Front Genet [JOURNAL]
Sun
200 papers
Charting the immune terrain: a novel risk model for thyroid cancer prognosis.
Qi Q, Cai X, Lv Q
Front Genet · 2026 · PMID 42100346 · Full text
OBJECTIVES: To construct a prognostic risk model for thyroid cancer based on immune genes and analyze the correlation between immune genes and immune infiltration. METHODS: A retrospective study was conducted on 180 pati...
OBJECTIVES: To construct a prognostic risk model for thyroid cancer based on immune genes and analyze the correlation between immune genes and immune infiltration. METHODS: A retrospective study was conducted on 180 patients with thyroid cancer treated in our hospital during May 2022 to April 2025. Based on the prognosis, the subjects were graded as good prognosis group of 126 cases and poor prognosis group of 54 cases. The influencing factors were analyzed by a binary logistic regression model, receiver operating characteristic curve and goodness of fit test. Single sample gene set enrichment analysis was used to perform immune infiltration analysis on the expression matrix of peripheral blood mononuclear cells. The GSEA algorithm was used to calculate the abundance of tumor associated immune cell infiltration. Pearson correlation analysis was used to investigate the correlation. The TCGA-THCA database was used to analyze the differential expression of genes, as well as the correlation with clinical pathological features. RESULTS: The expression levels of CDK1, B3GNT7, S100A9, and MMP9 genes were higher in the poor prognosis group than the good prognosis group ( < 0.05). A prognostic prediction model was constructed according to formula [1/1 + exp (4.125 + 1.250 × CDK1 + 1.880 × B3GNT7 + 0.920 × S100A9 + 1.050 × MMP9)]. The average C-index of the model was 0.919 (95% CI: 0.882-0.961). The AUC of the prognosis prediction model was 0.880. The poor prognosis group had much lower infiltration abundance of B lymphocytes, CD4T lymphocytes, and CD8T lymphocytes, and higher infiltration abundance of neutrophils and macrophages than the good prognosis group ( < 0.05). CDK1, B3GNT7, S100A9, and MMP9 were negatively correlated with the infiltration abundance of B lymphocytes, CD4T lymphocytes, and CD8T lymphocytes, and positively correlated with the infiltration abundance of neutrophils and macrophages ( < 0.05). Further analysis from the TCGA-THCA database showed that the high expression of S100A9 and MMP9 was correlated with advanced lymph node metastasis (pN stage), distant metastasis (pM stage) and overall TNM stage (). CONCLUSION: CDK1, B3GNT7, S100A9, and MMP9 were independent risk factors for poor prognosis in thyroid cancer. The prognostic prediction model may provide objective evidence for early screening of high-risk cases in clinical practice.
The role of pluripotency regulators in triple-negative breast cancer immune response.
López-Santana C, Mendez-Rivera F, Bernal-Estévez DA
Front Genet · 2026 · PMID 42100345 · Full text
Triple-negative breast cancer (TNBC) is defined by the absence of estrogen, progesterone, and HER2 receptor expression. A critical challenge in managing TNBC is its high concentration of cancer stem cells (CSCs), which d...
Triple-negative breast cancer (TNBC) is defined by the absence of estrogen, progesterone, and HER2 receptor expression. A critical challenge in managing TNBC is its high concentration of cancer stem cells (CSCs), which drives chemotherapy resistance and correlates with poor patient survival. In normal physiology, stem cell pluripotency and differentiation are governed by core transcription factors (such as Oct4, Sox2, Nanog, Klf4, and c-Myc) alongside key signaling networks, including the Notch, Wnt/β-catenin, and Sonic Hedgehog (Shh) pathways. During carcinogenesis, aberrant activation of these regulators in TNBC not only promotes the self-renewal of tumor cells but also actively facilitates immune evasion. Specifically, overexpressed pluripotency transcription factors enable cancer cells to downregulate antigen presentation molecules (e.g., MHC class I) and secrete immunomodulatory cytokines. Concurrently, dysregulated signaling, such as the Wnt/β-catenin pathway, inhibits dendritic cell maturation and recruits Myeloid-Derived Suppressor Cells (MDSCs) and regulatory T cells (Tregs) into the tumor microenvironment, thereby blunting the anti-tumor T cell response. This review examines the role of key pluripotency regulators in TNBC-mediated immune evasion, highlighting emerging immunotherapeutic strategies targeting these networks and summarizing current clinical research.
