Search Results (135)

Background/Objectives: Wolf–Hirschhorn syndrome (WHS; OMIM #194190) is a rare neurodevelopmental disorder, caused by deletions in the distal short arm of chromosome 4. It is characterized by developmental delay, epilepsy, intellectual disability, and distinctive facial dysmorphism. Clinical presentation varies widely, complicating prognosis and individualized care. Methods: We assembled a cohort of 140 individuals with genetically confirmed WHS from Spain and Latin-America, and developed and validated a multidimensional, Clinician-Reported Outcome Assessment (ClinRO) based on the Global Functional Assessment of the Patient (GFAP), derived from standardized clinical questionnaires and weighted by HPO (Human Phenotype Ontology) term frequencies. The GFAP score quantitatively captures key functional domains in WHS, including neurodevelopment, epilepsy, comorbidities, and age-corrected developmental milestones (selected based on clinical experience and disease burden). Results: Higher GFAP scores are associated with worse clinical outcomes. GFAP showed strong correlations with deletion size, presence of additional genomic rearrangements, sex, and epilepsy severity. Ward’s clustering and discriminant analyses confirmed GFAP’s discriminative power, classifying over 90% of patients into clinically meaningful groups with different prognoses. Conclusions: Our findings support GFAP as a robust, WHS-specific ClinRO that may aid in stratification, prognosis, and clinical management. This tool may also serve future interventional studies as a standardized outcome measure. Beyond its clinical utility, GFAP also revealed substantial social implications. This underscores the broader socioeconomic burden of WHS and the potential value of GFAP in identifying high-support families that may benefit from targeted resources and services. Full article

Background/Objectives: The 3-dimensional (3D) architecture of the genome in the nucleus of a living cell plays an unexpected yet fundamental regulatory role in cell biology. As an imprint of the cellular genetic, epigenetic and metabolic status, it discriminates pathological conditions through conditional changes to long-range 3D interactions (up to 300 kb) and thus could act as a powerful molecular biomarker linked closely to clinical outcomes. Methods: Here an assessment is made of the latest paradigm shift in molecular biology from a supply chain where information flows from DNA to RNA to protein, to the concept of heritable 3D folding of the genome reflecting the epigenetic and metabolic state of the cell, and which serves as a molecular biomarker for complex clinical outcome. Results: While biomarkers based on individual components of the supply chain fail to accurately reflect clinical outcomes, 3D genomics offers highly informative insights, exemplified for immuno-oncology and prostate cancer diagnosis by clinical tests of superior performance, already in practice in the US and UK. Conclusions: A more complete understanding of human biology will require models that account for the flow of information to and from the 3D genomic architecture in living cells, together with its regulation and logic. Integrating these principles into biomarker discovery and therapeutic design, along with other frontline approaches in precision medicine, including multi-omics and other system-level tools, will be essential for advancing precision medicine beyond its current limitations. Full article

Background: Antimicrobial resistance (AMR) is a critical global health threat, with AMR Salmonella enterica serovar Typhimurium strains being a major foodborne pathogen. Integrons, a type of mobile genetic element, capture and transfer resistance genes, thereby playing a role in the spread of AMR. Objectives: This study aimed to characterize the locations of integrons carrying AMR genes within the whole genomes of 32 Salmonella Typhimurium isolates collected from dairy cattle by two U.S. Veterinary Diagnostic Laboratories between 2009 and 2012. Methods: Class I integrons were sequenced from PCR-amplified products. DNA was extracted, quantified, barcoded, and sequenced on the Illumina MiSeq platform. Whole genome sequences were trimmed and assembled using the SPAdes assembler in Geneious Prime^®, and plasmids were identified with the PlasmidFinder pipeline in Linux. Integron locations were determined by aligning their sequences with whole genome contigs and plasmids, while AMR genes were identified through BLAST with the MEGARes 3.0 database and confirmed by alignment with isolate, plasmid, and integron sequences. Statistical analysis was applied to compare the proportions of isolates harboring integrons on their chromosome versus plasmids and also to examine the associations between integron presence and AMR gene presence. Results: Seven plasmid types were identified from all isolates: IncFII(S) (n = 14), IncFIB(S) (n = 13), IncC (n = 7), Inc1-I(Alpha) (n = 3), and ColpVC, Col(pAHAD28), and Col8282 (1 isolate each). Of the 32 isolates, 16 (50%) carried at least one size of integron. Twelve of them carried both 1000 and 1200 bp; 3 carried only 1000 bp and 1 carried 1800 bp integrons. Of the 15 isolates that carried 1000 bp integron, 12 harbored it on IncFIB(S) plasmids, 2 on IncC plasmids, and 1 on the chromosome. The 1200 bp integrons from all 12 isolates were located on chromosomes. There were significant positive associations between the presence of integrons and the presence of several AMR genes including sul1, aadA2, blaCARB-2, qacEdelta1, tet(G), and floR (p < 0.05). AMR genes were located as follows: aadA2 on IncFIB(S) and IncC plasmids; bla_CMY-2 on IncC plasmid; qacEdelta1 on IncFIB(S), IncC, and chromosome; bla_CARB-2, floR, tet(A) and tet(G) on the chromosome. Conclusions: The findings highlight the genomic and plasmid complexity of Salmonella Typhimurium which is impacted by the presence and location of integrons, and this study provides genomic insights that can inform efforts to enhance food safety and protect both animal and public health. Full article

