STAR Protoc. 2025 Apr 5;6(2):103745. doi: 10.1016/j.xpro.2025.103745. Online ahead of print.

ABSTRACT

Nanoflow liquid chromatography-tandem mass spectrometry (nLC-MS) benefits untargeted metabolomics by enhancing sensitivity and integrating proteomics for the same sample. Here, we present a protocol to enable nLC-MS for dual metabolomics and proteomics. We describe steps for solid-phase micro-extraction (SPME)-assisted metabolite cleaning and enrichment, which avoids capillary column blockage. We then detail nLC-MS data acquisition and analysis. This protocol has been applied in diverse specimens including biofluids, cell lines, and tissues. For complete details on the use and execution of this protocol, please refer to Lin et al.1.

PMID:40188434 | DOI:10.1016/j.xpro.2025.103745

Sci Data. 2025 Apr 5;12(1):579. doi: 10.1038/s41597-025-04854-8.

ABSTRACT

The spotted lanternfly (Lycorma delicatula) is a planthopper species (Hemiptera: Fulgoridae) native to China but invasive in South Korea, Japan, and the United States where it is a significant threat to agriculture. Genomic resources are critical to both management of this species and understanding the genomic characteristics of successful invaders. We report an annotated, haplotype-phased, chromosome-level genome assembly for the spotted lanternfly using PacBio long-read sequencing, Hi-C technology, and RNA-seq. The 2.2 Gbp genome comprises 13 chromosomes, and whole genome resequencing of eighty-two adults indicated chromosome four as the sex chromosome and a corresponding XO sex-determination system. We identified over 12,000 protein-coding genes and performed functional annotation, facilitating the identification of candidate genes that may hold importance for spotted lanternfly control. The assemblies and annotations were highly complete with over 96% of BUSCO genes complete regardless of the database (i.e., Eukaryota, Arthropoda, Insecta). This reference-quality genome will serve as an important resource for development and optimization of management practices for the spotted lanternfly and invasive species genomics as a whole.

PMID:40188159 | DOI:10.1038/s41597-025-04854-8

Nat Commun. 2025 Apr 5;16(1):3268. doi: 10.1038/s41467-025-58615-7.

ABSTRACT

Ubiquitylation of H2A (H2AK119ub1) by the polycomb repressive complexe-1 plays a key role in the initiation of facultative heterochromatin formation in somatic cells. Here we evaluate the contribution of sperm derived H2AK119ub1 to embryo development. In Xenopus laevis we found that H2AK119ub1 is present during spermiogenesis and into early embryonic development, highlighting its credential for a role in the transmission of epigenetic information from the sperm to the embryo. In vitro treatment of sperm with USP21, a H2AK119ub1 deubiquitylase, just prior to injection to egg, results in developmental defects associated with gene upregulation. Sperm H2AK119ub1 editing disrupts egg factor mediated paternal chromatin remodelling processes. It leads to post-replication accumulation of H2AK119ub1 on repeat element of the genome instead of CpG islands. This shift in post-replication H2AK119ub1 distribution triggered by sperm epigenome editing entails a loss of H2AK119ub1 from genes misregulated in embryos derived from USP21 treated sperm. We conclude that sperm derived H2AK119ub1 instructs egg factor mediated epigenetic remodelling of paternal chromatin and is required for embryonic development.

PMID:40188103 | DOI:10.1038/s41467-025-58615-7

J Mol Biol. 2025 Apr 3:169124. doi: 10.1016/j.jmb.2025.169124. Online ahead of print.

ABSTRACT

The RNA World hypothesis predicts that self-replicating RNAs evolved before DNA genomes and coded proteins. Despite widespread support for the RNA World, self-replicating RNAs have yet to be identified in a natural context, leaving a key 'missing link' for this explanation of the origin of life. Inspired by recent work showing that condensates of charged polymers are capable of catalyzing chemical reactions, we consider a catalytic RNA condensate as a candidate for the self-replicating RNA. Specifically, we propose that short, low-complexity RNA polymers formed catalytic condensates capable of templated RNA polymerization. Because the condensate properties depend on the RNA sequences, RNAs that formed condensates with improved polymerization and demixing capacity would be amplified, leading to a 'condensate chain reaction' and evolution by natural selection. Many of the needed properties of this self-replicating RNA condensate have been realized experimentally in recent studies and our predictions could be tested with current experimental and theoretical tools. Our theory addresses central problems in the origins of life: (i) the origin of compartmentalization, (ii) the error threshold for the accuracy of templated replication, (iii) the free energy cost of maintaining an information-rich population of replicating RNA polymers. Furthermore, we note that the extant nucleolus appears to satisfy many of the requirements of an evolutionary relic for the model we propose. More generally, we suggest that future work on the origin of life would benefit from condensate-centric biophysical models of RNA evolution.

