Strong off-target antibody reactivity to malarial antigens induced by RTS,S/AS01E vaccination is associated with increased protection

The RTS,S/AS01E vaccine targets the circumsporozoite protein (CSP) of the Plasmodium falciparum parasite. Using protein microarrays, levels of IgG to 1,000 P. falciparum antigens were measured in 2,138 infants (age 6-12 weeks) and children (age 5-17 months) from 6 African sites of the phase 3 trial, sampled before and at four longitudinal visits after vaccination. One month post-vaccination, IgG responses to 17% of all probed antigens showed differences between RTS,S/AS01E and comparator vaccination groups, whereas no prevaccination differences were found. A small subset of antigens presented IgG levels reaching 4- to 8 fold increases in the RTS,S/AS01E group, comparable in magnitude to anti-CSP IgG levels (~11-fold increase). They were strongly cross-correlated and correlated with anti CSP levels, waning similarly over time and re-increasing with the booster dose. Such an intriguing phenomenon may be due to cross-reactivity of anti-CSP antibodies with these antigens. RTS,S/AS01E vaccinees with strong off target IgG responses had an estimated lower clinical malaria incidence after adjusting for age group, site and post-vaccination anti-CSP levels. RTS,S/AS01E-induced IgG may bind strongly not only to CSP, but to unrelated malaria antigens, and this seems to either confer, or at least be a marker of, increased protection from clinical malaria.

A Quantitative Systems Approach to Define Novel Effects of Tumour p53 Mutations on Binding Oncoprotein MDM2

Understanding transient protein interactions biochemically at the proteome scale remains a long-standing challenge. Current tools developed to study protein interactions in high-throughput measure stable protein complexes and provide binary readouts; they do not elucidate dynamic and weak protein interactions in a proteome. The majority of protein interactions are transient and cover a wide range of affinities. Nucleic acid programmable protein arrays (NAPPA) are self-assembling protein microarrays produced by freshly translating full-length proteins in situ on the array surface. Herein, we have coupled NAPPA to surface plasmon resonance imaging (SPRi) to produce a novel label-free platform that measures many protein interactions in real-time allowing the determination of the KDs and rate constants. The developed novel NAPPA-SPRi technique showed excellent ability to study protein-protein interactions of clinical mutants of p53 with its regulator MDM2. Furthermore, this method was employed to identify mutant p53 proteins insensitive to the drug nutlin-3, currently in clinical practice, which usually disrupts the p53-MDM2 interactions. Thus, significant differences in the interactions were observed for p53 mutants on the DNA binding domain (Arg-273-Cys, Arg-273-His, Arg-248-Glu, Arg-280-Lys), on the structural domain (His-179-Tyr, Cys-176-Phe), on hydrophobic moieties in the DNA binding domain (Arg-280-Thr, Pro-151-Ser, Cys-176-Phe) and hot spot mutants (Gly-245-Cys, Arg-273-Leu, Arg-248-Glu, Arg-248-Gly), which signifies the importance of point mutations on the MDM2 interaction and nutlin3 effect, even in molecular locations related to other protein activities.

Dysregulated Serum Lipid Metabolism Promotes the Occurrence and Development of Diabetic Retinopathy Associated With Upregulated Circulating Levels of VEGF-A, VEGF-D, and PlGF

