Advanced Computational Tools Research Articles

LINEAGE: Label-free identification of endogenous informative single-cell mitochondrial RNA mutation for lineage analysis

Single-cell RNA-sequencing (scRNA-seq) has become a powerful tool for biomedical research by providing a variety of valuable information with the advancement of computational tools. Lineage analysis based on scRNA-seq provides key insights into the fate of individual cells in various systems. However, such analysis is limited by several technical challenges. On top of the considerable computational expertise and resources, these analyses also require specific types of matching data such as exogenous barcode information or bulk assay for transposase-accessible chromatin with high throughput sequencing (ATAC-seq) data. To overcome these technical challenges, we developed a user-friendly computational algorithm called "LINEAGE" (label-free identification of endogenous informative single-cell mitochondrial RNA mutation for lineage analysis). Aiming to screen out endogenous markers of lineage located on mitochondrial reads from label-free scRNA-seq data to conduct lineage inference, LINEAGE integrates a marker selection strategy by feature subspace separation and de novo "low cross-entropy subspaces" identification. In this process, the mutation type and subspace-subspace "cross-entropy" of features were both taken into consideration. LINEAGE outperformed three other methods, which were designed for similar tasks as testified with two standard datasets in terms of biological accuracy and computational efficiency. Applied on a label-free scRNA-seq dataset of BRAF-mutated cancer cells, LINEAGE also revealed genes that contribute to BRAF inhibitor resistance. LINEAGE removes most of the technical hurdles of lineage analysis, which will remarkably accelerate the discovery of the important genes or cell-lineage clusters from scRNA-seq data.

Kinetic Modeling of Saccharomyces cerevisiae Central Carbon Metabolism: Achievements, Limitations, and Opportunities.

Central carbon metabolism comprises the metabolic pathways in the cell that process nutrients into energy, building blocks and byproducts. To unravel the regulation of this network upon glucose perturbation, several metabolic models have been developed for the microorganism Saccharomyces cerevisiae. These dynamic representations have focused on glycolysis and answered multiple research questions, but no commonly applicable model has been presented. This review systematically evaluates the literature to describe the current advances, limitations, and opportunities. Different kinetic models have unraveled key kinetic glycolytic mechanisms. Nevertheless, some uncertainties regarding model topology and parameter values still limit the application to specific cases. Progressive improvements in experimental measurement technologies as well as advances in computational tools create new opportunities to further extend the model scale. Notably, models need to be made more complex to consider the multiple layers of glycolytic regulation and external physiological variables regulating the bioprocess, opening new possibilities for extrapolation and validation. Finally, the onset of new data representative of individual cells will cause these models to evolve from depicting an average cell in an industrial fermenter, to characterizing the heterogeneity of the population, opening new and unseen possibilities for industrial fermentation improvement.

Omics Approaches to Assess Flavor Development in Cheese.

Cheese is characterized by a rich and complex microbiota that plays a vital role during both production and ripening, contributing significantly to the safety, quality, and sensory characteristics of the final product. In this context, it is vital to explore the microbiota composition and understand its dynamics and evolution during cheese manufacturing and ripening. Application of high-throughput DNA sequencing technologies have facilitated the more accurate identification of the cheese microbiome, detailed study of its potential functionality, and its contribution to the development of specific organoleptic properties. These technologies include amplicon sequencing, whole-metagenome shotgun sequencing, metatranscriptomics, and, most recently, metabolomics. In recent years, however, the application of multiple meta-omics approaches along with data integration analysis, which was enabled by advanced computational and bioinformatics tools, paved the way to better comprehension of the cheese ripening process, revealing significant associations between the cheese microbiota and metabolites, as well as their impact on cheese flavor and quality.

