Applications of Peptide Microarrays in Autoantibody, Infection, and Cancer Detection

Grötzinger, Carsten

doi:10.1007/978-1-0716-2732-7_1

Cited by 2 publications

(4 citation statements)

References 73 publications

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“…While infectious disease diagnostics and associated classification approaches are one possible use-case of the approaches described above, the key conclusions of the work are more general. First, measured binding of serum antibodies to a very sparse and nearly random subset of all possible 10-mer peptides (~10 5 out of ~10 12 ) is sufficient to capture all the relevant diseasespecific information available from the array data (Figs 4, 5 and 6). Secondly, the representation of IgG binding is statistically comprehensive, as demonstrated by the high correlation (~0.96) between predicted and measured binding using peptide sequences not involved in the training of the network (Fig 2B).…”

Section: Discussionmentioning

confidence: 99%

“…Large-scale peptide microarray technology provides an alternate approach and has been used to measure binding of protein targets to large numbers of peptide sequences in the analysis of protein-protein and peptide-protein interactions [5,[12][13][14][15]. Antibody reactivity profiling (epitope mapping) has been one of the main research fields employing the technology [12,15,16]. As peptide arrays have become more available and affordable, they have become attractive for looking broadly at sequence-binding relationships.…”

Section: Introductionmentioning

confidence: 99%

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Modeling the sequence dependence of differential antibody binding in the immune response to infectious disease

Chowdhury

Taguchi²,

Kelbauskas

et al. 2023

PLoS Comput Biol

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Past studies have shown that incubation of human serum samples on high density peptide arrays followed by measurement of total antibody bound to each peptide sequence allows detection and discrimination of humoral immune responses to a variety of infectious diseases. This is true even though these arrays consist of peptides with near-random amino acid sequences that were not designed to mimic biological antigens. This “immunosignature” approach, is based on a statistical evaluation of the binding pattern for each sample but it ignores the information contained in the amino acid sequences that the antibodies are binding to. Here, similar array-based antibody profiles are instead used to train a neural network to model the sequence dependence of molecular recognition involved in the immune response of each sample. The binding profiles used resulted from incubating serum from 5 infectious disease cohorts (Hepatitis B and C, Dengue Fever, West Nile Virus and Chagas disease) and an uninfected cohort with 122,926 peptide sequences on an array. These sequences were selected quasi-randomly to represent an even but sparse sample of the entire possible combinatorial sequence space (~1012). This very sparse sampling of combinatorial sequence space was sufficient to capture a statistically accurate representation of the humoral immune response across the entire space. Processing array data using the neural network not only captures the disease-specific sequence-binding information but aggregates binding information with respect to sequence, removing sequence-independent noise and improving the accuracy of array-based classification of disease compared with the raw binding data. Because the neural network model is trained on all samples simultaneously, a highly condensed representation of the differential information between samples resides in the output layer of the model, and the column vectors from this layer can be used to represent each sample for classification or unsupervised clustering applications.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling the sequence dependence of differential antibody binding in the immune response to infectious disease

Chowdhury

Taguchi²,

Kelbauskas

et al. 2023

PLoS Comput Biol

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show abstract

“…The advent of peptide microarray (PM) technology has enabled inquiries into a broad variety of questions in biomolecular recognition, primarily focusing on protein-protein and peptide-protein interactions ( 23 – 27 ). Ab reactivity profiling (epitope mapping) has been one of the main research fields where the use of this technology resulted in a number of impactful discoveries ( 23 , 27 , 28 ) demonstrating the utility of the approach in discovering biologically relevant information. When used in combination with random peptide libraries, the approach offers a way for interrogating the combinatorial binding space agnostically without any a priori knowledge about the pathogen underlying a particular immune response.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Highly heterogenous humoral immune response in Lyme disease patients revealed by broad machine learning-assisted antibody binding profiling with random peptide arrays

Kelbauskas,

Legutki,

Woodbury

2024

Front. Immunol.

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IntroductionLyme disease (LD), a rapidly growing public health problem in the US, represents a formidable challenge due to the lack of detailed understanding about how the human immune system responds to its pathogen, the Borrelia burgdorferi bacterium. Despite significant advances in gaining deeper insight into mechanisms the pathogen uses to evade immune response, substantial gaps remain. As a result, molecular tools for the disease diagnosis are lacking with the currently available tests showing poor performance. High interpersonal variability in immune response combined with the ability of the pathogen to use a number of immune evasive tactics have been implicated as underlying factors for the limited test performance.MethodsThis study was designed to perform a broad profiling of the entire repertoire of circulating antibodies in human sera at the single-individual level using planar arrays of short linear peptides with random sequences. The peptides sample sparsely, but uniformly the entire combinatorial sequence space of the same length peptides for profiling the humoral immune response to a B.burg. infection and compare them with other diseases with etiology similar to LD and healthy controls.ResultsThe study revealed substantial variability in antibody binding profiles between individual LD patients even to the same antigen (VlsE protein) and strong similarity between individuals diagnosed with Lyme disease and healthy controls from the areas endemic to LD suggesting a high prevalence of seropositivity in endemic healthy control.DiscussionThis work demonstrates the utility of the approach as a valuable analytical tool for agnostic profiling of humoral immune response to a pathogen.

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Applications of Peptide Microarrays in Autoantibody, Infection, and Cancer Detection

Cited by 2 publications

References 73 publications

Modeling the sequence dependence of differential antibody binding in the immune response to infectious disease

Modeling the sequence dependence of differential antibody binding in the immune response to infectious disease

Highly heterogenous humoral immune response in Lyme disease patients revealed by broad machine learning-assisted antibody binding profiling with random peptide arrays

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