Biotechnol Biofuels Bioprod. 2025 Aug 1;18(1):86. doi: 10.1186/s13068-025-02685-8.

ABSTRACT

3-Methyl-1-butanol (3MB) is a promising renewable solvent, drop-in fuel, and precursor for various industrial products, including flavors, fragrances, and surfactants. Due to the myriad of intertwined biosynthetic pathways that share metabolic precursors, conventional metabolic engineering strategies to overproduce 3MB in yeast have typically resulted in yields that are far too low for economic viability. However, because 3MB is naturally produced by yeast, 100 million liter of 3MB are already produced annually as a byproduct of bioethanol fermentations. Despite its significant commercial value, this 3MB fraction is currently discarded due to its low relative concentration within the fusel alcohol mixture. Here, we present a novel strategy to produce 3MB along with the conventional bioethanol fermentation, leveraging the existing bioethanol industry by valorizing the discarded fusel alcohol byproduct stream. We first identified a robust industrially relevant chassis strain and explored different strategies to alleviate the valine and leucine feedback inhibition within the 3MB pathway, showing that mutating the leucine-inhibition site of Leu4p increased 3MB yield by 2.9-fold. Finally, we tested in silico-predicted gene deletion targets to reduce the byproduct acetate. Our final engineered strain achieved a 4.4-fold increase in 3MB yield compared to the wild type (1.5 mg/g sugars), average productivity of 5 mg/Lh, and a 3MB proportion increase from 42 to 71% within the fusel alcohol mix, while ethanol production remained comparable to the Ethanol Red® industrial reference. Our study thus opens a new route for co-producing 3MB and ethanol from sugarcane molasses in Saccharomyces cerevisiae, laying the groundwork toward an economically viable and sustainable approach for 3MB production alongside existing bioethanol production.

PMID:40751251 | DOI:10.1186/s13068-025-02685-8

Nat Med. 2025 Aug 1. doi: 10.1038/s41591-025-03904-3. Online ahead of print.

NO ABSTRACT

PMID:40750934 | DOI:10.1038/s41591-025-03904-3

Commun Chem. 2025 Aug 1;8(1):225. doi: 10.1038/s42004-025-01610-2.

ABSTRACT

Drug discovery starts with the identification of a "hit" compound that, following a long and expensive optimization process, evolves into a drug candidate. Bigger screening collections increase the odds of finding more and better hits. For this reason, large pharmaceutical companies have invested heavily in high-throughput screening (HTS) collections that can contain several million compounds. However, this figure pales in comparison with the emergent on-demand chemical collections, which have recently reached the trillion scale. These chemical collections are potentially transformative for drug discovery, as they could deliver many diverse and high-quality hits, even reaching lead-like starting points. But first, it will be necessary to develop computational tools capable of efficiently navigating such massive virtual collections. To address this challenge, we have conceived an innovative strategy that explores the chemical universe from the bottom up, performing a systematic search on the fragment space (exploration phase), to then mine the most promising areas of on-demand collections (exploitation phase). Using a hierarchy of increasingly sophisticated computational methods to remove false positives, we maximize the success probability and minimize the overall computational cost. A basic implementation of the concept has enabled us to validate the strategy prospectively, allowing the identification of new BRD4 (BD1) binders with potencies comparable to stablished drug candidates.

PMID:40750916 | DOI:10.1038/s42004-025-01610-2

BMC Genomics. 2025 Aug 1;26(1):712. doi: 10.1186/s12864-025-11863-9.

