Adv Pharmacol. 2025;103:1-22. doi: 10.1016/bs.apha.2025.01.006. Epub 2025 Feb 13.

ABSTRACT

In the current scenario of pandemics, drug discovery and design have undergone a significant transformation due to the integration of advanced computational methodologies. These methodologies utilize sophisticated algorithms, machine learning, artificial intelligence, and high-performance computing to expedite the drug development process, enhances accuracy, and reduces costs. Machine learning and AI have revolutionized predictive modeling, virtual screening, and de novo drug design, allowing for the identification and optimization of novel compounds with desirable properties. Molecular dynamics simulations provide a detailed insight into protein-ligand interactions and conformational changes, facilitating an understanding of drug efficacy at the atomic level. Quantum mechanics/molecular mechanics methods offer precise predictions of binding energies and reaction mechanisms, while structure-based drug design employs docking studies and fragment-based design to improve drug-receptor binding affinities. Network pharmacology and systems biology approaches analyze polypharmacology and biological networks to identify novel drug targets and understand complex interactions. Cheminformatics explores vast chemical spaces and employs data mining to find patterns in large datasets. Computational toxicology predicts adverse effects early in development, reducing reliance on animal testing. Bioinformatics integrates genomic, proteomic, and metabolomics data to discover biomarkers and understand genetic variations affecting drug response. Lastly, cloud computing and big data technologies facilitate high-throughput screening and comprehensive data analysis. Collectively, these computational innovations are driving a paradigm shift in drug discovery and design, making it more efficient, accurate, and cost-effective.

PMID:40175036 | DOI:10.1016/bs.apha.2025.01.006

J Am Coll Radiol. 2025 Mar 31:S1546-1440(25)00195-4. doi: 10.1016/j.jacr.2025.03.006. Online ahead of print.

NO ABSTRACT

PMID:40174871 | DOI:10.1016/j.jacr.2025.03.006

Nephron. 2025 Apr 2:1-11. doi: 10.1159/000544754. Online ahead of print.

ABSTRACT

BACKGROUND: Although there have been reports of relapse or worsening of membranous nephropathy after receiving vaccines against coronavirus disease 2019 (COVID-19), the causal relationship or association between them has not been established. This study aimed to investigate the occurrence of relapse or worsening of membranous nephropathy following inactivated COVID-19 vaccination.

METHODS: Patients who had been diagnosed with membranous nephropathy before receiving their first dose of vaccination, or before March 1, 2021, for unvaccinated patients, were included in the study. All patients were monitored at the Membranous Nephropathy Clinic of Huashan Hospital, Fudan University. The reasons for not receiving vaccines were investigated. The impact of COVID-19 vaccination on membranous nephropathy was assessed by comparing the relapse or worsening of membranous nephropathy within 12 months in vaccinated and unvaccinated patients with proteinuria <3.5 g/d. The baseline variables were balanced using cardinality matching.

RESULTS: A total of 353 patients with membranous nephropathy were included in the study, with 186 (53%) having received inactivated COVID-19 vaccines. Among the 167 unvaccinated participants, 114 (68%) expressed concerns about the possibility of disease relapse, and 47 (28%) were worried about the vaccine's efficacy due to their immunosuppressive therapy. Of the 239 participants with proteinuria <3.5 g/d, 152 were vaccinated, and 16 (11%) experienced a relapse or worsening of the disease during the follow-up period, which was similar to the 14 (16%) observed in the unvaccinated group. Following cardinality matching, there was no difference in the rate of relapse or worsening between the two groups, with 10 (13%) in the vaccinated group and 11 (15%) in the unvaccinated group (hazard ratio 0.98, 95% confidence interval 0.42-2.33).

CONCLUSION: Getting the inactivated COVID-19 vaccine may not increase risk of relapse or worsening in patients with membranous nephropathy.

PMID:40174580 | DOI:10.1159/000544754

Reprod Biomed Online. 2024 Nov 14;50(5):104707. doi: 10.1016/j.rbmo.2024.104707. Online ahead of print.

