Neuropathy target esterase activity predicts retinopathy among PNPLA6 disorders

Liu, J.; He, Y.; Lwin, C.; Han, M.; Guan, B.; Niak, A.; Bender, C.; Moore, N.; Huryn, L. A.; Sergeev, Y.; Qian, H.; Zeng, Y.; Dong, L.; Liu, P.; Li, J.; Haugen, C. J.; Prasov, L.; Shi, R.; Dollfus, H.; Aristodemou, P.; Laich, Y.; Nemeth, A. H.; Taylor, J.; Downes, S.; Krawczynski, M.; Meunier, I.; Strassberg, M.; Tenney, J.; Gao, J.; Shear, M. A.; Moore, A. T.; Duncan, J. L.; Menendez, B.; Hull, S.; Vincent, A.; Siskind, C. E.; Traboulsi, E. I.; Blackstone, C.; Sisk, R.; Utz, V.; Webster, A. R.; Michaelides, M.; Arno, G.; Synofzik, M.; Hufnagel, R. B.

doi:10.1101/2023.06.09.544373

Home/Archive/bioRxiv

bioRxiv · 10.1101/2023.06.09.544373

Neuropathy target esterase activity predicts retinopathy among PNPLA6 disorders

View original source ↗Export metadata ↓

Abstract

Biallelic pathogenic variants in the PNPLA6 gene cause a broad spectrum of disorders leading to gait disturbance, visual impairment, anterior hypopituitarism, and hair anomalies. PNPLA6 encodes Neuropathy target esterase (NTE), yet the role of NTE dysfunction on affected tissues in the large spectrum of associated disease remains unclear. We present a clinical meta-analysis of a novel cohort of 23 new patients along with 95 reported individuals with PNPLA6 variants that implicate missense variants as a driver of disease pathogenesis. Measuring esterase activity of 46 disease-associated and 20 common variants observed across PNPLA6-associated clinical diagnoses unambiguously reclassified 10 variants as likely pathogenic and 36 variants as pathogenic, establishing a robust functional assay for classifying PNPLA6 variants of unknown significance. Estimating the overall NTE activity of affected individuals revealed a striking inverse relationship between NTE activity and the presence of retinopathy and endocrinopathy. This phenomenon was recaptured in vivo in an allelic mouse series, where a similar NTE threshold for retinopathy exists. Thus, PNPLA6 disorders, previously considered allelic, are a continuous spectrum of pleiotropic phenotypes defined by an NTE genotype:activity:phenotype relationship. This relationship and the generation of a preclinical animal model pave the way for therapeutic trials, using NTE as a biomarker.

Explore related subjects

neuroscience Preprint

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Liu, J., He, Y., Lwin, C., Han, M., Guan, B., Niak, A., Bender, C., Moore, N., Huryn, L. A., Sergeev, Y., Qian, H., Zeng, Y., Dong, L., Liu, P., Li, J., Haugen, C. J., Prasov, L., Shi, R., Dollfus, H., Aristodemou, P., Laich, Y., Nemeth, A. H., Taylor, J., Downes, S., Krawczynski, M., Meunier, I., Strassberg, M., Tenney, J., Gao, J., Shear, M. A., Moore, A. T., Duncan, J. L., Menendez, B., Hull, S., Vincent, A., Siskind, C. E., Traboulsi, E. I., Blackstone, C., Sisk, R., Utz, V., Webster, A. R., Michaelides, M., Arno, G., Synofzik, M., Hufnagel, R. B.. 2023-06-11. Neuropathy target esterase activity predicts retinopathy among PNPLA6 disorders. https://doi.org/10.1101/2023.06.09.544373

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

The Unreasonable Effectiveness of Cell Types in Describing Neuronal Physiological Features

