Search Results (5,101)

Salivary adenoid cystic carcinoma (ACC) is a highly aggressive salivary gland malignancy characterized by infiltrative growth patterns that hinder complete resection. Lacking effective chemotherapy, recurrent or metastatic ACC remains clinically incurable. This research aimed to develop an efficient culture system for ACC organoids, which can preserve tumor heterogeneity and establish a reliable drug screening platform. Under our optimized culture conditions, ACC organoids grew rapidly and successfully recapitulated three characteristic histopathological patterns. Whole-genome sequencing (WGS) further confirmed they mirrored the genomic features of their parental tumors, including significantly mutated genes, non-coding regulatory region mutations, copy number variation, and minor allele frequency. RNA sequencing confirmed that ACC organoids recapitulated the MYB-NFIB fusion gene. At the protein level, these organoids contained multiple cellular components, including epithelial cells, mesenchymal cells, K7+ duct cells, a-SMA+ myoepithelial cells, K5+ basement membrane cells, and CD44+ tumor stem cells, with proper spatial distribution patterns. With an 88% success rate, the first ACC organoid platform, incorporating normal salivary gland (SG) organoids as toxicity controls, enabled high-throughput drug testing within two weeks. In conclusion, we developed an efficient culture system for ACC organoids that can preserve tumor heterogeneity and establish a reliable drug screening platform for mechanistic studies and personalized precision therapy research. Full article

Seed health testing is a cornerstone of global food security, yet traditional diagnostic workflows often struggle to balance speed, sensitivity, and regulatory confidence under low-prevalence and heterogeneous seed lot conditions. This review synthesizes recent advances in molecular diagnostics (PCR, qPCR, LAMP, and digital PCR), non-destructive imaging technologies (hyperspectral, X-ray, and thermal imaging), and data-driven analytical approaches for pathogen detection in seeds. Emphasis is placed on the practical integration of these tools within high-throughput, ISO/IEC 17025-compliant laboratory workflows, highlighting their respective strengths, limitations, and roles in risk-based decision-making. Comparative discussions address cost, sensitivity, turnaround time, and field deployability across diagnostic platforms, supported by crop- and pathogen-specific examples. Emerging approaches such as CRISPR-based biosensing, advanced sequencing, and imaging-assisted analytics are discussed in the context of validation, regulatory acceptance, and operational feasibility. By focusing on implementation rather than conceptual frameworks, this review provides a pragmatic reference for laboratories, regulators, and seed companies seeking to modernize seed health testing while safeguarding trade integrity and biosecurity. Full article

Phytoplasmas are obligate bacterial pathogens transmitted by phloem-feeding insects and responsible for severe diseases in numerous crops worldwide. In Iran, insect-associated phytoplasma transmission pathways remain poorly resolved, particularly at fine phylogenetic and vector-specific scales. Here, we investigated phytoplasma strains detected in four plant species, grapevine (Vitis vinifera), soybean (Glycine max), barberry (Berberis vulgaris), and the weed Conyza canadensis, and in three potential insect vectors (Tropidocephala prasina, Eysarcoris ventralis, and Nysius graminicola) collected from distinct agroecosystems across Iran. Phytoplasmas were characterized by using nearly full-length 16S rRNA gene sequences and a multilocus dataset of protein-coding genes obtained through a targeted next-generation sequencing approach. Five phytoplasma strains belonging to ribosomal groups 16SrI, 16SrVI, 16SrIX, and 16SrXII were identified, including two novel ribosomal subgroups, 16SrI-AS and 16SrIX-K. Several previously unreported plant–phytoplasma and insect–phytoplasma associations were documented. Comparative phylogenetic analyses revealed that ribosomal and multilocus markers capture complementary evolutionary signals, with protein-coding genes providing additional resolution beyond 16S-based classification. These findings highlight the potential role of diverse hosts and polyphagous insects, not yet confirmed as vectors, in phytoplasma circulation and underscore how high-throughput next-generation sequencing and multilocus approaches advance our understanding of phytoplasma diversity and evolution. Full article

