Genome Med. 2025 Jul 15;17(1):78. doi: 10.1186/s13073-025-01508-7.

ABSTRACT

BACKGROUND: Previous case-control studies have reported aberrations of the gut microbiota in individuals with prediabetes. The primary objective of the present study was to explore the dynamics of the gut microbiota of individuals with prediabetes over 4 years with a secondary aim of relating microbiota dynamics to temporal changes of metabolic phenotypes.

METHODS: The study included 486 European patients with prediabetes. Gut microbiota profiling was conducted using shotgun metagenomic sequencing and the same bioinformatics pipelines at study baseline and after 4 years. The same phenotyping protocols and core laboratory analyses were applied at the two timepoints. Phenotyping included anthropometrics and measurement of fasting plasma glucose and insulin levels, mean plasma glucose and insulin under an oral glucose tolerance test (OGTT), 2-h plasma glucose after an OGTT, oral glucose insulin sensitivity index, Matsuda insulin sensitivity index, body mass index, waist circumference, and systolic and diastolic blood pressure. Measures of the dynamics of bacterial microbiota were related to concomitant changes in markers of host metabolism.

RESULTS: Over 4 years, significant declines in richness were observed in gut bacterial and viral species and microbial pathways accompanied by significant changes in the relative abundance and the genetic composition of multiple bacterial species. Additionally, bacterial-viral interactions diminished over time. Despite the overall reduction in bacterial richness and microbial pathway richness, 80 dominant core bacterial species and 78 core microbial pathways were identified at both timepoints in 99% of the individuals, representing a resilient component of the gut microbiota. Over the same period, individuals with prediabetes exhibited a significant increase in glycemia and insulinemia alongside a significant decline in insulin sensitivity. Estimates of the gut bacterial microbiota dynamics were significantly correlated with temporal impairments in host metabolic health.

CONCLUSIONS: In this 4-year prospective study of European patients with prediabetes, the gut microbiota exhibited major changes in taxonomic composition, bacterial species genetics, and microbial functional potentials, many of which paralleled an aggravation of host metabolism. Whether the temporal gut microbiota changes represent an adaptation to the progression of metabolic abnormalities or actively contribute to these in prediabetes cases remains unsettled.

TRIAL REGISTRATION: The Diabetes Research on Patient Stratification (DIRECT) study, an exploratory observational study initiated on October 15, 2012, was registered on ClinicalTrials.gov under the number NCT03814915.

PMID:40665409 | DOI:10.1186/s13073-025-01508-7

BMC Bioinformatics. 2025 Jul 15;26(1):178. doi: 10.1186/s12859-025-06212-2.

ABSTRACT

BACKGROUND: Gene regulatory network (GRN) models provide mechanistic understanding of genetic interactions that regulate gene expression and, consequently, influence cellular behavior. Dysregulated gene expression plays a critical role in disease progression and treatment response, making GRN models a promising tool for precision medicine. While researchers have built many models to describe specific subsets of gene interactions, more comprehensive models that cover a broader range of genes are challenging to build. This necessitates the development of approaches for improving the models through model merging.

RESULTS: We present LM-Merger, a workflow for semi-automatically merging logical GRN models. The workflow consists of five main steps: (a) model identification, (b) model standardization and annotation, (c) model verification, (d) model merging, and (e) model evaluation. We demonstrate the feasibility and benefit of this workflow with two pairs of published models pertaining to acute myeloid leukemia (AML). The integrated models were able to retain the predictive accuracy of the original models, while expanding coverage of the biological system. Notably, when applied to a new dataset, the integrated models outperformed the individual models in predicting patient response.

CONCLUSIONS: This study highlights the potential of logical model merging to advance systems biology research and our understanding of complex diseases. By enabling the construction of more comprehensive models, LM-Merger facilitates deeper insights into disease mechanisms and enhances predictive modeling for precision medicine applications.

CLINICAL TRIAL NUMBER: Not applicable.

PMID:40665244 | DOI:10.1186/s12859-025-06212-2

Sci Rep. 2025 Jul 16;15(1):25678. doi: 10.1038/s41598-025-11592-9.

