Search Results (185)

Background: Virtual screening is a widely adopted technique for the discovery of novel pharmacologically active compounds; however, the risk of identifying false positive hits remains a major challenge. Aim: The aim of this study was to perform a validated structure-based drug design screening to discover multitarget pyrrole-based molecules as selective dual-acting monoamine oxidase (MAO) and acetylcholinesterase (AChE) inhibitors. Methods: The study employed validated docking protocols using Glide (Schrödinger) and GOLD (CCDC), integrating ligand enrichment analysis and robust Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) rescoring. These methods were applied to a custom-designed database of pyrrole-based compounds. The top-ranked hits were synthesized and validated through in vitro tests, demonstrating significant inhibitory activities against MAO-A, MAO-B, AChE, and Butyrylcholinesterase (BChE). Results: The docking protocols achieved favorable hit rates, with 25.93% for AChE inhibitors and 44.44% for MAO-B inhibitors. Additionally, structure–activity relationship analysis revealed key substituent effects that significantly influence binding affinity and selectivity. Two compounds, EM-DC-19 (2-(2,5-dimethyl-1H-pyrrol-1-yl)-3-(2H-imidazol-4-yl)propanoic acid) and EM-DC-27 ([4-(2,5-dimethyl-1H-pyrrol-1-yl)phenyl]acetic acid), were identified as selective MAO-B inhibitors with additional moderate AChE inhibitory activity, demonstrating IC₅₀ values of 0.299 ± 0.10 µM and 0.344 ± 0.10 µM against MAO-B, and 76.15 ± 6.12 µM and 375.20 ± 52.99 µM against AChE, respectively. The absence of statistically significant inhibitory effects of these lead compounds on MAO-A and BChE (IC₅₀ > 100 µM) underscores their selective inhibitory activity towards MAO-B and AChE. Furthermore, both compounds demonstrated low neurotoxicity and significant neuroprotective and antioxidant effects in rat brain synaptosomes, mitochondria, and microsomes. These effects were particularly evident in models of 6-hydroxydopamine-induced neurotoxicity (6-OHDA) and oxidative stress induced by tert-butyl hydroperoxide and Fe²⁺/ascorbic acid. Conclusions: The findings suggest that these multitarget compounds hold promise for further development, with potential for structural modifications to enhance their enzyme inhibitory and neuroprotective properties. Full article

Background: Reproductive efficiency is a significant hurdle to the sustainability of the beef cattle industry. Method: This study employed transcriptomic profiling to investigate endometrial gene expression differences in heifers with divergent fertility outcomes. Caruncular endometrial samples from fertile (n = 7) and subfertile (n = 5) heifers were subjected to RNA-Seq analysis, yielding 894 differentially expressed genes (DEGs) (p ≤ 0.05 and |log2FC| ≥ 0.5). Results: The MAPK (Mitogen-activated protein kinase) and Rap1 (Ras-associated protein 1) signaling pathways and immune response regulation were identified among the over-represented pathways underlying the DEGs. Transcriptional regulators, such as DUSP2, DUSP10, and MAPK13, were downregulated in subfertile heifers, suggesting disrupted signal transduction and immune function. Gene co-expression network analysis showed network rewiring and increased connectivity of genes related to cilium organization, motility, and microtubule-based processes in the subfertile group. Over-represented hub genes were enriched in the subfertile endometrium, including DNAH2, DNAI2, DNAAF4, CCDC65, and the transcription factor FOXJ1. Our results suggest that impaired ciliary function and disrupted MAPK and immune signaling could potentially contribute to subfertility. Conclusions: This study highlights novel molecular signatures in the uterine endometrium that may serve as predictive markers of fertility potential in beef heifers, providing a foundation for targeted strategies to improve reproductive performance in cattle. Full article

