Plant Methods. 2025 Jul 18;21(1):99. doi: 10.1186/s13007-025-01422-4.

ABSTRACT

BACKGROUND: Xylophilus ampelinus is a plant pathogenic bacterium that causes bacterial blight in grapevines, which can lead to severe yield losses and economic damage. Owing to its fastidious growth on culture media, detection is primarily based on molecular methods. However, existing tests have produced inconsistent results, particularly when used to detect latent infections and non-validated matrices. There is a risk of false-positive results, with economic consequences such as restrictions on international trade. To enhance the diagnostics of X. ampelinus, a genome-informed approach was utilised to identify new potential targets for specific detection. On the basis of these sequences, multiple real-time PCR assays were designed, and their specificity and sensitivity were assessed, as well as their performance validated across three different grapevine tissues, including leaves, roots, and xylem.

RESULTS: The newly designed real-time PCR assays were evaluated via high throughput testing for specificity and sensitivity and compared with a reference assay. The most promising assays were selected and validated in different grapevine tissues and included in a test performance study to validate their reproducibility and robustness. Three new assays (Xamp_BA_2, TXmp22.4, and Xamp_BA_7) demonstrated high specificity and sensitivity for X. ampelinus detection. The Xamp_BA_2 assay exhibited the best overall performance, offering high diagnostic sensitivity and robustness across diverse plant matrices. Importantly, the assays exhibited no cross-reactivity with non-target bacterial species and maintained high detection accuracy across diverse grapevine tissue types.

CONCLUSIONS: The newly developed real-time PCR assays provide an enhanced diagnostic framework for the detection of X. ampelinus in various plant matrices, significantly improving the applicability of molecular testing. The Xamp_BA_2 assay demonstrates superior performance and is recommended for routine diagnostics, with other validated assays being employed for confirmation of identification. The development of these new assays represents a significant expansion of our toolkit for the precise detection of X. ampelinus in grapevines, with the potential to contribute to the mitigation of grapevine bacterial blight, the prevention of yield losses, and the protection of international trade in grapevine material. Further implementation of these assays will support regulatory and phytosanitary efforts to mitigate the spread of X. ampelinus.

PMID:40676692 | DOI:10.1186/s13007-025-01422-4

Br J Cancer. 2025 Jul 17. doi: 10.1038/s41416-025-03106-1. Online ahead of print.

ABSTRACT

BACKGROUND: Elevating major histocompatibility complex class I (MHC-I) levels in tumour cells can boost antitumour immunity and enhance immunotherapy for colorectal cancer (CRC). Screening an FDA-approved drug library showed that MEK inhibitors (MEKis) significantly increase MHC-I expression in CRC cells, though the mechanisms and antitumour effects of MEKis, as well as their impact on gut microbiota, remain unclear.

METHODS: Dual-luciferase reporter system was employed to screen MHC-I inducers. MHC-I expression was analysed using qRT-PCR, flow cytometry, and western blot. OT-I TCR transgenic mice, subcutaneous mouse tumour models, RNA-seq, and ChIP-qPCR were used to identify the underlying mechanism. Gut microbiota was depleted using antibiotics cocktail and analysed via Shotgun sequencing, 16S rRNA sequencing and nontargeted metabolomic sequencing.

RESULTS: MEKis, particularly cobimetinib, increased MHC-I expression by inhibiting PRMT5-mediated repression of NLRC5, boosting CD8+ T cell-mediated immunity and enhancing PD-L1 blockade efficacy. Cobimetinib also altered gut microbiota, reducing L-arginine via arginase production, which compromised antitumour immunity. Arginase inhibition or L-arginine supplementation restored immune responses.

CONCLUSIONS: This study uncovers a novel mechanism of MEKi-induced MHC-I expression and highlights the interplay between gut microbiota and antitumour immunity, providing insights for MEKi-based CRC immunotherapy.

