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Rationally designed antimicrobial peptides with high selectivity and efficiency against phytopathogenic Ralstonia solanecearum

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Science of the Total Environment 2025年 976卷 179354页

作者： Manik, Melvalia Cristin Darai, Nitchakan Rungrotmongkol, Thanyada Duan, Lian Harada, Ryuhei Shigeta, Yasuteru Hengphasatporn, Kowit Vangnai, Alisa S. Biotechnology Program Faculty of Science Chulalongkorn University Bangkok10330 Thailand Futuristic Science Research Center School of Science Walailak University Nakhon Si Thammarat80160 Thailand Drug and Cosmetics Excellence Center Walailak University Thasala Nakhon Si Thammarat80160 Thailand Program in Bioinformatics and Computational Biology Chulalongkorn University Bangkok10330 Thailand Center for Computational Sciences University of Tsukuba 1-1-1 Tennodai Ibaraki Tsukuba305-8577 Japan Center of Excellence in Biocatalyst and Sustainable Biotechnology Faculty of Science Chulalongkorn University Bangkok10330 Thailand Department of Biochemistry Faculty of Science Chulalongkorn University Bangkok10330 Thailand

Ralstonia solanacearum, the causative agent of bacterial wilt, poses a global threat to agriculture, necessitating urgent and sustainable solutions as traditional methods lose efficacy. This study developed WRF-13, a synthetic antimicrobial peptide (AMP) designed to mimic natural AMPs, exhibiting potent antibacterial and anti-biofilm activity with high specificity against R. solanacearum. Mechanistic studies, including microscopy and computational analyses, demonstrated that WRF-13 disrupts the bacterial membrane. WRF-13 remained stable across a wide pH (5–8) and temperature (25–50 °C) range, essential for field applications, and showed no detectable toxicity to mammalian or plant cells at elevated concentrations. Greenhouse trials confirmed its efficacy in reducing bacterial wilt severity up to 65 %, highlighting its potential to protect crops from R. solanacearum infection. Overall, this study highlights WRF-13 as a targeted solution for managing bacterial wilt and advancing sustainable agriculture. © 2025 Elsevier B.V.

关键词： Antimicrobial peptides Bacterial wilt Crop protection In silico-peptide design Ralstonia solanacearum

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Pseudomonas aeruginosa-derived DnaJ induces TLR2 expression through TLR10-mediated activation of the PI3K-SGK1 pathway in macrophages

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Microbes and Infection 2025年 105481页

作者： Lee, Jaehoo Jin, Yongxin Wu, Weihui Lee, Yeji Ha, Un-Hwan Department of Biotechnology and Bioinformatics Korea University Sejong 30019 South Korea Interdisciplinary Graduate Program for Artificial Intelligence Smart Convergence Technology Korea University Sejong 30019 South Korea State Key Laboratory of Medicinal Chemical Biology Key Laboratory of Molecular Microbiology and Technology of the Ministry of Education Department of Microbiology Nankai University Tianjin 300071 China

TLR2 is a key component of the innate immune system, responsible for recognizing Gram-positive bacterial components and initiating inflammatory signaling cascades that activate defense responses. However, little is known about the regulatory effects of Pseudomonas aeruginosa (P. aeruginosa) on TLR2 expression. In this study, we investigated the potential link between P. aeruginosa-derived DnaJ and TLR2 expression in macrophages, as well as the activation of downstream signaling pathways. Our findings revealed that DnaJ significantly induced TLR2 expression in a dose- and time-dependent manner, predominantly affecting TLR2 with minimal impact on other TLRs, such as TLR4 and TLR5, which detect bacterial PAMPs. The DnaJ-mediated TLR2 induction was driven by activation of the PI3K-SGK1 signaling pathway, with TLR10 playing a crucial role in facilitating these effects. This increase in TLR2 expression led to enhanced production of inflammatory cytokines in response to secondary Staphylococcus aureus infections, indicating a role in boosting host defense mechanisms. In conclusion, these findings suggest that P. aeruginosa-derived DnaJ promotes TLR2 expression via TLR10-mediated activation of the PI3K-SGK1 pathway, thereby enhancing host immune responses against Gram-positive bacterial infections. © 2025 Institut Pasteur

