8 research outputs found
Identifying Biologically Relevant Mechanisms And Biomarkers Using Novel Bioinformatics Methods
No full textThere is a tremendous need to analyze molecular and patient clinical data to identify biomarkers, biological mechanisms, or to simply classify samples accurately. Issues such as: i) limited tools to diagnose many diseases, ii) not considering biological interactions, or iii) damaged DNA samples could cause a challenge in identifying valuable insights. In this work, I try to address these issues by developing different bioinformatic frameworks.First, I present three frameworks to identify i) Sarcoidosis biomarkers, ii) Tuberculosis biomarkers and iii) Cystic fibrosis (CF) biomarkers. I identified Sarcoidosis biomarkers I applied them to classify Sarcoidosis samples from non-Sarcoidosis (healthy controls, Tuberculosis, and lung cancer) with a sensitivity of 0.92 and specificity of 0.88. I identified 10 TB biomarkers and applied them to classify TB samples versus non-TB (healthy controls and sarcoidosis). The area under the receiver operating characteristics (ROC) curve for the top 10 biomarkers was 1 with a sensitivity of 1 and a specificity of 1. I identified 20 CF biomarkers and used them to classify CF from non-CF (healthy controls and lung cancer). The mean area under the ROC curve for the CF biomarkers was 0.97 with a sensitivity of 0.99 and specificity of 0.95. Second, I present a method that can construct networks of genes that can be considered putative mechanisms. A major challenge in life science research is understanding the mechanism involved in a given phenotype. The putative mechanisms constructed by this approach are not limited to the set of DE genes, but also considers all known and relevant gene-gene interactions. We analyzed three real datasets for which both the causes of the phenotype, as well as the true mechanisms were known. We show that the method identified the correct mechanisms when applied on microarray datasets from mouse. We compared the results of our method with the results of the classical approach, showing that our method produces more meaningful biological insights. Third, I propose a classification method that is able to analyze genomic data and assign an individual to a particular population/group. A current challenge in forensic evidence is to classify samples using genomic data accurately. Fragmented DNA due to degradation is a common problem with samples from crime scenes. The proposed classification method can use SNPs from as little as 10% of the DNA in the human genome to identify the population background of a sample. I compared the performance of the proposed method with three other classification methods: i) naive Bayes, ii) Random Forest, and iii) BIASLESS. The accuracy, sensitivity, specificity, and F1 score values yielded by the proposed classifier were 0.963, 0.798, 0.983, and 0.827, respectively. The results show that the proposed method outperforms the existing methods. Finally, I present the findings of analyzing clinical data for 81 COVID-19 ICU patients. The coronavirus disease (COVID-19) is a highly transmissible viral infection caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). I show evidence that mean platelet volume (MPV) reflects platelet activation and activation of coagulation cascades and plays a major role in the development of acute renal failure. Furthermore, I show that the glomerular filtration rate (GFR) values are deteriorating after day three for patients with acute renal injury (AKI). Such findings will help with the treatment
Identifying biologically relevant putative mechanisms in a given phenotype comparison.
No full textA major challenge in life science research is understanding the mechanism involved in a given phenotype. The ability to identify the correct mechanisms is needed in order to understand fundamental and very important phenomena such as mechanisms of disease, immune systems responses to various challenges, and mechanisms of drug action. The current data analysis methods focus on the identification of the differentially expressed (DE) genes using their fold change and/or p-values. Major shortcomings of this approach are that: i) it does not consider the interactions between genes; ii) its results are sensitive to the selection of the threshold(s) used, and iii) the set of genes produced by this approach is not always conducive to formulating mechanistic hypotheses. Here we present a method that can construct networks of genes that can be considered putative mechanisms. The putative mechanisms constructed by this approach are not limited to the set of DE genes, but also considers all known and relevant gene-gene interactions. We analyzed three real datasets for which both the causes of the phenotype, as well as the true mechanisms were known. We show that the method identified the correct mechanisms when applied on microarray datasets from mouse. We compared the results of our method with the results of the classical approach, showing that our method produces more meaningful biological insights
Novel T7 Phage Display Library Detects Classifiers for Active Mycobacterium Tuberculosis Infection
No full textTuberculosis (TB) is caused by Mycobacterium tuberculosis (MTB) and transmitted through inhalation of aerosolized droplets. Eighty-five percent of new TB cases occur in resource-limited countries in Asia and Africa and fewer than 40% of TB cases are diagnosed due to the lack of accurate and easy-to-use diagnostic assays. Currently, diagnosis relies on the demonstration of the bacterium in clinical specimens by serial sputum smear microscopy and culture. These methods lack sensitivity, are time consuming, expensive, and require trained personnel. An alternative approach is to develop an efficient immunoassay to detect antibodies reactive to MTB antigens in bodily fluids, such as serum. Sarcoidosis and TB have clinical and pathological similarities and sarcoidosis tissue has yielded MTB components. Using sarcoidosis tissue, we developed a T7 phage cDNA library and constructed a microarray platform. We immunoscreened our microarray platform with sera from healthy (n = 45), smear positive TB (n = 24), and sarcoidosis (n = 107) subjects. Using a student t-test, we identified 192 clones significantly differentially expressed between the three groups at a False Discovery Rate (FDR) <0.01. Among those clones, we selected the top ten most significant clones and validated them on independent test set. The area under receiver operating characteristics (ROC) for the top 10 significant clones was 1 with a sensitivity of 1 and a specificity of 1. Sequence analyses of informative phage inserts recognized as antigens by active TB sera may identify immunogenic antigens that could be used to develop therapeutic or prophylactic vaccines, as well as identify molecular targets for therapy
