Approaches for the study of cancer: towards the integration of genomics, proteomics and metabolomics

Screening of metabolites in the treatment of liver cancer xenografts HepG2/ADR by psoralen-loaded lipid nanoparticles

OBJECTIVE

Our study aimed to find potential biomarkers for drug resistance in liver cancer cells using metabolomics and further to evaluate the potential of psoralen-loaded polymer lipid nanoparticles (PSO-PLNs) to reverse the resistance of cells to doxorubicin.

METHODS

We used LC-MS-based non-targeted metabolomics, also known as global metabolite profiling, to screen in serum and urine of mice engrafted with a liver cancer cell line sensitive (HepG2/S) or resistant to doxorubicin (HepG2/ADR) for differentially regulated metabolites. We subsequently quantified the abundance of these metabolites in serum and the urine of mice. The mice were engrafted with HepG2 cells resistant against doxorubicin and were treated with I) doxorubicin, II) a combination of doxorubicin and psoralen and III) a combination of doxorubicin and psoralen packed in polymer lipid nanoparticles.

RESULTS

Metabolites found to be differentially present in urine of mice engrafted with resistant HepG2 cells were: hippuric acid, hyaluronic acid, pantothenic acid, and betaine; retinoic acid and α-linolenic acid were found to be reduced in serum samples of mice with HepG2 cells resistant to doxorubicin. The targeted analysis showed that the degree of regression of metabolic markers in groups differed: treatment group 2 had stronger degree of regression than treatment group 1 and the negative control group had the smallest, which indicates that the PSO-PLNs have superior properties compared with other treatments.

CONCLUSION

Psoralen reverses drug resistance of liver cancer cells and its efficacy can be increased by encapsulation in polymer lipid nanoparticles.

Investigating an increase in Florida manatee mortalities using a proteomic approach

Two large-scale Florida manatee (Trichechus manatus latirostris) mortality episodes were reported on separate coasts of Florida in 2013. The east coast mortality episode was associated with an unknown etiology in the Indian River Lagoon (IRL). The west coast mortality episode was attributed to a persistent Karenia brevis algal bloom or 'red tide' centered in Southwest Florida. Manatees from the IRL also had signs of cold stress. To investigate these two mortality episodes, two proteomic experiments were performed, using two-dimensional difference in gel electrophoresis (2D-DIGE) and isobaric tags for relative and absolute quantification (iTRAQ) LC-MS/MS. Manatees from the IRL displayed increased levels of several proteins in their serum samples compared to controls, including kininogen-1 isoform 1, alpha-1-microglobulin/bikunen precursor, histidine-rich glycoprotein, properdin, and complement C4-A isoform 1. In the red tide group, the following proteins were increased: ceruloplasmin, pyruvate kinase isozymes M1/M2 isoform 3, angiotensinogen, complement C4-A isoform 1, and complement C3. These proteins are associated with acute-phase response, amyloid formation and accumulation, copper and iron homeostasis, the complement cascade pathway, and other important cellular functions. The increased level of complement C4 protein observed in the red tide group was confirmed through the use of Western Blot.

Integrated analysis of long non-coding RNAs and mRNA profiles reveals potential sex-dependent biomarkers of bevacizumab/erlotinib response in advanced lung cancer

Background

While lung cancer patient outcomes are well-recognized to vary as a function of patient sex, there has been insufficient research regarding the relationship between patient sex and EGFR(Epidermal growth factor receptor) response efficacy. The present study therefore sought to identify novel sex-related biomarkers of bevacizumab/erlotinib (BE) responses in non-small cell lung cancer (NSCLC) patients.

Methods

The exon array data in the Gene Expression Omnibus (GEO) dataset were analyzed in order to identify patterns of mRNA and lncRNA expression associated with BE resistance in NSCLC. These differentially expressed (DE) lncRNAs and mRNAs were identified via DE Analysis Filtering. These DE mRNAs were then assessed for their potential functional roles via pathway enrichment analyses, with overlapping functions possibly associated with the BE resistance. The mRNAs in these overlapping groups were then assessed for their correlations with patient survival, and lncRNA-mRNA co-expression networks were generated for each patient subset. A protein-protein interaction (PPI) network was also generated based upon these DE mRNAs.

Results

In females we identified 172 DE lncRNAs and 1766 DE mRNAs associated with BE responses, while in males we identified 78 DE lncRNAs and 485 DE mRNAs associated with such responses. Based on the overlap between these two datasets, we identified a total of 37 GO functions and 18 pathways associated with BE responses. Co-expression and PPI networks suggested that the key lncRNAs and mRNAs associated with these BE response mechanisms weredifferent in the male and female patients.