Molecular Kinetic Modeling of Catalytic Naphtha Reforming: A Review of Complexities and Solutions

ABSTRACT Kinetic modeling is receiving more attention in recent years due to the availability of advanced computational tools and enhancement in the accuracy of analytical techniques. These advances facilitate investigation of the chemical transformations on a molecular level rather than on the bulk properties, such as density, distillation cuts, or octane number. Molecular kinetic modeling of catalytic naphtha reforming is of particular interest as it processes light petroleum fraction that enable full molecular-level analysis. Moreover, the process is an important source of valuable chemicals, hydrogen, and high-octane transportation fuel. Over the years, the goal of the kinetic modeling has evolved from predicting the octane number or other properties of reformate to tracking a particular molecule, such as benzene. Several kinetic models are published in the last decade – each of them strived to adopt somewhat different approach either in the complexity of proposed reaction network or in the methodology of estimating the kinetic parameters. The approaches that have been considered in formulating the rate expressions include: (i) classical power-law model; (ii) models based on Langmuir-Hinshelwood–Hougen–Watson kinetics; (iii) structure-oriented kinetics approach; and (iv) single-event fundamental model. The review presents a systematic comparison of these kinetic models in depth as well as their limitations. An appraisal of the mathematical methods for estimation of kinetic parameters and the computational tools employed for determination of the numerical values of these parameters is made. Finally, the current trends and outlook of this field is presented.

Non-invasive embedded system hardware/firmware anomaly detection based on the electric current signature

Quality control is a critical aspect of the modern electronic circuit industry. In addition to being a pre-requisite to proper functioning, circuit quality is closely related to safety, security, and economic issues. Quality control has been reached through system testing. Meanwhile, device miniaturization and multilayer Printed Circuit Boards have increased the electronic circuit test complexity considerably. Hence, traditional test processes based on manual inspections have become outdated and inefficient. More recently, the concept of Advanced Manufacturing or Industry 4.0 has enabled the manufacturing of customized products, tailored to the changing customers’ demands. This scenario points out additional requirements for electronic system testing: it demands a high degree of flexibility in production processes, short design and manufacturing cycles, and cost control. Thus, there is a demand for circuit testing systems that present effectiveness and accessibility without placing numerous test points. This work is focused on automated test solutions based on machine learning, which are becoming popular with advances in computational tools. We present a new testing approach that uses autoencoders to detect firmware or hardware anomalies based on the electric current signature. We built a test set-up using an embedded system development board to evaluate the proposed approach. We implemented six firmware versions that can run independently on the test board – one of them is considered anomaly-free. In order to obtain a reference frame to our results, two other classification techniques (a computer vision algorithm and a random forest classification model) were employed to detect anomalies on the same development board. The outcomes of the experiments demonstrated that the proposed test method is highly effective. For several test scenarios, the correct detection rate was above 99%. Test results showed that autoencoder and random forest approaches are effective. However, random forests require all data classes to be trained. Training an autoencoder, on the other hand, only requires the reference (anomaly-free) class.

FIBRE4YARDS: Fibre Composite Manufacturing Technologies for the Automation and Modular Construction in Shipyards

FIBRE4YARDS project aims to improve the competitiveness of small and medium European shipyards working with Fibre Reinforced Polymers (FRP). To this extent, the project expects to bring new automated cost-efficient production technologies to the shipyard (adaptive moulds, ATP/AFP, 3D printing, curved pultrusion profiles, hot stamping, innovative composite connections) from other competitive industrial sectors, and to transfer, adapt and combine them to improve FRP shipyards’ production and maintenance. In order to incorporate all new manufacturing technologies into the ship design, FIBRE4YARDS will develop new advanced computational tools and numerical formulations specially developed to characterize the new procedures. Ship design will also take into account the environmental impact of the materials and manufacturing technologies used, based on a Life Cycle Assessment. All new production procedures will be implemented in a new Shipyard 4.0 environment which, among other features, will be interconnected thanks to IoT technologies, and will be continuously monitored to ensure high quality processes using a digital twin of the shipyard. Special care will be taken to ensure that all technologies proposed by FIBRE4YARDS project comply with the actual standards, writing new guidelines when those do not exist, and that can be implemented in European Shipyards smoothly and straightforward. With the proposed approach, shipyards will improve their production capacity and the quality of the ships manufactured, making them more safe and efficient.

Computational simulations and assessment of two approaches for x-ray phase contrast imaging

X-ray phase-contrast imaging is a high-resolution imaging that permits an increase of the perceptibility of the details in three-dimensional objects, such as human tissues compared to conventional absorption imaging. There are different approaches for implementing phase-contrast imaging and their introduction into clinical practice requires advanced computational tools. A long-term goal of our research is the development of computational models of breast phase-contrast imaging. The aim of this study is to develop a software module for implementing grating-based phase-contrast imaging. For this purpose, an existing in-house software application for x-ray imaging with a function to model and simulate propagation-based phase-contrast x-ray images has been extended to include a model of grating-based imaging. To test the new functionality, four computational phantoms reflecting features, which can be screened in the real breast tissue and which differ in their complexity, were designed. Planar x-ray images in absorption, propagation-based and grating-based modes were generated and compared. Results showed improved visual appearance of the simulated objects in images obtained by simulating grating-based imaging setup. The developed subroutine is planned to be experimentally validated at synchrotron facility. The new software functionality will be exploited in studies related to new x-ray imaging techniques for breast screening and diagnosing.