The genus Blunervirus comprises plant viruses that infect a diverse range of plants, but no blunervirus has been reported infecting elm trees (Ulmus parvifolia) in China to date. Using high-throughput sequencing and reverse-transcription PCR assays, a novel blunervirus, tentatively named elm blunervirus 1 (ElmBlV1), was identified from a symptomatic elm plant (Ulmus parvifolia) in China. The genome of ElmBlV1 harbors canonical molecular features of blunerviruses and comprises six RNA segments (RNAs1–6), with RNA5 and 6 being two additional genomic components not reported in known blunerviruses. Sequence analyses revealed amino acid (aa) identity of ElmBlV1 proteins ranging from 25.9% (polyprotein encoded by RNA1) to 64.2% (movement protein encoded by RNA4) relative to reported blunerviruses and include five orphan open reading frames. Phylogenetically, ElmBlV1 is most closely related to blueberry necrotic ring blotch virus. Furthermore, ElmBlV1 P37 localizes to both plasmodesmata and the nucleus. Additionally, the RNA reads mapping revealed high read coverage was observed on RNAs3–4 for this virus. To our knowledge, this is the first report of a blunervirus infecting an elm tree in China. Our results enrich the diversity of known viruses in the genus of Blunervirus and expand our understanding of their genomic characteristics and molecular biology. Full article

High-throughput sequencing generally results in a list of genes. Which functional groups of genes among the DEGs are meaningful underlying factors to the differential biological/biomedical conditions under investigation? The process to find answers to this question can be called biological functional class enrichment analysis (FunCEA). R is a robust platform for FunCEA due to its accessibility by general users and availability of well-developed R packages for enrichment analysis and visualization, as well as for knowledge databases. Bench scientists in biomedical sciences need accessible and easy-to-understand protocols for FunCEA. This R tutorial provides detailed R scripts or command lines for FunCEA, as well as for data processing and visualization of the enrichment results. It keeps bench scientists in mind and provides supportive and apprehensible descriptions of the R scripts for each task (enrichment analysis, enrichment data processing, and visualization). It describes detailed procedures for the two popular FunCEA methods, the so-called over-representation analysis (ORA) and functional class scoring (FCS). The introduced FunCEA here uses three basic knowledge databases: gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and reactome. R codes for various visualizations (dot plot, term-gene network plot, enrichment map plot, ridge plot, and GSEA plot) are presented and annotated. Since all analyses are conducted in R, no commercial software is needed, yet clusterProfiler can directly access the latest KEGG knowledge database. Full article

A hybrid membrane bioreactor (HMBR) enhances treatment performance by simultaneously utilizing organisms on both suspended and attached sludge, yet the microbial mechanisms underpinning their efficiency remain poorly understood. In this study, we investigate spatial variability within microbial communities in HMBRs and correlate this factor with pollutant removal capacity. High-throughput sequencing results revealed significant differences in community structure between suspended sludge, suspended media surfaces, and membrane module surfaces. Suspended sludge exhibited the highest species richness, whereas microbial communities on suspended media resembled those within the sludge, contrasting markedly with membrane surface communities. Key functional groups were enriched at specific locations: Pseudomonas and Comamonas dominate the surface of the suspension culture medium and participate in nitrification; phosphorus-accumulating organisms (PAOs), primarily from the Flavobacteriales and Planctomycetaceae phyla, were most abundant on suspended media surfaces. This spatial partitioning of functional microbes indicates cooperative division of labor. Media surfaces serve as primary sites for nitrification and phosphorus removal, whilst suspended sludge flocs and membrane module surfaces are the principal contributors to denitrification. The results of this study provide microbiological evidence for optimizing HMBR design and operation, confirming that spatial community structure is a key factor influencing performance. Full article

