Antibody landscape of C57BL/6 mice cured of B78 melanoma via a combined radiation and immunocytokine immunotherapy regimen.

Sera of immune mice that were previously cured of their melanoma through a combined radiation and immunocytokine immunotherapy regimen consisting of 12 Gy of external beam radiation and the intratumoral administration of an immunocytokine (anti-GD2 mAb coupled to IL-2) with long-term immunological memory showed strong antibody-binding against melanoma tumor cell lines via flow cytometric analysis. Using a high-density whole-proteome peptide array (of 6.090.593 unique peptides), we assessed potential protein-targets for antibodies found in immune sera. Sera from 6 of these cured mice were analyzed with this high-density, whole-proteome peptide array to determine specific antibody-binding sites and their linear peptide sequence. We identified thousands of peptides that were targeted by these 6 mice and exhibited strong antibody binding only by immune (after successful cure and rechallenge), not naïve (before tumor implantation) sera and developed a robust method to detect these differentially targeted peptides. Confirmatory studies were done to validate these results using 2 separate systems, a peptide ELISA and a smaller scale peptide array utilizing a slightly different technology. To the best of our knowledge, this is the first study of the full set of germline encoded linear peptide-based proteome epitopes that are recognized by immune sera from mice cured of cancer via radio-immunotherapy. We furthermore found that although the generation of B-cell repertoire in immune development is vastly variable, and numerous epitopes are identified uniquely by immune serum from each of these 6 immune mice evaluated, there are still several epitopes and proteins that are commonly recognized by at least half of the mice studied. This suggests that every mouse has a unique set of antibodies produced in response to the curative therapy, creating an individual "fingerprint." Additionally, certain epitopes and proteins stand out as more immunogenic, as they are recognized by multiple mice in the immune group.

Genome-wide epitope mapping across multiple host species reveals significant diversity in antibody responses to Coxiella burnetii vaccination and infection.

Coxiella burnetii is an important zoonotic bacterial pathogen of global importance, causing the disease Q fever in a wide range of animal hosts. Ruminant livestock, in particular sheep and goats, are considered the main reservoir of human infection. Vaccination is a key control measure, and two commercial vaccines based on formalin-inactivated C. burnetii bacterins are currently available for use in livestock and humans. However, their deployment is limited due to significant reactogenicity in individuals previously sensitized to C. burnetii antigens. Furthermore, these vaccines interfere with available serodiagnostic tests which are also based on C. burnetii bacterin antigens. Defined subunit antigen vaccines offer significant advantages, as they can be engineered to reduce reactogenicity and co-designed with serodiagnostic tests to allow discrimination between vaccinated and infected individuals. This study aimed to investigate the diversity of antibody responses to C. burnetii vaccination and/or infection in cattle, goats, humans, and sheep through genome-wide linear epitope mapping to identify candidate vaccine and diagnostic antigens within the predicted bacterial proteome. Using high-density peptide microarrays, we analyzed the seroreactivity in 156 serum samples from vaccinated and infected individuals to peptides derived from 2,092 open-reading frames in the C. burnetii genome. We found significant diversity in the antibody responses within and between species and across different types of C. burnetii exposure. Through the implementation of three different vaccine candidate selection methods, we identified 493 candidate protein antigens for protein subunit vaccine design or serodiagnostic evaluation, of which 65 have been previously described. This is the first study to investigate multi-species seroreactivity against the entire C. burnetii proteome presented as overlapping linear peptides and provides the basis for the selection of antigen targets for next-generation Q fever vaccines and diagnostic tests.

Assessing Immune Factors in Maternal Milk and Paired Infant Plasma Antibody Binding to Human Rhinoviruses.

Before they can produce their own antibodies, newborns are protected from infections by transplacental transfer of maternal IgG antibodies and after birth through breast milk IgA antibodies. Rhinovirus (RV) infections are extremely common in early childhood, and while RV infections often result in only mild upper respiratory illnesses, they can also cause severe lower respiratory illnesses such as bronchiolitis and pneumonia. We used high-density peptide arrays to profile infant and maternal antibody reactivity to capsid and full proteome sequences of three human RVs - A16, B52, and C11. Numerous plasma IgG and breast milk IgA RV epitopes were identified that localized to regions of the RV capsid surface and interior, and also to several non-structural proteins. While most epitopes were bound by both IgG and IgA, there were several instances where isotype-specific and RV-specific binding were observed. We also profiled 62 unique RV-C dominant protein loop sequences characteristic of this species' capsid VP1 protein. Many of these RV-C sites were highly bound by IgG from one-year-old infants, indicating recent or ongoing active infections, or alternatively, a level of cross-reactivity among homologous RV-C sites.

