Detection of Antibiotic Resistance Genes in the Wastewater Microbial Metagenome

The existential threat of emerging antibiotic resistance in microbial communities poses significant risks to public health. In particular, wastewater can serve as a point of confluence for pharmaceuticals and antibiotic-resistant bacteria from urban and agricultural settings. While this is a prime environment for genetic drift and horizontal transfer of antibiotic resistance genes (ARGs) and mobile genetic elements, it also presents an opportunity for resistome monitoring via shotgun metagenomic sequencing and downstream analysis. This project reports the application of a hybrid assembly approach for the detection of ARGs within DNA derived from a wastewater sample collected from the San José-Santa Clara Regional Wastewater Facility, which serves a significant portion of the San Francisco Bay Area. Hybrid assembly (with polishing) of Nanopore-derived long reads and Illumina-derived short reads resulted in detection of additional ARGs compared to a previously-performed short-read-based approach

Performance Observability and Monitoring of High Performance Computing with Microservices

Traditionally, High Performance Computing (HPC) softwarehas been built and deployed as bulk-synchronous, parallel executables based on the message-passing interface (MPI) programming model. The rise of data-oriented computing paradigms and an explosion in the variety of applications that need to be supported on HPC platforms have forced a re-think of the appropriate programming and execution models to integrate this new functionality. In situ workflows demarcate a paradigm shift in HPC software development methodologies enabling a range of new applications --- from user-level data services to machine learning (ML) workflows that run alongside traditional scientific simulations. By tracing the evolution of HPC software developmentover the past 30 years, this dissertation identifies the key elements and trends responsible for the emergence of coupled, distributed, in situ workflows. This dissertation's focus is on coupled in situ workflows involving composable, high-performance microservices. After outlining the motivation to enable performance observability of these services and why existing HPC performance tools and techniques can not be applied in this context, this dissertation proposes a solution wherein a set of techniques gathers, analyzes, and orients performance data from different sources to generate observability. By leveraging microservice components initially designed to build high performance data services, this dissertation demonstrates their broader applicability for building and deploying performance monitoring and visualization as services within an in situ workflow. The results from this dissertation suggest that: (1) integration of performance data from different sources is vital to understanding the performance of service components, (2) the in situ (online) analysis of this performance data is needed to enable the adaptivity of distributed components and manage monitoring data volume, (3) statistical modeling combined with performance observations can help generate better service configurations, and (4) services are a promising architecture choice for deploying in situ performance monitoring and visualization functionality. This dissertation includes previously published and co-authored material and unpublished co-authored material

Performance analysis for parallel programs from multicore to petascale

Cutting-edge science and engineering applications require petascale computing. Petascale computing platforms are characterized by both extreme parallelism (systems of hundreds of thousands to millions of cores) and hybrid parallelism (nodes with multicore chips). Consequently, to effectively use petascale resources, applications must exploit concurrency at both the node and system level --- a difficult problem. The challenge of developing scalable petascale applications is only partially aided by existing languages and compilers. As a result, manual performance tuning is often necessary to identify and resolve poor parallel and serial efficiency. Our thesis is that it is possible to achieve unique, accurate, and actionable insight into the performance of fully optimized parallel programs by measuring them with asynchronous-sampling-based call path profiles; attributing the resulting binary-level measurements to source code structure; analyzing measurements on-the-fly and postmortem to highlight performance inefficiencies; and presenting the resulting context- sensitive metrics in three complementary views. To support this thesis, we have developed several techniques for identifying performance problems in fully optimized serial, multithreaded and petascale programs. First, we describe how to attribute very precise (instruction-level) measurements to source-level static and dynamic contexts in fully optimized applications --- all for an average run-time overhead of a few percent. We then generalize this work with the development of logical call path profiling and apply it to work-stealing-based applications. Second, we describe techniques for pinpointing and quantifying parallel inefficiencies such as parallel idleness, parallel overhead and lock contention in multithreaded executions. Third, we show how to diagnose scalability bottlenecks in petascale applications by scaling our our measurement, analysis and presentation tools to support large-scale executions. Finally, we provide a coherent framework for these techniques by sketching a unique and comprehensive performance analysis methodology. This work forms the basis of Rice University's HPCTOOLKIT performance tools