PMID:40187684 | DOI:10.1016/j.jmb.2025.169124

Cell. 2025 Apr 2:S0092-8674(25)00283-1. doi: 10.1016/j.cell.2025.03.010. Online ahead of print.

ABSTRACT

Despite the fundamental importance of gut microbes, the genetic basis of their colonization remains largely unexplored. Here, by applying cross-species genotype-habitat association at the tree-of-life scale, we identify conserved microbial gene modules associated with gut colonization. Across thousands of species, we discovered 79 taxonomically diverse putative colonization factors organized into operonic and non-operonic modules. They include previously characterized colonization pathways such as autoinducer-2 biosynthesis and novel processes including tRNA modification and translation. In vivo functional validation revealed YigZ (IMPACT family) and tRNA hydroxylation protein-P (TrhP) are required for E. coli intestinal colonization. Overexpressing YigZ alone is sufficient to enhance colonization of the poorly colonizing MG1655 E. coli by >100-fold. Moreover, natural allelic variations in YigZ impact inter-strain colonization efficiency. Our findings highlight the power of large-scale comparative genomics in revealing the genetic basis of microbial adaptations. These broadly conserved colonization factors may prove critical for understanding gastrointestinal (GI) dysbiosis and developing therapeutics.

PMID:40187346 | DOI:10.1016/j.cell.2025.03.010

Mol Biotechnol. 2025 Apr 5. doi: 10.1007/s12033-025-01417-5. Online ahead of print.

ABSTRACT

Viral diseases pose a significant threat to public health, highlighting the importance of understanding protein-protein interactions between hosts and viruses for therapeutic development. However, this process is often expensive and time-consuming, especially given the rapid evolution of viruses. Machine learning algorithms and artificial intelligence have emerged as powerful tools for efficiently identifying these interactions. This study aims to develop a machine learning-based model to predict protein interactions between viral pathogens and human hosts while analyzing the factors influencing these interactions. The prediction model was constructed using three machine learning algorithms: Random Forest (RF), XGBoost (XGB), and Artificial Neural Networks (ANN). Each algorithm underwent rigorous testing. The modeling features included physicochemical properties, motifs, and amino acid sequences. Model performance was evaluated using fitness, accuracy, precision, sensitivity, and specificity metrics, with validation conducted via the K-fold method. The accuracy of the RF, XGB, and ANN models was 87%, 86%, and 86%, respectively. By integrating dimensionality reduction and clustering techniques, the accuracy of the RF model improved to 90%. Traditionally, studying host-pathogen interactions is labor intensive and costly. The integration of machine learning algorithms into this field significantly enhances the efficiency of analyzing viral pathogen-human host interactions. This study demonstrates the effectiveness of such an approach and provides valuable insights for future research. The results are accessible to researchers through a web application at http://vhi.sysbiomed.ir .

PMID:40186829 | DOI:10.1007/s12033-025-01417-5

Mol Ther. 2025 Apr 3:S1525-0016(25)00275-8. doi: 10.1016/j.ymthe.2025.04.001. Online ahead of print.

ABSTRACT

Multi-modal datasets have grown exponentially in the last decade. This has created an enormous demand for machine learning models that can predict complex outcomes by leveraging cellular, molecular and humoral profiles. Corresponding inference of mechanisms can help uncover new therapeutic targets. Here, we discuss how biological principles guide the design of predictive models and how interpretable machine learning can lead to novel mechanistic insights. We provide descriptions of multiple learning techniques and how suited they are to domain adaptations. Finally, we talk about broad learning capabilities of foundation models on large datasets and whether they can be used to provide meaningful inference about biological datasets.

