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The organelle genomes of N. jatamansi were sequenced and examined to explore their structure, evolution, and possible functional implications. The chloroplast genome was constructed as a singular circular entity of 155,225 bp, whereas the mitochondrial genome exhibited considerable complexity, divided into 14 contigs totaling 1,229,747 bp, along with several sub-circular formations. Comparative analysis within the Caprifoliaceae family revealed that four genes (rps19, rpl22, rpl20, and matK) exhibited high sequence variability, suggesting their potential as molecular markers for the identification of species. In addition, three genes (clpP, ycf1, and ycf2) exhibited ka/ks ratios greater than one, which implies positive selection. Intracellular (between chloroplast and mitochondria) gene transfer analysis revealed the integration of six chloroplast-derived genes, and repeat analysis identified 47,980 repeat pairs in the mitochondrial genome, which spans 2.64 Mb. It is likely that these traits add to the structural complexity of the mitochondrial genome. Predictions based on climate show that N. jatamansi may be able to find more ideal habitat over the next 60 years. These results give us a useful genetic resource for studying evolution and set the stage for future research into how the species can be used in medicinal applications. Full article

As apex predators, felids (Felidae) face unresolved phylogenetic controversies due to their recent rapid speciation and remarkable morphological conservatism. Previous studies, often relying on a limited number of genetic markers, were constrained by insufficient data and conflicting phylogenetic signals, leaving these disputes unresolved. Therefore, establishing a robust phylogenetic framework based on larger-scale genomic data is crucial. This study integrated complete mitogenomes from 37 species representing all major felid genera to characterize genomic diversity, selection pressures, and phylogenetic relationships. Results revealed conserved gene content and arrangement patterns but significant intergenic variation in nucleotide composition, with the light-strand encoded ND6 exhibiting pronounced strand-specific bias. Nucleotide diversity was highest in ND4L (Pi = 0.132) and ATP6 (Pi = 0.131), suggesting their utility as novel markers for species delimitation and population studies. Selection pressure analysis indicated strong purifying selection on cytochrome oxidase subunits (e.g., COX1 Ka/Ks = 0.00327) but relaxed constraints on ATP8 (Ka/Ks = 0.12304). Phylogenies reconstructed from the complete 13PCGs + 2rRNAs dataset (showing high congruence between maximum likelihood and Bayesian methods) clearly delineated Felidae into two primary clades (Pantherinae and Felinae), confirming monophyly of all genera and positioning Neofelis nebulosa as the basal lineage within Pantherinae. Crucially, exclusion of ND6 (12PCGs + 2rRNAs) yielded topologies congruent with the complete 13PCGs + 2rRNAs dataset, whereas single-gene or limited multi-gene datasets produced inconsistent trees (particularly at genus-level nodes). This demonstrates that near-complete mitogenomic data (≥12PCGs + 2rRNAs) are essential for reconstructing robust felid phylogenetic frameworks. Our study provides insights into carnivoran mitogenome evolution. Full article

Background: Copy number variations (CNVs), also referred to as large genomic rearrangements (LGRs), represent a crucial component of BRCA1/2 (BRCA) testing. Next-generation sequencing (NGS) has become an established approach for detecting LGRs by combining sequencing data with dedicated bioinformatics pipelines. However, CNV detection in formalin-fixed paraffin-embedded (FFPE) samples remains technically challenging, and there is the need to implement a robust and optimized analysis strategy for routine clinical practice. Methods: This study evaluated 40 FFPE ovarian cancer (OC) samples from patients undergoing BRCA testing. The performance of the amplicon-based NGS Diatech Myriapod^® NGS BRCA1/2 panel (Diatech Pharmacogenetics, Jesi, Italy) was assessed for its ability to detect BRCA CNVs and results were compared to two hybrid capture-based reference assays. Results: Among the 40 analyzed samples (17 CNV-positive and 23 CNV-negative for BRCA genes), the Diatech pipeline showed a good concordance with the reference method—all CNVs were correctly identified in 16 cases with good enough sequencing quality. Only one result was inconclusive due to low sequencing quality. Conclusions: These findings support the clinical utility of NGS-based CNV analysis in FFPE samples when combined with appropriate bioinformatics tools. Integrating visual inspection of CNV plots with automated CNV calling improves the reliability of CNV detection and enhances the interpretation of results from tumor tissue. Accurate CNV detection directly from tumor tissue may reduce the need for additional germline testing, thus shortening turnaround times. Nevertheless, blood-based testing remains mandatory to determine whether detected BRCA CNVs are of hereditary or somatic origin, particularly in cases with a strong clinical suspicion of inherited predisposition due to young age and a personal and/or family history of OC. Full article

