Insects across different orders have convergently evolved adaptations to toxic cardiac glycosides (CGs), which are derived either from their diet or via endogenous synthesis. Previous studies on CG-resistance focused on changes in ATPα that is the direct inhibition target of CGs. Adaptation of whole organisms to toxic CGs could involve orchestrated changes at multiple genes and at multiple biological levels. Here, we explore this possibility by using whole genome sequences to identify several signatures of molecular convergence across multiple CG-adapted species. We identify gene families that changed convergently in CG-adapted species, including one member of stable fatty acyl-CoA reductase, CG5065, carboxylesterases and gustatory receptors that expanded in two of the three species. We find a number of candidate genes under positive selection in all CG-adapted species. We also identify convergent amino acid substitutions that have independently evolved in CG-adapted insects, including a conserved gene involved in the septate junction, Gliotactin (Gli). We used CRISPR-Cas9 to generate viable, homozygous Gli knock-in Drosophila lines with the convergent substitution. Through egg-larva and larva-adult survival experiments, we found that mutant flies consistently exhibit a lower survival rate compared to wild-type lines. Transmission electron microscopy (TEM) analysis of stage 17 embryos showed that in Gli mutants, the dihedral angles of bicellular membranes near the tricellular junction (TCJ) were unequal, and electron-dense materials were absent in the TCJ center. We propose that this convergently evolved Gli variant may contribute to CG adaptation by modulating epithelial permeability, potentially facilitating the sequestration of toxic CGs.

The ancestral recombination graph (ARG) is the model of choice in statistical genetics to model population ancestries. Software capable of inferring ARGs on a genome scale within a reasonable amount of time are now widely available for most practical use cases. While the inverse problem of inferring ancestries from a sample of haplotypes has seen major progress in the last decade, it does not enjoy the same level of advancement as its counterpart. Up until recently, even moderately sized samples could only be handled using heuristics. In recent years, the possibility of model-based inference for datasets closer to "real world" scenarios has become a reality, largely due to the development of threading-based algorithms. This article introduces Moonshine.jl, a Julia package that has the ability, among other things, to infer ARGs for samples of thousands of human haplotypes of sizes on the order of hundreds of megabases within a reasonable amount of time. On recent hardware, our package is able to infer an ARG for samples of densely haplotyped (over one marker/kilobase) human chromosomes of sizes up to 10,000 in well under a day on data simulated by msprime. Scaling up simulation on a compute cluster is straightforward since each ARG is inferred independently using a single thread. While model-based, it does not resort to threading but rather places restrictions on probability distributions typically used in simulation software in order to enforce sample consistency. In addition to being efficient, a strong emphasis is placed on ease of use and integration into the biostatistical software ecosystem.

Uncharted microbe-drug relationships constitute an under-exploited reservoir of therapeutic leads. In this manuscript, we introduced a hybrid framework named BRMDA by coupling a bilinear attention network with a random-forest classifier to systematically expose latent microbe-drug associations. Firstly, BRMDA integrated multiple drug-centric, microbe-centric, and disease-centric similarity profiles, along with experimentally validated microbe-drug associations, to construct a unified heterogeneous graph. And then, the bilinear attention network and random-forest classifier were employed to compute the predicted scores for potential microbe-drug associations based on the newly constructed unified heterogeneous graph. Next, benchmarking experiments were conducted under a rigorous five-fold cross-validation protocol using the MDAD dataset to validate the prediction performance of BRMDA. Additionally, case studies were further performed, focusing on front-line antibiotics including amoxicillin and ciprofloxacin as well as clinically relevant pathogens including Bacillus cereus and Mycobacterium tuberculosis, to evaluate the translational validity of the proposed model. Intensive experimental results demonstrated that BRMDA outperformed seven state-of-the-art competitors in terms of both AUC and AUPR, and 9 out of the top 10 associations predicted by the model were corroborated by independent literature evidence. These findings underscored the accuracy and translational potential of BRMDA, offering a data-driven compass for antimicrobial discovery and microbe-oriented therapeutic design.