Since 2022, global outbreaks of monkeypox virus (MPXV) have been repeatedly designated by the World Health Organization (WHO) as a public health emergency of international concern (PHEIC), underscoring the urgent need to elucidate the multidimensional mechanisms underlying viral evolution and transmission. Current understanding remains largely focused on genomic variation, while the critical role of epigenetic regulation has been considerably overlooked. To address this gap, this study integrates high-throughput evolutionary genomic analysis with whole-genome DNA methylation profiling. Using parallel Illumina and Nanopore sequencing platforms, we comprehensively characterized two clinically derived MPXV isolates collected locally. The results revealed that both isolates belonged to the C.1.1 ancestral lineage, diverging into distinct clades (E.3 and E.4, respectively, supporting the presence of at least two independent viral introduction events into the region, each followed by limited local transmission. They had accrued a considerable number of single-nucleotide polymorphisms (SNPs), with APOBEC3-associated substitutions constituting 84.8% and 77.6% of all observed mutations. Furthermore, both 5-hydroxymethylcytosine (5hmC) and N6-methyladenine (6mA) modifications were identified and found to be preferentially enriched within the inverted terminal repeats (ITRs) regions of MPXV genome in both viral strains; moreover, the E.4 lineage viral strain exhibits a markedly more intricate and compositionally diversified modification landscape, a pattern that indicates appreciable epigenetic heterogeneity among MPXV lineages. Our study furnishes a multi-omics framework that presents a systematic evolutionary feature of two clinical MPXV isolates and their genomic DNA 5hmC and 6mA modification topologies, and enhances our understanding of MPXV viral adaptation and diversification. Full article

Phages, the most abundant entities on Earth, exhibit a complex interplay with bacteria, especially within environmental biofilms, resulting in an ecological arms race. This study investigates the interaction between phages and bacteria in the drains of beef-processing plants using high-throughput sequencing and metagenomic analysis. Metagenomic data collected from 75 drain samples from beef-processing plants were analyzed to investigate phage–bacterial interactions. First, assembled contigs were screened to identify viral sequences, which were then taxonomically annotated to determine the viral composition, including phages. Functional annotation of these viral sequences provided information about the viral genes and their roles in bacterial interactions specifically associated with attack and counterattack of bacteria. In parallel, bacterial contigs were examined to identify genes associated with antiphage defense systems, providing insights into the strategies adapted by bacteria to resist phage infection. Taxonomic annotation of viral sequences from the bulk metagenomic data revealed the presence of phages targeting Pseudomonas, Klebsiella, and Enterococcus. The higher abundance of Pseudomonas phages aligns with our previous study, where Pseudomonas was identified as the dominant bacterial genus, suggesting potential copersistence of phages and their hosts. Functional annotation of phage contigs revealed infective and lysis-related genes, highlighting their potential role in bacterial attack. Conversely, bacterial contigs encoded antiphage defense systems, including CRISPR-Cas, restriction–modification, and other defense-related genes. The study also uncovered the presence of anti-CRISPR proteins in phages, suggesting a counterattack on the bacterial defense. These findings provide evidence for phage attack, bacterial defense, and phage counterattack and may showcase the ongoing coevolutionary arms race between phages and bacteria. While this evidence looks promising, these results remain preliminary and further studies are needed to validate these findings. Still, this study provides a foundational understanding of bacteria–phage coexistence in beef-processing plant drains and paves the way for further explorations of these intricate interactions and their possible applications in controlling pathogenic microorganisms within biofilms. Full article

Transfer RNA-derived small RNAs (tsRNAs) represent an emerging category of small non-coding RNAs generated through specific cleavage of precursor or mature tRNAs. Increasingly recognized as pivotal players in the pathogenesis of complex malignancies, tsRNAs not only regulate cancer progression but also hold promising clinical potential for cancer diagnosis and treatment. This review highlights recent advances in the application of high-throughput sequencing technologies in the systematic identification of tsRNAs, with a focus on their roles in cancer diagnosis, prognostic assessment, and targeted therapy. Delving into the translational medicine dimensions of tsRNAs may provide novel strategies for molecular diagnosis and therapeutic interventions in oncology. Full article

