Biology subjects

zhu, z.

Publications and source records attributed to zhu, z..

4 recordsLinked to original sources

bioRxiv2026

Dronedarone hydrochloride reverses obesity-related metabolic syndrome while preserving skeletal muscle mass

Obesity-driven metabolic syndrome poses a critical global threat, yet standard therapies like GLP-1 receptor agonists trigger substantial lean mass wasting, with muscle loss accounting for up to 40% of reduced weight. Here we identify a non-canonical metabolic application for dronedarone hydrochloride, an anti-arrhythmic benzofuran derivative. In diet-induced and ob/ob obese mice, short-term dronedarone hydrochloride administration dose-dependently reduces food intake, clears visceral and subcutaneous adiposity, and reverses steatohepatitis. Head-to-head trials show that dronedarone hydrochloride achieves glycemic control and fat clearance non-inferior to semaglutide, tirzepatide, and empagliflozin, but uniquely and completely preserves skeletal muscle mass. Mechanistically, dronedarone hydrochloride operates independently of central hypothalamic appetite-regulating neuropeptides and the peripheral leptin pathway. By decoupling fat reduction from sarcopenia, our findings establish dronedarone hydrochloride as a muscle-sparing therapeutic candidate for metabolic syndrome.

pharmacology and toxicology↗

bioRxiv2025

Global and cross-omics feature aggregation improves single-cell multi-omics integration and clustering

Sequencing technology advancements enable the simultaneous measurement of multiple omics within single cells, thereby facilitating single-cell multi-omics analysis. Most existing deep multi-omics integration methods learn shared or omics-specific representations from multiple omics by omics-wise aggregation. However, these approaches often fail to account for the structural relationships among all cells. Here, we introduce scCODI, an innovative cross-omics deep learning model to integrate single-cell multi-omics data for cell clustering. This model processes paired omics datasets, combining single-cell transcriptomics with either epigenomics or proteomics data to facilitate a more comprehensive exploration of cellular heterogeneity. Specifically, the shared representation of two omics is derived through cross-omics feature aggregation, fully leveraging the complementarity of different omics within the same cell. Additionally, we align the omic-specific and shared representations of each cell using a global relationship-guided contrastive learning module, which enhances the similarity between the shared and omic-specific representations of the same cell. Extensive experiments demonstrate that scCODI outperforms previous methods in single-cell multi-omics data integration and cell clustering, as evidenced by its performance across nine paired multi-omics datasets. This makes it a promising tool for decoding complex biological mechanisms. The code is available at https://github.com/SXUI-BDSI/scCODI

bioinformatics↗

bioRxiv2019

Genome-wide identification of long non-coding RNAs and their regulatory networks involved in Apis mellifera ligustica response to Nosema ceranae infection

Long non-coding RNAs (lncRNAs) are a diverse class of transcripts that structurally resemble mRNAs but do not encode proteins, and lncRNAs have been proved to play pivotal roles in a wide range of biological processes in animals and plants. However, knowledge of expression pattern and potential role of honeybee lncRNAs response to Nosema ceranae infection is completely unknown. Here, we performed whole transcriptome strand-specific RNA sequencing of normal midguts of Apis mellifera ligustica workers (Am7CK, Am10CK) and N. ceranae-inoculated midguts (Am7T, Am10T), followed by comprehensive analyses using bioinformatic and molecular approaches. A total of 6353 A. m. ligustica lncRNAs were identified, including 4749 conserved lncRNAs and 1604 novel lncRNAs. These lncRNAs had low sequence similarities with other known lncRNAs in other species; however, their structural features were similar with counterparts in mammals and plants, including shorter exon and intron length, lower exon number, and lower expression level, compared with protein-coding transcripts. Further, 111 and 146 N. ceranae-responsive lncRNAs were identified from midguts at 7 day post inoculation (dpi) and 10 dpi compared with control midguts. 12 differentially expressed lncRNAs (DElncRNAs) were shared by Am7CK vs Am7T and Am10CK vs Am10T comparison groups, while the numbers of unique ones were 99 and 134, respectively. Functional annotation and pathway analysis showed the DElncRNAs may regulate the expression of neighboring genes by acting in cis. Moreover, we discovered 27 lncRNAs harboring eight known miRNA precursors and 513 lncRNAs harboring 2257 novel miRNA precursors. Additionally, hundreds of DElncRNAs and their target miRNAs were found to form complex competitive endogenous RNA (ceRNA) networks, suggesting these DElncRNAs may act as miRNA sponges. Furthermore, DElncRNA-miRNA-mRNA networks were constructed and investigated, the result demonstrated that part of DElncRNAs were likely to participate in regulating the material and energy metabolism as well as cellular and humoral immune during host responses to N. ceranae invasion. Finally, the expression pattern of 10 DElncRNAs was validated using RT-qPCR, confirming the reliability of our sequencing data. Our findings revealed here offer not only a rich genetic resource for further investigation of the functional roles of lncRNAs involved in A. m. ligustica response to N. ceranae infection, but also a novel insight into understanding host-pathogen interaction during microsporidiosis of honeybee.

bioinformatics↗

bioRxiv2019

Prediction of antiviral drugs against African Swine Fever Viruses based on protein-protein interaction analysis

The African swine fever virus (ASFV) has severely influenced the swine industry of the world. Unfortunately, there is no effective antiviral drug or vaccine against the virus until now. Identification of new anti-ASFV drugs is urgently needed. Here, an up-to-date set of protein-protein interactions (PPIs) between ASFV and swine were curated by integration of PPIs from multiple sources. Thirty-two swine proteins were observed to interact with ASFVs and were defined as AIPs. They were found to play a central role in the swine PPI network, with significant larger degree, betweenness and smaller shortest path length than other swine proteins. Some of AIPs also interacted with several other viruses and could be taken as potential targets of drugs for broad-spectrum effect, such as HSP90AB1. Finally, the antiviral drugs which targeted AIPs and ASFV proteins were predicted. Several drugs with either broad-spectrum effect or high specificity on AIPs were identified, such as Polaprezinc. This work could not only deepen our understanding towards the ASFV-swine interactions, but also help for the development of effective antiviral drugs against the ASFVs.

bioinformatics↗