Methods Mol Biol. 2025;2880:331-344. doi: 10.1007/978-1-0716-4276-4_17.

ABSTRACT

A significantly low success rate of human clinical studies has long been attributed to a capability gap, namely, an ineffective translation of the animal data to the human context. To bridge this capability gap, several correcting measures have been evaluated; using a strict guideline to select animal models for a given disease and implementing alternative models such as tissues-on-chip are some of them. Current hypothesis tells that there is a basic similarity in responding to a stress between human and those mammals that precede human in the phylogenetic tree; however, the corresponding molecular mechanisms are not exactly the same across these species. Therefore, strategic manipulations are necessary to curate those candidates from animal data that would have high translational potential. Hence, we developed an analytical tool that can screen the in vivo results, such as genomic, proteomic, epigenomic data with two primary objectives. The first objective is to identify those molecules that are sequentially conserved across the phylogenetic tree. The second objective is to find those molecules that would similarly perturb across the phylogenetic tree in responding to a stress of interest. A machine learning (ML) algorithm converges these two sets of molecules to curate the common features, which would demonstrate phylogenetic homology in their molecular makeups and characteristic similarity across the phylogenetic tree. This ML-pipeline would be most beneficial in those scenarios, such as the rare diseases or chemical-biological-radiation-nuclear (CBRN)-exposed samples, where the inventory of human samples is minimum. This strategy is surely at a risk in overlooking the human-exclusive signatures; nevertheless, this ML-approach is poised to refine the animal data to generate results of high translational potential with minimum false positive and false negative entries.

PMID:39900768 | DOI:10.1007/978-1-0716-4276-4_17

Methods Mol Biol. 2025;2880:69-99. doi: 10.1007/978-1-0716-4276-4_4.

ABSTRACT

The discovery of genes that code for a specific enzymatic activity is important in various fields of life science and provides valuable biotechnological tools. Many genes that contribute to the production of secondary metabolites and specialized metabolic pathways are still not identified. Due to the great diversity of metabolic functions found in nature and their rapid evolutionary adaptation, we need precise but high-throughput approaches for a targeted search based on minimal prior knowledge. In this chapter, we describe a transcriptomics pipeline that was used to search for candidate genes coding for a specific enzymatic activity in a nonmodel species. We generated and combined short- and long-read transcriptomic data to obtain reliable full-length transcript sequences along with information on allelic variation, isoform expression, and condition-specific expression. Based on protein domain annotations of coding sequences and transcriptomic data, we selected candidate genes for activity assays. We provide detailed instructions for analysis and quality control steps in our pipeline that can be applied to other biological questions.

PMID:39900755 | DOI:10.1007/978-1-0716-4276-4_4

Methods Mol Biol. 2025;2880:1-16. doi: 10.1007/978-1-0716-4276-4_1.

ABSTRACT

Saliva, a readily available and noninvasive biofluid, has emerged as a promising source for gene expression studies, offering a window into both local and systemic gene expression patterns. The salivary transcriptome and miRNome hold valuable information about the physiological and pathological processes occurring in the oral cavity and throughout the body.This chapter delves into the potential of saliva as a noninvasive sampling method, exploring its utility in gene expression profiling for various applications. It provides an overview of the components contributing to the salivary transcriptome and discusses the challenges associated with salivary RNA analysis. We highlight the applications of salivary gene expression studies in biomarker discovery for oral and systemic diseases.While discussing various saliva collection techniques, here we focus on the procedure for RNA extraction, including microRNA (miRNA) from the OMNIgene™ SALIVA DNA and RNA device, OMR-610 (DNA Genotek Inc., Ottawa, Ontario, Canada). Herein, we provide the detailed methodologies for RNA extraction for salivary transcriptomics and the miRNome, thus providing a resource for researchers interested in leveraging the diagnostic and prognostic potential of saliva for personalized medicine and precision health initiatives.

PMID:39900752 | DOI:10.1007/978-1-0716-4276-4_1

Circulation. 2025 Feb 4;151(5):e35-e36. doi: 10.1161/CIRCULATIONAHA.124.072688. Epub 2025 Feb 3.

NO ABSTRACT

PMID:39899631 | DOI:10.1161/CIRCULATIONAHA.124.072688

PLoS Genet. 2025 Feb 3;21(2):e1011582. doi: 10.1371/journal.pgen.1011582. Online ahead of print.