Shader optimization and specialization

In the field of real-time graphics for computer games, performance has a significant effect on the player’s enjoyment and immersion. Graphics processing units (GPUs) are hardware accelerators that run small parallelized shader programs to speed up computationally expensive rendering calculations. This thesis examines optimizing shader programs and explores ways in which data patterns on both the CPU and GPU can be analyzed to automatically speed up rendering in games. Initially, the effect of traditional compiler optimizations on shader source-code was explored. Techniques such as loop unrolling or arithmetic reassociation provided speed-ups on several devices, but different GPU hardware responded differently to each set of optimizations. Analyzing execution traces from numerous popular PC games revealed that much of the data passed from CPU-based API calls to GPU-based shaders is either unused, or remains constant. A system was developed to capture this constant data and fold it into the shaders’ source-code. Re-running the game’s rendering code using these specialized shader variants resulted in performance improvements in several commercial games without impacting their visual quality

A grid computing framework for commercial simulation packages

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.An increased need for collaborative research among different organizations, together with continuing advances in communication technology and computer hardware, has facilitated the development of distributed systems that can provide users non-trivial access to geographically dispersed computing resources (processors, storage, applications, data, instruments, etc.) that are administered in multiple computer domains. The term grid computing or grids is popularly used to refer to such distributed systems. A broader definition of grid computing includes the use of computing resources within an organization for running organization-specific applications. This research is in the context of using grid computing within an enterprise to maximize the use of available hardware and software resources for processing enterprise applications. Large scale scientific simulations have traditionally been the primary benefactor of grid computing. The application of this technology to simulation in industry has, however, been negligible. This research investigates how grid technology can be effectively exploited by simulation practitioners using Windows-based commercially available simulation packages to model simulations in industry. These packages are commonly referred to as Commercial Off-The-Shelf (COTS) Simulation Packages (CSPs). The study identifies several higher level grid services that could be potentially used to support the practise of simulation in industry. It proposes a grid computing framework to investigate these services in the context of CSP-based simulations. This framework is called the CSP-Grid Computing (CSP-GC) Framework. Each identified higher level grid service in this framework is referred to as a CSP-specific service. A total of six case studies are presented to experimentally evaluate how grid computing technologies can be used together with unmodified simulation packages to support some of the CSP-specific services. The contribution of this thesis is the CSP-GC framework that identifies how simulation practise in industry may benefit from the use of grid technology. A further contribution is the recognition of specific grid computing software (grid middleware) that can possibly be used together with existing CSPs to provide grid support. With its focus on end-users and end-user tools, it is intended that this research will encourage wider adoption of grid computing in the workplace and that simulation users will derive benefit from using this technology

Service and cloud computing supporting genomic analysis of the mammalian species

 This research focused on building Software as a Service clouds to support mammalian genomic applications such as personalized medicine. Outcomes of this research included a Software as a Service cloud framework, the Uncinus research cloud and novel genomic analysis software. Results have been published in high ranking peer-reviewed international journals

Thousand and one amino acid kinase 2 (TAOK2) modulates Hippo pathway activity and impacts on synaptic plasticity

