Cancer Sci. 2025 Feb 3. doi: 10.1111/cas.16412. Online ahead of print.

ABSTRACT

BRCA1 is one of the causative genes for hereditary breast and ovarian cancer syndrome with a high risk of early-onset breast cancer. Whereas olaparib (OLA), an inhibitor of poly-ADP-ribose polymerase, has been applied as adjuvant therapy to those cancer patients, its effect on ovarian reproductive function remains unelucidated. Recently, a rat model (MUT; Brca1(L63X/+) mutation) mimicking a human BRCA1 pathogenic variant has been established. Using this model, we evaluated the effects of OLA on ovarian reproductive function in comparison with the wild-type (WT) rats. MUT showed a significantly reduced number of primordial follicles and subfertility in accordance with aging. Oxidative stress was significantly elevated in the young MUT granulosa cells (GCs) accompanied by increased mTOR but decreased PTEN signals. OLA administration in MUT further decreased primordial follicles, with gene set enrichment analysis, indicating upregulated DNA repair pathways. Furthermore, a combination of OLA and cyclophosphamide (CPA) induced empty primordial follicles, recognized as CPA-induced severe ovarian toxicity. Whereas OLA + CPA caused greater reduction in primordial follicles both in MUT and WT in comparison with CPA alone, MUT ovaries were more susceptible to oxidative stress, potentially depleting primordial follicles via activation of GCs and inducing oocyte death due to accumulated DNA damage by OLA treatment. Our findings in this preclinical model underscore the importance of evaluating ovarian reserve prior to chemotherapy by performing reproductive consultation with female patients with BRCA1 pathogenic variants.

PMID:39901592 | DOI:10.1111/cas.16412

BMC Gastroenterol. 2025 Feb 3;25(1):50. doi: 10.1186/s12876-025-03629-0.

ABSTRACT

BACKGROUND: Ulcerative colitis (UC) is a type of inflammatory bowel disease, and current treatments often fall short, necessitating new therapeutic options. Baicalin shows therapeutic promise in UC animal models, but a systematic review is needed.

METHODS: A systematic search was conducted across databases including PubMed, EBSCO, Web of Science, and Science Direct, up to March 2024, identifying randomized controlled trials (RCTs) examining baicalin's impact on UC in animal models. Seventeen studies were selected through manual screening. Meta-analyses and subgroup analyses utilized Rev Man 5.3 and Stata 15.0 software to assess symptom improvement.

RESULTS: From 1304 citations, 17 were analyzed. Baicalin significantly modulated various biomarkers: HCS (SMD = -3.91), DAI (MD = -2.75), spleen index (MD = -12.76), MDA (SMD = -3.88), IL-6 (SMD = -10.59), IL-1β (SMD = -3.98), TNF-α (SMD = -8.05), NF-κB (SMD = -5.46), TLR4 (MD = -0.38), RORγ (MD = -0.89), MCP-1 (MD = -153.25), MPO (SMD = -7.34), Caspase-9 (MD = -0.93), Caspase-3 (MD = -0.45), FasL (MD = -1.20)) and enhanced BWC (MD = 0.06), CL (MD = 1.39), ZO-1 (MD = 0.44), SOD (SMD = 3.04), IL-10 mRNA (MD = 3.14), and FOXP3 (MD = 0.45) levels. Baicalin's actions may involve the PI3K/AKT, TLR4/NF-κB, IKK/IKB, Bcl-2/Bax, Th17/Treg, and TLRs/MyD88 pathways. Optimal therapeutic outcomes were predicted at dosages of 60-150 mg/kg over 10-14 weeks.

CONCLUSION: Baicalin demonstrates a multifaceted therapeutic potential in UC, attributed to its anti-inflammatory, antioxidant, anti-apoptotic, and intestinal barrier repair properties. While higher doses and longer treatments appear beneficial, further research, particularly human clinical trials, is necessary to verify its effectiveness and safety in people.

PMID:39901089 | DOI:10.1186/s12876-025-03629-0

Nat Microbiol. 2025 Feb;10(2):527-540. doi: 10.1038/s41564-024-01914-4. Epub 2025 Feb 3.