ABSTRACT

A first ethanol exposure creates three memory-like states in Drosophila. Ethanol memory-like states appear genetically and behaviorally paralleled to the canonical learning and memory traces anesthesia-sensitive, anesthesia-resistant, and long-term memory ASM, ARM, and LTM. It is unknown if these ethanol memory-like states are also encoded by the canonical learning and memory circuitry that is centered on the mushroom bodies. We show that the three ethanol memory-like states, anesthesia-sensitive tolerance (AST) and anesthesia resistant tolerance (ART) created by ethanol sedation to a moderately high ethanol exposure, and chronic tolerance created by a longer low concentration ethanol exposure, each engage the mushroom body circuitry differently. Moreover, critical encoding steps for ethanol memory-like states reside outside the mushroom body circuitry, and within the mushroom body circuitry they are markedly distinct from classical memory traces. Thus, the first ethanol exposure creates distinct memory-like states in ethanol-specific circuits and impacts the function of learning and memory circuitry in ways that might influence the formation and retention of other memories.

PMID:39899623 | DOI:10.1371/journal.pgen.1011582

Diabetes Care. 2025 Feb 3:dc242039. doi: 10.2337/dc24-2039. Online ahead of print.

ABSTRACT

OBJECTIVE: Glucocorticoids (GCs) are potent anti-inflammatory drugs, but strategies to prevent side effects are lacking. We investigated whether metformin could prevent GC-related toxicity and explored the underlying mechanisms.

RESEARCH DESIGN AND METHODS: This single-center, randomized, placebo-controlled, double-blind, crossover trial compared metformin with placebo during high-dose GC treatment in 18 lean, healthy, male study participants. The trial was conducted at the University Hospital Basel, Switzerland. Participants received prednisone 30 mg/d in combination with metformin or placebo for two 7-day periods (1:1 randomization). The primary outcome, change in insulin sensitivity, was assessed using a two-sided paired t test. Before and after each study period, we conducted a mixed-meal tolerance test, blood metabolomics, and RNA sequencing of subcutaneous adipose tissue biopsy specimens.

RESULTS: Metformin improved insulin sensitivity as assessed by the Matsuda index (n = 17; mean change: -2.73 ± 3.55 SD for placebo, 2.21 ± 3.95 for metformin; mean difference of change -4.94 [95% CI, -7.24, -2.65)]; P < 0.001). Metabolomic and transcriptomic analyses revealed that metformin altered fatty acid flux in the blood and downregulated genes involved in fatty acid synthesis in adipose tissue. Metformin reduced markers of protein breakdown and bone resorption. Furthermore, metformin downregulated genes responsible for AMPK inhibition and affected GLP1 and bile acid metabolism.

CONCLUSIONS: Metformin prevents GC-induced insulin resistance and reduces markers of dyslipidemia, myopathy, and, possibly, bone resorption through AMPK-dependent and -independent pathways.

PMID:39899467 | DOI:10.2337/dc24-2039

Diabetes. 2025 Feb 3:db240360. doi: 10.2337/db24-0360. Online ahead of print.

ABSTRACT

Glucolipotoxicity, caused by combined hyperglycemia and hyperlipidemia, results in β-cell failure and type 2 diabetes via cellular stress-related mechanisms. Activating transcription factor 4 (Atf4) is an essential effector of stress response. We show here that Atf4 expression in β-cells is minimally required for glucose homeostasis in juvenile and adolescent mice but it is needed for β-cell function during aging and under obesity-related metabolic stress. Henceforth, Atf4-deficient β-cells older than 2 months after birth display compromised secretory function under acute hyperglycemia. In contrast, they are resistant to acute free fatty acid-induced dysfunction and reduced production of several factors essential for β-cell identity. Atf4-deficient β-cells down-regulate genes involved in protein translation. They also upregulate several lipid metabolism or signaling genes, likely contributing to their resistance to free fatty acid-induced dysfunction. These results suggest that Atf4 activation is required for β-cell identity and function under high glucose. But Atf4 activation paradoxically induces β-cell failure in high levels of free fatty acids. Different transcriptional targets of Atf4 could be manipulated to protect β-cells from metabolic stress-induced failure.

PMID:39899446 | DOI:10.2337/db24-0360

Elife. 2025 Feb 3;13:RP98934. doi: 10.7554/eLife.98934.

