Adjuvant-Mediated Epitope Specificity and Enhanced Neutralizing Activity of Antibodies Targeting Dengue Virus Envelope Protein

abstract: The heat-labile toxins (LT) produced by enterotoxigenic Escherichia coli display adjuvant effects to coadministered antigens, leading to enhanced production of serum antibodies. Despite extensive knowledge of the adjuvant properties of LT derivatives, including in vitro-generated non-toxic mutant forms, little is known about the capacity of these adjuvants to modulate the epitope specificity of antibodies directed against antigens. This study characterizes the role of LT and its non-toxic B subunit (LTB) in the modulation of antibody responses to a coadministered antigen, the dengue virus (DENV) envelope glycoprotein domain III (EDIII), which binds to surface receptors and mediates virus entry into host cells. In contrast to non-adjuvanted or alum-adjuvanted formulations, antibodies induced in mice immunized with LT or LTB showed enhanced virus-neutralization effects that were not ascribed to a subclass shift or antigen affinity. Nonetheless, immunosignature analyses revealed that purified LT-adjuvanted EDIII-specific antibodies display distinct epitope-binding patterns with regard to antibodies raised in mice immunized with EDIII or the alum-adjuvanted vaccine. Notably, the analyses led to the identification of a specific EDIII epitope located in the EF to FG loop, which is involved in the entry of DENV into eukaryotic cells. The present results demonstrate that LT and LTB modulate the epitope specificity of antibodies generated after immunization with coadministered antigens that, in the case of EDIII, was associated with the induction of neutralizing antibody responses. These results open perspectives for the more rational development of vaccines with enhanced protective effects against DENV infections.View the article as published at https://www.frontiersin.org/articles/10.3389/fimmu.2017.01175/ful

Obesity-linked insulin resistance, inflammation, and omega-3 fatty acids : exploring the anti-diabetic potential of novel omega-3 derived pro-resolution mediators

Tableau d'honneur de la Faculté des études supérieures et postdoctorales, 2011-2012L'objectif principal des études présentées dans cette thèse était d'étudier les effets métaboliques et anti-inflammatoires des acides gras oméga-3 et leurs dérivés bioactifs, nommés résolvines et protectines, dans le contexte d'obésité et d'insulino-résistance. Afin d'atteindre ce but, nous avons utilisé une nouvelle lignée de souris transgénique, fat-1, qui nous permet d'augmenter les niveaux d'acides gras oméga-3 sans modifier la diète expérimentale. Dans la première étude, nous avons démontré que le rétablissement des acides gras oméga-3 dans les souris obèses nourries avec une diète riche en gras, pouvait augmenter la synthèse des dérivés bioactifs des acides gras oméga-3, notamment les protectines, dans le tissu adipeux et le muscle squelettique. Ceci était associé à une meilleure capacité à résoudre une réponse inflammatoire aigùe et à une diminution de l'inflammation dans le tissu adipeux. De plus, les souris fat-1 obèses ont démontré une meilleure sensibilité à l'insuline et une plus grande tolérance au glucose. Tout cela avec un gain de poids et accretion de graisse équivalents à leurs homologues sauvages. Dans la seconde étude, nous avons analysé les effets métaboliques et antiinflammatoires de l'administration de la protectine DX dans des macrophages in vitro ainsi que dans les souris in vivo. En plus des activités anti-inflammatoires anticipées, nous avons pu observer que la protectine DX possède une activité antidiabétique unique qui pourrait être expliquée par une sécrétion d'interleukine-6 impliquée dans l'inhibition de l'expression des enzymes de la gluconéogenèse dans le foie. Dans la troisième étude, nous avons effectué une analyse comparative par biopuces du tissu adipeux des souris fat-1 obèses et des souris sauvages. Cette étude nous a permis de révéler cinq voies ciblées par des acides gras oméga-3 dans le tissu adipeux. Nous avons pu valider certaines cibles par PCR quantitatif et par histologie. L'ensemble de nos études démontre le rôle déterminant des acides gras oméga-3 et leurs dérivés, notamment les protectines, dans l'homéostasie métabolique. De plus, nous avons été en mesure d'identifier la protectine DX comme un nouvel agent thérapeutique qui possède un important potentiel pour le traitement du diabète de type 2 et de l'obésité

Inferring Genetic Co-dependencies To Identify New Vulnerabilities In Cancer

Translation of cancer genomic data into cancer therapies and companion diagnostics remains a primary challenge in personalized medicine. Much of this challenge is due to the difficulty of identifying genetic co-dependencies that lead to clinically actionable drug targets. Targeting many of the known essential gene products are not always selectively efficacious because these targets may be common to both malignant and benign cells. However, essential genes that are associated with particular genomic alterations in cancer cells, like those from synthetic lethality, can potentially provide a source of tumor-specific drug targets. To help aid novel drug discovery, I developed a computational approach called CLOvE, a multi-omic approach that identifies co-dependencies in pairs of genes. These co-dependencies are inferred from context-dependent changes in expression, where CLOvE assigns high scores to those genes with the greatest compensatory change in expression. These scores may suggest synthetic lethal interactions, which may uncover clinically actionable essential genes. These methods were developed in CCLE cell lines and validated with RNAi and CRISPR viability data. CLOvE identifies meaningful expression changes, assigns high scores to known essentials, reveals known synthetic lethal connections, and implicates many possible new connections. This approach could provide a tool to accelerate both target discovery and biomarker discovery, to develop drugs suitable for a specific cancer, and identify and stratify patients who may benefit from these treatments