ABSTRACT

Predicting bacterial transcriptional regulatory networks (TRNs) through computational methods is a core challenge in systems biology, and there is still a long way to go. Here we propose a powerful, general, and stable computational framework called PGBTR (Powerful and General Bacterial Transcriptional Regulatory networks inference method), which employs Convolutional Neural Networks (CNN) to predict bacterial transcriptional regulatory relationships from gene expression data and genomic information. PGBTR consists of two main components: the input generation step PDGD (Probability Distribution and Graph Distance) and the deep learning model CNNBTR (Convolutional Neural Networks for Bacterial Transcriptional Regulation inference). On the real Escherichia coli and Bacillus subtilis datasets, PGBTR outperforms other advanced supervised and unsupervised learning methods in terms of AUROC (Area Under the Receiver Operating Characteristic Curve), AUPR (Area Under Precision-Recall Curve), and F1-score. Moreover, PGBTR exhibits greater stability in identifying real transcriptional regulatory interactions compared to existing methods. PGBTR provides a new software tool for bacterial TRNs inference, and its core ideas can be further extended to other molecular network inference tasks and other biological problems using gene expression data.

PMID:40750847 | DOI:10.1186/s12864-025-11863-9

Commun Biol. 2025 Aug 1;8(1):1145. doi: 10.1038/s42003-025-08570-2.

ABSTRACT

Although the connection between COVID-19 and coagulopathy has been clear since the beginning of SARS-CoV-2 pandemic, the underlying molecular mechanisms remain elusive. Available data support that the hyper-coagulant state is sustained by systemic inflammation. Here we show that the SARS-CoV-2 main protease (Mpro) can play a direct role in the activation of coagulation. Adding Mpro to human plasma increased clotting probability by 3-fold. Enzymatic assays and degradomics analysis indicate that Mpro cleaves and activates coagulation factors VII and XII. This activity is compatible with an extended secondary specificity of Mpro for R↓X that diverge from its well-established preference for LQ↓X. This finding is supported by HDX-MS characterization of the Mpro complex with an Arg-containing inhibitor, as well as the proteolytic cleavage of the peptide FTRLR↓SLEN by Mpro. Overall, integrating biochemical, proteomics and structural biology experiments, we unveil a novel mechanism linking SARS-CoV-2 infection to thrombotic complications in COVID-19.

PMID:40750828 | DOI:10.1038/s42003-025-08570-2

Nat Commun. 2025 Aug 1;16(1):7055. doi: 10.1038/s41467-025-62200-3.

ABSTRACT

RNA medicine is an emerging groundbreaking technology for the prevention and treatment of disease. However, tools to deliver messenger RNA (mRNA) and other polyanions (circRNA, saRNA, pDNA, CRISPR-Cas, reprogramming factors) are required to advance current RNA therapies and address next generation challenges. Existing delivery systems often suffer from laborious syntheses, limited organ selectivity, formulation complexity, and undesired inflammatory responses. Here, we report novel mRNA delivery systems termed Discrete Immolative Guanidinium Transporters (DIGITs), which are synthesized convergently in as few as 4 steps. Unlike most cationic (ammonium) delivery systems, DIGITs are based on cationic guanidinium moieties, which complex mRNA at acidic pH and undergo irreversible neutralization at physiological pH to enable efficient RNA release. Systematic evaluation of structural variations and formulations have led to DIGIT/mRNA complexes that selectively target lung, spleen, and immature red blood cells in peripheral blood in female mice model. DIGIT/mRNA delivery systems show minimal toxicity based on cell viability and biochemical assays, supporting their future utility in biomedical applications.

PMID:40750796 | DOI:10.1038/s41467-025-62200-3

Nat Commun. 2025 Aug 1;16(1):6986. doi: 10.1038/s41467-025-62187-x.

ABSTRACT

Children and adults with 22q11.2 deletion syndrome (22q11.2DS) experience cognitive and emotional challenges and face a markedly increased risk for schizophrenia (SCZ), yet how this deletion alters early human brain development remains unclear. Using cerebral cortex organoids derived from individuals with 22q11.2DS and SCZ, we identify cell-type-specific developmental abnormalities. Single-cell RNA sequencing and experimental validation reveal delayed cortical neuron maturation, with increased neural progenitor proliferation and a reduced proportion of more mature neurons. We observe disrupted molecular programs linked to neuronal maturation, sparser neurites, and blunted glutamate-induced Ca²⁺ responses. The aberrant transcriptional profile is enriched for neuropsychiatric risk genes. MicroRNA profiling suggests that DGCR8 haploinsufficiency contributes to these effects via dysregulation of genes that control the pace of maturation. Protein-protein interaction network analysis highlights complementary roles for additional deleted genes. Our study reveals consistent developmental and molecular defects caused by 22q11.2 deletions, offering insights into disease mechanisms and therapeutic strategies.