ABSTRACT

Copy number variants (CNVs) are an important source of genetic variation underlying rapid adaptation and genome evolution. Whereas point mutation rates vary with genomic location and local DNA features, the role of genome architecture in the formation and evolutionary dynamics of CNVs is poorly understood. Previously, we found the GAP1 gene in Saccharomyces cerevisiae undergoes frequent amplification and selection in glutamine-limitation. The gene is flanked by two long terminal repeats (LTRs) and proximate to an origin of DNA replication (autonomously replicating sequence, ARS), which likely promote rapid GAP1 CNV formation. To test the role of these genomic elements on CNV-mediated adaptive evolution, we evolved engineered strains lacking either the adjacent LTRs, ARS, or all elements in glutamine-limited chemostats. Using a CNV reporter system and neural network simulation-based inference (nnSBI) we quantified the formation rate and fitness effect of CNVs for each strain. Removal of local DNA elements significantly impacts the fitness effect of GAP1 CNVs and the rate of adaptation. In 177 CNV lineages, across all four strains, between 26% and 80% of all GAP1 CNVs are mediated by Origin Dependent Inverted Repeat Amplification (ODIRA) which results from template switching between the leading and lagging strand during DNA synthesis. In the absence of the local ARS, distal ones mediate CNV formation via ODIRA. In the absence of local LTRs, homologous recombination can mediate gene amplification following de novo retrotransposon events. Our study reveals that template switching during DNA replication is a prevalent source of adaptive CNVs.

PMID:39899365 | DOI:10.7554/eLife.98934

iScience. 2025 Jan 4;28(2):111745. doi: 10.1016/j.isci.2025.111745. eCollection 2025 Feb 21.

ABSTRACT

Talin-1 (TLN1) is best known to activate integrin receptors and transmit mechanical stimuli to the actin cytoskeleton at focal adhesions. However, the localization of TLN1 is not restricted to focal adhesions. By utilizing both subcellular fractionations and confocal microscopy analyses, we show that TLN1 localizes to the nucleus in several human cell lines, where it is tightly associated with the chromatin. Importantly, small interfering RNA (siRNA)-mediated depletion of endogenous TLN1 triggers extensive changes in the gene expression profile of human breast epithelial cells. To determine the functional impact of nuclear TLN1, we expressed a TLN1 fusion protein containing a nuclear localization signal. Our findings revealed that the accumulation of nuclear TLN1 alters the expression of a subset of genes and impairs the formation of cell-cell clusters. This study introduces an additional perspective on the canonical view of TLN1 subcellular localization and function.

PMID:39898029 | PMC:PMC11787672 | DOI:10.1016/j.isci.2025.111745

iScience. 2025 Jan 2;28(2):111730. doi: 10.1016/j.isci.2024.111730. eCollection 2025 Feb 21.

ABSTRACT

Cell-fate decisions involve coordinated genome-wide expression changes, typically leading to a limited number of phenotypes. Although often modeled as simple toggle switches, these rather simplistic representations often disregard the complexity of regulatory networks governing these changes. Here, we unravel design principles underlying complex cell decision-making networks in multiple contexts. We show that the emergent dynamics of these networks and corresponding transcriptomic data are consistently low-dimensional, as quantified by the variance explained by principal component 1 (PC1). This low dimensionality in phenotypic space arises from extensive feedback loops in these networks arranged to effectively enable the formation of two teams of mutually inhibiting nodes. We use team strength as a metric to quantify these feedback interactions and show its strong correlation with PC1 variance. Using artificial networks of varied topologies, we also establish the conditions for generating canalized cell-fate landscapes, offering insights into diverse binary cellular decision-making networks.

PMID:39898023 | PMC:PMC11787609 | DOI:10.1016/j.isci.2024.111730

Heliyon. 2025 Jan 10;11(2):e41881. doi: 10.1016/j.heliyon.2025.e41881. eCollection 2025 Jan 30.