ABSTRACT

Epigallocatechin gallate (EGCG) compound (IMPHY000226) has the potential to modulate multiple molecular mechanisms involved in Parkinson's disease. Multiple targets such as SIRT3, FOXO1, PRKAA1, PPARGC1A, and CREBBP directly regulate reactive oxygen species levels and oxidative stress, suggesting that targeting these genes could help prevent further cellular damage. EGCG targets were identified using Swiss target prediction, revealing 31 targets modulated by EGCG. Specific keywords were used to identify 4663 targets related to PD modulation. The network was constructed and analyzed using the node and edge counts. Clustering analysis identified specific target groups with high edge counts and Kappa scores, indicating potential key players in PD modulation. The targets SIRT3, FOXO1, and PPARGC1A were predicted to have the highest binding energies via dual algorithm-based molecular docking studies. The MD simulation studies were performed for the highest-docked targets, SIRT3, FOXO1, and PPARGC1A, to assess the stability and interactions. The cell viability assays were conducted at various dosage concentrations for EGCG and resveratrol, which provided dose-dependent effects on cell survival. In the toxicity-induced group, the highest % cell viability of 94% and 81% was observed at a dosage of 6.25 µg/mL and 12.5 µg/mL. The toxicity-induced gene expression studies indicated that the EGCG upregulated the targets SOD2, FOXO1, and GPx. EGCG and resveratrol upregulated the targets SOD2, FOXO1, and GPx at a dosage concentration of 12.5 µg/mL. EGCG was found to be more potent than the resveratrol molecule, indicating that EGCG can be used as an anti-Parkinson agent.

PMID:40664965 | DOI:10.1038/s41598-025-11592-9

Sci Rep. 2025 Jul 15;15(1):25638. doi: 10.1038/s41598-025-11825-x.

ABSTRACT

Tomato brown rugose fruit virus (ToBRFV) is a single-stranded positive-sense RNA virus that targets tomato and pepper plants and is causing significant damage to crops in some regions of the world. ToBRFV is a highly contagious virus that is stable and rapidly spreads by mechanical methods and seeds. As a result, it may spread both locally and over long distances, and it is now recognized as a pandemic in plants. This study investigates the effectiveness of the systems CRISPR-Cas12a and CRISPR-Cas9, in conjugation with recombinase polymerase amplification (RPA), to detect ToBRFV in tomato plant samples collected from the field. The trans-cleavage activity of both nucleases, Cas12a and Cas9, was exploited to process a probe labelled with fluorescein and biotin to be resolved on a lateral flow device, thereby enabling a visual readout. We were able to detect the RNA genome of the virus in about 1 h at a low constant temperature. These results could pave way to offer a rapid, sensitive, and specific method for on-site detection of ToBRFV.

PMID:40664931 | DOI:10.1038/s41598-025-11825-x

NPJ Syst Biol Appl. 2025 Jul 15;11(1):78. doi: 10.1038/s41540-025-00558-2.

ABSTRACT

Adaptation to an environment is enabled by the accumulation of beneficial mutations. How do adaptive trajectories and pleiotropic effects of adaptation change in response to "subtle" changes in the environment? Since there exists no molecular framework to quantify "subtle" environmental change, designing experiments to answer this question has been challenging. In this work, we address this question by studying the effects of evolution in environments which differ solely in the way sugars are presented to a bacterial population. Specifically, we focus on glucose and galactose, which can be supplied to an E. coli population as a mixture of glucose and galactose, lactose, or melibiose. We evolve six replicate populations of E coli for 300 generations in these three chemically correlated or "synonymous" environments, and show that the adaptive responses of these populations are not similar. When tested for pleiotropic effects of fitness in a range of non-synonymous environments, our results show that despite uncorrelated adaptive changes, the nature of pleiotropic effects is largely predictable based on the fitness of the ancestor in the non-home environments. Overall, our results highlight how subtle changes in the environment can alter adaptation, but despite sequence-level variations, pleiotropy is qualitatively predictable.