PMID:40676225 | DOI:10.1038/s41416-025-03106-1

EMBO J. 2025 Jul 17. doi: 10.1038/s44318-025-00501-5. Online ahead of print.

ABSTRACT

Eukaryotic genomes replicate in a defined temporal order called the replication timing (RT) program. RT is developmentally regulated with the potential to drive cell fate transitions, but mechanisms controlling RT remain elusive. We previously identified "Early Replication Control Elements" (ERCEs), cis-acting elements necessary for early RT, domain-wide transcription, 3D chromatin architecture and compartmentalization in mouse embryonic stem cells (mESCs), but deletions identifying ERCEs were large and encompassed many putative regulatory elements. Here, we show that ERCEs are compound elements, whose RT activity can largely be accounted for by multiple binding sites for diverse master transcription factors (subERCEs). While deletion of subERCEs had large effects on both transcription and replication timing, deleting transcription start sites eliminated nearly all transcription with only moderate effects on replication timing. Our results suggest a model in which subERCEs are a class of transcriptional enhancers that can also organize chromatin domains structurally to support early replication timing, potentially providing a feed-forward loop to drive robust epigenomic change during cell fate transitions.

PMID:40676214 | DOI:10.1038/s44318-025-00501-5

Sci Rep. 2025 Jul 18;15(1):26026. doi: 10.1038/s41598-025-08090-3.

ABSTRACT

The increasing frequency of antibiotic resistant bacteria and their dissemination in environmental microbiomes is a critical health concern. Water quality assessment and AMR surveillance are broadly focused on commonly found Enterobacterales, and mainly on the faecal indicator E. coli. In this study, we analysed antibiotic resistance and biofilm formation in 14 environmental isolates belonging to six neglected species. Genetic diversity was assessed by ERIC-PCR. Identified as Cronobacter sakazakii (1), Kluyvera intermedia (1), Leclercia adecarboxylata (1), Raoultella ornithinolytica (8), Raoultella terrigena (1), and Yersinia massiliensis (2), each isolate had a unique and distinct AMR profile. The isolates demonstrated intrinsic resistance to erythromycin and increased resistance to ampicillin and tetracycline. None of the isolates exhibited carbapenem resistance. Ten isolates were MDR. Thirteen out of the 24 investigated ARGs were detected in bacterial genomes. Except for carbapenemases, various β-lactamases (blaTEM, blaCTX-M), and also tet, sul, erm, mef and qnr genes were found. A strong positive correlation was observed between the phenotypic and genotypic resistance. Due to its discriminatory power at the taxonomic level, ERIC-PCR fingerprinting provided a reliable and accurate molecular typing. Negative correlations between the number of ERIC bands, the magnitude of resistance and the biofilm score indicate that strains with abundant ERIC sequences are less likely to be resistant and to adhere to surfaces. This suggests that a high genome plasticity and adaptability prevents specific survival strategies and deserves further attention.

PMID:40676003 | DOI:10.1038/s41598-025-08090-3

Nat Commun. 2025 Jul 17;16(1):6591. doi: 10.1038/s41467-025-62094-1.

NO ABSTRACT

PMID:40675995 | DOI:10.1038/s41467-025-62094-1

Yi Chuan. 2025 Jul 20;47(7):813-820. doi: 10.16288/j.yczz.24-366.

ABSTRACT

The Brassica genus includes three diploid species and three amphidiploid species which were formed by the hybridization and genome duplication between pairs of the three diploid species. The evolution process of Brassica species has been used as a classic case to explain the mechanism of speciation and chromosome number variation in undergraduate genetics teaching. In this study, the authors designed a comprehensive experiment using Brassica plants as the experimental teaching material of genetics. The experimental system includes morphological observation, chromosome observation, and analysis of chromosome ploidy and karyotype. This experimental system includes experiments from macro to micro, will help students to understand the relationship between chromosome ploidy and speciation, and will help them to master the theory of polyploid formation and its application in breeding practice.

