Matching Items (298)
Description
Sulfadiazine (SD), one of broad-spectrum antibiotics, exhibits limited biodegradation in wastewater treatment due to its chemical structure, which requires initial mono-oxygenation reactions to initiate its biodegradation. Intimately coupling UV photolysis with biodegradation, realized with the internal loop photobiodegradation reactor, accelerated SD biodegradation and mineralization by 35 and 71 %, respectively. The main organic products from photolysis were 2-aminopyrimidine (2-AP), p-aminobenzenesulfonic acid (ABS), and aniline (An), and an SD-photolysis pathway could be identified using C, N, and S balances. Adding An or ABS (but not 2-AP) into the SD solution during biodegradation experiments (no UV photolysis) gave SD removal and mineralization rates similar to intimately coupled photolysis and biodegradation. An SD biodegradation pathway, based on a diverse set of the experimental results, explains how the mineralization of ABS and An (but not 2-AP) provided internal electron carriers that accelerated the initial mono-oxygenation reactions of SD biodegradation. Thus, multiple lines of evidence support that the mechanism by which intimately coupled photolysis and biodegradation accelerated SD removal and mineralization was through producing co-substrates whose oxidation produced electron equivalents that stimulated the initial mono-oxygenation reactions for SD biodegradation.
ContributorsPan, Shihui (Author) / Yan, Ning (Author) / Liu, Xinyue (Author) / Wang, Wenbing (Author) / Zhang, Yongming (Author) / Liu, Rui (Author) / Rittmann, Bruce (Author) / Biodesign Institute (Contributor) / Swette Center for Environmental Biotechnology (Contributor)
Created2014-11-01
Description
Heterochromatin is a repressive chromatin compartment essential for maintaining genomic integrity. A hallmark of heterochromatin is the presence of specialized nonhistone proteins that alter chromatin structure to inhibit transcription and recombination. It is generally assumed that heterochromatin is highly condensed. However, surprisingly little is known about the structure of heterochromatin or its dynamics in solution. In budding yeast, formation of heterochromatin at telomeres and the homothallic silent mating type loci require the Sir3 protein. Here, we use a combination of sedimentation velocity, atomic force microscopy and nucleosomal array capture to characterize the stoichiometry and conformation of Sir3 nucleosomal arrays. The results indicate that Sir3 interacts with nucleosomal arrays with a stoichiometry of two Sir3 monomers per nucleosome. We also find that Sir3 fibres are less compact than canonical magnesium-induced 30 nm fibres. We suggest that heterochromatin proteins promote silencing by ‘coating’ nucleosomal arrays, stabilizing interactions between nucleosomal histones and DNA.
ContributorsSwygert, Sarah G. (Author) / Manning, Benjamin J. (Author) / Senapati, Subhadip (Author) / Kaur, Parminder (Author) / Lindsay, Stuart (Author) / Demeler, Borries (Author) / Peterson, Craig L. (Author) / Biodesign Institute (Contributor) / Single Molecule Biophysics (Contributor)
Created2014-08-01
Description
We studied the microbial community structure of pilot two-stage membrane biofilm reactors (MBfRs) designed to reduce nitrate (NO[subscript 3]–) and perchlorate (ClO[subscript 4]–) in contaminated groundwater. The groundwater also contained oxygen (O[subscript 2]) and sulfate (SO[2 over 4]–), which became important electron sinks that affected the NO[subscript 3]– and ClO[subscript 4]– removal rates. Using pyrosequencing, we elucidated how important phylotypes of each “primary” microbial group, i.e., denitrifying bacteria (DB), perchlorate-reducing bacteria (PRB), and sulfate-reducing bacteria (SRB), responded to changes in electron-acceptor loading. UniFrac, principal coordinate analysis (PCoA), and diversity analyses documented that the microbial community of biofilms sampled when the MBfRs had a high acceptor loading were phylogenetically distant from and less diverse than the microbial community of biofilm samples with lower acceptor loadings. Diminished acceptor loading led to SO[2 over 4]– reduction in the lag MBfR, which allowed Desulfovibrionales (an SRB) and Thiothrichales (sulfur-oxidizers) to thrive through S cycling. As a result of this cooperative relationship, they competed effectively with DB/PRB phylotypes such as Xanthomonadales and Rhodobacterales. Thus, pyrosequencing illustrated that while DB, PRB, and SRB responded predictably to changes in acceptor loading, a decrease in total acceptor loading led to important shifts within the “primary” groups, the onset of other members (e.g., Thiothrichales), and overall greater diversity.