ABSTRACT

Infertility, adverse pregnancy outcomes and genital infections are global concerns. The reproductive tract microbiome appears to play a crucial role in the physiology of both the female and male reproductive tracts. Despite the presence of thousands of microbes in body fluids shared during unprotected sexual intercourse, they have traditionally been studied separately, with greater emphasis on the female (mostly vaginal) microbiome, and the interaction between these microbiomes in a sexually active couple has been overlooked. This review introduces the concept of the 'seminovaginal microbiome' - the collective microbiota of both partners, transferred and shared during sexual interaction. By synthesizing the existing body of next-generation sequencing-based literature, this review establishes the first holistic view of how these microbiomes interact, influence reproductive health and affect assisted reproductive technique outcomes, as well as the occurrence of microbe-associated diseases such as sexually transmitted infections, prostatitis, bacterial vaginosis and candidiasis. Additionally, the microbial interplay in homosexual couples and transgender individuals is discussed.

PMID:40174296 | DOI:10.1016/j.rbmo.2024.104707

Plant Physiol. 2025 Apr 2:kiaf126. doi: 10.1093/plphys/kiaf126. Online ahead of print.

ABSTRACT

Potato (Solanum tuberosum) is highly water and space efficient but susceptible to abiotic stresses such as heat, drought, and flooding, which are severely exacerbated by climate change. Our understanding of crop acclimation to abiotic stress, however, remains limited. Here, we present a comprehensive molecular and physiological high-throughput profiling of potato (Solanum tuberosum, cv. Désirée) under heat, drought, and waterlogging applied as single stresses or in combinations designed to mimic realistic future scenarios. Stress responses were monitored via daily phenotyping and multi-omics analyses of leaf samples comprising proteomics, targeted transcriptomics, metabolomics, and hormonomics at several timepoints during and after stress treatments. Additionally, critical metabolites of tuber samples were analyzed at the end of the stress period. We performed integrative multi-omics data analysis using a bioinformatic pipeline that we established based on machine learning and knowledge networks. Waterlogging produced the most immediate and dramatic effects on potato plants, interestingly activating ABA responses similar to drought stress. In addition, we observed distinct stress signatures at multiple molecular levels in response to heat or drought and to a combination of both. In response to all treatments, we found a downregulation of photosynthesis at different molecular levels, an accumulation of minor amino acids, and diverse stress-induced hormones. Our integrative multi-omics analysis provides global insights into plant stress responses, facilitating improved breeding strategies toward climate-adapted potato varieties.

PMID:40173380 | DOI:10.1093/plphys/kiaf126

Sci Adv. 2025 Apr 4;11(14):eado0173. doi: 10.1126/sciadv.ado0173. Epub 2025 Apr 2.

ABSTRACT

Understanding the heterogeneity of fibroblasts depends on decoding the complexity of cell subtypes, their origin, distribution, and interactions with other cells. Here, we integrated 249,156 fibroblasts from 73 studies across 10 tissues to present a single-cell atlas of fibroblasts. We provided a high-resolution classification of 18 fibroblast subtypes. In particular, we revealed a previously undescribed cell population, TSPAN8+ chromatin remodeling fibroblasts, characterized by high expression of genes with functions related to histone modification and chromatin remodeling. Moreover, TSPAN8+ chromatin remodeling fibroblasts were detectable in spatial transcriptome data and multiplexed immunofluorescence assays. Compared with other fibroblast subtypes, TSPAN8+ chromatin remodeling fibroblasts exhibited higher scores in cell differentiation and resident fibroblast, mainly interacting with endothelial cells and T cells through ligand VEGFA and receptor F2R, and their presence was associated with poor prognosis. Our analyses comprehensively defined the shared and specific characteristics of fibroblast subtypes across tissues and provided a user-friendly data portal, Fibroblast Atlas.

PMID:40173240 | DOI:10.1126/sciadv.ado0173

Neurodegener Dis Manag. 2025 Apr 2:1-3. doi: 10.1080/17582024.2025.2488230. Online ahead of print.

