Probing the Rhipicephalus bursa sialomes in potential anti-tick vaccine candidates : a reverse vaccinology approach

In the wake of the ‘omics’ explosion of data, reverse vaccinology approaches are being applied more readily as an alternative for the discovery of candidates for next generation diagnostics and vaccines. Promising protective antigens for the control of ticks and tick-borne diseases can be discovered by mining available omics data for immunogenic epitopes. The present study aims to explore the previously obtained Rhipicephalus bursa sialotranscriptome during both feeding and Babesia infection, to select antigenic targets that are either membrane-associated or a secreted protein, as well as unique to the ectoparasite and not present in the mammalian host. Further, they should be capable of stimulating T and B cells for a potential robust immune response, and be non-allergenic or toxic to the host. From the R. bursa transcriptome, 5706 and 3025 proteins were identified as belonging to the surfaceome and secretome, respectively. Following a reverse genetics immunoinformatics pipeline, nine preferred candidates, consisting of one transmembrane-related and eight secreted proteins, were identified. These candidates showed a higher predicted antigenicity than the Bm86 antigen, with no homology to mammalian hosts and exposed regions. Only four were functionally annotated and selected for further in silico analysis, which examined their protein structure, surface accessibility, flexibility, hydrophobicity, and putative linear B and T-cell epitopes. Regions with overlapping coincident epitopes groups (CEGs) were evaluated to select peptides that were further analyzed for their physicochemical characteristics, potential allergenicity, toxicity, solubility, and potential propensity for crystallization. Following these procedures, a set of three peptides from the three R. bursa proteins were selected. In silico results indicate that the designed epitopes could stimulate a protective and long-lasting immune response against those tick proteins, reflecting its potential as anti-tick vaccines The immunogenicity of these peptides was evaluated in a pilot immunization study followed by tick feeding to evaluate its impact on tick behavior and pathogen transmission. Combining in silico methods with in vivo immunogenicity evaluation enabled the screening of vaccine candidates prior to expensive infestation studies on the definitive ovine host animals.Spreadsheet S1 – SurfaceomeSpreadsheet S2 – SecretomeSpreadsheet S3 – MARVELSpreadsheet S4 – EVASINSpreadsheet S5 - RICINFundação para a Ciência e Tecnologia (FCT)http://www.mdpi.com/journal/biomedicinespm2021BiochemistryForestry and Agricultural Biotechnology Institute (FABI)GeneticsMicrobiology and Plant PathologyPlant Production and Soil Scienc

