Computational and experimental tools of MiRNAs in cancer

MicroRNAs (miRNAs) are short non-protein coding and single-stranded small RNA molecules with a critical role in the regulation of gene expression. These molecules are crucial regulatory elements in diverse biological processes such as apoptosis, development, and progression. miRNA genes have been associated with various human diseases, particularly cancer, and considered as a new biomarker. After the discovery of miRNAs, many researches have focused on identifying and characterizing miRNA genes in cancer. The various expression levels of miRNAs between cancer cells and normal cells are very crucial to diagnosis, prognosis, and treatment of many cancers. Many computational and experimental tools have been employed to characterize miRNAs. However, there exist some challenges in identifying miRNA using both computational and experimental tools due to miRNA features. The present review briefly introduced miRNA biology and certain computational and experimental tools for identifying and profiling miRNAs in cancer. Furthermore, we presented the advantages and challenges of these tools. © 2020, Shriaz University of Medical Sciences. All rights reserved

Computational and Experimental Approaches to Reveal the Effects of Single Nucleotide Polymorphisms with Respect to Disease Diagnostics

DNA mutations are the cause of many human diseases and they are the reason for natural differences among individuals by affecting the structure, function, interactions, and other properties of DNA and expressed proteins. The ability to predict whether a given mutation is disease-causing or harmless is of great importance for the early detection of patients with a high risk of developing a particular disease and would pave the way for personalized medicine and diagnostics. Here we review existing methods and techniques to study and predict the effects of DNA mutations from three different perspectives: in silico, in vitro and in vivo. It is emphasized that the problem is complicated and successful detection of a pathogenic mutation frequently requires a combination of several methods and a knowledge of the biological phenomena associated with the corresponding macromolecules

Using DNA microarrays to study host-microbe interactions.

Complete genomic sequences of microbial pathogens and hosts offer sophisticated new strategies for studying host-pathogen interactions. DNA microarrays exploit primary sequence data to measure transcript levels and detect sequence polymorphisms, for every gene, simultaneously. The design and construction of a DNA microarray for any given microbial genome are straightforward. By monitoring microbial gene expression, one can predict the functions of uncharacterized genes, probe the physiologic adaptations made under various environmental conditions, identify virulence-associated genes, and test the effects of drugs. Similarly, by using host gene microarrays, one can explore host response at the level of gene expression and provide a molecular description of the events that follow infection. Host profiling might also identify gene expression signatures unique for each pathogen, thus providing a novel tool for diagnosis, prognosis, and clinical management of infectious disease

Advances in Nematode Identification: A Journey from Fundamentals to Evolutionary Aspects

Nematodes are non-segmented roundworms evenly distributed with various habitats ranging to approximately every ecological extremity. These are the least studied organisms despite being the most diversified group. Nematodes are the most critical equilibrium-maintaining factors, having implications on the yield and health of plants as well as well-being of animals. However, taxonomic knowledge about nematodes is scarce. As a result of the lack of precise taxonomic features, nematode taxonomy remains uncertain. Morphology-based identification has proved inefficacious in identifying and exploring the diversity of nematodes, as there are insufficient morphological variations. Different molecular and new evolving methodologies have been employed to augment morphology-based approaches and bypass these difficulties with varying effectiveness. These identification techniques vary from molecular-based targeting DNA or protein-based targeting amino acid sequences to methods for image processing. High-throughput approaches such as next-generation sequencing have also been added to this league. These alternative approaches have helped to classify nematodes and enhanced the base for increased diversity and phylogeny of nematodes, thus helping to formulate increasingly more nematode bases for use as model organisms to study different hot topics about human well-being. Here, we discuss all the methods of nematode identification as an essential shift from classical morphometric studies to the most important modern-day and molecular approaches for their identification. Classification varies from DNA/protein-based methods to the use of new emerging methods. However, the priority of the method relies on the quality, quantity, and availability of nematode resources and down-streaming applications. This paper reviews all currently offered methods for the detection of nematodes and known/unknown and cryptic or sibling species, emphasizing modern-day methods and budding molecular techniques

