Literature Watch
Simpler Protein Domain Identification Using Spectral Clustering
Proteins. 2025 Feb 13. doi: 10.1002/prot.26808. Online ahead of print.
ABSTRACT
The decomposition of a biomolecular complex into domains is an important step to investigate biological functions and ease structure determination. A successful approach to do so is the SPECTRUS algorithm, which provides a segmentation based on spectral clustering applied to a graph coding inter-atomic fluctuations derived from an elastic network model. We present SPECTRALDOM, which makes three straightforward and useful additions to SPECTRUS. For single structures, we show that high quality partitionings can be obtained from a graph Laplacian derived from pairwise interactions-without normal modes. For sets of homologous structures, we introduce a Multiple Sequence Alignment mode, exploiting both the sequence based information (MSA) and the geometric information embodied in experimental structures. Finally, we propose to analyze the clusters/domains delivered using the so-called D $$ D $$ -family-matching algorithm, which establishes a correspondence between domains yielded by two decompositions, and can be used to handle fragmentation issues. Our domains compare favorably to those of the original SPECTRUS, and those of the deep learning based method Chainsaw. Using two complex cases, we show in particular that SPECTRALDOM is the only method handling complex conformational changes involving several sub-domains. Finally, a comparison of SPECTRALDOM and Chainsaw on the manually curated domain classification ECOD as a reference shows that high quality domains are obtained without using any evolutionary related piece of information. SPECTRALDOM is provided in the Structural Bioinformatics Library, see http://sbl.inria.fr and https://sbl.inria.fr/doc/Spectral_domain_explorer-user-manual.html.
PMID:39945423 | DOI:10.1002/prot.26808
A Veterinary DICOM-Based Deep Learning Denoising Algorithm Can Improve Subjective and Objective Brain MRI Image Quality
Vet Radiol Ultrasound. 2025 Mar;66(2):e70015. doi: 10.1111/vru.70015.
ABSTRACT
In this analytical cross-sectional method comparison study, we evaluated brain MR images in 30 dogs and cats with and without using a DICOM-based deep-learning (DL) denoising algorithm developed specifically for veterinary patients. Quantitative comparison was performed by measuring signal-to-noise (SNR) and contrast-to-noise ratios (CNR) on the same T2-weighted (T2W), T2-FLAIR, and Gradient Echo (GRE) MR brain images in each patient (native images and after denoising) in identical regions of interest. Qualitative comparisons were then conducted: three experienced veterinary radiologists independently evaluated each patient's T2W, T2-FLAIR, and GRE image series. Native and denoised images were evaluated separately, with observers blinded to the type of images they were assessing. For each image type (native and denoised) and pulse sequence type image, they assigned a subjective grade of coarseness, contrast, and overall quality. For all image series tested (T2W, T2-FLAIR, and GRE), the SNRs of cortical gray matter, subcortical white matter, deep gray matter, and internal capsule were statistically significantly higher on images treated with DL denoising algorithm than native images. Similarly, for all image series types tested, the CNRs between cortical gray and white matter and between deep gray matter and internal capsule were significantly higher on DL algorithm-treated images than native images. The qualitative analysis confirmed these results, with generally better coarseness, contrast, and overall quality scores for the images treated with the DL denoising algorithm. In this study, this DICOM-based DL denoising algorithm reduced noise in 1.5T MRI canine and feline brain images, and radiologists' perceived image quality improved.
PMID:39945204 | DOI:10.1111/vru.70015
MUNet: a novel framework for accurate brain tumor segmentation combining UNet and mamba networks
Front Comput Neurosci. 2025 Jan 29;19:1513059. doi: 10.3389/fncom.2025.1513059. eCollection 2025.
ABSTRACT
Brain tumors are one of the major health threats to humans, and their complex pathological features and anatomical structures make accurate segmentation and detection crucial. However, existing models based on Transformers and Convolutional Neural Networks (CNNs) still have limitations in medical image processing. While Transformers are proficient in capturing global features, they suffer from high computational complexity and require large amounts of data for training. On the other hand, CNNs perform well in extracting local features but have limited performance when handling global information. To address these issues, this paper proposes a novel network framework, MUNet, which combines the advantages of UNet and Mamba, specifically designed for brain tumor segmentation. MUNet introduces the SD-SSM module, which effectively captures both global and local features of the image through selective scanning and state-space modeling, significantly improving segmentation accuracy. Additionally, we design the SD-Conv structure, which reduces feature redundancy without increasing model parameters, further enhancing computational efficiency. Finally, we propose a new loss function that combines mIoU loss, Dice loss, and Boundary loss, which improves segmentation overlap, similarity, and boundary accuracy from multiple perspectives. Experimental results show that, on the BraTS2020 dataset, MUNet achieves DSC values of 0.835, 0.915, and 0.823 for enhancing tumor (ET), whole tumor (WT), and tumor core (TC), respectively, and Hausdorff95 scores of 2.421, 3.755, and 6.437. On the BraTS2018 dataset, MUNet achieves DSC values of 0.815, 0.901, and 0.815, with Hausdorff95 scores of 4.389, 6.243, and 6.152, all outperforming existing methods and achieving significant performance improvements. Furthermore, when validated on the independent LGG dataset, MUNet demonstrated excellent generalization ability, proving its effectiveness in various medical imaging scenarios. The code is available at https://github.com/Dalin1977331/MUNet.