Der Hippo-Signalweg ist ein evolutionär konservierter Signalweg und spielt eine entscheidende Rolle bei der Kontrolle der Gewebehomöostase, der Zelldifferenzierung und der richtigen Entwicklung der Organgröße durch die Regulierung der Zellproliferation und Apoptose. Der Hippo-Signalweg wurde als Tumoursuppressor-Signalweg identifiziert und ist an verschiedenen Krebsarten beteiligt. Darüber hinaus deutet Vieles darauf hin, dass der Hippo-Signalweg in mehreren Stadien an der neuronalen Entwicklung beteiligt ist, von der Proliferation neuronaler Stammzellen (NSCs) bis zur Apoptose neuronaler Zelltypen. Dieser Signalweg kann nicht nur auf Signale reagieren, die das Wachstum fördern oder begrenzen, sondern auch verschiedene zelluläre Signale integrieren, einschließlich mechano-sensorischer Stimuli und Stresssignale. Auf molekularer Ebene reguliert der Hippo-Signalweg die Aktivität des Co-Transkriptionsaktivators YAP1, der der Haupteffektor des Signalwegs ist. Der Kern des Weges besteht aus einer sog. Kinasekassette, die die Kinasen STK3/4 und LATS1/2 umfasst, die wiederum die Aktivität von YAP1 einschränken. Die Aktivität des Signalwegs wird darüber hinaus durch Polaritätsproteine wie Mitglieder der WWC-Familie oder Proteine der AMOT-Familie und weitere Kinasen und Strukturproteine, aber auch Membranrezeptoren gesteuert. Im ersten Teil der Studie verwendete ich das zellbasierte genetisch kodierte Split-TEV-Testsystem, um Protein-Protein-Wechselwirkungen zwischen Kernkomponenten und Hauptregulatoren des Hippo-Signalwegs zu analysieren. Das Ziel war es, bisher verborgene Wechselwirkungen zwischen Komponenten zu identifizieren, die die Aktivität des Hippo-Signalwegs modulieren könnten. Als zentralen Modulator der Hippo-Signalisierung habe ich TAOK2 identifiziert. TAOK2 bindet an LATS1 und phosphoryliert dieses. Die Überexpression von TAOK2 erhöhte die Phosphorylierung von LATS1, verringerte die YAP1-Transkriptionsaktivität und führte zu einer verringerten Proliferation von HEK293-Zellen. Dagegen führte eine Herunterregulierung von TAOK2 zu einer Reduzierung der LATS1-Phosphorylierung und einer erhöhten Proliferationsrate. Diese Beobachtungen aus der Zellkultur korrelieren mit einer reduzierten TAOK2- Expression und einer reduzierten Überlebensrate von Patienten, die an bestimmten Krebsarten, wie z.B. Lungen- und Pankreas-Adenokarzinome oder Gliome niedrigen Malignitätsgrades, gelitten haben. Da der Hippo-Signal-Weg auch an der Proliferation neuronaler Vorläufer und der Entwicklung von Neuronen beteiligt ist, beschreibt der zweite Teil der Studie mögliche Rollen von TAOK2 in synaptischen Signalnetzwerken. Vorarbeiten aus unserem Labor an Mäusen, bei denen Taok2 spezifisch in Neuronen im Gehirn während der frühen Phasen der Nervenentwicklung mit einer Emx1-Cre-Line ausgeschaltet wurde, zeigten mit einem milden Hyperaktivitätsphänotyp und leichten kognitiven Defizite ein verändertes Verhaltensprofil. Darüber hinaus haben Kollegen in Mäusen, die einen kompletten Taok2-Knockout (Taok2-ko) tragen, einen eindeutigen Hyperaktivitätsphänotyp sowie Defizite in der Kognition und im Angst- und Sozialverhalten beschrieben. Um diese Phänotypen auf zellulärer Ebene besser zu verstehen, habe ich in meiner Arbeit den Effekt einer Taok2-Inaktivierung in primären kortikalen Neuronenkulturen aus der Maus untersucht. Eine zelluläre Profilierung anhand des multiparametrischen cisProfiler-Assays in Taok2-ko-Neuronen zeigte, dass die genetische Deletion von Taok2 die Aktivität von MAP-Kinase-Signalwegen verringerte, die nach synaptischer Aktivität, z.B. durch Stimulation mit AMPA, aktiviert werden. RNAseq-Analysen in primären Maus-Neuronen mit TAOK2-Überexpression und shRNA-vermittelter Taok2-Inaktivierung wiesen gemeinsame differentiell regulierte Gene (DEGs) auf, die für den Zellzyklus und den Notch-Signalweg angereichert waren. Auf Proteinebene verursachte die Inaktivierung von Taok2 in primären Mausneuronen, die mit AMPA bzw. NMDA stimuliert wurden, eine Verringerung des Phosphorylierungsniveaus der MAP-Kinase Erk. Eine Inaktivierung von