Study of microRNAs-21/221 as potential breast cancer biomarkers in Egyptian women

microRNAs (miRNAs) play an important role in cancer prognosis. They are small molecules, approximately 17–25 nucleotides in length, and their high stability in human serum supports their use as novel diagnostic biomarkers of cancer and other pathological conditions. In this study, we analyzed the expression patterns of miR-21 and miR-221 in the serum from a total of 100 Egyptian female subjects with breast cancer, fibroadenoma, and healthy control subjects. Using microarray-based expression profiling followed by real-time polymerase chain reaction validation, we compared the levels of the two circulating miRNAs in the serum of patients with breast cancer (n = 50), fibroadenoma (n = 25), and healthy controls (n = 25). The miRNA SNORD68 was chosen as the housekeeping endogenous control. We found that the serum levels of miR-21 and miR-221 were significantly overexpressed in breast cancer patients compared to normal controls and fibroadenoma patients. Receiver Operating Characteristic (ROC) curve analysis revealed that miR-21 has greater potential in discriminating between breast cancer patients and the control group, while miR-221 has greater potential in discriminating between breast cancer and fibroadenoma patients. Classification models using k-Nearest Neighbor (kNN), Naïve Bayes (NB), and Random Forests (RF) were developed using expression levels of both miR-21 and miR-221. Best classification performance was achieved by NB Classification models, reaching 91% of correct classification. Furthermore, relative miR-221 expression was associated with histological tumor grades. Therefore, it may be concluded that both miR-21 and miR-221 can be used to differentiate between breast cancer patients and healthy controls, but that the diagnostic accuracy of serum miR-21 is superior to miR-221 for breast cancer prediction. miR-221 has more diagnostic power in discriminating between breast cancer and fibroadenoma patients. The overexpression of miR-221 has been associated with the breast cancer grade. We also demonstrated that the combined expression of miR-21 and miR-221can be successfully applied as breast cancer biomarkers

Evaluation of qPCR-Based Assays for Leprosy Diagnosis Directly in Clinical Specimens

The increased reliability and efficiency of the quantitative polymerase chain reaction (qPCR) makes it a promising tool for performing large-scale screening for infectious disease among high-risk individuals. To date, no study has evaluated the specificity and sensitivity of different qPCR assays for leprosy diagnosis using a range of clinical samples that could bias molecular results such as difficult-to-diagnose cases. In this study, qPCR assays amplifying different M. leprae gene targets, sodA, 16S rRNA, RLEP and Ag 85B were compared for leprosy differential diagnosis. qPCR assays were performed on frozen skin biopsy samples from a total of 62 patients: 21 untreated multibacillary (MB), 26 untreated paucibacillary (PB) leprosy patients, as well as 10 patients suffering from other dermatological diseases and 5 healthy donors. To develop standardized protocols and to overcome the bias resulted from using chromosome count cutoffs arbitrarily defined for different assays, decision tree classifiers were used to estimate optimum cutoffs and to evaluate the assays. As a result, we found a decreasing sensitivity for Ag 85B (66.1%), 16S rRNA (62.9%), and sodA (59.7%) optimized assay classifiers, but with similar maximum specificity for leprosy diagnosis. Conversely, the RLEP assay showed to be the most sensitive (87.1%). Moreover, RLEP assay was positive for 3 samples of patients originally not diagnosed as having leprosy, but these patients developed leprosy 5–10 years after the collection of the biopsy. In addition, 4 other samples of patients clinically classified as non-leprosy presented detectable chromosome counts in their samples by the RLEP assay suggesting that those patients either had leprosy that was misdiagnosed or a subclinical state of leprosy. Overall, these results are encouraging and suggest that RLEP assay could be useful as a sensitive diagnostic test to detect M. leprae infection before major clinical manifestations

Classification of Dengue Fever Patients Based on Gene Expression Data Using Support Vector Machines