PMID:39944950 | PMC:PMC11814164 | DOI:10.3389/fncom.2025.1513059
Application of deep learning for real-time detection, localization, and counting of the malignant invasive weed Solanum rostratum Dunal
Front Plant Sci. 2025 Jan 29;15:1486929. doi: 10.3389/fpls.2024.1486929. eCollection 2024.
ABSTRACT
Solanum rostratum Dunal (SrD) is a globally harmful invasive weed that has spread widely across many countries, posing a serious threat to agriculture and ecosystem security. A deep learning network model, TrackSolanum, was designed for real-time detection, location, and counting of SrD in the field. The TrackSolanmu network model comprises four modules: detection, tracking, localization, and counting. The detection module uses YOLO_EAND for SrD identification, the tracking module applies DeepSort for multi-target tracking of SrD in consecutive video frames, the localization module determines the position of the SrD through center-of-mass localization, and the counting module counts the plants using a target ID over-the-line invalidation method. The field test results show that for UAV video at a height of 2m, TrackSolanum achieved precision and recall of 0.950 and 0.970, with MOTA and IDF1 scores of 0.826 and 0.960, a counting error rate of 2.438%, and FPS of 17. For UAV video at a height of 3m, the model reached precision and recall of 0.846 and 0.934, MOTA and IDF1 scores of 0.708 and 0.888, a counting error rate of 4.634%, and FPS of 79. Thus, the TrackSolanum supports real-time SrD detection, offering crucial technical support for hazard assessment and precise management of SrD.
PMID:39944948 | PMC:PMC11814178 | DOI:10.3389/fpls.2024.1486929
Unraveling Alveolar Fibroblast and Activated Myofibroblast Heterogeneity and Differentiation Trajectories During Lung Fibrosis Development and Resolution in Young and Old Mice
Aging Cell. 2025 Feb 13:e14503. doi: 10.1111/acel.14503. Online ahead of print.
ABSTRACT
Idiopathic pulmonary fibrosis (IPF) is an age-associated disease characterized by the irreversible accumulation of excessive extracellular matrix components by activated myofibroblasts (aMYFs). Following bleomycin administration in young mice, fibrosis formation associated with efficient resolution takes place limiting the clinical relevance of this model for IPF. In this study, we used aged mice in combination with bleomycin administration to trigger enhanced fibrosis formation and delayed resolution as a more relevant model for IPF. Alveolosphere assays were carried out to compare the alveolar resident mesenchymal niche activity for AT2 stem cells in young versus old mice. Lineage tracing of the Acta2+ aMYFs in old mice exposed to bleomycin followed by scRNAseq of the lineage-traced cells isolated during fibrosis formation and resolution was performed to delineate the heterogeneity of aMYFs during fibrosis formation and their fate during resolution. Integration of previously published similar scRNAseq results using young mice was carried out. Our results show that alveolar resident mesenchymal cells from old mice display decreased supporting activity for AT2 stem cells. Our findings suggest that the cellular and molecular mechanisms underlying the aMYFs formation and differentiation towards the Lipofibroblast phenotype are mostly conserved between young and old mice. In addition to persistent fibrotic signaling in aMYF from old mice during resolution, we also identified differences linked to interleukin signaling in old versus young alveolar fibroblast populations before and during bleomycin injury. Importantly, our work confirms the relevance of a subcluster of aMYFs in old mice that is potentially relevant for future management of IPF.
PMID:39945330 | DOI:10.1111/acel.14503
Identifying health risk determinants and molecular targets in patients with idiopathic pulmonary fibrosis via combined differential and weighted gene co-expression analysis
Front Genet. 2025 Jan 29;15:1496462. doi: 10.3389/fgene.2024.1496462. eCollection 2024.