Preeclampsia (PE) is a major cause of maternal and perinatal morbidity, and early prediction is critical for timely intervention. This study aimed to identify predictive biomarkers for PE through transcriptomic analysis of second-trimester amniotic fluid supernatant (AFS) collected prior to clinical symptom onset. AFS samples from women who later developed PE (n = 7) and matched controls (n = 7) underwent RNA sequencing to identify differentially expressed genes (DEGs). Candidate genes were validated by real-time PCR in HTR-8/SVneo cells exposed to fluid shear stress at 3, 10, and 20 dyn/cm² for 24 h, mimicking the hemodynamic environment of PE, and siRNA-mediated knockdown was used to assess effects on trophoblast migration and invasion. RNA sequencing revealed 19 DEGs, with 3 upregulated and 16 downregulated genes in the PE group. HOOK2 emerged as the most significantly upregulated gene. Four candidate genes, including HOOK2, CCDC160, CKB, and PARP15, were selected for further validation. HOOK2 mRNA expression significantly increased with higher shear stress levels, consistent with sequencing data. Knockdown of HOOK2 led to a significant increase in trophoblast invasion, while migration showed no significant change. These findings suggest that HOOK2 may serve as a promising early biomarker for PE by modulating trophoblast invasiveness under altered hemodynamic conditions, with potential to improve risk stratification and personalized monitoring in pregnancy. Full article

Large-scale vegetation loss induced by surface coal mining constitutes a critical driver of regional ecological degradation. However, the applicability of existing change detection methodologies based on remote sensing within complex mining areas under diverse climatic conditions remains systematically unverified. To address this gap and reveal nationwide disturbance patterns, this study systematically evaluates the performance of two algorithms—Continuous Change Detection and Classification (CCDC) and Landsat-based Detection of Trends in Disturbance and Recovery (LandTrendr)—in identifying vegetation loss across three major climatic zones of China (the humid, semi-humid, and semi-arid zones). Based on the optimal algorithm, the vegetation loss year and loss magnitude across all of China’s surface coal mining areas from 1990 to 2020 were accurately identified, enabling the reconstruction of the comprehensive, nationwide spatio-temporal pattern of mining-induced vegetation loss over the past 30 years. The results show that: (1) CCDC demonstrated superior stability and significantly higher accuracy (OA = 0.82) than LandTrendr (OA = 0.31) in identifying loss years across all zones. (2) The cumulative vegetation loss area reached 1429.68 km², with semi-arid zones accounting for 86.76%. Temporal analysis revealed a continuous expansion of the loss area from 2003 to 2013, followed by a distinct inflection point and decline during 2014–2016 attributable to policy-driven regulations. (3) Further analysis revealed significant variations in the average magnitude of loss across different climatic zones, namely semi-arid (0.11), semi-humid (0.21), and humid (0.25). These findings underscore the imperative for region-specific restoration strategies to ensure effective conservation outcomes. This study provides a systematic quantification and analysis of long-term, nationwide evolution patterns and regional differentiation characteristics of vegetation loss induced by surface coal mining in China, offering critical support for sustainable development decision-making in balancing energy development and ecological conservation. Full article

In recent years, the availability of automated change detection algorithms in Google Earth Engine has permitted the cloud-based processing of large quantities of satellite imagery. Models such as the Continuous Change Detection and Classification (CCDC), CCDC-Spectral Mixture Analysis (CCDC-SMA), and Landsat-based Detection of Trends in Disturbance and Recovery (LandTrendr) allow users to exploit decades of Earth Observations (EOs), leveraging the Landsat archive and data from other sensors to detect disturbances in forest ecosystems. Despite the wide adoption of these methods, robust documentation, and a growing community of users, little research has systematically detailed their tuning process in mangrove environments. This work aims to identify the best practices for applying these models to monitor changes within mangrove forest cover, which has been declining gradually in Belize the last several decades. Partnering directly with the Belizean Forest Department, our team developed a replicable, efficient methodology to annually update the country’s mangrove extent, employing EO-based change detection. We ran a series of model variations in both CCDC-SMA and LandTrendr to identify the parameterizations best suited to identifying change in Belizean mangroves. Applying the best performing model run to the starting 2017 mangrove extent, we estimated a total loss of 540 hectares in mangrove coverage by 2024. Overall accuracy across thirty variations in model runs of LandTrendr and CCDC-SMA ranged from 0.67 to 0.75. While CCDC-SMA generally detected more disturbances and had higher precision for true changes, LandTrendr runs tended to have higher recall. Our results suggest LandTrendr offered more flexibility in balancing precision and recall for true changes compared to CCDC-SMA, due to its greater variety of adjustable parameters. Full article