Taok2 in Neuronen führt somit unter definierten Bedingungen zu einer verringerten Weiterleitung der synaptischen Aktivität in Neuronen, was die in Taok2-ko-Mäusen beobachteten Verhaltensdefizite auf molekularer Ebene erklären könnte.The Hippo-signaling pathway, which is an evolutionarily conserved pathway, has a crucial function in the field of controlling the homeostasis of different tissues, cell differentiation across diverse organisms, and coordinates the proper size of organ during development by regulating the proliferation process and the apoptosis of cells. It has been identified that Hippo-signaling is a tumor-suppressing pathway and has been related with various types of cancers. In addition, accumulating researchers indicate that Hippo-signaling pathway participates the neuronal development at multiple stages, involving in the proliferation process of neural stem cells (NSCs) and neuronal death. This pathway not only responds to signals promoting or limiting growth, but also integrates diverse cellular cues including mechano-sensory inputs and energy stress. At the molecular level, Hippo signaling regulates the activity of the co-transcriptional activator YAP1, which is the main effector of the pathway. Two kinases: STK3/4 and LATS1/2, which can moderate the activity of YAP1, comprise a kinase cassette that composes the key component of the Hippo pathway. Upstream, the polarity proteins, such as WWC family members or AMOT family proteins, other kinases, scaffolding proteins, and transmembrane receptors control the activity of the pathway. In the first part of the study, I used the cell-based genetically encoded split TEV assay method to profile protein-protein interactions among Hippo pathway core components and major regulators to identify so far hidden interactions among components that may modulate Hippo pathway activity. I identified TAOK2 as central modulator of Hippo signaling. TAOK2 binds to and phosphorylate LATS1. Overexpression of TAOK2 reduced YAP1 transcriptional activity and led to decreased proliferation of HEK293 cells. In contrast, downregulation of TAOK2 led to a reduction in LATS1 phosphorylation levels and an increased proliferation. These observations from cell culture correlate with reduced TAOK2 expression and survival in patients who have suffered from certain cancers, such as lung and pancreatic adenocarcinomas or low-grade gliomas. Due to the involvement of this pathway in the proliferation of neural precursors and development of neurons as well, the second part of this study describes the potential role of TAOK2 in synaptic signaling networks. Preliminary work from our lab in mice in which Taok2 was specifically inactivated in neurons in the brain during early neurodevelopment using an Emx1-Cre driver line showed an altered behavioral profile with a mild hyperactivity phenotype and mild cognitive deficits. In addition, colleagues have described a distinct hyperactivity phenotype in mice carrying a complete Taok2 knockout (Taok2-ko), as well as deficits in social behavior, cognition and anxiety. To better understand these phenotypes at the molecular level, my work investigated the effect of Taok2 inactivation in primary murine cortical neuron cultures. A cellular profiling using the multiparametric cisProfiler assay in Taok2-ko neurons showed that the genetic inactivation of Taok2 reduced the activity of MAP kinase signaling which is, e.g., after AMPA stimulation, activated upon synaptic transmission. RNAseq-based analyses for neurons either with overexpressed human TAOK2 or shRNA-depleted Taok2 shared differentially regulated genes (DEGs) that were enriched for the cell cycle and Notch signaling. At the protein level, an inactivation of Taok2 in neurons, stimulated with AMPA or NMDA, caused a reduction in the phosphorylation levels of the MAP kinase Erk1/2. An inactivation of Taok2 in neurons thus led to reduced transmission of synaptic activity in neurons under defined conditions, which could explain the behavioral deficits observed in Taok2-ko mice at the molecular level