ABSTRACT
INTRODUCTION: Idiopathic pulmonary fibrosis (IPF) is a rare but debilitating lung disease characterized by excessive fibrotic tissue accumulation, primarily affecting individuals over 50 years of age. Early diagnosis is challenging, and without intervention, the prognosis remains poor. Understanding the molecular mechanisms underlying IPF pathogenesis is crucial for identifying diagnostic markers and therapeutic targets.
METHODS: We analyzed transcriptomic data from lung tissues of IPF patients using two independent datasets. Differentially expressed genes (DEGs) were identified, and their functional roles were assessed through pathway enrichment and tissue-specific expression analysis. Protein-protein interaction (PPI) networks and co-expression modules were constructed to identify hub genes and their associations with disease severity. Machine learning approaches were applied to identify genes capable of differentiating IPF patients from healthy individuals. Regulatory signatures, including transcription factor and microRNA interactions, were also explored, alongside the identification of potential drug targets.
RESULTS: A total of 275 and 167 DEGs were identified across two datasets, with 67 DEGs common to both. These genes exhibited distinct expression patterns across tissues and were associated with pathways such as extracellular matrix organization, collagen fibril formation, and cell adhesion. Co-expression analysis revealed DEG modules correlated with varying IPF severity phenotypes. Machine learning analysis pinpointed a subset of genes with high discriminatory power between IPF and healthy individuals. PPI network analysis identified hub proteins involved in key biological processes, while functional enrichment reinforced their roles in extracellular matrix regulation. Regulatory analysis highlighted interactions with transcription factors and microRNAs, suggesting potential mechanisms driving IPF pathogenesis. Potential drug targets among the DEGs were also identified.
DISCUSSION: This study provides a comprehensive transcriptomic overview of IPF, uncovering DEGs, hub proteins, and regulatory signatures implicated in disease progression. Validation in independent datasets confirmed the relevance of these findings. The insights gained here lay the groundwork for developing diagnostic tools and novel therapeutic strategies for IPF.
PMID:39944354 | PMC:PMC11813903 | DOI:10.3389/fgene.2024.1496462
Pathophysiology of small airways in idiopathic pulmonary fibrosis (IPF): the silent zone
Expert Rev Respir Med. 2025 Feb 13. doi: 10.1080/17476348.2025.2467341. Online ahead of print.
ABSTRACT
INTRODUCTION: Idiopathic pulmonary fibrosis (IPF) isa chronic, progressive lung disease characterized by distorted alveolar structureand reduced lung compliance, and impaired ventilation-perfusion. Small airwaydisease (SAD) is often termed a 'quietzone' due to its asymptomatic nature. Around 30-40% of IPF patients exhibit SAD,which is associated with worse prognosis, higher fibrosis and emphysema scores,and elevated mortality risk. We used PubMed and Google Scholar for literaturesearch.
AREAS COVERED: This review explores thepathophysiology of small airways in IPF, focusing on 1. risk factors, includingage, gender, smoking and occupational dust exposure, and ozone. 2. Diagnosticchallenges: SAD is difficult to detect through traditional spirometry or high-resolutioncomputed tomography imaging due to resolutionlimitations. 3. Early physiologicalchanges of small airways include airway wall thickening, lumen distortion, andreduced terminal bronchioles, preceding microscopic fibrosis, occurs in the earlyprocess of IPF. 4. Pathological mechanisms: The review examines the underlyingmechanisms driving small airway disease in IPF.
EXPERT OPINION: A comprehensive approach is essential to improve the understanding andmanagement of SAD in IPF. Priorities include identifying therapeutic targets,advanced imaging and functional assessments. Forced oscillation technique should be introduced for early detection for smallairway abnormalities in IPF.
PMID:39943815 | DOI:10.1080/17476348.2025.2467341
The Impact of Adverse Events in Transbronchial Lung Cryobiopsy on Histopathological Diagnosis
J Clin Med. 2025 Jan 23;14(3):731. doi: 10.3390/jcm14030731.