PMID:40675766 | DOI:10.16288/j.yczz.24-366

Plant J. 2025 Jul;123(2):e70294. doi: 10.1111/tpj.70294.

ABSTRACT

Advances in engineering of bioenergy crops were driven over the past years by adapting technological breakthroughs and accelerating conventional applications but also exposed intriguing challenges. New tools revealed rich interconnectivity in the exponentially growing and dynamic 'big' omics data' of metabolomes, transcriptomes, and genomes at previously inaccessible magnitude (global, cross-species, meta-) and resolution (single cell). Insights enabled fresh hypotheses and stimulated disciplines such as functional genomics with discovery of broad regulatory networks and their determinants, that is, DNA parts, including promoters, regulatory elements, and transcription factors. Their rational design, assembly into increasingly complex blueprints, and installation into diverse chassis is an existing frontier that may benefit from emerging technologies to address bottlenecks. Interweaving nature-inspired to fully synthetic parts has already allowed building of fine-tuned regulatory circuits, or new-to-nature metabolic routes insulated from the biological context of the chassis species. Similarly, developments and the evolving need for unifying principles in plant transformation and species-agnostic technologies highlight future opportunities for engineering the next generation of bioenergy plants.

PMID:40674648 | DOI:10.1111/tpj.70294

Science. 2025 Jul 17;389(6757):235-236. doi: 10.1126/science.adz5035. Epub 2025 Jul 17.

ABSTRACT

Designed protein pockets recognize intrinsically disordered protein regions.

PMID:40674482 | DOI:10.1126/science.adz5035

Cancer Rep (Hoboken). 2025 Jul;8(7):e70240. doi: 10.1002/cnr2.70240.

ABSTRACT

BACKGROUND AND AIMS: Lung cancer remains a leading cause of cancer-related deaths worldwide, with early detection critical for improving prognosis. Traditional machine learning (ML) models have shown limited generalizability in clinical settings. This study proposes a deep learning-based approach using transfer learning to accurately segment lung tumor regions from CT scans and classify images as cancerous or noncancerous, aiming to overcome the limitations of conventional ML models.

METHODS: We developed a two-stage model utilizing a ResNet50 backbone within a U-Net architecture for lesion segmentation, followed by a multi-layer perceptron (MLP) for binary classification. The model was trained on publicly available CT scan datasets and evaluated on an independent clinical dataset from Hazrat Rasool Hospital, Iran. Training employed binary cross-entropy and Dice loss functions. Data augmentation, dropout, and regularization were used to enhance model generalizability and prevent overfitting.

RESULTS: The model achieved 94% accuracy on the real-world clinical test set. Evaluation metrics, including F1 score, Matthews correlation coefficient (MCC), Cohen's kappa, and Dice index, confirmed the model's robustness and diagnostic reliability. In comparison, traditional ML models performed poorly on external test data despite high training accuracy, highlighting a significant generalization gap.

CONCLUSION: This research presents a reliable deep learning framework for lung cancer detection that outperforms traditional ML approaches on external validation. The results demonstrate its potential for clinical deployment. Future work will focus on prospective validation, interpretability techniques, and integration into hospital workflows to support real-time decision making and regulatory compliance.

PMID:40674395 | DOI:10.1002/cnr2.70240

Elife. 2025 Jul 17;13:RP104255. doi: 10.7554/eLife.104255.