Qibo: a framework for quantum simulation with hardware acceleration

We present Qibo, a new open-source software for fast evaluation of quantum circuits and adiabatic evolution which takes full advantage of hardware accelerators. The growing interest in quantum computing and the recent developments of quantum hardware devices motivates the development of new advanced computational tools focused on performance and usage simplicity. In this work we introduce a new quantum simulation framework that enables developers to delegate all complicated aspects of hardware or platform implementation to the library so they can focus on the problem and quantum algorithms at hand. This software is designed from scratch with simulation performance, code simplicity and user friendly interface as target goals. It takes advantage of hardware acceleration such as multi-threading Central Processing Unit (CPU), single Graphics Processing Unit (GPU) and multi-GPU devices.

Systematic Optimization of Ambient Ionization Ion Mobility Mass Spectrometry for Rapid Separation of Isomers.

Current methods typically used for metabolite screening and disease diagnosis often require extensive sample preparation, which increases analysis time and associated costs. While ambient ionization techniques enable the analysis of various samples in complex matrices with little or no sample preparation in a short time (typically within a minute), their reduced selectivity, even when coupled with high-resolution mass spectrometers, limits their application in certain fields. In this study, we have optimized the coupling of paper spray (PS) and leaf spray (LS) ambient ionization techniques with a commercially available ion mobility mass spectrometer (IM-MS) and demonstrated the separation of geometric and constitutional isomers. Ambient ionization techniques allow simultaneous introduction and ionization of samples, while background noise and matrix interference from paper and leaf substrates are filtered out by IM separation, resulting in high sensitivity and selectivity of the PS-IM-MS and LS-IM-MS workflows. In addition, we introduced a novel approach to perform single-field collision cross section (CCS) measurements, which resulted in CCS values that differ by 0.15% and 0.25% from traditional stepped-field and single-field methods, respectively. In addition, we used advanced computational tools to confidently identify analyte structures by comparing CCS values from experimental IM measurements and theoretical calculations. These results suggest that the coupling of ambient ionization methods with ion mobility techniques enables rapid, sensitive, and highly selective analysis that can be used in different fields, such as agrochemical screening and disease diagnostics.

Immunoinformatics: Pushing the boundaries of immunology research and medicine

Immunology has come a long way, from its early religious beginnings thousands of years ago, to the explosion of immunological data in the 21st century. Thanks to discoveries in immunology, our world has seen tremendous progress in how we understand and treat disease. However, a lot of unmet clinical needs remain, which require focused, real-time collaboration at the clinical and scientific research forefronts. Moreover, the current exponential growth in the generation of research data makes it impossible to handle, analyze, visualize, and interpret such data without the use of advanced computational tools. We think immunoinformatics- a discipline at the intersection of immunology and computer science- will greatly increase efficiency in research productivity and disease treatment.This perspective paper aims to emphasize the role of immunoinformatics toward pushing the boundaries of immunology research. It will also illustrate its clinical applications, including disease prevention, diagnosis, prognosis, treatment, monitoring, as well as in drug discovery.We believe informatics approaches will be implemented increasingly more frequently in research. Thus, here we also discuss a set of fundamental prerequisites to facilitate the efficient and ethical integration of informatics in research and ensure immunological advancements provide maximum benefits to society.