NO ABSTRACT

PMID:40173153 | DOI:10.1080/17582024.2025.2488230

PLoS Biol. 2025 Apr 2;23(4):e3003072. doi: 10.1371/journal.pbio.3003072. eCollection 2025 Apr.

ABSTRACT

An alga that abandoned photosynthesis? This Primer explores a PLOS Biology study showing that a single horizontal gene transfer event allowed the diatom Nitzschia sing1 to evolve a complete enzymatic machinery to break down alginate from brown algae, unlocking a new ecological niche.

PMID:40173128 | DOI:10.1371/journal.pbio.3003072

J Agric Food Chem. 2025 Apr 2. doi: 10.1021/acs.jafc.4c12671. Online ahead of print.

ABSTRACT

Methanol is an ideal one-carbon (C1) feedstock for bioconversion into multicarbon value-added compounds. Biocatalytic approaches to methanol conversion provide sustainable and environmentally friendly alternatives to conventional methods. This process is facilitated by methanol-oxidizing enzymes, including alcohol oxidase (AOx). Here, we report an AOx from Pestalotiopsis fici (PfAOx) with the highest methanol oxidation activity and efficient heterologous expression compared to other AOxs. To investigate the bioconversion of a multicarbon compound (C4 chemical, 2-keto-4-hydroxybutyrate, 2-KHB) from cost-effective methanol, we developed a one-pot enzyme system including PfAOx and pyruvate aldolase from Deinococcus radiodurans (DrADL) with catalase from Bos taurus (BtCAT, commercially available enzyme) to remove toxic H2O2. The optimal reaction conditions for 2-KHB production using PfAOx, DrADL, and BtCAT were determined as pH 8.0, 35 °C, 0.9 mg mL-1 PfAOx, 0.3 mg mL-1 DrADL, 1.5 mg mL-1 BtCAT, 150 mM methanol, 100 mM pyruvate, and 5 mM Mg2+ with shaking at 200 rpm. Under these reaction conditions, 88.8 mM (10.4 g L-1) of 2-KHB was produced for 75 min, representing a 74.0-fold higher yield compared to previously reported 2-KHB production systems from methanol and pyruvate. This study demonstrates a promising multi-enzyme cascade approach for the bioconversion of methanol into valuable compounds.

PMID:40173089 | DOI:10.1021/acs.jafc.4c12671

Proc Natl Acad Sci U S A. 2025 Apr 8;122(14):e2412310122. doi: 10.1073/pnas.2412310122. Epub 2025 Apr 2.

ABSTRACT

High tumor mutational burden (TMB-H) is a predictive biomarker for the responsiveness of cancer to immune checkpoint inhibitor (ICI) therapy that indicates whether immune cells can sufficiently recognize cancer cells as nonself. However, about 30% of all cancers from The Cancer Genome Atlas (TCGA) are classified as immune-desert tumors lacking T cell infiltration despite TMB-H. Since the underlying mechanism of these immune-desert tumors has yet to be unraveled, there is a pressing need to transform such immune-desert tumors into immune-inflamed tumors and thereby enhance their responsiveness to anti-PD1 therapy. Here, we present a systems framework for identifying immuno-oncotargets, based on analysis of gene regulatory networks, and validating the effect of these targets in transforming immune-desert into immune-inflamed tumors. In particular, we identify DEAD-box helicases 54 (DDX54) as a master regulator of immune escape in immune-desert lung cancer with TMB-H and show that knockdown of DDX54 can increase immune cell infiltration and lead to improved sensitivity to anti-PD1 therapy.

PMID:40172969 | DOI:10.1073/pnas.2412310122

Elife. 2025 Apr 2;13:RP92536. doi: 10.7554/eLife.92536.