ContributorsOntiveros-Valencia, Aura (Author) / Tang, Youneng (Author) / Zhao, Heping (Author) / Friese, David (Author) / Overstreet, Ryan (Author) / Smith, Jennifer (Author) / Evans, Patrick (Author) / Rittmann, Bruce (Author) / Krajmalnik-Brown, Rosa (Author) / Biodesign Institute (Contributor) / Swette Center for Environmental Biotechnology (Contributor) / Julie Ann Wrigley Global Institute of Sustainability (Contributor) / School of Sustainability (Contributor)
Created2014-07-01
Description
The Combined Activated Sludge-Anaerobic Digestion Model (CASADM) quantifies the effects of recycling anaerobic-digester (AD) sludge on the performance of a hybrid activated sludge (AS)-AD system. The model includes nitrification, denitrification, hydrolysis, fermentation, methanogenesis, and production/utilization of soluble microbial products and extracellular polymeric substances (EPS). A CASADM example shows that, while effluent COD and N are not changed much by hybrid operation, the hybrid system gives increased methane production in the AD and decreased sludge wasting, both caused mainly by a negative actual solids retention time in the hybrid AD. Increased retention of biomass and EPS allows for more hydrolysis and conversion to methane in the hybrid AD. However, fermenters and methanogens survive in the AS, allowing significant methane production in the settler and thickener of both systems, and AD sludge recycle makes methane formation greater in the hybrid system.
ContributorsYoung, Michelle (Author) / Marcus, Andrew (Author) / Rittmann, Bruce (Author) / Biodesign Institute (Contributor) / Swette Center for Environmental Biotechnology (Contributor)
Created2013-08-13
Description
Background
Multicellular organisms consist of cells of many different types that are established during development. Each type of cell is characterized by the unique combination of expressed gene products as a result of spatiotemporal gene regulation. Currently, a fundamental challenge in regulatory biology is to elucidate the gene expression controls that generate the complex body plans during development. Recent advances in high-throughput biotechnologies have generated spatiotemporal expression patterns for thousands of genes in the model organism fruit fly Drosophila melanogaster. Existing qualitative methods enhanced by a quantitative analysis based on computational tools we present in this paper would provide promising ways for addressing key scientific questions.
Results
We develop a set of computational methods and open source tools for identifying co-expressed embryonic domains and the associated genes simultaneously. To map the expression patterns of many genes into the same coordinate space and account for the embryonic shape variations, we develop a mesh generation method to deform a meshed generic ellipse to each individual embryo. We then develop a co-clustering formulation to cluster the genes and the mesh elements, thereby identifying co-expressed embryonic domains and the associated genes simultaneously. Experimental results indicate that the gene and mesh co-clusters can be correlated to key developmental events during the stages of embryogenesis we study. The open source software tool has been made available at http://compbio.cs.odu.edu/fly/.
Conclusions
Our mesh generation and machine learning methods and tools improve upon the flexibility, ease-of-use and accuracy of existing methods.
Multicellular organisms consist of cells of many different types that are established during development. Each type of cell is characterized by the unique combination of expressed gene products as a result of spatiotemporal gene regulation. Currently, a fundamental challenge in regulatory biology is to elucidate the gene expression controls that generate the complex body plans during development. Recent advances in high-throughput biotechnologies have generated spatiotemporal expression patterns for thousands of genes in the model organism fruit fly Drosophila melanogaster. Existing qualitative methods enhanced by a quantitative analysis based on computational tools we present in this paper would provide promising ways for addressing key scientific questions.
Results
We develop a set of computational methods and open source tools for identifying co-expressed embryonic domains and the associated genes simultaneously. To map the expression patterns of many genes into the same coordinate space and account for the embryonic shape variations, we develop a mesh generation method to deform a meshed generic ellipse to each individual embryo. We then develop a co-clustering formulation to cluster the genes and the mesh elements, thereby identifying co-expressed embryonic domains and the associated genes simultaneously. Experimental results indicate that the gene and mesh co-clusters can be correlated to key developmental events during the stages of embryogenesis we study. The open source software tool has been made available at http://compbio.cs.odu.edu/fly/.
Conclusions
Our mesh generation and machine learning methods and tools improve upon the flexibility, ease-of-use and accuracy of existing methods.
ContributorsZhang, Wenlu (Author) / Feng, Daming (Author) / Li, Rongjian (Author) / Chernikov, Andrey (Author) / Chrisochoides, Nikos (Author) / Osgood, Christopher (Author) / Konikoff, Charlotte (Author) / Newfeld, Stuart (Author) / Kumar, Sudhir (Author) / Ji, Shuiwang (Author) / Biodesign Institute (Contributor) / Center for Evolution and Medicine (Contributor) / College of Liberal Arts and Sciences (Contributor) / School of Life Sciences (Contributor)
Created2013-12-28
Learning Sparse Representations for Fruit-Fly Gene Expression Pattern Image Annotation and Retrieval
Description
Background
Fruit fly embryogenesis is one of the best understood animal development systems, and the spatiotemporal gene expression dynamics in this process are captured by digital images. Analysis of these high-throughput images will provide novel insights into the functions, interactions, and networks of animal genes governing development. To facilitate comparative analysis, web-based interfaces have been developed to conduct image retrieval based on body part keywords and images. Currently, the keyword annotation of spatiotemporal gene expression patterns is conducted manually. However, this manual practice does not scale with the continuously expanding collection of images. In addition, existing image retrieval systems based on the expression patterns may be made more accurate using keywords.