PMID:40186352 | DOI:10.1016/j.ymthe.2025.04.001

Gut Microbes. 2025 Dec;17(1):2489074. doi: 10.1080/19490976.2025.2489074. Epub 2025 Apr 4.

ABSTRACT

The human gut microbiome, crucial in various diseases, can be utilized to develop diagnostic models through machine learning (ML). The specific tools and parameters used in model construction such as data preprocessing, batch effect removal and modeling algorithms can impact model performance and generalizability. To establish an generally applicable workflow, we divided the ML process into three above-mentioned steps and optimized each sequentially using 83 gut microbiome cohorts across 20 diseases. We tested a total of 156 tool-parameter-algorithm combinations and benchmarked them according to internal- and external- AUCs. At the data preprocessing step, we identified four data preprocessing methods that performed well for regression-type algorithms and one method that excelled for non-regression-type algorithms. At the batch effect removal step, we identified the "ComBat" function from the sva R package as an effective batch effect removal method and compared the performance of various algorithms. Finally, at the ML algorithm selection step, we found that Ridge and Random Forest ranked the best. Our optimized work flow performed similarly comparing with previous exhaustive methods for disease-specific optimizations, thus is generally applicable and can provide a comprehensive guideline for constructing diagnostic models for a range of diseases, potentially serving as a powerful tool for future medical diagnostics.

PMID:40186338 | DOI:10.1080/19490976.2025.2489074

EMBO J. 2025 Apr 4. doi: 10.1038/s44318-025-00424-1. Online ahead of print.

ABSTRACT

The mammary epithelium derives from multipotent mammary stem cells (MaSCs) that engage into differentiation during embryonic development. However, adult MaSCs maintain the ability to reactivate multipotency in non-physiological contexts. We previously reported that Notch1 activation in committed basal cells triggers a basal-to-luminal cell fate switch in the mouse mammary gland. Here, we report conservation of this mechanism and found that in addition to the mammary gland, constitutive Notch1 signaling induces a basal-to-luminal cell fate switch in adult cells of the lacrimal gland, the salivary gland, and the prostate. Since the lineage transition is progressive in time, we performed single-cell transcriptomic analysis on index-sorted mammary cells at different stages of lineage conversion, generating a temporal map of changes in cell identity. Combining single-cell analyses with organoid assays, we demonstrate that cell proliferation is indispensable for this lineage conversion. We also reveal the individual transcriptional landscapes underlying the cellular plasticity switching of committed mammary cells in vivo with spatial and temporal resolution. Given the roles of Notch signaling in cancer, these results may help to better understand the mechanisms that drive cellular transformation.

PMID:40186028 | DOI:10.1038/s44318-025-00424-1

EMBO Rep. 2025 Apr 4. doi: 10.1038/s44319-025-00437-1. Online ahead of print.

ABSTRACT

Genes regulating the finger-like cellular protrusions-filopodia have long been implicated in cancer metastasis. However, depleting the flat lamellipodia but retaining filopodia drastically hampers cell migration on spread surface, obscuring the role of filopodia in cell motility. It has been noticed recently that cells under confinement may employ distinct migratory machineries. However, the regulating factors have mainly been focused on cell blebbing, nuclear deformation and cell rear contractility, without much emphasis on cell protrusions and even less on filopodia. Here, by micropore-based screening, we identified espin as an active regulator for confined migration and that its overexpression was associated with metastasis. In comparison to fascin, espin showed stronger actin bundling in vitro and induced shorter and thicker filopodia in cells. Combining the imaging-compatible microchannels and DNA-based tension probes, we uncovered that espin overexpression induced excessive filopodia at the leading edge and along the sides, exerting force for confined migration. Our results demonstrate an important role for filopodia and the regulating protein-espin in confined cell migration and shed new light on cytoskeletal mechanisms underlying metastasis.

PMID:40185977 | DOI:10.1038/s44319-025-00437-1

Sci Rep. 2025 Apr 4;15(1):11544. doi: 10.1038/s41598-025-90592-1.