Myeloproliferative neoplasms, particularly the Philadelphia chromosome-negative (Ph-negative) subtypes such as essential thrombocythemia, polycythemia vera, and primary myelofibrosis, present diagnostic challenges due to overlapping morphological features and clinical heterogeneity. Traditional diagnostic approaches, including imaging and histopathological analysis, are often limited by interobserver variability, delayed diagnosis, and subjective interpretations. To address these limitations, we propose a novel framework that integrates handcrafted and automatic feature extraction techniques for improved classification of Ph-negative myeloproliferative neoplasms. Handcrafted features capture interpretable morphological and textural characteristics. In contrast, automatic features utilize deep learning models to identify complex patterns in histopathological images. The extracted features were used to train machine learning models, with hyperparameter optimization performed using Optuna. Our framework achieved high performance across multiple metrics, including precision, recall, F1 score, accuracy, specificity, and weighted average. The concatenated probabilities, which combine both feature types, demonstrated the highest mean weighted average of 0.9969, surpassing the individual performances of handcrafted (0.9765) and embedded features (0.9686). Statistical analysis confirmed the robustness and reliability of the results. However, challenges remain in assuming normal distributions for certain feature types. This study highlights the potential of combining domain-specific knowledge with data-driven approaches to enhance diagnostic accuracy and support clinical decision-making. Full article

Genomic instability has been increasingly recognized over the past decade as a fundamental driver of cancer initiation and progression, largely owing to its association with specific genes and cellular mechanisms that offer therapeutic potential. However, a comprehensive molecular framework that captures the interconnected processes underlying this phenomenon remains elusive. In this study, we focused on polo-like kinase 1 (PLK1), a key cell cycle regulator frequently overexpressed in diverse human tumors, to reconstruct a regulatory network that consolidates pre-existing biological knowledge exclusively related to pathways involved in genome stability maintenance and cancer. The resulting model integrates nine biological processes, 1030 reactions, and 716 molecular species to form a literature-supported network in which PLK1 serves as a central regulatory node. However, rather than depicting an isolated PLK1-centric system, this network reflects a broader and more complex architecture of interrelated genomic instability mechanisms. As expected, the simulations reproduced known behaviors associated with PLK1 dysregulation, reinforcing the well-established role of the kinase in genome destabilization. Importantly, this model also enables the exploration of additional, less-characterized dynamics, including the potential involvement of genes such as kif2c, incenp, and other regulators of chromosomal segregation and DNA repair, which appear to contribute to instability events downstream of PLK1. While these findings are grounded in mechanistic simulations and require further experimental validation, gene expression and survival analyses across tumor types support their clinical relevance by linking them to poor prognosis in specific cancers. Overall, the model provides a systemic and adaptable foundation for studying PLK1-related genomic instability, enabling both the reinforcement of known mechanisms and discovery of candidate genes and circuits that may drive tumorigenesis through compromised genome integrity across distinct cancer contexts. Full article

