The goal of pathway analysis is to identify the pathways significantly impacted in a given phenotype. Many current methods are based on algorithms that 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, standalone, 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.

Cystic 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.

The 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.

Tuberculosis (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.

A 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.