The number of clinical investigations and approved applications of adeno-associated virus (AAV) based transgene product (TP) delivery has grown steadily. There also has been a growing interest in understanding how anti-AAV and anti-TP immune responses affect the safety and efficacy of these gene therapy treatments. While considerations related to anti-AAV immunity have been discussed in other works, this manuscript focuses on the assessment of anti-TP immune responses, including both humoral and cellular responses. The development of anti-TP antibodies or a cytotoxic cellular response may lead to increased clearance of the TP, elimination of AAV-transduced cells, and consequently, affect the overall durability and efficacy of the treatment. Additionally, the binding and neutralization of residual endogenous protein by anti-TP antibodies might further worsen the clinical condition under treatment. Several topics are explored in this manuscript, including immunogenicity risk factors that can be considered when evaluating the overall risk and impact of anti-TP immunogenicity, potential implications of anti-TP immunogenicity, the importance of assessing anti-TP immunogenicity, and the commonly used analytical methodologies. The manuscript proposes an approach to determining the scope of anti-TP immunogenicity assessment for clinical and non-clinical studies, based on the TP nature, other intrinsic and extrinsic risk factors. Authored by a group of scientists involved in AAV-based therapeutic development from various industry organizations, the manuscript aims to provide recommendations and guidance to industry sponsors, academic laboratories, and regulatory agencies working on AAV-based modalities, with the goal of achieving a more consistent approach to the assessment of anti-TP immune response.

The number of approved or investigational late phase viral vector gene therapies (GTx) has been rapidly growing. The adeno-associated virus vector (AAV) technology continues to be the most used GTx platform of choice. The presence of pre-existing anti-AAV immunity has been firmly established and is broadly viewed as a potential deterrent for successful AAV transduction with a possibility of negative impact on clinical efficacy and a connection to adverse events. Recommendations for the evaluation of humoral, including neutralizing and total antibody based, anti-AAV immune response have been presented elsewhere. This manuscript aims to cover considerations related to the assessment of anti-AAV cellular immune response, including review of correlations between humoral and cellular responses, potential value of cellular immunogenicity assessment, and commonly used analytical methodologies and parameters critical for monitoring assay performance. This manuscript was authored by a group of scientists involved in GTx development who represent several pharma and contract research organizations. It is our intent to provide recommendations and guidance to the industry sponsors, academic laboratories, and regulatory agencies working on AAV-based GTx viral vector modalities with the goal of achieving a more consistent approach to anti-AAV cellular immune response assessment.

The number of viral vector-based gene therapies (GTx) continues to grow with two products (Zolgensma® and Luxturna®) approved in the USA as of March 2021. To date, the most commonly used vectors are adeno-associated virus-based (AAV). The pre-existing humoral immunity against AAV (anti-AAV antibodies) has been well described and is expected as a consequence of prior AAV exposure. Anti-AAV antibodies may present an immune barrier to successful AAV transduction and hence negatively impact clinical efficacy and may also result in adverse events (AEs) due to the formation of large immune complexes. Patients may be screened for the presence of anti-AAV antibodies, including neutralizing (NAb) and total binding antibodies (TAb) prior to treatment with the GTx. Recommendations for the development and validation of anti-AAV NAb detection methods have been presented elsewhere. This manuscript covers considerations related to anti-AAV TAb-detecting protocols, including the advantages of the use of TAb methods, selection of assay controls and reagents, and parameters critical to monitoring assay performance. This manuscript was authored by a group of scientists involved in GTx development representing eleven organizations. It is our intent to provide recommendations and guidance to industry sponsors, academic laboratories, and regulatory agencies working on AAV-based GTx viral vector modalities with the goal of achieving a more consistent approach to anti-AAV TAb assessment.

Adeno-associated virus (AAV)-based gene therapies have recently shown promise as a novel treatment for hereditary diseases. Due to the viral origin of the vector capsid, however, cellular immune response may be elicited that could eliminate transduced target cells. To monitor cellular immune responses in clinical trials, we optimized and bioanalytically validated a sensitive, robust, and reliable interferon-gamma (IFN-gamma) enzymelinked immunospot (ELISpot) assay. For method performance validation, human peripheral blood mononuclear cells (PBMCs) were stimulated with peptides derived from AAV5 capsid proteins and the encoded transgene product, human blood clotting factor VIII (FVIII), in addition to positive controls, such as peptides from the 65-kDa phosphoprotein of cytomegalovirus. We statistically assessed the limit of detection and confirmatory cutpoint, evaluated precision and linearity, and confirmed specificity using HIV peptides. Robustness parameter ranges and sample stability periods were established. The validated IFN-gamma ELISpot assay was then implemented in an AAV5-FVIII gene therapy clinical trial. Cellular immune responses against the AAV5 capsid were observed in most participants as soon as 2 weeks following dose administration; only limited responses against the transgene product were detected. These data underscore the value of using validated methods for monitoring cellular immunity in AAV gene therapy trials.

