Cis-regulatory evolution of the recently expanded Ly49 gene family
(2024) Nature Communications, 15 (1), art. no. 4839, .
Fan, C.a b , Xing, X.a b , Murphy, S.J.H.c d , Poursine-Laurent, J.e , Schmidt, H.a b , Parikh, B.A.f , Yoon, J.e , Choudhary, M.N.K.a b , Saligrama, N.c f g h i , Piersma, S.J.e j , Yokoyama, W.M.e f , Wang, T.a b k
a Department of Genetics, Washington University School of Medicine, St. Louis, 63110, United States
b The Edison Family Center for Genome Sciences & amp; Systems Biology, Washington University School of Medicine, St. Louis, 63110, United States
c Department of Neurology, Washington University School of Medicine, St. Louis, 63110, United States
d Medical Scientist Training Program, Washington University School of Medicine, St. Louis, 63110, United States
e Division of Rheumatology, Department of Medicine, Washington University School of Medicine, St. Louis, 63110, United States
f Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, 63110, United States
g Bursky Center for Human Immunology and Immunotherapy Programs, Washington University School of Medicine, St. Louis, 63110, United States
h Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, 63110, United States
i Center for Brain Immunology and Glia (BIG), Washington University School of Medicine, St. Louis, 63110, United States
j Siteman Cancer Center, Washington University School of Medicine, St. Louis, 63110, United States
k McDonnell Genome Institute, Washington University School of Medicine, St. Louis, 63110, United States
Abstract
Comparative genomics has revealed the rapid expansion of multiple gene families involved in immunity. Members within each gene family often evolved distinct roles in immunity. However, less is known about the evolution of their epigenome and cis-regulation. Here we systematically profile the epigenome of the recently expanded murine Ly49 gene family that mainly encode either inhibitory or activating surface receptors on natural killer cells. We identify a set of cis-regulatory elements (CREs) for activating Ly49 genes. In addition, we show that in mice, inhibitory and activating Ly49 genes are regulated by two separate sets of proximal CREs, likely resulting from lineage-specific losses of CRE activity. Furthermore, we find that some Ly49 genes are cross-regulated by the CREs of other Ly49 genes, suggesting that the Ly49 family has begun to evolve a concerted cis-regulatory mechanism. Collectively, we demonstrate the different modes of cis-regulatory evolution for a rapidly expanding gene family. © The Author(s) 2024.
Document Type: Article
Publication Stage: Final
Source: Scopus
Relationship between sex biases in gene expression and sex biases in autism and Alzheimer’s disease
(2024) Biology of Sex Differences, 15 (1), art. no. 47, .
Fass, S.B.a b , Mulvey, B.a b c , Chase, R.a b , Yang, W.a d , Selmanovic, D.a b , Chaturvedi, S.M.a b , Tycksen, E.a d , Weiss, L.A.f g h , Dougherty, J.D.a b e i
a Department of Genetics, Washington University School of Medicine, 660 S. Euclid Ave, Saint Louis, MO 63110, United States
b Department of Psychiatry, Washington University School of Medicine, 660 S. Euclid Ave, Saint Louis, MO 63110, United States
c Lieber Institute for Brain Development, 855 North Wolfe St. Ste 300, Baltimore, MD 21205, United States
d McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO 63110, United States
e Intellectual and Developmental Disabilities Research Center, Washington University School of Medicine, 660 S. Euclid Ave, Saint Louis, MO 63110, United States
f Institute for Human Genetics, University of California, San Francisco, 513 Parnassus Ave, HSE901, San Francisco, CA 94143, United States
g Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, 513 Parnassus Ave, HSE901, San Francisco, CA 94143, United States
h Weill Institute for Neurosciences, University of California, San Francisco, 513 Parnassus Ave, HSE901, San Francisco, CA 94143, United States
i Department of Genetics, 4566 Scott Ave., St. Louis, MO 63110-1093, United States
Abstract
Background: Sex differences in the brain may play an important role in sex-differential prevalence of neuropsychiatric conditions. Methods: In order to understand the transcriptional basis of sex differences, we analyzed multiple, large-scale, human postmortem brain RNA-Seq datasets using both within-region and pan-regional frameworks. Results: We find evidence of sex-biased transcription in many autosomal genes, some of which provide evidence for pathways and cell population differences between chromosomally male and female individuals. These analyses also highlight regional differences in the extent of sex-differential gene expression. We observe an increase in specific neuronal transcripts in male brains and an increase in immune and glial function-related transcripts in female brains. Integration with single-nucleus data suggests this corresponds to sex differences in cellular states rather than cell abundance. Integration with case–control gene expression studies suggests a female molecular predisposition towards Alzheimer’s disease, a female-biased disease. Autism, a male-biased diagnosis, does not exhibit a male predisposition pattern in our analysis. Conclusion: Overall, these analyses highlight mechanisms by which sex differences may interact with sex-biased conditions in the brain. Furthermore, we provide region-specific analyses of sex differences in brain gene expression to enable additional studies at the interface of gene expression and diagnostic differences. Graphical Abstract: (Figure presented.) © The Author(s) 2024.