Extreme climate events—such as heatwaves, floods, and droughts—are increasingly affecting ecosystems, with the global average temperature projected to rise by up to 3 °C (IPCC, 2023) due to anthropogenic greenhouse gas emissions. These changes pose critical challenges to food security, as evidenced by 733 million people facing hunger in 2024. In response, crop modeling considering different climate change scenarios has become a valuable tool to guide the development of climate-resilient agricultural strategies. Despite its nutritional importance and capacity to thrive across diverse environments, Ipomoea batatas (sweet potato) remains understudied in terms of potential spatial distribution forecasting, particularly in regions of high agrobiodiversity such as northwestern South America. Therefore, in this study we modeled the projected distribution of wild and landrace sweet potato genepools in the northern Andes under four future timeframes using seven machine learning algorithms. Our results predicted a 50% reduction in the climatically suitable range for the wild genepool by 2081, coupled with an average altitudinal shift from 1537 to 2216 m above sea level (a.s.l.). For landraces, a 36% reduction was projected by 2080, with a shift from 62 to 1995 m a.s.l. By the end of the century, suitable zones for both wild and cultivated genepools are expected to converge in high-altitude regions such as the Colombian Massif, with additional remnants of wild populations near the mountain range of Farallones de Cali. This modeling approach provides essential insights into the spatial dynamics of I. batatas under climate change, highlighting the need for ex situ conservation planning in vulnerable regions as well as assisted migration to more suitable areas. Future research should integrate edaphic and biotic interaction data to better approach the realized niche of the species and understand potential responses under a niche conservatism assumption, as well as genomic data to account for the species’ intrinsic adaptative potential, overall informing conservation, germplasm mobilization, and pre-breeding strategies that may ultimately secure the role of sweet potato in resilient food systems. Full article

Cotton fiber quality improvement remains a fundamental challenge in breeding programs due to the complex genetic architecture underlying fiber development. The narrow genetic base of upland cotton (Gossypium hirsutum L.) and the quantitative nature of fiber quality traits necessitate innovative approaches for identifying and incorporating superior alleles from related species. We developed a BC₆F₂ population by introgressing chromosome segments from the sea island cotton variety Xinhai 36 (G. barbadense) into the upland cotton variety Xinluzhong 60 (G. hirsutum). Based on fiber strength phenotyping, we constructed two DNA bulks representing extreme phenotypes (20 superior and 12 inferior individuals) for bulked segregant analysis sequencing (BSA-Seq). High-throughput sequencing generated 225.13 Gb of raw data with average depths of 20× for parents and 30× for bulks. SNP calling and annotation were performed using GATK and ANNOVAR against the upland cotton reference genome (TM-1). BSA-Seq analysis identified 13 QTLs primarily clustered within a 1.6 Mb region (20.6–22.2 Mb) on chromosome A10. Within this region, we detected nonsynonymous mutation genes involving a total of six genes. GO and KEGG enrichment analyses revealed significant enrichment for carbohydrate metabolic processes, protein modification, and secondary metabolite biosynthesis pathways. Integration with transcriptome data prioritized GH_A10G1043, encoding a β-amylase family protein, as the key candidate gene. Functional validation through overexpression and RNAi knockdown in Arabidopsis thaliana demonstrated that GH_A10G1043 significantly regulates starch content and β-amylase activity, though without visible morphological alterations. This study successfully identified potential genomic regions and candidate genes associated with cotton fiber strength using chromosome segment substitution lines combined with BSA-Seq. The key candidate gene GH_A10G1043 provides a valuable target for marker-assisted selection in cotton breeding programs. Our findings establish a foundation for understanding the molecular mechanisms of fiber quality formation and offer genetic resources for developing superior cotton varieties with enhanced fiber strength. Full article

Rhizoctonia solani root rot (RSRR) is a major disease that significantly reduces soybean yields, causing substantial economic losses to global soybean production. To elucidate the genetic basis of RSRR resistance, 310 soybean germplasm accessions were evaluated using the disease severity index (DSI) following inoculation with R. solani. Among these accessions, 46.13% were susceptible, and only 2.26% exhibited high resistance. Utilizing resequencing data consisting of 738,561 Single Nucleotide Polymorphism (SNP) loci, a genome-wide association study (GWAS) was performed by integrating both general linear model (GLM) and mixed linear model (MLM) approaches, resulting in the identification of 21 SNPs significantly associated with resistance on chromosomes 3, 13, 15, 16, 17, and 18, and six candidate genes. RT-qPCR expression analysis revealed that four genes, including Glyma.03G166300, Glyma.03G168100, Glyma.13G212700, and Glyma.13G212300, were significantly upregulated in resistant genotypes after inoculation. Furthermore, Cleaved Amplified Polymorphic Sequences (CAPS) and Kompetitive Allele Specific PCR (KASP) molecular markers were successfully developed based on the RSRR-associated SNPs S3_38086892, S3_38247290, and S13_32595026, providing effective tools for marker-assisted selection (MAS). The findings strengthen our genetic knowledge concerning RSRR resistance and contribute to the molecular breeding of resistant soybean cultivars. Full article