Vector-pathogen interactomics: connecting the dots

As carraças e doenças associadas a carraças têm um impacto negativo considerável na saúde humana e animal. Rhipicephalus bursa é uma carraça multihospedeiro hematófaga e é o principal vetor de Babesia ovis, um hemoparasita altamente patogénico em pequenos ruminantes, que pode levar a uma taxa de mortalidade de 30- 50% em animais suscetíveis e, indiretamente contribuir para um impacto socioeconómico negativo na sociedade humana. O controlo de carraças e doenças associadas depende principalmente do uso de fármacos, que apresentam grandes desvantagens, como a contaminação de alimentos e ambiente e o aumento da resistência, reforçando assim a necessidade de medidas alternativas, como a vacinação. Com base na premissa de que as glândulas salivares da carraça têm um papel crucial no comportamento hematófago e na transmissão de agentes patogénicos, o objetivo principal deste trabalho é aumentar o conhecimento sobre a interação R. bursa-B. ovis neste tecido, de forma a identificar novos candidatos a antigénios protetores para o desenvolvimento de vacinas. Assim sendo, os sialotranscritos e as sialoproteínas de R. bursa foram analisados em diferentes condições, para compreender melhor os processos de alimentação e infeção e contribuir para o desenvolvimento de novas vacinas anti-carraça e doenças associadas. A análise comparativa dos transcriptomas e proteomas revelou que a alimentação por sangue induz a produção de moléculas por parte da carraça, o que se traduziu no aumento da expressão genética e da síntese proteica. Além disso, os dados mostram que a combinação de estímulos (alimentação e infeção) influenciou positivamente a expressão genética, mas negativamente a tradução, podendo sugerir a manipulação de B. ovis no sialoma de R. bursa. Estes resultados aliados a diferentes metodologias como RNA de interferência (in vitro e in vivo) e vacinologia reversa, permitem explorar a maquinaria celular da carraça e identificar vários alvos como potenciais antigénios para vacinas. Os ensaios de silenciamento revelaram o impacto direto de algumas moléculas na sobrevivência da carraça e a sua fixação ao hospedeiro (como a putativa “Vitelogenin-3” e uma proteína do “cement”), enquanto que outros demonstraram um efeito duplo divergente na sobrevivência do vetor e do agente patogénico (como a “lachesin” e a “UB2N”). A análise imunoinformática dos dados anteriores de sequenciação permitiu a identificação de proteínas/peptídeos capazes de induzir, no hospedeiro vertebrado, uma resposta imunológica forte e robusta contra o vetor e o agente patogénico. Nesta análise, uma proteína membranar (proteína contendo domínios “Marvel”) e duas secretórias (uma “Evasin” e uma proteína contendo domínios de “Ricin”) foram selecionadas e promissores "immunological kernels" foram encontrados, contendo características ideais de uma vacina baseada em peptídeos, sem causar alergia e toxicidade. Além disso, a integração de diferentes análises ómicas de diferentes espécies de carraças foi usada como uma estratégia para pesquisar e caracterizar vias biológicas conservadas, a fim de selecionar novos alvos capazes de impactar uma ampla gama de espécies de carraças e bloquear a transmissão de vários agentes patogénicos transmitidos por estas. Deste estudo, destacou-se a via de biossíntese de folato, ao observar que durante a infeção da carraça, quer por bactéria quer por protozoário, a expressão de genes relacionados com esta via era aumentada. No entanto, ensaios de silenciamento numa linha celular de carraça mostraram que, a curto prazo, a redução da expressão de um gene relacionado ao folato (gch-I), não exorta alterações significativas nas células de carraça ou no comportamento do agente patogénico em termos de invasão ou multiplicação. Estudos aplicados e ensaios de vacinação precisam ser conduzidos para validar o potencial desses alvos promissores para o desenvolvimento de abordagens anti-carraça e de bloqueio de transmissão de doenças.health. Rhipicephalus bursa is a hematophagous multi-host tick and the main vector of Babesia ovis, a highly pathogenic hemoparasite in small ruminants, which leads to a 30-50 % of mortality rate in susceptible animals and, indirectly, to a negative socioeconomic impact in human society. Tick and disease control rely mainly in the use of chemotherapy and acaracides, which has major drawbacks including food and environment contamination and the increase of resistance, reinforcing the need for alternatives measures, such as vaccination. Based on the premise that tick salivary glands have a crucial role on hematophagous behaviour and on pathogen transmission, the main objective of this research was to increase the understanding on the Rhipicephalus bursa- Babesia ovis interaction in this organ, in order to find new protective antigen candidates for vaccine design. Thus, the R. bursa sialotranscripts and sialoproteins were screened under different conditions, to better understand the feeding and infection processes and contribute for the development of new anti-tick and tick-borne diseases. The comparative analyses of the transcriptomes and proteomes revealed that blood feeding induces the production of tick molecules, which was translated by the increased gene expression and protein synthesis. Moreover, the data unveiled that the combination of stimuli (feeding and infection) influenced positively gene expression but negatively translation, suggesting that B. ovis might manipulate R. bursa sialome. These results allied to interference RNA (in vitro and in vivo) and reverse vaccinology, allowed to explore the tick cellular machinery and pinpointed several targets as potential vaccine antigens. The silencing assays revealed the direct impact of some molecules in tick survival and attachment to the host (such as putative Vitellogenin-3 and a Cement protein), while others demonstrated a divergent dual-effect on both vector and parasite survival (such as Lachesin and UB2N). Immunoinformatic analysis of the previous sequencing data allowed the identification of proteins/peptides capable of elicit, in the vertebrate host, a strong and robust immune response against both vector and pathogen. In this experiment, one membrane-related (Marvel-containing protein) and two secreted (a Evasin and a ricin-containing protein) proteins were selected and promising “immunological kernels” were found to have ideal characteristics for an anti-tick peptide-based vaccine, without causing allergy and toxicity. Furthermore, the integration of different omics analyses from different tick species was used as a strategy to search and characterize conserved biological pathways in order to select new targets able to impact a wide range of tick vectors and block the transmission of several transmitted pathogens. From this study the folate biosynthesis pathway stood out by observing that during tick infection, by either bacteria or protozoan, the expression of genes related to this pathway were increased. However, silencing assays in a tick cell line demonstrate that, in a short term, the reduction of expression of a folate-related gene (gch-I), did not lead to significant changes in tick cells or pathogen behaviour of invasion or multiplication. Applied studies and vaccination trials need to be conducted to validate the potencial of these promising targets for the development of anti-tick and transmission blocking approaches