Virtual exhibition for design workshops. Some experiences at DiARC_ University of Naples “Federico II

In the following we describe some conceptual steps and problematic nodes concerning the theme of new and integrable ways of laboratory teaching aided by advanced computer tools developed at the Architectural and Urban Composition Studio or Final Architectural Studio of the five-year single- cycle degree courses in Architecture and the Master’s degree course in Architectural Studio at the DiARC of the University of Naples “Federico II” held – - in the academic years 2019-20 and 2020-21 – and coordinated by Renato Capozzi with the collaboration of Nicola Campanile, Gennaro Di Costanzo, Roberta Esposito, Oreste Lubrano, Claudia Sansò and Francesca Spacagna. The contribution, starting from a questioning of the potentialities but also of the limits of a didactics of the project according to the D.a.D. or blended modality according to a wider perspective of heterotopic sense of Foucauldian matrix in the paragraph “Real VS Virtual”, is articulated in three more technical related paragraphs: “The 3D models”; “Elaboration of the sharing interface”; “Experiences of virtual exhibitions”. The essay ends with some provisional “Conclusions” that reflect on the actual potentialities and development prospects of the combined use of the technologies employed. While the text “3D Models” analyses the main techniques for the production of virtual models to define the spheroidal environment in which the exhibition is to be located and the fundamental elements (graphics and models) for the construction of the exhibition’s itinerary or multiple itineraries, the following section describes the phases of elaboration of the multimedia product to be shared on the web or through other media, offering the user autonomous navigation in the exhibition spaces and a strong interactivity of its contents. At the end, the main experiences of virtual exhibitions produced in 2020 and 2021 are reported and exhibited for the first time on 28th November 2020 (only for a part related to the annus terribilis 2020) in the Researcher’s European night, promoted by MEET me TONIGHT “Faccia a Faccia con la ricerca”, Link city | DiARC UNINA neaPòlis Scuola Politecnica e delle scienze di base – Università degli Studi di Napoli “Federico II” through the system: Jitsi Meetings.

Abstract PO-039: Antiproliferative activity of inhibitors of RAD51, singly and in combination with chemotherapy drugs, against pancreatic cancer cell lines

Abstract Despite the innovations in chemotherapeutic treatment of pancreatic cancer, the generally poor outcome of this disease begs the search for novel targets. RAD51 is a critical component of homologous recombination DNA repair, binding with BRCA2 and forming polymeric filaments essential for its function. RAD51 is often elevated in cancer cells compared to normal cells, particularly in pancreatic cancer: RAD51 is a negative prognostic biomarker and promotes tumor cell proliferation in that disease (Cancer Cell Int 19: 356, 2019; doi: 10.1186/s12935-019-1077-6). Therefore, RAD51 is an attractive target for anticancer treatment in that its elevated level and activity in tumor cells provide a potential level of selectivity for such an agent, increasing the therapeutic index. We demonstrated previously that novel inhibitors of RAD51, IBR2 [2-(Benzylsulfonyl)-1-(1H-indol-3-yl)-1,2-dihydroisoquinoline] and IBR120 (an isoindolinyl derivative of IBR2) (Eur J Med Chem. 96:196-208, 2015; doi: 10.1016/j.ejmech.2015.04.021), not only inhibited proliferation of a range of tumor cell lines at micromolar concentrations but also acted in combination with commonly used cytotoxic chemotherapy and molecularly targeted drugs to synergistically inhibit proliferation (J Pharmacol Expt Ther, 364: 46-54, 2018; doi.org/10.1124/jpet.117.241661). To improve ADME properties, enhance activity, and increase potential to selectively target RAD51, advanced computational tools and rational approaches were employed to predict possible modification to the IBR chemical backbone. This effort resulted in discovery of the compound JKYN-1. As a model system in which to test the activity of JKYN-1 in vitro, human pancreatic cancer cell lines PANC-1, Capan-1 and Capan-2 were used. All lines have mutant K-Ras and p53, and Capan-1 has mutant BRCA2 (https://www.cancer.gov/research/key-initiatives/ras; https://web.expasy.org/cellosaurus). JKYN-1 or its water-soluble methylsulfonate salt (JKYN-1-mesylate) inhibit proliferation of PANC-1 cells approximately 5 times more effectively than IBR120 and inhibit Capan-1 and Capan-2 cells even better. Against PANC-1, JKYN-1-mesylate inhibits proliferation synergistically in combination with afatinib (inhibitor of epidermal growth factor receptor) or with IBR120, and is additive with gemcitabine. Biochemical characterization of the interaction between RAD51 and these inhibitors demonstrated the following: (1) IBR2 and IBR120 promote disassembly of RAD51 multimers in an ex vivo native polyacrylamide gel electrophoresis assay using clonal constructs of RAD51, and (2) JKYN-1 binds to RAD51 with higher affinity than IBR120 and B02, a commercially available RAD51 inhibitor, in a surface plasmon resonance spectroscopy assay. Therefore, JKYN-1 is a potential novel small molecule therapeutic agent for treatment of pancreatic cancer, both as a single agent and in combination with standard chemotherapy drugs. Citation Format: Peter Ferguson, Mark D. Vincent, Yousef Najajreh, Brian Shilton, Stephen Ritter, Rima Al-awar, Richard Marcellus, Mohammed Mohammed, Methvin Isaac, James Koropatnnick. Antiproliferative activity of inhibitors of RAD51, singly and in combination with chemotherapy drugs, against pancreatic cancer cell lines [abstract]. In: Proceedings of the AACR Virtual Special Conference on Pancreatic Cancer; 2021 Sep 29-30. Philadelphia (PA): AACR; Cancer Res 2021;81(22 Suppl):Abstract nr PO-039.