ABSTRACT

In eukaryotes, protein kinase signaling is regulated by a diverse array of post-translational modifications, including phosphorylation of Ser/Thr residues and oxidation of cysteine (Cys) residues. While regulation by activation segment phosphorylation of Ser/Thr residues is well understood, relatively little is known about how oxidation of cysteine residues modulate catalysis. In this study, we investigate redox regulation of the AMPK-related brain-selective kinases (BRSK) 1 and 2, and detail how broad catalytic activity is directly regulated through reversible oxidation and reduction of evolutionarily conserved Cys residues within the catalytic domain. We show that redox-dependent control of BRSKs is a dynamic and multilayered process involving oxidative modifications of several Cys residues, including the formation of intramolecular disulfide bonds involving a pair of Cys residues near the catalytic HRD motif and a highly conserved T-loop Cys with a BRSK-specific Cys within an unusual CPE motif at the end of the activation segment. Consistently, mutation of the CPE-Cys increases catalytic activity in vitro and drives phosphorylation of the BRSK substrate Tau in cells. Molecular modeling and molecular dynamics simulations indicate that oxidation of the CPE-Cys destabilizes a conserved salt bridge network critical for allosteric activation. The occurrence of spatially proximal Cys amino acids in diverse Ser/Thr protein kinase families suggests that disulfide-mediated control of catalytic activity may be a prevalent mechanism for regulation within the broader AMPK family.

PMID:40172959 | DOI:10.7554/eLife.92536

Bioinformatics. 2025 Apr 2:btaf149. doi: 10.1093/bioinformatics/btaf149. Online ahead of print.

ABSTRACT

SUMMARY: In single-cell transcriptomics, inconsistent cell type annotations due to varied naming conventions and hierarchical granularity impede data integration, machine learning applications, and meaningful evaluations. To address this challenge, we developed the unified Hierarchical Annotation Framework (uHAF), which includes organ-specific hierarchical cell type trees (uHAF-T) and a mapping tool (uHAF-Agent) based on large language models. uHAF-T provides standardized hierarchical references for 38 organs, allowing for consistent label unification and analysis at different levels of granularity. uHAF-Agent leverages GPT-4 to accurately map diverse and informal cell type labels onto uHAF-T nodes, streamlining the harmonization process. By simplifying label unification, uHAF enhances data integration, supports machine learning applications, and enables biologically meaningful evaluations of annotation methods. Our framework serves as an essential resource for standardizing cell type annotations and fostering collaborative refinement in the single-cell research community.

AVAILABILITY AND IMPLEMENTATION: uHAF is publicly available at: https://uhaf.unifiedcellatlas.org and https://github.com/SuperBianC/uhaf.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:40172934 | DOI:10.1093/bioinformatics/btaf149

Mycorrhiza. 2025 Apr 2;35(2):26. doi: 10.1007/s00572-025-01188-6.

ABSTRACT

Like other plant-microbe symbioses, the establishment of orchid mycorrhiza (ORM) is likely to require specific communication and metabolic adjustments between the two partners. However, while modulation of plant and fungal metabolism has been investigated in fully established mycorrhizal tissues, the molecular changes occurring during the pre-symbiotic stages of the interaction remain largely unexplored in ORM. In this study, we investigated the pre-symbiotic responses of the ORM fungus Tulasnella sp. SV6 to plantlets of the orchid host Serapias vomeracea in a dual in vitro cultivation system. The fungal mycelium was harvested prior to physical contact with the orchid roots and the fungal transcriptome and metabolome were analyzed using RNA-seq and untargeted metabolomics approaches. The results revealed distinct transcriptomic and metabolomic remodelling of the ORM fungus in the presence of orchid plantlets, as compared to the free-living condition. The ORM fungus responds to the presence of the host plant with a significant up-regulation of genes associated with protein synthesis, amino acid and lipid biosynthesis, indicating increased metabolic activity. Metabolomic analysis supported the RNA-seq data, showing increased levels of amino acids and phospholipids, suggesting a remodelling of cell structure and signalling during the pre-symbiotic interaction. In addition, we identified an increase of transcripts of a small secreted protein that may play a role in early symbiotic signalling. Taken together, our results suggest that Tulasnella sp. SV6 may perceive information from orchid roots, leading to a readjustment of its transcriptomic and metabolomic profiles.

PMID:40172721 | DOI:10.1007/s00572-025-01188-6

Philos Trans A Math Phys Eng Sci. 2025 Apr 2;383(2293):20240412. doi: 10.1098/rsta.2024.0412. Epub 2025 Apr 2.

