. Subsequent analyses identified candidate variants within these regulatory regions that associate with reproductive traits, in which the enhancer SNP rs346038249 likely affects transcriptional activity through allele-dependent transcription factor binding. Collectively, our findings provide mechanistic insights into pig embryo implantation and nominate candidate regulatory variants that merit further functional validation for their potential role in pig genetic improvement.

Embryo implantation↗

PubMed2026

The H3K27me3 reader GmLHP1 impairs Phytophthora sojae resistance by repressing ethylene precursor accumulation in soybean.

Phytophthora root rot, caused by Phytophthora sojae, is a devastating soilborne disease of soybean (Glycine max). However, the epigenetic regulation of soybean responses to P. sojae remains incompletely understood. Here, using genetic, molecular and biochemical approaches, we characterized the functions of LIKE HETEROCHROMATIN PROTEIN 1 (GmLHP1) and its downstream regulatory network. We demonstrated that GmLHP1, as a reader of H3K27me3, negatively regulates soybean resistance to P. sojae. GmLHP1 binds to H3K27me3 peptides in vitro and colocalizes with H3K27me3 marks genome-wide in vivo. The integrated chromatin immunoprecipitation sequencing and RNA sequencing analysis identified the ethylene biosynthesis pathway gene 1-AMINO-CYCLOPROPANE-1-CARBOXYLATE SYNTHASE 18 (GmACS18) as being enriched for H3K27me3 and bound by GmLHP1, leading to its transcriptional downregulation. Notably, GmLHP1 associates with the GmACS18 promoter by directly binding to AATTAA motifs and recognizing H3K27me3 marks. Moreover, GmACS18 enhances defense against P. sojae by accumulating the ethylene precursor 1-aminocyclopropane-1-carboxylic acid (ACC). Further analysis unveiled that recognition of H3K27me3 by GmLHP1 is essential for regulating soybean resistance to P. sojae through repressing GmACS18 transcription and decreasing ACC accumulation. Our findings reveal a novel epigenetic regulatory mechanism in which the H3K27me3 reader GmLHP1 blocks soybean resistance to P. sojae by repressing ethylene precursor ACC accumulation.

ACC accumulation↗

PubMed2005

New adaptive color quantization method based on self-organizing maps.

Color quantization (CQ) is an image processing task popularly used to convert true color images to palletized images for limited color display devices. To minimize the contouring artifacts introduced by the reduction of colors, a new competitive learning (CL) based scheme called the frequency sensitive self-organizing maps (FS-SOMs) is proposed to optimize the color palette design for CQ. FS-SOM harmonically blends the neighborhood adaptation of the well-known self-organizing maps (SOMs) with the neuron dependent frequency sensitive learning model, the global butterfly permutation sequence for input randomization, and the reinitialization of dead neurons to harness effective utilization of neurons. The net effect is an improvement in adaptation, a well-ordered color palette, and the alleviation of underutilization problem, which is the main cause of visually perceivable artifacts of CQ. Extensive simulations have been performed to analyze and compare the learning behavior and performance of FS-SOM against other vector quantization (VQ) algorithms. The results show that the proposed FS-SOM outperforms classical CL, Linde, Buzo, and Gray (LBG), and SOM algorithms. More importantly, FS-SOM achieves its superiority in reconstruction quality and topological ordering with a much greater robustness against variations in network parameters than the current art SOM algorithm for CQ. A most significant bit (MSB) biased encoding scheme is also introduced to reduce the number of parallel processing units. By mapping the pixel values as sign-magnitude numbers and biasing the magnitudes according to their sign bits, eight lattice points in the color space are condensed into one common point density function. Consequently, the same processing element can be used to map several color clusters and the entire FS-SOM network can be substantially scaled down without severely scarifying the quality of the displayed image. The drawback of this encoding scheme is the additional storage overhead, which can be cut down by leveraging on existing encoder in an overall lossy compression scheme.

Algorithms↗

PubMed2005

Self-organizing topological tree for online vector quantization and data clustering.

The self-organizing Maps (SOM) introduced by Kohonen implement two important operations: vector quantization (VQ) and a topology-preserving mapping. In this paper, an online self-organizing topological tree (SOTT) with faster learning is proposed. A new learning rule delivers the efficiency and topology preservation, which is superior of other structures of SOMs. The computational complexity of the proposed SOTT is O(log N) rather than O(N) as for the basic SOM. The experimental results demonstrate that the reconstruction performance of SOTT is comparable to the full-search SOM and its computation time is much shorter than the full-search SOM and other vector quantizers. In addition, SOTT delivers the hierarchical mapping of codevectors and the progressive transmission and decoding property, which are rarely supported by other vector quantizers at the same time. To circumvent the shortcomings of clustering performance of classical partition clustering algorithms, a hybrid clustering algorithm that fully exploit the online learning and multiresolution characteristics of SOTT is devised. A new linkage metric is proposed which can be updated online to accelerate the time consuming agglomerative hierarchical clustering stage. Besides the enhanced clustering performance, due to the online learning capability, the memory requirement of the proposed SOTT hybrid clustering algorithm is independent of the size of the data set, making it attractive for large database.

Algorithms↗