Integrated metabolomics and bioactivity analysis of root extract as a natural inducer of resistance to rice bacterial blight.
Mansoori A, Mohan M, Das SN … +2 more , Kumar R, Kumar A
Front Genet · 2026 · PMID 42095025 · Full text
BACKGROUND: Synthetic pesticides are widely used in agriculture to manage pests and reduce yield loss. Phytochemicals with antioxidant and antibacterial activities have great potential for treating plant diseases and red...
BACKGROUND: Synthetic pesticides are widely used in agriculture to manage pests and reduce yield loss. Phytochemicals with antioxidant and antibacterial activities have great potential for treating plant diseases and reducing the use of synthetic chemicals. Identifying compounds from various plant species is crucial for their potential agricultural applications. METHODS: In the present study, was screened for potential antioxidant, antimicrobial, and bacterial blight protection abilities. Methanol and aqueous extracts of root was tested for their polyphenol content, antioxidant potential, metabolomics and antimicrobial study. RESULTS: Results revealed that methanol extract exhibited higher phytochemical content and antioxidant activity. FTIR examination of extracts identified functional groups such as OH, C-H, C=C, and C-N, indicating the presence of distinct metabolites. The GC-MS investigation indicated the existence of 59 metabolites, several of which had previously been described as antimicrobial agents. Furthermore, antibacterial studies confirmed the antimicrobial effect of methanol extract against pv. (). Moreover, prediction of antimicrobial metabolites, particularly 7-hydroxy-4-methylcoumarin-3-acetic acid, was confirmed through molecular docking study with D-alanine-D-alanine ligase A (DdlA) and the peptide deformylase (PDF) protein of Finally, the study evaluated the effectiveness of m root extract against bacterial blight disease, finding a significant reduction in lesions in pre-treatment and also showing their efficacy in post-treatment. Effect of extract was also observed in the photosynthetic status of rice by measuring chlorophyll A fluorescence. CONCLUSION: is a promising plant for its versatile role as an antioxidant, antimicrobial, and bacterial blight disease protection in rice.
Nanopore-mA-finder, a novel mA site caller for Nanopore DRS data.
Yang Y, Yu L, Mo L … +3 more , Qi C, Song W, Jin H
Front Genet · 2026 · PMID 42095024 · Full text
INTRODUCTION: -methyladenosine (mA) is a pivotal RNA modification involved in diverse biological and pathological processes. Compared to the mA detection methods based on second-generation sequencing, Nanopore direct RN...
INTRODUCTION: -methyladenosine (mA) is a pivotal RNA modification involved in diverse biological and pathological processes. Compared to the mA detection methods based on second-generation sequencing, Nanopore direct RNA sequencing (DRS) offers the unique advantage of capturing native modifications. METHODS: Here, we present Nanopore-mA-Finder (NP-mFinder), a reference-free mA prediction computational framework that employs the XGBoost model in the mRNA exonic region and a hard-voting ensemble of XGBoost and random forest models in the poly(A) region. RESULTS AND DISCUSSION: NP-mFinder can determine mA sites as well as estimate their methylation levels from Guppy basecalled DRS data. After training with DRS data of in -transcribed RNA, NP-mFinder achieved high performance on held-out test datasets (area under the curve (AUC) ≈0.90; accuracy, precision, recall, and F1-score >0.80). Comparing with canonical m6A detection methods, it recovered 20% of meRIP-seq-defined m6A sites in yeast, and 27% of our HEK293 site prediction overlapped with miCLIP calls. Although single-base overlap with existing DRS-based tools of EpiNano and mAFiA was limited, 73% of our identified mA-containing genes were validated by at least one of them. Benchmarking our method with GLORI v2.0 revealed concordance of 28% at a site level and 85% at a gene level, as well as a mild correlation on mA level estimations. Notably, NP-mFinder achieved 93% precision in detecting mA within the "AAAAA" sequence context in the mRNA exonic region of HEK293T DRS data when compared to high-confidence mA site annotation in GLORI v2.0, demonstrating the good performance of our method in the region possessing a stretch of continuous A-sequences. Moreover, our method predicted that m6A might exist in the human HEK293 poly(A) region, suggesting a possibly conserved phenomenon of a modified poly(A) tail beyond the previously reported T. brucei variant surface glycoprotein (VSG) transcripts. Together, these results established NP-mFinder as a robust and versatile tool for transcriptome-wide m6A profiling with DRS data at single-read resolution.