ABSTRACT
Background: Transbronchial lung cryobiopsy (TBLC) has a high incidence of adverse events. This study aimed to investigate the relationship between the occurrence of these events and the condition of the pathology samples or pathological diagnosis in TBLC. Methods: We studied 102 patients who underwent TBLC for the diagnosis of interstitial lung disease. We analyzed the association between the condition or diagnosis of pathology samples and the occurrence of TBLC-related adverse events, including hemorrhage, pneumothorax, and acute exacerbation of interstitial lung disease. Results: The adverse events occurred in 19 patients (18.6%), of which hemorrhage was the most common (14 patients, 13.7%). The patients who experienced adverse events, especially hemorrhage, were less likely to have successful sampling with TBLC and showed lower diagnostic confidence in the pathology results. The diagnostic confidence was level A in 50 cases (49.0%) and level C in 23 cases (22.6%). TBLC-related adverse events, including hemorrhage, were significantly more common in patients with lower pathological confidence levels. Conclusions: TBLC-related adverse events, particularly hemorrhage, can lead to fewer successful samples and lower levels of diagnostic confidence.
PMID:39941401 | DOI:10.3390/jcm14030731
Echocardiographic Assessment of Biventricular Mechanics in Patients with Mild-to-Moderate Idiopathic Pulmonary Fibrosis: A Systematic Review and Meta-Analysis
J Clin Med. 2025 Jan 22;14(3):714. doi: 10.3390/jcm14030714.
ABSTRACT
Background: Over the last few years, a few imaging studies have performed conventional transthoracic echocardiography (TTE) implemented with speckle tracking echocardiography (STE) for the assessment of biventricular mechanics in patients with non-advanced idiopathic pulmonary fibrosis (IPF). This systematic review and meta-analysis aimed at evaluating the overall effect of mild-to-moderate IPF on the main indices of biventricular systolic function assessed by TTE and STE. Methods: All imaging studies assessing right ventricular (RV)-global longitudinal strain (GLS), left ventricular (LV)-GLS, tricuspid annular plane systolic excursion (TAPSE), and left ventricular ejection fraction (LVEF) in IPF patients vs. healthy controls, selected from PubMed, Scopus, and EMBASE databases, were included. Continuous data (RV-GLS, LV-GLS, TAPSE, and LVEF) were pooled as standardized mean differences (SMDs) comparing the IPF group with healthy controls. The SMD of RV-GLS was calculated using the random-effect model, whereas the SMDs of LV-GLS, TAPSE, and LVEF were calculated using the fixed-effect model. Results: The full texts of 6 studies with 255 IPF patients and 195 healthy controls were analyzed. Despite preserved TAPSE and LVEF, both RV-GLS and LV-GLS were significantly, although modestly, reduced in the IPF patients vs. the controls. The SMD was large (-1.01, 95% CI -1.47, -0.54, p < 0.001) for RV-GLS, medium (-0.62, 95% CI -0.82, -0.42, p < 0.001) for LV-GLS, small (-0.42, 95% CI -0.61, -0.23, p < 0.001) for TAPSE, and small and not statistically significant (-0.20, 95% CI -0.42, 0.03, p = 0.09) for LVEF assessment. Between-study heterogeneity was high for the studies assessing RV-GLS (I2 = 80.5%), low-to-moderate for those evaluating LV-GLS (I2 = 41.7%), and low for those measuring TAPSE (I2 = 16.4%) and LVEF (I2 = 7.63%). The Egger's test yielded a p-value of 0.60, 0.11, 0.31, and 0.68 for the RV-GLS, LV-GLS, TAPSE, and LVEF assessment, respectively, indicating no publication bias. On meta-regression analysis, none of the moderators was significantly associated with effect modification for RV-GLS (all p > 0.05). The sensitivity analysis supported the robustness of the results. Conclusions: RV-GLS impairment is an early marker of subclinical myocardial dysfunction in mild-to-moderate IPF. STE should be considered for implementation in clinical practice for early detection of RV dysfunction in IPF patients without advanced lung disease.
PMID:39941384 | DOI:10.3390/jcm14030714
Mitochondrial COX3 and tRNA Gene Variants Associated with Risk and Prognosis of Idiopathic Pulmonary Fibrosis
Int J Mol Sci. 2025 Feb 6;26(3):1378. doi: 10.3390/ijms26031378.