Genomic studies on the impact of host/virus interaction in EBV infection using massively parallel high throughput sequencing

Epstein-Barr virus is one of the most common viral infections in humans and, once acquired, persists within its host throughout their life. EBV therefore represents an ex- tremely successful virus, having evolved complex strategies to evade the host’s innate and adaptive immune response during both initial and persistent stages of infection. While infection is mostly harmless in the majority of cases, EBV has the ability to be oncogenic in some individuals, and is associated with a wide range of malignancies as well as non-cancerous diseases. To generate new and useful insights into the evolution of EBV interactions with its host, a hybridization-based target enrichment methodology was optimised to enable whole genome sequencing of EBV directly from clinical samples. This allowed the gen- eration of whole genome sequences of EBV directly from blood for the first time. This methodology was subsequently applied to a number of distinct EBV sample col- lections and the resulting data used to investigate the intra- and inter-host variation in various clinical settings, such as infectious mononucleosis and immunosuppression with chronic EBV infection. Additionally, the number of available whole genomes from East Asia is expanded by eleven (unique) novel genomes from primary infection from a NPC- non-endemic area. These sequences were used for a comparative analysis between NPC- and non-NPC-derived EBV genomes and a number of sites were determined differenti- ating these two groups. Finally, comparative genomic analyses of world-wide EBV strain diversity were per- formed using genome sequences generated here in conjunction with a large number of publicly available EBV genome sequences. The comprehensive data sets generated, which included measures of diversity, selection, and linkage, were used to identify poten- tial targets of T cell immunity. In addition, the population structure of EBV was analysed to better understand the forces that have shaped the evolution of EBV

Following the trail of cellular signatures : computational methods for the analysis of molecular high-throughput profiles