BACKGROUND:The MG-RAST API provides search capabilities and delivers organism and function data as well as raw or annotated sequence data via the web interface and its RESTful API. For casual users, however, RESTful APIs are hard to learn and work with.RESULTS:We created the graphical MG-RAST API explorer to help researchers more easily build and export API queries; understand the data abstractions and indices available in MG-RAST; and use the results presented in-browser for exploration, development, and debugging.CONCLUSIONS:The API explorer lowers the barrier to entry for occasional or first-time MG-RAST API users.

Folker Meyer MCS Argonne National Laboratory Argonne, IL, USA folker@anl.gov Robert D. Finn Sequence Families Team, EMBL-EBI European Bioinformatics Institute, Cambridge, UK rdf@ebi.ac.uk Wolfgang Gerlach Data Science and Learning Argonne National Laboratory Argonne, IL, USA wgerlach@mcs.anl.gov Alex L Mitchell Sequence Families Team, EMBL-EBI European Bioinformatics Institute, Cambridge, UK mitchell@ebi.ac.uk

High performance computing (HPC) applications, such as metagenomics and other big data systems, need to store and analyze huge volumes of semi-structured data. Such applications often rely on NoSQL-based datastores, and optimizing these databases is a challenging endeavor, with over 50 configuration parameters in Cassandra alone. As the application executes, database workloads can change rapidly from read-heavy to write-heavy ones, and a system tuned with a read-optimized configuration becomes suboptimal when the workload becomes write-heavy. In this paper, we present a method and a system for optimizing NoSQL configurations for Cassandra and ScyllaDB when running HPC and metagenomics workloads. First, we identify the significance of configuration parameters using ANOVA. Next, we apply neural networks using the most significant parameters and their workload-dependent mapping to predict database throughput, as a surrogate model. Then, we optimize the configuration using genetic algorithms on the surrogate to maximize the workload-dependent performance. Using the proposed methodology in our system (Rafiki), we can predict the throughput for unseen workloads and configuration values with an error of 7.5% for Cassandra and 6.9-7.8% for ScyllaDB. Searching the configuration spaces using the trained surrogate models, we achieve performance improvements of 41% for Cassandra and 9% for ScyllaDB over the default configuration with respect to a read-heavy workload, and also significant improvement for mixed workloads. In terms of searching speed, Rafiki, using only 1/10000-th of the searching time of exhaustive search, reaches within 15% and 9.5% of the theoretically best achievable performances for Cassandra and ScyllaDB, respectively---supporting optimizations for highly dynamic workloads.

As technologies change, MG-RAST is adapting. Newly available software is being included to improve accuracy and performance. As a computational service constantly running large volume scientific workflows, MG-RAST is the right location to perform benchmarking and implement algorithmic or platform improvements, in many cases involving trade-offs between specificity, sensitivity and run-time cost. The work in [Glass EM, Dribinsky Y, Yilmaz P, et al. ISME J 2014;8:1-3] is an example; we use existing well-studied data sets as gold standards representing different environments and different technologies to evaluate any changes to the pipeline. Currently, we use well-understood data sets in MG-RAST as platform for benchmarking. The use of artificial data sets for pipeline performance optimization has not added value, as these data sets are not presenting the same challenges as real-world data sets. In addition, the MG-RAST team welcomes suggestions for improvements of the workflow. We are currently working on versions 4.02 and 4.1, both of which contain significant input from the community and our partners that will enable double barcoding, stronger inferences supported by longer-read technologies, and will increase throughput while maintaining sensitivity by using Diamond and SortMeRNA. On the technical platform side, the MG-RAST team intends to support the Common Workflow Language as a standard to specify bioinformatics workflows, both to facilitate development and efficient high-performance implementation of the community's data analysis tasks.