VARIATIONS IN MICROARRAY BASED GENE EXPRESSION PROFILING: IDENTIFYING SOURCES AND IMPROVING RESULTS

Two major issues hinder the application of microarray based gene expression profiling in clinical laboratories as a diagnostic or prognostic tool. The first issue is the sheer volume and high-dimensionality of gene expression data from microarray experiments, which require advanced algorithms to extract meaningful gene expression patterns that correlate with biological impact. The second issue is the substantial amount of variation in microarray gene expression data, which impairs the performance of analysis method and makes sharing or integrating microarray data very difficult. Variations can be introduced by all possible sources including the DNA microarray technology itself and the experimental procedures. Many of these variations have not been characterized, measured, or linked to the sources. In the first part of this dissertation, a decision tree learning method was demonstrated to perform as well as more popularly accepted classification methods in partitioning cancer samples with microarray data. More importantly, results demonstrate that variation introduced into microarray data by tissue sampling and tissue handling compromised the performance of classification methods. In the second part of this dissertation, variations introduced by the T7 based in vitro transcription labeling methods were investigated in detail. Results demonstrated that individual amplification methods significantly biased gene expression data even though the methods compared in this study were all derivatives of the T7 RNA polymerase based in vitro transcription labeling approach. Variations observed can be partially explained by the number of biotinylated nucleotides used for labeling and the incubation time of the in vitro transcription experiments. These variations can generate discordant gene expression results even using the same RNA samples and cannot be corrected by post experiment analysis including advanced normalization techniques. Studies in this dissertation stress the concept that experimental and analytical methods must work together. This dissertation also emphasizes the importance of standardizing the DNA microarray technology and experimental procedures in order to optimize gene expression analysis and create quality standards compatible with the clinical application of this technology. These findings should be taken into account especially when comparing data from different platforms, and in standardizing protocols for clinical applications in pathology

An Analysis of Global Gene Expression Resulting from Exposure to Energetic Materials

AN ANALYSIS OF GLOBAL GENE EXPRESSION RESULTING FROM EXPOSURE TO ENERGETIC MATERIALS A Dissertation Presented for the Doctor of Philosophy Degree University of Tennessee, Knoxville VERNON LASHAWN MCINTOSH JR. August 2010 Dedication This dissertation is dedicated to my family. My mother and father Debra and Vernon McIntosh instilled in me the respect for academic excellence and the drive maximize my potential. Early on, my younger brother Kyle started showing signs of a shared interest in biology thus my desire to be a positive role model for him kept me motivated. Last but certainly not least, my loving wife and best friend Nichole has been there to offer love and support throughout my entire undergraduate and graduate degrees. It’s difficult to imagine making it this far without her (and that’s not just because she paid the bills). Abstract Characteristic transcriptional biomarkers have been identified for microbial cultures exposed to 2, 4, 6-trinitrotoluene (TNT), 2, 6-dinitrotoluene (DNT), or triacetone-triperoxide (TATP). This study describes the generation of expression profiles for exposure to each compound, the functional significance of each response, and the identification of the characteristic alterations in gene expression associated with exposure to each compound. Expression profiles were generated from a total of three different candidate organisms: Escherichia coli, Saccharomyces cerevisiae, and Pseudomonas putida. Common to all three organisms, TNT exposure resulted in increased expression of genes involved in toxin resistance and drug efflux systems. The S.cerevisiae and E.coli expression profiles were both characterized by increased expression of genes involved in iron-sulfur cluster assembly, sulfur containing amino acids, sulfate transport and assimilation and the metabolism of nitrogen compounds. Only E.coli and Saccharomyces were used to generate DNT induced expression profiles; both profiles exhibited high degrees of similarity with each organism’s respective TNT profiles. This was especially true of the E.coli profile where 25 of the 30 alterations were also observed after exposure to TNT. A computational discriminant functional analysis was performed to identify characteristic biomarkers for each exposure. For each compound a set of transcriptional biomarkers (10 or less) was developed. An additional set of biomarkers was developed encompassing both TNT and DNT exposure. These sets of genes serve as a transcriptional fingerprint for exposure to each respective compound. The sensitivity and specificity of each transcriptional fingerprint is sufficient to correctly identify exposure to energetic materials against a background of non-energetic compound exposures. This study makes several novel contributions to the greater body of scientific knowledge: • This is the first documented study of the interactions of TATP in any biological system. • This is the first comprehensive gene expression study of the TNT response by P. putida, E.coli or E.coli. • This is the first application of computational class prediction in the development of biomarkers for exposure to energetic material

