Eur J Cancer Prev

DNA methylation has emerged as a promising target linking environmental exposures and cancer. The World Trade Center (WTC) responders sustained exposures to potential carcinogens, resulting in an increased risk of cancer. Previous studies of cancer risk in WTC-exposed responders were limited by the deficiency in quantitative and individual information on exposure to carcinogens. The current study introduces a new exposure-ranking index (ERI) for estimating cancer-related acute and chronic exposures, which aimed to improve the ability of future analyses to estimate cancer risk. An epigenome-wide association study based on DNA methylation and a weighted gene co-expression network analysis were carried out to identify cytosine-phosphate-guanosine (CpG) sites, modules of correlated CpG sites, and biological pathways associated with the new ERI. Methylation was profiled on blood samples using Illumina 450K Beadchip. No significant epigenome-wide association was found for ERI at a false discovery rate of 0.05. Several cancer-related pathways emerged in pathway analyses for the top ranking genes from epigenome-wide association study as well as enriched module from the weighted gene co-expression network analysis. The current study was the first DNA methylation study that aimed to identify methylation signature for cancer-related exposure in the WTC population. No CpG sites survived multiple testings adjustment. However, enriched gene sets involved in cancer, were identified in both acute and chronic ERIs, supporting the view that multiple genes play a role in this complex exposure.20192020-05-01T00:00:00ZU01 OH010416/OH/NIOSH CDC HHS/United StatesU01 OH010987/OH/NIOSH CDC HHS/United StatesP30 ES005022/ES/NIEHS NIH HHS/United States30001286PMC6329666762

Smart-RRBS for single-cell methylome and transcriptome analysis

The integration of DNA methylation and transcriptional state within single cells is of broad interest. Several single-cell dual- and multi-omics approaches have been reported that enable further investigation into cellular heterogeneity, including the discovery and in-depth study of rare cell populations. Such analyses will continue to provide important mechanistic insights into the regulatory consequences of epigenetic modifications. We recently reported a new method for profiling the DNA methylome and transcriptome from the same single cells in a cancer research study. Here, we present details of the protocol and provide guidance on its utility. Our Smart-RRBS (reduced representation bisulfite sequencing) protocol combines Smart-seq2 and RRBS and entails physically separating mRNA from the genomic DNA. It generates paired epigenetic promoter and RNA-expression measurements for ~24% of protein-coding genes in a typical single cell. It also works for micro-dissected tissue samples comprising hundreds of cells. The protocol, excluding flow sorting of cells and sequencing, takes ~3 d to process up to 192 samples manually. It requires basic molecular biology expertise and laboratory equipment, including a PCR workstation with UV sterilization, a DNA fluorometer and a microfluidic electrophoresis system

Unveiling the frontiers of deep learning: innovations shaping diverse domains

Deep learning (DL) enables the development of computer models that are capable of learning, visualizing, optimizing, refining, and predicting data. In recent years, DL has been applied in a range of fields, including audio-visual data processing, agriculture, transportation prediction, natural language, biomedicine, disaster management, bioinformatics, drug design, genomics, face recognition, and ecology. To explore the current state of deep learning, it is necessary to investigate the latest developments and applications of deep learning in these disciplines. However, the literature is lacking in exploring the applications of deep learning in all potential sectors. This paper thus extensively investigates the potential applications of deep learning across all major fields of study as well as the associated benefits and challenges. As evidenced in the literature, DL exhibits accuracy in prediction and analysis, makes it a powerful computational tool, and has the ability to articulate itself and optimize, making it effective in processing data with no prior training. Given its independence from training data, deep learning necessitates massive amounts of data for effective analysis and processing, much like data volume. To handle the challenge of compiling huge amounts of medical, scientific, healthcare, and environmental data for use in deep learning, gated architectures like LSTMs and GRUs can be utilized. For multimodal learning, shared neurons in the neural network for all activities and specialized neurons for particular tasks are necessary.Comment: 64 pages, 3 figures, 3 table

2023- The Twenty-seventh Annual Symposium of Student Scholars

The full program book from the Twenty-seventh Annual Symposium of Student Scholars, held on April 18-21, 2023. Includes abstracts from the presentations and posters.https://digitalcommons.kennesaw.edu/sssprograms/1027/thumbnail.jp