Purpose: This study aims to explore the correlations of arteriosclerosis-associated plasma indices with various severity levels of diabetic retinopathy (DR) and to test the hypothesis that elevated circulating level of known angiogenic cytokines induced by hyperglycemia is associated with dyslipidemia on DR.Methods: This cross-sectional study consists of 131 patients with type 2 diabetes. The patients were categorized based on their DR status into those with no DR (diabetes mellitus, DM), non-proliferative diabetic retinopathy (NPDR), and proliferative diabetic retinopathy (PDR) groups. The biochemical profile including fasting glucose, glycated hemoglobin (HbA1c), lipid profile were estimated, plasma angiogenic cytokines (vascular endothelial growth factor, VEGF-A, -C, -D) and placental growth factor (PlGF) were analyzed by protein microarrays. The atherogenic plasma index (API) was defined as low-density lipoprotein cholesterol/high-density lipoprotein cholesterol (LDL-C/HDL-C); atherogenic index (AI) was calculated as (TC-(HDL-C))/HDL-C and atherogenic index of plasma (AIP) was defined as log (TG/HDL-C).Results: No significant differences were detected in the duration of hypertension, age, and gender between the three groups. Serum TC and LDL-C, AI, and API in the NPDR group and PDR group were significantly higher than those in the DM group. The circulating level of PlGF, VEGF-A, and VEGF-C were significantly correlated with the severity of DR. VEGF-D is a risk factor independent of API (Z = −2.61, P = 0.009) and AI (Z = −2.40, P = 0.016). Multivariate logistic regression showed that AI and API are strong risk factors for the occurrence and severity of DR. Associated with AI and API, VEGF-D and PlGF contribute to DR: VEGF-D [AI: P = 0.038, odd ratio (OR) = 1.38; VEGF-D: P = 0.002, OR = 1.00. API: P = 0.027, OR = 1.56, VEGF-D:P = 0.002, OR = 1.00] and PlGF [AI: P = 0.021, OR = 1.43; VEGF-D: P = 0.004, OR = 1.50. API: P = 0.011, OR = 1.66; VEGF-D: P = 0.005, OR = 1.49].Conclusions: Total cholesterol (TC) and LDL-C are risk factors for presence of any DR. Atherogenic index and API are novel and better predictive indicators for the occurrence and severity of DR in comparion with the traditional lipid profiles. Abnormal lipid metabolism are associated with the upregulation of circulating cytokines that are linked to the severity of DR.

Identification of Tumor Antigens in Ovarian Cancers Using Local and Circulating Tumor-Specific Antibodies

Ovarian cancers include several disease subtypes and patients often present with advanced metastatic disease and a poor prognosis. New biomarkers for early diagnosis and targeted therapy are, therefore, urgently required. This study uses antibodies produced locally in tumor-draining lymph nodes (ASC probes) of individual ovarian cancer patients to screen two separate protein microarray platforms and identify cognate tumor antigens. The resulting antigen profiles were unique for each individual cancer patient and were used to generate a 50-antigen custom microarray. Serum from a separate cohort of ovarian cancer patients encompassing four disease subtypes was screened on the custom array and we identified 28.8% of all ovarian cancers, with a higher sensitivity for mucinous (50.0%) and serous (40.0%) subtypes. Combining local and circulating antibodies with high-density protein microarrays can identify novel, patient-specific tumor-associated antigens that may have diagnostic, prognostic or therapeutic uses in ovarian cancer.

A Pre-Processing Pipeline to Quantify, Visualize and Reduce Technical Variation in Protein Microarray Studies

Technical variation, or variation from non-biological sources, is present in most laboratory assays. Correcting for this variation enables analysts to extract a biological signal that informs questions of interest. However, each assay has different sources and levels of technical variation and the choice of correction methods can impact downstream analyses. Compared to similar assays such as DNA microarrays, relatively few methods have been developed and evaluated for protein microarrays, a versatile tool for measuring levels of various proteins in serum samples. Here, we propose a pre-processing pipeline to correct for some common sources of technical variation in protein microarrays. The pipeline builds upon an existing normalization method by using controls to reduce technical variation. We evaluate our method using data from two protein microarray studies, and by simulation. We demonstrate that pre-processing choices impact the fluorescent-intensity based ranks of proteins, which in turn, impact downstream analysis.1Impact StatementProtein microarrays are in wide use in cancer research, infectious disease diagnostics and biomarker identification. To inform research and practice in these and other fields, technical variation must be corrected using normalization and pre-processing. Current protein microarray studies use a variety of normalization methods, many of which were developed for DNA microarrays, and therefore are based on assumptions and data that are not ideal for protein microarrays. To address this issue, we develop, evaluate, and implement a pre-processing pipeline that corrects for technical variation in protein microarrays. We show that pre-processing and normalization directly impact the validity of downstream analysis, and protein-specific approaches are essential.