关键词： DnaJ Pseudomonas aeruginosa SGK1 TLR10 TLR2

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HiPoNet: A Topology-Preserving Multi-View Neural Network For High Dimensional Point Cloud and Single-Cell Data

arXiv

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arXiv 2025年

作者： Viswanath, Siddharth Madhu, Hiren Bhaskar, Dhananjay Kovalic, Jake Johnson, Dave Ying, Rex Tape, Christopher Adelstein, Ian Perlmutter, Michael Krishnaswamy, Smita Department of Computer Science Yale University New Haven United States Department of Genetics Yale University New Haven United States Department of Applied Physics Yale University New Haven United States Program in Computing Boise State University Idaho United States Cell Communication Lab Department of Oncology University College London Cancer Institute London United Kingdom Department of Mathematics Yale University New Haven United States Department of Mathematics Boise State University Idaho United States Computational Biology and Bioinformatics Program Yale University New Haven United States Wu-Tsai Institute Yale University New Haven United States Program for Applied Math Yale University New Haven United States

In this paper, we propose HiPoNet, an end-to-end differentiable neural network for regression, classification, and representation learning on high-dimensional point clouds. Single-cell data can have high dimensionality exceeding the capabilities of existing methods for point clouds which are tailored for 3D data. Moreover, modern single-cell and spatial experiments now yield entire cohorts of datasets (i.e. one on every patient), necessitating models that can process large, high-dimensional point clouds at scale. Most current approaches build a single nearest-neighbor graph, discarding important geometric information. In contrast, HiPoNet forms higher-order simplicial complexes through learnable feature reweighting, generating multiple data views that disentangle distinct biological processes. It then employs simplicial wavelet transforms to extract multiscale features—capturing both local and global topology. We empirically show that these components preserve topological information in the learned representations, and that HiPoNet significantly outperforms state-of-the-art point-cloud and graph-based models on single cell. We also show an application of HiPoNet on spatial transcriptomics datasets using spatial co-ordinates as one of the views. Overall, HiPoNet offers a robust and scalable solution for high-dimensional data analysis. © 2025, CC BY.

关键词： Network topology

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Genomic data processing with GenomeFlow

arXiv

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arXiv 2025年

作者： Park, Junseok Maury, Eduardo A. Oh, Changhoon Shin, Donghoon Denisko, Danielle Lee, Eunjung Alice Division of Genetics and Genomics Boston Children’s Hospital 3 Blackfan Circle BostonMA United States Department of Pediatrics Harvard Medical School 25 Shattuck St BostonMA United States Bioinformatics Integrative Genomics Program Harvard/MIT MD-PHD Program Harvard Medical School BostonMA United States Program in Medical and Population Genetics Broad Institute of MIT and Harvard CambridgeMA United States Graduate School of Information Yonsei University 50 Yonsei-ro Seodaemun-gu Seoul Korea Republic of Department of Human Centered Design and Engineering University of Washington SeattleWA United States Department of Biomedical Informatics Harvard Medical School BostonMA United States

Advances in genome sequencing technologies generate massive amounts of sequence data that are increasingly analyzed and shared through public repositories. On-demand infrastructure services on cloud computing platforms enable the processing of such large-scale genomic sequence data in distributed processing environments with a significant reduction in analysis time. However, parallel processing on cloud computing platforms presents many challenges to researchers, even skillful bioinformaticians. In particular, it is difficult to design a computing architecture optimized to reduce the cost of computing and disk storage as genomic data analysis pipelines often employ many heterogeneous tools with different resource requirements. To address these issues, we developed GenomeFlow, a tool for automated development of computing architecture and resource optimization on Google Cloud Platform, which allows users to process a large number of samples at minimal cost. We outline multiple use cases of GenomeFlow demonstrating its utility to significantly reduce computing time and cost associated with analyzing genomic and transcriptomic data from hundreds to tens of thousands of samples from several consortia. Here, we describe a step-by-step protocol on how to use GenomeFlow for a common genomic data processing task. We introduce this example protocol geared toward a bioinformatician with little experience in cloud computing. © 2025, CC BY.

关键词： Cloud platforms

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Synthetic Control Removes Spurious Discoveries from Double Dipping in Single-Cell and Spatial Transcriptomics Data Analyses

Synthetic Control Removes Spurious Discoveries from Double ...