A novel prognostic model for ovarian cancer: construction, validation, and therapeutic insights.
Yu T, Gao M, Yu Y … +1 more , Wang B
Front Genet · 2026 · PMID 42095023 · Full text
BACKGROUND: Ovarian cancer (OV) is the most lethal gynaecological malignancy worldwide. Palmitoylation, a reversible post-translational lipid modification, has been implicated in tumourigenesis, growth, metastasis and ap...
BACKGROUND: Ovarian cancer (OV) is the most lethal gynaecological malignancy worldwide. Palmitoylation, a reversible post-translational lipid modification, has been implicated in tumourigenesis, growth, metastasis and apoptosis across multiple cancers. However, its impact on immune infiltration, therapeutic response and clinical outcomes in ovarian cancer remains insufficiently explored. METHODS: We obtained transcriptome data and clinical information pertaining to ovarian cancer from the Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Gene Expression Omnibus (GEO) databases. A prognostic model based on palmitoylation-related genes was constructed using univariate Cox and Lasso-Cox regression for feature selection. The predictive performance of the model was assessed via Kaplan-Meier (KM) survival analysis and receiver operating characteristic (ROC) curve evaluation. RESULTS: We developed a five-gene prognostic prediction model utilizing palmitoylation-related genes derived from TCGA samples of epithelial ovarian cancer patients. The validity of this model was confirmed using patient samples from both TCGA and GEO datasets. KM analysis demonstrated that our prognostic model effectively distinguished between high-risk and low-risk groups, correlating with poorer or more favorable outcomes respectively. According to ROC curve analysis, our model exhibited superior predictive accuracy compared to traditional clinical factors alone. Additionally, analyses regarding immune cell infiltration, expression levels of immune checkpoints, as well as drug sensitivity further support potential treatment strategies for ovarian cancer. CONCLUSION: The prognostic model developed in this study has the potential to enhance our understanding of the role of palmitic acid-related genes in ovarian cancer, providing new insights into prognosis prediction and treatment strategies for patients with ovarian cancer.
Genomic basis of adaptation to cardiac glycosides in three insect orders.
Zhu K, Zhu C, Zhen Y
Front Genet · 2026 · PMID 42095022 · Full text
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...
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 (). We used CRISPR-Cas9 to generate viable, homozygous knock-in 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 variant may contribute to CG adaptation by modulating epithelial permeability, potentially facilitating the sequestration of toxic CGs.
Framework for assessing genetic variation in livestock using demographic, pedigree, and genomic measures.
Mészáros G, Curik I, Ouedraogo D … +9 more , Windig J, Leroy G, Utsunomiya YT, Burger P, Colli L, Xu C, Boettcher P, Looft C, Soelkner J
Front Genet · 2026 · PMID 42095021 · Full text
Genetic variation within livestock populations underpins global food security, resilience, and the long-term sustainability of breeding programs. Despite its fundamental role, harmonized approaches for assessing and moni...
