ID Genomics (United States)

In cortical and hippocampal pyramidal neurons, backpropagating action potentials (bAPs) play a central role in dendritic signaling, synaptic integration, and spike-timing-dependent plasticity (STDP). In most experimental and theoretical frameworks, bAPs are implicitly treated as reliable signals that faithfully inform dendritic synapses of somatic spiking. Here, we review experimental evidence demonstrating that this assumption is often violated. In large portions of the pyramidal neuron dendritic tree, particularly in distal apical branches and apical tuft dendrites, bAP amplitude exhibits pronounced spatial and temporal variability, including: (i) activity-dependent attenuation, (ii) frequency-dependent amplification, (iii) branch-specific propagation failures, and (iv) trial-to-trial stochastic AP flickering. We summarize five experimentally documented forms of bAP variability and discuss how stochastic backpropagation may shape synaptic plasticity in computational neuroscience, especially STDP, by introducing probabilistic gates that limit the coincidence of: (i) dendritic depolarization (bAP) and (ii) synaptic input (EPSP). Finally, we consider broader implications of the AP flickering in dendrites for cortical information processing, including redundancy, averaging, evidence accumulation, and error-correcting strategies in cortical circuits.

Background: Autoimmune and cardiovascular diseases are diagnostically complex, especially when multiple rare disorders co-occur in a single patient. This study presents a comprehensive genomic analysis of a 50-year-old female patient with mixed ancestry and a history of thrombotic thrombocytopenic purpura (TTP), systemic lupus erythematosus (SLE), and left ventricular non-compaction cardiomyopathy (LVNC).

Influenza A subclade K viruses caused high infection rates in the 2025/2026 Northern Hemisphere season, raising concerns about antigenic drift and reduced vaccine effectiveness. We measured antibody responses in matched human pre- and post-vaccination sera against a vaccine-like as well as subclade K isolates. Pre-existing immunity to subclade K variants was noted with seasonal influenza vaccination boosting titers two-fold against subclade K and three-fold against the vaccine-like strain, indicating little antigenic drift. These data contrast ferret-based predictions of marked antigenic drift.

Major Depressive Disorder (MDD) is a debilitating psychiatric disorder that is a leading cause of disability worldwide. Although treatment with antidepressants, such as Selective Serotonin Reuptake Inhibitors (SSRIs), has demonstrated clinical efficacy, the "trial and error" approach in choosing the most effective antidepressant treatment for each patient allows for only a subset of patients to achieve response to the first line of treatment. Circular RNAs (circRNAs), are highly stable and brain-enriched non-coding RNAs that are mainly derived from the backsplicing and covalent joining of exons and introns of protein-coding genes. They are known to be important for brain development and function, cross the blood-brain-barrier, and be highly sensitive to changes in both synaptic activity and neuronal receptor signaling. Here we present evidence that expression of the brain-enriched circRNA, CDR1as, is associated with symptomatic response to SSRI treatment, and regulated by serotonin and Brain-Derived Neurotrophic Factor (BDNF) receptor activity. We present data using circRNA-specific PCR in baseline whole blood samples from two independent cohorts, drawn from the Establishing moderators and biosignatures of antidepressant response in clinical care (EMBARC) and the Biomarkers of ANTidepressant RESponse (ANTARES) clinical studies, showing that before treatment CDR1as is differentially expressed between future symptomatic responders and non-responders to treatment with the SSRI sertraline. Additional data from naturalistic antidepressant response studies further highlight the association between CDR1as and antidepressant effects of SSRIs as a class. In addition, we show that CDR1as levels are altered following sertraline treatment in responders with the trajectory of change post-treatment associated with long-term remission. Furthermore, we report that levels of CDR1as in the blood can specifically predict remission with SSRI treatment, but not response/remission with Placebo or Bupropion treatments. Lastly, we provide evidence in animal mechanistic and neuronal culture studies, suggesting mouse Cdr1as is strongly regulated by 5-HT2A and BDNF receptor signaling. Taken together, our data identify a brain-enriched circRNA associated with known mechanisms of antidepressant response that can serve as a blood biomarker for predicting response and remission with SSRI treatment.