ABSTRACT

Anterior eye disorders including dry eye syndrome, keratitis, chemical burns, and trauma have varying prevalence rates in the world. Classical dosage forms based-topical ophthalmic drugs are popular treatments for managing corneal diseases. However, current dosage forms of ocular drugs can be associated with major challenges such as the short retention time in the presence of ocular barriers. Developing alternative therapeutic methods is required to overcome drug bioavailability from ocular barriers. Nanocarriers are major platforms and promising candidates for the administration of ophthalmic drugs in an adjustable manner. This paper briefly introduces the advantages, disadvantages, and characteristics of delivery systems for the treatment of corneal diseases. Additionally, advanced technologies such as 3D printing are being considered to fabricate ocular drug carriers and determine drug dosages for personalized treatment. This comprehensive review is gathered through multiple databases such as Google Scholar, PubMed, and Web of Science. It explores information around "ocular drug delivery systems'', "nano-based drug delivery systems'', "engineered nanocarriers'', and "advanced technologies to fabricate personalized drug delivery systems''.

PMID:39897787 | PMC:PMC11783021 | DOI:10.1016/j.heliyon.2025.e41881

Curr Res Pharmacol Drug Discov. 2025 Jan 9;8:100212. doi: 10.1016/j.crphar.2024.100212. eCollection 2025.

ABSTRACT

Non-small-cell lung cancer (NSCLC) represents a predominant histological subtype of lung cancer, characterized by high incidence and mortality rates. Despite significant advancements in therapeutic strategies and a deeper understanding of targeted therapies in recent years, tumor resistance remains an inevitable challenge, leading to poor prognostic outcomes. Several studies have indicated that sphingosine kinase 1 (SPHK1) plays a regulatory role in epidermal growth factor receptor (EGFR) signaling, and its elevated expression may be associated with resistance to EGFR tyrosine kinase inhibitors (EGFR-TKIs). Furthermore, the catalytic product of SPHK1, sphingosine 1-phosphate (S1P), along with its receptor, sphingosine 1-phosphate receptor 3 (S1PR3), plays a regulatory role in the function of the EGFR. However, the specific effects of the SPHK1/S1P/S1PR3 axis on EGFR in NSCLC, as well as the combined effects of SPHK1/S1P/S1PR3 inhibitors with the EGFR-TKI gefitinib, remain to be elucidated. In the present study, we investigated the correlation between SPHK1 expression levels and the survival rates of NSCLC patients, the relationship between SPHK1 or S1PR3 and EGFR, and the impact of SPHK1 expression on the half-maximal inhibitory concentration (IC50) of gefitinib in NSCLC. In A549 cells, the phosphorylation of EGFR was significantly reduced following SPHK1 knockdown. Utilizing SPHK1/S1P/S1PR3 inhibitors, namely PF543, TY52156, and FTY720, we established that the SPHK1/S1P/S1PR3 axis modulates EGFR activation in NSCLC. Furthermore, these signaling inhibitors enhanced the anti-proliferative efficacy of the EGFR-TKI gefitinib. RNA sequencing analysis revealed substantial alterations in 85 differentially expressed genes in NSCLC cells treated with the combination of FTY720 and gefitinib. These genes were predominantly associated with pathways such as axon guidance, microRNAs in cancer, and the JAK-STAT signaling pathway, among others. Overall, targeting the SPHK1/S1P/S1PR3 signaling pathway represents a promising therapeutic strategy to enhance gefitinib sensitivity in NSCLC.

PMID:39896887 | PMC:PMC11787445 | DOI:10.1016/j.crphar.2024.100212

bioRxiv [Preprint]. 2025 Jan 20:2025.01.16.633337. doi: 10.1101/2025.01.16.633337.

ABSTRACT

The BioModels Repository contains over 1000 manually curated mechanistic models drawn from published literature, most of which are encoded in the Systems Biology Markup Language (SBML). This community-based standard formally specifies each model, but does not describe the computational experimental conditions to run a simulation. Therefore, it can be challenging to reproduce any given figure or result from a publication with an SBML model alone. The Simulation Experiment Description Markup Language (SED-ML) provides a solution: a standard way to specify exactly how to run a specific experiment that corresponds to a specific figure or result. BioModels was established years before SED-ML, and both systems evolved over time, both in content and acceptance. Hence, only about half of the entries in BioModels contained SED-ML files, and these files reflected the version of SED-ML that was available at the time. Additionally, almost all of these SED-ML files had at least one minor mistake that made them invalid. To make these models and their results more reproducible, we report here on our work updating, correcting and providing new SED-ML files for 1055 curated mechanistic models in BioModels. In addition, because SED-ML is implementation-independent, it can be used for verification , demonstrating that results hold across multiple simulation engines. Here, we use a wrapper architecture for interpreting SED-ML, and report verification results across five different ODE-based biosimulation engines. Our work with SED-ML and the BioModels collection aims to improve the utility of these models by making them more reproducible and credible.