Transcriptome Analysis of Non‐Coding RNAs in Livestock Species: Elucidating the Ambiguity

The recent remarkable development of transcriptomics technologies, especially next generation sequencing technologies, allows deeper exploration of the hidden landscapes of complex traits and creates great opportunities to improve livestock productivity and welfare. Non-coding RNAs (ncRNAs), RNA molecules that are not translated into proteins, are key transcriptional regulators of health and production traits, thus, transcriptomics analyses of ncRNAs are important for a better understanding of the regulatory architecture of livestock phenotypes. In this chapter, we present an overview of common frameworks for generating and processing RNA sequence data to obtain ncRNA transcripts. Then, we review common approaches for analyzing ncRNA transcriptome data and present current state of the art methods for identification of ncRNAs and functional inference of identified ncRNAs, with emphasis on tools for livestock species. We also discuss future challenges and perspectives for ncRNA transcriptome data analysis in livestock species

TOWARDS EFFICIENT PRESENTATION AND INTERACTION IN VISUAL DATA ANALYSIS

The "data explosion'' since the era of the Internet has increased data size tremendously, from several hundred Megabytes to millions of Terabytes. Large amounts of data may not fit into memory, and a proper way of handling and processing the data is necessary. Besides, analyses of such large scale data requires complex and time consuming algorithms. On the other hand, humans play an important role in steering and driving the data analysis, while there are often times when people have a hard time getting an overview of the data or knowing which analysis to run. Sometimes they may not even know where to start. There is a huge gap between the data and understanding. An intuitive way to facilitate data analysis is to visualize it. Visualization is understandable and illustrative, while using it to support fast and rapid data exploration of large scale datasets has been a challenge for a long time. In this dissertation, we aim to facilitate efficient visual data exploration of large scale datasets from two perspectives: efficiency and interaction. The former indicates how users could understand the data efficiently, this depends on various factors, such as how fast data is processed and how data is presented, while the latter focuses more on the users: how they deal with the data and why they interact with the system in a particular way. In order to improve the efficiency of data exploration, we have looked into two steps in the visualization pipeline: rendering and processing (computations). We first address visualization rendering of large dataset through a thorough evaluation of web-based visualization performance. We evaluate and understand the page loading effects of Scalable Vector Graphics (SVG), a popular image format for interactive visualization on the web browsers. To understand the scalability of individual elements in SVG based visualization, we conduct performance tests on different types of charts, in different phases of rendering process. From the results, we have figured out optimization techniques and guidelines to achieve better performance when rendering SVG visualization. Secondly, we present a pure browser based distributed computing framework (VisHive) that exploits computational power from co-located idle devices for visualization. The VisHive framework speeds up web-based visualization, which is originally designed for single computer and cannot make use of additional computational resources on the client side. It takes advantage of multiple devices that today's users often have access to. VisHive constructs visualization applications that can transparently connect multiple devices into an ad-hoc cluster for local computation. It requires no specific software to be downloaded for setup. To achieve a more interactive data analysis process, we first propose a proactive visual analytics system (DataSite) that enable users to analyze the data smoothly with a list of pre-defined algorithms. DataSite provides results through selecting and executing computations using automatic server-side computation. It utilizes computational resources exhaustively during data analysis to reduce the burden of human thinking. Analyzing results identified by these background processes are surfaced as status updates in a feed on the front-end, akin to posts in a social media feed. DataSite effectively turns data analysis into a conversation between the user and the computer, thereby reducing the cognitive load and domain knowledge requirements on users. Next we apply the concept of proactive data analysis to genomic data, and explore how to improve data analysis through adaptive computations in bioinformatics domain. We build Epiviz Feed, a web application that supports proactive visual and statistical analysis of genomic data. It addresses common and popular biological questions that may be asked by the analyst, and shortens the time of processing and analyzing the data with automatic computations. We further present a computational steering mechanism for visual analytics that prioritizes computations performed on the dataset leveraging the analyst's navigational behavior in the data. The web-based system, called Sherpa, provides computational modules for genomic data analysis, where independent algorithms calculate test statistics relevant to biological inferences about gene regulation in various tumor types and their corresponding normal tissues

