MSTPublications: February 2021
General Purpose Structure-Based Drug Discovery Neural Network Score Functions with Human-Interpretable Pharmacophore Maps.
Brown BP, Mendenhall J, Geanes AR, Meiler J.
J Chem Inf Model. 2021 Jan 26. doi: 10.1021/acs.jcim.0c01001. Online ahead of print.
The BioChemical Library (BCL) is an academic open-source cheminformatics toolkit comprising ligand-based virtual high-throughput screening (vHTS) tools such as quantitative structure-activity/property relationship (QSAR/QSPR) modeling, small molecule flexible alignment, small molecule conformer generation, and more. Here, we expand the capabilities of the BCL to include structure-based virtual screening. We introduce two new score functions, BCL-AffinityNet and BCL-DockANNScore, based on novel distance-dependent signed protein-ligand atomic property correlations. Both metrics are conventional feed-forward dropout neural networks trained on the new descriptors. We demonstrate that BCL-AffinityNet is one of the top performing score functions on the comparative assessment of score functions 2016 affinity prediction and affinity ranking tasks. We also demonstrate that BCL-AffinityNet performs well on the CSAR-NRC HiQ I and II test sets. Furthermore, we demonstrate that BCL-DockANNScore is competitive with multiple state-of-the-art methods on the docking power and screening power tasks. Finally, we show how our models can be decomposed into human-interpretable pharmacophore maps to aid in hit/lead optimization. Altogether, our results expand the utility of the BCL for structure-based scoring to aid small molecule discovery and design. BCL-AffinityNet, BCL-DockANNScore, and the pharmacophore mapping application, as well as the remainder of the BCL cheminformatics toolkit, are freely available with an academic license at the BCL Commons site hosted on http://meilerlab.org/.
PreQual: An automated pipeline for integrated preprocessing and quality assurance of diffusion weighted MRI images.
Cai LY, Yang Q, Hansen CB, Nath V, Ramadass K, Johnson GW, Conrad BN, Boyd BD, Begnoche JP, Beason-Held LL, Shafer AT, Resnick SM, Taylor WD, Price GR, Morgan VL, Rogers BP, Schilling KG, Landman BA.
Magn Reson Med. 2021 Feb 3. doi: 10.1002/mrm.28678. Online ahead of print.
Purpose: Diffusion weighted MRI imaging (DWI) is often subject to low signal-to-noise ratios (SNRs) and artifacts. Recent work has produced software tools that can correct individual problems, but these tools have not been combined with each other and with quality assurance (QA). A single integrated pipeline is proposed to perform DWI preprocessing with a spectrum of tools and produce an intuitive QA document.
Methods: The proposed pipeline, built around the FSL, MRTrix3, and ANTs software packages, performs DWI denoising; inter-scan intensity normalization; susceptibility-, eddy current-, and motion-induced artifact correction; and slice-wise signal drop-out imputation. To perform QA on the raw and preprocessed data and each preprocessing operation, the pipeline documents qualitative visualizations, quantitative plots, gradient verifications, and tensor goodness-of-fit and fractional anisotropy analyses.
Results: Raw DWI data were preprocessed and quality checked with the proposed pipeline and demonstrated improved SNRs; physiologic intensity ratios; corrected susceptibility-, eddy current-, and motion-induced artifacts; imputed signal-lost slices; and improved tensor fits. The pipeline identified incorrect gradient configurations and file-type conversion errors and was shown to be effective on externally available datasets.
Conclusions: The proposed pipeline is a single integrated pipeline that combines established diffusion preprocessing tools from major MRI-focused software packages with intuitive QA.
Machine learning predicts risk of cerebrospinal fluid shunt failure in children: a study from the hydrocephalus clinical research network.
Hale AT, Riva-Cambrin J, Wellons JC, Jackson EM, Kestle JRW, Naftel RP, Hankinson TC, Shannon CN; Hydrocephalus Clinical Research Network.
Childs Nerv Syst. 2021 Jan 30. doi: 10.1007/s00381-021-05061-7. Online ahead of print.