Antibody signatures of asymptomatic Plasmodium falciparum malaria infections measured from dried blood spots

Background Screening malaria-specific antibody responses on protein microarrays can help identify immune factors that mediate protection against malaria infection, disease, and transmission, as well as markers of past exposure to both malaria parasites and mosquito vectors. Most malaria protein microarray work has used serum as the sample matrix, requiring prompt laboratory processing and a continuous cold chain, thus limiting applications in remote locations. Dried blood spots (DBS) pose minimal biohazard, do not require immediate laboratory processing, and are stable at room temperature for transport, making them potentially superior alternatives to serum. The goals of this study were to assess the viability of DBS as a source for antibody profiling and to use DBS to identify serological signatures of low-density Plasmodium falciparum infections in malaria-endemic regions of Myanmar. Methods Matched DBS and serum samples from a cross-sectional study in Ingapu Township, Myanmar were probed on protein microarrays populated with P. falciparum antigen fragments. Signal and trends in both sample matrices were compared. A case-control study was then performed using banked DBS samples from malaria-endemic regions of Myanmar, and a regularized logistic regression model was used to identify antibody signatures of ultrasensitive PCR-positive P. falciparum infections. Results Approximately 30% of serum IgG activity was recovered from DBS. Despite this loss of antibody activity, antigen and population trends were well-matched between the two sample matrices. Responses to 18 protein fragments were associated with the odds of asymptomatic P. falciparum infection, albeit with modest diagnostic characteristics (sensitivity 58%, specificity 85%, negative predictive value 88%, and positive predictive value 52%). Conclusions Malaria-specific antibody responses can be reliably detected, quantified, and analysed from DBS, opening the door to serological studies in populations where serum collection, transport, and storage would otherwise be impossible. While test characteristics of antibody signatures were insufficient for individual diagnosis, serological testing may be useful for identifying exposure to asymptomatic, low-density malaria infections, particularly if sero-surveillance strategies target individuals with low previous exposure as sentinels for population exposure.

Autoantibody profiles associated with clinical features in psychotic disorders

Autoimmune processes are suspected to play a role in the pathophysiology of psychotic disorders. Better understanding of the associations between auto-immunoglobulin G (IgG) repertoires and clinical features of mental illness could yield novel models of the pathophysiology of psychosis, and markers for biological patient stratification. We undertook cross-sectional detection and quantification of auto-IgGs in peripheral blood plasma of 461 people (39% females) with established psychotic disorder diagnoses. Broad screening of 24 individuals was carried out on group level in eight clinically defined groups using planar protein microarrays containing 42,100 human antigens representing 18,914 proteins. Autoantibodies indicated by broad screening and in the previous literature were measured using a 380-plex bead-based array for autoantibody profiling of all 461 individuals. Associations between autoantibody profiles and dichotomized clinical characteristics were assessed using a stepwise selection procedure. Broad screening and follow-up targeted analyses revealed highly individual autoantibody profiles. Females, and people with family histories of obesity or of psychiatric disorders other than schizophrenia had the highest overall autoantibody counts. People who had experienced subjective thought disorder and/or were treated with clozapine (trend) had the lowest overall counts. Furthermore, six autoantibodies were associated with specific psychopathology symptoms: anti-AP3B2 (persecutory delusions), anti-TDO2 (hallucinations), anti-CRYGN (initial insomnia); anti-APMAP (poor appetite), anti-OLFM1 (above-median cognitive function), and anti-WHAMMP3 (anhedonia and dysphoria). Future studies should clarify whether there are causal biological relationships, and whether autoantibodies could be used as clinical markers to inform diagnostic patient stratification and choice of treatment.