The consequences of DNA methylation maintenance deficiency in human Embryonic Stem Cells

A methyl group deposited on cytosines incorporated into the sequence of the DNA, so called DNA methylation, decorates the genomes of a large number of species, from archaea to man. Over the last two decades, a large body of research discovered that this small chemical moiety elicits a profound effect on the gene expression program. In particular, DNA methylation restricts transcriptionally active regions of the genome, therefore ensuring a faithful interpretation of the regulatory information encoded in the DNA sequence. This fundamental role played by the methylation of DNA helps define cell identity at a molecular level, thus it enables a biologically complex transition such as from a zygote to an organism to occur in a unidirectional and orchestrated manner. Perturbations in the pattern of DNA methylation have been frequently found in pathological processes such as tumorigenesis. The pattern of DNA methylation decorating the genome of a cell is precisely copied during cell division by maintenance machinery composed of the DNMT1 enzyme and its associated proteins. The absence of DNMT1 elicits a wide-range of deleterious effects, from loss of cell fitness of in vitro cultured cells to embryonic lethality and loss of homeostasis of somatic tissues. Previous studies reported pleiotropic effects and mutually exclusive phenotypes of DNMT1 knockout depending on the design of the study – from apoptosis and genomic instability to accelerated cell cycle and trans-differentiation. How exactly these phenotypes arise in a response to DNMT1 deficiency is unknown. We employed the state-of-the-art next generation sequencing technologies and coupled them with molecular and cell biology techniques to elucidate the causes for the loss of fitness of DNMT1-deficient human embryonic stem cells. In contrast to previous studies, we did not observe the proposed DNA damage or genomic instability. Our work demonstrated that an acute depletion of DNMT1 results in a uniform decay of DNA methylation that we characterized in depth at a single cell level. Interestingly, our transcriptome profiling in single cells followed by functional validations revealed a change in the way how the transcriptional machinery interprets the genome in the absence of DNMT1. The loss of global DNA methylation without its maintenance machinery resulted in transcriptional changes mainly related to some gonad-specific genes and also a few genes encoding key players of a signaling transduction pathway. This finding inspired us to discover that the cells deficient for DNMT1 display a lower threshold for activating transcription once challenged with external stimuli. Our findings therefore provide new insights into how genome deficient for cytosine methylation becomes transcriptionally amenable, thus capable to integrate and respond to new signals from the environment. Our work lays a foundation for future studies on how such process leads to developmental defects and disease states

A profile of differential DNA methylation in sporadic human prion disease blood: precedent, implications and clinical promise

Sporadic Creutzfeldt-Jakob Disease (sCJD) is a rare but devastating neurodegenerative disorder characterised by misfolding, propagation and deposition of the prion protein in the brain, leading to neuronal death and rapid cognitive and functional decline. As there is no obvious genetic cause of sCJD, the epigenetic status of sCJD patients may clarify spontaneous prion disease aetiology or reveal biomarkers of the disease. Blood from patients was profiled to document genome-wide differential DNA methylation. // 38 loci were identified as being differentially methylated in sCJD blood, including two which associated with disease severity as measured by the MRC Scale score. Of 7 loci considered for replication, 5 showed similar effects in a second cohort of patients, but not in patients of Alzheimer’s disease, iatrogenic CJD, or inherited prion disease, suggesting these effects are specific to the sporadic form of CJD. Notably hypomethylation at a site in the promoter of AIM2, an inflammasome component, retained its association with disease severity. // Hypomethylation of FKBP5, a gene known to regulate the cellular response to cortisol, prompted further investigation which revealed that circulating cortisol is indeed elevated in sCJD patients. Profiling of frontal cortex-derived DNA showed that differential methylation observed in blood is absent from the brain methylome. // Machine learning classification of sCJD based on genome-wide methylation data was able to classify sCJD and healthy control status with an accuracy of 87.04%. This is an appreciable level of accuracy but importantly sets precedence for further classification of prion patients in more complex clinical and research settings, as well as assisting differential diagnosis of less conventional rapid dementias

Emergence of ciprofloxacin heteroresistance in foodborne Salmonella enterica serovar Agona