Computational Auxiliary for the Progress of Sodium-Ion Solid-State Electrolytes.

All-solid-state sodium batteries (ASSBs) have attracted ever-increasing attention due to their enhanced safety, high energy density, and the abundance of raw materials. One of the remaining key issues for the practical ASSB is the lack of good superionic and electrochemical stable solid-state electrolytes (SEs). Design and manufacturing specific functional materials used as high-performance SEs require an in-depth understanding of the transport mechanisms and electrochemical properties of fast sodium-ion conductors on an atomic level. On account of the continuous progress and development of computing and programming techniques, the advanced computational tools provide a powerful and convenient approach to exploit particular functional materials to achieve that aim. Herein, this review primarily focuses on the advanced computational methods and ion migration mechanisms of SEs. Second, we overview the recent progress on state-of-the-art solid sodium-ion conductors, including Na-β-alumina, sulfide-type, NASICON-type, and antiperovskite-type sodium-ion SEs. Finally, we outline the current challenges and future opportunities. Particularly, this review highlights the contributions of the computational studies and their complementarity with experiments in accelerating the study progress of high-performance sodium-ion SEs for ASSBs.

Advanced computation in cardiovascular physiology: new challenges and opportunities.

Recent developments in computational physiology have successfully exploited advanced signal processing and artificial intelligence tools for predicting or uncovering characteristic features of physiological and pathological states in humans. While these advanced tools have demonstrated excellent diagnostic capabilities, the high complexity of these computational 'black boxes' may severely limit scientific inference, especially in terms of biological insight about both physiology and pathological aberrations. This theme issue highlights current challenges and opportunities of advanced computational tools for processing dynamical data reflecting autonomic nervous system dynamics, with a specific focus on cardiovascular control physiology and pathology. This includes the development and adaptation of complex signal processing methods, multivariate cardiovascular models, multiscale and nonlinear models for central-peripheral dynamics, as well as deep and transfer learning algorithms applied to large datasets. The width of this perspective highlights the issues of specificity in heartbeat-related features and supports the need for an imminent transition from the black-box paradigm to explainable and personalized clinical models in cardiovascular research. This article is part of the theme issue 'Advanced computation in cardiovascular physiology: new challenges and opportunities'.

Computer visualization and dynamic study of new families of root-solvers

The considered topic, the dynamic study of new root solvers using computer tools, is actually a “bridge” between computer science and applied mathematics. The goal of this paper is the construction and dynamic study of two new one-parameter families for solving nonlinear equations using advanced computer tools such as symbolic computation, computer graphics and multi-precision arithmetic. First family, based on Popovski’s third order method (Popovski, 1980), is modified and adapted for finding multiple zeros of differentiable functions. Its dynamic study is performed using basins of attraction and associated quantitative data. In the second part this one-point family for a single zero serves for the derivation of a very efficient family of iterative methods with corrections for the simultaneous determination of all multiple zeros of algebraic polynomials. The convergence analysis, performed by the help of symbolic computation in computer algebra system Mathematica, has shown that the order of convergence of the proposed family is four, five and six, depending of the type of used corrective approximations. A very fast convergence rate is obtained without any additional evaluations of a given polynomial P and its derivatives P′ and P′′, which points to the high computational efficiency of new methods. Choosing different values of the involved parameter, the presented family generates a variety of simultaneous methods. Employing multi-precision arithmetic, it has been shown by numerical experiments that four particular methods from the family produce approximations of very high accuracy. Computer visualization, carried out by plotting trajectories of zero approximations, points to the stability and robustness of the proposed simultaneous methods and indicates a conjecture on their globally convergent properties, one of the most important features of simultaneous methods for solving polynomial equations.

