DNA methylation has long served as one of molecular epidemiology’s most informative bridges between environmental exposures and disease. The addition of a methyl group to cytosine bases, typically at CpG dinucleotides, can silence genes, alter enhancer activity, and reshape chromatin architecture, and these marks are known to respond dynamically to external stimuli such as diet, stress, pollutants, and tobacco smoke. Tobacco smoking is widely regarded as one of the most powerful exogenous modifiers of the human epigenome, producing methylation changes that persist for years, and in some cases decades, after cessation. Because of this durability and sensitivity, methylation signatures of smoking have been proposed as objective biomarkers of exposure that could complement self-reported smoking history, which is known to be imperfect and prone to recall bias.

Until now, however, the field’s largest meta-analyses of smoking-related methylation have been constrained by limited ancestral diversity among participants and by sample sizes that, while impressive, could not support well-powered comparisons between populations. Most prior epigenome-wide association studies of smoking drew predominantly from cohorts of European ancestry, leaving open the question of whether the methylation marks discovered in those populations generalize to people of African, Admixed American, Asian, or other ancestries. Genetic ancestry influences patterns of genetic variation that in turn shape methylation, through mechanisms such as meQTLs, or methylation quantitative trait loci, where nearby genetic variants control methylation levels at specific sites. Differences in allele frequencies and linkage disequilibrium across populations therefore have the potential to alter both the detection and the magnitude of exposure-related methylation associations.

The new analysis, led by Dennis Khodasevich of VA Palo Alto Health Care System and Stanford University School of Medicine together with Andres Cardenas and colleagues, exploited the extraordinary scale and diversity of the Million Veteran Program to address these gaps directly. The team performed epigenome-wide association analyses, or EWAS, comparing methylation measured across the genome between current smokers, former smokers, and never smokers. In the overall study population, the researchers identified 40,529 methylation sites significantly associated with current smoking and 4,240 sites associated with former smoking after applying Bonferroni correction, the stringent multiple-testing threshold that guards against false discoveries when testing hundreds of thousands of positions simultaneously. These numbers represent by far the most comprehensive catalog of smoking-associated methylation ever assembled, dwarfing previous efforts and revealing an enormous breadth of low-magnitude signals that smaller studies could not reliably detect.

Crucially, the team then repeated the analyses within ancestry-specific strata, examining European, African, and Admixed American subgroups separately. The results revealed a striking degree of commonality: between 95 and 100 percent of the associations identified in each ancestry-specific analysis were also detected in the overall population, indicating that the fundamental epigenetic response to smoking is broadly shared across human populations. Yet the study also uncovered evidence of ancestry-specific associations and differences in the magnitude of methylation changes between groups, suggesting that while the core smoking response is conserved, genetic background and environmental context modulate its precise epigenomic expression. Across all analyses, a set of 31 CpG sites emerged as differentially methylated regardless of ancestry, forming a robust, universally responsive signature of smoking exposure.

The identification of these 31 common sites enabled one of the study’s most clinically oriented analyses: the construction of a methylation risk score for predicting atherosclerotic cardiovascular disease, or ASCVD. Smoking is a major driver of atherosclerosis, the pathological buildup of cholesterol-laden plaque in arterial walls that underlies coronary artery disease, peripheral arterial disease, and acute ischemic stroke. The researchers tested whether aggregating methylation values across the 31 shared CpG sites into a single quantitative score could improve prediction of time to ASCVD events beyond conventional three-category smoking status, which distinguishes current, former, and never smokers. The methylation risk score achieved a concordance statistic of 0.673, compared with 0.668 for smoking status alone, a modest but meaningful improvement that illustrates how epigenetic information can capture gradations of biological exposure and residual physiological effect that coarse behavioral categories miss.

The concordance difference, though small in absolute terms, carries conceptual significance for precision medicine. A person’s smoking history exists on a continuum of intensity, duration, and time since cessation, and the methylation mark integrates this exposure over biological time in ways that self-report cannot. By encoding the cumulative epigenetic consequence of smoking into a risk score, clinicians may eventually gain a more nuanced tool for cardiovascular risk stratification, particularly for patients whose reported smoking history is incomplete or inconsistent. The score’s performance also underscores that methylation is not merely a passive record of exposure; it may reflect downstream molecular pathways, such as inflammatory and endothelial processes, that mediate the link between smoking and arterial disease.