BACKGROUND: Bacterial heteroresistance has been increasingly identified as an important phenomenon for many antibiotic/bacterium combinations. OBJECTIVES: To investigate ciprofloxacin heteroresistance in Salmonella and characterize mechanisms contributing to ciprofloxacin heteroresistance. METHODS: Ciprofloxacin-heteroresistant Salmonella were identified by population analysis profiling (PAP). Target mutations and the presence of PMQR genes were detected using PCR and sequencing. Expression of acrB, acrF and qnrS was conducted by quantitative RT-PCR. Competition ability and virulence were also compared using pyrosequencing, blue/white screening, adhesion and invasion assays and a Galleria model. Two subpopulations were whole-genome sequenced using Oxford Nanopore and Illumina platforms. RESULTS: PAP identified one Salmonella from food that yielded a subpopulation demonstrating heteroresistance to ciprofloxacin at a low frequency (10-9 to 10-7). WGS and PFGE analyses confirmed that the two subpopulations were isogenic, with six SNPs and two small deletions distinguishing the resistant from the susceptible. Both subpopulations possessed a T57S substitution in ParC and carried qnrS. The resistant subpopulation was distinguished by overexpression of acrB and acrF, a deletion within rsxC and altered expression of soxS. The resistant population had a competitive advantage against the parental population when grown in the presence of bile salts but was attenuated in the adhesion and invasion of human intestinal cells. CONCLUSIONS: We determined that heteroresistance resulted from a combination of mutations in fluoroquinolone target genes and overexpression of efflux pumps associated with a deletion in rsxC. This study warns that ciprofloxacin heteroresistance exists in Salmonella in the food chain and highlights the necessity for careful interpretation of antibiotic susceptibility

Program and Proceedings: The Nebraska Academy of Sciences 1880-2012

PROGRAM FRIDAY, APRIL 20, 2012 REGISTRATION FOR ACADEMY, Lobby of Lecture wing, Olin Hall Aeronautics and Space Science, Session A, Olin 249 Aeronautics and Space Science, Session B, Olin 224 Collegiate Academy, Biology Session A, Olin B Chemistry and Physics, Section A, Chemistry, Olin A Applied Science and Technology, Olin 325 Biological and Medical Sciences, Session A, Olin 112 Biological and Medical Sciences, Session B, Smith Callen Conference Center Junior Academy, Judges Check-In, Olin 219 Junior Academy, Senior High REGISTRATION, Olin Hall Lobby Chemistry and Physics, Section B, Physics, Planetarium Collegiate Academy, Chemistry and Physics, Session A, Olin 324 Junior Academy, Senior High Competition, Olin 124, Olin 131 Aeronautics and Space Science, Poster Session, Olin 249 NWU Health and Sciences Graduate School Fair, Olin and Smith Curtiss Halls Aeronautics and Space Science, Poster Session, Olin 249 MAIBEN MEMORIAL LECTURE, OLIN B Buffalo Bruce McIntosh, Research Ecologist with Western Nebraska Resources Council, The Status of Nebraska\u27s Native Aspen LUNCH, PATIO ROOM, STORY STUDENT CENTER (pay and carry tray through cafeteria line, or pay at NAS registration desk) Aeronautics Group, Conestoga Room Anthropology, Olin 111 Biological and Medical Sciences, Session C, Olin 112 Biological and Medical Sciences, Session D, Smith Callen Conference Center Chemistry and Physics, Section A, Chemistry, Olin A Chemistry and Physics, Section B, Physics, Planetarium Collegiate Academy, Biology Session A, Olin B Collegiate Academy, Biology Session B, Olin 249 Collegiate Academy, Chemistry and Physics, Session B, Olin 324 Earth Science, Olin 224 History/Philosophy of Science, Olin 325 Junior Academy, Judges Check-In, Olin 219 Junior Academy, Junior High REGISTRATION, Olin Hall Lobby Junior Academy, Senior High Competition, (Final), Olin 110 Teaching of Science and Math, Olin 325 Junior Academy, Junior High Competition, Olin 124, Olin 131 NJAS Board/Teacher Meeting, Olin 219 BUSINESS MEETING, OLIN B AWARDS RECEPTION for NJAS, Scholarships, Members, Spouses, and Guests First United Methodist Church, 2723 N 50th Street, Lincoln, N