The availability of genome sequences and single-nucleotide polymorphism (SNP) chips allows us to investigate the various genomic characteristics of species by exploring genetic diversity to aid genetic selection. The SNP chip is a cost-effective genotyping platform essential for molecular breeding of livestock. In this study, we developed a liquid SNP capture chip suitable for five Hubei (China) indigenous beef cattle breeds based on whole-genome sequencing datasets. The panel consisted of 42,686 SNPs (~40 K). These SNPs were evenly distributed on each bovine chromosome, with the majority of SNPs having minor allele frequencies >0.05 and located within intergenic regions. The performance evaluation of this SNP chip panel was proceeded by genotyping 200 individuals, revealing that this panel has a high SNP call rate of 99.48%. The SNP chip panel was further used to examine the population structure of a beef cattle population with 205 individuals and demonstrated the ability to differentiate between foreign and indigenous cattle breeds. The SNP chip was also used to determine the runs of homozygosity (ROH) within a local Hubei beef cattle population of 195 individuals. We identified 2547 ROH and several genes associated with economically important traits in the study population. Our findings demonstrate that this chip not only contributes to the understanding of the genetic characteristics of local beef cattle breeds but also provides valuable genetic information for future breeding programs, thereby improving their production efficiency and economic value. Full article

Genome-editing technology has advanced significantly since the 2020 Nobel Prize in Chemistry was awarded for the development of clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated protein 9 (Cas9). While CRISPR–Cas9 has become widely used in academic research, its social implementation has lagged due to unresolved patent disputes and slower progress in gene function analysis. To address this, new approaches bypassing direct gene function analysis are needed, with bioinformatics and next-generation sequencing (NGS) playing crucial roles. NGS is essential for sequencing the genome of target species, but challenges such as data quality, genome heterogeneity, ploidy, and small individual sizes persist. Despite these issues, advancements in sequencing technologies, like PacBio high-fidelity (HiFi) long reads and high-throughput chromosome conformation capture (Hi-C), have improved genome sequencing. Bioinformatics contributes to genome editing through off-target prediction and target gene selection, both of which require accurate genome sequence information. In this review, I will give updates on the development of genome editing and bioinformatics technologies with a focus on the rapid progress in genome sequencing. Full article

Avermectin is a widely used insecticide for pest control, such as the Plutella xylostella. Despite its efficacy in pest management, concerns have been raised regarding its effect on non-target species, such as the important economic insect silkworm (Bombyx mori). We aimed to investigate the effects of avermectin application on the 3D genome architecture of silkworm midgut using high-throughput techniques such as high-throughput chromosome conformation capture (Hi-C) coupled with RNA sequencing (RNA-seq) and Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq). Midgut tissue samples, during the fifth instar and three days, were collected from silkworm larvae fed mulberry leaves (control group) and leaves soaked in avermectin solution for 12 h (experimental group). Our findings revealed that avermectin treatment led to significant changes, including 386 differentially expressed genes (252 up-regulated, 134 down-regulated) and increased chromatin accessibility, particularly those involved in immune response, metabolism, and cellular stress pathways. The Hi-C data revealed more intense spatial interactions in the experimental group, leading to increased expression of detoxification proteins like ABC transporter. This study provides important insights into the molecular basis of pesticide resistance and a foundational basis for further research on the genetic and epigenetic mechanisms of insect stress responses. Full article

The 3D organization of chromatin in the nucleus plays a critical role in regulating gene expression and maintaining cellular functions in eukaryotic cells. High-throughput chromosome conformation capture (Hi-C) and its derivative technologies have been developed to map genome-wide chromatin interactions at the population and single-cell levels. However, insufficient sequencing depth and high noise levels in bulk Hi-C data, particularly in single-cell Hi-C (scHi-C) data, result in low-resolution contact matrices, thereby limiting diverse downstream computational analyses in identifying complex chromosomal organizations. To address these challenges, we developed a transformer-based deep learning model, HiCENT, to impute and enhance both scHi-C and Hi-C contact matrices. Validation experiments on large-scale bulk Hi-C and scHi-C datasets demonstrated that HiCENT achieves superior enhancement effects compared to five popular methods. When applied to real Hi-C data from the GM12878 cell line, HiCENT effectively enhanced 3D structural features at the scales of topologically associated domains and chromosomal loops. Furthermore, when applied to scHi-C data from five human cell lines, it significantly improved clustering performance, outperforming five widely used methods. The adaptability of HiCENT across different datasets and its capacity to improve the quality of chromatin interaction data will facilitate diverse downstream computational analyses in 3D genome research, single-cell studies and other large-scale omics investigations. Full article