Over the last three decades, high-throughput techniques, such as next-generation sequencing, microarrays, or mass spectrometry, have revolutionized biomedical research by enabling scientists to generate detailed molecular profiles of biological samples on a large scale. These profiles are usually complex, high-dimensional, and often prone to technical noise, which makes a manual inspection practically impossible. Hence, powerful computational methods are required that enable the analysis and exploration of these data sets and thereby help researchers to gain novel insights into the underlying biology. In this thesis, we present a comprehensive collection of algorithms, tools, and databases for the integrative analysis of molecular high-throughput profiles. We developed these tools with two primary goals in mind. The detection of deregulated biological processes in complex diseases, like cancer, and the identification of driving factors within those processes. Our first contribution in this context are several major extensions of the GeneTrail web service that make it one of the most comprehensive toolboxes for the analysis of deregulated biological processes and signaling pathways. GeneTrail offers a collection of powerful enrichment and network analysis algorithms that can be used to examine genomic, epigenomic, transcriptomic, miRNomic, and proteomic data sets. In addition to approaches for the analysis of individual -omics types, our framework also provides functionality for the integrative analysis of multi-omics data sets, the investigation of time-resolved expression profiles, and the exploration of single-cell experiments. Besides the analysis of deregulated biological processes, we also focus on the identification of driving factors within those processes, in particular, miRNAs and transcriptional regulators. For miRNAs, we created the miRNA pathway dictionary database miRPathDB, which compiles links between miRNAs, target genes, and target pathways. Furthermore, it provides a variety of tools that help to study associations between them. For the analysis of transcriptional regulators, we developed REGGAE, a novel algorithm for the identification of key regulators that have a significant impact on deregulated genes, e.g., genes that show large expression differences in a comparison between disease and control samples. To analyze the influence of transcriptional regulators on deregulated biological processes,, we also created the RegulatorTrail web service. In addition to REGGAE, this tool suite compiles a range of powerful algorithms that can be used to identify key regulators in transcriptomic, proteomic, and epigenomic data sets. Moreover, we evaluate the capabilities of our tool suite through several case studies that highlight the versatility and potential of our framework. In particular, we used our tools to conducted a detailed analysis of a Wilms' tumor data set. Here, we could identify a circuitry of regulatory mechanisms, including new potential biomarkers, that might contribute to the blastemal subtype's increased malignancy, which could potentially lead to new therapeutic strategies for Wilms' tumors. In summary, we present and evaluate a comprehensive framework of powerful algorithms, tools, and databases to analyze molecular high-throughput profiles. The provided methods are of broad interest to the scientific community and can help to elucidate complex pathogenic mechanisms.Heutzutage werden molekulare Hochdurchsatzmessverfahren, wie Hochdurchsatzsequenzierung, Microarrays, oder Massenspektrometrie, regelmäßig angewendet, um Zellen im großen Stil und auf verschiedenen molekularen Ebenen zu charakterisieren. Die dabei generierten Datensätze sind in der Regel hochdimensional und oft verrauscht. Daher werden leistungsfähige computergestützte Anwendungen benötigt, um deren Analyse zu ermöglichen. In dieser Arbeit präsentieren wir eine Reihe von effektiven Algorithmen, Programmen, und Datenbaken für die Analyse von molekularen Hochdurchsetzdatensätzen. Diese Ansätze wurden entwickelt, um deregulierte biologische Prozesse zu untersuchen und in diesen wichtige Schlüsselmoleküle zu identifizieren. Zusätzlich wurden eine Reihe von Analysen durchgeführt um die verschiedenen Methoden zu evaluieren. Zu diesem Zweck haben wir insbesondere eine Wilmstumor Studie durchgeführt, in der wir verschiedene regulatorische Mechanismen und dazugehörige Biomarker identifizieren konnten, die für die erhöhte Malignität von Wilmstumoren mit blastemreichen Subtyp verantwortlich sein könnten. Diese Erkenntnisse könnten in der Zukunft zu einer verbesserten Behandlung dieser Tumore führen. Diese Ergebnisse zeigen eindrucksvoll, dass unsere Ansätze in der Lage sind, verschiedene molekulare Hochdurchsatzmessungen auszuwerten und dabei helfen können pathogene Mechanismen im Zusammenhang mit Krebs oder anderen komplexen Krankheiten aufzuklären

Divergence of an introduced population of the swimbladder-nematode Anguillicola crassus - a transcriptomic perspective

Differential gene-expression in A. crassus populations was assessed using next generation sequencing on the 454 and Illumina platforms and genetic components of differences were isolated in cross-inoculation experiments with both Asian and European host-species and parasite populations. Heritable change was large in comparison to the effect of modification in different host-environments. Subunits of the respiratory chain showed divergent expression patterns in European vs. Asian parasites

