Her, Y. | Search bioRxiv

bioRxiv2026

CK2 variant function and disease modelling in Drosophila reveal allelic heterogeneity and Wnt/β-catenin-mediated phenotypes

Heterozygous pathogenic variants in CSNK2A1 or CSNK2B encoding the Casein Kinase 2 (CK2) protein complex, lead to pediatric neurodevelopmental disorders, Okur-Chung Neurodevelopmental Syndrome (OCNDS) and Poirier-Bienvenu Neurodevelopmental Syndrome (POBINDS). OCNDS and POBINDS are characterized by a range of symptoms, including developmental delay, intellectual disability, facial dysmorphism, and seizures. Despite over 250 reported cases of OCNDS and POBINDS, we do not fully understand how specific alterations in CK2 relate to the heterogeneity observed in patients. To investigate this, we used the fruit fly, Drosophila melanogaster, as a model system. To assess variant impact, we co-expressed human CSNK2A1 and CSNK2B reference or disease-causing variants in flies. In parallel, we determined the role of Drosophila CkII in the developing and mature nervous system, specifically in neurons and glia. We found that 12/13 variants tested act as full or partial loss-of-function with one CSNK2A1 variant showing gain-of-function. Phospho-proteomic studies in neurons revealed separate signatures for loss- and gain-of-function variants. We found that neuronal and glial CkII is critical for organismal development. Reduction of neuronal CkII in the adult nervous system causes motor and seizure-like phenotypes. Finally, given the known role of CK2 in potentiating Wnt/{beta}-catenin signalling, we show that Wnt agonists partially rescue phenotypes associated with adult-specific neuronal reduction of CkII. This work generates Drosophila models of CSNK2A1 and CSNK2B expression to functionally assess variant impact, as well as an adult-specific neuronal loss-of-function model for drug screening and mechanistic studies.

genetics↗

bioRxiv2025

ATOMIC: A graph attention neural network for ATOpic dermatitis prediction on human gut MICrobiome

Atopic dermatitis (AD) is a chronic inflammatory skin disease driven by complex interactions among genetic, environmental, and microbial factors; however, its etiology remains unclear. Recent studies have reported the role of gut microbiota dysbiosis in AD pathogenesis, leading to increased interest in microbiome-targeted therapeutic strategies such as probiotics and fecal microbiota transplantation. Building on these findings, recent advances in computational modeling have introduced machine learning and deep learning-based approaches to capture the nonlinear relationships between gut microbiota and diseases. However, these models focus on diseases other than AD and often fail to capture complex microbial interactions or incorporate microbial genomic information, thereby offering limited interpretability. To address these limitations, we propose ATOMIC, an interpretable graph attention network-based model that incorporates microbial co-expression networks to predict AD. Microbial co-expression networks incorporate microbial genomic information as node features, thereby enhancing their ability to capture functionally relevant microbial patterns. To train and test our model, we collected and processed 99 gut microbiome samples from adult patients with AD and healthy controls at Kangwon National University Hospital (KNUH). As a result, ATOMIC achieved an AUROC of 0.810 and an AUPRC of 0.927 on the KNUH dataset. Furthermore, ATOMIC identified microbes potentially associated with AD prediction and proposed candidate microbial biomarkers that may inform future therapeutic strategies. To facilitate future research, we publicly released a gut microbial abundance dataset from KNUH. The source code and processed abundance data are available from ATOMIC GitHub repository at https://www.github.com/KU-MedAI/ATOMIC. Author summaryAtopic dermatitis (AD) is a chronic inflammatory skin disease affecting approximately 120 million people worldwide, often leading to a reduced quality of life, including sleep disturbances and stress. Current treatment methods rarely achieve complete remission, and the precise cause of AD remains unclear. The association between imbalances in the gut microbiota and the pathogenesis of AD has highlighted the gut microbiota as a promising target for therapeutic strategies. Exploring the potential role of the gut microbiota in modulating host immune responses is likely to have a positive impact on future AD treatment. Consequently, the growing need to develop machine learning and deep learning-based predictive models using gut microbiome data. However, existing models often suffer from limited interpretability owing to their black-box nature and frequently overlook microbial interactions and genomic contexts. To address these limitations, we propose a novel model called ATOMIC, which integrates microbial co-expression networks and genomic information to predict AD. Our model achieved superior performance in terms of AUROC, AUPRC, and F1 scores compared with existing models and provided interpretable insights through an attention mechanism, thereby offering promising potential for identifying relevant biomarkers. Together, these results highlight ATOMICs potential for the accurate prediction of AD and for identifying biologically meaningful microbial signatures.

systems biology↗