Nat Commun. 2025 Jul 18;16(1):6644. doi: 10.1038/s41467-025-61977-7.

ABSTRACT

The functional characterization of host-gut microbiome interactions remains limited by the sensitivity of current metaproteomic approaches. Here, we present uMetaP, an ultra-sensitive workflow combining advanced LC-MS technologies with an FDR-validated de novo sequencing strategy, novoMP. uMetaP markedly expands functional coverage and improves the taxonomic detection limit of the gut dark metaproteome by 5000-fold, enabling precise detection and quantification of low-abundance microbial and host proteins. Applied to a mouse model of intestinal injury, uMetaP revealed host-microbiome functional networks underlying tissue damage, beyond genomic findings. Orthogonal validation using transcriptomic data from Crohn's disease patients confirmed key host protein alterations. Furthermore, we introduce the concept of a druggable metaproteome, mapping functional targets within the host and microbiota. By redefining the sensitivity limits of metaproteomics, uMetaP provides a highly valuable framework for advancing microbiome research and developing therapeutic strategies for microbiome-related diseases.

PMID:40681571 | DOI:10.1038/s41467-025-61977-7

NPJ Syst Biol Appl. 2025 Jul 19;11(1):80. doi: 10.1038/s41540-025-00561-7.

ABSTRACT

Silver nanoparticles (AgNPs) are widely used in industrial and biomedical applications, however, their toxicity mechanisms at the molecular level are not completely understood. To address this gap, we investigate the temporal dynamics of gene expression in human lung epithelial cells exposed to AgNPs, integrating transcriptomic analysis, gene ontology (GO) enrichment, protein-protein interaction (PPI) networks, and dynamic simulations. GO analysis highlights early activation of ribosomal biogenesis and stress pathways, transitioning DNA repair and cell cycle regulation at later stages. PPI networks identify ribosomal proteins and DNA damage regulators as key hub genes. Dynamic simulations modeled gene expression changes over 48 hours, uncovering sequential activation of stress response genes, followed by DNA repair attempts and apoptotic signaling as cellular damage persisted. Through modeling the interplay between molecular responses and cell viability, the simulations provided a predictive temporal framework for advancing nanotoxicology research, providing insights into AgNPs-induced molecular disturbances, contributing to safety assessments.

PMID:40681532 | DOI:10.1038/s41540-025-00561-7

Nat Commun. 2025 Jul 19;16(1):6649. doi: 10.1038/s41467-025-61693-2.

ABSTRACT

Plastic pollution is one of most daunting sustainability challenges. Multi-functional and biodegradable plastics are critical for both desirable end-of-life outcomes and petrochemical plastics replacement. Current bioplastics are either: short of mechanical properties, like polyhydroxybutyrate (PHB); lack room temperature biodegradability, like polylactic acid (PLA); or lack the functionality to create additional values. Here, we present the bioinspired Layered, Ecological, Advanced, and multi-Functional Film (LEAFF), for sustainable plastic packaging. This biomimetic composite, based on the structure of the natural plant leaf, synergistically improves mechanical strength while empowering PLA for rapid ambient soil biodegradability, achieving complete degradation in 5 weeks. The film is also highly transparent and water stable, and achieves high gas barrier properties to improve food shelf life and reduce waste. The biomimetic design showcases the synergistic advantage leveraged by the LEAFF's multilayer structure to enhance mechanical performance while simultaneously retaining biodegradability and achieving multifunctionality for broad applications.

PMID:40681509 | DOI:10.1038/s41467-025-61693-2

Nat Commun. 2025 Jul 19;16(1):6651. doi: 10.1038/s41467-025-61904-w.

ABSTRACT

PINK1/Parkin-mediated ubiquitin-dependent mitophagy is a critical negative regulatory machinery for browning in the inguinal white adipose tissue (iWAT). However, the precise regulatory mechanism underlying PINK1/Parkin-mediated mitophagy during browning of iWAT remains largely unknown. Here we report that PNPLA7, an Endoplasmic Reticulum and mitochondria-associated membrane (MAM) protein, inhibits browning of iWAT by promoting PINK1/Parkin-mediated mitophagy upon cold challenge or β3-adrenergic receptor agonist treatment. With genetic manipulation in mice, we show that adipose tissue overexpressing PNPLA7 induces mitophagy, abolishes iWAT browning and interrupts adaptive thermogenesis. Conversely, conditional ablation of PNPLA7 in adipose tissue promotes browning of iWAT, resulting in enhanced adaptive thermogenesis. Mechanistically, PNPLA7 interacts with Parkin to promote mitochondrial recruitment of Parkin for mitophagy activation and mitochondria degradation by disrupting PKA-induced phosphorylation of Parkin under cold challenge. Taken together, our findings suggest that PNPLA7 is a critical regulator of mitophagy that resists cold-induced browning of iWAT, thus providing a direct mechanistic link between mitophagy and browning of iWAT.