PMID:40664788 | DOI:10.1038/s41540-025-00558-2

OMICS. 2025 Jul 16. doi: 10.1177/15578100251359300. Online ahead of print.

ABSTRACT

The Rugose Spiraling Whitefly (RSW) (Aleurodicus rugioperculatus Martin), a pest native to Central America, infests coconut palms and has been introduced to other regions of the world including North America (e.g., Florida) and Southeast Asia. In India, RSW was first reported in 2016, and rapidly expanded to multiple states nationwide. Currently, RSW has growing global relevance as an agricultural insect pest. In addition to coconut, the RSW exhibits a broad host range, causing damage to various palms, fruit crops such as guava, vegetables, and ornamental shrubs. In this study, we present a high-quality draft genome assembly for this insect pest, generated using Pacific Bioscience long-read HiFi sequencing. The assembled genome spans 1.10 Gb, with a contig N50 value of 10.23 Mb. Approximately 521 Mb of sequences, accounting for 47.30% of the genome, were identified as repeat elements. The assembly includes 35,884 predicted coding sequences and exhibits high completeness, with 98.4% of Benchmarking Universal Single-Copy Orthologs genes recovered for the core insect gene set. The sequencing of the RSW genome offers valuable insights into the biology of one of the most significant and pervasive agricultural pests. The expansion of gene families associated with insecticide resistance may indicate this pest's ability to metabolize selective insecticides. These data have the potential to greatly enhance strategies for managing the RSW insect population size and limiting its invasive capacity for pest control. Additionally, the genome provides a foundation for comparative studies of whitefly genomes, and possibly informing the future design and development of novel insecticides.

PMID:40664507 | DOI:10.1177/15578100251359300

Genome Res. 2025 Jul 15. doi: 10.1101/gr.280274.124. Online ahead of print.

ABSTRACT

The original 100.3 Mb reference genome for Caenorhabditis elegans, generated from the wild-type laboratory strain N2, has been crucial for analysis of C. elegans since 1998 and has been considered complete since 2005. Unexpectedly, this long-standing reference was shown to be incomplete in 2019 by a genome assembly from the N2-derived strain VC2010. Moreover, genetically divergent versions of N2 have arisen over decades of research and hindered reproducibility of C. elegans genetics and genomics. Here we provide a 106.4 Mb gap-free, telomere-to-telomere genome assembly of C. elegans, generated from CGC1, an isogenic derivative of the N2 strain. We use improved long-read sequencing and manual assembly of 43 recalcitrant genomic regions to overcome deficiencies of prior N2 and VC2010 assemblies and to assemble tandem repeat loci, including a 772 kb sequence for the 45S rRNA genes. Although many differences from earlier assemblies come from repeat regions, unique additions to the genome are also found. Of 19,972 protein-coding genes in the N2 assembly, 19,790 (99.1%) encode products that are unchanged in the CGC1 assembly. The CGC1 assembly also may encode 183 new protein-coding and 163 new ncRNA genes. CGC1 thus provides both a completely defined reference genome and corresponding isogenic wild-type strain for C. elegans, allowing unique opportunities for model and systems biology.

PMID:40664475 | DOI:10.1101/gr.280274.124

J R Soc Interface. 2025 Jul;22(228):20250146. doi: 10.1098/rsif.2025.0146. Epub 2025 Jul 16.

ABSTRACT

Sudden and gradual changes from permissive to resistant hosts affect viral fitness, virulence and rates of molecular evolution. We analysed the roles of stochasticity and selection in evolving populations of Sindbis virus under different rates of host replacement. First, approximate Markov models within the Wright-Fisher diffusion framework revealed a reduction in effective population size by approximately half under sudden host changes. These scenarios were also associated with fewer weak beneficial mutations. Second, genetic distance between populations at consecutive time points indicated that populations undergoing gradual host changes evolved steadily until the original host disappeared. Distances to the ancestral sequence in these cases exhibited occasional leapfrog phenomena, where the rise of certain haplotypes is not predictable based on their relatedness to previously dominant ones. In contrast, populations exposed to sudden changes exhibited less-stable compositions and diverged from the ancestral sequence at a consistent rate. Third, we observed that the distribution of allele frequencies followed Taylor's Power Law. Both treatments exhibited high levels of allele aggregation and significant fluctuations, with neutral, beneficial and deleterious alleles distinguishable by their behaviour and position on Taylor's plot. Finally, we found evidence that the host replacement regime influences the temporal distribution of mutations across the genome.