The study’s findings on former smoking are particularly noteworthy. The 4,240 significant sites associated with former smoking, together with what the authors describe as extensive low-magnitude associations, demonstrate that the epigenome retains detectable traces of past tobacco exposure long after smoking ceases. Some methylation changes at well-characterized loci, such as sites in the AHRR and F2RL3 genes that previous literature has repeatedly linked to smoking, are known to partially but incompletely revert after cessation. The persistence of these marks raises the possibility that they contribute to the elevated cardiovascular risk that former smokers continue to carry for years after quitting, although the current study establishes association rather than causation, and disentangling whether methylation changes drive disease or merely track it remains a central challenge for the field.

Methodologically, the work exemplifies the power of biobank-scale epigenomics. The Million Veteran Program, a nationwide cohort of US Veterans who consented to genomic research linked to comprehensive electronic health records, provided the sample size needed to detect effects of small magnitude and to stratify analyses by ancestry without sacrificing statistical power. The researchers acknowledged the Veterans whose participation made the study possible, and the analysis was conducted under VA research infrastructure with appropriate ethical oversight and informed consent from all participants. The consortium-based authorship, credited to the VA Million Veteran Program alongside individual investigators from Stanford, the University of Utah, the University of Pennsylvania, and VA medical centers, reflects the collaborative scale that modern genomic epidemiology increasingly demands.

The implications of the study extend across several domains. For basic epigenomics, the massive catalog of smoking-responsive sites provides a rich resource for investigating the biological pathways that tobacco combustion products perturb, including xenobiotic metabolism, oxidative stress, and immune regulation. For genetic epidemiology, the confirmation that ancestry-specific methylation signals exist alongside broadly shared ones reinforces the importance of diverse cohorts and ancestry-aware analytical designs in biomarker discovery, helping to prevent the inequities that arise when tools calibrated in one population are deployed uncritically in another. For clinical medicine, the methylation risk score, while not yet ready for routine deployment, offers a proof of concept that epigenetic biomarkers can add incremental predictive value to established risk factors for the leading cause of death worldwide.

Future work will need to validate the 31-site score in independent, ancestrally diverse cohorts, refine its calibration, and test whether it improves clinical decision-making in prospective settings. Researchers will also want to probe the ancestry-specific associations more deeply, examining whether genetic variation at methylation quantitative trait loci or differing exposure patterns explain the between-group differences in association magnitude. As epigenome-wide association studies continue to scale up, the smoking methylation signature documented in the Million Veteran Program stands as both a landmark catalog and a template for how large, diverse, deeply phenotyped cohorts can illuminate the molecular conversations between environment, genome, and disease.

Subject of Research: Ancestry-specific DNA methylation signatures of smoking and associations with atherosclerotic cardiovascular disease risk: findings from the Million Veteran Program

Article Title: Ancestry-specific DNA methylation signatures of smoking and associations with atherosclerotic cardiovascular disease risk: findings from the Million Veteran Program

Article References: Khodasevich, D., Hilliard, A. T., Barad, A., Zhou, J., Guarischi-Sousa, R., Clarke, S. L., Pridgen, K. M., Lynch, J. A., Chang, K.-M., Tsao, P. S., Assimes, T. L., Cardenas, A., & VA Million Veteran Program (2026). Ancestry-specific DNA methylation signatures of smoking and associations with atherosclerotic cardiovascular disease risk: findings from the Million Veteran Program. Genome Medicine. https://doi.org/10.1186/s13073-026-01761-4

Image Credits: AI Generated

DOI: 10.1186/s13073-026-01761-4

Keywords: Ancestry-specific, methylation, signatures, smoking, associations, atherosclerotic, cardiovascular, disease, risk, findings, Million, Veteran

The team started with the publicly available sequencing dataset GSE203206, downloaded from the Gene Expression Omnibus repository, which contains transcriptomic data from 39 Alzheimer’s disease samples and 8 normal control samples. Using t-tests implemented in the limma R Bioconductor package, they identified 958 differentially expressed genes, with a strikingly symmetric result of 479 genes upregulated and 479 genes downregulated in the diseased brains. That balance alone is noteworthy, because it suggests widespread, bidirectional remodeling of the transcriptome rather than a simple overshoot of one or two processes, and it gives researchers a rich catalogue of candidate players to investigate.