PMID:40681495 | DOI:10.1038/s41467-025-61904-w

OMICS. 2025 Jul 18. doi: 10.1177/15578100251359566. Online ahead of print.

ABSTRACT

Phytophthora meadii is a polyphagous oomycete causing fatal diseases in economically important cash crops such as rubber, arecanut, cardamom, and other crops and plants of economic significance. Although information on the proteogenomic and proteomic analysis is available for several Phytophthora species, no information on the proteome repertoire of P. meadii is available. In the present study, a gel-free protein annotation was performed using liquid chromatography with tandem mass spectrometry analysis of the P. meadii hyphae, followed by bioinformatics analysis. The results were compared with a global Phytophthora proteome database-based search and an in-house P. meadii genome database, along with RefSeq proteome databases of other selected species of Phytophthora. A total of 7725 and 3979 proteins were exclusively matched with global and in-house databases, respectively. Basic Local Alignment Search Tool analysis showed 209 unique peptide sequences belonging to 85 proteins of P. meadii. Gene Ontology-based functional analysis of the P. meadii mycelial proteome categorized the proteins based on their role in cellular components, molecular functions, and biological processes. Kyoto Encyclopedia of Genes and Genomes pathway and protein-protein network analysis further revealed the role of these proteins in growth and development functions. In addition, proteins potentially involved in virulence, infections in the host system, and several signaling mechanisms were deduced. The current study is the first report on the P. meadii mycelial proteins under optimum growth conditions. These omics data also have socioeconomic implications since Phytophthora causes disease in a wide range of economically noteworthy crops and forest ecosystems.

PMID:40681316 | DOI:10.1177/15578100251359566

Int J Biol Macromol. 2025 Jul 16:146089. doi: 10.1016/j.ijbiomac.2025.146089. Online ahead of print.

ABSTRACT

Our previous research revealed aberrant serum N-glycan profiles in non-small-cell lung cancer (NSCLC), but the specific protein sources remain unclear. While immunoglobulin G (IgG) N-glycosylation has been implicated in cancer, its alterations in NSCLC are not well defined. Herein, we profiled the serum IgG N-glycome of 314 NSCLC patients and 364 healthy controls using a high-throughput MALDI-TOF-MS platform. Lectin-based enzyme-linked immunosorbent assay (ELISA) was applied for orthogonal validation. Machine learning was employed to construct a glycan-based diagnostic model. Two-sample Mendelian randomization (MR) analysis was performed to access potential causal relationships. The findings suggested positive correlations between matched IgG and whole-serum N-glycans. Compared with controls, NSCLC patients exhibited distinct IgG glycosylation patterns, including decreased galactosylation, monosialylation, and bisecting N-acetylglucosamine, alongside increased agalactosylation. The lectin-based assay confirmed the reductions in IgG galactosylation and sialylation. An eight-glycan panel demonstrated robust capability for NSCLC discrimination. MR analysis further revealed an inverse association between the IgG FS1/FS2 ratio and NSCLC risk. In conclusion, this study identified dysregulated IgG N-glycan signatures in NSCLC and proposed a pathogenic role for specific glycosylation traits. The findings unveil the potential of IgG glycans as non-invasive biomarkers and provide novel insights into the pathogenesis and therapeutic strategies for NSCLC.

PMID:40680951 | DOI:10.1016/j.ijbiomac.2025.146089

Cancer Cell. 2025 Jul 16:S1535-6108(25)00269-7. doi: 10.1016/j.ccell.2025.06.019. Online ahead of print.