ABSTRACT

Oocyte meiotic divisions represent a critical process in sexual reproduction, as a diploid non-dividing oocyte is transformed into a haploid fertilizable egg, as a prelude for the subsequent embryonic divisions and differentiation. Although cell differentiation and proliferation are governed by transcription, oocyte maturation and early embryonic divisions depend entirely on changes in protein abundance and post-translational modifications. Here, we analyze the abundance and phosphorylation of proteins during Xenopus oocyte meiotic maturation. We reveal significant shifts in protein stability, related to spindle assembly, DNA replication, and RNA-binding. Our analysis pinpoints broad changes in phosphorylation correlating with key cytological meiotic milestones, noteworthy changes in membrane trafficking, nuclear envelope disassembly, and modifications in microtubule dynamics. Additionally, specific phosphorylation events target regulators of protein translation, Cdk1 and the Mos/MAPK pathway, thereby providing insight into the dynamics of Cdk1 activity, as related to the meiotic cell cycle. This study sheds light on the orchestration of protein dynamics and phosphorylation events during oocyte meiotic divisions, providing a rich resource for understanding the molecular pathways orchestrating meiotic progression in the frog, and most likely applicable to other vertebrate species.

PMID:40674131 | DOI:10.7554/eLife.104255

Expert Rev Proteomics. 2025 Jul 17. doi: 10.1080/14789450.2025.2536061. Online ahead of print.

ABSTRACT

INTRODUCTION: . The ability of cancer cells to disseminate from the primary tumor and form metastatic lesions frequently leads to fatal outcomes. Recently, however, it has been recognized that this process is driven by complex interactions between the cancer and the neighboring cells, and, overall, made possible by a supportive tumor microenvironment. The emergence of high-throughput technologies is expected to bring much needed clarity to unraveling the players and intricate communication pathways that promote metastatic progression.

AREAS COVERED: In this report, the impact of mass spectrometry and proteomic technologies on deciphering the cross-talk between cancer and tumor microenvironment cells is discussed. Focus is placed on the role of cell-membrane and secretome proteins as the main enablers of this cross-talk, and on the challenges presented by metastatic tumors that evolve in the brain. Future prospects are assessed in the context of recent biology, technology, and data analysis breakthroughs.

EXPERT OPINION: Advancements in high-throughput proteomic technologies, complemented by the exciting potential of new disease model systems and data processing abilities of artificial intelligence, are expected to bring groundbreaking progress in deciphering the fundamental biological mechanisms that support cancer behavior and metastatic development, revealing novel therapeutic targets, and guiding innovative intervention approaches.

PMID:40673921 | DOI:10.1080/14789450.2025.2536061

FEBS Lett. 2025 Jul 17. doi: 10.1002/1873-3468.70115. Online ahead of print.

ABSTRACT

Understanding the biochemical pathways and interorgan cross talk underlying type 2 diabetes (T2D) is essential for elucidating its pathophysiology. These pathways provide a mechanistic framework linking molecular dysfunction to clinical phenotypes, enabling patient stratification based on dominant metabolic disturbances. Advances in multi-omics, including genomics, transcriptomics, proteomics, microbiomics, and metabolomics, offer a systems-level view connecting genetic variants and regulatory elements to disease traits. Single-cell technologies further refine this perspective by identifying cell-type-specific drivers of β-cell failure, hepatic glucose dysregulation, and adipose inflammation. AI-driven analytics and machine learning integrate these high-dimensional datasets, uncovering molecular signatures and regulatory networks involved in insulin signaling, lipid metabolism, mitochondrial function, and immune-metabolic cross talk. This review synthesizes current evidence on T2D's molecular architecture, emphasizing key pathways such as PI3K-Akt, AMPK, mTOR, JNK, and sirtuins. It also explores the role of gut microbiota in modulating host metabolism and inflammation. Adopting a pathway-centric systems biology approach moves beyond statistical associations toward mechanistic insight. Integrating multi-omics with AI-based modeling represents a transformative strategy for stratifying patients and guiding precision therapies in diabetes care. Impact statement This review translates complex biochemical pathways into therapeutic direction for type 2 diabetes, addressing a critical gap between molecular research and clinical care. By integrating multi-omics, AI, and systems biology, it empowers the scientific community to develop targeted interventions that reduce the global burden of this escalating metabolic disease.

PMID:40673471 | DOI:10.1002/1873-3468.70115

Data Brief. 2025 Jun 23;61:111821. doi: 10.1016/j.dib.2025.111821. eCollection 2025 Aug.