To make biological sense of that gene list, the researchers ran Gene Ontology and pathway enrichment analyses. The upregulated genes clustered mainly around response to stimulus, cytoplasmic functions, small molecule binding and signal transduction, painting a picture of a brain under persistent stress, actively reorganizing its signaling machinery. The downregulated genes, by contrast, were enriched for multicellular organism development, cell junction biology, ion binding and cardiac conduction, hinting that fundamental structural and developmental programs are being quietly dismantled as the disease progresses. These divergent functional signatures reinforce the idea that Alzheimer’s is not one pathway gone wrong but an entire coordinated system drifting out of tune.

The next step was network analysis. By mapping the differentially expressed genes onto a protein-protein interaction network, the team built a graph containing 4,886 nodes and 10,342 edges, an enormous molecular web from which they extracted the most connected hubs. Ten genes rose to the top: HSP90AA1, FN1, KIT, YAP1, LSM2, SKP1, EIF5A2, TAF9, DDX39B and CDK7. Several of these names will be familiar to neuroscientists. HSP90AA1 encodes a heat shock protein chaperone already flagged by proteomic studies in the entorhinal cortex of Alzheimer’s patients, while FN1, the fibronectin gene, has recently been linked through rare genetic variants to protection against the notorious APOE ε4 risk factor.

Beyond static expression patterns, the study probed the regulatory layers that might control these hub genes. Constructing a microRNA-hub gene regulatory network, the authors identified hsa-mir-545-3p and hsa-miR-548f-5p as microRNAs that could help orchestrate Alzheimer’s pathology by fine-tuning multiple hub genes simultaneously. Similarly, a transcription factor-hub gene network implicated PLAG1 and MEF2A as master regulators that may be involved in disease development. This kind of multi-level regulation matters because it suggests that upstream control points, rather than individual downstream genes, could offer the most efficient targets for intervention, and because non-coding RNAs and transcription factors are increasingly seen as versatile biomarkers in neurodegeneration.

Perhaps the most clinically intriguing part of the work is the drug-hub gene interaction analysis, which predicted four existing drug molecules as candidates for Alzheimer’s treatment: Sulindac, Infliximab, Norfloxacin and Gemcitabine. The logic of repurposing is simple and appealing. These compounds already have established safety profiles, known pharmacokinetics and, in some cases, mechanisms that touch inflammation or cell survival, processes central to Alzheimer’s biology. Sulindac is a nonsteroidal anti-inflammatory drug, Infliximab is an antibody targeting tumor necrosis factor, and both speak to the chronic inflammatory component that has shadowed Alzheimer’s research for decades. Gemcitabine and Norfloxacin add further molecular diversity to the candidate pool.

To test these predictions computationally, the researchers performed molecular docking between hub gene products and corresponding active molecules. The docking analysis revealed that Isocryptomerin and Macrophylloside D, natural product-derived compounds, showed strong binding activities to HSP90AA1 and FN1 respectively. Docking scores cannot substitute for wet-lab validation, but favorable computed binding poses provide a rational starting point for medicinal chemists and suggest that these molecules are worth experimental follow-up as potential modulators of the disease’s most central protein hubs.

The study also evaluated the clinical diagnostic potential of the hub genes using receiver operating characteristic curve analysis, a standard method for estimating how well a biomarker separates diseased from healthy samples. Strong diagnostic performance would mean that measuring these genes, or the proteins and microRNAs they encode, could one day help identify Alzheimer’s earlier and more reliably than current cognitive assessments and imaging alone. With disease-modifying therapies finally emerging, the window for meaningful intervention depends critically on early detection, making reliable molecular biomarkers one of the field’s most valuable goals.

As with any computational study, caveats apply. The findings are hypotheses generated from correlation in a modest sample of 39 disease and 8 control brains, and hub genes identified by network centrality do not automatically equal causal drivers. The predicted drug interactions and docking results require validation in cell and animal models before any clinical translation. Nevertheless, the pipeline used here mirrors successful approaches in other diseases and provides a transparent, reproducible map of where future experimental effort should be directed.

