Estimating Autoantibody Signatures To Detect Autoimmune Disease Patient Subsets

Abstract

SummaryAutoimmune diseases are characterized by highly specific immune responses against molecules in self-tissues. Different autoimmune diseases are characterized by distinct immune responses, making autoantibodies useful for diagnosis and prediction. In many diseases, the targets of autoantibodies are incompletely defined. Although the technologies for autoantibody discovery have advanced dramatically over the past decade, each of these techniques generates hundreds of possibilities, which are onerous and expensive to validate. We set out to establish a method to greatly simplify autoantibody discovery, using a pre-filtering step to define subgroups with similar specificities based on migration of radiolabeled, immunoprecipitated proteins on sodium dodecyl sulfate (SDS) gels and autoradiography [Gel Electrophoresis and band detection on Autoradiograms (GEA)]. Human recognition of patterns is not optimal when the patterns are complex or scattered across many samples. Multiple sources of errors -including irrelevant intensity differences and warping of gels -have challenged automation of pattern discovery from autoradiograms.In this paper, we address these limitations using a Bayesian hierarchical model with shrinkage * To whom correspondence should be addressed.. CC-BY-NC 4.0 International license peer-reviewed) is the author/funder. It is made available under aThe copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/128199 doi: bioRxiv preprint first posted online Apr. 18, 2017; 2 Z. Wu and others priors for pattern alignment and spatial dewarping. The Bayesian model combines information from multiple gel sets and corrects spatial warping for coherent estimation of autoantibody signatures defined by presence or absence of a grid of landmark proteins. We show the pre-processing creates more clearly separated clusters and improves the accuracy of autoantibody subset detection via hierarchical clustering. Finally, we demonstrate the utility of the proposed methods with GEA data from scleroderma patients.

A Bayesian approach to restricted latent class models for scientifically structured clustering of multivariate binary outcomes

“…Further the model assumes binary response/nonresponse to each protein, rather than quantifying the response as a real value. This modeling choice follows the convention that clinicians interpret the presence or absence of bands corresponding to each protein on the images obtained from GEA (Wu et al, 2019).…”

“…We seek to identify components of the machines and to quantify the variations in their occurrence among individuals and estimate patient subsets. The binary responses 𝒀 𝑖 indicate the observed presence or absence of proteins at equispaced molecular weight landmarks as produced by a preprocessing method (Wu et al, 2019) applied to GEA data. We ran four gels, each loaded with IPs performed using sera from 19 different patients, and one reference lane.…”

“…The statistical goal is to incorporate the scientific constraint when clustering patients into subsets with distinct responses to the machines. The choice of binary rather than continuous measures of autoantibodies is specific to the gel electrophoresis and autoradiogram (GEA) technology that produced the data where the amount of protein is not informative about clustering patients (Wu et al, 2019).…”

SLE: reconciling heterogeneity

“…Many clinical and basic science investigators use an exclusive definition of SLE that accepts only classification criteria-defined patients and rejects (for ethical and practical reasons) patients with dementia, pregnancy, comorbid illness or specific forms of treatment 22–24. Practising physicians use an inclusive definition that gathers under one name all patients with lupus spectrum illness, including those with typical SLE (criteria fulfilling), overlap syndromes (typical SLE associated with another definable autoimmune illness), undifferentiated autoimmune syndrome ( UAS ) (lupus-like illness that does not fulfil criteria),25–27 SLE-associated antibodies only (diagnostic autoantibodies but no clinical illness)28 and cutaneous SLE (skin disease without systemic manifestations) 29…”

A Bayesian Approach to Restricted Latent Class Models for Scientifically-Structured Clustering of Multivariate Binary Outcomes

“…The choice of binary rather than continuous measures of autoantibodies is specific to the gel electrophoresis and autoradiogram (GEA) technology that produced the data where the amount of protein is not informative about clustering patients (Wu et al, 2019).…”