Background: Colorectal cancer (CRC) is characterized by the uncontrolled growth of malignant colonic or rectal crypt epithelium. About 85% of CRCs evolve through a stepwise progression from advanced precancerous adenoma lesions. A better understanding of the evolution from adenoma to carcinoma can provide a window of opportunity not only for early detection and therapeutic intervention but potentially also for cancer prevention strategies. Methods: This study investigates the heterogeneous methylation, copy-number alteration (CNA), and mutation signals of histological adenoma subtypes in the context of progression from normal colon to advanced precancerous lesions (APLs) and early-stage CRC. Results: Differential methylation analysis revealed 2321 significantly altered regions among APLs: 137 hypermethylated regions in serrated vs. tubular, 2093 in serrated vs. tubulovillous, and 91 in tubular vs. tubulovillous adenoma subtypes. The most differentiating pathways for serrated adenomas belonged to cAMP signaling and the regulation of pluripotency of stem cells, while regions separating tubular and tubulovillous subtypes were enriched for WNT signaling. CNA events were mostly present in tubular or tubulovillous adenomas, with the most frequent signals being seen in chromosomes 7, 12, 19, and 20. In contrast, early-stage CRC exhibited signals in chromosomes 7, 8, and 20, indicating different processes between APL and early-stage CRC. Mutations reinforce subtype-level differences, showing specific alterations in each subtype. Conclusions: These findings are especially important for developing early detection or cancer prevention tests trying to capture adenoma signatures. Full article

Conventional cancer drugs are small molecules that target specific pathways. We introduced PCMS, a 26 kDa supramolecule combining sensors (S), molecular motors (M), and switching molecules (C), integrated within a fourth-generation PAMAM structure (P). PCMS identifies and deactivates cancer cell nucleus dynamics. A decade ago, we demonstrated programmable, clock-like interactions among the S-C-M components. In this study, we captured images of fractal patterns formed by chromosomal compartments and developed a theoretical model of their fractal dynamics. We showed that the nucleus behaves like a cavity, producing resonance effects similar to Chladni patterns. When the external agent, PCMS, interacts with this cavity, it generates a fractal pattern. We identified and mapped five key phase transitions that ultimately lead to the breakdown of cancer cell nuclei. Full article

(1) Background: Fetal chromosomal examination is a critical component of modern prenatal testing. Traditionally, maternal serum biomarkers such as free β-human chorionic gonadotropin (Free β-HCG) and pregnancy-associated plasma protein A (PAPPA) have been employed for screening, achieving a detection rate of approximately 90% for fetuses with Down syndrome, albeit with a false positive rate of 5%. While amniocentesis remains the gold standard for the prenatal diagnosis of chromosomal abnormalities, including Down syndrome and Edwards syndrome, its invasive nature carries a significant risk of complications, such as infection, preterm labor, or miscarriage, occurring at a rate of 7 per 1000 procedures. Beyond Down syndrome and Edwards syndrome, other chromosomal abnormalities, such as trisomy of chromosomes 9, 16, or Barr bodies, pose additional diagnostic challenges. Non-invasive prenatal testing (NIPT) has emerged as a powerful alternative for fetal genetic screening by leveraging maternal blood sampling. However, due to the extremely low abundance of fetal cells in maternal circulation, NIPT based on fetal cells faces substantial technical challenges. (2) Methods: Fetal nucleated red blood cells (FnRBCs) were first identified in maternal circulation in a landmark study published in The Lancet in 1959. Due to their fetal origin and presence in maternal peripheral blood, FnRBCs represent an ideal target for non-invasive prenatal testing (NIPT). In this study, we introduce a novel self-assembled cell array (SACA) chip system, a microfluidic-based platform designed to efficiently settle and align cells into a monolayer at the chip’s base within five minutes using lateral flow dynamics and gravity. This system is integrated with a fully automated, multi-channel fluorescence scanning module, enabling the real-time imaging and molecular profiling of fetal cells through fluorescence-tagged antibodies. By employing a combination of Hoechst+/CD71+/HbF+/CD45− markers, the platform achieves the precise enrichment and isolation of FnRBCs at the single-cell level from maternal peripheral blood. (3) Results: The SACA chip system effectively reduces the displacement of non-target cells by 31.2%, achieving a single-cell capture accuracy of 97.85%. This isolation and enrichment system for single cells is well suited for subsequent genetic analysis. Furthermore, the platform achieves a high purity of isolated cells, overcoming the concentration detection limit of short tandem repeat (STR) analysis, demonstrating its capability for reliable non-invasive prenatal testing. (4) Conclusions: This study demonstrates that the SACA chip, combined with an automated image positioning system, can efficiently isolate single fetal nucleated red blood cells (FnRBCs) from 50 million PBMCs in 2 mL of maternal blood, completing STR analysis within 120 min. With higher purification efficiency compared to existing NIPT methods, this platform shows great promise for prenatal diagnostics and potential applications in other clinical fields. Full article