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29th International Conference on Research in Computational Molecular Biology, RECOMB 2025

作者： Song, Dongyuan Chen, Siqi Lee, Christy Li, Kexin Ge, Xinzhou Li, Jingyi Jessica Department of Genetics and Genome Sciences University of Connecticut Health Center FarmingtonCT06032 United States Interdepartmental Program of Bioinformatics University of California Los Angeles Los AngelesCA90095-7246 United States Department of Statistics and Data Science University of California Los Angeles Los AngelesCA90095-1554 United States Department of Statistics Oregon State University CorvallisOR97331-4606 United States

ISBN: (纸本)9783031902512

Double dipping is a common analytical pitfall in single-cell and spatial transcriptomics data analysis: after a clustering algorithm finds clusters as putative cell types or spatial domains, statistical tests are applied to the same data to identify differentially expressed (DE) genes as potential cell-type or spatial-domain markers. Because the genes that contribute to clustering are inherently likely to be identified as DE genes, double dipping can result in false-positive markers, especially when clusters are spurious, leading to ambiguously defined cell types or spatial domains. To address this challenge, we propose ClusterDE, a statistical method designed to identify post-clustering DE genes as reliable markers of cell types and spatial domains, while controlling the false discovery rate (FDR) regardless of clustering quality. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Biomolecules

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Depression increases cancer mortality by 23–83%: a meta-analysis of 65 studies across five major cancer types

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GeroScience 2025年 1-17页

作者： Ungvari, Zoltan Fekete, Mónika Buda, Annamaria Lehoczki, Andrea Fekete, János Tibor Varga, Péter Ungvari, Anna Győrffy, Balázs Vascular Cognitive Impairment Neurodegeneration and Healthy Brain Aging Program Department of Neurosurgery University of Oklahoma Health Sciences Center Oklahoma City OK United States Stephenson Cancer Center University of Oklahoma Oklahoma City OK United States Oklahoma Center for Geroscience and Healthy Brain Aging University of Oklahoma Health Sciences Center Oklahoma City OK United States Department of Health Promotion Sciences College of Public Health University of Oklahoma Health Sciences Center Oklahoma City OK United States IDoctoral College/Institute of Preventive Medicine and Public Health International Training Program in Geroscience Semmelweis University Budapest Hungary Institute of Preventive Medicine and Public Health Semmelweis University Semmelweis University Budapest Hungary Jozsef Fodor Center for Prevention and Healthy Aging Semmelweis University Budapest Hungary Health Sciences Division Doctoral College Semmelweis University Budapest Hungary Dept. of Bioinformatics Semmelweis University Budapest 1094 Hungary Cancer Biomarker Research Group Institute of Molecular Life Sciences HUN-REN Research Centre for Natural Sciences Budapest 1117 Hungary Dept. of Biophysics Medical School University of Pecs Pecs 7624 Hungary

Depression is a prevalent but often underrecognized comorbidity among cancer patients. Emerging evidence suggests that psychological distress may adversely impact cancer outcomes, but the magnitude of its effect on survival remains unclear. This meta-analysis evaluates the association between depression diagnosed after cancer diagnosis and cancer-specific and all-cause mortality across major cancer types. A systematic search of PubMed, Web of Science, Google Scholar, and the Cochrane Library was conducted to identify cohort studies examining the impact of depression on cancer mortality. Studies were included if they assessed clinically diagnosed depression or depressive symptoms using validated scales and reported hazard ratios (HRs) for mortality outcomes. A random-effects meta-analysis was performed to pool HR estimates, with heterogeneity assessed via Cochran’s Q and I² statistics. Funnel plots and Egger’s test were used to evaluate publication bias. A total of 65 cohort studies were included. Depression was associated with significantly increased cancer-specific mortality in colorectal cancer (HR 1.83, 95% CI 1.47–2.28), breast cancer (HR 1.23, 95% CI 1.13–1.34), lung cancer (HR 1.59, 95% CI 1.36–1.86), and prostate cancer (HR 1.74, 95% CI 1.36–2.23). When considering mixed cancer types, depression was linked to a 38% increased risk of cancer mortality (HR 1.38, 95% CI 1.20–1.60). Significant heterogeneity was observed across studies (I² range 56–98%), suggesting variations in study populations and methodologies. Sensitivity analyses confirmed the robustness of the findings, and trial sequential analysis indicated sufficient evidence for a conclusive association. Depression after cancer diagnosis is associated with a significantly increased risk of cancer-specific mortality across multiple cancer types. These findings highlight the urgent need for integrating routine mental health screening and interventions into oncology care. Future resea