Transcriptome annotation in the cloud: complexity, best practices and cost

BACKGROUND The NIH Science and Technology Research Infrastructure for Discovery, Experimentation, and Sustainability (STRIDES) initiative provides NIH-funded researchers cost-effective access to commercial cloud providers, such as Amazon Web Services (AWS) and Google Cloud Platform (GCP). These cloud providers represent an alternative for the execution of large computational biology experiments like transcriptome annotation, which is a complex analytical process that requires the interrogation of multiple biological databases with several advanced computational tools. The core components of annotation pipelines published since 2012 are BLAST sequence alignments using annotated databases of both nucleotide or protein sequences almost exclusively with networked on-premises compute systems. FINDINGS We compare multiple BLAST sequence alignments using AWS and GCP. We prepared several Jupyter Notebooks with all the code required to submit computing jobs to the batch system on each cloud provider. We consider the consequence of the number of query transcripts in input files and the effect on cost and processing time. We tested compute instances with 16, 32, and 64 vCPUs on each cloud provider. Four classes of timing results were collected: the total run time, the time for transferring the BLAST databases to the instance local solid-state disk drive, the time to execute the CWL script, and the time for the creation, set-up, and release of an instance. This study aims to establish an estimate of the cost and compute time needed for the execution of multiple BLAST runs in a cloud environment. CONCLUSIONS We demonstrate that public cloud providers are a practical alternative for the execution of advanced computational biology experiments at low cost. Using our cloud recipes, the BLAST alignments required to annotate a transcriptome with ∼500,000 transcripts can be processed in <2 hours with a compute cost of ∼$200-$250. In our opinion, for BLAST-based workflows, the choice of cloud platform is not dependent on the workflow but, rather, on the specific details and requirements of the cloud provider. These choices include the accessibility for institutional use, the technical knowledge required for effective use of the platform services, and the availability of open source frameworks such as APIs to deploy the workflow.

Structural Biology in the Clouds: The WeNMR-EOSC Ecosystem.

Structural biology aims at characterizing the structural and dynamic properties of biological macromolecules at atomic details. Gaining insight into three dimensional structures of biomolecules and their interactions is critical for understanding the vast majority of cellular processes, with direct applications in health and food sciences. Since 2010, the WeNMR project (www.wenmr.eu) has implemented numerous web-based services to facilitate the use of advanced computational tools by researchers in the field, using the high throughput computing infrastructure provided by EGI. These services have been further developed in subsequent initiatives under H2020 projects and are now operating as Thematic Services in the European Open Science Cloud portal (www.eosc-portal.eu), sending >12 millions of jobs and using around 4,000 CPU-years per year. Here we review 10 years of successful e-infrastructure solutions serving a large worldwide community of over 23,000 users to date, providing them with user-friendly, web-based solutions that run complex workflows in structural biology. The current set of active WeNMR portals are described, together with the complex backend machinery that allows distributed computing resources to be harvested efficiently.

Getting up close and personal with molecules in virtual reality

Virtual reality (VR) tools let chemists explore proteins and molecules, build and test potential drugs, and collaborate with colleagues remotely. In this video, C&EN reporter Sam Lemonick tries out a VR app that incorporates visualizations of molecules with advanced computational tools—offering a new way for chemists to develop drugs and address other problems—and learns how VR could help scientists use robotic lab equipment. Researchers who are using VR to study SARS-CoV-2 say the tools are giving them new insights about the virus and vaccines. But one scientist cautions that the cost of equipment could limit VR’s potential to address global problems. Learn more at cenm.ag/virtualresearch .