ABSTRACT

Tempe is a superior plant-based protein source that provides a diverse array of nutritional benefits as a result of the presence of bioactive metabolites. Nevertheless, there is a scarcity of information regarding the metabolomics profile between organic and conventional tempe and the fate of these metabolites after in vitro digestion. This report examines the metabolomic profile of soybean as raw material and tempe prior to and following the in vitro digestion process. We obtained a comprehensive set of metabolomic data using ultra-high-performance liquid chromatography coupled with high-resolution mass spectrometry (UHPLC-HRMS). The metabolomics dataset organized into Excel sheets and structured according to polarity, mass to charge ratio (m/z), retention time, feature name, biological replicates and controls. This data offers preliminary insights into the metabolite profile of tempe samples, encompassing source material soybean, tempe, and tempe digesta.

PMID:40673189 | PMC:PMC12266556 | DOI:10.1016/j.dib.2025.111821

Front Microbiol. 2025 Jul 2;16:1641452. doi: 10.3389/fmicb.2025.1641452. eCollection 2025.

ABSTRACT

[This corrects the article DOI: 10.3389/fmicb.2024.1470054.].

PMID:40673146 | PMC:PMC12266492 | DOI:10.3389/fmicb.2025.1641452

Comput Struct Biotechnol J. 2025 Jun 6;27:2747-2756. doi: 10.1016/j.csbj.2025.05.040. eCollection 2025.

ABSTRACT

Gene-gene co-expression network analysis has been widely applied to bulk RNA sequencing and microarray data to investigate different phenotypes and compound exposures. Recently, it has also been applied to single cell RNA sequencing data. However, the impact of different network models, data processing pipelines, and analysis strategies on downstream interpretations has not yet been characterized. Here we study the impact of network models and analysis strategies on the resulting interpretations from analyses of cell differentiation and cell state over time using gene-gene co-expression networks. Our results suggest that the network modeling choice has less impact on downstream results than the network analysis strategy selected. The largest differences in biological interpretation were observed between the node-based and community-based network analysis methods (strategies). In addition, we observe a difference between single time point and combined time point modeling.

PMID:40673123 | PMC:PMC12266514 | DOI:10.1016/j.csbj.2025.05.040

bioRxiv [Preprint]. 2025 Jul 10:2025.07.06.663404. doi: 10.1101/2025.07.06.663404.

ABSTRACT

Transplantation tolerance without the need for lifelong immunosuppression is a central goal in transplant immunology yet prior sensitization events remain a major barrier to achieving stable tolerance. In reproductive immunology by contrast, pregnancy represents a spontaneous model of tolerance where the semi-allogeneic fetus evades rejection even in multiparous or previously sensitized mothers. CD8 + T cell phenotypes of tolerance and rejection have been previously reported in transplant and pregnancy, but the transcriptional states of donor and fetus-specific CD4 + T cells remain poorly defined. To address this, we performed Single-cell RNA-sequencing (ScRNA-seq) on endogenous, donor-specific CD4 + T cells across models of naïve or paternally skin sensitized pregnancy as well as in a model of allogeneic heart transplants with or without co-stimulation blockade-induced tolerance. Our systems biology approach allowed us to identify shared and distinct transcriptional clusters of donor-specific CD4 + Foxp3 neg T conventional (Tconvs) and Foxp3 pos regulatory (Tregs) T cells from peripheral lymphoid tissue. We expectedly found regulatory populations restricted to tolerance and pregnancy but were surprised to find significant overlap in activated follicular and non-follicular effector phenotypes in rejection and successful pregnancy. We also showed these murine populations were relevant and enriched in human datasets of health and disease respectively. These findings highlight context-dependent differentiation programs of antigen-specific CD4 + T conventional and regulatory cells and provide new insights into their responses to allogeneic conflict at the intersection of transplant and reproductive immunology.