ABSTRACT

Rapid recurrence is common in triple-negative breast cancer (TNBC). To better understand drivers of recurrence, we use imaging mass cytometry to characterize the tumor phenotype landscapes of 215 TNBC patients. We observe high intertumor heterogeneity with eleven tumor cell phenotypes, each of which dominates in an individual patient, and identify a tumor cell phenotype with reduced basoluminal lineage fidelity and stem-like traits that is correlated with rapid disease recurrence. Scoring of tumor-CD8+ T cell interactions identifies patients with inflamed tumors and high HLADR expression. We combine these features in multi-omics analyses of 8 cohorts with 3737 patients across all molecular subtypes to propose five prognostic breast cancer subtypes distinguished by tumor cytokeratin expression profiles and CD8+ T cell spatial patterns. This stratification scheme has direct clinical implications: inflamed tumors show good prognosis and high immunotherapy response rates, whereas patients dominated by basoluminal tumor cells have poor prognosis.

PMID:40680742 | DOI:10.1016/j.ccell.2025.06.019

Curr Opin Chem Biol. 2025 Jul 17;87:102621. doi: 10.1016/j.cbpa.2025.102621. Online ahead of print.

NO ABSTRACT

PMID:40680589 | DOI:10.1016/j.cbpa.2025.102621

Int J Med Inform. 2025 Jun 19;203:106015. doi: 10.1016/j.ijmedinf.2025.106015. Online ahead of print.

ABSTRACT

INTRODUCTION: The rapid integration of artificial intelligence (AI) into healthcare presents significant governance challenges, requiring balanced approaches that safeguard safety, efficacy, equity, and trust (SEET). This study proposes a cognitive framework to guide AI governance, addressing tradeoffs between speed, scope, and capability.

OBJECTIVE: To develop a structured governance model that harmonizes stakeholder perspectives, focusing on multi-dimensional challenges and ethical principles essential for AI in healthcare.

METHODS: A multidisciplinary team convened at the Blueprints for Trust conference, organized by the American Medical Informatics Association (AMIA), and the Division of Clinical Informatics at Beth Israel Deaconess Medical Center. Following extensive discussions with 190 participants across sectors, three governance models were identified to address specific domains: (1) Clinical Decision Support (CDS), (2) Real-World Evidence (RWE), (3) Consumer Health (CH).

RESULTS: Three governance models emerged, tailored to CDS, RWE, and CH domains. Key recommendations include establishing a Health AI Consumer Consortium for patient-centered oversight, initiating voluntary accreditation and certification frameworks, and piloting risk-level-based standards. These models balance rapid adaptation with SEET-focused safeguards through transparency, inclusivity, and ongoing learning.

CONCLUSION: A proactive, constraint-based governance framework is critical for responsible AI integration in healthcare. This structured, multi-stakeholder approach provides a roadmap for ethical, transparent governance that can evolve with technological advancements, enhancing trust and safety in healthcare AI applications.

PMID:40680319 | DOI:10.1016/j.ijmedinf.2025.106015

Sci Adv. 2025 Jul 18;11(29):eadx8105. doi: 10.1126/sciadv.adx8105. Epub 2025 Jul 18.

ABSTRACT

Interleukin-2 (IL-2) regulates immune homeostasis by fine-tuning the balance between effector and regulatory T (Treg) cells. To identify regulators of IL-2 signaling, we performed genome-wide CRISPR-knockout screening in IL-2-dependent cells derived from a patient with adult T cell leukemia (ATL) and found enrichment of single guide RNAs targeting PRDM1, which encodes B lymphocyte-induced maturation protein 1 (BLIMP1). BLIMP1 inhibits IL-2 production by T cells; however, its role in IL-2 signaling remains unknown. Here, we show that overexpressing Prdm1 down-regulated IL-2 signaling, whereas Prdm1-deficiency enhanced IL-2 signaling in mouse CD4+ T cells and Treg cells with augmented IL-2 signaling in T cells from influenza-infected mice and during adoptive T cell transfer-induced colitis. Deleting PRDM1 in human CD4+ T cells and Treg cells also increased IL-2 signaling. Furthermore, CD4+ T cells from patients with ATL expressed less BLIMP1 and had enhanced IL-2 signaling, whereas overexpressing PRDM1 in ATL cells suppressed IL-2 signaling. Thus, BLIMP1 inhibits IL-2 signaling during normal and pathophysiological responses, suggesting that manipulating BLIMP1 could have therapeutic potential.