Purpose: While conventional statistical approaches have been used to identify risk factors for cerebrospinal fluid (CSF) shunt failure, these methods may not fully capture the complex contribution of clinical, radiologic, surgical, and shunt-specific variables influencing this outcome. Using prospectively collected data from the Hydrocephalus Clinical Research Network (HCRN) patient registry, we applied machine learning (ML) approaches to create a predictive model of CSF shunt failure.
Methods: Pediatric patients (age < 19 years) undergoing first-time CSF shunt placement at six HCRN centers were included. CSF shunt failure was defined as a composite outcome including requirement for shunt revision, endoscopic third ventriculostomy, or shunt infection within 5 years of initial surgery. Performance of conventional statistical and 4 ML models were compared.
Results: Our cohort consisted of 1036 children undergoing CSF shunt placement, of whom 344 (33.2%) experienced shunt failure. Thirty-eight clinical, radiologic, surgical, and shunt-design variables were included in the ML analyses. Of all ML algorithms tested, the artificial neural network (ANN) had the strongest performance with an area under the receiver operator curve (AUC) of 0.71. The ANN had a specificity of 90% and a sensitivity of 68%, meaning that the ANN can effectively rule-in patients most likely to experience CSF shunt failure (i.e., high specificity) and moderately effective as a tool to rule-out patients at high risk of CSF shunt failure (i.e., moderately sensitive). The ANN was independently validated in 155 patients (prospectively collected, retrospectively analyzed).
Conclusion: These data suggest that the ANN, or future iterations thereof, can provide an evidence-based tool to assist in prognostication and patient-counseling immediately after CSF shunt placement.
Instrument Gauge and Type in Uveal Melanoma Fine Needle Biopsy: Implications for Diagnostic Yield and Molecular Prognostication.
Klofas LK, Bogan CM, Coogan AC, Schultenover SJ, Weiss VL, Daniels AB.
Am J Ophthalmol. 2021 Jan;221:83-90. doi: 10.1016/j.ajo.2020.08.014. Epub 2020 Aug 18.
Purpose: To systematically evaluate and compare the effects of using small-gauge needles and vitrectors on the ability to obtain adequate diagnostic and prognostic uveal melanoma biopsy specimens.
Design: Comparative evaluation of biopsy instruments.
Methods: Survival of uveal melanoma cells was evaluated in vitro following needle aspiration. Five therapeutically enucleated eyes were sampled in triplicate for ex vivo diagnostic biopsy experiments with 25 gauge (25 G) needle, 27 gauge (27 G) needle, and 27 G vitrector. During surgery in 8 patients, paired diagnostic transscleral fine needle aspiration biopsies were performed using both 25 G and 27 G needles. A review of cytologic specimens was performed by a panel of 3 expert cytopathologists. A retrospective chart review was performed to evaluate 100 consecutive tumors undergoing prognostic biopsy for gene expression profiling to assess the relationship between needle gauge and prognostic adequacy.
Results: No significant cell shearing of uveal melanoma cells occurred in vitro with 25 G, 27 G, or 30 G needles. For ex vivo biopsy samples, diagnostic yield was 100% using 25 G needle (5/5) or 27 G vitrector (5/5) but 60% using a 27 G needle (3/5). For in vivo samples, no difference in diagnostic yield was found between 25 G (75%, 6/8) or 27 G (75%, 6/8) needle sizes. Of 100 molecular prognostic biopsy samples evaluated, 65 were obtained using 27 G needles; for these biopsies, the prognostic yield was 65/65 (100%).
Conclusions: For diagnostic biopsy of uveal melanoma, a larger-gauge needle or a 27 G vitrector may have better overall cellularity and diagnostic yield when compared to a 27 G needle. However, for much more common molecular prognostic testing, a 27 G needle provided adequate sample in 100% (65/65) of cases, and a larger needle provided no additional benefit.
Topologically associating domain boundaries that are stable across diverse cell types are evolutionarily constrained and enriched for heritability.
McArthur E, Capra JA.
Am J Hum Genet. 2021 Feb 4;108(2):269-283. doi: 10.1016/j.ajhg.2021.01.001.