Abstract HER2DX is a genomic test developed to guide treatment decisions in newly diagnosed HER2-positive (HER2+) breast cancer. It analyzes the expression of 27 genes grouped into 4 biological signatures: immune/immunoglobulin (IGG), luminal differentiation, proliferation, and HER2 amplicon expression. The test provides three distinct outputs: a prognostic relapse risk score, a pathologic complete response (pCR) likelihood score, and a quantitative ERBB2 mRNA score. This study investigates the relationship between HER2DX-derived outputs and standard histopathologic features in early-stage HER2+ breast cancer. The standardized HER2DX genomic test provided by Reveal Genomics was performed on paraffin tumor samples from newly diagnosed patients with early-stage HER2+ breast cancer, treated across 24 hospitals from January 2022 to June 2025. Histopathologic evaluation of hematoxylin-eosin-stained slides included tumor grade, stromal tumor-infiltrating lymphocytes (TILs) percentage, spatial TIL distribution, and presence of tertiary lymphoid structures (TLS). Hormone receptor (HR) status and Ki67 proliferative index were assessed locally by immunohistochemistry (IHC), while HER2 status was determined using IHC and in situ hybridization. Associations between HER2DX scores and pathologic features were assessed using appropriate statistical tests for continuous variables and categorical comparisons. HER2DX scores and their underlying biological signatures were analyzed in 410 HER2+ tumors. HR+ disease represented 75.7% of cases. The distribution of tumor grade, HER2 IHC status, HR status, Ki67 index, stromal TILs, TLS, and TIL spatial patterns is summarized in the Table. The HER2DX pCR score was significantly associated with all histopathologic features evaluated, the relapse-risk score five of these features (including Ki67 and TILs), and the HER2DX ERBB2 score with HER2 and HR status. Higher stromal TIL levels were moderately correlated with the IGG signature (r=0.59, p<0.001), and the inflamed TIL distribution was associated with increased IGG signature expression and higher HER2DX pCR scores (p<0.001). TLS presence also correlated with elevated IGG signature expression (p<0.001). HR+ tumors exhibited higher luminal signature expression (p<0.001), while HER2 IHC 3+ tumors had increased HER2 amplicon signature and HER2DX ERBB2 scores (p<0.001). Finally, higher Ki67 levels were moderately correlated with the proliferation signature (r=0.50, p<0.001). The HER2DX genomic test shows significant associations with multiple histopathologic features in early-stage HER2+ breast cancer, although no single feature can fully explain the outputs of the test. These findings support the notion that HER2DX integrates complex biological and histological dimensions, offering complementary information that may enhance personalized treatment decision-making. Citation Format: E. Sanfeliu, A. Martinez-Romero, M. Marín-Aguilera, S. Cobo, B. González-Farré, E. Hernandez, P. Jares, J. Puig-Butillé, M. Muñoz, R. Gómez-Bravo, M. Tapia, C. Tebar, C. Saura, S. Escrivà-de-Romaní, J. Soberino, J. Cortés, S. Morales, K. Amillano, L. Paré, P. Villagrasa, W. Buckingham, F. Pardo, J. Parker, F. Brasó-Maristany, E. Ciruelos, R. Sánchez-Bayona, O. Martinez-Sáez, J. Cejalvo, A. Prat. Biologic and Pathologic Correlations of the HER2DX Genomic Test in HER2+ Disease [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2025; 2025 Dec 9-12; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(4 Suppl):Abstract nr PS3-10-24.