Taken together, the work offers new insights into Alzheimer’s pathogenesis by nominating ten hub genes, two microRNAs, two transcription factors and several repurposable compounds as candidate diagnostic and therapeutic markers. It exemplifies a broader trend in biomedical research: as public sequencing archives grow, computational biology can extract biomedical value from data that already exists, at a fraction of the cost of new clinical trials. For a disease that affects tens of millions of people worldwide and still defies a cure, every new molecular lead, however preliminary, matters. The hub genes, microRNAs and transcription factors identified here now join the growing arsenal of targets that researchers worldwide will test in the years ahead.

The dataset at the heart of this study, GSE203206, is one of a growing number of publicly deposited transcriptomic datasets in the Gene Expression Omnibus, a repository maintained by the National Center for Biotechnology Information. Because such archives are openly accessible, any laboratory with computational resources can reanalyze raw sequencing reads, apply its own statistical thresholds, and cross-check published conclusions. This transparency has become a cornerstone of modern genomics, allowing independent teams to validate biomarker candidates across independent cohorts, a step that will be essential before any of the ten hub genes identified here can be considered a robust diagnostic marker.

Several of the hub genes carry biological stories that extend well beyond Alzheimer’s research. YAP1, for instance, is the key effector of the Hippo signaling pathway, a conserved cascade best known for controlling organ size and cell proliferation, and it has been increasingly implicated in neural regeneration and glial responses to injury. CDK7 is a cyclin-dependent kinase that functions as part of the transcriptional machinery, phosphorylating the RNA polymerase II tail and thereby regulating the expression of broad gene programs, which makes its dysregulation potentially consequential for many downstream pathways at once. LSM2 and SKP1 participate in RNA processing and ubiquitin-mediated protein degradation respectively, both processes that intersect with the protein homeostasis failures characteristic of neurodegenerative disease.

The involvement of HSP90AA1 is particularly interesting from a therapeutic standpoint. Heat shock protein 90 acts as a molecular chaperone that stabilizes numerous client proteins, many of which are involved in signaling cascades, and chaperone overload has been proposed as a mechanism by which misfolded and aggregated proteins, such as tau and amyloid-beta species, persist in the aging brain. Inhibitors of this chaperone have been explored in oncology for years, meaning that a substantial body of pharmacological knowledge and chemical tool compounds already exists and could be adapted for neurodegeneration research.

The microRNA findings also fit into a broader literature. MicroRNAs are short non-coding RNAs that each typically regulate dozens to hundreds of messenger RNAs, so a single microRNA shift can ripple across entire pathways. Previous work has documented widespread microRNA alterations in Alzheimer’s brain tissue and even in circulating blood, fueling interest in these molecules as minimally invasive biomarkers detectable in plasma or cerebrospinal fluid. The specific candidates reported here, hsa-mir-545-3p and hsa-miR-548f-5p, now join that expanding catalogue and can be tested for reproducibility in independent sample sets.

Molecular docking itself deserves a note of context. The method models how a small molecule fits into the three-dimensional structure of a target protein and estimates binding strength computationally, often within hours and at negligible cost compared with laboratory screening. Its predictions, however, are only as good as the protein structures and scoring functions used, and docking affinities frequently fail to translate into cellular activity. Natural products such as Isocryptomerin, derived from coniferous plants, and Macrophylloside D offer chemical diversity that synthetic libraries sometimes lack, which is why they attract attention as starting scaffolds. The sensible next steps are biochemical binding assays, neuronal cell models, and ultimately animal studies to determine whether any of these computational leads survives contact with biological reality.

Subject of Research: Identification of Candidate Biomarkers and Signaling Pathways Associated with Alzheimer’s Disease Using Bioinformatics Analysis of Next Generation Sequencing Data and Molecular Docking Studies

Article Title: Identification of Candidate Biomarkers and Signaling Pathways Associated with Alzheimer’s Disease Using Bioinformatics Analysis of Next Generation Sequencing Data and Molecular Docking Studies

Article References: Identification of Candidate Biomarkers and Signaling Pathways Associated with Alzheimer’s Disease Using Bioinformatics Analysis of Next Generation Sequencing Data and Molecular Docking Studies. (n.d.). https://doi.org/10.1007/s12126-026-09665-9