A comprehensive comparative analysis was conducted on the nucleotide and amino acid sequences of intact phiLM21-like prophages (phiLM21-LPhs), which currently represent the most prevalent prophages in Sinorhizobium meliloti—a symbiotic partner of Fabaceae plants. Remarkably, the nucleotide sequences of 25 phiLM21-LPhs, identified across 36 geographically dispersed S. meliloti strains, covered no more than 34% of the phiLM21 phage genome. All prophages were integrated into specific isoacceptor tRNA genes and carried a tyrosine-type integrase gene; however, this integration did not exhibit features of tRNA-dependent lysogeny. Only one-fifth of phiLM21-LPhs encoded the minimal set of regulators for lysogenic/lytic cycle transitions, while the remainder contained either uncharacterized regulatory elements or appeared to be undergoing genomic “anchoring” within the host bacterium. The phiLM21-LPhs harbored open reading frames (ORFs) of diverse origins (phage-derived, bacterial, and unknown), yet over half of these ORFs had undeterminable functions, representing genetic “dark matter”. The observed diversification of intact phiLM21-like prophages likely stems from recombination events involving both virulent/temperate phages and phylogenetically remote bacterial taxa. The evolutionary and biological significance of the substantial genetic “dark matter” within these prophages in soil saprophytic bacteria remains an unresolved question. Full article

Cold stress severely limits legume productivity, threatening global food security, particularly in climate-vulnerable regions. This review synthesizes advances in understanding and enhancing cold tolerance in key legumes (chickpea, soybean, lentil, and cowpea), addressing three core questions: (1) molecular/physiological foundations of cold tolerance; (2) how emerging technologies accelerate stress dissection and breeding; and (3) integration strategies and deployment challenges. Legume cold tolerance involves conserved pathways (e.g., ICE-CBF-COR, Inducer of CBF Expression, C-repeat Binding Factor, Cold-Responsive genes) and species-specific mechanisms like soybean’s GmTCF1a-mediated pathway. Multi-omics have identified critical genes (e.g., CaDREB1E in chickpea, NFR5 in pea) underlying adaptive traits (membrane stabilization, osmolyte accumulation) that reduce yield losses by 30–50% in tolerant genotypes. Technologically, AI and high-throughput phenotyping achieve >95% accuracy in early cold detection (3–7 days pre-symptoms) via hyperspectral/thermal imaging; deep learning (e.g., CNN-LSTM hybrids) improves trait prediction by 23% over linear models. Genomic selection cuts breeding cycles by 30–50% (to 3–5 years) using GEBVs (Genomic estimated breeding values) from hundreds of thousands of SNPs (Single-nucleotide polymorphisms). Advanced sensors (LIG-based, LoRaWAN) enable real-time monitoring (±0.1 °C precision, <30 s response), supporting precision irrigation that saves 15–40% water while maintaining yields. Key barriers include multi-omics data standardization and cost constraints in resource-limited regions. Integrating molecular insights with AI-driven phenomics and multi-omics is revolutionizing cold-tolerance breeding, accelerating climate-resilient variety development, and offering a blueprint for sustainable agricultural adaptation. Full article

The Long Interspersed Element-1 (L1) retrotransposon is an ancient genetic parasite that comprises a significant part of the human genome. ORF2p is a multifunctional enzyme with endonuclease (EN) and reverse transcriptase (RT) activities that mediate target-primed reverse transcription of RNA into DNA. Structural studies of LINE-1 ORF2p consistently show a single Mg²⁺ cation in the reverse transcriptase active site, conflicting with the common DNA polymerase mechanism which involves two divalent cations. We explored a reaction pathway of the DNA elongation based on the recent high-resolution ternary complex structure of the ORF2p. The combined quantum and molecular mechanics approach at the QM (PBE0-D3/6-31G**)/MM (CHARMM) level is employed for biased umbrella sampling molecular dynamics simulations followed by umbrella integration utilized to obtain the free energy profile. The nucleotidyl transfer reaction proceeds in a single step with a free energy barrier of 15.1 ± 0.8 kcal/mol, and 7.8 ± 1.2 kcal/mol product stabilization relative to reagents. Concerted nucleophilic attack by DNA O3′ and proton transfer to Asp703 occur without a second catalytic metal ion. Estimated rate constant ∼60 s⁻¹ aligns with RT kinetics, while analysis of the Laplacian of the electron density along the cleaving P-O bond identifies a dissociative mechanism. Full article

