A novel splice site variant in DEGS1 leads to aberrant splicing and loss of DEGS1 enzyme activity, a VUS resolved - Holly C. Beale; Victor Tse; Joanna Y. Lee; Jon Akutagawa; Yusuph Mavura; Brandon Saint-John; Allison Cheney; Dennis R. Mulligan; Guillermo Chacaltana; Martin Gutierrez; Jessica Tenney; Joseph T. Shieh; et al. (Human Genetics, 2026)
Pathogenic DEGS1 variants have been reported in individuals with autosomal recessive hypomyelinating leukodystrophy 18 (HLD18; MIM# 618404). Here we describe three participants with HLD features and a previously unreported homozygous DEGS1 5′ splice site variant, c.825+4_825 + 5delAGinsTT (NM_003676.4). We used next-generation DNA and transcriptome sequencing, cell-based splicing assays, and tandem mass spectrometry to detect and characterize the variant’s impact on DEGS1 expression. We then performed RNA structure probing and conventional antisense oligonucleotide screening to investigate molecular mechanisms for potential therapeutic intervention. We show that the splice site variant: (1) was sufficient to induce exon two skipping in most detected transcripts; (2) resulted in structural changes to the 5′ and 3′ splice site regions using RNA structure probing; and (3) corresponds to plasma sphingolipid profiles consistent with loss of sphingolipid delta(4)-desaturase activity. Our RNA and lipidomic evidence proved that the DEGS1 variant c.825+4_825 + 5delAGinsTT is pathogenic and suggested a mechanistic model that explains how exon two skipping is induced.

Interpretable deep learning reveals spatiotemporal MRI features of brain aging that align with neurodegeneration - Nikhil N. Chaudhari; Owen M. Vega; Phoebe Imms; Jaron M. Kawamura; Nahian F. Chowdhury; Tamara Jafar; Andrei Irimia; Michael Weiner; Paul Aisen; Ronald Petersen; Clifford R. Jack; William Jagust; et al. (GeroScience, 2026)
Cortical thinning and atrophy are hallmarks of brain aging that have been characterized using magnetic resonance imaging (MRI). Brain aging involves many neuroanatomic features whose effects on brain structure remain unexplored. To address this challenge, we trained interpretable deep neural networks (DNNs) to estimate brain age (BA) from T 1 -weighted ( T 1 w) MRI. By identifying MRI features unapparent to humans, DNNs can find aging-related structural alterations above and beyond cortical thickness and atrophy. Using a novel approach to DNN interpretability, we mapped brain aging progression in 25,539 cognitively normal UK Biobank adults aged 45–83 years. Cortical aging is found to involve anatomic features becoming prominent during the 50s within frontolateral, mesolimbic, cuneal, and occipitotemporal regions. From these foci, aging-related features propagate to adjacent areas at rates peaking in the 60s. Cortical thinning and atrophy do not trend closely with neurodegeneration, but important DNN-identifiable anatomic features have spatiotemporal dynamics that match those of amyloid or tau. Our results challenge the assumption that MRI cannot map anatomic features trending with neurodegeneration. Interpretable DNNs can empower MRI to quantify anatomic aging as a process of spatial feature expansion from focal regions into nearby structures in the sequence of neurodegenerative pathology. Traditional morphometrics explain only ∼1% of variance in DNN-identifiable features, which clarifies why the former are insensitive to anatomic changes involving neurodegeneration. Our results conceptualize brain aging in the context of spatial and temporal parallels between anatomic senescence and neuropathology. These findings may help to map cognitively normal adults’ neurodegenerative anatomy even without PET measurements. Graphical Abstract

Sex and APOE genotype specific brain regional vulnerability to Alzheimer's Disease - Qi Zeng; Minghui Wang; Erming Wang; Xianxiao Zhou; Peng Xu; Vahram Haroutunian; Dongming Cai; Bin Zhang; Michael Weiner; Paul Aisen; Ronald Petersen; Clifford R. Jack; et al. (GeroScience, 2026)
Alzheimer’s disease (AD) disproportionately affects women and carriers of the apolipoprotein E ε4 allele (APOE4), yet little is known about how sex and APOE interact to influence white matter (WM) integrity during disease progression. We integrated diffusion MRI and matched blood transcriptomic data to investigate these interactions and their underlying biological mechanisms. WM microstructure was quantified using diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI), and regional vulnerability was assessed with a composite vulnerability score (CVS) derived from associations between diffusion features and AD severity across clinical traits in each of the four sex-APOE groups (female or male, with or without APOE4). Brain parcellation with the Eve atlas revealed regions consistently affected across sex-APOE groups (e.g., parahippocampal and superior temporal gyri) and regions specific to individual groups (e.g., the cingulum in females with APOE4 and the middle frontal gyrus in males without APOE4). Gene co-expression network analysis of the matched blood expression data identified gene subnetworks linked to group-specific regional vulnerability, including a muscle tissue morphogenesis module regulated by NEURL1B and HIST1H2BN associated with middle frontal gyrus vulnerability. These findings demonstrate that sex and APOE genotype jointly shape region-specific WM vulnerability and its molecular signatures in AD. Understanding these interactions provides novel mechanistic insights and may inform precision approaches to drug development, biomarker discovery, and clinical trial design for AD.