ABSTRACT
Idiopathic pulmonary fibrosis (IPF) has been associated with mitochondrial dysfunction. We investigated whether mitochondrial DNA variants in peripheral blood leukocytes (PBLs), which affect proteins of the respiratory chain and mitochondrial function, could be associated with an increased risk and poor prognosis of IPF. From 2020 to 2022, we recruited 36 patients (age: 75.3 ± 8.5; female: 19%) with IPF, and 80 control subjects (age: 72.3 ± 9.0; female: 27%). The mitochondrial genome of peripheral blood leukocytes was determined using next-generation sequencing. During a 45-month follow-up, 10 (28%) patients with IPF remained stable and the other 26 (72%) progressed, with 12 (33%) mortalities. IPF patients had more non-synonymous (NS) variants (substitution/deletion/insertion) in mitochondrial COX3 gene (coding for subunit 3 of complex IV of the respiratory chain), and more mitochondrial tRNA variants located in the anticodon (AC) stem, AC loop, variable loop, T-arm, and T-loop of the tRNA clover-leaf structure in PBLs than the control group. The succumbed IPF patients were older, had lower initial diffusion capacity, and higher initial fibrosis score on high-resolution computerized tomography (HRCT) than the alive group. NS variants in mitochondrial COX3 gene and tRNA variants in PBLs were associated with shorter survival. Our study shows that (1) leukocyte mitochondrial COX3 NS variants are associated with risk and prognosis of IPF; (2) leukocyte mitochondrial tRNA variants located in the AC stem, AC loop, variable loop, T-arm, and T-loop of the tRNA clover-leaf structure are associated with risk, and the presence of tRNA variants is associated with poor prognosis of IPF.
PMID:39941146 | DOI:10.3390/ijms26031378
Folate depletion impact on the cell cycle results in restricted primary root growth in Arabidopsis
Plant Mol Biol. 2025 Feb 13;115(2):31. doi: 10.1007/s11103-025-01554-0.
ABSTRACT
Folates are vital one carbon donors and acceptors for a whole range of key biochemical reactions, including the biosynthesis of DNA building blocks. Plants use one carbon metabolism as a jack of all trades in their growth and development. Depletion of folates impedes root growth in Arabidopsis thaliana, but the mechanistic basis behind this function is still obscure. A global transcriptomic study hinted that folate depletion may cause misregulation of cell cycle progression. However, investigations on a direct connection thereof are scarce. We confirmed the effect of methotrexate (MTX), a folate biosynthesis inhibitor, on the expression of cell cycle genes. Subsequently, we determined the effect of MTX on root morphology and cell cycle progression through phase-specific cell cycle reporter analyses. Our study reveals that folate depletion affects the expression of cell cycle regulatory genes in roots, thereby suppressing cell cycle progression. We confirmed, through DNA labelling by EdU, that MTX treatment leads to arrest in the S phase of meristematic cells, likely due to the lack of DNA precursors. Further, we noted an accumulation of the A-type CYCA3;1 cyclin at the root tip, suggesting a possible link with the observed loss of apical dominance. Overall, our study shows that the restricted cell division and cell cycle progression is one of the reasons behind the loss of primary root growth upon folate depletion.
PMID:39946030 | DOI:10.1007/s11103-025-01554-0
Taste-Guided Isolation of Bitter Compounds from the Mushroom <em>Amaropostia stiptica</em> Activates a Subset of Human Bitter Taste Receptors
J Agric Food Chem. 2025 Feb 13. doi: 10.1021/acs.jafc.4c12651. Online ahead of print.
ABSTRACT
Bitter taste perception cautions humans against the ingestion of potentially toxic compounds. However, current knowledge about natural bitter substances and their activation of human bitter taste receptors (TAS2Rs) is biased toward substances from flowering plants, whereas other sources are underrepresented. Although numerous mushrooms taste bitter, the corresponding substances and receptors are unexplored. Three previously undescribed triterpene glucosides, named oligoporins D-F, together with the known oligoporins A and B, were isolated from Amaropostia stiptica. The structures of oligoporins D-F were determined using spectroscopic analyses. The isolated oligoporins and the bitter indolalkaloid infractopicrin from Cortinarius infractus were functionally screened with all TAS2Rs. For all compounds, at least one responding receptor was identified. Oligoporin D activated TAS2R46 already at a submicromolar concentration and thus belongs to the family of most potent bitter agonists. The addition of mushroom compounds to the list of cognate TAS2R activators lowers the existing bias of knowledge about bitter agonists.
PMID:39945763 | DOI:10.1021/acs.jafc.4c12651
Deciphering reductive dehalogenase specificity through targeted mutagenesis of chloroalkane reductases
Appl Environ Microbiol. 2025 Feb 13:e0150124. doi: 10.1128/aem.01501-24. Online ahead of print.