PMID:40750773 | DOI:10.1038/s41467-025-62187-x

Sci Rep. 2025 Aug 1;15(1):28105. doi: 10.1038/s41598-025-08384-6.

ABSTRACT

Bone marrow fibrosis (BMF) impairs normal hematopoietic functions in patients. The overactivation of the TGF-β signaling pathway is regarded as one of the offenders causing disease progression. Thus, factors capable of regulating TGF-β secretion hold great potential in reversing fibrotic diseases. One such factor is histone deacetylase inhibitors (HDACis), which can modulate the expression of TGF-β. Our previous study successfully synthesized a selective HDAC6 inhibitor, J22352, for pulmonary fibrosis; however, the treatment efficacies on BMF remain unclear. Therefore, in this study, we treated bone marrow-derived myofibroblasts with J22352. The results showed that J22352 significantly reduced cell viability, induced apoptosis, and inhibited extracellular matrix (ECM) accumulation. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis was employed to disclose potential mechanisms, identifying 334 differentially expressed proteins (DEPs). The DEPs were involved in cell apoptosis, programmed cell death, ECM deposition, and collagen formation. These results suggest that J22352 efficiently alleviated BMF by inducing cell apoptosis and inhibiting ECM deposition. This study introduces a novel selective HDAC6 inhibitor as a potential option for slowing down the progression of BMF. We aim to provide a promising selective HDACi for clinical medicine through a detailed analysis of its mechanisms and efficacy, offering new prospects in the field.

PMID:40750768 | DOI:10.1038/s41598-025-08384-6

Sci Rep. 2025 Aug 1;15(1):28088. doi: 10.1038/s41598-025-13871-x.

ABSTRACT

Proteins localized to peroxisomes, particularly those expressed under specific conditions or in low abundance, are often undetected by routine proteomics methods due to detection sensitivity limits. In silico identification and experimental validation of peroxisomal targeting signals (PTSs) offer a reliable alternative. We demonstrate that SYM, a non-canonical plant PTS-1 signal, functions similarly in Aspergillus nidulans, as GFP tagged with a SYM C-terminal tripeptide localizes to peroxisomes. One of two native A. nidulans proteins with C-terminal SYM tripeptide shows weak peroxisomal localization alongside cytoplasmic presence, indicating that only a subset of proteins with non-canonical signals access peroxisomes. In silico analysis of 1,010 fungal genomes identified diverse SYM-proteins with variable functions, suggesting that non-canonical PTS-1 signals may evolve spontaneously. Two-thirds of SYM-proteins are predicted to localize to specific intracellular compartments other than the peroxisome. We propose that despite their predicted localization, these proteins possessing SYM as a non-canonical peroxisomal signal might also have peroxisomal presence. Among SYM-proteins, pectinesterases, known plant pathogen virulence factors, were frequent. Notably, 25% of fungal pectinesterases harbor non-canonical PTS-1 signals, suggesting that partial peroxisomal localization of pectinesterases has evolved convergently. This suggests that partial peroxisomal localization may enhance protein functional flexibility, contributing to the organism's adaptability.

PMID:40750654 | DOI:10.1038/s41598-025-13871-x

NPJ Syst Biol Appl. 2025 Aug 1;11(1):85. doi: 10.1038/s41540-025-00565-3.