Background: Oncogenic fusions of MAML2 with CRTC1, CRTC3, YAP1, and NR1D1 retain the MAML2 transactivating domain (TAD) and are believed to drive aberrant gene transcription. While the oncogenic roles of these known fusions have been established, we aimed to identify novel MAML2 fusions across a range of human malignancies. Methods: DNA and RNA sequencing were performed on tumor samples submitted to Caris Life Sciences. MAML2 fusions were identified from RNA transcripts and filtered to include only known pathogenic fusions or recurrent, in-frame fusions containing a C-terminal MAML2 TAD. Fusion burden was defined as the number of unique fusion isoforms per sample. Results: Among 180,124 tumor samples, 143 specimens harbored MAML2 fusions with a MAML2 TAD: >50% of specimens harbored known fusions, but novel fusions with MTMR2 (31/143), SESN3 (11/143), CCDC82 (6/143), FAM76B (4/143), and ATXN3 (3/143) were also identified. Compared to the known fusions, the novel fusions generally had lower expressions (median: 8 vs. 13 junction reads/sample, p = 0.0064), higher fusion burdens (median: 6 vs. 2 unique fusion isoforms/sample, p < 0.0001), more frequent TP53 co-mutations (80% vs. 11.5%, p < 0.0001), and no clear association with the tissue of origin. Excluding ATXN3::MAML2, the novel fusion partners were located near MAML2 in the genome, likely arose from duplications or deletions, and occurred in samples harboring concurrent mutations. In contrast, ATXN3::MAML2 arose via interchromosomal translocation, occurred in samples with a low fusion burden, and was not associated with TP53 mutations. Conclusions: We identified novel MAML2 fusion partners, most of which likely represent passenger alterations, possibly arising from genomic instability or impaired p53 function. However, ATXN3::MAML2 fusions, previously reported in a pre-cancerous pancreatic disease case, may represent a pathogenic alteration warranting further investigation. Full article

Mugalzhar horses are a relatively young native breed of Kazakhstan, prized for meat and milk production and adaptation. This study was conducted to investigate genetic diversity and pinpoint genomic regions associated with selection signatures in this breed using whole-genome sequence data. Variant calling yielded a total of 21,722,393 high-quality variants, including 19,495,163 SNPs and 2,227,230 indels. Most variants were located in introns and intergenic regions, while only 1.94% were exonic. Estimates of genetic diversity were moderate, with expected and observed heterozygosity and nucleotide diversity of 0.2325, 0.2402, and 0.0021, respectively. We identified nine adaptive candidate genes (SCAPER, FHAD1, MMP15, ADGRE1, CMKLR1, MRPL15, ZNF667, CCDC66, and LOC100055310), harboring high-impact exonic variants in the homozygote state for an alternative allele. No deleterious segregating variants associated with Mendelian traits were found in this population, while seven variants linked to coat color and gaitedness were detected in a low frequency heterozygous state. Our findings suggest that there are certain genomic regions subjected to ancient selection footprints during the ancestor breed formation and adaptation. The outcome of this study serves as a foundation for future genomic-driven strategies, a broader utilization of this breed, and a reference for genomic studies on other horse breeds. Full article