AUTHOR SUMMARY: Reproducing computationally-derived scientific results seems like it should be straightforward, but is often elusive. Code is lost, file formats change, and knowledge of what was done is only partially recorded and/or forgotten. Model repositories such as BioModels address this failing in the Systems Biology domain by encoding models in a standard format that can reproduce a figure from the paper from which it was drawn. Here, we delved into the BioModels repository to ensure that every curated model additionally contained instructions on what to do with that model, and then tested those instructions on a variety of simulation platforms. Not only did this improve the BioModels repository itself, but also improved the infrastructure necessary to run these validation comparisons in the future.

AUTHOR CONTRIBUTIONS: LS: Writing, Conceptualization, Data Curation, Investigation, Methodology, Project Administration, Software, Validation. RMS: Reading, Writing, Data Curation, Methodology TN: Reading, Data Curation, Methodology HH: Reading JK: Conceptualization, Data Curation, Investigation, Methodology, Software. BS: Software LD: Software IIM: Reading, Conceptualization, Funding JCS: Software, Methodology EA: Reading, Writing AAP: Software MLB: Reading, Writing JH: Writing, Methodology EM: Reading, Writing DPN: Reading, Writing, Methodology JG: Reading, Writing, Methodology HMS: Reading, Writing, Funding.

PMID:39896466 | PMC:PMC11785046 | DOI:10.1101/2025.01.16.633337

J Biomol Struct Dyn. 2025 Feb 3:1-14. doi: 10.1080/07391102.2025.2460069. Online ahead of print.

ABSTRACT

Lung squamous cell carcinoma (LUSC) is a type of non-small cell lung cancer that is the most common and deadly type of lung cancer, originating from the cells lining the bronchi. The progression of LUSC is influenced by various factors, such as genetic, viral, environmental and hormonal factors, immune system response, and smoking history. Despite extensive studies aimed at improving patient survival, the role of gender-specific molecular variants in LUSC progression remains unclear. Using a systems biology approach, combining differential gene expression, network analysis, and machine learning, aberrant mRNA and ncRNAs implicated in LUSC have been identified to improve patient survival, stratify patients and develop novel prognostic strategies. Furthermore, a systematic analysis of the prognostic implications and functional annotations of the molecular variants results in the filtering of key protein-coding genes and non-coding RNAs that are involved in tumor progression. We found several common molecular variants in both genders, including 4 mRNA, 4 miRNAs, and 27 lncRNAs. Among the shared lncRNAs, 5 were novel for both genders. These were found to have a poor prognostic performance in patients with lung cancer. The key players are involved in DNA replication, nucleotide excision repair, complement and coagulation cascades, and estrogen signaling pathways. In this study, we report lncRNAs (PVT1, FAM13A-AS1, LINC00461, NAV2-AS5, PRICKLE2-AS1, and VCAN-AS1) that may function as oncogenes or tumor suppressors by regulating the expression of coding genes, such as RAB24, HECW2, LGR4, and FKBP5. These lncRNAs and coding genes may play important roles in LUSC development and progression.

PMID:39895519 | DOI:10.1080/07391102.2025.2460069

Ann Rheum Dis. 2025 Feb 1:S0003-4967(25)00078-0. doi: 10.1016/j.ard.2025.01.022. Online ahead of print.

ABSTRACT

OBJECTIVES: To analyse the immune mechanisms of diffuse cutaneous systemic sclerosis (dcSSc) skin disease focusing on CD8+ T-cell responses in the affected skin of patients because chronic inflammation, vasculopathy, and extensive cutaneous fibrosis are prominent features of dcSSc skin disease, causing pain and disability in patients, with no effective therapy.

METHODS: Single-cell transcriptomics and epigenomics were applied to well-characterised patient skin samples to identify transcriptomes and key regulators of skin-resident CD8+ T-cell subsets. Multicolor immunofluorescence miscoscopy was used to validate molecular findings. Ex vivo skin explant assays were used to functionally characterise dysfunctional CD8+ T-cell subsets on nonlesional autologous skin.