Conclusions

This work is the first to conduct a global profiling of the relationship between lncRNA and mRNA expression patterns, patient sex, and BE responses in individuals suffering from NSCLC. Together these results suggest that the integrative lncRNA-mRNA expression analyses may offer invaluable new therapeutic insights that can guide the tailored treatment of lung cancer in order to ensure optimal BE responses.

Muscle metabolomics analysis reveals potential biomarkers of exercise‑dependent improvement of the diaphragm function in chronic obstructive pulmonary disease

Decreased diaphragm function is a crucial factor leading to reduced ventilatory efficiency and worsening of quality of life in chronic obstructive pulmonary disease (COPD). Exercise training has been demonstrated to effectively improve the function of the diaphragm. However, the mechanism of this process has not been identified. The emergence of metabolomics has allowed the exploration of new ideas. The present study aimed to analyze the potential biomarkers of exercise-dependent enhancement of diaphragm function in COPD using metabolomics. Sprague Dawley rats were divided into three groups: COPD + exercise group (CEG); COPD model group (CMG); and control group (CG). The first two groups were exposed to cigarette smoke for 16 weeks to establish a COPD model. Then, the rats in the CEG underwent aerobic exercise training for 9 weeks. Following confirmation that exercise effectively improved the diaphragm function, a gas chromatography tandem time-of-flight mass spectrometry analysis system was used to detect the differential metabolites and associated pathways in the diaphragm muscles of the different groups. Following exercise intervention, the pulmonary function and diaphragm contractility of the CEG rats were significantly improved compared with those of the CMG rats. A total of 36 different metabolites were identified in the comparison between the CMG and the CG. Pathway enrichment analysis indicated that these different metabolites were involved in 17 pathways. A total of 29 different metabolites were identified in the comparison between the CMG and the CEG, which are involved in 14 pathways. Candidate biomarkers were selected, and the pathways analysis of these metabolites demonstrated that 2 types of metabolic pathways, the nicotinic acid and nicotinamide metabolism and arginine and proline metabolism pathways, were associated with exercise-induced pulmonary rehabilitation.

Molecular Systems Biology of Neurodevelopmental Disorders, Rett Syndrome as an Archetype

Neurodevelopmental disorders represent a challenging biological and medical problem due to their genetic and phenotypic complexity. In many cases, we lack the comprehensive understanding of disease mechanisms necessary for targeted therapeutic development. One key component that could improve both mechanistic understanding and clinical trial design is reliable molecular biomarkers. Presently, no objective biological markers exist to evaluate most neurodevelopmental disorders. Here, we discuss how systems biology and "omic" approaches can address the mechanistic and biomarker limitations in these afflictions. We present heuristic principles for testing the potential of systems biology to identify mechanisms and biomarkers of disease in the example of Rett syndrome, a neurodevelopmental disorder caused by a well-defined monogenic defect in methyl-CpG-binding protein 2 (MECP2). We propose that such an approach can not only aid in monitoring clinical disease severity but also provide a measure of target engagement in clinical trials. By deepening our understanding of the "big picture" of systems biology, this approach could even help generate hypotheses for drug development programs, hopefully resulting in new treatments for these devastating conditions.

Meta-analysis for genome-wide association studies using case-control design: application and practice

This review aimed to arrange the process of a systematic review of genome-wide association studies in order to practice and apply a genome-wide meta-analysis (GWMA). The process has a series of five steps: searching and selection, extraction of related information, evaluation of validity, meta-analysis by type of genetic model, and evaluation of heterogeneity. In contrast to intervention meta-analyses, GWMA has to evaluate the Hardy-Weinberg equilibrium (HWE) in the third step and conduct meta-analyses by five potential genetic models, including dominant, recessive, homozygote contrast, heterozygote contrast, and allelic contrast in the fourth step. The 'genhwcci' and 'metan' commands of STATA software evaluate the HWE and calculate a summary effect size, respectively. A meta-regression using the 'metareg' command of STATA should be conducted to evaluate related factors of heterogeneities.