Predicting the progression of MCI and Alzheimer’s disease on structural brain integrity and other features with machine learning - Marthe Mieling; Mushfa Yousuf; Nico Bunzeck; Michael Weiner; Paul Aisen; Ronald Petersen; Clifford R. Jack; William Jagust; Susan Landau; Monica Rivera-Mindt; Leslie M. Shaw; Edward B. Lee; et al. (GeroScience, 2025)
Machine learning (ML) on structural MRI data shows high potential for classifying Alzheimer’s disease (AD) progression, but the specific contribution of brain regions, demographics, and proteinopathy remains unclear. Using Alzheimer’s Disease Neuroimaging Initiative (ADNI) data, we applied an extreme gradient-boosting algorithm and SHAP (SHapley Additive exPlanations) values to classify cognitively normal (CN) older adults, those with mild cognitive impairment (MCI) and AD dementia patients. Features included structural MRI, CSF status, demographics, and genetic data. Analyses comprised one cross-sectional multi-class classification (CN vs. MCI vs. AD dementia, n = 568) and two longitudinal binary-class classifications (CN-to-MCI converters vs. CN stable, n = 92; MCI-to-AD converters vs. MCI stable, n = 378). All classifications achieved 70–77% accuracy and 61–83% precision. Key features were CSF status, hippocampal volume, entorhinal thickness, and amygdala volume, with a clear dissociation: hippocampal properties contributed to the conversion to MCI, while the entorhinal cortex characterized the conversion to AD dementia. The findings highlight explainable, trajectory-specific insights into AD progression.

Next Generation Risk Assessment approaches for advanced nanomaterials: Current status and future perspectives - Danail Hristozov; Elena Badetti; Paolo Bigini; Andrea Brunelli; Susan Dekkers; Luisa Diomede; Shareen H. Doak; Wouter Fransman; Agnieszka Gajewicz-Skretna; Elisa Giubilato; Laura Gómez-Cuadrado; Roland Grafström; et al. (NanoImpact, 2024)

Social Mycology: Using Social Media Networks in the Management of Aspergillosis and Other Mycoses - Neil R. H. Stone (Mycopathologia, 2023)

Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity - Chiara Fallerini; Nicola Picchiotti; Margherita Baldassarri; Kristina Zguro; Sergio Daga; Francesca Fava; Elisa Benetti; Sara Amitrano; Mirella Bruttini; Maria Palmieri; Susanna Croci; Mirjam Lista; et al. (Human Genetics, 2022)
The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management.

Linking Chlorophyll Concentration and Wind Patterns Using Satellite Data in the Central and Northern California Current System - Hally B. Stone; Neil S. Banas; Parker MacCready; Raphael M. Kudela; Bridget Ovall (Frontiers in Marine Science, 2020)

Creating positive synergies between risk management and transfer to accelerate food system climate resilience - Shahbaz Mushtaq; Jarrod Kath; Roger Stone; Ross Henry; Peter Läderach; Kathryn Reardon-Smith; David Cobon; Torben Marcussen; Neil Cliffe; Paul Kristiansen; Frederik Pischke (Climatic Change, 2020)

Elevated carbon dioxide decreases the adverse effects of higher temperature and drought stress by mitigating oxidative stress and improving water status in Arabidopsis thaliana - Mohammad I. Abo Gamar; Anna Kisiala; R. J. Neil Emery; Edward C. Yeung; Sophia L. Stone; Mirwais M. Qaderi (Planta, 2019)

Genomic epidemiology of Cryptococcus yeasts identifies adaptation to environmental niches underpinning infection across an African HIV / AIDS cohort - Mathieu Vanhove; Mathew A. Beale; Johanna Rhodes; Duncan Chanda; Shabir Lakhi; Geoffrey Kwenda; Sile Molloy; Natasha Karunaharan; Neil Stone; Thomas S. Harrison; Tihana Bicanic; Matthew C. Fisher (Molecular Ecology, 2017)
Emerging infections caused by fungi have become a widely recognized global phenomenon and are causing an increasing burden of disease. Genomic techniques are providing new insights into the structure of fungal populations, revealing hitherto undescribed fine‐scale adaptations to environments and hosts that govern their emergence as infections. Cryptococcal meningitis is a neglected tropical disease that is responsible for a large proportion of AIDS ‐related deaths across Africa; however, the ecological determinants that underlie a patient's risk of infection remain largely unexplored. Here, we use genome sequencing and ecological genomics to decipher the evolutionary ecology of the aetiological agents of cryptococcal meningitis, Cryptococcus neoformans and Cryptococcus gattii , across the central African country of Zambia. We show that the occurrence of these two pathogens is differentially associated with biotic (macroecological) and abiotic (physical) factors across two key African ecoregions, Central Miombo woodlands and Zambezi Mopane woodlands. We show that speciation of Cryptococcus has resulted in adaptation to occupy different ecological niches, with C. neoformans found to occupy Zambezi Mopane woodlands and C. gattii primarily recovered from Central Miombo woodlands. Genome sequencing shows that C. neoformans causes 95% of human infections in this region, of which over three‐quarters belonged to the globalized lineage VNI . We show that VNI infections are largely associated with urbanized populations in Zambia. Conversely, the majority of C. neoformans isolates recovered in the environment belong to the genetically diverse African‐endemic lineage VNB , and we show hitherto unmapped levels of genomic diversity within this lineage. Our results reveal the complex evolutionary ecology that underpins the reservoirs of infection for this, and likely other, deadly pathogenic fungi.

Poster abstracts - J. Ferrie; M. Shipley; F. Cappuccio; E. Brunner; M. Miller; M. Kumari; M. Marmot; A. Coenen; J. L. Castillo; F. Araya; G. Bustamante; L. Montecino; et al. (Sleep and Biological Rhythms, 2007)

Evidence and Implications of Recent Climate Change in Northern Alaska and Other Arctic Regions - Larry D. Hinzman; Neil D. Bettez; W. Robert Bolton; F. Stuart Chapin; Mark B. Dyurgerov; Chris L. Fastie; Brad Griffith; Robert D. Hollister; Allen Hope; Henry P. Huntington; Anne M. Jensen; Gensuo J. Jia; et al. (Climatic Change, 2005)