PMID:40680114 | DOI:10.1126/sciadv.adx8105

PLoS Pathog. 2025 Jul 18;21(7):e1013303. doi: 10.1371/journal.ppat.1013303. eCollection 2025 Jul.

ABSTRACT

Fungi are important and hyperdiverse organisms, yet chronically understudied. Most fungal clades have no reference genomes, impeding our understanding of their ecosystem functions and use as solutions in health and biotechnology. Also, opportunities for training in fungal biology and genomics are lacking, creating a bottleneck that hinders the recruitment and cultivation of a talented future mycological workforce. To address these issues, we developed Myco-Ed, an educational program offering training and scientific contributions through genome sequencing and analysis. Myco-Ed empowers students to pursue careers in fungal biology while improving fungal resources. Myco-Ed has been piloted at 12 institutions (15 classrooms) ranging from online e-Campuses to R1 universities, resulting in hundreds of fungal observations and many new high-quality reference genomes.

PMID:40680048 | DOI:10.1371/journal.ppat.1013303

PLoS One. 2025 Jul 18;20(7):e0328905. doi: 10.1371/journal.pone.0328905. eCollection 2025.

ABSTRACT

Genome-editing technologies hold significant potential across various biotechnological fields, yet concerns about possible risks, including off-target mutations, remain. To ensure safe and effective application, these unintended mutations must be rigorously examined and minimized. Computational approaches are anticipated to streamline the detection of off-target mutations; however, the performance of current prediction tools is limited, likely owing to insufficient knowledge of off-target mutation characteristics. In this study, we collected experimentally validated off-target mutation data and conducted a large-scale analysis of 177 nonredundant datasets obtained from six studies. We developed a method to assess the statistical significance of sequence pattern similarity and diversity between off-target sites. This method is based on a comparison of ordered relative entropy values for aligned target sequences, and it was compared with two other methods on the basis of Euclidean distance and the Pearson correlation coefficient. The three methods demonstrated clear correlations, indicating their validity. These methods were applied to 238 dataset pairs for the same target site, and it was revealed that off-target sequence patterns were quite similar across different experimental conditions, such as varying cell lines and independent experiments, suggesting that the intrinsic properties of the Cas-sgRNA-DNA complex play a key role in determining cleavage sites. However, newly engineered enzymes and those from different bacterial sources occasionally display unique off-target patterns, indicating the need for comprehensive evaluation of each new enzyme to develop reliable prediction tools. The insights gained from this study are expected to contribute to a better understanding of off-target mutation characteristics and support the development of more accurate computational prediction methods.

PMID:40680043 | DOI:10.1371/journal.pone.0328905

J Am Chem Soc. 2025 Jul 18. doi: 10.1021/jacs.5c04424. Online ahead of print.

ABSTRACT

Traditional methods for identifying selective protease substrates have primarily relied on synthetic libraries of linear peptides, which offer limited sequence and structural diversity. Here, we present an approach that leverages phage display technology to screen large libraries of chemically modified cyclic peptides, enabling the identification of highly selective substrates for a protease of interest. Our method uses a reactive chemical linker to cyclize peptides on the phage surface, while simultaneously incorporating an affinity tag and a fluorescent reporter. The affinity tag enables capture of the phage library and subsequent release of phages expressing optimal substrates upon incubation with a protease of interest. The addition of a turn-on fluorescent reporter allows direct quantification of cleavage efficiency throughout each selection round. The resulting identified substrates can then be chemically synthesized, optimized and validated using recombinant enzymes and cells. We demonstrate the utility of this approach using Fibroblast Activation Protein α (FAPα) and the related proline-specific protease, dipeptidyl peptidase-4 (DPP4), as targets. Phage selection and subsequent optimization identified substrates with selectivity for each target that have the potential to serve as valuable tools for applications in basic biology and fluorescence image-guided surgery (FIGS). Overall, our strategy provides a rapid and unbiased platform for effectively discovering highly selective, non-natural protease substrates, overcoming key limitations of existing methods.

PMID:40679920 | DOI:10.1021/jacs.5c04424

Bull Exp Biol Med. 2025 Jul 18. doi: 10.1007/s10517-025-06432-9. Online ahead of print.