ABSTRACT

The emergence of single-cell transcriptomics has given us novel views of gene expression heterogeneity and cellular trajectories in development and disease at unprecedented resolution. However, an overarching theoretical framework to interpret single-cell gene expression data is lacking. Here we argue that dynamical systems theory can provide an interpretable, causal, and quantitative perspective to understand and analyze these enormously rich data sets, in addition to yielding potential benefits for health care.

PMID:40750599 | DOI:10.1038/s41540-025-00565-3

Exp Cell Res. 2025 Jul 30:114688. doi: 10.1016/j.yexcr.2025.114688. Online ahead of print.

NO ABSTRACT

PMID:40750009 | DOI:10.1016/j.yexcr.2025.114688

J Biol Chem. 2025 Jul 30:110542. doi: 10.1016/j.jbc.2025.110542. Online ahead of print.

ABSTRACT

HLA class I alleles of archaic origin may have been retained in modern humans because they provide immunity against diseases to which archaic humans had evolved resistance. According to this model, archaic introgressed alleles were somehow distinct from those that evolved in African populations. Here we show that HLA-B*73:01, a rare allotype with putative archaic origins, has a relatively rare peptide binding motif with an unusually long-tailed peptide length distribution. We also find that HLA-B*73:01 combines a restricted and unique peptidome with high-cell surface expression, characteristics that make it well-suited to combat one or a number of closely-related pathogens. Furthermore, a crystal structure of HLA-B*73:01 in complex with KIR2DL2 highlights differences from previously solved structures with HLA-C molecules. These molecular characteristics distinguish HLA-B*73:01 from other HLA class I alleles previously investigated and may have provided early modern human migrants that inherited this allele with a selective advantage as they colonized Europe and Asia.

PMID:40749828 | DOI:10.1016/j.jbc.2025.110542

Curr Biol. 2025 Jul 26:S0960-9822(25)00879-6. doi: 10.1016/j.cub.2025.07.013. Online ahead of print.

ABSTRACT

The meaning of a visual image depends on context-a mouse sees an expanding visual stimulus when approaching a dark refuge or when a cat approaches them, and distinguishing between the two is a matter of life and death. The superior colliculus (SC) is an evolutionarily ancient hub essential for survival behaviors like approach and avoidance of threats.1,2 We therefore combined virtual reality and neural recordings to ask whether matching visual stimuli to self-motion alters behavior and neural activity in SC. We first measured locomotion behavior and neural activity while animals approached an object in virtual reality or while the same object loomed at them. In both contexts, vision dominated activity in the superficial layers of SC (SCs), whereas locomotion had more influence on activity in the intermediate layers (SCim). In addition, animals instinctively slowed their locomotion when nearing the object or when the object neared them. To directly test animals' ability to distinguish self from object motion, we replayed the visual images generated during object approach. Locomotion behavior often changed during replay, showing animals can determine whether visual motion is matched to their self-movement. Further, decoders trained on locomotion behavior or on population activity in SC, particularly in SCim, were able to reliably discriminate self-movement and object movement contexts. We conclude that both mouse behavior and SC activity distinguish the context of visual motion and can thus discriminate motion arising from an animal's own movement and that of an external agent.

PMID:40749678 | DOI:10.1016/j.cub.2025.07.013

Plant Physiol. 2025 Aug 1:kiaf345. doi: 10.1093/plphys/kiaf345. Online ahead of print.

NO ABSTRACT

PMID:40749083 | DOI:10.1093/plphys/kiaf345

STAR Protoc. 2025 Jul 31;6(3):104000. doi: 10.1016/j.xpro.2025.104000. Online ahead of print.

ABSTRACT

We present a protocol for isolating highly purified crypt epithelial cells from the mouse intestine for single-cell RNA sequencing (scRNA-seq). Optimized for the mouse jejunum, it can be adapted to all intestinal tracts, including the colon. The pipeline incorporates automated community detection of cell populations (ACDC), a time- and memory-efficient Python package for automated graph-based optimal clustering of large scRNA-seq datasets. We demonstrate its usage to identify cellular populations in an intestinal stem cell dataset and generate publication-ready figures. For complete details on the use and execution of this protocol, please refer to Malagola et al.1.