m 1 A modification, as a pivotal RNA epigenetic modification, plays a central regulatory role in the pathogenesis and progression of complex human diseases, including cancer. Exploring the potential associations between m 1 As and diseases are an important approach to revealing the molecular mechanism of disease onset. However, traditional biological experiments have the limitations of time-consuming and labor-intensive, resulting in an extremely scarce amount of verified m 1 A-disease association data. Meanwhile, the existing computational prediction methods are mostly limited to specific application scenarios and rely solely on the direct correlation data between m 1 As and diseases. They do not fully integrate multi-dimensional biological information and thus are unable to achieve efficient and accurate association predictions. In view of this, this study proposes a method for predicting the association between m 1 A modification and diseases based on a ternary heterogeneous network and GCN. By introducing circRNA as an intermediate connection node, a ternary association network of m 1 A-circRNA-disease is constructed, which effectively enriches the dimension of feature information for both m 1 A and diseases. Meanwhile, leveraging the feature learning capability of Graph Convolutional Network, the extraction and representation of their features are realized. The experimental results demonstrate that the proposed approaches significantly outperforms existing mainstream methods in predictive performance, substantially enhancing the accuracy and reliability of m 1 A-disease association prediction. Furthermore, case validation has further confirmed that the predicted candidate m 1 A sites participate in regulating disease-related gene expression networks by modulating core processes such as RNA localization, stability, and translation efficiency, thereby providing novel insights into the investigation of disease pathogenesis.

Objective: Eclampsia severely endangers maternal and neonatal health, being a major contributor to emergency admissions, maternal mortality, and long-term complications. This study aimed to identify reliable biomarkers and explore potential therapeutic targets for improving the diagnosis, prevention, and management of eclampsia.Methods: Differential gene expression analysis was performed on the GSE60438 dataset. Weighted Gene Co-expression Network Analysis (WGCNA) was used to construct gene modules and screen modules associated with pre-eclampsia. Gene Ontology (GO) and Gene Set Enrichment Analysis (GSEA) were employed to annotate the biological functions and pathways of candidate genes. Immune cell infiltration was evaluated via the xCell algorithm. LASSO regression was utilized to identify hub genes, which was validated by RT-qPCR and Western blot in clinical samples (placental tissues and serum from pre-eclampsia patients). DPP4 knockdown experiments were conducted in HTR-8 cells to assess its effects on pro-inflammatory cytokines (IL-6, TNF-α) and trophoblast cell functions (migration, invasion, lumen formation). Additionally, the p65/NLRP3/ASC/Caspase-1 signaling pathway was examined to clarify the underlying molecular mechanism.Results: A total of 4,642 upregulated and 2,193 downregulated genes were identified in pre-eclampsia samples. WGCNA revealed nine gene modules, with the red module showing the strongest positive correlation and the magenta module exhibiting a negative correlation with pre-eclampsia. GO analysis indicated enrichment of candidate genes in chromosome organization, mitochondrial function, and DNA repair. GSEA identified key immune-related pathways, including cytokine production and chemokine signaling. LASSO regression pinpointed DPP4 as a hub gene, which was significantly upregulated in pre-eclampsia clinical samples. DPP4 knockdown in HTR-8 cells reduced IL-6 and TNF-α levels, impaired trophoblast migration, invasion, and lumen formation, and inhibited the phosphorylation of p65, NLRP3, ASC, and Caspase-1 in the p65/NLRP3/ASC/Caspase-1 signaling pathway.Conclusion: Targeting DPP4 may serve as an innovative strategy for regulating inflammatory signaling in eclampsia, with potential to alleviate maternal symptoms and improve pregnancy outcomes.