ABSTRACT

Understanding the multicellular organization of stem cells is vital for determining the mechanisms that coordinate cell fate decision-making during differentiation; these mechanisms range from neighbor-to-neighbor communication to tissue-level biochemical gradients. Current methods for quantifying multicellular patterning tend to capture the spatial properties of cell colonies at a fixed scale and typically rely on human annotation. We present a computational pipeline that utilizes topological data analysis to generate quantitative, multiscale descriptors which capture the shape of data extracted from 2D multichannel microscopy images. By applying our pipeline to certain stem cell colonies, we detected subtle differences in patterning that reflect distinct spatial organization associated with loss of pluripotency. These results yield insight into putative directed cellular organization and morphogen-mediated, neighbor-to-neighbor signaling. Because of its broad applicability to immunofluorescence microscopy images, our pipeline is well-positioned to serve as a general-purpose tool for the quantitative study of multicellular pattern formation.

PMID:40185811 | DOI:10.1038/s41598-025-90592-1

Nat Commun. 2025 Apr 4;16(1):3106. doi: 10.1038/s41467-025-58272-w.

ABSTRACT

"To act or not to act" is a fundamental decision made in daily life. However, it is unknown how the relevant signals are transmitted to the secondary motor cortex (M2), which is the cortical origin of motor initiation. Here, we found that in a decision-making task in male mice, inputs from the thalamus to M2 positively regulated the action while inputs from the lateral part of the orbitofrontal cortex (LO) negatively regulated it. The motor thalamus that received the basal ganglia outputs transmitted action value-related signals to M2 regardless of whether the animal acted or not. By contrast, a large subpopulation of LO inputs showed decreased activity before and during the action, regardless of the action value. These results suggest that M2 integrates the positive signal of the action value from the motor thalamus with the negative action-biased signal from the LO to finally determine whether to act or not.

PMID:40185746 | DOI:10.1038/s41467-025-58272-w

Comput Biol Med. 2025 Apr 3;190:110064. doi: 10.1016/j.compbiomed.2025.110064. Online ahead of print.

ABSTRACT

The rapidly advancing field of artificial intelligence (AI) has transformed numerous scientific domains, including biology, where a vast and complex volume of data is available for analysis. This paper provides a comprehensive overview of the current state of AI-driven methodologies in genomics, proteomics, and systems biology. We discuss how machine learning algorithms, particularly deep learning models, have enhanced the accuracy and efficiency of embedding sequences, motif discovery, and the prediction of gene expression and protein structure. Additionally, we explore the integration of AI in the embedding and analysis of biological networks, including protein-protein interaction networks and multi-layered networks. By leveraging large-scale biological data, AI techniques have enabled unprecedented insights into complex biological processes and disease mechanisms. This work underlines the potential of applying AI to complex biological data, highlighting current applications and suggesting directions for future research to further explore AI in this rapidly evolving field.

PMID:40184941 | DOI:10.1016/j.compbiomed.2025.110064

Int Immunopharmacol. 2025 Apr 3;154:114515. doi: 10.1016/j.intimp.2025.114515. Online ahead of print.

ABSTRACT

BACKGROUND: Sulfur mustard (SM) is a highly lethal chemical warfare agent that induces severe health complications in exposed individuals. Gaining insights into the metabolic changes caused by SM exposure is essential for understanding its underlying mechanisms and developing effective diagnostic and therapeutic interventions.

METHODS: In this investigation, we utilized proton nuclear magnetic resonance (H-NMR) spectroscopy to conduct metabolomic analysis in patients diagnosed with mustard lung disease (MLD) using a non-targeted approach. Metabolite measurements were conducted on plasma and urine samples collected from a total of 54 individuals, including 20 individuals with mild MLD, 20 individuals with moderate MLD, and 14 healthy individuals. Multivariate and univariate analyses were applied to identify metabolites that distinguish between the different groups, and enrichment analysis was performed to unveil the underlying biochemical pathways involved.

RESULTS: The obtained metabolic profile had the potential to differentiate moderate from healthy plasma, but not from mild patients using multivariate analysis. Sixteen metabolites from plasma were considered significantly different between the moderate and control groups (VIP > 1 and p < 0.05) that these metabolites involved in fatty acid and amino acid metabolism. Utilizing all 16 metabolites as a combined panel, we were able to distinguish between the moderate and control groups, achieving an area under the curve (AUC) of 0.854. Moreover, 6 and 8 urinary metabolites were detected between mild vs. control and moderate vs. control groups, respectively. Fourteen metabolites exhibited significant fold changes (FC) (FC < 0.66 or FC > 1.5; p < 0.05). These metabolites are involved in amino acid and nicotinate metabolism.