Background: The enhancer–promoter interaction (EPI) is a critical component of gene regulatory networks, playing a significant role in understanding the complexity of gene expression. Traditional EPI prediction methods focus on one-to-one interactions, neglecting more complex one-to-many and many-to-many patterns. To address this gap, we utilize graph neural networks to comprehensively explore all interaction patterns between enhancers and promoters, capturing complex regulatory relationships for more accurate predictions. Methods: In this study, we introduce a novel EPI prediction framework, GATv2EPI, based on dynamic graph attention neural networks. GATv2EPI leverages epigenetic information from enhancers, promoters, and their surrounding regions and organizes interactions into a network to comprehensively explore complex EPI regulatory patterns, including one-to-one, one-to-many, and many-to-many relationships. To avoid overfitting and ensure diverse data representation, we implemented a connectivity-based sampling method for dataset partitioning, which constructs graphs for each chromosome and assigns entire connected subgraphs to training or test sets, thereby preventing information leakage and ensuring comprehensive chromosomal representation. Results: In experiments conducted on four cell lines—NHEK, IMR90, HMEC, and K562—GATv2EPI demonstrated superior EPI recognition accuracy compared to existing similar methods, with a training time improvement of 95.29% over TransEPI. Conclusions: GATv2EPI enhances EPI prediction accuracy by capturing complex topological structure information from gene regulatory networks through graph neural networks. Additionally, our results emphasize the importance of epigenetic features surrounding enhancers and promoters in EPI prediction. Full article

(1) Background: Chitons (Mollusca, Polyplacophora) are relatively primitive species in Mollusca that allow the study of biomineralization. Although mitochondrial genomes have been isolated from Polyplacophora, there is no genomic information at the chromosomal level; (2) Methods: Here we report a chromosome-level genome assembly for Acanthochiton rubrolineatus using PacBio (Pacific Biosciences, United States) reads and high-throughput chromosome conformation capture (Hi-C) data; (3) Results: The assembly spans 1.08 Gb with a contig N50 of 3.63 Mb and 99.97% of the genome assigned to eight chromosomes. Among the 32,291 predicted genes, 76.32% had functional predictions. The divergence time of Brachiopoda and Mollusca was ~550.8 Mya (million years ago), and that of A. rubrolineatus and other mollusks was ~548.5 Mya; (4) Conclusions: This study not only offers high-quality reference sequences for the Acanthochiton rubrolineatus genome, but also establishes groundwork for investigating the mechanisms of Polyplacophora biomineralization and its evolutionary history. This research will aid in uncovering the genetic foundations of molluscan adaptations across diverse environments. Full article

Clustered regularly interspaced short palindromic repeats (CRISPR)-Cas (CRISPR-associated proteins) systems provide acquired heritable protection to bacteria and archaea against selfish DNA elements, such as viruses. These systems must be tightly regulated because they can capture DNA fragments from foreign selfish elements, and also occasionally from self-chromosomes, resulting in autoimmunity. Most known species from the halophilic archaeal genus Haloferax contain type I-B CRISPR-Cas systems, and the strongest hotspot for self-spacer acquisition by H. mediterranei was a locus that contained a putative transposable element, as well as the gene HFX_2341, which was a very frequent target for self-targeting spacers. To test whether this gene is CRISPR-associated, we investigated it using bioinformatics, deletion, over-expression, and comparative transcriptomics. We show that HFX_2341 is a global transcriptional regulator that can repress diverse genes, since its deletion results in significantly higher expression of multiple genes, especially those involved in nutrient transport. When over-expressed, HFX_2341 strongly repressed the transcript production of all cas genes tested, both those involved in spacer acquisition (cas1, 2 and 4) and those required for destroying selfish genetic elements (cas3 and 5–8). Considering that HFX_2341 is highly conserved in haloarchaea, with homologs that are present in species that do not encode the CRISPR-Cas system, we conclude that it is a global regulator that is also involved in cas gene regulation, either directly or indirectly. Full article