Topologically associating domains (TADs) are fundamental units of three-dimensional (3D) nuclear organization. The regions bordering TADs-TAD boundaries-contribute to the regulation of gene expression by restricting interactions of cis-regulatory sequences to their target genes. TAD and TAD-boundary disruption have been implicated in rare-disease pathogenesis; however, we have a limited framework for integrating TADs and their variation across cell types into the interpretation of common-trait-associated variants. Here, we investigate an attribute of 3D genome architecture-the stability of TAD boundaries across cell types-and demonstrate its relevance to understanding how genetic variation in TADs contributes to complex disease. By synthesizing TAD maps across 37 diverse cell types with 41 genome-wide association studies (GWASs), we investigate the differences in disease association and evolutionary pressure on variation in TADs versus TAD boundaries. We demonstrate that genetic variation in TAD boundaries contributes more to complex-trait heritability, especially for immunologic, hematologic, and metabolic traits. We also show that TAD boundaries are more evolutionarily constrained than TADs. Next, stratifying boundaries by their stability across cell types, we find substantial variation. Compared to boundaries unique to a specific cell type, boundaries stable across cell types are further enriched for complex-trait heritability, evolutionary constraint, CTCF binding, and housekeeping genes. Thus, considering TAD boundary stability across cell types provides valuable context for understanding the genome’s functional landscape and enabling variant interpretation that takes 3D structure into account.
Prevalence of Decreased Sound Tolerance (Hyperacusis) in Individuals With Autism Spectrum Disorder: A Meta-Analysis.
Williams ZJ, Suzman E, Woynaroski TG.
Ear Hear. 2021 Feb 9. doi: 10.1097/AUD.0000000000001005. Online ahead of print.
Objectives: Hyperacusis, defined as decreased tolerance to sound at levels that would not trouble most individuals, is frequently observed in individuals with autism spectrum disorder (ASD). Despite the functional impairment attributable to hyperacusis, little is known about its prevalence or natural history in the ASD population. The objective of this study was to conduct a systematic review and meta-analysis estimating the current and lifetime prevalence of hyperacusis in children, adolescents, and adults with ASD. By precisely estimating the burden of hyperacusis in the ASD population, the present study aims to enhance recognition of this particular symptom of ASD and highlight the need for additional research into the causes, prevention, and treatment of hyperacusis in persons on the spectrum.
Design: We searched PubMed and ProQuest to identify peer-reviewed articles published in English after January 1993. We additionally performed targeted searches of Google Scholar and the gray literature, including studies published through May 2020. Eligible studies included at least 20 individuals with diagnosed ASD of any age and reported data from which the proportion of ASD individuals with current and/or lifetime hyperacusis could be derived. To account for multiple prevalence estimates derived from the same samples, we utilized three-level Bayesian random-effects meta-analyses to estimate the current and lifetime prevalence of hyperacusis. Bayesian meta-regression was used to assess potential moderators of current hyperacusis prevalence. To reduce heterogeneity due to varying definitions of hyperacusis, we performed a sensitivity analysis on the subset of studies that ascertained hyperacusis status using the Autism Diagnostic Interview-Revised (ADI-R), a structured parent interview.
Results: A total of 7783 nonduplicate articles were screened, of which 67 were included in the review and synthesis. Hyperacusis status was ascertained in multiple ways across studies, with 60 articles employing interviews or questionnaires and seven using behavioral observations or objective measures. The mean (range) age of samples in the included studies was 7.88 years (1.00 to 34.89 years). The meta-analysis of interview/questionnaire measures (k(3) = 103, nASD = 13,093) estimated the current and lifetime prevalence of hyperacusis in ASD to be 41.42% (95% CrI, 37.23 to 45.84%) and 60.58% (50.37 to 69.76%), respectively. A sensitivity analysis restricted to prevalence estimates derived from the ADI-R (k(3) = 25, nASD = 5028) produced similar values. The estimate of current hyperacusis prevalence using objective/observational measures (k(3) = 8, nASD = 488) was 27.30% (14.92 to 46.31%). Heterogeneity in the full sample of interview/questionnaire measures was substantial but not significantly explained by any tested moderator. However, prevalence increased sharply with increasing age in studies using the ADI-R (BF10 = 93.10, R2Het = 0.692).