Background: Symptomatic infection by dengue virus (DENV) can range from dengue fever (DF) to dengue haemorrhagic fever (DHF), however, the determinants of DF or DHF progression are not completely understood. It is hypothesised that host innate immune response factors are involved in modulating the disease outcome and the expression levels of genes involved in this response could be used as early prognostic markers for disease severity. Methodology/Principal Findings: mRNA expression levels of genes involved in DENV innate immune responses were measured using quantitative real time PCR (qPCR). Here, we present a novel application of the support vector machines (SVM) algorithm to analyze the expression pattern of 12 genes in peripheral blood mononuclear cells (PBMCs) of 28 dengue patients (13 DHF and 15 DF) during acute viral infection. The SVM model was trained using gene expression data of these genes and achieved the highest accuracy of ,85% with leave-one-out cross-validation. Through selective removal of gene expression data from the SVM model, we have identified seven genes (MYD88, TLR7, TLR3, MDA5, IRF3, IFN-a and CLEC5A) that may be central in differentiating DF patients from DHF, with MYD88 and TLR7 observed to be the most important. Though the individual removal of expression data of five other genes had no impact on the overall accuracy, a significant combined role was observed when the SVM model of the two main genes (MYD88 and TLR7) was re-trained to include the five genes, increasing the overall accuracy to ,96%. Conclusions/Significance: Here, we present a novel use of the SVM algorithm to classify DF and DHF patients, as well as to elucidate the significance of the various genes involved. It was observed that seven genes are critical in classifying DF and DHF patients: TLR3, MDA5, IRF3, IFN-a, CLEC5A, and the two most important MYD88 and TLR7. While these preliminary results are promising, further experimental investigation is necessary to validate their specific roles in dengue disease

Experimental and computational exploration of enzyme sequence space

Millions of enzymes with desirable features or new exciting activities can be found in organisms occupying diverse niches all around the earth. However, enzyme studies tend to be biased towards characterisation of representatives from eukaryotes, model organisms, or disease-causing bacteria. As such, a large number of enzymes still remains underexplored. The so-called sequence space of proteins - all possible protein sequences - is even greater when we include not only natural sequences, but also the ones designed by human or artificial intelligence. This thesis explores various reasons, approaches, and outcomes of investigation of large enzymatic sequence spaces.\ua0In the first part of my work, I focused on investigation of a natural sequence space of oxidases using a high-throughput activity profiling platform. A functional screen of an industrially important class of enzymes, S-2-hydroxyacid oxidases (EC 1.1.3.15), revealed that nearly 80% of the class is misannotated. Further exploration of annotations to public databases indicated that similar errors of annotations can be found in other enzyme classes. A broader activity profiling of 1.1.3.x oxidases resulted in the discovery of two novel microbial enzymes: N-acetyl-hexosamine oxidase, and a novel type of long-chain alcohol oxidase.\ua0Natural enzymes often need to be improved in order to be industrially applied, for example to become more stable, or accept non-natural substrates. A novel, and constantly developing, approach for enzyme design involves the use of machine learning (ML) tools. Second part of my work focused on screening an enzyme sequence space designed by generative adversarial networks. Our work proved that ML methods can generate fully functional enzymes that mimic sequences present in nature.Enzyme assays are necessary to get a full understanding of how enzymes work. Traditional kinetic assays are time- and reagent-consuming and as a result a limited number of variants and conditions are being tested for each target. In my final work I described a novel approach for enzyme kinetic studies, by adaptation of a microfluidic qPCR device

Classification of COVID-19 Cases: The Customized Deep Convolutional Neural Network and Transfer Learning Approach

The recent advancements under the umbrella of artificial intelligence (AI) open opportunities to tackle complex problems related to image analysis. Recently, the proliferation of COVID-19 brought multiple challenges to medical practitioners, such as precise analysis and classification of COVID-19 cases. Deep learning (DL) and transfer learning (TL) techniques appear to be attractive solutions. To provide the precise classification of COVID-19 cases, this article presents a customized Deep Convolutional Neural Network (DCNN) and pre-trained TL model approach. Our pipeline accommodated several popular pre-trained TL models, namely DenseNet121, ResNet50, InceptionV3, EfficientNetB0, and VGG16, to classify COVID-19 positive and negative cases. We evaluated and compared the performance of these models with a wide range of measures, including accuracy, precision, recall, and F1 score for classifying COVID-19 cases based on chest X-rays. The results demonstrate that our customized DCNN model performed well with randomly assigned weights, achieving 98.5% recall and 97.0% accuracy