Genetic variants in the dynein axonemal heavy chain 17 (DNAH17) have been implicated in multiple morphological abnormalities of the sperm flagella (MMAF) and asthenoteratozoospermia (ATZS). The aim of this study is to identify novel DNAH17 variants associated with MMAF and ATZS and to evaluate the impact of these variants on sperm motility, sperm morphology, the ultrastructure of sperm flagella, and the outcomes of intracytoplasmic sperm injection (ICSI). In the present study, we identified one novel homozygous and three compound heterozygous DNAH17 variants in one consanguineous and three nonconsanguineous families. Semen analysis revealed significant reductions in sperm motility and notable morphological changes. Transmission electron microscopy (TEM) revealed the absence of outer dynein arms (ODAs), loss of peripheral doublet microtubules (DMTs) 4-7, and multiple mitochondrial sheath (MS) abnormalities in the sperm flagella. Functional analyses indicated that these variants disrupted the expression and localization of the DNAH17 protein in the sperm flagella. Tandem mass tagging proteomics and immunofluorescence demonstrated a significant reduction in the expression of proteins associated with the assembly of ODA-associated proteins into the axoneme from individuals with DNAH17 variants. Notably, successful pregnancies were achieved through ICSI combined with assisted oocyte activation (AOA) treatment in the female partner of these probands. This study identified seven novel homozygous or compound heterozygous DNAH17 variants in both consanguineous and nonconsanguineous families, highlighting their detrimental effects on sperm motility, morphology, and flagellar ultrastructure. These findings provide valuable insights for the genetic diagnosis of MMAF and may enhance future genetic counseling strategies.

Cytogenetic analysis encompasses a suite of standard-of-care diagnostic testing methods that is applied routinely in cases of acute myeloid leukemia (AML) to assess chromosomal changes that are clinically relevant for risk classification and treatment decisions. In this study, we assess the use of Genomic Proximity Mapping® (GPM) for cytogenomic analysis of AML diagnostic specimens for detection of cytogenetic risk variants included in the European Leukemia Network (ELN) risk stratification guidelines. Archival patient samples (n = 48) from the Fred Hutchinson Cancer Center (FH) leukemia bank with historical clinical cytogenetic data were processed for GPM and analyzed with the CytoTerra cloud-based analysis platform. Genomic proximity mapping showed 100% concordance for all specific variants that have associated impacts on risk stratification as defined by ELN 2022 criteria and 78% concordance when considering all variants reported by the FH Cytogenetics Lab. Notably, the percentage of blasts (ranging from 5- 96%) did not have a clear effect on the ability to detect these variants. In two cases, GPM identified a recurrent inv(9)(p13.3p13.1). These findings demonstrate GPM's effectiveness for the evaluation of known AML-associated risk variants and a source for biomarker discovery.

This study investigated the probiotic properties of Lactobacillus paracasei (LAB 815) isolated from Gansu Jiangshui and Saccharomyces cerevisiae (SC 8(3)), along with their functional activities and fermentation performances in Sonchus oleraceus L. (S. oleraceus) Jiangshui. Both LAB (815) and SC 8(3) had good gastrointestinal tolerance and auto-aggregation ability, and neither strain showed hemolytic activity. The functional activity assays showed that the fermented S. oleraceus Jiangshui (F2) produced through co-fermentation with LAB 815 and SC 8(3) exhibited superior biological activities, including α-glucosidase and α-amylase inhibitory effects, ACE inhibitory activity, strong antioxidant capacity, and significant antimicrobial activity against pathogenic bacteria. The results of simulated gastrointestinal fluid test showed that the inhibition rates of α-glucosidase, α-amylase and ACE decreased in all groups (F1, F2, F3 and F4), indicating a partial loss of these enzyme inhibitory activities after gastrointestinal transit. However, the F2 group maintained relatively higher activity compared to the other groups and maintained inhibition rates of approximately 50%. The fermentation performance results showed that the F2 produced acid more rapidly and achieved a greater pH reduction compared to other groups (F1, F3, F4). Metabolite analysis showed that F2 resulted in a significant increase in the major metabolites such as amino acids (+339.954 mg/100 mL), alcohols (+29.118%), esters (+25.295%) and organic acids (+15.12%). The sensory evaluation results showed that the overall acceptability of the S. oleraceus Jiangshui was significantly improved. The present findings could confirm the key role of co-fermentation of LAB 815 and SC 8(3) in improving the taste quality and potential health functions of S. oleraceus Jiangshui.

