Lipidomic Profiling Identifies a Novel Lipid Signature Associated with Ethnicity-Specific Disparity of Bladder Cancer

Bladder Cancer (BLCA) is the ninth most frequently diagnosed cancer globally and the sixth most common cancer in the US. African Americans (AA) exhibit half the BLCA incidence compared to European Americans (EA), but they have a 70% higher risk of cancer-related death; unfortunately, this disparity in BLCA mortality remains poorly understood. In this study, we have used an ethnicity-balanced cohort for unbiased lipidomics profiling to study the changes in the lipid fingerprint for AA and EA BLCA tissues collected from similar geographical regions to determine a signature of ethnic-specific alterations. We identified 86 lipids significantly altered between self-reported AA and EA BLCA patients from Augusta University (AU) cohort. The majority of altered lipids belong to phosphatidylcholines (PCs), phosphatidylethanolamines (PEs), ly sophosphatidylcholines (lysoPCs), phosphatidylserines (PSs), and diglycerides (DGs). Interestingly, levels of four lysoPCs (lyso PCs 20:3, lyso PCs 22:1, lyso PCs 22:2, and lyso PCs 26:1) were elevated while, in contrast, the majority of the PCs were reduced in AA BLCA. Significant alterations in long-chain monounsaturated (MonoUN) and polyunsaturated (PolyUN) lipids were also observed between AA and EA BLCA tumor tissues. These first-in-field results implicate ethnic-specific lipid alterations in BLCA.

Fundamental limitations on efficiently forecasting certain epidemic measures in network models

The ongoing COVID-19 pandemic underscores the importance of developing reliable forecasts that would allow decision makers to devise appropriate response strategies. Despite much recent research on the topic, epidemic forecasting remains poorly understood. Researchers have attributed the difficulty of forecasting contagion dynamics to a multitude of factors, including complex behavioral responses, uncertainty in data, the stochastic nature of the underlying process, and the high sensitivity of the disease parameters to changes in the environment. We offer a rigorous explanation of the difficulty of short-term forecasting on networked populations using ideas from computational complexity. Specifically, we show that several forecasting problems (e.g., the probability that at least a given number of people will get infected at a given time and the probability that the number of infections will reach a peak at a given time) are computationally intractable. For instance, efficient solvability of such problems would imply that the number of satisfying assignments of an arbitrary Boolean formula in conjunctive normal form can be computed efficiently, violating a widely believed hypothesis in computational complexity. This intractability result holds even under the ideal situation, where all the disease parameters are known and are assumed to be insensitive to changes in the environment. From a computational complexity viewpoint, our results, which show that contagion dynamics become unpredictable for both macroscopic and individual properties, bring out some fundamental difficulties of predicting disease parameters. On the positive side, we develop efficient algorithms or approximation algorithms for restricted versions of forecasting problems.

Multi-omics Integration Analysis Robustly Predicts High-Grade Patient Survival and Identifies CPT1B Effect on Fatty Acid Metabolism in Bladder Cancer

PURPOSE

The perturbation of metabolic pathways in high-grade bladder cancer has not been investigated. We aimed to identify a metabolic signature in high-grade bladder cancer by integrating unbiased metabolomics, lipidomics, and transcriptomics to predict patient survival and to discover novel therapeutic targets.

EXPERIMENTAL DESIGN

We performed high-resolution liquid chromatography mass spectrometry (LC-MS) and bioinformatic analysis to determine the global metabolome and lipidome in high-grade bladder cancer. We further investigated the effects of impaired metabolic pathways using in vitro and in vivo models.

RESULTS

We identified 519 differential metabolites and 19 lipids that were differentially expressed between low-grade and high-grade bladder cancer using the NIST MS metabolomics compendium and lipidblast MS/MS libraries, respectively. Pathway analysis revealed a unique set of biochemical pathways that are highly deregulated in high-grade bladder cancer. Integromics analysis identified a molecular gene signature associated with poor patient survival in bladder cancer. Low expression of CPT1B in high-grade tumors was associated with low FAO and low acyl carnitine levels in high-grade bladder cancer, which were confirmed using tissue microarrays. Ectopic expression of the CPT1B in high-grade bladder cancer cells led to reduced EMT in in vitro, and reduced cell proliferation, EMT, and metastasis in vivo.

CONCLUSIONS

Our study demonstrates a novel approach for the integration of metabolomics, lipidomics, and transcriptomics data, and identifies a common gene signature associated with poor survival in patients with bladder cancer. Our data also suggest that impairment of FAO due to downregulation of CPT1B plays an important role in the progression toward high-grade bladder cancer and provide potential targets for therapeutic intervention.