Author Keywords
Alzheimer’s; Autism; Brain; Expression; Human; Immune; Neuronal; rna-seq; Sex; Sex-bias
Document Type: Article
Publication Stage: Final
Source: Scopus
Prevalence of Suicidality in Adolescents With Newly Diagnosed Focal Epilepsy at Diagnosis and Over the Following 36 Months
(2024) Neurology, 103 (1), p. e209397.
Greenwood, H.T., French, J., Ferrer, M., Jandhyala, N., Thio, L.L., Dlugos, D.J., Park, K.L., Kanner, A.M., Human Epilepsy Project Investigators
From the Department of Neurology (H.T.G., J.F., M.F., N.J.), and Department of Pediatrics (M.F.), NYU Grossman School of Medicine, New York; Department of Neurology (L.L.T.), Washington University in St. Louis School of Medicine, MO; Department of Pediatrics and Neurology (D.J.D.), Children’s Hospital of Philadelphia, PA; Department of Pediatrics and Neurology (K.L.P.), University of Colorado School of Medicine, Aurora; and Department of Neurology (A.M.K.), Miller School of Medicine, University of Miami, FL
Abstract
BACKGROUND AND OBJECTIVES: Individuals with epilepsy have increased risk of suicidal ideation (SI) and behaviors when compared with the general population. This relationship has remained largely unexplored in adolescents. We investigated the prevalence of suicidality in adolescents with newly diagnosed focal epilepsy within 4 months of treatment initiation and over the following 36 months. METHODS: This was a post hoc analysis of the enrollment and follow-up data from the Human Epilepsy Project, an international, multi-institutional study that enrolled participants between 2012 and 2017. Participants enrolled were 11-17 years of age within 4 months of treatment initiation for focal epilepsy. We used data from the Columbia Suicide Severity Rating Scale (C-SSRS), administered at enrollment and over the 36-month follow-up period, along with data from medical records. RESULTS: A total of 66 adolescent participants were enrolled and completed the C-SSRS. At enrollment, 14 (21%) had any lifetime SI and 5 (8%) had any lifetime suicidal behaviors (SBs). Over the following 36 months, 6 adolescents reported new onset SI and 5 adolescents reported new onset SB. Thus, the lifetime prevalence of SI within this population increased from 21% to 30% (14-20 adolescents), and the lifetime prevalence of SB increased from 8% to 15% (5-10). DISCUSSION: The prevalence of suicidality in adolescents with newly diagnosed focal epilepsy reported in our study is consistent with previous findings of significant suicidality observed in epilepsy. We identify adolescents as an at-risk population at the time of epilepsy diagnosis and in the following years.
Document Type: Article
Publication Stage: Final
Source: Scopus
AmyloidPETNet: Classification of Amyloid Positivity in Brain PET Imaging Using End-to-End Deep Learning
(2024) Radiology, 311 (3), p. e231442.