Image Credits: AI Generated

DOI: 10.1007/s12126-026-09665-9

Keywords: Identification, Candidate, Biomarkers, Signaling, Pathways, Associated, Alzheimer, Disease, Bioinformatics, Analysis, Next, Generation

The heart of the new study is a technique called total infectome metatranscriptomics. Unlike targeted polymerase chain reaction assays, which can only detect organisms a researcher already suspects, RNA-based metatranscriptomic sequencing captures the full transcriptional activity of every microbe in a sample—RNA viruses, actively replicating DNA viruses, bacteria, fungi, and other eukaryotic microorganisms alike. Between 2021 and 2025, the team conducted epidemiological surveys across major grass carp-producing regions of China, including provinces in the Yangtze, Pearl, and Yellow River basins, confirming that OWS has spread well beyond its point of first documentation. From affected and healthy fish, the researchers dissected eight organs each—liver, spleen, kidney, intestine, gill, brain, muscle, and skin—and processed every organ as an independent sequencing library, generating 80 metatranscriptomes. After quality filtering and ribosomal RNA depletion, 8.09 billion high-quality reads remained, averaging 101.2 million per library, providing the depth needed for broad pathogen discovery.

The sequencing results revealed a strikingly complex microbial landscape. Across all libraries, the team identified 107 dominant microbial species: 32 viruses, 65 bacteria, and 10 eukaryotic microorganisms, spanning nine RNA viral supergroups, two DNA viral families, seven bacterial phyla, and seven eukaryotic phyla. Bacteria constituted the largest share of detected organisms at 60.7 percent, followed by RNA viruses at 22.4 percent, eukaryotes at 9.3 percent, and DNA viruses at 7.5 percent. Perhaps most tellingly, 68 of the 107 species—63.6 percent—were putatively novel, indicating that the majority of the grass carp-associated infectome had never been characterized before. Among the known and emerging agents were a grass carp hepacivirus, the first of its kind detected in this host; Chinook salmon nidovirus 1, previously reported only from salmonids; and a divergent aquareovirus the team named Shunde grass carp aquareovirus. Several parasitic eukaryotes from groups including Cnidaria, Euglenozoa, Fornicata, and Platyhelminthes also appeared in the dataset.

Detection alone, however, cannot establish causation—a lesson that has repeatedly frustrated disease investigators in aquaculture. To prioritize candidates, the researchers applied a comparative infectomics framework, quantifying microbial abundance as reads per million non-rRNA reads and retaining 33 taxa above a threshold of RPM ≥ 1. Differential abundance analysis, using a criterion of at least a fourfold change with a false discovery rate below 0.05, showed that overall microbial profiles clearly separated diseased fish from healthy controls. Among the enriched taxa, one organism stood out decisively: Flavobacterium psychrophilum was detected in every diseased individual, across multiple organs, and at its highest abundance in muscle and skin—precisely the tissues where OWS lesions were most severe. By contrast, parasitic eukaryotes such as Ichthyobodonidae, Trypanosomatidae, and Thelohanellus species showed inconsistent, sporadic occurrence, and the RNA viruses enriched in diseased fish phylogenetically clustered with invertebrate-associated lineages whose abundance correlated with parasite loads rather than direct infection of the fish.

With F. psychrophilum prioritized as the leading candidate, the team moved to experimental validation. The bacterium was isolated from lesion-associated muscle tissue of naturally diseased fish, yielding pale-yellow colonies on TYES agar after incubation at 15 degrees Celsius. Sequencing of the 16S rRNA gene placed the representative isolate, designated GC30-154, firmly within the F. psychrophilum clade with maximum bootstrap support. Healthy grass carp were then challenged by intramuscular injection with graded doses ranging from 10^4 to 10^8 colony-forming units. Control fish injected with buffer remained entirely healthy, while infected fish developed clinical signs beginning four days post-injection, with morbidity climbing in a dose-dependent fashion from 25 percent at the lowest dose to 100 percent at the highest. Mortality followed the same pattern, reaching 95 percent by day 18 in the highest-dose group, and the bacterium was successfully re-isolated from the lesions of deceased fish—satisfying key elements of Koch’s postulates.