Genetic variation within livestock populations underpins global food security, resilience, and the long-term sustainability of breeding programs. Despite its fundamental role, harmonized approaches for assessing and monitoring genetic variation across data sources remain limited. This review provides an integrated framework for assessing genetic variation in livestock using demographic, pedigree, and genomic data, developed by FAO experts and international collaborators. Demographic indicators offer essential insight into population size, sex ratio, and reproductive structure, while pedigree data allow detailed evaluation of genetic relatedness, inbreeding, and effective population size ( ) over time. Genomic information now provides unprecedented accuracy in characterizing allelic variation, population structure with admixture, and the dynamics of inbreeding and drift. Each data source differs in availability, resolution, and interpretive limits; therefore, complementary use of demographic, pedigree, and genomic measures is recommended for effective monitoring and decision-making. This framework outlines the main properties, applications, and constraints of these approaches and provides guidance on selecting appropriate indicators for monitoring genetic variation within and among livestock populations. Its implementation supports the objectives of the Global Plan of Action for Animal Genetic Resources and the Kunming-Montreal Global Biodiversity Framework, contributing to evidence-based management of livestock diversity worldwide.
Polydactyly and syndactyly in a Chinese family with Floating-Harbor syndrome: an expansion of the clinical phenotype.
Tang J, Cao Y, Huang D … +3 more , Wang C, Jiang X, Zhu J
Front Genet · 2026 · PMID 42095020 · Full text
Floating-Harbor syndrome (FLHS) is a rare neurodevelopmental and skeletal disorder caused by truncating variants in exons 33 and 34 of the gene. It is characterized by distinctive facial features, delayed bone age, shor...
Floating-Harbor syndrome (FLHS) is a rare neurodevelopmental and skeletal disorder caused by truncating variants in exons 33 and 34 of the gene. It is characterized by distinctive facial features, delayed bone age, short stature, and moderate intellectual disability. While digital anomalies have been reported in approximately half of the more than 100 known cases, the phenotypic spectrum continues to expand. Here, we describe a family in which two individuals were identified with FLHS. Both the proband and her mother presented with typical manifestations, including classic facial dysmorphism, short stature, intellectual disability, brachydactyly, and clinodactyly. Moreover, the proband exhibited a novel combination of polydactyly and syndactyly affecting the right fifth and sixth toes, a feature previously unreported in FLHS. Additionally, she had complications including anemia, feeding difficulties, recurrent infections, epilepsy, and thrombosis. Whole-exome sequencing identified a heterozygous c.7330C>T (p.Arg2444Ter) mutation in both affected individuals. The proband also harbored compound heterozygous mutations in (c.609G>A/p.Trp203Ter and c.565C>T/p.Arg189Cys), potentially explaining some extra-skeletal symptoms. In summary, this study describes the first case of FLHS concurrently presenting with both polydactyly and syndactyly. Our work broadens the known phenotypic range of this rare syndrome.
Editorial: Genetic underpinnings of Alzheimer's and Parkinson's: insights and innovations.
Marongiu R, Cordato D, Reiss AB
Front Genet · 2026 · PMID 42088780 · Full text
Abstract loading — click title to view on PubMed.
Correction: BRMDA: prediction model for potential microbe-drug associations based on bilinear attention networks and random forest.
Yu G, Chen F, Chen H … +5 more , Tang S, Liang M, Liu X, Zeng B, Wang L
Front Genet · 2026 · PMID 42088779 · Full text
[This corrects the article DOI: 10.3389/fgene.2026.1757318.].
[This corrects the article DOI: 10.3389/fgene.2026.1757318.].
Network-based multi-omics approaches to identify molecular signatures associated with pregnancy status in beef heifers.
Brown TC, Banerjee P, Dyce PW … +3 more , Falkenberg S, Rodning SP, Diniz WJS
Front Genet · 2026 · PMID 42083569 · Full text
Fertility is a multifactorial trait and a key determinant of productivity and sustainability in beef cattle production. Identifying molecular mechanisms and biomarkers associated with fertility could improve the predicti...