The power and promise of genetic mapping from Plasmodium falciparum crosses utilizing human liver-chimeric mice.

Genetic crosses are most powerful for linkage analysis when progeny numbers are high, parental alleles segregate evenly and numbers of inbred progeny are minimized. We previously developed a novel genetic crossing platform for the human malaria parasite Plasmodium falciparum, an obligately sexual, hermaphroditic protozoan, using mice carrying human hepatocytes (the human liver-chimeric FRG NOD huHep mouse) as the vertebrate host. We report on two genetic crosses-(1) an allopatric cross between a laboratory-adapted parasite (NF54) of African origin and a recently patient-derived Asian parasite, and (2) a sympatric cross between two recently patient-derived Asian parasites. We generated 144 unique recombinant clones from the two crosses, doubling the number of unique recombinant progeny generated in the previous 30 years. The allopatric African/Asian cross has minimal levels of inbreeding and extreme segregation distortion, while in the sympatric Asian cross, inbred progeny predominate and parental alleles segregate evenly. Using simulations, we demonstrate that these progeny provide the power to map small-effect mutations and epistatic interactions. The segregation distortion in the allopatric cross slightly erodes power to detect linkage in several genome regions. We greatly increase the power and the precision to map biomedically important traits with these new large progeny panels.

Immunoreactive peptide maps of SARS-CoV-2.

Serodiagnosis of SARS-CoV-2 infection is impeded by immunological cross-reactivity among the human coronaviruses (HCoVs): SARS-CoV-2, SARS-CoV-1, MERS-CoV, OC43, 229E, HKU1, and NL63. Here we report the identification of humoral immune responses to SARS-CoV-2 peptides that may enable discrimination between exposure to SARS-CoV-2 and other HCoVs. We used a high-density peptide microarray and plasma samples collected at two time points from 50 subjects with SARS-CoV-2 infection confirmed by qPCR, samples collected in 2004-2005 from 11 subjects with IgG antibodies to SARS-CoV-1, 11 subjects with IgG antibodies to other seasonal human coronaviruses (HCoV), and 10 healthy human subjects. Through statistical modeling with linear regression and multidimensional scaling we identified specific peptides that were reassembled to identify 29 linear SARS-CoV-2 epitopes that were immunoreactive with plasma from individuals who had asymptomatic, mild or severe SARS-CoV-2 infections. Larger studies will be required to determine whether these peptides may be useful in serodiagnostics.

Comprehensive Profiling of the Rheumatoid Arthritis Antibody Repertoire.

OBJECTIVE: Autoantibodies against citrullinated proteins are found in 64-89% of rheumatoid arthritis (RA) patients, with 88-99% specificity. This study was undertaken to create an unbiased, comprehensive profile of serum antibodies against the human proteome, including the citrullinome and the homocitrullinome, in RA patients, using a high-density peptide array. METHODS: Our high-density peptide array, consisting of >4.6 million peptides, contained the entire annotated human proteome. The 20,246 proteins were represented as overlapping 16-mer peptides. In addition to native peptides, citrullinated and homocitrullinated peptides were included, as substitutions for arginine and lysine, and provided a comprehensive screen against all possible epitopes. Twenty-six serum samples (from 8 controls and 18 RA patients) were profiled on the high-density peptide array. Using RA-specific epitopes, we constructed an 8-epitope diagnostic biomarker on a Gyrolab xPlore instrument with a cohort of 92 serum samples (from 29 controls and 63 RA patients). The diagnostic biomarker was further validated with an independent cohort of 181 serum samples (from 54 controls and 127 RA patients). RESULTS: In the initial cohort the diagnostic performance of the 8-epitope biomarker yielded 96.6% specificity and 92.1% sensitivity. The overall diagnostic performance in the validation cohort was 94.4% specificity and 85% sensitivity. In both cohorts, the performance of the 8-epitope diagnostic biomarker compared favorably against the Abnova cyclic citrullinated peptide 2 (CCP2) assay. Using data from the peptide array, we identified novel RA-specific epitopes and formed the basis of a new RA diagnostic assay. CONCLUSION: Comprehensive antibody profiling using a high-density peptide array not only identified novel RA-specific epitopes but also allowed us to construct a novel diagnostic biomarker that is as specific as and more sensitive than the Abnova CCP2 assay.