Summit: Automated Analysis of Arrayed Single-Cell Gel Electrophoresis

New pipelines are required to automate the quantitation of emerging high-throughput electrophoretic (EP) assessment of DNA damage, or proteoform expression in single cells. EP cytometry consists of thousands of Western blots performed on a microscope slide-sized gel microwell array for single cells. Thus, EP cytometry images pose an analysis challenge that blends requirements for accurate and reproducible analysis encountered for both standard Western blots and protein microarrays. Here, we introduce the Summit algorithm to automate array segmentation, peak background subtraction, and Gaussian fitting for EP cytometry. The data structure storage of parameters allows users to perform quality control on identically processed data, yielding a ~6.5% difference in coefficient of quartile variation (CQV) of protein peak area under the curve (AUC) distributions measured by four users. Further, inspired by investigations of background subtraction methods to reduce technical variation in protein microarray measurements, we aimed to understand the trade-offs between EP cytometry analysis throughput and variation. We found an 11%–50% increase in protein peaks that passed quality control with a subtraction method similar to microarray “average on-boundary” versus an axial subtraction method. The background subtraction method only mildly influences AUC CQV, which varies between 1% and 4.5%. Finally, we determined that the narrow confidence interval for peak location and peak width parameters from Gaussian fitting yield minimal uncertainty in protein sizing. The AUC CQV differed by only ~1%–2% when summed over the peak width bounds versus the 95% peak width confidence interval. We expect Summit to be broadly applicable to other arrayed EP separations, or traditional Western blot analysis.

Argonaute Autoantibodies as Biomarkers in Autoimmune Neurologic Diseases

ObjectiveTo identify and characterize autoantibodies (Abs) as novel biomarkers for an autoimmune context in patients with central and peripheral neurologic diseases.MethodsTwo distinct approaches (immunoprecipitation/mass spectrometry–based proteomics and protein microarrays) and patients' sera and CSF were used. The specificity of the identified target was confirmed by cell-based assay (CBA) in 856 control samples.ResultsUsing the 2 methods as well as sera and CSF of patients with central and peripheral neurologic involvement, we identified Abs against the family of Argonaute proteins (mainly AGO1 and AGO2), which were already reported in systemic autoimmunity. AGO-Abs were mostly of immunoglobulin G 1 subclass and conformation dependent. Using CBA, AGO-Abs were detected in 21 patients with a high suspicion of autoimmune neurologic diseases (71.4% were women; median age 57 years) and only in 4/856 (0.5%) controls analyzed by CBA (1 diagnosed with small-cell lung cancer and the other 3 with Sjögren syndrome). Among the 21 neurologic patients identified, the main clinical presentations were sensory neuronopathy (8/21, 38.1%) and limbic encephalitis (6/21, 28.6%). Fourteen patients (66.7%) had autoimmune comorbidities and/or co-occurring Abs, whereas AGO-Abs were the only autoimmune biomarker for the remaining 7/21 (33.3%). Thirteen (61.9%) patients were treated with immunotherapy; 8/13 (61.5%) improved, and 3/13 (23.1%) remained stable, suggesting an efficacy of these treatments.ConclusionsAGO-Abs might be potential biomarkers of autoimmunity in patients with central and peripheral nonparaneoplastic neurologic diseases. In 7 patients, AGO-Abs were the only biomarkers; thus, their identification may be useful to suspect the autoimmune character of the neurologic disorder.Classification of EvidenceThis study provides Class III evidence that AGO-Abs are more frequent in patients with autoimmune neurologic diseases than controls.

Autoimmune Responses in Oncology: Causes and Significance

Specific anti-tumor immune responses have proven to be pivotal in shaping tumorigenesis and tumor progression in solid cancers. These responses can also be of an autoimmune nature, and autoantibodies can sometimes be present even before the onset of clinically overt disease. Autoantibodies can be generated due to mutated gene products, aberrant expression and post-transcriptional modification of proteins, a pro-immunogenic milieu, anti-cancer treatments, cross-reactivity of tumor-specific lymphocytes, epitope spreading, and microbiota-related and genetic factors. Understanding these responses has implications for both basic and clinical immunology. Autoantibodies in solid cancers can be used for early detection of cancer as well as for biomarkers of prognosis and treatment response. High-throughput techniques such as protein microarrays make parallel detection of multiple autoantibodies for increased specificity and sensitivity feasible, affordable, and quick. Cancer immunotherapy has revolutionized cancer treatments and has made a considerable impact on reducing cancer-associated morbidity and mortality. However, immunotherapeutic interventions such as immune checkpoint inhibition can induce immune-related toxicities, which can even be life-threatening. Uncovering the reasons for treatment-induced autoimmunity can lead to fine-tuning of cancer immunotherapy approaches to evade toxic events while inducing an effective anti-tumor immune response.