Identification of a Tolerogenic Factor V Peptide and Its Potential Role in FVIII Tolerance Induction

Inhibitory Anti-Drug Antibody (ADA) responses interfere with FVIII replacement efficacy in 25-30% of Hemophilia A (HA), greatly increasing patient morbidity and treatment costs. As an extension of previous work on tolerance-inducing peptides in IgG (Tregitopes) we investigated whether there were peptides in other ubiquitous serum proteins that could have high homology to peptides found in Factor VIII. We hypothesized that tolerance to cross-conserved peptides in other prevalent proteins might explain why anti-FVIII antibodies fail to develop in some severely FVIII-deficient HA patients. Using advanced computational modeling tools, we discovered in Factor V a potent regulatory peptide (FV1) with an immunologic profile (HLA and TCR binding) that is homologous to a non-identical peptide in FVIII. We postulated that treatment with a FV1-peptide containing biologic may be able to invoke tolerance to Factor VIII in patients who have anti-FVIII antibodies (Figure 1).Our preliminary studies support this hypothesis. We developed an ex vivo assay using human PBMC (peripheral blood mononuclear cells) in which the recall response to tetanus toxoid, a well-known immunogenic protein for which there is a T memory response, was found to be robust and strongly inhibited by FV1 but not by other FV peptides. Other FV peptides with similar HLA-binding properties did not suppress tetanus toxoid response.FV1 peptide also strongly inhibits proliferation of central and effector memory CD4 T and effector CD8 T cells without affecting cell viability. FV1 peptide at 4 mM reduced proliferation of effector and central memory T cells by more than 80% over the levels seen with 0.5 mg/ml TT and no peptide on a wide set of donors with HLA-DRB1 haplotypes covering the nine major supertypes in the human population. A peptide homologue to FV1 found in Factor VIII shows similar inhibitory activity on activated CD4 T cells.Using this assay, we are determining the evolution of markers on regulatory T cells, Antigen Presenting Cells, and effector T cells as well as the cytokine secretion profile over time in order to identify specific parameters associated with cell populations and soluble factors mediating immune suppression in response to FV1. This is the first time that we have reported that FV1 peptide may play a role in the induction of tolerance to FVIII in HA patients; the important implications of this work for treatment of HA, ADA and reduction of immunogenicity discussed. [Display omitted] DisclosuresGuillen:Epivax, Inc.: Employment. Rosenberg:Food & Drug Administration: Employment. Lelias:EpiVax, Inc.: Employment. Hindocha:EpiVax, Inc.: Employment. Terry:EpiVax, Inc.: Employment. Martin:EpiVax, Inc.: Equity Ownership. De Groot:EpiVax, Inc.: Equity Ownership.

DECODING THE GENE EXPRESSION PROGRAM EVOLVED IN ODONTOGENIC KERATOCYST BY THE COMBINATION OF DEEP TRANSCRIPTOMICS ANALYSES AND COMPUTATIONAL BIOLOGY TOOLS

<h3>Background</h3> <i>Transcriptomics</i> analysis of odontogenic keratocysts (OKCs) has been described in 3 microarray-based studies utilizing fresh-frozen or formalin-fixed paraffin-embedded samples. To date, RNA-sequencing (RNA-seq) technology has been applied only for the transcriptomics analysis of OKC-derived fibroblasts. <h3>Objective</h3> RNA-seq analysis of the entire OKC tissue will enable the identification of the gene expression profile that accompanies OKC development. <h3>Methods</h3> Total RNA was extracted from formalin-fixed paraffin-embedded samples of 3 OKCs and 3 dental follicles of third molars. RNA library preparation and 150-bp paired-end sequencing were implemented using an Illumina Hiseq4000 Sequencer followed by application of advanced computational biology tools; for example, edgeR software package and Galaxy platform. <h3>Results</h3> RNA-sequencing resulted in ∼15 to 40 million paired-end reads per sample. A total of 1335 differentially expressed genes (DEGs) between OKCs and dental follicles (fold change ≥2) were captured. Gene ontology classification analysis identified markers of epidermal cell development and differentiation (e.g., DSP, TFAP2A, FOXM1) among the top upregulated DEGs, markers of extracellular matrix organization and nervous system regulation (e.g., ADAMTS14, COL3A1, NAV3), among the top downregulated DEGs, and various transcription factors in both categories exemplified by the upregulation of the epidermal master regulator TP63 in OKCs. <h3>Conclusions</h3> This is the first RNA-seq-based study of the entire OKC. The preliminarily results show significant upregulation of genes commuting to epidermis development/differentiation, a finding consistent with OKC histopathologic phenotype. The identification of novel DEGs is critical to access unknown elements of the molecular mechanism underlying OKC pathogenesis and biologic behavior, with potential for future therapeutic applications.