Antibody responses to Zika virus proteins in pregnant and non-pregnant macaques.

The specificity of the antibody response against Zika virus (ZIKV) is not well-characterized. This is due, in part, to the antigenic similarity between ZIKV and closely related dengue virus (DENV) serotypes. Since these and other similar viruses co-circulate, are spread by the same mosquito species, and can cause similar acute clinical syndromes, it is difficult to disentangle ZIKV-specific antibody responses from responses to closely-related arboviruses in humans. Here we use high-density peptide microarrays to profile anti-ZIKV antibody reactivity in pregnant and non-pregnant macaque monkeys with known exposure histories and compare these results to reactivity following DENV infection. We also compare cross-reactive binding of ZIKV-immune sera to the full proteomes of 28 arboviruses. We independently confirm a purported ZIKV-specific IgG antibody response targeting ZIKV nonstructural protein 2B (NS2B) that was recently reported in ZIKV-infected people and we show that antibody reactivity in pregnant animals can be detected as late as 127 days post-infection (dpi). However, we also show that these responses wane over time, sometimes rapidly, and in one case the response was elicited following DENV infection in a previously ZIKV-exposed animal. These results suggest epidemiologic studies assessing seroprevalence of ZIKV immunity using linear epitope-based strategies will remain challenging to interpret due to susceptibility to false positive results. However, the method used here demonstrates the potential for rapid profiling of proteome-wide antibody responses to a myriad of neglected diseases simultaneously and may be especially useful for distinguishing antibody reactivity among closely related pathogens.

Prediction of fine-tuned promoter activity from DNA sequence.

The quantitative prediction of transcriptional activity of genes using promoter sequence is fundamental to the engineering of biological systems for industrial purposes and understanding the natural variation in gene expression. To catalyze the development of new algorithms for this purpose, the Dialogue on Reverse Engineering Assessment and Methods (DREAM) organized a community challenge seeking predictive models of promoter activity given normalized promoter activity data for 90 ribosomal protein promoters driving expression of a fluorescent reporter gene. By developing an unbiased modeling approach that performs an iterative search for predictive DNA sequence features using the frequencies of various k-mers, inferred DNA mechanical properties and spatial positions of promoter sequences, we achieved the best performer status in this challenge. The specific predictive features used in the model included the frequency of the nucleotide G, the length of polymeric tracts of T and TA, the frequencies of 6 distinct trinucleotides and 12 tetranucleotides, and the predicted protein deformability of the DNA sequence. Our method accurately predicted the activity of 20 natural variants of ribosomal protein promoters (Spearman correlation r = 0.73) as compared to 33 laboratory-mutated variants of the promoters (r = 0.57) in a test set that was hidden from participants. Notably, our model differed substantially from the rest in 2 main ways: i) it did not explicitly utilize transcription factor binding information implying that subtle DNA sequence features are highly associated with gene expression, and ii) it was entirely based on features extracted exclusively from the 100 bp region upstream from the translational start site demonstrating that this region encodes much of the overall promoter activity. The findings from this study have important implications for the engineering of predictable gene expression systems and the evolution of gene expression in naturally occurring biological systems.

Plasmodium falciparum genetic crosses in a humanized mouse model.

Genetic crosses of phenotypically distinct strains of the human malaria parasite Plasmodium falciparum are a powerful tool for identifying genes controlling drug resistance and other key phenotypes. Previous studies relied on the isolation of recombinant parasites from splenectomized chimpanzees, a research avenue that is no longer available. Here we demonstrate that human-liver chimeric mice support recovery of recombinant progeny for the identification of genetic determinants of parasite traits and adaptations.

Predicting functional and regulatory divergence of a drug resistance transporter gene in the human malaria parasite.