Testing environmental DNA sampling and predictive modeling as means to investigate wood frog (Rana sylvatica) distribution in Alaska and Northern Canada

Thesis (M.S.) University of Alaska Fairbanks, 2017Global amphibian declines over the past 30+ years have led to a greater awareness of amphibian conservation issues. Few amphibian species occur in northern landscapes, however, and the species that do occur are widely dispersed and at the northern extent of their range. Accordingly, amphibian research is not prioritized in northern landscapes. Deficient monitoring practices have resulted in incomplete distribution knowledge that impedes the management of wood frogs (Rana sylvatica) in Alaska and northern Canada. I developed an environmental DNA detection assay to complement monitoring practices at the northern extent of the wood frog's range. This assay was tested to be species-specific, allowing it to be implemented in areas where wood frogs may co-occur with other amphibian species. It can detect wood frog DNA in environmental samples to a concentration of 1.83 x 10⁻³ pg/μL. I further demonstrate that environmental DNA occurrence data can be used to predict wood frog distribution in the Fairbanks North Star Borough. I combined environmental DNA occurrence data with environmental GIS data and analyzed the resulting dataset with machine learning algorithms to define an ecological niche for the wood frog. This niche, when extrapolated to the landscape, results in a species distribution model that attains 74% predictive accuracy. Lastly, I conducted an environmental DNA mega-transect survey along the Elliot/Dalton Highway corridor in Alaska. I combined the results of this survey with citizen science occurrence data from past and current monitoring projects to create a set of alternative occurrence data. This alternative data was combined with environmental GIS data and analyzed with machine learning algorithms to create a species distribution model that achieves 92% predictive accuracy across Alaska and the Yukon Territory, Canada. These results improve upon prior species distribution models developed for wood frogs in Alaska. They provide deeper insights into potential wood frog distribution at high latitudes and elevations in Alaska, where anecdotal observations have previously been recorded. Adoption and widespread use of an environmental DNA monitoring protocol in under-sampled regions of Alaska and northern Canada will generate larger datasets with wider geographic coverage, leading to models with even higher predictive accuracy. Alternative data, including that obtained from environmental DNA and citizen science monitoring, can boost efforts to further develop baseline knowledge of wood frog occurrence in these areas. Species distribution models generated in this research can help guide these efforts. Increasing knowledge of wood frog distribution may assist conservation managers to designate critical habitat, study climate impacts, and make more informed decisions regarding amphibians in northern landscapes.Chapter 1 Development, validation, and evaluation of an assay for the detection of wood frogs (Rana sylvatica) in environmental DNA -- Chapter 2 Application of environmental DNA-based occurrence data in modeling wood frog (Rana sylvatica) distribution in Interior Alaska -- Chapter 3 A reassessment of wood frog (Rana sylvatica) distribution in Alaska and northern Canada based on environmental DNA and citizen science -- Conclusion -- References -- Appendices

pcrEfficiency: a Web tool for PCR amplification efficiency prediction

<p>Abstract</p> <p>Background</p> <p>Relative calculation of differential gene expression in quantitative PCR reactions requires comparison between amplification experiments that include reference genes and genes under study. Ignoring the differences between their efficiencies may lead to miscalculation of gene expression even with the same starting amount of template. Although there are several tools performing PCR primer design, there is no tool available that predicts PCR efficiency for a given amplicon and primer pair.</p> <p>Results</p> <p>We have used a statistical approach based on 90 primer pair combinations amplifying templates from bacteria, yeast, plants and humans, ranging in size between 74 and 907 bp to identify the parameters that affect PCR efficiency. We developed a generalized additive model fitting the data and constructed an open source Web interface that allows the obtention of oligonucleotides optimized for PCR with predicted amplification efficiencies starting from a given sequence.</p> <p>Conclusions</p> <p>pcrEfficiency provides an easy-to-use web interface allowing the prediction of PCR efficiencies prior to web lab experiments thus easing quantitative real-time PCR set-up. A web-based service as well the source code are provided freely at <url>http://srvgen.upct.es/efficiency.html</url> under the GPL v2 license.</p