ABSTRACT

The reliability of existing diagnostic methods for thyroid neoplasms remains questionable, which necessitates the search for alternative approaches. The use of nontarget proteomic analysis for diagnosing oncological diseases is gaining traction and represents an efficient method for multiplex analysis. This study analyzed 372 blood plasma samples collected from patients with histologically confirmed thyroid pathologies treated at the National Medical Research Center for Endocrinology in 2019-2021. The samples were obtained prior to surgical intervention. Sample preparation involved the reduction and alkylation of disulfide bonds, followed by proteolysis and purification using specialized cartridges. Proteomic analysis was performed using nanoflow HPLC coupled with high-resolution mass spectrometry in a data-dependent acquisition mode. Peptides were identified using FragPipe software, and their suitability for biomarker discovery was assessed. From this analysis, 60 candidate proteins for thyroid disease biomarkers were identified, and sequences of 2930 peptides were evaluated. Further evaluation of nine candidate proteins revealed 31 peptides with high suitability scores for quantification. These peptides can be consolidated into a single biomarker panel that will be further developed for the risk stratification of patients with thyroid diseases.

PMID:40679521 | DOI:10.1007/s10517-025-06432-9

Cardiol Rev. 2025 Jul 18. doi: 10.1097/CRD.0000000000000999. Online ahead of print.

ABSTRACT

Corin and furin are protetic enzymes central to the activation of natriuretic peptides (NPs), which regulate cardiovascular homeostasis. Recent insights suggest that disruptions in the Corin-Furin axis-via genetic polymorphisms, aberrant post-translational modifications, or disease-associated downregulation-contribute to the pathogenesis of hypertension, heart failure, and myocardial fibrosis. This study examines current challenges in enzymatic stability, pharmacodynamics, and delivery of corin- and furin-based therapies, emphasizing translational barriers and the need for precision medicine. We review preclinical models demonstrating the therapeutic promise of recombinant corin and furin inhibitors, as well as the limitations posed by species-specificity, short half-lives, and incomplete pharmacogenomic data. Multiomics platforms and systems biology approaches are highlighted as essential tools for identifying actionable targets, guiding patient stratification, and integrating corin genotyping into clinical care. Emerging strategies include engineered proteases, small-molecule modulators, and RNA-based interventions aimed at restoring proteolytic balance and enhancing NP signaling. While clinical application remains nascent, these findings underscore the therapeutic potential of targeting local NP-processing mechanisms. A comprehensive understanding of corin and furin function, regulation, and interactomes is critical for developing personalized interventions in cardiovascular disease.

PMID:40679493 | DOI:10.1097/CRD.0000000000000999

J Ind Microbiol Biotechnol. 2025 Jul 18:kuaf019. doi: 10.1093/jimb/kuaf019. Online ahead of print.

ABSTRACT

The thiopeptide GE2270A is a clinically relevant, ribosomally synthesised and post-translationally modified peptide (RiPP) naturally produced by Planobispora rosea. Due to the genetically intractable nature of P. rosea, heterologous expression is considered a possible route to yield improvement. In this study, we focused on improving GE2270A production through heterologous expression of the biosynthetic gene cluster (BGC) in the model organism Streptomyces coelicolor M1146. A statistically significant yield improvement was obtained in the S. coelicolor system through the data-driven rational engineering of the BGC, including the introduction of additional copies of key biosynthetic and regulatory genes. However, despite our best effort, the highest production level observed in the strains generated in this study is 12 × lower than published titres achieved in the natural producer and 50 × lower than published titres obtained using Nonomuraea ATCC 39727 as expression host. These results suggest that, while using the most genetically amenable strain as host can be the right choice when exploring different BGC designs, the choice of the most suitable host has a major effect on the achievable yield and should be carefully considered. The analysis of the multi-omics data obtained in this study suggests an important role of PbtX in GE2270A biosynthesis and provides insights into the differences in production metabolic profiles between the different strains.

PMID:40679465 | DOI:10.1093/jimb/kuaf019

Plant Physiol. 2025 Jul 18:kiaf312. doi: 10.1093/plphys/kiaf312. Online ahead of print.

NO ABSTRACT

PMID:40679385 | DOI:10.1093/plphys/kiaf312

J Pharm Anal. 2025 Jun;15(6):101295. doi: 10.1016/j.jpha.2025.101295. Epub 2025 Apr 9.