关键词： Anxiety Breast cancer Cancer mortality Colorectal cancer Depression Lung cancer Mental health interventions Mortality Oncology Post-diagnosis Prognosis Prostate cancer Psycho-oncology Psychological distress Psychosocial determinants Survival

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Network-based method to infer the contributions of proteins to the etiology of drug side effects

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Frontiers of Electrical and Electronic Engineering in China 2015年第3期10卷 124-134页

作者： Rui Li (1) Ting Chen (1) (2) Shao Li (1) 1.MOE Key Laboratory of Bioinformatics and Bioinformatics Division TNLIST Department of Automation Tsinghua University Beijing 100084 China 2.Program in Computational Biology and Bioinformatics University of Southern California Los Angeles CA 90089 USA

Studying the molecular mechanisms that underlie the relationship between drugs and the side effects they produce is critical for drug discovery and drug development. Currently, however, computational methods are still unavailable to assess drug-protein interactions with the aim of globally inferring the contributions of various classes of proteins toward the etiology of side effects. In this work, we integrated data reflecting drug-side effect relationships, drug- target relationships, and protein-protein interactions to develop a novel network-based probabilistic model, SidePro, to evaluate the contributions of proteins toward the etiology of side effects. For a given side effect, the method applies an expectation--maximization algorithm and a diffusion kernel-based approach to estimate each protein＇s contribution. We applied this method to a wide range of side effects and validated the results using cross-validation and records from the Side Effect Resource database. We also studied a specific side effect, nephrotoxicity, which is known to be associated with the irrational use of the Chinese herbal compound triptolide, a diterpenoid epoxide in the Thunder of God Vine, Tripterygium wilfordii （Lei-Gong-Teng）. Using triptolide as an example, we scored the target proteins of triptolide using our model and investigated the high-scoring proteins and their related biological processes. The results demonstrated that our model could differentiate between the potential side effect targets and therapeutic targets of triptolide. Overall, the proposed model could accurately pinpoint the molecular mechanisms of drug side effects, thus making contribution to safe and effective drug development.

关键词： network pharmacology drug targets side effects triptolide

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CIWARS: A Web Server for Antibiotic Resistance Surveillance Using Longitudinal Metagenomic Data

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Journal of Molecular Biology 2025年

作者： Emon, Muhit Islam Cheung, Yat Fei Stoll, James Rumi, Monjura Afrin Brown, Connor Choi, Joung Min Moumi, Nazifa Ahmed Ahmed, Shafayat Song, Haoqiu Sein, Justin Yao, Shunyu Khan, Ahmad Gupta, Suraj Kulkarni, Rutwik Butt, Ali Vikesland, Peter Pruden, Amy Zhang, Liqing Department of Computer Science Virginia Tech Blacksburg 24060 VA United States Fralin Life Science Institute Virginia Tech Blacksburg 24060 VA United States Department of Civil and Environmental Engineering Virginia Tech Blacksburg 24060 VA United States The Interdisciplinary PhD Program in Genetics Bioinformatics and Computational Biology Virginia Tech Blacksburg 24060 VA United States

The rise of antibiotic resistance (AR) poses a substantial threat to human and animal health, food security, and economic stability. Wastewater-based surveillance (WBS) has emerged as a powerful strategy for population-level AR monitoring, providing valuable data to guide public health and policy decisions. Metagenomic sequencing is especially promising, as it can yield comprehensive profiles of antibiotic resistance genes (ARGs) and other genes relevant to AR in a single run. However, online analytical platforms to facilitate analysis of longitudinal metagenomic data are lacking. To address this, we introduce CyberInfrastructure for Waterborne Antibiotic Resistance Surveillance (CIWARS), a web server configured for characterizing key AR trends from longitudinal metagenomic WBS data. CIWARS offers comprehensive profiling of ARGs and taxonomic profiling of pathogen-associated bacterial taxonomic groups, identifies potential associations of ARGs with mobile genetic elements (MGEs) and pathogen-containing taxa, and assesses resistome risk based on the co-occurrence of ARGs, MGEs, and pathogen-like sequences. Additionally, it detects anomalous AR indicators over time, aiding in identifying potential events of concern, such as the emergence of resistant strains or outbreaks. Through interactive temporal data visualization, CIWARS enables AR monitoring and can serve as a tool to inform effective and timely interventions to mitigate the spread and transmission of AR. Here, CIWARS is demonstrated using longitudinal metagenomic data from a wastewater treatment plant (WWTP) influent and effluent, but it can be extended to any environment. CIWARS provides a valuable tool to support global efforts to combat the evolution and spread of AR, while also guiding agricultural and public health efforts aimed at optimizing antibiotic use. The web server is freely available at https://***/. © 2025 Elsevier Ltd