ABSTRACT
Reductive dehalogenases (RDases) are essential in the anaerobic degradation of various organohalide contaminants. This family of enzymes has broad sequence diversity, but high structural conservation. There have been few studies assessing how RDase amino acid sequences affect their substrate selectivity. Here, we focus on two chloroalkane RDases, CfrA and DcrA, which have 95% protein sequence identity but have diverged to have opposite substrate preferences. CfrA dechlorinates chloroform (CF) and 1,1,1-trichloroethane (TCA) but not 1,1-dichloroethane (DCA), while DcrA will dechlorinate 1,1-DCA but not CF or 1,1,1-TCA. We mutated several residues in the active site of CfrA to investigate a change in substrate preference and to identify which wild-type residues contribute the most to substrate specialization. We determined that no individual residue solely dictates substrate discrimination, but both Y80W and F125W mutations were needed to force CfrA to prefer 1,1-DCA as a substrate. When using 1,1,2-TCA as a substrate, CfrA predominately performs hydrogenolysis to 1,2-DCA, yet the introduction of the double mutant changed this preference to dihaloelimination (forming vinyl chloride). We use predictive protein models and substrate docking to predict what interactions are made between the enzyme and substrate to aid in selection. The residues of significance identified in this study are consistent with those identified from chloroethene RDases, suggesting residue locations with a particularly high impact on activity.IMPORTANCEReductive dehalogenases (RDases) play an integral role in the removal of chlorinated solvents from the environment. These enzymes have specificity toward different chlorinated compounds, and it is known that natural variants of highly similar RDases can have distinct activities. How specific differences in protein sequence influence activity is largely unknown. In this study, we demonstrate that mutating a few residues within the active site of CfrA-a chloroform and trichloroethane-specific dehalogenase-changes its substrate preference to dichloroethane. We determine that only two mutations are needed to disrupt the native activity, underscoring the nuances in substrate-structure relationships in RDases. Though we are still far from predicting function from the sequence, this knowledge can give some insight into engineering RDases for new target contaminants.
PMID:39945532 | DOI:10.1128/aem.01501-24
Dachsous is a key player in epithelial wound closure by modulating cell shape changes and tissue mechanics
J Cell Sci. 2025 Feb 13:jcs.263674. doi: 10.1242/jcs.263674. Online ahead of print.
ABSTRACT
Epithelia are vital tissues in multicellular organisms, acting as barriers between external and internal environments. Simple epithelia, such as pg those in embryos and the adult gut, have the remarkable ability to repair wounds efficiently, making them ideal for studying epithelial repair mechanisms. In these tissues, wound closure involves the coordinated action of a contractile actomyosin cable at the wound edge and collective cell movements around the wound. However, the dynamics of cell-cell interactions during this process remain poorly understood. Here, we demonstrate that Dachsous (Ds), an atypical cadherin associated with Planar Cell Polarity, is crucial for efficient epithelial repair in the Drosophila embryo. We show that the absence of Ds alters tissue mechanics and cell shape changes and rearrangements, leading to slower wound closure. Additionally, we reveal that Occluding Junctions are necessary for the proper apical localization of Ds, uncovering an unanticipated interaction between these two molecular complexes. This study identifies Ds as a novel key player in epithelial repair and highlights the need for further investigating the molecular mechanisms by which Ds modulates cell shape and tissue morphogenesis.
PMID:39945479 | DOI:10.1242/jcs.263674
Correction: Downregulation of c-SRC kinase CSK promotes castration resistant prostate cancer and pinpoints a novel disease subclass
Oncotarget. 2025 Feb 12;16:65-66. doi: 10.18632/oncotarget.28693.
NO ABSTRACT
PMID:39945472 | DOI:10.18632/oncotarget.28693
Detection of β-D-glucuronidase activity in environmental samples using 4-fluorophenyl β-D-glucuronide and <sup>19</sup>F NMR
Anal Methods. 2025 Feb 13. doi: 10.1039/d4ay01723d. Online ahead of print.
ABSTRACT
Common methods for establishing the presence of enteric bacteria polluting water supplies, or in other samples, rely on detecting the hydrolysis of model glucuronide substrates by glucuronidases to release a phenolic product quantifiable by absorbance or fluorescence. Substrates include the β-D-glucuronides of p-nitrophenol, and umbelliferyl or quercetin derivatives. One limitation is that it may be difficult or impossible to quantify the released phenolic moiety in samples that are strongly coloured or, that contain fluorescent compounds. Exploiting the sensitivity available from the 19F nucleus to changes in chemical environment which can be detected by 19F NMR spectroscopy, and the almost complete absence of 19F from naturally-occurring samples containing organic matter, which provides background-free signals, we propose a model substrate; 4-fluorophenyl β-D-glucuronide (4FP-glucuronide). The 19F NMR chemical shift position of 4FP-glucuronide changes from -121.0 ppm upon hydrolysis to release 4-fluorophenol, at -124.9 ppm (at pH 6.8), enabling detection of β-glucuronidase activity. We illustrate the use of this substrate with environmental samples from forest soil, standing water, and mud from cattle pasture. Each of these would challenge conventional methods, owing to their opacity or the presence of coloured organic material. The technique enables detection of glucuronidases, a widely-used proxy for enteric bacteria, extending the scope of testing beyond water to include environmental and other challenging samples.