Beef production is an important component of the world’s food supply, with production being near 59 million tons in 2023 (USDA, 2023). Enhancing our understanding of the factors influencing metabolism will lead to improvements in production efficiency. Using RNA-seq and WGCNA of longissimus muscle samples, gene expression and metabolic pathway analyses were performed to examine relationships with ultrasound and body mass variables. In this study, body weight (BW), ultrasound back fat (BF), ultrasound muscle depth (MD), and body condition score (BCS) were traits recorded for 18 cull beef cows. As expected, all production-related traits monitored (WT, BF, MD, and BCS) in this study exhibited a positive correlation with each other. Large-scale transcriptome analyses were performed using RNA extracted from longissimus dorsi muscles. Weighted correlation network analysis (WGCNA) was employed to associate changes in traits with gene expression. In WGCNA, the dark-green module demonstrated a positive correlation (cor) with all traits, with the highest observed for BF (cor = 0.45, p = 0.07) and MD (cor = 0.45, p = 0.07). Functional analysis of the dark-green module highlighted olfactory transduction (p = 0.03) and RNA processing as significantly correlated (p = 0.08) with production traits. Additionally, the hematopoietic cell lineage pathway was reported as the most significant negative correlation with muscle depth (cor = −0.71, p = 0.001). We identified four hub genes (i.e., SEPTIN9, NONO, CCDC88C, and CACNA2D3) showing relationships with the traits measured. These findings provide further understanding of the molecular mechanisms influencing muscle and fat accretion in cull beef cows. Full article

Avocado orchards are among the most profitable and fastest-growing commodity crops in Mexico, especially in the area known as the “Avocado Belt”. Several efforts have been made to monitor their expansion; however, there is currently no method that can be easily updated to track this expansion. The main objective of this study was to monitor the expansion of avocado orchards from 1993 to 2024, using the Continuous Change Detection and Classification (CCDC) algorithm and Landsat 5, 7, 8, and 9 imagery. Presence/absence maps of avocado orchards corresponding to 1 January of each year were used to perform a trajectory analysis, identifying eight possible change trajectories. Finally, maps from 2020 to 2023 were verified using reference data and very-high-resolution images. The maps showed a level of agreement = 0.97, while the intersection over union for the avocado orchard class was 0.62. The main results indicate that the area occupied by avocado orchards more than tripled from 1993 to 2024, from 64,304.28 ha to 200,938.32 ha, with the highest expansion occurring between 2014 and 2024. The trajectory analysis confirmed that land conversion to avocado orchards is generally permanent and happens only once (i.e., gain without alternation). The method proved to be a robust approach for monitoring avocado orchard expansion and could be an attractive alternative for regularly updating this information. Full article

Background/Objectives: Glioblastomas are a part of adult-type diffuse gliomas, the most common and most aggressive primary brain tumors in adults (glioblastoma, IDH-wildtype). The identification of the genetic factors associated with glioblastoma could be an important contribution to the diagnosis and early prevention of this disease. We compiled data from the global literature and analyzed clinically relevant variants implicated in glioblastoma risk. Methods: PubMed, Web of Science, and Scopus were used as databases. Associations between the SNPs and glioblastoma risk were calculated as a measure of pooled odds ratios (ORs) and 95% confidence intervals. Pearson’s analysis was used for epidemiological correlation (only p-values less than 0.05 were statistically significant), and data were obtained from the World Health Organization platform and the 1000 Genomes Project. Statistical analysis was performed using Review Manager (RevMan) 5.4 and BioEstat 5.0. Results: CCDC26 rs891835 G/T, G/G, and G/T-G/G genotypes were analyzed and determined to increase glioblastoma risk (G/T OR = 1.96, 95% CI: 1.38–2.77, p = 0.0002, I² = 0%; G/G OR = 1.33, 95% CI: 0.46–3.85, p = 0.60, I² = 0%; G/T − G/G OR = 1.96, 95% CI: 1.39–2.76, p = 0.0001, I² = 0%). Epidemiological correlation also demonstrated that the higher the frequency of the CCDC26 rs891835 variant, the higher the incidence of that variant in the European population. Conclusions: CCDC26 rs891835 may serve as a predictive biomarker for glioblastoma, IDH-wildtype risk and may influence higher glioblastoma incidence rates in the European population. Full article