ABSTRACT

Combined with elastic network model (ENM), the perturbation response scanning (PRS) has emerged as a robust technique for pinpointing allosteric interactions within proteins. Here, we proposed the PRS analysis of drug-target networks (DTNs), which could provide a promising avenue in network medicine. We demonstrated the utility of the method by introducing a deep learning and network perturbation-based framework, for drug repurposing of multiple sclerosis (MS). First, the MS comorbidity network was constructed by performing a random walk with restart algorithm based on shared genes between MS and other diseases as seed nodes. Then, based on topological analysis and functional annotation, the neurotransmission module was identified as the "therapeutic module" of MS. Further, perturbation scores of drugs on the module were calculated by constructing the DTN and introducing the PRS analysis, giving a list of repurposable drugs for MS. Mechanism of action analysis both at pathway and structural levels screened dihydroergocristine as a candidate drug of MS by targeting a serotonin receptor of serotonin 2B receptor (HTR2B). Finally, we established a cuprizone-induced chronic mouse model to evaluate the alteration of HTR2B in mouse brain regions and observed that HTR2B was significantly reduced in the cuprizone-induced mouse cortex. These findings proved that the network perturbation modeling is a promising avenue for drug repurposing of MS. As a useful systematic method, our approach can also be used to discover the new molecular mechanism and provide effective candidate drugs for other complex diseases.

PMID:40678478 | PMC:PMC12268079 | DOI:10.1016/j.jpha.2025.101295

Synth Syst Biotechnol. 2025 Jun 14;10(4):1140-1149. doi: 10.1016/j.synbio.2025.06.005. eCollection 2025 Dec.

ABSTRACT

The complex transcriptional regulatory relationships among genes influence gene expression levels and play a crucial role in determining cellular phenotypes. In this study, we propose a novel, distributed, large-scale transcriptional regulatory neural network model (DLTRNM), which integrates prior knowledge into the reconstruction of pre-trained machine learning models, followed by fine-tuning. Using Saccharomyces cerevisiae as a case study, the curated transcriptional regulatory relationships are used to define the interactions between transcription factors (TFs) and their target genes (TGs). Subsequently, DLTRNM is pre-trained on pan-transcriptomic data and fine-tuned with time-series data, enabling it to accurately predict regulatory correlations. Additionally, DLTRNM can help identify potential key TFs, thereby simplifying the complex and interrelated transcriptional regulatory networks (TRNs). It can also complement previously reported transcriptional regulatory subnetworks. DLTRNM provides a powerful tool for studying transcriptional regulation with reduced computational demands and enhanced interpretability. Thus, this study marks a significant advancement in systems biology for understanding the complex transcriptional regulation within cells.

PMID:40678148 | PMC:PMC12268561 | DOI:10.1016/j.synbio.2025.06.005

Mol Ther Nucleic Acids. 2025 Jun 16;36(3):102606. doi: 10.1016/j.omtn.2025.102606. eCollection 2025 Sep 9.

ABSTRACT

In the field of cancer therapy, bispecific T cell engagers (BiTEs) have demonstrated significant potential. However, their clinical application is constrained by challenges in production and limited plasma half-life. In vitro-transcribed (IVT) mRNA formulations emerge as a promising alternative, offering adaptability and cost-efficiency. Yet, the intricate relationship between mRNA dosage, antibody production, and the distribution of mRNA and proteins requires a deeper understanding. To address these issues, we present a novel physiologically based pharmacokinetic (PBPK) model to characterize the pharmacokinetics of BiTEs. This model predicts the distribution patterns of both recombinant and mRNA-encoded BiTEs by extending an established PBPK model with a hierarchical multiscale framework calibrated and validated using preclinical data from existing literature. The extended PBPK model can be adapted to various mRNA-based therapeutic formulations, facilitating in-silico exploration of different drug administration scenarios. It can provide valuable support for optimizing dose and schedule and allows the efficient investigation of drug distribution at a whole-body scale. This approach promises to enhance the personalization and effectiveness of cancer therapies, reduce research time and costs, and significantly advance the development of mRNA-based BiTEs for cancer treatment.

PMID:40677727 | PMC:PMC12269393 | DOI:10.1016/j.omtn.2025.102606