Metagenomic sequencing has produced significant amounts of data in recent years. For example, as of summer 2013, MG-RAST has been used to annotate over 110,000 data sets totaling over 43 Terabases. With metagenomic sequencing finding even wider adoption in the scientific community, the existing web-based analysis tools and infrastructure in MG-RAST provide limited capability for data retrieval and analysis, such as comparative analysis between multiple data sets. Moreover, although the system provides many analysis tools, it is not comprehensive. By opening MG-RAST up via a web services API (application programmers interface) we have greatly expanded access to MG-RAST data, as well as provided a mechanism for the use of third-party analysis tools with MG-RAST data. This RESTful API makes all data and data objects created by the MG-RAST pipeline accessible as JSON objects. As part of the DOE Systems Biology Knowledgebase project (KBase, http://kbase.us) we have implemented a web services API for MG-RAST. This API complements the existing MG-RAST web interface and constitutes the basis of KBase's microbial community capabilities. In addition, the API exposes a comprehensive collection of data to programmers. This API, which uses a RESTful (Representational State Transfer) implementation, is compatible with most programming environments and should be easy to use for end users and third parties. It provides comprehensive access to sequence data, quality control results, annotations, and many other data types. Where feasible, we have used standards to expose data and metadata. Code examples are provided in a number of languages both to show the versatility of the API and to provide a starting point for users. We present an API that exposes the data in MG-RAST for consumption by our users, greatly enhancing the utility of the MG-RAST service.

Access to data plays a major role in designing and performing efficient data computation and analyses in a distributed environment. Existing approaches access data via a variety of methods and offer various benefits and drawbacks based on the use case. Our original use case was the computational analysis of environmental sequence data, or metagenomics. Unlike other workflows that often reduce the dataset size dramatically within the first few processing steps, owing to biologially-motivated data compression. Metagenomic data compresses poorly, and so metagenomic workflows add to the size of the data set along the processing pipeline. Thus, wide-area, high-throughput access to the data is essential.To address this problem, we developed Shock, a data store for files, their associated metadata, and indexes that allow Shock to provide different views into the data. Shock comprises three major components: a web service that provides a RESTful API, backend data storage for files, and storage for object metadata. Shock has proven to be a stable data store for MG-RAST, an application that served over 40,000 users in 2014 on a server that houses more than 3 million data objects. Moreover, Shock provides both subselection and high-performance file transfer capabilities that serve most usages.

MG-RAST (http://metagenomics.anl.gov) is an open-submission data portal for processing, analyzing, sharing and disseminating metagenomic datasets. The system currently hosts over 200,000 datasets and is continuously updated. The volume of submissions has increased 4-fold over the past 24 months, now averaging 4 terabasepairs per month. In addition to several new features, we report changes to the analysis workflow and the technologies used to scale the pipeline up to the required throughput levels. To show possible uses for the data from MG-RAST, we present several examples integrating data and analyses from MG-RAST into popular third-party analysis tools or sequence alignment tools.

Recently, Linux container technology has been gaining attention as it promises to transform the way software is developed and deployed. The portability and ease of deployment makes Linux containers an ideal technology to be used in scientific workflow platforms. Skyport utilizes Docker Linux containers to solve software deployment problems and resource utilization inefficiencies inherent to all existing scientific workflow platforms. As an extension to AWE/Shock, our data analysis platform that provides scalable workflow execution environments for scientific data in the cloud, Skyport greatly reduces the complexity associated with providing the environment necessary to execute complex workflows.

The cost of DNA sequencing has plummeted in recent years. The consequent data deluge has imposed big burdens for data analysis applications. For example, MG-RAST, a production open-public metagenome annotation service, has experienced increasingly large amount of data submission and has demanded scalable resources for the computational needs. To address this problem, we have developed a scalable platform to port MG-RAST workloads into the cloud, where elastic computing resources can be used on demand. To efficiently utilize such resources, however, one must understand the characteristics of the application workloads. In this paper, we characterize the MG-RAST workloads running in the cloud, from the perspectives of computation, I/O, and data transfer. Insights from this work will help guide application enhancement, service operation, and resource management for MG-RAST and similar big data applications demanding elastic computing resources.