PMID:40672265 | PMC:PMC12265706 | DOI:10.1101/2025.07.06.663404

bioRxiv [Preprint]. 2025 Jul 9:2025.05.23.655795. doi: 10.1101/2025.05.23.655795.

ABSTRACT

Computational models in systems biology are often underdetermined-that is, there is little data relative to the complexity and size of the model. The lack of data is primarily due to limits in our ability to observe specific biological systems and restricts the utility of computational models. However, there are a growing number of experimental databases in biology. While these databases provide more observations, they often do not have observations that match the system of interest exactly. For example, database measurements might be collected at different experimental conditions or on a different scale compared to the system of interest. Here, we investigate what information can be gleaned from generalizing databases across these differences in the context of modeling a specific system - cell signaling. Ultimately, our goal is to better determine models of specific systems, thereby increasing their utility. To do this, we propose a novel, multiscale, probabilistic framework. We use this framework to integrate measurements of protein structure from the Protein Data Bank and measurements of amino acid sequence from the Universal Protein Resource into the parameter inference of cell signaling models. Then, we quantify exactly what information is gained from these measurements when modeling cell signaling. We choose to investigate the utility of these databases in the context of dynamic cell signaling models because experimental measurements of the variables of interest, protein dynamics, are still quite limited. We find that we can successfully integrate measurements from these databases to significantly improve parameter estimation of signaling models. The impact of sequence and structure measurements on model predictions depends on the sensitivity of the prediction to perturbations in the parameter values. Overall, this study demonstrates that measurements of protein structure and amino acid sequence can be leveraged to better inform parameters in models of cell signaling.

AUTHOR SUMMARY: Computational models of cell signaling have provided mechanistic insights into complex biological systems, including in physiological and disease settings. Accurate and predictive modeling critically depends on the precise estimation of model parameters, which is often hindered by the limited availability of experimental data. In this study, we present a novel multiscale probabilistic inference framework that broadens the scope of data types that can be leveraged for parameter estimation for models of cell signaling. The framework integrates a machine learning pipeline with a generalizable parameter inference approach, enabling the use of experimental data across scales. Specifically, we demonstrate that incorporating protein amino acid sequence and 3D structural data enhances parameter estimation compared to traditional measurements such as protein concentrations over time. Improving parameter estimation increases the robustness and applicability of cell signaling models. Ultimately, our framework facilitates use of a broader range of data and supports the development of predictive computational models that increase our understanding of cell signaling.

PMID:40672255 | PMC:PMC12265541 | DOI:10.1101/2025.05.23.655795

Curr Dev Nutr. 2025 Jun 9;9(7):107484. doi: 10.1016/j.cdnut.2025.107484. eCollection 2025 Jul.

ABSTRACT

BACKGROUND: A novel ready-to-use microbiome-directed food (MDF) has been developed for the management of acute malnutrition using ingredients that promote repair of the gut microbiota of undernourished children.

OBJECTIVES: This study aims to assess the acceptability of MDF compared with standard nutritional care among children with acute malnutrition.

METHODS: Two randomized crossover trials consisting of 2 14-d periods of at-home consumption were conducted. Children aged 6 to <24 mo with severe acute malnutrition (SAM) or moderate acute malnutrition (MAM) were individually randomized in a 1:1 ratio to the sequence of receiving MDF then standard nutritional care, or vice versa. Standard nutritional care consisted of ready-to-use therapeutic food for SAM and ready-to-use supplementary food for MAM. The primary outcome was at-home acceptability, defined as the return of ≥75% of sachets empty after the 14-d at-home consumption period. The primary analysis was a noninferiority analysis, in which MDF was considered noninferior if the lower bound of the 95% confidence interval (CI) of the difference in at-home acceptability comparing MDF with standard nutritional care was within -20 percentage points. Secondary outcomes included caregiver's perception of the child's liking, as well as caregiver willingness to use in the future and preference between the 2 foods.