Alkaline stress, driven by high pH and carbonate accumulation, results in severe physiological damage in plants. While the molecular mechanisms underlying alkaline tolerance have been partially elucidated in many crops, they remain largely unexplored in wheat. We hypothesize that alkaline stress tolerance in wheat is genotype-dependent. This study employed an integrated multi-omics approach to assess alkaline stress responses, combining genome-wide association study (GWAS) and RNA-seq analyses. Systematic phenotyping revealed severe alkaline stress-induced root architecture remodeling—with 57% and 73% length reductions after 1- and 3-day treatments, respectively—across 258 accessions. Analysis of the GWAS results identified nine significant alkaline tolerance QTLs on chromosomes 1A, 3B, 3D, 4A, and 5B, along with 285 associated candidate genes. Using contrasting genotypes—Dingxi 38 (tolerant) and TDP.D-27 (sensitive)—as experimental materials, physiological analyses demonstrated that root elongation was less inhibited in Dingxi 38 under alkaline stress compared to TDP.D-27, with superior root integrity observed in the tolerant genotype. Concurrently, Dingxi 38 exhibited enhanced reactive oxygen species (ROS) scavenging capacity. Subsequent RNA-seq analysis identified differentially expressed genes (DEGs) involved in ion homeostasis, oxidative defense, and cell wall remodeling. Integrated GWAS and RNA-seq analyses allowed for the identification of seven high-confidence candidate genes, including transcription factors (MYB38, bHLH148), metabolic regulators (ATP-PFK3), and transporters (OCT7), elucidating a mechanistic basis for adaptation to alkaline conditions. These findings advance our understanding of alkaline tolerance in wheat and provide candidate targets for molecular breeding of saline- and alkaline-tolerant crops. Full article

Background: Pancreatic ductal adenocarcinoma (PDAC) is a highly heterogeneous malignancy, characterized by low tumor cellularity, a dense stromal response, and intricate cellular and molecular interactions within the tumor microenvironment (TME). Although bulk omics technologies have enhanced our understanding of the molecular landscape of PDAC, the specific contributions of non-malignant immune and stromal components to tumor progression and therapeutic response remain poorly understood. Methods: We explored genome-wide DNA methylation and transcriptomic data from the Cancer Genome Atlas Pancreatic Adenocarcinoma cohort (TCGA-PAAD) to profile the immune composition of the TME and uncover gene co-expression networks. Bioinformatic analyses included DNA methylation profiling followed by hierarchical deconvolution, epigenetic age estimation, and a weighted gene co-expression network analysis (WGCNA). Results: The unsupervised clustering of methylation profiles identified two major tumor groups, with Group 2 (n = 98) exhibiting higher tumor purity and a greater frequency of KRAS mutations compared to Group 1 (n = 87) (p < 0.0001). The hierarchical deconvolution of DNA methylation data revealed three distinct TME subtypes, termed hypo-inflamed (immune-deserted), myeloid-enriched, and lymphoid-enriched (notably T-cell predominant). These immune clusters were further supported by co-expression modules identified via WGCNA, which were enriched in immune regulatory and signaling pathways. Conclusions: This integrative epigenomic–transcriptomic analysis offers a robust framework for stratifying PDAC patients based on the tumor immune microenvironment (TIME), providing valuable insights for biomarker discovery and the development of precision immunotherapies. Full article

Clear cell ovarian carcinoma is a rare and aggressive histologic subtype of epithelial ovarian cancer, characterized by a chemoresistant phenotype and distinct immunogenomic features. Despite early-phase trials showing a limited response to immune checkpoint inhibitors (ICIs), emerging evidence reveals a biologically diverse tumor immune microenvironment, with implications for the efficacy of immunotherapies. Preclinical studies highlight paradoxical associations between immune infiltration and prognosis, as well as genomic drivers—including KRAS, MYC, PI3KCA, TP53, PTEN, and ARID1A—that shape immune evasion and checkpoint ligand expression. Clinically, ICI monotherapy yields modest benefit, while combination regimens—particularly dual checkpoint blockade and targeted co-inhibition—offer improved outcomes. Biomarkers such as PD-L1 CPS ≥ 1%, ARID1A mutations, elevated tumor mutational burden, and PIK3CA alterations emerge as promising predictors of therapeutic response. This review integrates current preclinical and clinical data to propose a precision immunotherapy framework tailored to the immunogenomic landscape of clear cell ovarian carcinoma. Full article