Serum Metabolic Profiling Identified a Distinct Metabolic Signature in Bladder Cancer Smokers: A Key Metabolic Enzyme Associated with Patient Survival

BACKGROUND

The current system to predict the outcome of smokers with bladder cancer is insufficient due to complex genomic and transcriptomic heterogeneities. This study aims to identify serum metabolite-associated genes related to survival in this population.

METHODS

We performed LC/MS-based targeted metabolomic analysis for >300 metabolites in serum obtained from two independent cohorts of bladder cancer never smokers, smokers, healthy smokers, and healthy never smokers. A subset of differential metabolites was validated using Biocrates absoluteIDQ p180 Kit. Genes associated with differential metabolites were integrated with a publicly available cohort of The Cancer Genome Atlas (TCGA) to obtain an intersecting signature specific for bladder cancer smokers.

RESULTS

Forty metabolites (FDR < 0.25) were identified to be differential between bladder cancer never smokers and smokers. Increased abundance of amino acids (tyrosine, phenylalanine, proline, serine, valine, isoleucine, glycine, and asparagine) and taurine were observed in bladder cancer smokers. Integration of differential metabolomic gene signature and transcriptomics data from TCGA cohort revealed an intersection of 17 genes that showed significant correlation with patient survival in bladder cancer smokers. Importantly, catechol-O-methyltransferase, iodotyrosine deiodinase, and tubulin tyrosine ligase showed a significant association with patient survival in publicly available bladder cancer smoker datasets and did not have any clinical association in never smokers.

CONCLUSIONS

Serum metabolic profiling of bladder cancer smokers revealed dysregulated amino acid metabolism. It provides a distinct gene signature that shows a prognostic value in predicting bladder cancer smoker survival.

IMPACT

Serum metabolic signature-derived genes act as a predictive tool for studying the bladder cancer progression in smokers.

Measurement of methylated metabolites using Liquid Chromatography-Mass Spectrometry and its biological application

Methylation aberrations play an important role in many metabolic disorders including cancer. Methylated metabolites are direct indicators of metabolic aberrations, and currently, there is no Liquid chromatography - Mass spectrometry (LC-MS) based method available to cover all classes of methylated metabolites at low detection limits. In this study, we have developed a method for the detection of methylated metabolites, and it's biological application. In this approach, we used a HILIC based HPLC with MS to measure methylated organic acids, amino acids, and nucleotides. These metabolites were separated from each other by their hydrophobic interactions and analyzed by targeted metabolomics of single reaction monitoring by positive and negative mode of electrospray ionization. These metabolites were quantified, and the interday reproducibility was <10% relative standard deviation. Furthermore, by applying this method, we identified high levels of methylated metabolites in bladder cancer cell lines compared to benign cells. In vitro treatment of cancer cells with methylation inhibitor, 5- aza-2'-deoxycytidine showed a decrease in these methylated metabolites. This data indicates that HPLC analysis using this HILIC based method could be a powerful tool for measuring methylated metabolites in biological specimens. This method is rapid, sensitive, selective, and precise to measure methylated metabolites.

Optimizing antibody expression: The nuts and bolts

Antibodies are extensively utilized entities in biomedical research, and in the development of diagnostics and therapeutics. Many of these applications require high amounts of antibodies. However, meeting this ever-increasing demand of antibodies in the global market is one of the outstanding challenges. The need to maintain a balance between demand and supply of antibodies has led the researchers to discover better means and methods for optimizing their expression. These strategies aim to increase the volumetric productivity of the antibodies along with the reduction of associated manufacturing costs. Recent years have witnessed major advances in recombinant protein technology, owing to the introduction of novel cloning strategies, gene manipulation techniques, and an array of cell and vector engineering techniques, together with the progress in fermentation technologies. These innovations were also highly beneficial for antibody expression. Antibody expression depends upon the complex interplay of multiple factors that may require fine tuning at diverse levels to achieve maximum yields. However, each antibody is unique and requires individual consideration and customization for optimizing the associated expression parameters. This review provides a comprehensive overview of several state-of-the-art approaches, such as host selection, strain engineering, codon optimization, gene optimization, vector modification and process optimization that are deemed suitable for enhancing antibody expression.

Discovery of association rules in medical data

Data mining is a technique for discovering useful information from large databases. This technique is currently being profitably used by a number of industries. A common approach for information discovery is to identify association rules which reveal relationships among different items. In this paper, we use this approach to analyse a large database containing medical-record data. Our aim is to obtain association rules indicating relationships between procedures performed on a patient and the reported diagnoses. Random sampling was used to obtain these association rules. After reviewing the basic concepts associated with data mining, we discuss our approach for identifying association rules and report on the rules generated.