ABSTRACT

The local arrangement of microbes can profoundly impact community assembly, function and stability. However, our understanding of the spatial organization of the human gut microbiome at the micron scale is limited. Here we describe a high-throughput and streamlined method called Split-And-pool Metagenomic Plot-sampling sequencing (SAMPL-seq) to capture spatial co-localization in a complex microbial consortium. The method obtains microbial composition of micron-scale subcommunities through split-and-pool barcoding. SAMPL-seq analysis of the healthy human gut microbiome identified bacterial taxa pairs that consistently co-occurred both over time and across multiple individuals. These co-localized microbes organize into spatially distinct groups or 'spatial hubs' dominated by Bacteroidaceae, Ruminococcaceae and Lachnospiraceae families. Using inulin as a dietary perturbation, we observed reversible spatial rearrangement of the gut microbiome where specific taxa form new local partnerships. Spatial metagenomics using SAMPL-seq can unlock insights into microbiomes at the micron scale.

PMID:39901058 | DOI:10.1038/s41564-024-01914-4

Nat Biotechnol. 2025 Feb 3. doi: 10.1038/s41587-025-02576-1. Online ahead of print.

NO ABSTRACT

PMID:39901026 | DOI:10.1038/s41587-025-02576-1

Nat Genet. 2025 Feb 3. doi: 10.1038/s41588-024-02069-y. Online ahead of print.

ABSTRACT

A pan-transcriptome describes the transcriptional and post-transcriptional consequences of genome diversity from multiple individuals within a species. We developed a barley pan-transcriptome using 20 inbred genotypes representing domesticated barley diversity by generating and analyzing short- and long-read RNA-sequencing datasets from multiple tissues. To overcome single reference bias in transcript quantification, we constructed genotype-specific reference transcript datasets (RTDs) and integrated these into a linear pan-genome framework to create a pan-RTD, allowing transcript categorization as core, shell or cloud. Focusing on the core (expressed in all genotypes), we observed significant transcript abundance variation among tissues and between genotypes driven partly by RNA processing, gene copy number, structural rearrangements and conservation of promotor motifs. Network analyses revealed conserved co-expression module::tissue correlations and frequent functional diversification. To complement the pan-transcriptome, we constructed a comprehensive cultivar (cv.) Morex gene-expression atlas and illustrate how these combined datasets can be used to guide biological inquiry.

PMID:39901014 | DOI:10.1038/s41588-024-02069-y

Nat Commun. 2025 Feb 3;16(1):1277. doi: 10.1038/s41467-025-56489-3.

ABSTRACT

Eukaryotic translation elongation factor 1A (eEF1A) is a highly abundant, multi-domain GTPase. Post-translational steps essential for eEF1A biogenesis are carried out by bespoke chaperones but co-translational mechanisms tailored to eEF1A folding remain unexplored. Here, we use AlphaPulldown to identify Ypl225w (also known as Chp1, Chaperone 1 for eEF1A) as a conserved yeast protein predicted to stabilize the N-terminal, GTP-binding (G) domain of eEF1A against its misfolding propensity, as predicted by computational simulations and validated by microscopy analysis of ypl225wΔ cells. Proteomics and biochemical reconstitution reveal that Ypl225w functions as a co-translational chaperone by forming dual interactions with the eEF1A G domain nascent chain and the UBA domain of ribosome-bound nascent polypeptide-associated complex (NAC). Lastly, we show that Ypl225w primes eEF1A nascent chains for binding to GTP as part of a folding mechanism tightly coupled to chaperone recycling. Our work shows that an ATP-independent chaperone can drive vectorial folding of nascent chains by co-opting G protein nucleotide binding.

PMID:39900909 | DOI:10.1038/s41467-025-56489-3

NPJ Precis Oncol. 2025 Feb 3;9(1):35. doi: 10.1038/s41698-025-00804-0.

ABSTRACT

The rapid development of deep learning has revolutionized medical image processing, including analyzing whole slide images (WSIs). Despite the demonstrated potential for characterizing gene mutations directly from WSIs in certain cancers, challenges remain due to image resolution and reliance on manual annotations for acute myeloid leukemia (AML). We, therefore, propose a deep learning model based on multiple instance learning (MIL) with ensemble techniques to predict gene mutations from AML WSIs. Our model predicts NPM1 mutations and FLT3-ITD without requiring patch-level or cell-level annotations. Using a dataset of 572 WSIs, the largest database with both WSI and genetic mutation information, our model achieved an AUC of 0.90 ± 0.08 for NPM1 and 0.80 ± 0.10 for FLT3-ITD in the testing cohort. Additionally, we found that blasts are pivotal indicators for gene mutation predictions, with their proportions varying between mutated and standard WSIs, highlighting the clinical potential of AML WSI analysis.

PMID:39900774 | DOI:10.1038/s41698-025-00804-0

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