ABSTRACT

During infectious disease outbreaks, the time-dependent reproduction number ([Formula: see text]) can be estimated to monitor pathogen transmission. In previous work, we developed a simulation-based method for estimating [Formula: see text] from temporally aggregated disease incidence data (e.g. weekly case reports). While that approach is straightforward to use, it assumes implicitly that all cases are reported and the computation can be slow when applied to large datasets. In this article, we extend our previous approach and develop a computationally efficient simulation-based method for estimating [Formula: see text] in real-time accounting for both temporal aggregation of incidence data and under-reporting (with a fixed reporting probability per case). Using simulated data, we show that failing to consider stochastic under-reporting can lead to inappropriately precise estimates, including scenarios in which the true [Formula: see text] value lies outside inferred credible intervals more often than expected. We then apply our approach to data from the 2018 to 2020 Ebola outbreak in the Democratic Republic of the Congo (DRC), again exploring the effects of case under-reporting. Finally, we show how our method can be extended to account for temporal variations in reporting. Given information about the level of case reporting, our framework can be used to estimate [Formula: see text] during future outbreaks with under-reported and temporally aggregated case data.This article is part of the theme issue 'Uncertainty quantification for healthcare and biological systems (Part 2)'.

PMID:40172553 | DOI:10.1098/rsta.2024.0412

Infect Immun. 2025 Apr 2:e0033324. doi: 10.1128/iai.00333-24. Online ahead of print.

ABSTRACT

Babesia microti is the primary cause of human babesiosis in North America. Despite the emergence of the disease in recent years, the pathogenesis and immune response to B. microti infection remain poorly understood. Studies in laboratory mice have shown a critical role for macrophages in the elimination of parasites and infected red blood cells (iRBCs). Importantly, the underlying mechanisms that activate macrophages are still unknown. Recent evidence identified the release of extracellular vesicles (EVs) from Babesia iRBCs. EVs are spherical particles released from cell membranes under natural or pathological conditions that have been suggested to play roles in host-pathogen interactions among diseases caused by protozoan parasites. The present study examined whether EVs released from cultured Babesia iRBCs could activate macrophages and alter cytokine secretion. An analysis of vesicle size in EV fractions from Babesia iRBCs showed diverse populations in the <100 nm size range compared to EVs from uninfected RBCs. In co-culture experiments, EVs released by B. microti iRBCs appeared to be associated with macrophage membranes and cytoplasm, indicating uptake of these vesicles in vitro. Interestingly, the incubation of macrophages with EVs isolated from Babesia iRBC culture supernatants resulted in the activation of NF-κB and modulation of pro-inflammatory cytokines. These results support a role for Babesia-derived EVs in macrophage activation and provide new insights into the mechanisms involved in the induction of the innate immune response during babesiosis.

PMID:40172538 | DOI:10.1128/iai.00333-24

Adv Sci (Weinh). 2025 Apr 2:e2412775. doi: 10.1002/advs.202412775. Online ahead of print.

ABSTRACT

Chronic noncommunicable diseases (NCDS) are often characterized by gradual onset and slow progression, but the difficulty in early prediction remains a substantial health challenge worldwide. This study aims to explore the interconnectedness of disease occurrence through multi-omics studies and validate it in large-scale electronic health records. In response, the research examined multi-omics data from 160 sub-healthy individuals at high altitude and then a deep learning model called Omicsformer is developed for detailed analysis and classification of routine blood samples. Omicsformer adeptly identified potential risks for nine diseases including cancer, cardiovascular conditions, and psychiatric conditions. Analysis of risk trajectories from 20 years of large clinical patients confirmed the validity of the group in preclinical risk assessment, revealing trends in increased disease risk at the time of onset. Additionally, a straightforward NCDs risk prediction system is developed, utilizing basic blood test results. This work highlights the role of multiomics analysis in the prediction of chronic disease risk, and the development and validation of predictive models based on blood routine results can help advance personalized medicine and reduce the cost of disease screening in the community.

