Publications

Visceral Leishmaniasis, caused by Leishmania infantum, is a tropical neglected disease and the most dangerous form of Leishmaniasis. It occurs zoonotically, with domestic transmission posing risks to humans as dogs have high susceptibility and are natural reservoirs of the parasite. Given their epidemiological role, improvements are needed in diagnosing Canine Visceral Leishmaniasis (CVL). Thus, we mapped linear epitopes from the rLiNTPDase2 antigen through peptide microarray and identified six positive epitopes. Validation through peptide ELISA revealed three promising peptides with accuracies of 78.6%, 85.92%, and 79.59%. Their combination yielded 97.58% accuracy. Negative epitopes were also found, which interacted with CVL-negative and Chagas Disease positive samples. Their removal from the rLiNTPDase2 sequence resulted in the rNT2.neg, which obtained enhanced specificity over rLiNTPDase2. The rNT2.neg validation achieved 87.50% sensitivity, 90.55% specificity, and 93.5% accuracy within 127 CVL-positive and 96 CVL-negative samples. Therefore, three peptides and rNT2.neg show significant promise for CVL diagnosis.

Introduction: Immunoinformatic tools can be used to predict schistosome-specific B-cell epitopes with little sequence identity to human proteins and antigens other than the target. This study reports an approach for identifying schistosome peptides mimicking linear B-cell epitopes using in-silico tools and peptide microarray immunoassay validation. Method: Firstly, a comprehensive literature search was conducted to obtain published schistosome-specific peptides and recombinant proteins with the best overall diagnostic performances. For novel peptides, linear B-cell epitopes were predicted from target recombinant proteins using ABCpred, Bcepred and BepiPred 2.0 in-silico tools. Together with the published peptides, predicted peptides with the highest probability of being B-cell epitopes and the lowest sequence identity with proteins from human and other pathogens were selected. Antibodies against the peptides were measured in sera, using peptide microarray immunoassays. Area under the ROC curve was calculated to assess the overall diagnostic performances of the peptides. Results: Peptide AA81008-19-30 had excellent and acceptable diagnostic performances for discriminating S. mansoni and S. haematobium positives from healthy controls, with AUC values of 0.8043 and 0.7326 respectively for IgG. Peptides MS3_10186-123-131, MS3_10385-339-354, SmSPI-177-193, SmSPI-379-388, MS3-10186-40-49 and SmS-197-214 had acceptable diagnostic performances for discriminating S. mansoni positives from healthy controls with AUC values ranging from 0.7098 to 0.7763 for IgG. Peptides SmSPI-359-372, Smp126160-438-452 and MS3 10186-25-41 had acceptable diagnostic performances for discriminating S. mansoni positives from S. mansoni negatives with AUC values of 0.7124, 0.7156 and 0.7115 respectively for IgG. Peptide MS3-10186-40-49 had an acceptable diagnostic performance for discriminating S. mansoni positives from healthy controls, with an AUC value of 0.7413 for IgM. Conclusion: One peptide with a good diagnostic performance and nine peptides with acceptable diagnostic performances were identified using the immunoinformatic approach and peptide microarray validation. There is need for evaluation of the peptides with true negatives and a good standard positive reference.

Rapid design, screening, and characterization of biorecognition elements (BREs) is essential for the development of diagnostic tests and antiviral therapeutics needed to combat the spread of viruses such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). To address this need, we developed a high-throughput pipeline combining in silico design of a peptide library specific for SARS-CoV-2 spike (S) protein and microarray screening to identify binding sequences. Our optimized microarray platform allowed the simultaneous screening of ~ 2.5 k peptides and rapid identification of binding sequences resulting in selection of four peptides with nanomolar affinity to the SARS-CoV-2 S protein. Finally, we demonstrated the successful integration of one of the top peptides into an electrochemical sensor with a clinically relevant limit of detection for S protein in spiked saliva. Our results demonstrate the utility of this novel pipeline for the selection of peptide BREs in response to the SARS-CoV-2 pandemic, and the broader application of such a platform in response to future viral threats.

The identification of specific biomarkers for Zika infection and its clinical complications is fundamental to mitigate the infection spread, which has been associated with a broad range of neurological sequelae. , The identification of specific biomarkers for Zika infection and its clinical complications is fundamental to mitigate the infection spread, which has been associated with a broad range of neurological sequelae. We present the characterization of antibody responses in serum samples from individuals infected with Zika, presenting non-severe (classical) and severe (neurological disease) phenotypes, with high-density peptide arrays comprising the Zika NS1 and NS2B proteins. The data pinpoints one strongly IgG-targeted NS2B epitope in non-severe infections, which is absent in Zika patients, where infection progressed to the severe phenotype. This differential IgG profile between the studied groups was confirmed by multivariate data analysis. Molecular dynamics simulations and circular dichroism have shown that the peptide in solution presents itself in a sub-optimal conformation for antibody recognition, which led us to computationally engineer an artificial protein able to stabilize the NS2B epitope structure. The engineered protein was used to interrogate paired samples from mothers and their babies presenting Zika-associated microcephaly and confirmed the absence of NS2B IgG response in those samples. These findings suggest that the assessment of antibody responses to the herein identified NS2B epitope is a strong candidate biomarker for the diagnosis and prognosis of Zika-associated neurological disease.

The WHO declared the COVID-19 outbreak a public health emergency of international concern. The causative agent of this acute respiratory disease is a newly emerged coronavirus, named SARS-CoV 2, which originated in China in late 2019. Exposure to SARS‑CoV‑2 leads to multifaceted disease outcomes from asymptomatic infection to severe pneumonia, acute respiratory distress and potentially death. Understanding the host immune response is crucial for the development of interventional strategies. Humoral responses play an important role in defending viral infections and are therefore of particular interest. With the aim to resolve SARS-CoV-2-specific humoral immune responses at the epitope level, we screened clinically well-characterized sera from COVID-19 patients with mild and severe disease outcome using high-density peptide microarrays covering the entire proteome of SARS-CoV-2. Moreover, we determined the longevity of epitope-specific antibody responses in a longitudinal approach. Here we present IgG and IgA-specific epitope signatures from COVID-19 patients, which may serve as discriminating prognostic or predictive markers for disease outcome and/or could be relevant for intervention strategies

In this manuscript we describe the engineering of a biomarker for the diagnosis and prognosis of Zika-associated neurological disease. Although the causal association between congenital Zika virus (ZIKV) infection and neurological manifestations has been well documented in the recent years, biomarkers for proper diagnostic and disease outcome still remain to be defined. Combining high-density peptide array and multivariate analysis, we have identified an ZIKV epitope that is associated to a lack of IgG antibody response in patients with severe neurological symptoms. An engineered chimera was developed to discriminate between mild and severe clinical forms of the disease.