Association studies have linked many genetic variants to a variety of phenotypes but understanding the biological mechanisms underlying these signals remains a major challenge. Since genes operate within complex networks, statistical interactions between genetic mutations that reflect biological pathways are expected to exist. However, their discovery has been hampered by the vast search space of variant combinations and the multiplicatively small expected effect sizes of interactions. To increase power, we created a test for interaction between single-nucleotide polymorphisms (SNPs) and groups of other variants with a direct effect on a phenotype aggregated in a polygenic score (PGS) which can be performed for any quantitative trait. In realistic simulations, this method avoids false positives and is well powered to find interaction networks. We apply it to 97 quantitative phenotypes in European samples in the UK Biobank and identify 144 independent interactions affecting 52 different traits, including important disease risk variants at genes such as APOE, FTO or TCF7L2. We develop approaches to refine identified signals and detect 38 pairwise interactions between SNPs. These include known interactions between ABO, FUT2 and TREH affecting alkaline phosphatase levels, which are shown to be part of a larger network including PIGC and FUT6, as well as an interaction for eosinophil levels between IL33 and ALOX15, two genes whose functional interaction has recently been implicated in asthma. Finally, we propose a method to partition PGSs according to the binding sites of more than 1100 transcription factors using the HOCOMOCO motif database and test for interactions involving functionally partitioned scores. We identify 12 interactions affecting eight traits, two of which directly reflect known regulatory relationships such as that between TCF7L2 (a key regulator of glucose metabolism) and the transcription factor KDM2A, which are known to interact functionally within the Wnt signalling pathway, affecting glycated haemoglobin levels. This work substantially extends the set of known epistatic effects for human phenotypes and shows how statistical interactions can reflect biological interdependencies between genes.

Multi-omics data are instrumental in obtaining a comprehensive picture of complex biological systems. This is particularly useful for women's health conditions such as endometriosis, which has been historically understudied despite having a high prevalence (around 10% of women of reproductive age). Subsequently, endometriosis has limited genetic characterization: current genome-wide association studies explain only 11% of its 47% total estimated heritability, underscoring the need for integrative approaches. Graph representations provide an intuitive and meaningful way to harmonize biological data, using nodes to represent biological concepts (e.g., genes, single nucleotide polymorphisms, proteins, and phenotypes) and edges to represent their relationships. We present DRIVE-KG (Disease Risk Inference and Variant Exploration Knowledge Graph), which uses a heterogeneous graph representation to integrate data from diverse multi-omics datasets. We trained two distinct models using DRIVE-KG: a link prediction model to suggest associations between SNPs and two pilot phenotypes (endometriosis and obesity), and a graph convolutional network (GCN) for patient-level classification of endometriosis/adenomyosis as a combined phenotype. We conducted patient-level classification using data from 1,441 Penn Medicine BioBank participants with gold standard chart-reviewed endometriosis/adenomyosis status. The link prediction model uncovered 66 high-confidence (model score ≥ 0.95) candidate SNP-endometriosis associations, representing largely distinct genetic signals (R2 < 0.1). These variants were enriched for obesity/body mass index traits (24.2%), lipid metabolism (6%), and depressive disorders (4.5%), showing agreement with emerging hypotheses about endometriosis etiology. In contrast, of the high-confidence, candidate SNP-obesity associations that could be evaluated using LDlink, 38.22% were in high linkage disequilibrium (R2 ≥ 0.8) with known obesity or comorbidity associations. The GCN to classify patient endometriosis/adenomyosis status had an F1 score of 0.752 compared to 0.698 for a genetic risk score. Despite this moderate improvement, we found that the GCN learned meaningful stratification of underlying adenomyosis signal and severe endometriosis grades. Together, these results demonstrate that heterogeneous integration of multi-omics data is valuable for diverse downstream tasks-including discovery and clinical prediction-particularly for understudied diseases where traditional genomic approaches are insufficient.