Machine Learning Methods for Cancer Immunology

Tumours are highly heterogeneous collections of tissues characterised by a repertoire of heavily mutated and rapidly proliferating cells. Evading immune destruction is a fundamental hallmark of cancer, and elucidating the contextual basis of tumour-infiltrating leukocytes is pivotal for improving immunotherapy initiatives. However, progress in this domain is hindered by an incomplete characterisation of the regulatory mechanisms involved in cancer immunity. Addressing this challenge, this thesis is formulated around a fundamental line of inquiry: how do we quantitatively describe the immune system with respect to tumour heterogeneity? Describing the molecular interactions between cancer cells and the immune system is a fundamental goal of cancer immunology. The first part of this thesis describes a three-stage association study to address this challenge in pancreatic ductal adenocarcinoma (PDAC). Firstly, network-based approaches are used to characterise PDAC on the basis of transcription factor regulators of an oncogenic KRAS signature. Next, gene expression tools are used to resolve the leukocyte subset mixing proportions, stromal contamination, immune checkpoint expression and immune pathway dysregulation from the data. Finally, partial correlations are used to characterise immune features in terms of KRAS master regulator activity. The results are compared across two independent cohorts for consistency. Moving beyond associations, the second part of the dissertation introduces a causal modelling approach to infer directed interactions between signaling pathway activity and immune agency. This is achieved by anchoring the analysis on somatic genomic changes. In particular, copy number profiles, transcriptomic data, image data and a protein-protein interaction network are integrated using graphical modelling approaches to infer directed relationships. Generated models are compared between independent cohorts and orthogonal datasets to evaluate consistency. Finally, proposed mechanisms are cross-referenced against literature examples to test for legitimacy. In summary, this dissertation provides methodological contributions, at the levels of associative and causal inference, for inferring the contextual basis for tumour-specific immune agency.This PhD was supported by the Cancer Research UK and Engineering and Physical Sciences Research Council Imaging Centre in Cambridge and Manchester (C197/A16465

Selected Works in Bioinformatics

This book consists of nine chapters covering a variety of bioinformatics subjects, ranging from database resources for protein allergens, unravelling genetic determinants of complex disorders, characterization and prediction of regulatory motifs, computational methods for identifying the best classifiers and key disease genes in large-scale transcriptomic and proteomic experiments, functional characterization of inherently unfolded proteins/regions, protein interaction networks and flexible protein-protein docking. The computational algorithms are in general presented in a way that is accessible to advanced undergraduate students, graduate students and researchers in molecular biology and genetics. The book should also serve as stepping stones for mathematicians, biostatisticians, and computational scientists to cross their academic boundaries into the dynamic and ever-expanding field of bioinformatics