PMID:40664232 | DOI:10.1098/rsif.2025.0146

Biomaterials. 2025 Jul 10;325:123548. doi: 10.1016/j.biomaterials.2025.123548. Online ahead of print.

ABSTRACT

Parkinson's disease (PD) is a progressive neurodegenerative disorder that primarily affects dopaminergic neurons in the substantia nigra. Its multifactorial pathogenic mechanisms include oxidative stress, mitochondrial dysfunction, α-synuclein aggregation, neuroinflammation, and alterations in the gut microbiome, ultimately leading to neuronal deficits and debilitating motor and nonmotor symptoms. Although conventional therapies provide temporary relief, their efficacy tends to wane over time or produce adverse effects. Exosome-based therapeutic strategies are a promising alternative, and we highlight the unique advantages of exosomes, including their biocompatibility, low immunogenicity, and ability to cross the blood-brain barrier, thereby facilitating the targeted delivery of neuroprotective and anti-inflammatory medications to affected regions. We also discuss recent advances in exosome engineering to improve cargo loading, enhance cell specificity and improve efficacy. However, large-scale exosome production, targeted delivery and long-term safety remain major challenges. Early-phase clinical trials of exosome-based therapies in other neurodegenerative conditions have demonstrated acceptable tolerability, and ongoing preclinical studies in PD models suggest potential efficacy, laying the groundwork for future clinical translation.

PMID:40664089 | DOI:10.1016/j.biomaterials.2025.123548

PLoS Biol. 2025 Jul 15;23(7):e3003275. doi: 10.1371/journal.pbio.3003275. Online ahead of print.

ABSTRACT

Extrachromosomal circular DNA (eccDNA) has been described in several eukaryotic species and has been shown to impact phenomena as diverse as cancer and herbicide tolerance. EccDNA is thought to arise mainly through transposable element (TE) mobilization. Because studies based on short-read sequencing cannot efficiently identify full-length eccDNA forms generated from TEs, we employed the CIDER-Seq pipeline based on long-read sequencing, to obtain full-length eccDNAs from Arabidopsis. The generated eccDNA datasets identified centromeric/pericentromeric regions as hotspots of eccDNAs with several eccDNA molecules originating from Helitron and LTR TEs. To investigate the role of epigenetic marks on TE-derived eccDNA biogenesis, we studied Arabidopsis methylation mutants dcl3, rdr6, ros1, and ddm1. Contrasting the TE-suppression previously reported in the hypermethylated ros1 mutants, we identified activation of TEs in ros1, specifically of LTR/Gypsy TEs. An enrichment of LTR/Copia elements was identified in actively dividing calli and the shoot apical meristem (SAM). Uncharacterized "variable TEs" with high eccDNA and expression were identified in the SAM, including ATCOPIA58. Together, our study reveals the genomic origins of eccDNAs and delineates the link between epigenetic regulation, transposon mobilization, and eccDNA biogenesis.

PMID:40663590 | DOI:10.1371/journal.pbio.3003275

Elife. 2025 Jul 15;13:RP100193. doi: 10.7554/eLife.100193.

ABSTRACT

Ketosis-prone diabetes mellitus (KPD) is a subtype of type 2 diabetes, which presents much like type 1 diabetes, with dramatic hyperglycemia and ketoacidosis. Although KPD patients are initially insulin-dependent, after a few months of insulin treatment, roughly 70% undergo near-normoglycemia remission and can maintain blood glucose without insulin, as in early type 2 diabetes or prediabetes. Here, we propose that these phenomena can be explained by the existence of a fast, reversible glucotoxicity process, which may exist in all people but be more pronounced in those susceptible to KPD. We develop a simple mathematical model of the pathogenesis of KPD, which incorporates this assumption, and show that it reproduces the phenomenology of KPD, including variations in the ability for patients to achieve and sustain remission. These results suggest that a variation of our model may be able to quantitatively describe variations in the course of remission among individuals with KPD.