Fertility is a multifactorial trait and a key determinant of productivity and sustainability in beef cattle production. Identifying molecular mechanisms and biomarkers associated with fertility could improve the prediction of reproductive potential in beef heifers. Herein, by combining transcriptomic and proteomic data from peripheral white blood cells (PWBCs) collected before the time of artificial insemination (AI), we investigated molecular differences between fertile and subfertile beef heifers (n = 6 per group) classified based on their reproductive outcomes. RNA-Sequencing and untargeted proteomics identified 230 differentially expressed genes (DEGs; ≤ 0.05 and |log2FC| ≥ 0.5) and 70 differentially abundant proteins (DAPs; ≤ 0.05) between groups. Over-representation analyses revealed that these molecules were associated with cell cycle regulation, metabolism, and immune-related pathways, including chemokine and JAK-STAT signaling ( ≤ 0.01). Data integration revealed limited overlap between DEGs and DAPs ( and ). Among these, expression was previously reported to be progesterone-responsive, supporting its potential role in early pregnancy establishment. Network analyses revealed distinct regulatory patterns between groups (|r ≥ 0.95| and ≤ 0.05). At the transcript level, subfertile heifers exhibited increased connectivity, indicating potential compensatory transcriptional rewiring. We identified 92 regulatory impact factor (RIF) genes with potential modulatory roles, including . Epigenetic transcription factors, including and were also rewired, suggesting an interplay between hormone signaling and chromatin regulation that modulates transcript expression and consequently fertility outcomes. Our results show that PWBCs reflect systemic molecular changes associated with fertility status and represent a promising, non-invasive source for biomarker discovery. This integrative multi-omics approach provided novel insights into the regulatory networks underlying fertility in beef heifers, highlighting the value of integrating multi-omics to identify key pathways and molecular targets to improve reproductive efficiency in beef production systems.
Sez6l promotes neuropathic pain via Wnt5a/Ca pathways in dorsal root ganglion.
Hu L, Chen J
Front Genet · 2026 · PMID 42083568 · Full text
BACKGROUND: Neuropathic pain (NP) is a prevalent chronic pain disorder that severely impairs the physical and mental health of patients, affecting 6.9%-10% of the general population. The dorsal root ganglion (DRG) is a c...
BACKGROUND: Neuropathic pain (NP) is a prevalent chronic pain disorder that severely impairs the physical and mental health of patients, affecting 6.9%-10% of the general population. The dorsal root ganglion (DRG) is a crucial locus in the pathogenesis of NP. However, the underlying mechanisms by which DRGs contribute to this condition remain incompletely understood. METHODS: High-throughput sequencing data of DRGs was downloaded from the Gene Expression Omnibus (GEO) and integrated for analysis. Differential expression analysis combined with multiple machine learning methods was employed to identify candidate genes associated with NP in DRGs. The spared nerve injury (SNI) model was used to assess gene expression patterns. Small interfering RNA-mediated knockdown of the target gene was performed to evaluate its functional role. Bioinformatics analysis and chromatin immunoprecipitation (ChIP) experiments were conducted to explore the transcriptional regulation of the target gene. RESULTS: Sez6l was identified as a candidate gene upregulated in DRGs. In the SNI model, Sez6l was significantly upregulated. Knockdown of Sez6l reduced the expression levels of inflammatory cytokines (IL-6, TNF-α, and IL-1β) and alleviated mechanical allodynia and thermal hyperalgesia in SNI mice. Bioinformatics analysis and ChIP experiments suggested that Foxo1 may enhance the transcription and expression of Sez6l. Mechanistically, Sez6l promoted NP by activating the Wnt5a/Ca signaling pathway in DRGs. CONCLUSION: Our findings suggest that Sez6l, which is transcriptionally regulated by Foxo1, facilitates neuropathic pain through activating the Wnt5a/Ca signaling pathway in DRGs.
Case report: co-inheritance of familial lecithin-cholesterol acyltransferase deficiency and α-Thalassemia.
Zhu Y, Liu C, Tang W … +3 more , Jiang Y, Mao L, Huang M
Front Genet · 2026 · PMID 42080096 · Full text
BACKGROUND: Familial lecithin-cholesterol acyltransferase () deficiency and α-thalassemia are rare autosomal recessive disorders. Although both disease-causing genes reside on chromosome 16, their physical distance typic...