RESULTS: We identified 2 major developmentally connected CD8+ T-cell subpopulations that were expanded in SSc skin lesions compared with healthy control skin. The first was a heterogeneous subset of effector-memory CD8+KLRB1+IL7R+ cells characterised by increased cytolytic and Tc2/Tc17 effector functions that appear to induce tissue damage and fibrosis in early-stage dcSSc skin lesions. The second, found primarily in patients with late-stage disease, was an exhausted CD8+KLRG1+IL7R- subset that exhibited transcriptional features of long-lived effector cells, likely contributing to chronic inflammation. Significantly, both subsets were also expanded in other benign dermatoses, implicating these cell populations in the pathogenesis of chronic human skin inflammation.

CONCLUSIONS: This study provides new insight into core regulatory programmes modulating skin-resident CD8+ T-cell plasticity and identifies distinct CD8+ T-cell subpopulations that contribute to initiation and chronicity of inflammatory responses in systemic sclerosis skin lesions. These findings reveal prospective molecular targets for new therapeutic strategies against this incurable disease.

PMID:39894688 | DOI:10.1016/j.ard.2025.01.022

J Adv Res. 2025 Jan 31:S2090-1232(25)00071-2. doi: 10.1016/j.jare.2025.01.052. Online ahead of print.

ABSTRACT

BACKGROUND: The development of pangenomes has revolutionized genomic studies by capturing the complete genetic diversity within a species. Pangenome assembly integrates data from multiple individuals to construct a comprehensive genomic landscape, revealing both core and accessory genomic elements. This approach enables the identification of novel genes, structural variations, and gene presence-absence variations, providing insights into species evolution, adaptation, and trait variation. Representing pangenomes requires innovative visualization formats that effectively convey the complex genomic structures and variations.

AIM: This review delves into contemporary methodologies and recent advancements in constructing pangenomes, particularly in plant genomes. It examines the structure of pangenome representation, including format comparison, conversion, visualization techniques, and their implications for enhancing crop improvement strategies.

KEY SCIENTIFIC CONCEPTS OF REVIEW: Earlier comparative studies have illuminated novel gene sequences, copy number variations, and presence-absence variations across diverse crop species. The concept of a pan-genome, which captures multiple genetic variations from a broad spectrum of genotypes, offers a holistic perspective of a species' genetic makeup. However, constructing a pan-genome for plants with larger genomes poses challenges, including managing vast genome sequence data and comprehending the genetic variations within the germplasm. To address these challenges, researchers have explored cost-effective alternatives to encapsulate species diversity in a single assembly known as a pangenome. This involves reducing the volume of genome sequences while focusing on genetic variations. With the growing prominence of the pan-genome concept in plant genomics, several software tools have emerged to facilitate pangenome construction. This review sheds light on developing and utilizing software tools tailored for constructing pan-genomes in plants. It also discusses representation formats suitable for downstream analyses, offering valuable insights into the genetic landscape and evolutionary dynamics of plant species. In summary, this review underscores the significance of pan-genome construction and representation formats in resolving the genetic architecture of plants, particularly those with complex genomes. It provides a comprehensive overview of recent advancements, aiding in exploring and understanding plant genetic diversity.

PMID:39894347 | DOI:10.1016/j.jare.2025.01.052

Int J Biol Macromol. 2025 Jan 31:140567. doi: 10.1016/j.ijbiomac.2025.140567. Online ahead of print.

ABSTRACT

BACKGROUND: Staphylococcus aureus (S. aureus) is a globally prevalent foodborne pathogen responsible for significant public health concerns. Staphylococcal food poisoning (SFP) results from staphylococcal enterotoxins (SEs) produced by specific strains of S. aureus. Rapid and effective detection of SEs remains a significant challenge for public health authorities. Aptamers, short single-stranded DNA(ssDNA), RNA, or synthetic xeno nucleic acid (XNA) molecules, exhibit high affinity for binding to their specific targets. Due to their unique properties, including low production costs, ease of chemical modification, high thermal stability, and reproducibility, aptamers present a viable alternative to antibodies for diagnostic and therapeutic applications.

OBJECTIVES: This research aimed to isolate high-affinity ssDNA aptamers with specificity for staphylococcal enterotoxin D (SED).

METHODS: The systematic evolution of ligands by the exponential enrichment (SELEX) method was utilized to identify specific aptamers. These aptamers were then validated using enzyme-linked apta-sorbent assay (ELASA) and surface plasmon resonance (SPR) to assess their binding characteristics and affinity.