Platelet Diagnostics:A novel liquid biomarker

The aim of this thesis is to find a novel liquid biomarker for the detection of cancer and to optimize treatment. The first chapter gives an introduction to the oncology biomarker field and focuses on platelets and their role in cancer. In part 1, we evaluate extracellular vesicles (EVs). EVs are small vesicles released by all types of cells, including tumor cells, into the circulation. They carry protein kinases and can be isolated from plasma. We demonstrate that AKT and ERK kinase protein levels in EVs reflect the cellular expression levels and treatment with kinase inhibitors alters their concentration, depending on the clinical response to the drug. Therefore, EVs may provide a promising biomarker biosource for monitoring of treatment responses. Part 2 starts with reviews describing the function and role of platelets in greater depth. Chapter 3 focusses on thrombocytogenesis and several biological processes in which platelets play a role. Furthermore, the RNA processing machineries harboured by platelets are discussed. Both chapter 3 and 4 evaluate the change platelets undergo after being exposed to tumor and its environment. The exchange of biomolecules with tumor cells results in educated platelets, so-called tumor educated platelets (TEPs). TEPs play a role in several hallmarks of cancer and have the ability to respond to systemic alterations making them an interesting biomarker. In chapter 5 the diagnostic potential of platelets is first discussed. We determine their potential by sequencing the RNA of 283 platelet samples, of which 228 are patients with cancer, and 55 are healthy controls. We reach an accuracy of 96%. Furthermore, we are able to pinpoint the location of the primary tumor with an accuracy of 71%. In part 3, our developed thromboSeq platform is taken to the next level. Several potential confounding factors are taken into account such as age and comorbidity. We show that particle-swarm optimization (PSO)-enhanced algorithms enable efficient selection of RNA biomarker panels. In a validation cohort we apply these algorithms to non-small-cell lung cancer and reach an accuracy of 88% in late stage (n=518) and early-stage 81% accuracy. Finally, in chapter 7 we describe our wet- and dry-lab protocols in detail. This includes platelet RNA isolation, mRNA amplification, and preparation for next-generation sequencing. The dry-lab protocol describes the automated FASTQ file pre-processing to quantified gene counts, quality controls, data normalization and correction, and swarm intelligence-enhanced support vector machine (SVM) algorithm development. Part 4 focuses on central nervous system (CNS) malignancies especially on glioblastoma. Chapter 8 gives an overview of the different liquid biomarkers for diffuse glioma, the most common primary CNS malignancy. In chapter 9 we assess the specificity of the platelet education due to glioblastoma by comparing the RNA profile of TEPs from glioblastoma patients with a neuroinflammatory disease and brain metastasis patients. This results in a detection accuracy of 80%. Secondly, analysis of patients with glioblastoma versus healthy controls in an independent validation series provide a detection accuracy of 95%. Furthermore, we describe the potential value of platelets as a monitoring biomarker for patients with glioma, distinguishing pseudoprogression from real tumor progression. In part 5 thromboSeq is applied to breast cancer diagnostics both as a screening tool in the general population and in a high risk population, BRCA mutated women. In chapter 11 we first apply our technique to an inflammatory condition, multiple sclerosis (MS). Platelet RNA is used as input for the development of a diagnostic MS classifier capable of detecting MS with 80% accuracy in the independent validation series. In the final part we conclude this thesis with a general discussion of the main findings and suggestions for future research

Analysis, Visualization, and Machine Learning of Epigenomic Data

The goal of the Encyclopedia of DNA Elements (ENCODE) project has been to characterize all the functional elements of the human genome. These elements include expressed transcripts and genomic regions bound by transcription factors (TFs), occupied by nucleosomes, occupied by nucleosomes with modified histones, or hypersensitive to DNase I cleavage, etc. Chromatin Immunoprecipitation (ChIP-seq) is an experimental technique for detecting TF binding in living cells, and the genomic regions bound by TFs are called ChIP-seq peaks. ENCODE has performed and compiled results from tens of thousands of experiments, including ChIP-seq, DNase, RNA-seq and Hi-C. These efforts have culminated in two web-based resources from our lab—Factorbook and SCREEN—for the exploration of epigenomic data for both human and mouse. Factorbook is a peak-centric resource presenting data such as motif enrichment and histone modification profiles for transcription factor binding sites computed from ENCODE ChIP-seq data. SCREEN provides an encyclopedia of ~2 million regulatory elements, including promoters and enhancers, identified using ENCODE ChIP-seq and DNase data, with an extensive UI for searching and visualization. While we have successfully utilized the thousands of available ENCODE ChIP-seq experiments to build the Encyclopedia and visualizers, we have also struggled with the practical and theoretical inability to assay every possible experiment on every possible biosample under every conceivable biological scenario. We have used machine learning techniques to predict TF binding sites and enhancers location, and demonstrate machine learning is critical to help decipher functional regions of the genome