Identifying markers of cell identity from single-cell omics data

Einzelzell-Omics-Daten stehen derzeit im Fokus der Entwicklung computergestützter Methoden in der Molekularbiologie und Genetik. Einzelzellexperimenten lieferen dünnbesetzte, hochdimensionale Daten über zehntausende Gene oder hunderttausende regulatorische Regionen in zehntausenden Zellen. Diese Daten bieten den Forschenden die Möglichkeit, Gene und regulatorische Regionen zu identifizieren, welche die Bestimmung und Aufrechterhaltung der Zellidentität koordinieren. Die gängigste Strategie zur Identifizierung von Zellidentitätsmarkern besteht darin, die Zellen zu clustern und dann Merkmale zu finden, welche die Cluster unterscheiden, wobei davon ausgegangen wird, dass die Zellen innerhalb eines Clusters die gleiche Identität haben. Diese Annahme ist jedoch nicht immer zutreffend, insbesondere nicht für Entwicklungsdaten bei denen sich die Zellen in einem Kontinuum befinden und die Definition von Clustergrenzen biologisch gesehen potenziell willkürlich ist. Daher befasst sich diese Dissertation mit Clustering-unabhängigen Strategien zur Identifizierung von Markern aus Einzelzell-Omics-Daten. Der wichtigste Beitrag dieser Dissertation ist SEMITONES, eine auf linearer Regression basierende Methode zur Identifizierung von Markern. SEMITONES identifiziert (Gruppen von) Markern aus verschiedenen Arten von Einzelzell-Omics-Daten, identifiziert neue Marker und übertrifft bestehende Marker-Identifizierungsansätze. Außerdem ermöglicht die Identifizierung von regulatorischen Markerregionen durch SEMITONES neue Hypothesen über die Regulierung der Genexpression während dem Erwerb der Zellidentität. Schließlich beschreibt die Dissertation einen Ansatz zur Identifizierung neuer Markergene für sehr ähnliche, dennoch underschiedliche neurale Vorlauferzellen im zentralen Nervensystem von Drosphila melanogaster. Ingesamt zeigt die Dissertation, wie Cluster-unabhängige Ansätze zur Aufklärung bisher uncharakterisierter biologischer Phänome aus Einzelzell-Omics-Daten beitragen.Single-cell omics approaches are the current frontier of computational method development in molecular biology and genetics. A single single-cell experiment provides sparse, high-dimensional data on tens of thousands of genes or hundreds of thousands of regulatory regions (i.e. features) in tens of thousands of cells (i.e. samples). This data provides researchers with an unprecedented opportunity to identify those genes and regulatory regions that determine and coordinate cell identity acquisition and maintenance. The most common strategy for identifying cell identity markers consists of clustering the cells and then identifying differential features between these clusters, assuming that cells within a cluster share the same identity. This assumption is, however, not guaranteed to hold, particularly for developmental data where cells lie along a continuum and inferring cluster boundaries becomes non-trivial and potentially biologically arbitrary. In response, this thesis presents clustering-independent strategies for marker feature identification from single-cell omics data. The primary contribution of this thesis is a linear regression-based method for marker feature identification from single-cell omics data called SEMITONES. SEMITONES can identify markers or marker sets from diverse single-cell omics data types, identifies novel markers, outperforms existing marker identification approaches. The thesis also describes how the identification of marker regulatory regions by SEMITONES enables the generation of novel hypotheses regarding gene regulation during cell identity acquisition. Lastly, the thesis describes the clustering-independent identification of novel marker genes for highly similar yet distinct neural progenitor cells in the Drosophila melanogaster central nervous system. Altogether, the thesis demonstrates how clustering-independent approaches aid the elucidation of yet uncharacterised biological patterns from single cell-omics data

Multivariate analysis of the immune response upon recent acquisition of Mycobacterium tuberculosis infection

Tuberculosis (TB), caused by the pathogen Mycobacterium tuberculosis (M.tb), is the leading cause of mortality due to an infectious agent worldwide. Based on data from an adolescent cohort study carried out from May 2005 to February 2009, we studied and compared the immune responses of individuals from four cohorts that were defined based on their longitudinal QFT results: the recent QFT converters, the QFT reverters, the persistent QFT positives and negatives. Analysis was based on the integration of different arms of the immune response, including adaptive and “innaptive” responses, measured on the cohorts. COMPASS was used to filter the adaptive dataset and identify bioligically meaningful subsets, while, for the innaptive dataset, we came up with a novel filtering method. Once the datasets were integrated, they were standardized using variance stabilizing (vast) standardization and missing values were imputed using a multiple factor analysis (MFA)-based approach. We first set out to define a set of immune features that changed during recent M.tb infection. This was achieved by employing the kmlShape clustering algorithm to the recent QFT converters. We identified 55 cell subsets to either increase or decrease post-infection. When we assessed how the associations between these changed pre- and post-infection using correlation networks, we found no notable differences. By comparing the recent QFT converters and the persistent QFT positives, a blood-based biomarker to distinguish between recent and established infection, namely ESAT6/CFP10-specific expression of HLA-DR on total Th1 cells, was identified using elastic net (EN) models (average AUROC = 0.87). The discriminatory ability of this variable was confirmed using two tree-based models. Lastly, to assess whether the QFT reverters are a biologically distinct group of individuals, we compared them to the persistent QFT positive and QFT negative individuals using a Projection to Latent Space Discriminant Analysis (PLS-DA) model. The results indicated that reverters appeared more similar to QFT negative individuals rather than QFT positive. Hence, QFT reversion may be associated with clearance of M.tb infection. Immune signatures associated with recent infection could be used to refine end-points of clinical trials testing vaccine efficacy against acquisition of M.tb infection, while immune signatures associated with QFT reversion could be tested as correlates of protection from M.tb infection