关键词： anomaly detection antibiotic resistance (AR) AR indicator profiling longitudinal metagenomics wastewater-based surveillance

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The Anticancer and Apoptotic Activity of the Endophytic Fungus Phomopsis Sp. Isolated from Soursop Leaves in Mcf-7 Breast Cancer Cells: In Vitro and Computational in Silico Studies

SSRN

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SSRN 2025年

作者： Dwicesaria, Maheswari Alfira Priosoeryanto, Bambang Pontjo Hasan, Akhmad Endang Zainal Irsal, Riyan Alifbi Putera Gholam, Gusnia Meilin Mahendra, Fachrur Rizal Suminto, Syaefudin Indonesia Department of Biochemistry Faculty of Mathematics and Natural Sciences IPB University Dramaga Campus Bogor16680 Indonesia School of Veterinary Medicine and Biomedicine IPB University Bogor16680 Indonesia Faculty of Engineering Kyushu University Nishi-ku Fukuoka819-0395 Japan Tropical Biopharmaca Research Center IPB University Bogor16128 Indonesia Bioinformatics Study Program Faculty of Mathematics and Natural Sciences IPB University Bogor16680 Indonesia

This study investigated the anticancer potential of the endophytic fungus Phomopsis sp., extracted from soursop leaves. The metabolite profile of Phomopsis sp. was analyzed and its anticancer and apoptotic activities were assessed in both human breast cancer cell (MCF-7) and normal Vero cell lines. Phomopsis sp. were cultivated in yeast malt broth, extracted with ethyl acetate, and separated using column chromatography. The brine shrimp lethality test gave ten fractions (F1–F10), with F1 demonstrating the highest cytotoxicity (LC50 = 2.33 µg/mL). The obtained fractions were evaluated for antiproliferative effects, revealing an IC50 of 5.65 µg/mL for MCF-7 cells and 5.21 µg/mL for Vero cells, indicating effective anti-proliferation through apoptosis, as confirmed by acridine orange-ethidium bromide staining. Liquid Chromatography-tandem Mass Spectrometry identified 38 active compounds in F1, including known anticancer agents such as cytosporone C, kynurenic acid, maraniol, (-)-caryophyllene oxide, and chalcone. Computational in silico analyses utilizing PASS online, molecular docking, ADMET/pharmacokinetic studies, density functional theory (DFT) analysis, and molecular dynamics (MD) simulations further validated chalcone as a promising anticancer candidate. Chalcone exhibited favorable ADMET/pharmacokinetic properties and achieved the highest docking score -8.392 kcal/mol among the compounds tested. MD simulations over 300 ns demonstrated that chalcone outperformed tamoxifen, with binding energy calculations revealing a value of -227.069 kJ/mol compared to -45.598 kJ/mol for tamoxifen. These findings highlight the therapeutic potential of Phomopsis sp. and its bioactive compounds, particularly chalcones, as candidates for effective anti-breast cancer therapies. © 2025, The Authors. All rights reserved.

关键词： Column chromatography

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BPS2025 - Biophysical insights into allosteric modulation and inhibition of viral proteases using MD simulations

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Biophysical Journal 2025年第3期124卷 61a-61a页

作者： Marion Q. LoPresti Kelsie M. King Anne M. Brown Virginia Tech Blacksburg VA USA Genetics Bioinformatics and Computational Biology Program Virginia Tech Christiansburg VA USA Department of Biochemistry Virginia Tech Blacksburg VA USA

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