Single-cell RNA sequencing (scRNA-seq) captures detailed gene expression profiles at scale, while patch-clamp recordings measure intrinsic neuronal electrophysiological properties. Modeling the relations between these two modalities remains a challenge. Here, we compare how well electrophysiological features can be predicted by traditional transcriptomic cell type classification, representations derived from a foundational model (scGPT) pretrained on large-scale scRNA-seq datasets, ion channel-coding genes, and highly variable genes. Using paired transcriptomic and electrophysiological patch-sequencing data from 495 human neurons from neurosurgical tissue, we find that cluster-level cell type representations consistently outperform highly variable gene selection, ion channel gene selection, and context-enriched scGPT embeddings. Notably, performance varies across model architectures and initializations, and the best results are obtained by combining the outputs of separate cell type and scGPT-based models. Together, these findings suggest that traditional discrete cellular classification is highly effective in predicting physiological features. For maximum performance it can be complemented by pretrained transformer models.

neuroscience↗

bioRxiv2026

A nonlinear inhibition pathway underlying cortical responses to tuned holographic optogenetic perturbations

Optogenetics enables causal manipulation of cortical activity. Perturbation responses can be counterintuitive due to network interactions, making theory essential for predicting them. Existing approaches often rely on linear approximations, which fail for many biologically relevant perturbations. Here we develop a nonlinear theory of responses to holographic perturbations in cell-type-specific recurrent networks with structured connectivity. We fit a nonlinear model to mouse V1 data, which shows cotuned-ensemble suppression: perturbing spatially clustered neurons with similar preferred orientations yields markedly stronger short-range suppression than perturbing untuned ensembles. We show that cotuned-ensemble suppression arises from a feature-tuned, nonlinear inhibition pathway implicating somatostatin-positive (SST) interneurons. The theory predicts that cotuned ensembles suppress parvalbumin-positive (PV) neurons but facilitate SST neurons, and links the degree of cotuned-ensemble suppression or facilitation to the variance of the SST response. This framework identifies mechanisms by which nonlinear inhibition sculpts cortical dynamics and establishes a predictive basis for targeted optogenetic interventions.

neuroscience↗

bioRxiv2026

Proteomic signatures of APOE ε4 across human tissues and cell types in Alzheimers disease

The apolipoprotein E {varepsilon}4 (APOE {varepsilon}4) allele is the strongest genetic risk factor for late-onset Alzheimers disease (AD). However, the underlying molecular mechanisms remain unclear. This study included 1691 participants from the Religious Orders Study and Rush Memory and Aging Project (ROSMAP), 1226 participants from the Accelerating Medicines Partnership - Alzheimers Disease (AMP-AD) Diverse Cohorts Study, and 735 participants from the Alzheimers Disease Neuroimaging Initiative (ADNI). To characterise APOE {varepsilon}4 molecular effects, we analysed proteomic data from plasma, cerebrospinal fluid (CSF), and induced pluripotent stem cell (iPSC)-derived astrocytes and neurons, as well as transcriptomic and proteomic data from multiple brain regions. The association of APOE {varepsilon}4 with AD neuropathology was also examined. APOE {varepsilon}4 carriers shared a plasma proteomic signature enriched for immune processes, irrespective of AD diagnosis. A machine learning classifier trained on this signature discriminated APOE {varepsilon}4 carriers from non-carriers in an independent cohort using CSF proteomics. APOE {varepsilon}4 carriage was associated with higher Braak stages and Consortium to Establish a Registry for Alzheimers Disease (CERAD) score. However, only limited APOE {varepsilon}4-associated transcriptomic and proteomic changes were observed in bulk brain tissue, with poor cross-layer concordance. Proteomic analyses of iPSC-derived astrocytes and neurons further revealed cell-type-specific APOE {varepsilon}4-associated changes. APOE {varepsilon}4 is associated with a consistent proteomic signature across plasma and CSF. Its molecular effects in the brain differ across cell types, brain regions and molecular layers. These findings support the need for cell-type-resolved multi-omic studies to elucidate how APOE {varepsilon}4 confers AD risk.

neuroscience↗