PMID:39945190 | DOI:10.1039/d4ay01723d
Transcriptomic signatures of severe acute mountain sickness during rapid ascent to 4,300 m
Front Physiol. 2025 Jan 29;15:1477070. doi: 10.3389/fphys.2024.1477070. eCollection 2024.
ABSTRACT
INTRODUCTION: Acute mountain sickness (AMS) is a common altitude illness that occurs when individuals rapidly ascend to altitudes ≥2,500 m without proper acclimatization. Genetic and genomic factors can contribute to the development of AMS or predispose individuals to susceptibility. This study aimed to investigate differential gene regulation and biological pathways to diagnose AMS from high-altitude (HA; 4,300 m) blood samples and predict AMS-susceptible (AMS+) and AMS-resistant (AMS─) individuals from sea-level (SL; 50 m) blood samples.
METHODS: Two independent cohorts were used to ensure the robustness of the findings. Blood samples were collected from participants at SL and HA. RNA sequencing was employed to profile gene expression. Differential expression analysis and pathway enrichment were performed to uncover transcriptomic signatures associated with AMS. Biomarker panels were developed for diagnostic and predictive purposes.
RESULTS: At HA, hemoglobin-related genes (HBA1, HBA2, and HBB) and phosphodiesterase 5A (PDE5A) emerged as key differentiators between AMS+ and AMS- individuals. The cAMP response element-binding protein (CREB) pathway exhibited contrasting regulatory patterns at SL and HA, reflecting potential adaptation mechanisms to hypoxic conditions. Diagnostic and predictive biomarker panels were proposed based on the identified transcriptomic signatures, demonstrating strong potential for distinguishing AMS+ from AMS- individuals.
DISCUSSION: The findings highlight the importance of hemoglobin-related genes and the CREB pathway in AMS susceptibility and adaptation to hypoxia. The differential regulation of these pathways provides novel insights into the biological mechanisms underlying AMS. The proposed biomarker panels offer promising avenues for the early diagnosis and prediction of AMS risk, which could enhance preventive and therapeutic strategies.
PMID:39944919 | PMC:PMC11813865 | DOI:10.3389/fphys.2024.1477070
Reconstruction and computational analysis of the microRNA regulation gene network in wheat drought response mechanisms
Vavilovskii Zhurnal Genet Selektsii. 2024 Dec;28(8):904-917. doi: 10.18699/vjgb-24-98.
ABSTRACT
Drought is a critical factor limiting the productivity of bread wheat (Triticum aestivum L.), one of the key agricultural crops. Wheat adaptation to water deficit is ensured by complex molecular genetic mechanisms, including the coordinated work of multiple genes regulated by transcription factors and signaling non-coding RNAs, particularly microRNAs (miRNAs). miRNA-mediated regulation of gene expression is considered one of the main mechanisms of plant resistance to abiotic stresses. Studying these mechanisms necessitates computational systems biology methods. This work aims to reconstruct and analyze the gene network associated with miRNA regulation of wheat adaptation to drought. Using the ANDSystem software and the specialized Smart crop knowledge base adapted for wheat genetics and breeding, we reconstructed a wheat gene network responding to water deficit, comprising 144 genes, 1,017 proteins, and 21 wheat miRNAs. Analysis revealed that miRNAs primarily regulate genes controlling the morphogenesis of shoots and roots, crucial for morphological adaptation to drought. The key network components regulated by miRNAs are the MYBa and WRKY41 family transcription factors, heat-shock protein HSP90, and the RPM1 protein. These proteins are associated with phytohormone signaling pathways and calcium-dependent protein kinases significant in plant water deficit adaptation. Several miRNAs (MIR7757, MIR9653a, MIR9671 and MIR9672b) were identified that had not been previously discussed in wheat drought adaptation. These miRNAs regulate many network nodes and are promising candidates for experimental studies to enhance wheat resistance to water deficiency. The results obtained can find application in breeding for the development of new wheat varieties with increased resistance to water deficit, which is of substantial importance for agriculture in the context of climate change.
PMID:39944815 | PMC:PMC11811492 | DOI:10.18699/vjgb-24-98
Ontologies in modelling and analysing of big genetic data
Vavilovskii Zhurnal Genet Selektsii. 2024 Dec;28(8):940-949. doi: 10.18699/vjgb-24-101.