Biomarker Discovery Using Statistical and Machine Learning Approaches on Gene Expression Data

My PhD is affiliated with the dCod 1.0 project (https://www.uib.no/en/dcod): decoding the systems toxicology of Atlantic cod (Gadus morhua), which aims to better understand how cods adapt and react to the stressors in the environment. One of the research topics is to discover the biomarkers which discriminate the fish under normal biological status and the ones that are exposed to toxicants. A biomarker, or biological marker, is an indicator of a biological state in response to an intervention, which can be for example toxic exposure (in toxicology), disease (for example cancer), or drug response (in precision medicine). Biomarker discovery is a very important research topic in toxicology, cancer research, and so on. A good set of biomarkers can give insight into the disease / toxicant response mechanisms and be useful to find if the person has the disease / the fish has been exposed to the toxicant. On the molecular level, a biomarker could be "genotype" - for instance a single nucleotide variant linked with a particular disease or susceptibility; another biomarker could be the level of expression of a gene or a set of genes. In this thesis we focus on the latter one, aiming to find out the informative genes that can help to distinguish samples from different groups from the gene expression profiling. Several transcriptomics technologies can be used to generate the necessary data, and among them, DNA microarray and RNA sequencing (RNA-Seq) have become the most useful methods for whole transcriptome gene expression profiling. Especially RNA-Seq has become an attractive alternative to microarrays since it was introduced. Prior to analysis of gene expression, the RNA-Seq data needs to go through a series of processing steps, so a workflow which can automate the process is highly required. Even though many workflows have been proposed to facilitate this process, their application is usually limited to such as model organisms, high-performance computers, computer fluent users, and so on. To fill these gaps, we developed a maximally general RNA-Seq analysis workflow: RNA-Seq Analysis Snakemake Workflow (RASflow), which is applicable to a wide range of applications and requires little programming skills. It takes the sequencing data as input, and maps them to either transcriptome or genome for quantification, and after that the gene expression profile can be achieved which afterwards goes through normalization and statistical tests to find out the differentially expressed genes. This work was presented in Paper I and Paper II. Differential expression analysis used in RASflow, together with other univariate methods are widely used in biomarker discovery for their simplicity and interpretability. But they rely on a hypothesis that variables are independent, so they can only identify variables that possess significant information by themselves. However, biological processes usually involve many variables that have complex interactions. Multivariate methods which take the interactions between variables into consideration are therefore also popular for biomarker discovery. To study whether there is a significant advantage of one over the other, we conducted a comparative study of various methods from these two categories and evaluated these methods on two aspects: stability and prediction accuracy, we found that a method’s performance is quite data-dependent. This work was presented in Paper III. Since the biomarker discovery methods perform quite differently on different datasets, then how to choose the most appropriate one for a particular dataset? One solution is to use the function perturbation strategy to combine the outputs from multiple methods. Function perturbation is capable of maintaining prediction accuracy compared with the original individual methods, but its stability is not satisfactory enough. On the other hand, data perturbation uses a similar ensemble learning logic: it firstly generates multiple datasets by resampling the original dataset and then combines the results from those datasets. Data perturbation has been proven to improve the stability of the biomarker discovery method. We therefore proposed a framework which combines function perturbation with data perturbation: Ensemble Feature Selection Integrating Stability (EFSIS) which achieves both high prediction accuracy and stability. This work was presented in Paper IV