RESULTS: In all, 128 children with SAM and 146 children with MAM were randomized. MDF was noninferior to standard nutritional care in terms of at-home acceptability among children with SAM (risk difference: -7.0; 95% CI lower bound: -11.6%) and among children with MAM (risk difference: -2.3%; 95% CI lower bound: -6.1%). There were no differences in caregiver willingness to use either food in future.

CONCLUSIONS: MDF is acceptable for the management of acute malnutrition in children aged 6 to <24 mo in Niger and should be further tested in other populations with a high prevalence of acute malnutrition. Effectiveness of the novel food will be assessed in forthcoming trials.

TRIAL REGISTRATION NUMBER: This trial was registered at clinicaltrials.gov as NCT05551819.

PMID:40672125 | PMC:PMC12266491 | DOI:10.1016/j.cdnut.2025.107484

ACS Infect Dis. 2025 Jul 17. doi: 10.1021/acsinfecdis.5c00286. Online ahead of print.

ABSTRACT

Clostridioides difficile infection can cause severe inflammation in the gastrointestinal (GI) tract, leading to diarrhea, colitis, and an increased risk of colorectal cancer. Colonization of C. difficile is associated with microbial community-level changes in the expression of polyamine and polyamine precursor biosynthesis genes. Polyamines are abundant cationic metabolites that serve indispensable functions for all kingdoms, particularly in gut homeostasis. Catabolism of the polyamine precursors arginine and ornithine offers C. difficile supplemental nutrition while subverting host immunity, yet existing models of C. difficile metabolism are incomplete regarding polyamines with comparable importance in the gut (e.g., spermidine). In this study, we conducted feeding studies with isotope-labeled polyamines and discovered a network of low-molecular-weight thiol (LMWT) molecules termed clostridithiols (CSHs) constructed from polyamines conjugated with N-acetylcysteine (NAC) moieties. NAC is clinically used as a mucolytic agent and is a well-established redox molecule. Through the analysis of a human microbiota diversity collection, we established that these previously uncharacterized hybrid metabolites are widely detected in Firmicutes and Bacteroidetes. A genetic screen using DNA from an alternative CSH producerBacteroides uniformis enabled the identification and validation of a two-gene operon, including a gene encoding a domain of unknown function, that was conserved in both producing organisms and other members of the microbiome. CSH abundance in GI mucosal biopsies positively correlated with colorectal cancer compared with matched healthy control samples. These studies indicate that human microbial metabolism broadly unites polyamine and LMWT functionalities to generate metabolites that may be associated with disease.

PMID:40671632 | DOI:10.1021/acsinfecdis.5c00286

J Proteome Res. 2025 Jul 17. doi: 10.1021/acs.jproteome.5c00220. Online ahead of print.

ABSTRACT

Protein phosphorylation is a ubiquitous post-translational modification (PTM) found across the kingdoms of life and is critical for the regulation of protein function in health and disease. Advances in high-throughput mass spectrometry have transformed our ability to interrogate the phosphoproteome. However, sample preparation methodologies optimized for phosphoproteomics have not kept pace, compromising the ability to fully exploit these technological advances. In this study, we present an optimized phosphoproteomics workflow using carboxylated SP3 magnetic beads, which have simplified proteomics sample preparation. By employing a washing step with 8 M urea and omitting the conventional C18 SPE cleanup, we demonstrate a significant improvement in phosphopeptide identifications, with application of this refined protocol to HEK-293T cell extracts increasing the number nearly 2-fold compared to standard SP3 techniques (7908 cf. 4129). We also observed substantial improvement in the detection of multiply phosphorylated peptides. Our findings suggest that the complexity of PTM cross-talk using current peptide-based proteomics workflows is currently under-represented and underscores the necessity of methodological innovations to better capture the intricacies of the phosphoproteome landscape.

PMID:40671572 | DOI:10.1021/acs.jproteome.5c00220