ABSTRACT
To systematize and effectively use the huge volume of experimental data accumulated in the field of bioinformatics and biomedicine, new approaches based on ontologies are needed, including automated methods for semantic integration of heterogeneous experimental data, methods for creating large knowledge bases and self-interpreting methods for analyzing large heterogeneous data based on deep learning. The article briefly presents the features of the subject area (bioinformatics, systems biology, biomedicine), formal definitions of the concept of ontology and knowledge graphs, as well as examples of using ontologies for semantic integration of heterogeneous data and creating large knowledge bases, as well as interpreting the results of deep learning on big data. As an example of a successful project, the Gene Ontology knowledge base is described, which not only includes terminological knowledge and gene ontology annotations (GOA), but also causal influence models (GO-CAM). This makes it useful not only for genomic biology, but also for systems biology, as well as for interpreting large-scale experimental data. An approach to building large ontologies using design patterns is discussed, using the ontology of biological attributes (OBA) as an example. Here, most of the classification is automatically computed based on previously created reference ontologies using automated inference, except for a small number of high-level concepts. One of the main problems of deep learning is the lack of interpretability, since neural networks often function as "black boxes" unable to explain their decisions. This paper describes approaches to creating methods for interpreting deep learning models and presents two examples of self-explanatory ontology-based deep learning models: (1) Deep GONet, which integrates Gene Ontology into a hierarchical neural network architecture, where each neuron represents a biological function. Experiments on cancer diagnostic datasets show that Deep GONet is easily interpretable and has high performance in distinguishing cancerous and non-cancerous samples. (2) ONN4MST, which uses biome ontologies to trace microbial sources of samples whose niches were previously poorly studied or unknown, detecting microbial contaminants. ONN4MST can distinguish samples from ontologically similar biomes, thus offering a quantitative way to characterize the evolution of the human gut microbial community. Both examples demonstrate high performance and interpretability, making them valuable tools for analyzing and interpreting big data in biology.
PMID:39944813 | PMC:PMC11813802 | DOI:10.18699/vjgb-24-101
Comparison of brain activity metrics in Chinese and Russian students while perceiving information referencing self or others
Vavilovskii Zhurnal Genet Selektsii. 2024 Dec;28(8):982-992. doi: 10.18699/vjgb-24-105.
ABSTRACT
Neurocomputing technology is a field of interdisciplinary research and development widely applied in modern digital medicine. One of the problems of neuroimaging technology is the creation of methods for studying human brain activity in socially oriented conditions by using modern information approaches. The aim of this study is to develop a methodology for collecting and processing psychophysiological data, which makes it possible to estimate the functional states of the human brain associated with the attribution of external information to oneself or other people. Self-reference is a person's subjective assessment of information coming from the external environment as related to himself/herself. Assigning information to other people or inanimate objects is evaluating information as a message about someone else or about things. In modern neurophysiology, two approaches to the study of self-referential processing have been developed: (1) recording brain activity at rest, then questioning the participant for self-reported thoughts; (2) recording brain activity induced by self-assigned stimuli. In the presented paper, a technology was tested that combines registration and analysis of EEG with viewing facial video recordings. The novelty of our approach is the use of video recordings obtained in the first stage of the survey to induce resting states associated with recognition of information about different subjects in later stages of the survey. We have developed a software and hardware module, i. e. a set of related programs and procedures for their application consisting of blocks that allow for a full cycle of registration and processing of psychological and neurophysiological data. Using this module, brain electrical activity (EEG) indicators reflecting individual characteristics of recognition of information related to oneself and other people were compared between groups of 30 Chinese (14 men and 16 women, average age 23.2 ± 0.4 years) and 32 Russian (15 men, 17 women, average age 22.1 ± 0.4 years) participants. We tested the hypothesis that differences in brain activity in functional rest intervals between Chinese and Russian participants depend on their psychological differences in collectivism scores. It was revealed that brain functional activity depends on the subject relevance of the facial video that the participants viewed between resting-state intervals. Interethnic differences were observed in the activity of the anterior and parietal hubs of the default-mode network and depended on the subject attribution of information. In Chinese, but not Russian, participants significant positive correlations were revealed between the level of collectivism and spectral density in the anterior hub of the default-mode network in all experimental conditions for a wide range of frequencies. The developed software and hardware module is included in an integrated digital platform for conducting research in the field of systems biology and digital medicine.
PMID:39944805 | PMC:PMC11811493 | DOI:10.18699/vjgb-24-105
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