Background: Embryoid bodies (EBs) play a central role in organoid engineering, where their formation fidelity and size critically influence downstream differentiation outcomes. Current EB production workflows primarily rely on retrospective quality assessment, which limits reproducibility in high-throughput culture systems. Objective: This study aimed to develop a prospective, non-invasive framework that integrates early-phase bright-field time-lapse imaging with a three-dimensional convolutional neural network to predict EB formation outcomes and final EB diameter within the microwell platform. Methods: Time-lapse image sequences collected during the first hours after cell seeding on the microwell array were used to train 3D-CNN models for classification (formation vs. non-formation) and regression (final diameter). A balanced dataset was constructed through under-sampling, and five-fold cross-validation with data augmentation was applied to evaluate model performance. Results: The classification model achieved an accuracy of 96.5%, reliably distinguishing between successful and failed EB formation using short-duration image sequences. The regression model predicted the final EB diameter with a mean absolute error of ±7.1 µm, reflecting strong agreement with measured values and capturing seeding-density-dependent size variations. Conclusions: Early aggregation dynamics captured by bright-field time-lapse imaging contain sufficient spatiotemporal information to enable accurate, prospective EB quality prediction. The proposed framework provides a label-free and automation-compatible strategy for improving reproducibility in large-scale EB manufacturing and supports the future development of adaptive and closed-loop organoid culture systems for clinical applications. Full article

DNAzymes are catalytically active single-stranded DNAs that fold into metal-ion-assisted architectures to mediate diverse reactions. Addressing the performance gap in biological settings, we establish a novel conceptual framework based on a continuous iteration workflow of selection, enhancement, and application. This paradigm integrates selection constraints, molecular engineering, and clinical context into a unified cycle. We summarize the evolution of SELEX toward application-driven selection incorporating functional/environmental constraints, deep-sequencing-enabled high-throughput activity readouts, droplet compartmentalization and structure- and computation-guided design. We further consolidate engineering strategies to improve stability, kinetics and controllability, including 2′-sugar modifications and XNA substitution, backbone and nucleobase functionalization, arm and secondary-structure engineering for switchable or split architectures and multivalent organization on nanocarriers or nucleic acid scaffolds to enhance local concentration, protection and targeted delivery. Finally, we survey applications in ultrasensitive biosensing and portable diagnostics, activatable and multimodal in vivo imaging, and therapies for cancer, inflammatory diseases and airway disorders, and outline translational priorities: data-driven design, next-generation delivery, standardized safety/PK-PD evaluation and scalable manufacturing, ultimately for clinical and point-of-care deployment. Full article

Aptamers are powerful tools for detecting and analyzing biomolecules that consist of proteins or nucleic acids. However, their application to aptamers against viroids—highly structured self-replicating RNAs—has not yet been explored. In this study, a magnetic bead- and high-throughput sequencing-based SELEX (MB-HTS-SELEX) protocol for selecting potential aptamers against potato spindle tuber viroid (PSTVd) is presented. Full-length biotinylated-PSTVd RNA was transcribed in vitro, immobilized on streptavidin-coated magnetic beads, and incubated with a library of ~3.32 × 10¹⁴ molecules of random single-stranded oligo-DNAs (oligo-ssDNAs) of 20, 30, or 40 nucleotides (L20, L30, or L40, respectively) flanked by primer binding sites for downstream PCR amplification. Simultaneous biotin labeling of the anti-aptamer strand of the resulting double-stranded DNA (dsDNA) amplicons facilitated strand separation using streptavidin-coated magnetic beads. After 10 selection rounds, high-throughput sequencing, followed by bioinformatics analysis of the generated sequences, allowed for the detection of several enriched sequences, representing putative PSTVd-binding aptamers. Subsequent pull-down assays showed that the most abundant oligo-ssDNA in L30 was docked on PSTVd molecules. This combination method may ameliorate the selection of high-affinity aptamers against PSTVd, reduce the number of selection cycles, time, and other costs of aptamer production, thereby promoting future massive and cost-effective viroid detection and characterization. Full article