The field of human and medical genomics is undergoing two major transformations (Figure 1). First, data density and dimensionality is increasing. Second, a combination of new and old analytical techniques-notably empowered by artificial intelligence (AI)-are enabling extraction of mechanistic insight and knowledge from these data. In this editorial we highlight a fraction of the many key papers recently published in this field, with a particular emphasis on those that were invited to our research topic with speculative insights based on literature review or research that reflected state-of-the-art methods, detailed recent developments, or highlighted pivotal accomplishments. A focus of this research topic was to showcase progress and pinpoint hurdles that need to be surmounted to push the boundaries of what is medically and technically feasible in genetics.Two years ago, at the launch of this research topic, we envisioned potential advances in the following categories: (1) novel genomic technologies and their clinical application; (2) genetic determinants of complex diseases; (3) ethical, legal, and social implications of genomic research; (4) personalized medicine and genomic tailoring; (5) advances in genomic sequencing techniques; and (6) integration of genomic data with AI and machine learning approaches. These fit into a broader arc of progress in our field. Human genomics fundamentally starts with the genome and ends with personalized medicine. Variant annotation remains the foundation of human genomics and must be built on careful observation of patients 1 reported on the scale of case reports 2 all the way up to population compilations. 3,4 AlphaMissense provides an example of advancing sophistication in variant annotation (https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2024.1487608/full). The insight is that variant annotation cannot end without consideration of complex genomic context. To advance mechanistic insights the field will increasingly incorporate epistasis, 5 synergies, population and ancestry context, 6 and whole genome interactions into this functional foundation. Viewing the genome as a web of interactions, and not as a linear construct is a necessary start. Pangenome representations (https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2025.1679660/full) illustrate an important step in this direction. Furthermore, The Peruvian Genome Project (https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2025.1614021/full) highlights the importance of worldwide pan-population data generation coupled to FAIR data governance (https://www.go-fair.org/fair-principles/). Once a foundation of data is generated, it must be analyzed. The insight is that this analysis must be increasingly mechanistic and holistic, focusing on Self-critique of the field must address major questions:Are current omics data collections big enough? Dense data drives twenty-first century research. 10 Even though modern data sets may comprise data on thousands of variables (e.g., a combination of plasma proteomics and metabolomics), these may not be enough to truly illuminate the dynamic state of the entire human systems and its environment. A combination of increasing technological efficiency, brute force of scale, and careful hypothesis driven selection of assayed biological targets will be necessary to drive complete insights (e.g., shifting from left to right in Figure 1). Are we examining enough dimensions? Many analyses focus on genomics coupled to blood proteomics, metabolomics, & epigenetics. The microbiome is gaining increasing attention. For better systems understanding, we will need to look at multiple tissues and cells (and their compartments) simultaneously, and to measure the entire system dynamically over time. Larger, less biased, recruitment must occur in clinical trials and studies. Continuous monitoring devices and solutions that enable remote trials with massive population participation will drive the future.As analyses get increasingly complex, it becomes more important to carefully design controls, and to red-team (systematically testing for flaws or biases) the computational workflows. Control analyses limit the ways our complex workflows can fool us into conclusions not supported by the data. The safeguards provided by controls become critically important given the widespread availability of AI-and ML-tools that automate data handling without necessarily exercising judgment or understanding. We must use systems thinking and rational epistemological approaches such as Hill's criteria to arrive at medically useful translatable knowledge. 7Are we creating papers and disseminating data that will be part of large meta-analyses? As research becomes increasingly collaborative and cross-discipline, we must write manuscripts that will be accessible and understood by lay people, students, experts, and AI agents. Datasets must be publicly available, easing any barriers to high-throughput automated access, following the FAIR

X-rays (XR) are electromagnetic waves capable of inducing significant biological effects in living organisms. Although widely used in medicine and industry, the impact of low-dose XR exposure on human health remains insufficiently characterized. XR can generate direct and indirect DNA damage such as single- and double-strand breaks, base modifications, and DNA-protein crosslinks, leading to chromosomal alterations that disrupt cellular homeostasis and may contribute to disease development. Although previous studies have reported general increases in cytogenetic damage at low exposures, they seldom provide detailed descriptions of which chromosomes are most affected, which structural or numerical alterations predominate, or how frequently each alteration occurs. This study aimed to characterize the type and frequency of chromosomal alterations and the spectrum of genetic damage, including both clonal and non-clonal alterations, in human lymphocytes exposed in vitro to a low X-ray dose (94.33mGy), using non-exposed samples as controls. Peripheral blood was collected from 12 healthy donors, and genetic damage was assessed using GTG-banding cytogenetics and the cytokinesis-block micronucleus assay. Irradiated samples exhibited a significantly higher frequency of chromosomal alterations and fragile sites compared with their respective controls (p ≤ 0.0093). Among numerical alterations, monosomies were the most frequent, with chromosomes 8 and 21 being the most commonly affected, detected in 50% of irradiated samples. Structural chromosomal alterations predominantly involved chromosomes 11, 16, and 17, while recurrent deletions included del(15)(q22) and del(16)(q12). Among heterochromatic variants, chtb(9)(q12) was the most frequent, and fra(9)(q12) represented the most prevalent fragile site. MN frequency increased significantly after irradiation (p = 0.0214), and women exhibited higher MN frequencies than men regardless of treatment (p = 0.0224). Overall, these findings indicate that low-dose XR exposure is associated with detectable chromosomal damage and underscore the relevance of biosafety practices and cytogenetic monitoring approaches in contexts involving XR exposure, even at doses traditionally considered safe.

This report describes a patient with a <i>de novo</i> missense variant in the N-terminal zinc-finger domain of <i>ZEB2</i> who presented with neurodevelopmental delay, atypical facial features, and recurrent respiratory infections, alongside a reduction in infection frequency during zinc supplementation. The variant is classified as likely pathogenic, and these observations expand the phenotypic variability potentially associated with <i>ZEB2</i> variants. Additional cases and functional studies are required to confirm any causal link between the variant, the observed phenotype, and the effects of zinc supplementation.