The rapid expansion of urban areas necessitates effective monitoring systems for sustainable development planning. Time-series change detection algorithms applied to satellite imagery offer promising solutions, but their comparative effectiveness specifically for urban land cover monitoring remains poorly understood. This study aims to systematically evaluate and optimize three widely used algorithms—LandTrendr, CCDC, and BFAST—selected for their proven capabilities in different land cover change contexts and distinct algorithmic approaches. Using Landsat 5/7/8 (TM/ETM+/OLI) time-series data from 2000 to 2020 and a globally distributed dataset of 200 sample locations spanning six continents, we assess these algorithms across multiple spectral bands and parameter settings for land cover change detection in urban areas. Our analysis reveals that CCDC achieves the highest accuracy (78.14% F1 score) when utilizing complete spectral information (bands B1–B7), outperforming both BFAST (74.32% F1 score with NDVI) and LandTrendr (71.29% F1 score with B1). We demonstrated that, contrary to conventional approaches that prioritize vegetation indices, visible light bands—particularly B1 and B2—achieve higher performance across multiple algorithms. For instance, in LandTrendr, B1 yielded an F1 score of 71.29%, whereas NDVI and EVI produced 56.19% and 53.16%, respectively. Similarly, in CCDC, B2 achieved an F1 score of 72.19%, while NDVI and EVI resulted in 68.57% and 65.33%, respectively. Our findings underscore that parameter optimization and band selection significantly impact detection accuracy, with variations up to 30% observed across different configurations. This comprehensive evaluation provides critical methodological guidance for satellite-based urban expansion monitoring and identifies specific optimization strategies to enhance the application of existing algorithms for urban land cover change detection. Full article

Background: Rheumatoid arthritis (RA) is a chronic autoimmune disorder that predominantly affects synovial joints, leading to inflammation, pain, and progressive joint damage. Despite therapeutic advancements, the molecular basis of established RA remains poorly defined. Methods: In this study, we conducted an untargeted plasma proteomic analysis using two-dimensional differential gel electrophoresis (2D-DIGE) and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) in samples from RA patients and healthy controls in the discovery phase. Results: Significantly (ANOVA, p ≤ 0.05, fold change > 1.5) differentially abundant proteins (DAPs) were identified. Notably, upregulated proteins included mitochondrial dicarboxylate carrier, hemopexin, and 28S ribosomal protein S18c, while CCDC124, osteocalcin, apolipoproteins A-I and A-IV, and haptoglobin were downregulated. Receiver operating characteristic (ROC) analysis identified CCDC124, osteocalcin, and metallothionein-2 with high diagnostic potential (AUC = 0.98). Proteins with the highest selected frequency were quantitatively verified by multiple reaction monitoring (MRM) analysis in the validation cohort. Bioinformatic analysis using Ingenuity Pathway Analysis (IPA) revealed the underlying molecular pathways and key interaction networks involved STAT1, TNF, and CD40. These central nodes were associated with immune regulation, cell-to-cell signaling, and hematological system development. Conclusions: Our combined proteomic and bioinformatic approaches underscore the involvement of dysregulated immune pathways in RA pathogenesis and highlight potential diagnostic biomarkers. The utility of these markers needs to be evaluated in further studies and in a larger cohort of patients. Full article