Introduction: Metabolic dysfunction associated steatotic liver disease (MASLD), previously termed as nonalcoholic fatty liver disease (NAFLD), is a growing global health concern, particularly in South Asia. While PNPLA3 is a well-recognized genetic contributor to MASLD, the role of other metabolic genes, such as ABCC8, remains unexplored in South Asian populations. In this study, we aim to investigate the genetic association and potential synergy between PNPLA3 (rs738409) and ABCC8 (rs146378237) variants in MASLD pathogenesis in a Pakistani cohort. Methods: A two-phased case–control study was conducted. Whole Exome Sequencing (WES) was performed on 6 MASLD cases and 6 healthy controls to identify relevant variants, followed by validation via Sanger sequencing in an extended MASLD cohort (n = 52). Variant frequencies were compared with 96 ethnically matched controls from the 1000 Genomes Project. Furthermore, the association of the variants with clinical, biochemical, and fibrotic parameters was assessed. Results: The PNPLA3 rs738409 G allele (MAF = 0.47) and ABCC8 rs146378237 T allele (MAF = 0.36) were significantly enriched in MASLD cases and strongly associated with cirrhosis. The TT genotype of ABCC8 was also linked to T2DM and low HDL levels. Importantly, eight MASLD patients harbored both GG (PNPLA3) and TT (ABCC8) genotype, and all were known cases of diabetes, suggesting a synergistic genetic interaction. Conclusions: This is the first report of ABCC8 rs146378237 in a South Asian MASLD cohort, revealing population-specific risk and a gene–gene interaction that may inform targeted screening and personalized management of MASLD in high-risk diabetic individuals. Full article

Terpenoids, which are essential pharmaceutical compounds, encounter significant production challenges due to their low yields in native plants and associated ecological concerns. This review summarizes recent advances in metabolic engineering strategies applied across three complementary platforms: native medicinal plants, microbial systems, and heterologous plant hosts. We present how the “Genomic Insights to Biotechnological Applications” paradigm, supported by multi-omics technologies such as genomics, transcriptomics, metabolomics, and related disciplines, contributes to advancing research in this field. These technologies enable the systematic identification of key biosynthetic genes and regulatory networks. CRISPR-based tools, enzyme engineering, and subcellular targeting are presented as pivotal transformative strategies in advancing metabolic engineering approaches. Strategic co-expression and optimization approaches have achieved substantial improvements in product yields, as demonstrated by a 25-fold increase in paclitaxel production and a 38% enhancement in artemisinin yield. Persistent challenges, such as metabolic flux balancing, cytotoxicity, and scale-up economics, are discussed in conjunction with emerging solutions, including machine learning and photoautotrophic chassis systems. We conclude by proposing a strategic roadmap for industrial translation that highlights the essential integration of systems biology and synthetic biology approaches to accelerate the transition of terpenoid biomanufacturing from discovery to commercial-scale application. Full article

The innate immune system protects against infection and cellular damage by recognizing conserved pathogen-associated molecular patterns (PAMPs) and damage-associated molecular patterns (DAMPs). Emerging evidence suggests that aberrant epigenetic modifications—such as altered DNA methylation and histone marks—can serve as immunogenic signals that activate pattern recognition receptor (PRR)-mediated immune surveillance. This review explores the concept that epigenetic marks may function as DAMPs or even mimic PAMPs. I highlight how unmethylated CpG motifs, which are typically suppressed using host methylation, are recognized as foreign via Toll-like receptor 9 (TLR9). I also examine how cytosolic DNA sensors, including cGAS, detect mislocalized or hypomethylated self-DNA resulting from genomic instability. In addition, I discuss how extracellular histones and nucleosomes released during cell death or stress can act as DAMPs that engage TLRs and activate inflammasomes. In the context of cancer, I review how epigenetic dysregulation can induce a “viral mimicry” state, where reactivation of endogenous retroelements produces double-stranded RNA sensed by RIG-I and MDA5, triggering type I interferon responses. Finally, I address open questions and future directions, including how immune recognition of epigenetic alterations might be leveraged for cancer immunotherapy or regulated to prevent autoimmunity. By integrating recent findings, this review underscores the emerging concept of the epigenome as a target of innate immune recognition, bridging the fields of immunology, epigenetics, and cancer biology. Full article