Detection of Cystic Fibrosis Serological Biomarkers Using a T7 Phage Display Library
No full textAbstractCystic fibrosis (CF) is an autosomal recessive disorder affecting the cystic fibrosis transmembrane conductance regulator (CFTR). CF is characterized by repeated lung infections leading to respiratory failure. Using a high-throughput method, we developed a T7 phage display cDNA library derived from mRNA isolated from bronchoalveolar lavage (BAL) cells and leukocytes of sarcoidosis patients. This library was biopanned to obtain 1070 potential antigens. A microarray platform was constructed and immunoscreened with sera from healthy (n = 49), lung cancer (LC) (n = 31) and CF (n = 31) subjects. We built 1,000 naïve Bayes models on the training sets. We selected the top 20 frequently significant clones ranked with student t-test discriminating CF antigens from healthy controls and LC at a False Discovery Rate (FDR) < 0.01. The performances of the models were validated on an independent validation set. The mean of the area under the receiver operating characteristic (ROC) curve for the classifiers was 0.973 with a sensitivity of 0.999 and specificity of 0.959. Finally, we identified CF specific clones that correlate highly with sweat chloride test, BMI, and FEV1% predicted values. For the first time, we show that CF specific serological biomarkers can be identified through immunocreenings of a T7 phage display library with high accuracy, which may have utility in development of molecular therapy.</jats:p
Platelets and renal failure in the SARS-CoV-2 syndrome
No full textThe coronavirus disease 19 (COVID-19) is a highly transmittable viral infection caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). SARS‐CoV‐2 utilizes metallocarboxyl peptidase angiotensin receptor (ACE) 2 to gain entry into human cells. Activation of several proteases facilitates the interaction of viral spike proteins (S1) and ACE2 receptor. This leads to cleavage of host ACE2 receptors. ACE2 activity counterbalances the angiotensin II effect, its loss may lead to elevated angiotensin II levels with modulation of platelet function, size and activity. COVID-19 disease encompasses a spectrum of systemic involvement far beyond respiratory failure alone. Several features of this disease, including the etiology of acute kidney injury (AKI) and the hypercoagulable state, remain poorly understood. Here, we show that there is a high incidence of AKI (81%) in the critically ill adults with COVID-19 in the setting of elevated D-dimer, elevated ferritin, C reactive protein (CRP) and lactate dehydrogenase (LDH) levels. Strikingly, there were unique features of platelets in these patients, including larger, more granular platelets and a higher mean platelet volume (MPV). There was a significant correlation between measured D-dimer levels and MVP; but a negative correlation between MPV and glomerular filtration rates (GFR) in critically ill cohort. Our data suggest that activated platelets may play a role in renal failure and possibly hypercoagulability status in COVID19 patients
Methods and approaches in the topology-based analysis of biological pathways
No full textThe goal of pathway analysis is to identify the pathways significantly impacted in a given phenotype. Many current methods are based on algorithms which consider pathways as simple gene lists, dramatically under-utilizing the knowledge that such pathways are meant to capture. During the past few years, a plethora of methods claiming to incorporate various aspects of the pathway topology have been proposed. These topology-based methods, sometimes referred to as ``third generation'', have the potential to better model the phenomena described by pathways. Although there is now a large variety of approaches used for this purpose, no review is currently available to offer guidance for potential users and developers. This review covers 22 such topology-based pathway analysis methods published in the last decade. We compare these methods based on: type of pathways analyzed (e.g. signaling or metabolic), input (subset of genes, all genes, fold changes, gene p-values, etc.), mathematical models, pathway scoring approaches, output (one or more pathway scores, p-values, etc.) and implementation (web-based, stand alone, etc.). We identify and discuss challenges, arising both in methodology and in pathway representation, including inconsistent terminology, different data formats, lack of meaningful benchmarks, and the lack of tissue and condition specificity
Crowd-sourced machine learning prediction of long COVID using data from the National COVID Cohort CollaborativeResearch in context
No full textSummary: Background: While many patients seem to recover from SARS-CoV-2 infections, many patients report experiencing SARS-CoV-2 symptoms for weeks or months after their acute COVID-19 ends, even developing new symptoms weeks after infection. These long-term effects are called post-acute sequelae of SARS-CoV-2 (PASC) or, more commonly, Long COVID. The overall prevalence of Long COVID is currently unknown, and tools are needed to help identify patients at risk for developing long COVID. Methods: A working group of the Rapid Acceleration of Diagnostics-radical (RADx-rad) program, comprised of individuals from various NIH institutes and centers, in collaboration with REsearching COVID to Enhance Recovery (RECOVER) developed and organized the Long COVID Computational Challenge (L3C), a community challenge aimed at incentivizing the broader scientific community to develop interpretable and accurate methods for identifying patients at risk of developing Long COVID. From August 2022 to December 2022, participants developed Long COVID risk prediction algorithms using the National COVID Cohort Collaborative (N3C) data enclave, a harmonized data repository from over 75 healthcare institutions from across the United States (U.S.). Findings: Over the course of the challenge, 74 teams designed and built 35 Long COVID prediction models using the N3C data enclave. The top 10 teams all scored above a 0.80 Area Under the Receiver Operator Curve (AUROC) with the highest scoring model achieving a mean AUROC of 0.895. Included in the top submission was a visualization dashboard that built timelines for each patient, updating the risk of a patient developing Long COVID in response to clinical events. Interpretation: As a result of L3C, federal reviewers identified multiple machine learning models that can be used to identify patients at risk for developing Long COVID. Many of the teams used approaches in their submissions which can be applied to future clinical prediction questions. Funding: Research reported in this RADx® Rad publication was supported by the National Institutes of Health. Timothy Bergquist, Johanna Loomba, and Emily Pfaff were supported by Axle Subcontract: NCATS-STSS-P00438