PMID:40171841 | DOI:10.1002/advs.202412775

iScience. 2025 Mar 4;28(4):112146. doi: 10.1016/j.isci.2025.112146. eCollection 2025 Apr 18.

ABSTRACT

Epidermal growth factor receptor (Egfr)-driven signaling regulates fundamental homeostatic processes. Dysregulated signaling via Egfr is implicated in numerous disease pathologies and distinct Egfr-associated disease etiologies are known to be tissue-specific. The molecular basis of this tissue-specificity remains poorly understood. Most studies of Egfr signaling to date have been performed in vitro or in tissue-specific mouse models of disease, which has limited insight into Egfr signaling patterns in healthy tissues. Here, we carried out integrated phosphoproteomic, proteomic, and transcriptomic analyses of signaling changes across various mouse tissues in response to short-term stimulation with the Egfr ligand Egf. We show how both baseline and Egf-stimulated signaling dynamics differ between tissues. Moreover, we propose how baseline phosphorylation and total protein levels may be associated with clinically relevant tissue-specific Egfr-associated phenotypes. Altogether, our analyses illustrate tissue-specific effects of Egf stimulation and highlight potential links between underlying tissue biology and Egfr signaling output.

PMID:40171493 | PMC:PMC11960661 | DOI:10.1016/j.isci.2025.112146

RSC Adv. 2025 Apr 1;15(13):9884-9890. doi: 10.1039/d4ra07801b. eCollection 2025 Mar 28.

ABSTRACT

This study focuses on the creation and application of an advanced impedimetric immunosensor designed for the sensitive detection of lymphoma cancer cells. The sensor was developed by modifying a glassy carbon electrode (GCE) with gold nanoparticles (AuNPs) and 3,3'-dithiodipropionic acid di(N-hydroxysuccinimide ester) boronic acid (AuNPs@DTSP-BA), followed by the attachment of rituximab monoclonal antibody. Incorporating the boronic acid (BA) component enabled effective oriented immobilization of the antibody, thereby improving the performance of the biosensor. Various spectroscopic techniques were used to characterize the immunosensor. The developed immunosensor demonstrated the ability to detect lymphoma cancer cells across a wide linear range of 100 to 50 000 cells per mL, with a detection sensitivity of 64 cells per mL.

PMID:40171289 | PMC:PMC11959537 | DOI:10.1039/d4ra07801b

Cancer Discov. 2025 Apr 2;15(4):685-701. doi: 10.1158/2159-8290.CD-24-0861.

ABSTRACT

Viewing the hallmarks as a sequence of adaptations captures the "why" behind the "how" of the molecular changes driving cancer. This eco-evolutionary view distils the complexity of cancer progression into logical steps, providing a framework for understanding all existing and emerging hallmarks of cancer and developing therapeutic interventions.

PMID:40170539 | DOI:10.1158/2159-8290.CD-24-0861

Sheng Wu Gong Cheng Xue Bao. 2025 Mar 25;41(3):1152-1178. doi: 10.13345/j.cjb.250065.

ABSTRACT

Biomanufacturing has emerged as a crucial driving force for efficient material conversion through engineered cells or cell-free systems. However, the intrinsic spatiotemporal heterogeneity, complexity, and dynamic characteristics of these processes pose significant challenges to systematic understanding, optimization, and regulation. This review summarizes essential methodologies for multi-omics data acquisition and analyses for bioprocesses and outlines modelling approaches based on multi-omics data. Furthermore, we explore practical applications of multi-omics and modelling in fine-tuning process parameters, improving fermentation control, elucidating stress response mechanisms, optimizing nutrient supplementation, and enabling real-time monitoring and adaptive adjustment. The substantial potential offered by integrating multi-omics with computational modelling for precision bioprocessing is also discussed. Finally, we identify current challenges in bioprocess optimization and propose the possible solutions, the implementation of which will significantly deepen understanding and enhance control of complex bioprocesses, ultimately driving the rapid advancement of biomanufacturing.

PMID:40170317 | DOI:10.13345/j.cjb.250065