PMID:40748762 | DOI:10.1016/j.xpro.2025.104000

Anal Chem. 2025 Aug 1. doi: 10.1021/acs.analchem.5c01400. Online ahead of print.

ABSTRACT

Gonorrhea has become an escalating public health issue due to the rapid emergence of antimicrobial resistance (AMR). Developing efficient and accurate detection of resistant strains is urgently needed for their management and treatment. We have developed the Multiplex Integrated RPA-CRISPR/Cas12a detection Assay (MIRCA) for simultaneous detection of Neisseria gonorrheae (Ng) and mutations with decreased susceptibility to cephalosporins. MIRCA enables multiplex detection of Ng and single-nucleotide polymorphisms in resistance-associated genes within 40 min, with high specificity and sensitivity (10-20 copies/reaction). Clinical evaluation showed 100% concordance with qPCR for Ng identification and Sanger sequencing for FC428 strain tracking. For predicting decreased-susceptibility strains with A501 mutations, MIRCA achieved 98.33% agreement with Sanger sequencing. Simulated tests demonstrated 100% consistency between MIRCA results in centrifuge tubes and microfluidic chips. This robust and cost-effective approach addresses current challenges in AMR surveillance. Its integration with microfluidic chip offers an affordable and user-friendly diagnostic solution, making it highly valuable for timely infectious disease diagnosis and resistance monitoring. It also holds significant potential for point-of-care testing in resource-limited areas.

PMID:40748632 | DOI:10.1021/acs.analchem.5c01400

J Endocrinol Invest. 2025 Aug 1. doi: 10.1007/s40618-025-02672-8. Online ahead of print.

ABSTRACT

PURPOSE: Childhood obesity is a global health problem that may be linked to changes in the gut microbiota. The aim of this pilot study was to link the clinical parameters of children with obesity, who underwent a dietary intervention, with their gut microbiota profiles to better understand the factors associated with weight loss.

METHODS: The study focused on 36 male children with obesity (aged 12-15 years) who were referred for treatment of primary obesity. Personal medical history information was collected at enrollment. Body composition was assessed before and after a three-week balanced hypocaloric diet with preserved nutritional value. Bacterial taxa were analyzed by 16S rRNA gene amplicon sequencing. Linear regression models and machine learning algorithms were used to determine how subjects' clinical factors and gut microbiota contribute to weight loss efficacy.

RESULTS: Results showed that higher abundance of Lachnospiraceae members (Blautia and Anaerostipes) prior to the diet intervention, and later introduction of non-dairy food were positively related to weight loss efficacy, while higher abundance of Erysipelotrichaceae UCG-003 and Faecalibacterium in the pre-diet samples was negatively correlated with BMI change. The change in the abundance of Enterobacteriaceae and Bacteroidetes members between the pre- and post-diet samples was negatively associated with delta BMI.

CONCLUSION: The efficacy of weight loss in male children with obesity under dietary treatment is related to the composition of the gut microbiota and the timing of the introduction of non-dairy food. Promoting beneficial gut bacteria could increase the success of dietary treatment for weight loss and improve long-term health outcomes in children with obesity.

PMID:40748420 | DOI:10.1007/s40618-025-02672-8

Plant Mol Biol. 2025 Aug 1;115(4):102. doi: 10.1007/s11103-025-01632-3.