CONCLUSION: Our study provides novel insights into the metabolic changes associated with MLD and highlights potential pathways involved in the disease progression. These findings have implications for the development of targeted diagnostic and therapeutic strategies for MLD.

PMID:40184812 | DOI:10.1016/j.intimp.2025.114515

Food Chem. 2025 Apr 1;482:144174. doi: 10.1016/j.foodchem.2025.144174. Online ahead of print.

ABSTRACT

A key barrier in transitioning to plant-based, more satiating diets, is the bitter taste of plant proteins. We hypothesize that both, a more bitter tasting (MBT) and a less bitter tasting (LBT) pea protein hydrolysate (PPH) can be digested in the stomach into bitter tasting peptides that stimulate proton secretion (PS) and serotonin release, as two of the key gastric satiety signals, via the functional involvement of bitter taste receptors (TAS2Rs). Using a sensory-guided LC-MS approach, we identified six bitter peptides that were released from LBT-PPH and MBT-PPH during gastric digestion in vitro. TAS2R4 and TAS2R43 involvement in PS and serotonin release was confirmed via CRISPR-Cas9 knockout experiments. Our hypothesis was proven with all six peptides equally stimulating PS in immortalized human gastric HGT-1 cells, and LBT-PPH-derived peptides eliciting a higher serotonin release in HGT-1 cells than MBT-PPH peptides, indicating a satiating potential of less bitter tasting protein hydrolysates.

PMID:40184744 | DOI:10.1016/j.foodchem.2025.144174

PLoS One. 2025 Apr 4;20(4):e0322319. doi: 10.1371/journal.pone.0322319. eCollection 2025.

ABSTRACT

[This corrects the article DOI: 10.1371/journal.pone.0289535.].

PMID:40184393 | DOI:10.1371/journal.pone.0322319

Cell Rep. 2025 Apr 3;44(4):115502. doi: 10.1016/j.celrep.2025.115502. Online ahead of print.

ABSTRACT

The endoplasmic reticulum (ER) is structurally and functionally diverse, yet how its functions are organized within morphological subdomains is incompletely understood. Utilizing TurboID-based proximity labeling and CRISPR knockin technologies, we map the proteomic landscape of the human ER network. Sub-organelle proteomics reveals enrichments of proteins into ER tubules, sheets, and the nuclear envelope. We uncover an ER-enriched actin-binding protein, calmin/CLMN, and define it as an ER-actin tether that localizes to focal adhesions adjacent to ER tubules. Mechanistically, we find that CLMN depletion perturbs adhesion disassembly, actin dynamics, and cell movement. CLMN-depleted cells display decreased polarization of ER-plasma membrane contacts and calcium signaling factor STIM1 and altered calcium signaling near ER-actin interfaces, suggesting that CLMN influences calcium signaling to facilitate F-actin/adhesion dynamics. Collectively, we map the sub-organelle proteome landscape of the ER, identify CLMN as an ER-actin tether, and describe a non-canonical mechanism by which ER tubules engage actin to regulate cell migration.

PMID:40184252 | DOI:10.1016/j.celrep.2025.115502

STAR Protoc. 2025 Apr 3;6(2):103724. doi: 10.1016/j.xpro.2025.103724. Online ahead of print.

ABSTRACT

Myocardial infarction (MI) and myocardial ischemia-reperfusion injury (MIRI) are major pathological conditions in cardiovascular disease, requiring in-depth study for effective therapy development. Here, we present a detailed protocol for establishing a mouse model using the squeeze technique to simulate MI and MIRI. Key steps include isoflurane-induced anesthesia, left anterior descending artery (LAD) ligation, and real-time monitoring. Additionally, we describe procedures for histological analysis, offering a comprehensive approach to investigating disease mechanisms and potential therapeutic strategies. For complete details on the use and execution of this protocol, please refer to Gao et al.1.

PMID:40184247 | DOI:10.1016/j.xpro.2025.103724

Bioinformatics. 2025 Apr 4:btaf150. doi: 10.1093/bioinformatics/btaf150. Online ahead of print.