Background: Obesity significantly impacts disease burden, with waist-to-hip ratio (WHR) as a key obesity indicator, but the genetic and biological pathways underlying WHR, particularly its sex-specific differences, remain poorly understood. Methods: This study explored WHR’s sexual dimorphism and its links to complex traits using cross-sectional surveys and genetic data from Giant and UK Biobank (UKB). We analyzed WHR heritability, performed tissue-specific transcriptome-wide association studies (TWAS) using FUSION, and conducted genetic correlation analyses with linkage disequilibrium score regression (LDSC) and Local Analysis of [co]Variant Association (LAVA). Polygenic scores (PGS) for WHR were constructed using the clumping and thresholding method (CT), and associations with complex traits were assessed via logistic or linear models. Results: The genetic analysis showed sex-specific heritability for WHR, with TWAS identifying female-specific (e.g., CCDC92) and male-specific (e.g., UQCC1) genes. Global genetic correlation analysis revealed sex-specific associations between WHR and 23 traits, while local analysis identified eight sex-specific loci across five diseases. Regression analysis highlighted sex-specific associations for 70 traits with WHR and 45 traits with WHR PGS, with stronger effects in females. Predictive models also performed better in females. Conclusions: This study underscores WHR’s sexual dimorphism and its distinct associations with complex traits, offering insights into sex-specific biological differences, health management, and clinical advancements. Full article

Background: Comprehensive molecular profiling is essential for precision oncology in non-small cell lung cancer (NSCLC). However, genomic data from Eastern European populations, including Romania, remain limited. Methods: We analyzed 398 consecutive NSCLC cases tested at the PATHOS Molecular Pathology Laboratory (Cluj-Napoca, Romania) between April 2024 and February 2025 using the Ion Torrent™ Genexus™ System and the Oncomine™ Dx Target Test, which evaluates SNVs/indels in 46 genes, fusions in 23 genes, and CNVs in 19 genes from FFPE samples. Results: The cohort was predominantly male (66%) with a median age of 67 years. Adenocarcinoma represented 70% of cases with known histology. Genomic profiling revealed a high frequency of actionable alterations. KRAS mutations were the most common (29.1%), with p.G12C detected in 10.3% of all the cases. EGFR mutations were present in 14.3% of patients, mostly exon 19 deletions and L858R substitutions. BRAF alterations (5.3%) included both V600E and non-V600E variants. RET alterations were detected as eight missense mutations, two canonical fusions (KIF5B–RET, CCDC6–RET), one amplification, and three transcript imbalances. EML4-ALK fusions (1.77%), ERBB2 mutations/amplifications (3.0%), and FGFR1/FGFR3 amplifications were also observed. Conclusions: This study provides the first large-scale molecular snapshot of NSCLC in Romania. While the overall genomic profiles align with Western populations, the higher frequency of KRAS p.G12C and FGFR amplifications highlights the value of region-specific data to support targeted therapies in Eastern Europe. Full article

Crohn’s disease (CD) is a chronic inflammatory disorder with potential progression to stricturing (B2) or penetrating (B3) phenotypes, leading to significant complications. Early identification of patients at risk for these complications is critical for personalized management. This study aimed to develop a predictive model using clinical data and a Korean-specific transcriptome-wide association study (TWAS) to forecast early progression in CD patients. A retrospective analysis of 430 Korean CD patients from 15 hospitals was conducted. Genotyping was performed using the Korea Biobank Array, and gene expression predictions were derived from a TWAS model based on terminal ileum data. Logistic regression models incorporating clinical and gene expression data predicted progression to B2 or B3 within 24 months of diagnosis. Among the cohort, 13.9% (60 patients) progressed to B2 and 16.9% (73 patients) to B3. The combined model achieved mean area under the curve (AUC) values of 0.788 for B2 and 0.785 for B3 progression. Key predictive genes for B2 included CCDC154, FAM189A2, and TAS2R19, while PUS7, CCDC146, and MLXIP were linked to B3 progression. This integrative model provides a robust approach for identifying high-risk CD patients, potentially enabling early, targeted interventions to reduce disease progression and associated complications. Full article