Transcriptome and Genome Analyses Applied to Aquaculture Research

Aquaculture is an important economic activity for food production all around the world that has experienced an exponential growth during the last few decades. However, several weaknesses and bottlenecks still need to be addressed in order to improve the aquaculture productive system. The recent fast development of the omics technologies has provided scientists with meaningful tools to elucidate the molecular basis of their research interests. This reprint compiles different works about the use of transcriptomics and genomics technologies in different aspects of the aquaculture research, such as immunity, stress response, development, sexual dimorphism, among others, in a variety of fish and shellfish, and even in turtles. Different transcriptome (mRNAs and non-coding RNAs (ncRNAs)), genome (Single Nucleotide Polymorphisms (SNPs)), and metatranscriptome analyses were conducted to unravel those different aspects of interest

Genetics of Animal Health and Disease in Livestock

Wood surface attributes can be established by examining its several different physical or chemical properties. Differences in the wood surfaces occur between the manufacturing and post-treatment processes as well. Understanding how their unique anisotropic molecular organization, chemical linkages, branching, and other molecular features govern micro- and macroscale accessibility is essential for coating and complex modification processes. It is therefore important for scientific as well as practical reasons to qualify and quantify the effects of wood surface treatments and modifications. Challenges still exist to fully understanding the effect of the numerous applied chemicals and the wide range of treatment processes on wood surfaces

Kernel Methods and Measures for Classification with Transparency, Interpretability and Accuracy in Health Care

Support vector machines are a popular method in machine learning. They learn from data about a subject, for example, lung tumors in a set of patients, to classify new data, such as, a new patient’s tumor. The new tumor is classified as either cancerous or benign, depending on how similar it is to the tumors of other patients in those two classes—where similarity is judged by a kernel. The adoption and use of support vector machines in health care, however, is inhibited by a perceived and actual lack of rationale, understanding and transparency for how they work and how to interpret information and results from them. For example, a user must select the kernel, or similarity function, to be used, and there are many kernels to choose from but little to no useful guidance on choosing one. The primary goal of this thesis is to create accurate, transparent and interpretable kernels with rationale to select them for classification in health care using SVM—and to do so within a theoretical framework that advances rationale, understanding and transparency for kernel/model selection with atomic data types. The kernels and framework necessarily co-exist. The secondary goal of this thesis is to quantitatively measure model interpretability for kernel/model selection and identify the types of interpretable information which are available from different models for interpretation. Testing my framework and transparent kernels with empirical data I achieve classification accuracy that is better than or equivalent to the Gaussian RBF kernels. I also validate some of the model interpretability measures I propose

Serotonergic modulation of the ventral pallidum by 5HT1A, 5HT5A, 5HT7 AND 5HT2C receptors

Introduction: Serotonin's involvement in reward processing is controversial. The large number of serotonin receptor sub-types and their individual and unique contributions have been difficult to dissect out, yet understanding how specific serotonin receptor sub-types contribute to its effects on areas associated with reward processing is an essential step. Methods: The current study used multi-electrode arrays and acute slice preparations to examine the effects of serotonin on ventral pallidum (VP) neurons. Approach for statistical analysis: extracellular recordings were spike sorted using template matching and principal components analysis, Consecutive inter-spike intervals were then compared over periods of 1200 seconds for each treatment condition using a student’s t test. Results and conclusions: Our data suggests that excitatory responses to serotonin application are pre-synaptic in origin as blocking synaptic transmission with low-calcium aCSF abolished these responses. Our data also suggests that 5HT1a, 5HT5a and 5HT7 receptors contribute to this effect, potentially forming an oligomeric complex, as 5HT1a antagonists completely abolished excitatory responses to serotonin application, while 5HT5a and 5HT7 only reduced the magnitude of excitatory responses to serotonin. 5HT2c receptors were the only serotonin receptor sub-type tested that elicited inhibitory responses to serotonin application in the VP. These findings, combined with our previous data outlining the mechanisms underpinning dopamine's effects in the VP, provide key information, which will allow future research to fully examine the interplay between serotonin and dopamine in the VP. Investigation of dopamine and serotonins interaction may provide vital insights into our understanding of the VP's involvement in reward processing. It may also contribute to our understanding of how drugs of abuse, such as cocaine, may hijack these mechanisms in the VP resulting in sensitization to drugs of abuse