BACKGROUND: Familial lecithin-cholesterol acyltransferase () deficiency and α-thalassemia are rare autosomal recessive disorders. Although both disease-causing genes reside on chromosome 16, their physical distance typically results in independent inheritance in non-consanguineous populations. Co-inheritance of both conditions has not been previously reported. CASE PRESENTATION: A 50-year-old Chinese man with childhood-onset corneal opacity and long-standing anemia presented with 2 months of progressive lower limb edema. Laboratory evaluation revealed nephrotic syndrome and markedly reduced high-density lipoprotein cholesterol (HDL-C). Renal biopsy showed characteristic glomerular lipid deposition, confirming deficiency. Genetic testing identified a homozygous mutation (c.355G>C, p.Gly119Arg), with both parents confirmed as heterozygous carriers. The patient had severe microcytic hypochromic anemia that did not fully align with the mild hemolytic anemia typical of deficiency. Given parental consanguinity, expanded genetic testing revealed co-inheritance of α-thalassemia (: -SEA/αα), explaining the hematological phenotype. OUTCOME: No specific treatment exists for deficiency. Symptomatic management with angiotensin-converting enzyme inhibitors and diuretics improved edema. The α-thalassemia trait is asymptomatic and requires no intervention; its diagnosis avoided unnecessary iron therapy and the associated risk of iron overload. Long-term follow-up will focus on renal function, proteinuria, lipid profile, and ocular findings. Genetic counseling will also be provided to the patient and their family. CONCLUSION: To our knowledge, this is the first reported case of co-inherited deficiency and α-thalassemia confirmed by both renal pathology and comprehensive genetic testing. The consanguineous background suggests possible co-transmission of distant recessive variants on the same chromosome. This case highlights the importance of considering coexisting genetic disorders in patients with consanguinity or unexplained multisystem involvement.
Exploring Ebola virus-associated gene expression through comparative analysis.
Rezapour M, Murphy SV, Ornelles DA … +6 more , Shupe TD, Walker SJ, Jacobson AR, Gurcan MN, McNutt PM, Atala A
Front Genet · 2026 · PMID 42080095 · Full text
INTRODUCTION: Ebola virus (EBOV) infection triggers intense host transcriptional responses that overlap extensively with those induced by other viral and bacterial pathogens. This overlap complicates the identification o...
INTRODUCTION: Ebola virus (EBOV) infection triggers intense host transcriptional responses that overlap extensively with those induced by other viral and bacterial pathogens. This overlap complicates the identification of EBOV-specific gene expression signatures and limits diagnostic specificity. Defining transcriptional markers that distinguish EBOV from other infections is essential for improving molecular diagnostics and advancing understanding of EBOV-specific host responses. METHODS: We developed a multi-step filtering framework using blood-derived RNA-Seq data from nonhuman primates and human cohorts organized into independent training and test sets. In the training cohort, differential expression analysis was performed using an edgeR-based GLMQL-MAS approach to identify EBOV-associated genes. Candidates were filtered against non-EBOV comparator datasets, including mpox virus, influenza, bacterial pneumonia, acute HIV-1 infection, and multiple SARS-CoV-2 variants, to remove broadly shared host-response genes. Genes included in the NanoString nCounter® Host Response Panel were additionally excluded. The resulting EBOV-specific signature was evaluated in independent EBOV and non-EBOV test cohorts using principal component analysis and logistic regression. Functional enrichment was assessed using KEGG pathways. RESULTS: Initial analysis identified numerous interferon-stimulated genes that were similarly upregulated across infections. After cross-infection filtering and NanoString exclusion, 281 EBOV-specific genes were identified. Optimization within the training cohort yielded a top-50 gene set that clearly separated EBOV from Non-EBOV samples. In the independent test cohort, classification performance improved substantially, with the F1 score increasing from 37.5% when all genes were used to 95.0% after applying the top-50 gene set. Enrichment analysis of the top-50 EBOV-specific genes revealed significant association with vascular, coagulation, secretory, and metabolic pathways. showed consistent upregulation in EBOV while remaining downregulated or inactive in comparator infections. DISCUSSION: Structured cross-pathogen filtering enables identification of EBOV-specific transcriptional features beyond shared antiviral responses. The validated gene signature generalizes across independent cohorts and highlights biologically distinct pathways, which supports its potential utility for host-based diagnostic development.