RESULTS: SELEX successfully identified aptamers with strong binding affinity to SED. Among the identified candidates, one aptamer, Aptamer 1, exhibited the highest specificity for SED with a dissociation constant (KD) of 4.4 ± 2.26 nM. The limit of detection (LOD) for SED using this aptamer was determined to be 45 nM.

CONCLUSIONS: The findings indicate that the ELASA system designed using the aptamer developed in this study demonstrates higher specificity, sensitivity, and reproducibility in detecting enterotoxin D. This novel aptamer offers significant potential for applications in diagnostic platforms targeting S. aureus enterotoxins.

PMID:39894103 | DOI:10.1016/j.ijbiomac.2025.140567

SLAS Discov. 2025 Jan 31:100220. doi: 10.1016/j.slasd.2025.100220. Online ahead of print.

ABSTRACT

Protein-nucleic acid phase separation has been implicated in many diseases such as viral infections, neurodegeneration, and cancer. There is great interest in identifying condensate modulators (CMODs), which are small molecules that alter the dynamics and functions of phase-separated condensates, as a potential therapeutic modality. Most CMODs were identified in cellular high-content screens (HCS) where micron-scale condensates were characterized by fluorescence microscopy. These approaches lack information on protein dynamics, are limited by microscope resolution, and are insensitive to subtle condensation phenotypes missed by overfit analysis pipelines. Here, we evaluate two alternative cell-based assays: high-throughput single molecule tracking (htSMT) and proximity-based condensate biosensors using NanoBIT (split luciferase) and NanoBRET (bioluminescence resonance energy transfer) technologies. We applied these methods to evaluate condensation of the SARS-CoV-2 nucleocapsid (N) protein under GSK3 inhibitor treatment, which we had previously identified in our HCS campaign to induce condensation with well-defined structure-activity relationships (SAR). Using htSMT, we observed robust changes in N protein diffusion as early as 3 hours post GSK3 inhibition. Proximity-based N biosensors also reliably reported on condensation, enabling the rapid assaying of large compound libraries with a readout independent of imaging. Both htSMT and proximity-based biosensors performed well in a screening format and provided information on CMOD activity that was complementary to HCS. We expect that this expanded toolkit for interrogating phase-separated proteins will accelerate the identification of CMODs for important therapeutic targets.

PMID:39894078 | DOI:10.1016/j.slasd.2025.100220

Cell Rep. 2025 Feb 1;44(2):115240. doi: 10.1016/j.celrep.2025.115240. Online ahead of print.

ABSTRACT

Despite the broad use of single-cell/nucleus RNA sequencing in plant research, accurate cluster annotation in less-studied plant species remains a major challenge due to the lack of validated marker genes. Here, we generated a single-cell RNA sequencing atlas of soil-grown wheat roots and annotated cluster identities by transferring annotations from publicly available datasets in wheat, rice, maize, and Arabidopsis. The predictions from our orthology-based annotation approach were next validated using untargeted spatial transcriptomics. These results allowed us to predict evolutionarily conserved tissue-specific markers and generate cell type-specific gene regulatory networks for root tissues of wheat and the other species used in our analysis. In summary, we generated a single-cell and spatial transcriptomics resource for wheat root apical meristems, including numerous known and uncharacterized cell type-specific marker genes and developmental regulators. These data and analyses will facilitate future cell type annotation in non-model plant species.

PMID:39893633 | DOI:10.1016/j.celrep.2025.115240

ISME J. 2025 Feb 2:wraf018. doi: 10.1093/ismejo/wraf018. Online ahead of print.

ABSTRACT

Microbes transform their environments using diverse enzymatic reactions. However, it remains challenging to measure microbial reaction rates in natural environments. Despite advances in global quantification of enzyme abundances, the individual relationships between enzyme abundances and their reaction rates have not been systematically examined. Using matched proteomic and reaction rate data from microbial cultures, we show that enzyme abundance is often insufficient to predict its corresponding reaction rate. However, we discovered that global proteomic measurements can be used to make accurate rate predictions of individual reaction rates (median R2 = 0.78). Accurate rate predictions required only a small number of proteins and they did not need explicit prior mechanistic knowledge or environmental context. These results indicate that proteomes are encoders of cellular reaction rates, potentially enabling proteomic measurements in situ to estimate the rates of microbially mediated reactions in natural systems.

PMID:39893571 | DOI:10.1093/ismejo/wraf018