Over the past decade, NGS technology has transformed the way to study and explore genomics, epigenomics, and transcriptomics. Improvements in throughput, and accuracy now allow researchers to move from bulk analyses to single-cell and high-resolution approaches, uncovering cellular heterogeneity and dynamic gene regulation that were previously unattainable. These advances, however, have amplified challenges associated with the broad range of applications feeding into sequencing technologies, including sample preparation, data management, integrative analysis, and interpretation. The articles in this special issue collectively address these challenges, offering solutions that advance both fundamental research and translational applications. Reliable sample preparation remains the foundation of any sequencing study. Bentz et al. present optimized high-throughput PacBio workflows for genomic DNA and Threose Nucleic Acid (TNA), demonstrating improvements in throughput, cost-efficiency, and analysis resolution. These methodological innovations illustrate how early-stage experimental improvements directly influence what can be measured and understood downstream. As data volume and complexity grow, effective management and accessibility become increasingly critical for the researchers. Large publicly available repositories, such as The Cancer Genome Atlas (TCGA), have become essential for integrative multi-omic studies, yet accurate data extraction and preprocessing remain critical to unlock their full potential. Baumann et al. address this challenge by introducing a tool that simplifies access to TCGA datasets, thus improving data retrieval and harmonization, and enabling reproducible analyses across genomics, epigenomics, and transcriptomics studies. The work highlights the importance of precise data handling to gain robust insights from multi-omic repositories. Computational strategies are central to interpreting high-dimensional datasets, particularly in single-cell studies where technical noise, sparsity, and batch effects complicate analysis. In this context, Elshiekh et al. review how deep learning and advanced computational models help in extracting meaningful biological signals from high-dimensional single-cell datasets, underlining the importance of integrating experimental and computational expertise to interpret complex omics data effectively. Complementing these strategies, Zhang et al. focus on transcriptional bursting models, demonstrating how modeling assumptions influence our understanding of gene expression variability and its relationship to phenotypic diversity and disease. Quantitative frameworks like these allow researchers to move from descriptive observations to mechanistic insights. Translating sequencing insights into clinical contexts remains a translational gap. Lin et al. investigate RNA N6-methyladenosine methylation and gene expression in colorectal cancer, using an integrative transcriptome-wide approach to uncover regulatory mechanisms directly relevant to precision oncology. Together with genomic and epigenomic analyses, RNA-focused studies provide a more comprehensive view of molecular regulation and help bridge molecular insight with the clinical application of high-resolution sequencing approaches. Similarly, single-cell technologies are also approaching clinical maturity, enabling detailed characterization of patient-specific cellular heterogeneity. The translation into clinical practice requires robust, adaptable pipelines that integrate high-resolution data with computational models and curated databases, providing a pathway toward biomarker discovery and personalized therapies. Taken together, the contributions to this Research Topic illustrate how sequencing has become the common integrative backbone of diverse high-throughput approaches, evolving from a data-generation tool into a framework for biological discovery and translational research. By advancing experimental protocols, improving data accessibility, and developing computational frameworks, these studies represent a step toward a more integrated and clinically relevant understanding of molecular biology.

ctDNA is a promising biomarker in oncology. However, its prognostic and predictive value in HCC remains underexplored. This meta-analysis aims to evaluate the prognostic impact of ctDNA in HCC and its predictive value for recurrence. A systematic review and meta-analysis were performed following PRISMA guidelines. PubMed, Embase, Web of Science, and CNKI were searched up to 1 June 2025, for studies assessing ctDNA in HCC patients with reported survival outcomes or predictive accuracy. Studies reporting hazard ratios for overall or disease-free survival, or AUCs for prediction, were included. Two reviewers independently screened studies and assessed quality using the Newcastle-Ottawa Scale (NOS). Meta-analyses used random- or fixed-effects models depending on heterogeneity, with sensitivity analyses performed to assess robustness. A total of 219 records were screened from PubMed, Embase, Web of Science, and CNKI, and 8 studies comprising 1,907 patients were included. ctDNA positivity was significantly associated with poorer OS, with a pooled HR of 2.34 (95% CI 1.96-2.78; p < 0.0001). Moderate heterogeneity was observed (I2 = 32.2%). Sensitivity analyses confirmed the robustness of this finding. Two studies assessed the predictive value of ctDNA for RFS, yielding a pooled AUC of 0.66 (95% CI 0.47-0.86; I2 = 65.7%). Discriminative accuracy was higher when ctDNA was detected postoperatively (AUC range: 0.57-0.77), suggesting its potential role in identifying minimal residual disease. ctDNA is associated with adverse prognosis in HCC and may offer moderate predictive accuracy for recurrence. Standardized protocols for sampling and analysis are required to facilitate broader clinical translation.