A critical step in plasmodial entry and subsequent survival is the establishment of a parasitophorous vacuolar membrane (PVM). This chapter focuses on the understanding of erythrocyte signaling mechanisms and trafficking of host receptors and parasite ligands implicated in PVM formation, transport functions of a tubulovesicular membrane network (TVN) extending from the PVM to the erythrocyte, and a secretome of ~300 parasite proteins exported to the erythrocyte, which indicates vast complexity in structural and antigenic remodeling of the host milieu required for virulence and parasite survival in the erythrocyte. Although major erythrocyte proteins are excluded, proteins resident in erythrocyte detergent-resistant membrane (DRM) rafts are trafficked to the malarial vacuole. Initial studies investigating the role of host rafts in malarial infection showed that depletion of modest amounts of erythrocyte cholesterol (~23%) led to abrogation of DRM rafts and parasite entry.

BACKGROUND:As the impact and prevalence of large-scale metagenomic surveys grow, so does the acute need for more complete and standards compliant metadata. Metadata (data describing data) provides an essential complement to experimental data, helping to answer questions about its source, mode of collection, and reliability. Metadata collection and interpretation have become vital to the genomics and metagenomics communities, but considerable challenges remain, including exchange, curation, and distribution. Currently, tools are available for capturing basic field metadata during sampling, and for storing, updating and viewing it. Unfortunately, these tools are not specifically designed for metagenomic surveys; in particular, they lack the appropriate metadata collection templates, a centralized storage repository, and a unique ID linking system that can be used to easily port complete and compatible metagenomic metadata into widely used assembly and sequence analysis tools.RESULTS:Metazen was developed as a comprehensive framework designed to enable metadata capture for metagenomic sequencing projects. Specifically, Metazen provides a rapid, easy-to-use portal to encourage early deposition of project and sample metadata.CONCLUSIONS:Metazen is an interactive tool that aids users in recording their metadata in a complete and valid format. A defined set of mandatory fields captures vital information, while the option to add fields provides flexibility.

The field of metagenomics is transforming our ability to study the enormous biomass and diversity of microbial life around us. Understanding this microbial world will lead to advances and practical applications in a broad range of fields. Metagenomic sequencing, provides unprecedented access to the thousands (or even millions) of microbes in an environment. Unlike 16S SSU rRNA amplicon sequencing, metagenomic sequencing (whole shotgun sequencing) provides information on not only who is in a community but what they are doing, extending understanding of community structure towards interactions within an environment. This talk will discuss the MG-RAST analysis pipeline starting from quality control assessment to annotation and an overview the interactive tools for comparative analysis. MG-RAST has analyzed over 60,000 WGS and amplicon datasets equaling approximately 20 Tbp.

The democratized world of sequencing is leading to numerous data analysis challenges; MG-RAST addresses many of these challenges for diverse datasets, including amplicon datasets, shotgun metagenomes, and metatranscriptomes. The changes from version 2 to version 3 include the addition of a dedicated gene calling stage using FragGenescan, clustering of predicted proteins at 90% identity, and the use of BLAT for the computation of similarities. Together with changes in the underlying software infrastructure, this has enabled the dramatic scaling up of pipeline throughput while remaining on a limited hardware budget. The Web-based service allows upload, fully automated analysis, and visualization of results. As a result of the plummeting cost of sequencing and the readily available analytical power of MG-RAST, over 78,000 metagenomic datasets have been analyzed, with over 12,000 of them publicly available in MG-RAST.

We provide a novel method, DRISEE (duplicate read inferred sequencing error estimation), to assess sequencing quality (alternatively referred to as "noise" or "error") within and/or between sequencing samples. DRISEE provides positional error estimates that can be used to inform read trimming within a sample. It also provides global (whole sample) error estimates that can be used to identify samples with high or varying levels of sequencing error that may confound downstream analyses, particularly in the case of studies that utilize data from multiple sequencing samples. For shotgun metagenomic data, we believe that DRISEE provides estimates of sequencing error that are more accurate and less constrained by technical limitations than existing methods that rely on reference genomes or the use of scores (e.g. Phred). Here, DRISEE is applied to (non amplicon) data sets from both the 454 and Illumina platforms. The DRISEE error estimate is obtained by analyzing sets of artifactual duplicate reads (ADRs), a known by-product of both sequencing platforms. We present DRISEE as an open-source, platform-independent method to assess sequencing error in shotgun metagenomic data, and utilize it to discover previously uncharacterized error in de novo sequence data from the 454 and Illumina sequencing platforms.