ABSTRACT

MOTIVATION: Precision medicine leverages patient-specific multimodal data to improve prevention, diagnosis, prognosis and treatment of diseases. Advancing precision medicine requires the non-trivial integration of complex, heterogeneous and potentially high-dimensional data sources, such as multi-omics and clinical data. In the literature several approaches have been proposed to manage missing data, but are usually limited to the recovery of subsets of features for a subset of patients. A largely overlooked problem is the integration of multiple sources of data when one or more of them are completely missing for a subset of patients, a relatively common condition in clinical practice.

RESULTS: We propose miss-Similarity Network Fusion (miss-SNF), a novel general-purpose data integration approach designed to manage completely missing data in the context of patient similarity networks. Miss-SNF integrates incomplete unimodal patient similarity networks by leveraging a non-linear message-passing strategy borrowed from the SNF algorithm. Miss-SNF is able to recover missing patient similarities and is "task agnostic", in the sense that can integrate partial data for both unsupervised and supervised prediction tasks. Experimental analyses on nine cancer datasets from The Cancer Genome Atlas (TCGA) demonstrate that miss-SNF achieves state-of-the-art results in recovering similarities and in identifying patients subgroups enriched in clinically relevant variables and having differential survival. Moreover, amputation experiments show that miss-SNF supervised prediction of cancer clinical outcomes and Alzheimer's disease diagnosis with completely missing data achieves results comparable to those obtained when all the data are available.

AVAILABILITY AND IMPLEMENTATION: miss-SNF code, implemented in R, is available at https://github.com/AnacletoLAB/missSNF.

SUPPLEMENTARY INFORMATION: Supplementary information are available at Bioinformatics online.

PMID:40184204 | DOI:10.1093/bioinformatics/btaf150

Metab Brain Dis. 2025 Apr 4;40(4):169. doi: 10.1007/s11011-025-01585-y.

ABSTRACT

Amyotrophic lateral sclerosis (ALS) and Parkinson's disease (PD) share overlapping molecular mechanisms, including estrogen signaling dysregulation, oxidative stress, and neuroinflammation. Standard treatments often lead to adverse effects due to unintended cross-talk with the estrogen signaling pathway. Identifying key regulatory genes and bioactive plant-derived compounds that modulate estrogen signaling without interfering with standard therapies offers a promising neuroprotective strategy. A network medicine and systems biology approach was used, beginning with the screening of 29 medicinal plants for ALS and 49 for PD, identifying 12 shared plants with neuroprotective potential. Bioactive compounds were screened for gene, protein, and pathway interactions, leading to target prediction (846 ALS-related and 690 PD-related targets) and disease association mining, which identified 93 overlapping genes (OGs). Protein-protein interaction (PPI) network analysis and MCODE clustering revealed ESR1, EGFR, and SRC as key hub-bottleneck (HB) genes, further validated via differential gene expression analysis. Gene ontology (GO) and pathway enrichment analyses revealed significant enrichment in estrogen signaling confirming the involvement of HB genes in neurodegenerative disease progression. Differential expression analysis confirmed ESR1 upregulation in ALS but downregulation in PD, suggesting a converse disease-specific regulatory pattern. Gene regulatory network (GRN) analysis identified hsa-miR-145-5p (ALS) and hsa-miR-181a-5p (PD) as key regulators, while FOXC1, GATA2, and TP53 emerged as crucial transcription factors (TFs) influencing disease progression. Molecular docking and MD simulations validated strong and stable interactions of Eupalitin (CYP19A1, -9.0 kcal/mol), Hesperetin (ESR1, -8.1 kcal/mol), and Sumatrol (PIK3CA, -8.9 kcal/mol). These phytochemicals, derived from Rosmarinus officinalis, Artemisia scoparia, Ocimum tenuiflorum, and Indigofera tinctoria, maintained stable hydrogen bonding and hydrophobic interactions for over 30% of a 25 ns simulation, supporting their therapeutic potential. The identification of ESR1, EGFR, and SRC as key targets, alongside estrogen signaling involvement, highlights the need for targeted nutraceutical interventions. These findings pave the way for safer, plant-based therapies that mitigate neurodegeneration while preserving estrogen signaling integrity, offering a promising adjuvant strategy alongside existing treatments.

PMID:40184012 | DOI:10.1007/s11011-025-01585-y