Results
In this article, we adapt advanced data mining and computer vision techniques to address the key challenges in annotating and retrieving fruit fly gene expression pattern images. To boost the performance of image annotation and retrieval, we propose representations integrating spatial information and sparse features, overcoming the limitations of prior schemes.
Conclusions
We perform systematic experimental studies to evaluate the proposed schemes in comparison with current methods. Experimental results indicate that the integration of spatial information and sparse features lead to consistent performance improvement in image annotation, while for the task of retrieval, sparse features alone yields better results.
Fruit fly embryogenesis is one of the best understood animal development systems, and the spatiotemporal gene expression dynamics in this process are captured by digital images. Analysis of these high-throughput images will provide novel insights into the functions, interactions, and networks of animal genes governing development. To facilitate comparative analysis, web-based interfaces have been developed to conduct image retrieval based on body part keywords and images. Currently, the keyword annotation of spatiotemporal gene expression patterns is conducted manually. However, this manual practice does not scale with the continuously expanding collection of images. In addition, existing image retrieval systems based on the expression patterns may be made more accurate using keywords.
Results
In this article, we adapt advanced data mining and computer vision techniques to address the key challenges in annotating and retrieving fruit fly gene expression pattern images. To boost the performance of image annotation and retrieval, we propose representations integrating spatial information and sparse features, overcoming the limitations of prior schemes.
Conclusions
We perform systematic experimental studies to evaluate the proposed schemes in comparison with current methods. Experimental results indicate that the integration of spatial information and sparse features lead to consistent performance improvement in image annotation, while for the task of retrieval, sparse features alone yields better results.
ContributorsYuan, Lei (Author) / Woodard, Alexander (Author) / Ji, Shuiwang (Author) / Jiang, Yuan (Author) / Zhou, Zhi-Hua (Author) / Kumar, Sudhir (Author) / Ye, Jieping (Author) / Biodesign Institute (Contributor) / Center for Evolution and Medicine (Contributor) / Ira A. Fulton Schools of Engineering (Contributor) / College of Liberal Arts and Sciences (Contributor) / School of Life Sciences (Contributor)
Created2012-05-23
Description
Coronaviruses are a significant group of viruses that cause enteric and respiratory infections in a variety of animals, including humans. Outbreaks of Severe Acute Respiratory Syndrome (SARS) and Middle Eastern Respiratory Syndrome (MERS) in the past 15 years has increased research into coronaviruses to gain an understanding of their structure and function so one day therapies and vaccines may be produced. These viruses have four main structural proteins: the spike, nucleocapsid, envelope, and membrane proteins. The envelope (E) protein is an integral membrane protein in the viral envelope that acts as a viroporin for transport of cations and plays an important role in pathogenesis and viral assembly. E contains a hydrophobic transmembrane domain with polar residues that is conserved across coronavirus species and may be significant to its function. This experiment looks at the possible role of one polar residue in assembly, the 15th residue glutamine, in the Mouse Hepatitis Virus (MHV) E protein. The glutamine 15 residue was mutated into positively charged residues lysine or arginine. Plasmids with these mutations were co-expressed with the membrane protein (M) gene to produce virus-like particles (VLPs). VLPs are produced when E and M are co-expressed together and model assembly of the coronavirus envelope, but they are not infectious as they do not contain the viral genome. Observing their production with the mutated E protein gives insight into the role the glutamine residue plays in assembly. The experiment showed that a changing glutamine 15 to positive charges does not appear to significantly affect the assembly of the VLPs, indicating that this specific residue may not have a large impact on viral assembly.