Fan, S., Ponisio, M.R., Xiao, P., Ha, S.M., Chakrabarty, S., Lee, J.J., Flores, S., LaMontagne, P., Gordon, B., Raji, C.A., Marcus, D.S., Nazeri, A., Ances, B.M., Bateman, R.J., Morris, J.C., Benzinger, T.L.S., Sotiras, A., Alzheimer’s Disease Neuroimaging Initiative
From the Department of Bioengineering, Rice University, Houston, Tex (S. Fan); Department of Radiology (S. Fan, M.R.P., P.X., S.M.H., J.J.L., S. Flores, P.L., B.G., C.A.R., D.S.M., A.N., T.L.S.B., A.S.), Charles F. and Joanne Knight Alzheimer Disease Research Center (B.G., B.M.A., R.B., J.C.M., T.L.S.B.), Department of Neurology (C.A.R., B.M.A., R.J.B., J.C.M.), and Institute for Informatics, Data Science and Biostatistics (A.S.), Washington University School of Medicine, 660 S Euclid Ave, Campus Box 8132, St Louis, MO 63110; Duke-NUS Medical School, Singapore (S. Fan); Department of Electrical and Systems Engineering, Washington University in St Louis, St Louis, Mo (S.C., A.S.); Brain Health Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Canada (A.N.); and Tracy Family SILQ Center for Neurodegenerative Biology, St Louis, Mo (R.J.B.)
Abstract
Background Visual assessment of amyloid PET scans relies on the availability of radiologist expertise, whereas quantification of amyloid burden typically involves MRI for processing and analysis, which can be computationally expensive. Purpose To develop a deep learning model to classify minimally processed brain PET scans as amyloid positive or negative, evaluate its performance on independent data sets and different tracers, and compare it with human visual reads. Materials and Methods This retrospective study used 8476 PET scans (6722 patients) obtained from late 2004 to early 2023 that were analyzed across five different data sets. A deep learning model, AmyloidPETNet, was trained on 1538 scans from 766 patients, validated on 205 scans from 95 patients, and internally tested on 184 scans from 95 patients in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) fluorine 18 (18F) florbetapir (FBP) data set. It was tested on ADNI scans using different tracers and scans from independent data sets. Scan amyloid positivity was based on mean cortical standardized uptake value ratio cutoffs. To compare with model performance, each scan from both the Centiloid Project and a subset of the Anti-Amyloid Treatment in Asymptomatic Alzheimer’s Disease (A4) study were visually interpreted with a confidence level (low, intermediate, high) of amyloid positivity/negativity. The area under the receiver operating characteristic curve (AUC) and other performance metrics were calculated, and Cohen κ was used to measure physician-model agreement. Results The model achieved an AUC of 0.97 (95% CI: 0.95, 0.99) on test ADNI 18F-FBP scans, which generalized well to 18F-FBP scans from the Open Access Series of Imaging Studies (AUC, 0.95; 95% CI: 0.93, 0.97) and the A4 study (AUC, 0.98; 95% CI: 0.98, 0.98). Model performance was high when applied to data sets with different tracers (AUC ≥ 0.97). Other performance metrics provided converging evidence. Physician-model agreement ranged from fair (Cohen κ = 0.39; 95% CI: 0.16, 0.60) on a sample of mostly equivocal cases from the A4 study to almost perfect (Cohen κ = 0.93; 95% CI: 0.86, 1.0) on the Centiloid Project. Conclusion The developed model was capable of automatically and accurately classifying brain PET scans as amyloid positive or negative without relying on experienced readers or requiring structural MRI. Clinical trial registration no. NCT00106899 © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Bryan and Forghani in this issue.
Document Type: Article
Publication Stage: Final
Source: Scopus
Astrocytes as a mechanism for contextually-guided network dynamics and function
(2024) PLoS Computational Biology, 20 (5), art. no. e1012186, .