The pathological picture in experimentally infected fish mirrored natural OWS with remarkable fidelity. Gross signs included focal erythema and swelling at the injection site, reddening around the pectoral-fin base, mild snout reddening, and tail erosion, while histopathology revealed severe muscle fiber degeneration, extensive vacuolation, and disruption of skin architecture—lesions closely resembling those in field cases, and concentrated in external and barrier tissues while liver, spleen, and kidney remained largely intact. Critically, the team then performed post-challenge total infectome analysis to rule out a role for secondary microbes in driving the experimental disease. Only F. psychrophilum appeared at consistently high abundance in infected animals, with the same muscle- and skin-dominant organ distribution seen in naturally diseased fish, while controls showed no signal whatsoever. The convergence of clinical signs, tissue pathology, mortality patterns, and infectome signatures established the bacterium as sufficient—and therefore the primary cause—of OWS.

A second layer of the investigation explained the disease’s peculiar seasonality. F. psychrophilum is classically regarded as a cold-water pathogen of salmonids, causing bacterial cold-water disease and rainbow trout fry syndrome, typically at temperatures below 10 degrees Celsius. Yet in grass carp the story was different. When challenged fish were held at constant temperatures of 10, 15, or 20 degrees Celsius, mortality was highest at 15 degrees—55 percent—with no deaths at the other temperatures during the observation period. More striking still was a temperature-shift experiment designed to mimic the overwintering-to-spring transition. Fish injected at 10 degrees and held there for 15 days showed no abnormalities; only when water temperature was gradually raised to 15 degrees did ulcers appear and mortality surge, reaching 95 percent within 14 days of warming. This thermal profile closely matches the late-overwintering and early-spring window in which natural OWS outbreaks occur, and it suggests the grass carp isolate may represent a host-adapted variant with altered temperature-dependent virulence.

Beyond the headline finding, the study carries broader implications for how infectious disease is investigated in complex systems. Aquatic environments teem with microbial diversity, and intensive aquaculture—shared water systems, high stocking densities, seasonal environmental stress—creates ideal conditions for polymicrobial communities to obscure etiology. The framework demonstrated here links epidemiological surveying, cohort-based comparative infectomics, targeted isolation, experimental infection, re-isolation, and post-challenge infectome validation into a coherent chain of evidence that converts unbiased pathogen discovery into causal inference. The authors emphasize that its success depends on careful attention to cohort representativeness, sampling coverage, and the detectability of pathogen-derived transcriptional signals, and that sampling and validation workflows must be tailored to the ecology of each disease system. Applied to OWS, the approach correctly demoted opportunistic eukaryotes and invertebrate-associated viruses that might otherwise have been mistaken for culprits, while flagging latent pathogen diversity—including novel hepaciviruses and nidoviruses—that could matter under future environmental or co-infection scenarios.

As aquaculture continues to expand and intensify worldwide, the connectivity between farming systems grows apace, raising the risk of pathogen transmission across previously separated host species and the emergence of new disease syndromes. The grass carp OWS resolution offers both a practical answer for producers—pointing toward surveillance and control of F. psychrophilum during spring warming—and a methodological template for wildlife, livestock, and even clinical medicine, where metagenomic detection increasingly outpaces causal interpretation. The study’s raw sequencing data have been deposited in a public aquatic pathogen platform, and all alignments and phylogenetic trees are openly available, reflecting the authors’ intent that the total infectome framework be adopted, adapted, and tested broadly. What began as an attempt to solve one stubborn disease in Chinese carp ponds may ultimately change how scientists everywhere distinguish the true cause of an outbreak from the microbial noise that surrounds it.

Subject of Research: A total infectome framework for resolving complex disease etiology in aquaculture

Article Title: A total infectome framework for resolving complex disease etiology in aquaculture

Article References: Xu, Y., Liu, H., Wu, W., Gu, Y., Zhang, N., Zhang, C., Zhou, R., Zhang, D., Weng, S., Shi, M., He, J., & He, J. (2026). A total infectome framework for resolving complex disease etiology in aquaculture. Advanced Biotechnology, 4(3), Article 31. https://doi.org/10.1007/s44307-026-00125-8

Image Credits: AI Generated

DOI: 10.1007/s44307-026-00125-8

Keywords: total, infectome, framework, resolving, complex, disease, etiology, aquaculture, scientific research