Beyond QTL and GWAS: how deep learning, graph models, and multi-omics are reshaping plant genomic prediction analysis.
Wang T, Tong R, Yuan Z … +1 more , Li Y
Front Genet · 2026 · PMID 42080094 · Full text
BACKGROUND: Traditional genetic mapping has advanced plant trait studies but struggles to capture epistasis, pleiotropy, and genotype-environment (G × E) interactions in genomic prediction (GP). Recently, artificial inte...
BACKGROUND: Traditional genetic mapping has advanced plant trait studies but struggles to capture epistasis, pleiotropy, and genotype-environment (G × E) interactions in genomic prediction (GP). Recently, artificial intelligence (AI) has provided innovative methods. MAIN BODY: This review outlines the transition from traditional frameworks to AI-enabled approaches for plant trait analysis. Specifically, major statistical and AI methods are summarized; current strategies for combining genomic, transcriptomic, metabolomic, phenotypic, and environmental data are described; and examinations are carried out over how graph-based and Transformer models represent regulatory networks and higher-order interactions. This paper further explores developments in multi-task learning, cross-population and cross-species transfer, and emerging foundation-style models. Key issues related to interpretability, reproducibility, data quality, and evaluation practices are considered in the context of practical deployment. CONCLUSION: AI-driven models are reshaping plant trait analysis by extending traditional association methods toward scalable, biologically informed prediction. Continued efforts in data standardization, transparent models, and validation across time and environments will determine the broader impact of these approaches in crop improvement.
Editorial: Exploring the intersection of animal breeding, genetics, and genomics in modern agriculture.
Davenport KM, Estrada-Reyes ZM, Wilson CS
Front Genet · 2026 · PMID 42080093 · Full text
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Brave new RNA world(s): from prebiotic chemistry to gene regulation and RNA technology.
Flores-García AA, Hernandez-Rodriguez A, López-Brito EI … +5 more , Nava-Domínguez DF, Pasapera SP, Pazos-Murillo RF, Pineda-Diaz T, Vázquez-Salazar A
Front Genet · 2026 · PMID 42064362 · Full text
RNA has long provided a plausible route by which heredity and catalysis could become linked in early evolution, and the same chemical versatility helps explain why RNA remains central to origin-of-life research, modern c...
RNA has long provided a plausible route by which heredity and catalysis could become linked in early evolution, and the same chemical versatility helps explain why RNA remains central to origin-of-life research, modern cell biology, and biotechnology. This review adopts a plural framing of RNA worlds to connect three regimes: a primordial RNA world constrained by geochemistry, a contemporary RNA world in which RNAs contribute to catalysis and regulation in cells, and an applied RNA world in which RNA is engineered as a programmable tool. Across these regimes, a common logic emerges from the mapping of sequence to structure to function under explicit constraints. In early evolution, cycling, interfaces, and confinement can generate heterogeneous oligomer pools and bias their persistence, whereas the transition toward Darwinian dynamics depends on copying fidelity, strand dynamics, and compartment coupled population structure. In cells and applications, noncoding RNA networks, RNA modifications, and RNA-guided targeting implement specificity in chemically complex environments, while laboratory selection and design must also confront constraints imposed by stability, delivery, and immune sensing. Across contexts, fitness landscapes and tradeoffs between peak performance and robustness provide experimental benchmarks and practical design principles for RNA function.
Different feed efficiency modeling approaches for the prediction of genomic breeding values in lactating dairy cows.
Chegini A, Negussie E, Kokkonen T … +1 more , Lidauer MH
Front Genet · 2026 · PMID 42058107 · Full text
Feed is the main cost of production in dairy farming. Any improvement in feed efficiency (FE) would increase marginal profit and sustainability and mitigate the environmental impact of dairy farming. In this study, we ap...