Conclusions: In this meta-analysis, we found a high prevalence of current and lifetime hyperacusis in individuals with ASD, with a majority of individuals on the autism spectrum experiencing hyperacusis at some point in their lives. The high prevalence of hyperacusis in individuals with ASD across the lifespan highlights the need for further research on sound tolerance in this population and the development of services and/or interventions to reduce the burden of this common symptom.
Hierarchical tumor heterogeneity mediated by cell contact between distinct genetic subclones.
Karthikeyan S, Waters IG, Dennison L, Chu D, Donaldson J, Shin DH, Rosen DM, Gonzalez-Ericsson PI, Sanchez V, Sanders ME, Pantone MV, Bergman RE, Davidson BA, Reed SC, Zabransky DJ, Cravero K, Kyker-Snowman K, Button B, Wong HY, Hurley PJ, Croessmann S, Park B.
J Clin Invest. 2021 Feb 2:143557. doi: 10.1172/JCI143557. Online ahead of print.
TRPV1 Supports Axogenic Enhanced Excitability in Response to Neurodegenerative Stress.
Risner ML, McGrady NR, Boal AM, Pasini S, Calkins DJ.
Front Cell Neurosci. 2021 Jan 11;14:603419. doi: 10.3389/fncel.2020.603419. eCollection 2020.
Prostaglandin I2 signaling licenses Treg suppressive function and prevents pathogenic reprogramming.
Norlander AE, Bloodworth MH, Toki S, Zhang J, Zhou W, Boyd KL, Polosukhin VV, Cephus JY, Ceneviva ZJ, Gandhi VD, Chowdhury NU, Charbonnier LM, Rogers LM, Wang J, Aronoff DM, Bastarache L, Newcomb DC, Chatila TA, Peebles RS Jr.
J Clin Invest. 2021 Feb 2:140690. doi: 10.1172/JCI140690. Online ahead of print.
Targeting In Vivo Metabolic Vulnerabilities of Th2 and Th17 Cells Reduces Airway Inflammation.
Contreras Healey DC, Cephus JY, Barone SM, Chowdhury NU, Dahunsi DO, Madden MZ, Ye X, Yu X, Olszewski K, Young K, Gerriets VA, Siska PJ, Dworski R, Hemler J, Locasale JW, Poyurovsky MV, Peebles RS Jr, Irish JM, Newcomb DC, Rathmell JC.
J Immunol. 2021 Feb 8:ji2001029. doi: 10.4049/jimmunol.2001029. Online ahead of print.
Influenza vaccine community outreach: Leveraging an interprofessional healthcare student workforce to immunize marginalized populations.
Brown SH, Fisher EL, Taylor AQ, Neuzil KE, Trump SW, Sack DE, Fricker GP, Miller RF.
Prev Med. 2021 Feb 17:106460. doi: 10.1016/j.ypmed.2021.106460. Online ahead of print.
Resting-state hippocampal networks related to language processing reveal unique patterns in temporal lobe epilepsy.
Whitten A, Jacobs ML, Englot DJ, Rogers BP, Levine KK, González HFJ, Morgan VL.
Epilepsy Behav. 2021 Feb 17;117:107834. doi: 10.1016/j.yebeh.2021.107834. Online ahead of print.
Antivirulence Strategies for the Treatment of Staphylococcus aureusInfections: A Mini Review.
Ford CA, Hurford IM, Cassat JE.
Front Microbiol. 2021 Jan 14;11:632706. doi: 10.3389/fmicb.2020.632706. eCollection 2020.
Impact of Cardiovascular Hemodynamics on Cognitive Aging.
Moore EE, Jefferson AL.
Arterioscler Thromb Vasc Biol. 2021 Feb 11:ATVBAHA120311909. doi: 10.1161/ATVBAHA.120.311909. Online ahead of print.