PMID:40662943 | DOI:10.7554/eLife.100193

Bioinformatics. 2025 Jul 1;41(Supplement_1):i1-i2. doi: 10.1093/bioinformatics/btaf271.

ABSTRACT

This editorial describes the review and selection process of full-length, original research papers submitted to the Proceedings track of the ISMB/ECCB 2025 conference.

PMID:40662842 | DOI:10.1093/bioinformatics/btaf271

Bioinformatics. 2025 Jul 1;41(Supplement_1):i580-i589. doi: 10.1093/bioinformatics/btaf231.

ABSTRACT

MOTIVATION: Replication timing (RT) refers to the order in which DNA loci are replicated during S phase. RT is cell-type specific and implicated in cellular processes including transcription, differentiation, and disease. RT is typically quantified genome-wide using two-fraction assays (e.g. Repli-Seq) which sort cells into early and late S phase fractions followed by DNA sequencing, yielding a ratio as the RT signal. While two-fraction RT data are widely available in multiple cell lines, it is limited in its ability to capture high-resolution RT features. To address this, high-resolution Repli-Seq, which quantifies RT across 16 fractions, was developed, but it is costly and technically challenging with very limited data generated to date.

RESULTS: Here, we developed Soffritto, a deep learning model that predicts high-resolution RT data using two-fraction RT data, histone ChIP-seq data, GC content, and gene density as input. Soffritto is composed of a Long Short-Term Memory (LSTM) module and a prediction module. The LSTM module learns long- and short-range interactions between genomic bins, while the prediction module is composed of a fully connected layer that outputs a 16-fraction probability vector for each bin using the LSTM module's embeddings as input. By performing both within cell line and cross-cell line training and testing for five human and mouse cell lines, we show that Soffritto is able to capture experimental 16-fraction RT signals with high accuracy, and the predicted signals allow detection of high-resolution RT patterns.

AVAILABILITY AND IMPLEMENTATION: Soffritto is available at https://github.com/ay-lab/Soffritto.

PMID:40662815 | DOI:10.1093/bioinformatics/btaf231

Bioinformatics. 2025 Jul 1;41(Supplement_1):i561-i570. doi: 10.1093/bioinformatics/btaf180.

ABSTRACT

SUMMARY: Dynamic gene expression is controlled by transcriptional and post-transcriptional regulation. Recent studies on transcriptional bursting and buffering have increasingly highlighted the dynamic gene regulatory mechanisms. However, direct measurement techniques still face various constraints and require complementary methodologies, which are both comprehensive and versatile. To address this issue, inference approaches based on transcriptome data and differential equation models representing the messenger RNA lifecycle have been proposed. However, the inference of complex dynamics under diverse experimental conditions and biological scenarios remains challenging. In this study, we developed a flexible modeling using physics-informed neural networks and demonstrated its performance using simulation and experimental data. Our model has the ability to computationally revalidate and visualize dynamic biological phenomena, such as transcriptional ripple, co-bursting, and buffering in a breast cancer cell line. Furthermore, our results suggest putative molecular mechanisms underlying these phenomena. We propose a novel approach for inferring transcriptional and post-transcriptional regulation and expect to offer valuable insights for experimental and systems biology.

AVAILABILITY AND IMPLEMENTATION: https://github.com/omuto/RVINN.

PMID:40662812 | DOI:10.1093/bioinformatics/btaf180

Bioinformatics. 2025 Jul 1;41(Supplement_1):i590-i598. doi: 10.1093/bioinformatics/btaf192.

ABSTRACT

MOTIVATION: As most proteins interact with other proteins to perform their respective functions, methods to computationally predict these interactions have been developed. However, flawed evaluation schemes and data leakage in test sets have obscured the fact that sequence-based protein-protein interaction (PPI) prediction is still an open problem. Recently, methods achieving better-than-random performance on leakage-reduced PPI data have been proposed.