BACKGROUND: The paradigm of resistance evolution to chemotherapeutic agents is that a key coding mutation in a specific gene drives resistance to a particular drug. In the case of resistance to the anti-malarial drug chloroquine (CQ), a specific mutation in the transporter pfcrt is associated with resistance. Here, we apply a series of analytical steps to gene expression data from our lab and leverage 3 independent datasets to identify pfcrt-interacting genes. Resulting networks provide insights into pfcrt's biological functions and regulation, as well as the divergent phenotypic effects of its allelic variants in different genetic backgrounds. RESULTS: To identify pfcrt-interacting genes, we analyze pfcrt co-expression networks in 2 phenotypic states - CQ-resistant (CQR) and CQ-sensitive (CQS) recombinant progeny clones - using a computational approach that prioritizes gene interactions into functional and regulatory relationships. For both phenotypic states, pfcrt co-expressed gene sets are associated with hemoglobin metabolism, consistent with CQ's expected mode of action. To predict the drivers of co-expression divergence, we integrate topological relationships in the co-expression networks with available high confidence protein-protein interaction data. This analysis identifies 3 transcriptional regulators from the ApiAP2 family and histone acetylation as potential mediators of these divergences. We validate the predicted divergences in DNA mismatch repair and histone acetylation by measuring the effects of small molecule inhibitors in recombinant progeny clones combined with quantitative trait locus (QTL) mapping. CONCLUSIONS: This work demonstrates the utility of differential co-expression viewed in a network framework to uncover functional and regulatory divergence in phenotypically distinct parasites. pfcrt-associated co-expression in the CQ resistant progeny highlights CQR-specific gene relationships and possible targeted intervention strategies. The approaches outlined here can be readily generalized to other parasite populations and drug resistances.

Correlation of phenotypic profiles using targeted proteomics identifies mycobacterial esx-1 substrates.

The Esx/WXG-100 (ESAT-6/Wss) exporters are multiprotein complexes that promote protein translocation across the cytoplasmic membrane in a diverse range of pathogenic and nonpathogenic bacterial species. The Esx-1 (ESAT-6 System-1) system mediates virulence factor translocation in mycobacterial pathogens, including the human pathogen Mycobacterium tuberculosis. Although several genes have been associated with Esx-1-mediated transport and virulence, the contribution of individual Esx-1 genes to export is largely undefined. A unique aspect of Esx-1 export is that several substrates require each other for export/stability. We exploited substrate "codependency" to identify Esx-1 substrates. We simultaneously quantified changes in the levels of 13 Esx-1 proteins from both secreted and cytosolic protein fractions generated from 16 Esx-1-deficient Mycobacterium marinum strains in a single experiment using MRM/SRM targeted mass spectrometry. This expansion of measurable Esx-1 proteins allowed us to define statistical rules for assigning novel substrates using phenotypic profiles of known Esx-1 substrates. Using this approach, we identified three additional Esx-1 substrates encoded by the esx-1 region. Our studies begin to address how disruption of specific genes affects several proteins in the Esx-1 complex. Overall, our findings illuminate relationships between Esx-1 proteins and create a framework for the identification of secreted substrates applicable to other protein exporters and pathways.

Networks' Characteristics Matter for Systems Biology.

A fundamental goal of systems biology is to create models that describe relationships between biological components. Networks are an increasingly popular approach to this problem. However, a scientist interested in modeling biological (e.g., gene expression) data as a network is quickly confounded by the fundamental problem: how to construct the network? It is fairly easy to construct a network, but is it the network for the problem being considered? This is an important problem with three fundamental issues: How to weight edges in the network in order to capture actual biological interactions? What is the effect of the type of biological experiment used to collect the data from which the network is constructed? How to prune the weighted edges (or what cut-off to apply)? Differences in the construction of networks could lead to different biological interpretations. Indeed, we find that there are statistically significant dissimilarities in the functional content and topology between gene co-expression networks constructed using different edge weighting methods, data types, and edge cut-offs. We show that different types of known interactions, such as those found through Affinity Capture-Luminescence or Synthetic Lethality experiments, appear in significantly varying amounts in networks constructed in different ways. Hence, we demonstrate that different biological questions may be answered by the different networks. Consequently, we posit that the approach taken to build a network can be matched to biological questions to get targeted answers. More study is required to understand the implications of different network inference approaches and to draw reliable conclusions from networks used in the field of systems biology.