Identification and expression analysis of peroxisome-targeted defence proteins mediating innate immunity in the model plant Arabidopsis thaliana

Master's thesis in Biological ChemistryPeroxisomes are single-membrane organelles that have oxidative metabolic functions. Peroxisomes carry out major functions such as lipid degradation, photorespiration and glyoxylate cycle. However, new functions have been recently reported such as peroxisome mediation in plant innate immunity. To elucidate more on peroxisomal roles in pathogen defence in plants, identification and expression analyses of both new and established peroxisome-targeted pathogen defence proteins in plants was investigated in this study. Subcellular localization analysis of four PTS1 carrying proteins with a pathogen annotation was done. In addition, gene expression analysis of established peroxisomal pathogen defence was carried out using Real-Time Quantitative PCR (qPCR). The four PTS1 carry proteins that whose subcellular localization was studied are NUDT7, NUDT15, CHAT homolog and ATP-BP. NUDT15 and CHAT homolog targeted punctuate subcellular structures, which were later confirmed to be peroxisomes in double labelling experiment with a peroxisomal marker. NUDT7 and ATP-BP failed to target any subcellular structures, were therefore, putatively reported to be cytosolic in this study. Expression analyses were done on three NHL proteins (NHL4, NHL6 and NHL25) and also on three IAN proteins (IAN8, IAN11 and IAN12) using wild type Arabidopsis Col 0 plants, by mimicking pathogen attack with exogenously applied defence hormone-salicylic acid. All the NHL and IAN genes were induced after salicylic acid treatment. In addition, co-expression analyses were done on the aforementioned NHL and IAN proteins (except for NHL25). NHL6 and IAN8 were co-expressed with other Arabidopsis defence proteins. Whereas NHL4, IAN11 and IAN12 were found not be co-expressed in the dataset generated. In conclusion, in this study, two new peroxisomal pathogen defence proteins were identified namely NUDT15 and CHAT homolog, and also NHL6 and NHL25 were induced by salicylic acid treatment

Precursor-derived in-water peracetic acid impacts on broiler performance, gut microbiota and antimicrobial resistance genes

Past antimicrobial misuse has led to the spread of antimicrobial resistance amongst pathogens, reportedly a major public health threat. Attempts to reduce the spread of antimicrobial resistant (AMR) bacteria are in place worldwide, among which finding alternatives to antimicrobials have a pivotal role. Such molecules could be used as “green alternatives” to reduce the bacterial load either by targeting specific bacterial groups or more generically, functioning as biocides when delivered in vivo. In this study, the effect of in-water peracetic acid as a broad-spectrum antibiotic alternative for broilers was assessed via hydrolysis of precursors sodium percarbonate and tetraacetylethylenediamine. Six equidistant peracetic acid levels were tested from 0 to 50 ppm using four pens per treatment and 4 birds per pen (i.e., 16 birds per treatment and 96 in total). Peracetic acid was administered daily from d 7 to 14 of age whilst measuring performance parameters and end-point bacterial concentration (qPCR) in crop, jejunum, and ceca, as well as crop 16S sequencing. PAA treatment, especially at 20, 30, and 40 ppm, increased body weight at d 14, and feed intake during PAA exposure compared to control (P < 0.05). PAA decreased bacterial concentration in the crop only (P < 0.05), which was correlated to better performance (P < 0.05). Although no differences in alpha- and beta-diversity were found, it was observed a reduction of Lactobacillus (P < 0.05) and Flectobacillus (P < 0.05) in most treatments compared to control, together with an increased abundance of predicted 4-aminobutanoate degradation (V) pathway. The analysis of the AMR genes did not point towards any systematic differences in gene abundance due to treatment administration. This, together with the rest of our observations could indicate that proximal gut microbiota modulation could result in performance amelioration. Thus, peracetic acid may be a valid antimicrobial alternative that could also positively affect performance