RESULTS: Here, we show that the use of ESM-2 protein embeddings explains this performance gain irrespective of model architecture. We compared the performance of models with varying complexity, per-protein, and per-token embeddings, as well as the influence of self- or cross-attention, where all models plateaued at an accuracy of 0.65. Moreover, we show that the tested sequence-based models cannot implicitly learn a contact map as an intermediate layer. These results imply that other input types, such as structure, might be necessary for producing reliable PPI predictions.

AVAILABILITY AND IMPLEMENTATION: All code for models and execution of the models is available at https://github.com/daisybio/PPI_prediction_study. Python version 3.8.18 and PyTorch version 2.1.1 were used for this study. The environment containing the versions of all other packages used can be found in the GitHub repository. The used data are available at https://doi.org/10.6084/m9.figshare.21591618.v3.

PMID:40662806 | DOI:10.1093/bioinformatics/btaf192

Bioinformatics. 2025 Jul 1;41(Supplement_1):i255-i264. doi: 10.1093/bioinformatics/btaf188.

ABSTRACT

SUMMARY: We introduce DNABERT-S, a tailored genome model that develops species-aware embeddings to naturally cluster and segregate DNA sequences of different species in the embedding space. Differentiating species from genomic sequences (i.e. DNA and RNA) is vital yet challenging, since many real-world species remain uncharacterized, lacking known genomes for reference. Embedding-based methods are therefore used to differentiate species in an unsupervised manner. DNABERT-S builds upon a pre-trained genome foundation model named DNABERT-2. To encourage effective embeddings to error-prone long-read DNA sequences, we introduce Manifold Instance Mixup (MI-Mix), a contrastive objective that mixes the hidden representations of DNA sequences at randomly selected layers and trains the model to recognize and differentiate these mixed proportions at the output layer. We further enhance it with the proposed Curriculum Contrastive Learning (C2LR) strategy. Empirical results on 28 diverse datasets show DNABERT-S's effectiveness, especially in realistic label-scarce scenarios. For example, it identifies twice more species from a mixture of unlabeled genomic sequences, doubles the Adjusted Rand Index (ARI) in species clustering, and outperforms the top baseline's performance in 10-shot species classification with just a 2-shot training.

AVAILABILITY AND IMPLEMENTATION: Model, codes, and data are publically available at https://github.com/MAGICS-LAB/DNABERT_S.

PMID:40662791 | DOI:10.1093/bioinformatics/btaf188

Bioinformatics. 2025 Jul 1;41(Supplement_1):i552-i560. doi: 10.1093/bioinformatics/btaf194.

ABSTRACT

MOTIVATION: Single-cell RNA sequencing (scRNA-seq) provides comprehensive gene expression data at a single-cell level but lacks spatial context. In contrast, spatial transcriptomics captures both spatial and transcriptional information but is limited by resolution, sensitivity, or feasibility. No single technology combines both the high spatial resolution and deep transcriptomic profiling at the single-cell level without tradeoffs. Spatial mapping tools that integrate scRNA-seq and spatial transcriptomics data are crucial to bridge this gap. However, we found that Tangram, one of the most prominent spatial mapping tools, provides inconsistent results over repeated runs.

RESULTS: We refine Tangram to achieve more consistent cell mappings and investigate the challenges that arise from data characteristics. We find that the mapping quality depends on the gene expression sparsity. To address this, we (1) train the model on an informative gene subset, (2) apply cell filtering, (3) introduce several forms of regularization, and (4) incorporate neighborhood information. Evaluations on real and simulated mouse datasets demonstrate that this approach improves both gene expression prediction and cell mapping. Consistent cell mapping strengthens the reliability of the projection of cell annotations and features into space, gene imputation, and correction of low-quality measurements. Our pipeline, which includes gene set and hyperparameter selection, can serve as guidance for applying Tangram on other datasets, while our benchmarking framework with data simulation and inconsistency metrics is useful for evaluating other tools or Tangram modifications.