ContributorsHaller, Sarah S. (Author) / Hogue, Brenda (Thesis director) / Liu, Wei (Committee member) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor) / Biodesign Institute (Contributor)
Created2017-05
Description
Dyslexia is a learning disability that negatively affects reading, writing, and spelling development at the word level in 5%-9% of children. The phenotype is variable and complex, involving several potential cognitive and physical concomitants such as sensory dysregulation and immunodeficiencies. The biological pathogenesis is not well-understood. Toward a better understanding of the biological drivers of dyslexia, we conducted the first joint exome and metabolome investigation in a pilot sample of 30 participants with dyslexia and 13 controls. In the metabolite analysis, eight metabolites of interest emerged (pyridoxine, kynurenic acid, citraconic acid, phosphocreatine, hippuric acid, xylitol, 2-deoxyuridine, and acetylcysteine). A metabolite-metabolite interaction analysis identified Krebs cycle intermediates that may be implicated in the development of dyslexia. Gene ontology analysis based on exome variants resulted in several pathways of interest, including the sensory perception of smell (olfactory) and immune system-related responses. In the joint exome and metabolite analysis, the olfactory transduction pathway emerged as the primary pathway of interest. Although the olfactory transduction and Krebs cycle pathways have not previously been described in the dyslexia literature, these pathways have been implicated in other neurodevelopmental disorders including autism spectrum disorder and obsessive-compulsive disorder, suggesting the possibility of these pathways playing a role in dyslexia as well. Immune system response pathways, on the other hand, have been implicated in both dyslexia and other neurodevelopmental disorders.
ContributorsNandakumar, Rohit (Author) / Dinu, Valentin (Thesis director) / Peter, Beate (Committee member) / Barrett, The Honors College (Contributor) / College of Health Solutions (Contributor)
Created2022-05
Description
High throughput transcriptome data analysis like Single-cell Ribonucleic Acid sequencing (scRNA-seq) and Circular Ribonucleic Acid (circRNA) data have made significant breakthroughs, especially in cancer genomics. Analysis of transcriptome time series data is core in identifying time point(s) where drastic changes in gene transcription are associated with homeostatic to non-homeostatic cellular transition (tipping points). In Chapter 2 of this dissertation, I present a novel cell-type specific and co-expression-based tipping point detection method to identify target gene (TG) versus transcription factor (TF) pairs whose differential co-expression across time points drive biological changes in different cell types and the time point when these changes are observed. This method was applied to scRNA-seq data sets from a SARS-CoV-2 study (18 time points), a human cerebellum development study (9 time points), and a lung injury study (18 time points). Similarly, leveraging transcriptome data across treatment time points, I developed methodologies to identify treatment-induced and cell-type specific differentially co-expressed pairs (DCEPs). In part one of Chapter 3, I presented a pipeline that used a series of statistical tests to detect DCEPs. This method was applied to scRNA-seq data of patients with non-small cell lung cancer (NSCLC) sequenced across cancer treatment times. However, this pipeline does not account for correlations among multiple single cells from the same sample and correlations among multiple samples from the same patient. In Part 2 of Chapter 3, I presented a solution to this problem using a mixed-effect model. In Chapter 4, I present a summary of my work that focused on the cross-species analysis of circRNA transcriptome time series data. I compared circRNA profiles in neonatal pig and mouse hearts, identified orthologous circRNAs, and discussed regulation mechanisms of cardiomyocyte proliferation and myocardial regeneration conserved between mouse and pig at different time points.
ContributorsNyarige, Verah Mocheche (Author) / Liu, Li (Thesis advisor) / Wang, Junwen (Thesis advisor) / Dinu, Valentin (Committee member) / Arizona State University (Publisher)
Created2022
Description
Advancements in high-throughput biotechnologies have generated large-scale multi-omics datasets encompassing diverse dimensions such as genomics, epigenomics, transcriptomics, proteomics, metabolomics, metagenomics, and phenomics. Traditionally, statistical and machine learning-based approaches utilize single-omics data sources to uncover molecular signatures, dissect complicated cellular mechanisms, and predict clinical results. However, to capture the multifaceted pathological mechanisms, integrative multi-omics analysis is needed that can provide a comprehensive picture of the disease. Here, I present three novel approaches to multi-omics integrative analysis. I introduce a single-cell integrative clustering method, which leverages multi-omics to enhance the resolution of cell subpopulations. Applied to a Cellular Indexing of Transcriptomes and Epitopes (CITE-Seq) dataset from human Acute Myeloid Lymphoma (AML) and control samples, this approach unveiled nuanced cell populations that otherwise remain elusive. I then shift the focus to a computational framework to discover transcriptional regulatory trios in which a transcription factor binds to a regulatory element harboring a genetic variant and subsequently differentially regulates the transcription level of a target gene. Applied to whole-exome, whole-genome, and transcriptome data of multiple myeloma samples, this approach discovered synergetic cis-acting and trans-acting regulatory elements associated with tumorigenesis. The next part of this work introduces a novel methodology that leverages the transcriptome and surface protein data at the single-cell level produced by CITE-Seq to model the intracellular protein trafficking process. Applied to COVID-19 samples, this approach revealed dysregulated protein trafficking associated with the severity of the infection.
ContributorsMudappathi, Rekha (Author) / Liu, Li (Thesis advisor) / Dinu, Valentin (Committee member) / Sun, Zhifu (Committee member) / Arizona State University (Publisher)
Created2023