Gong, L.a , Pasqualetti, F.b , Papouin, T.c , Ching, S.a
a Department of Electrical and Systems Engineering, Washington University, St. Louis, MO, United States
b Department of Mechanical Engineering, University of California, Riverside, CA, United States
c Department of Neuroscience, Washington University School of Medicine, St. Louis, MO, United States
Abstract
Astrocytes are a ubiquitous and enigmatic type of non-neuronal cell and are found in the brain of all vertebrates. While traditionally viewed as being supportive of neurons, it is increasingly recognized that astrocytes play a more direct and active role in brain function and neural computation. On account of their sensitivity to a host of physiological covariates and ability to modulate neuronal activity and connectivity on slower time scales, astrocytes may be particularly well poised to modulate the dynamics of neural circuits in functionally salient ways. In the current paper, we seek to capture these features via actionable abstractions within computational models of neuron-astrocyte interaction. Specifically, we engage how nested feedback loops of neuron-astrocyte interaction, acting over separated timescales, may endow astrocytes with the capability to enable learning in context-dependent settings, where fluctuations in task parameters may occur much more slowly than within-task requirements. We pose a general model of neuron-synapse-astrocyte interaction and use formal analysis to characterize how astrocytic modulation may constitute a form of meta-plasticity, altering the ways in which synapses and neurons adapt as a function of time. We then embed this model in a bandit-based reinforcement learning task environment, and show how the presence of time-scale separated astrocytic modulation enables learning over multiple fluctuating contexts. Indeed, these networks learn far more reliably compared to dynamically homogeneous networks and conventional non-network-based bandit algorithms. Our results fuel the notion that neuron-astrocyte interactions in the brain benefit learning over different time-scales and the conveyance of task-relevant contextual information onto circuit dynamics. © 2024 Gong et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding details
U.S. Department of DefenseDODW911NF2110312
U.S. Department of DefenseDOD
National Institutes of HealthNIHR01MH127163
National Institutes of HealthNIH
Document Type: Article
Publication Stage: Final
Source: Scopus
Pick a PACC: Comparing Domain-Specific and General Cognitive Composites in Alzheimer Disease Research
(2024) Neuropsychology, .
McKay, N.S.a , Millar, P.R.b , Nicosia, J.b , Aschenbrenner, A.J.b , Gordon, B.A.a , Benzinger, T.L.S.a , Cruchaga, C.C.c , Schindler, S.E.b , Morris, J.C.b , Hassenstab, J.b
a Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, United States
b Department of Neurology, Washington University School of Medicine, St. Louis, United States
c Department of Psychiatry, Washington University School of Medicine, St. Louis, United States
Abstract
Objective: We aimed to illustrate how complex cognitive data can be used to create domain-specific and general cognitive composites relevant to Alzheimer disease research. Method: Using equipercentile equating, we combined data from the Charles F. and Joanne Knight Alzheimer Disease Research Center that spanned multiple iterations of the Uniform Data Set. Exploratory factor analyses revealed four domain-specific composites representing episodic memory, semantic memory, working memory, and attention/processing speed. The previously defined preclinical Alzheimer disease cognitive composite (PACC) and a novel alternative, the Knight-PACC, were also computed alongside a global composite comprising all available tests. These three composites allowed us to compare the usefulness of domain and general composites in the context of predicting common Alzheimer disease biomarkers. Results: General composites slightly outperformed domain-specific metrics in predicting imaging-derived amyloid, tau, and neurodegeneration burden. Power analyses revealed that the global, Knight-PACC, and attention and processing speed composites would require the smallest sample sizes to detect cognitive change in a clinical trial, while the Alzheimer Disease Cooperative Study-PACC required two to three times as many participants. Conclusions: Analyses of cognition with the Knight-PACC and our domain-specific composites offer researchers flexibility by providing validated outcome assessments that can equate across test versions to answer a wide range of questions regarding cognitive decline in normal aging and neurodegenerative disease. © 2024 American Psychological Association
Author Keywords
Alzheimer; factor analysis; magnetic resonance imaging; positron emission tomography; preclinical Alzheimer disease cognitive composite
Document Type: Article
Publication Stage: Article in Press
Source: Scopus