ABSTRACT

The recognition of translational initiation sites (TISs) offers complementary insights into identifying genes encoding novel proteins or small peptides. Conventional computational methods primarily identify Ribo-seq-supported TISs and lack the capacity of systematic and global identification of TIS, especially for non-AUG sites in plants. Additionally, these methods are often unsuitable for evaluating the importance of mRNA sequence features for TIS determination. In this study, we present TISCalling, a robust framework that combines machine learning (ML) models and statistical analysis to identify and rank novel TISs across eukaryotes. TISCalling generalized and ranks important features common to multiple plant and mammalian species while identifying kingdom-specific features such as mRNA secondary structures and "G"-nucleotide contents. Furthermore, TISCalling achieved high predictive power for identifying novel viral TISs. Importantly, TISCalling provides prediction scores for putative TIS along plant transcripts, enabling prioritization of those of interest for further validation. We offer TISCalling as a command-line-based package [ https://github.com/yenmr/TISCalling ], capable of generating prediction models and identifying key sequence features. Additionally, we provide web tools [ https://predict.southerngenomics.org/TISCalling/ ] for visualizing pre-computed potential TISs, making it accessible to users without programming experience. The TISCalling framework offers a sequence-aware and interpretable approach for decoding genome sequences and exploring functional proteins in plants and viruses.

PMID:40748408 | DOI:10.1007/s11103-025-01632-3

Pediatr Allergy Immunol. 2025 Aug;36(8):e70164. doi: 10.1111/pai.70164.

ABSTRACT

BACKGROUND: COVID-19 prevention policies significantly influenced the prevalence and severity of allergic diseases. While lockdown measures in many countries reduced asthma exacerbations by limiting exposure to respiratory infections and environmental triggers, Taiwan's non-lockdown strategy provided a unique public health model. This study evaluates the impact of Taiwan's COVID-19 prevention policies on the prevalence and severity of allergic diseases in early adolescents.

METHOD: We collected 1519 questionnaires in 2021 and 1165 in 2023 from urban elementary schools. Self-reported data on asthma, allergic rhinitis, and atopic dermatitis were analyzed to assess changes in prevalence and severity. Comparisons were made between the two periods to determine the effects of public health measures on adolescent allergic diseases.

RESULTS: In 2021, our survey showed that 3% of participants had wheezing in the past 12 months, and 16.3% experienced nighttime cough. By 2023, both wheezing and nighttime cough significantly increased, along with a rise in rhinitis ever. In contrast, the prevalence of atopic dermatitis ever declined, as did the prevalence of recurrent eczema in the past 12 months over this period.

CONCLUSION: Taiwan's non-lockdown COVID-19 prevention strategy was associated with divergent trends in allergic diseases among early adolescents. Between 2021 and 2023, the prevalence of wheezing and allergic rhinitis increased, while atopic dermatitis and recurrent eczema declined. These findings suggest that pandemic-related behavioral changes, including mask-wearing and social distancing, may have influenced both respiratory and skin-related allergic conditions, highlighting the need to evaluate a broad range of health outcomes when assessing the impact of public health interventions.

PMID:40747654 | DOI:10.1111/pai.70164

iScience. 2025 Jul 2;28(8):113044. doi: 10.1016/j.isci.2025.113044. eCollection 2025 Aug 15.

ABSTRACT

Understanding the breadth and functional profile of T cell responses is crucial for assessing their role in immune surveillance of emerging SARS-CoV-2 variants. Sampling healthy individuals, we profiled the kinetics and polyfunctionality of T cell immunity elicited by mRNA vaccination. Modeling of anti-spike T cell responses against ancestral and variant strains suggested epitope immunodominance and cross-reactivity as major predictive determinants of T cell immunity. To identify immunodominant epitopes, we comprehensively mapped CD4+ and CD8+ T cell epitopes within non-spike proteins using samples from convalescent patients. We found that immunodominant epitopes mainly resided within regions that were minimally disrupted by emerging mutations. Conservation analysis across human coronaviruses and in silico alanine scanning highlighted the functional importance of mutationally constrained immunodominant regions. Collectively, these findings identify immunodominant T cell epitopes across the SARS-CoV-2 proteome that may enhance immune surveillance against emerging variants and inform next-generation vaccine designs providing broader and more durable protection.

PMID:40746995 | PMC:PMC12312045 | DOI:10.1016/j.isci.2025.113044