Feed is the main cost of production in dairy farming. Any improvement in feed efficiency (FE) would increase marginal profit and sustainability and mitigate the environmental impact of dairy farming. In this study, we applied single-step genomic best linear unbiased prediction to different feed-efficiency metrics using records collected from Nordic Red dairy cattle (RDC). The main objective was to compare different metrics in terms of their effectiveness in selecting more feed-efficient animals. Weekly observations (n = 22,071) of dry-matter intake records from 791 RDC cows collected from 1998 to 2021 were used in this study. The pedigree consisted of 5,604 individuals, of which 1,489 animals were genotyped. Different modeling approaches, including conventional residual feed intake (RFI), regression on expected feed intake (ReFI), two multi-trait residual feed efficiency indices (RFI and RZFE), and energy conversion efficiency (ECE) were analyzed. For the ReFI approach, two alternatives for predicting the expected feed intake, namely, a prediction equation tailored to the RDC data and a prediction equation based on Holstein dairy cow data proposed by the National Academies of Sciences, Engineering, and Medicine (NRC 2021), were compared. First, a BLUP model was developed, and the necessary variance components were estimated for each approach. Then, pedigree-based and genomic-enhanced breeding values (PEBV and GEBV, respectively) were estimated using either reduced or full datasets. For model validation, PEBV and GEBV estimated using the full dataset were regressed on PEBV and GEBV estimated using the reduced dataset, respectively, to measure bias, dispersion, and prediction accuracy (PAC). The heritability estimates of different residual metrics ranged from 0.23 for RFI to 0.30 for ReFI, and the repeatability estimates ranged from 0.48 to 0.52. The estimated heritability and repeatability of ECE were 0.23 and 0.56, respectively. For all metrics, the use of genomic information increased PAC. However, there were discrepancies between the metrics in terms of the magnitude of PAC, with the PAC being the highest for ReFI and the lowest for RFI. Similarly, ReFI had the lowest bias, while the highest bias was estimated for RFI. In addition, RZFE and ReFI showed lower dispersion. The correlations between GEBV of the residual metrics and the GEBV of ECE were lowest for RFI and RFI and highest for ReFI. Among the metrics compared, ReFI and RFI showed the highest effectiveness in selecting efficient cows. This indicates that the use of appropriate partial regression coefficients and the type of modeling are vital in breeding programs aimed at enhancing FE.
A deep learning model for predicting essential proteins based on an attention mechanism.
Zhou S, Zhou H, Chen S … +2 more , Xu Y, Wang L
Front Genet · 2026 · PMID 42058106 · Full text
INTRODUCTION: Essential proteins are key to cellular viability, yet their experimental identification is costly and time-consuming. METHODS: In this study, DLAM is introduced as a deep learning framework that integrates...
INTRODUCTION: Essential proteins are key to cellular viability, yet their experimental identification is costly and time-consuming. METHODS: In this study, DLAM is introduced as a deep learning framework that integrates four complementary biological cues, namely, domain composition, subcellular localization, orthology, and gene expression, together with a weighted protein-protein interaction network. The heterogeneous signals are encoded into compact representations and learned by an attention-enhanced network to score protein essentiality. RESULTS: On the DIP dataset, DLAM achieves consistently better performance than representative centrality measures and conventional machine-learning classifiers. In a further expanded baseline study based on the larger BioGRID dataset containing more proteins, we conducted comparative experiments between DLAM and four recently proposed deep learning methods (TCBB2021, EPGAT, BMC2022, and ACDMBI). On the BioGRID dataset, we evaluated DLAM using stratified five-fold cross-validation. Across folds, DLAM achieves consistently strong discrimination and ranking performance (reported as the mean ± std. for ROC-AUC and AP) and maintains a stable F1-score under a validation-selected decision threshold. This suggests that, under the same evaluation protocol, DLAM has strong ranking and discrimination capability. Moreover, it also exhibits good and stable performance on other metrics such as accuracy, precision, recall, and F-measure. DISCUSSION: These results indicate that jointly modeling multi-source biological information with interaction topology yields more reliable essential-protein prediction under class imbalance.
Moonshine.jl: a Julia package for genome-scale model-based ancestral recombination graph inference.
Fournier P, Larribe F
Front Genet · 2026 · PMID 42046720 · Full text
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 widel...
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.
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