Peptide-based vaccines offer a modular and readily manufacturable platform for both prophylactic and therapeutic immunization. However, their broader translation has been constrained by the limited capacity to predict protective immunity directly from sequence-level features. Recent advances in systems vaccinology and high-throughput immune profiling have substantially expanded the experimental evidence, while generative artificial intelligence now enables de novo design of peptide immunogens and multi-epitope antigens under precisely controlled constraints. This review approaches how these complementary developments are transforming peptide vaccine research, moving beyond classical reverse vaccinology and conventional epitope prediction toward integrated, data-driven design frameworks. We discuss key generative model architectures and conditioning strategies aligned with vaccine objectives, including approaches that account for structural presentation, antigen processing and population-level human leukocyte antigen (HLA) diversity. Central to this perspective is the requirement for rigorous experimental validation and for strengthening the computational–experimental feedback loop through iterative in vitro and in vivo testing informed by systems-level immune readouts. We highlight representative applications spanning infectious diseases, cancer immunotherapy and vector-borne vaccinology, and we outline major technical and translational challenges that must be addressed to enable robust real-world deployment. Finally, we propose future directions for precision peptide vaccinology, emphasizing standardized functional benchmarks, the development of richer curated datasets linking sequence space to immune outcomes, and the early incorporation of formulation and delivery constraints into generative design pipelines. Full article

Tuberculous meningitis is the most severe form of tuberculosis and remains associated with high mortality and substantial neurological disability, particularly among children and people living with HIV. Early diagnosis is challenging because of nonspecific clinical manifestations, the limited discriminatory value of cerebrospinal fluid cytochemical analysis, and the low sensitivity of conventional microbiological methods. This narrative review synthesizes contemporary evidence on the diagnostic approach to tuberculous meningitis, integrating clinical assessment, paraclinical cerebrospinal fluid findings, conventional microbiology, and molecular diagnostic tools. Clinical scoring systems, including the uniform case definition (Lancet consensus score), improve diagnostic stratification but do not replace microbiological confirmation. Molecular assays have transformed diagnostic pathways by enabling rapid detection of Mycobacterium tuberculosis, although their performance is influenced by bacillary burden, cerebrospinal fluid volume, HIV status, and disease stage. Complementary molecular techniques and advanced sequencing approaches provide additional diagnostic value in selected paucibacillary cases or when first-line tests are negative. Integrated diagnostic algorithms that combine clinical evaluation with stepwise molecular testing improve diagnostic accuracy and support earlier treatment initiation. Ongoing challenges include limited access to molecular platforms, variability in laboratory capacity, and the need for standardized multimodal diagnostic pathways applicable across diverse healthcare settings. Full article

Methane (CH₄) emissions present a significant challenge to both environmental sustainability and energy efficiency in ruminants, including beef cattle that are born in dairy herds. Although numerous approaches, including alterations in feed and the use of additives, are under investigation to mitigate these emissions, the genetic selection of animals that produce lower levels of methane offers the potential for enduring and cumulative advantages. Transcriptome analysis represents a crucial advancement in elucidating the networks and mechanisms through which the ruminant genome influences methane emissions. In the present study, methane emissions were measured using a GreenFeed system in beef-on-dairy cattle (n = 11). High-throughput RNA sequencing was conducted on animal blood samples, followed by differential gene expression analysis using methane production (g/d) as a continuous trait. The analysis identified eleven differentially expressed genes (DEGs), including six downregulated (KIAA1211L, LOC107131224, OSCP1, IL12B, LOC618859, FREM1) and five upregulated (DSCAML1, OSBP2, ACAN, PRSS16, CD1B) genes (Padj < 0.05) with one gene exhibiting potential biomarker characteristics. Gene and cell enrichment, as well as pathway analysis, suggested that nervous, immune, and endocrine systems may be involved in ruminal methane production by beef-on-dairy cattle. These findings highlight the potential of transcriptomic biomarkers to guide genetic selection strategies, offering a sustainable pathway to reduce methane emissions and enhance both environmental and agricultural efficiency. Full article