AVAILABILITY AND IMPLEMENTATION: The refinements for Tangram and our benchmarking pipeline are available at https://github.com/daisybio/Tangram_Refinement_Strategies.

PMID:40662790 | DOI:10.1093/bioinformatics/btaf194

Bioinformatics. 2025 Jul 1;41(Supplement_1):i533-i541. doi: 10.1093/bioinformatics/btaf186.

ABSTRACT

MOTIVATION: Gene regulation inference, a key challenge in systems biology, is crucial for understanding cell function, as it governs processes such as differentiation, cell state maintenance, signal transduction, and stress response. Leading methods utilize gene expression, chromatin accessibility, transcription factor (TF) DNA binding motifs, and prior knowledge. However, they overlook the fact that TFs must be in physical proximity to facilitate transcriptional gene regulation.

RESULTS: To fill the gap, we develop GRIP-Gene Regulation Inference by considering TF Proximity-a gene regulation inference method that directly considers the physical proximity between regulating TFs. Specifically, we use the distance in a protein-protein interaction (PPI) network to estimate the physical proximity between TFs. We design a novel Boolean convex program, which can identify TFs that not only can explain the gene expression of target genes (TGs) but also stay close in the PPI network. We propose an efficient algorithm to solve the Boolean relaxation of the proposed model with a theoretical tightness guarantee. We compare our GRIP with state-of-the-art methods (SCENIC+, DirectNet, Pando, and CellOracle) on inferring cell-type-specific (CD4, CD8, and CD 14) gene regulation using the PBMC 3k scMultiome-seq data and demonstrate its out-performance in terms of the predictive power of the inferred TFs, the physical distance between the inferred TFs, and the agreement between the inferred gene regulation and PCHiC data.

AVAILABILITY AND IMPLEMENTATION: https://github.com/EJIUB/GRIP.

PMID:40662784 | DOI:10.1093/bioinformatics/btaf186

Small. 2025 Jul 15:e2507240. doi: 10.1002/smll.202507240. Online ahead of print.

ABSTRACT

Perovskite nanocrystals with CsPbX3 (X = Cl, Br, or I) structure have attracted great attention for developing optoelectronic applications in the past few years. However, their low stability and the limited scalability of current synthesis methods pose major challenges for future applications. Here, a template-assisted strategy is reported for synthesizing CsPbCl3 nanocrystals within mesoporous silica nanoreactors. Powder state CsPbCl3 complex fabricated via this method demonstrated an exceptional solvent stability and allows for yttrium doping to further enhance optical performance. Halide tuning (Cl⁻ to Br⁻) and synthesis of lead-free perovskites such as Cs4Bi2MnCl12 enable broad spectral emission, supporting full-spectrum white LED fabrication. This low-cost, scalable, and reproducible method offers strong potential for advancing perovskite-based optoelectronic technologies.

PMID:40662342 | DOI:10.1002/smll.202507240

iScience. 2025 Jun 11;28(7):112852. doi: 10.1016/j.isci.2025.112852. eCollection 2025 Jul 18.

ABSTRACT

The Phloem-Associated RNA-Chaperone-Like (PARCL) protein is a plant-specific RNA-binding protein (RBP) that is highly abundant in the phloem. PARCL has been observed to form large biomolecular condensates that move within the phloem stream, potentially being involved in RNA transport. Here, we present results on unraveling drivers for PARCL's phase separation. We used coarse-grained molecular dynamics simulations to compute a residue interaction map that identifies candidate residues involved in phase separation. Subsequent simulations with mutations of candidate residues resulted in disrupted condensation, supporting their involvement in phase separation. We performed in vitro and in vivo experiments to validate these predictions. To investigate the RNA-binding of PARCL, we added microRNA to the simulations and identified a short region of PARCL that consistently made contact with the miRNA in agreement with bioinformatics predictions and experiments. We discuss the implications of our findings in terms of model-guided engineering of biomolecular condensates.

PMID:40662195 | PMC:PMC12256297 | DOI:10.1016/j.isci.2025.112852