Metabolomics method to comprehensively analyze amino acids in different domains

Amino acids play essential roles in both metabolism and the proteome. Many studies have profiled free amino acids (FAAs) or proteins; however, few have connected the measurement of FAA with individual amino acids in the proteome. In this study, we developed a metabolomics method to comprehensively analyze amino acids in different domains, using two examples of different sample types and disease models. We first examined the responses of FAAs and insoluble-proteome amino acids (IPAAs) to the Myc oncogene in Tet21N human neuroblastoma cells. The metabolic and proteomic amino acid profiles were quite different, even under the same Myc condition, and their combination provided a better understanding of the biological status. In addition, amino acids were measured in 3 domains (FAAs, free and soluble-proteome amino acids (FSPAAs), and IPAAs) to study changes in serum amino acid profiles related to colon cancer. A penalized logistic regression model based on the amino acids from the three domains had better sensitivity and specificity than that from each individual domain. To the best of our knowledge, this is the first study to perform a combined analysis of amino acids in different domains, and indicates the useful biological information available from a metabolomics analysis of the protein pellet. This study lays the foundation for further quantitative tracking of the distribution of amino acids in different domains, with opportunities for better diagnosis and mechanistic studies of various diseases.

Application of metabolomics in drug resistant breast cancer research

The metabolic profiles of breast cancer cells are different from normal mammary epithelial cells. Breast cancer cells that gain resistance to therapeutic interventions can reprogram their endogenous metabolism in order to adapt and proliferate despite high oxidative stress and hypoxic conditions. Drug resistance in breast cancer, regardless of subgroups, is a major clinical setback. Although recent advances in genomics and proteomics research has given us a glimpse into the heterogeneity that exists even within subgroups, the ability to precisely predict a tumor’s response to therapy remains elusive. Metabolomics as a quantitative, high through put technology offers promise towards devising new strategies to establish predictive, diagnostic and prognostic markers of breast cancer. Along with other “omics” technologies that include genomics, transcriptomics, and proteomics, metabolomics fits into the puzzle of a comprehensive systems biology approach to understand drug resistance in breast cancer. In this review, we highlight the challenges facing successful therapeutic treatment of breast cancer and the innovative approaches that metabolomics offers to better understand drug resistance in cancer.

Choline kinase inhibition in rheumatoid arthritis

OBJECTIVES

Little is known about targeting the metabolome in non-cancer conditions. Choline kinase (ChoKα), an essential enzyme for phosphatidylcholine biosynthesis, is required for cell proliferation and has been implicated in cancer invasiveness. Aggressive behaviour of fibroblast-like synoviocytes (FLS) in rheumatoid arthritis (RA) led us to evaluate whether this metabolic pathway could play a role in RA FLS function and joint damage.

METHODS

Choline metabolic profile of FLS cells was determined by (1)H magnetic resonance spectroscopy ((1)HMRS) under conditions of ChoKα inhibition. FLS function was evaluated using the ChoKα inhibitor MN58b (IC₅₀=4.2 μM). For arthritis experiments, mice were injected with K/BxN sera. MN58b (3 mg/kg) was injected daily intraperitoneal beginning on day 0 or day 4 after serum administration.

RESULTS

The enzyme is expressed in synovial tissue and in cultured RA FLS. Tumour necrosis factor (TNF) and platelet-derived growth factor (PDGF) stimulation increased ChoKα expression and levels of phosphocholine in FLS measured by Western Blot (WB) and metabolomic studies of choline-containing compounds in cultured RA FLS extracts respectively, suggesting activation of this pathway in RA synovial environment. A ChoKα inhibitor also suppressed the behaviour of cultured FLS, including cell migration and resistance to apoptosis, which might contribute to cartilage destruction in RA. In a passive K/BxN arthritis model, pharmacologic ChoKα inhibition significantly decreased arthritis in pretreatment protocols as well as in established disease.

CONCLUSIONS

These data suggest that ChoKα inhibition could be an effective strategy in inflammatory arthritis. It also suggests that targeting the metabolome can be a new treatment strategy in non-cancer conditions.

Metabolomics in paediatric oncology: a potential still to be exploited

Oncology is a branch of medicine in rapid evolution in the attempt to find innovative methods for early diagnosis and a better understanding of tumoral processes leading to the development of new therapies. Metabolomics is the emerging discipline among the "omics" sciences which makes it possible to further expand our knowledge concerning cancer biology. Different studies have revealed the potential role of metabolomics in gaining an understanding of pathophysiological processes in cancer, improving tumor staging, characterizing tumors and searching for biomarkers predictive of therapeutic responses. However, to date there are few works aimed at gaining deeper insights into infantile oncology through metabolomics.

Chemo- and bioinformatics resources for in silico drug discovery from medicinal plants beyond their traditional use: a critical review

An overview of databases andin silicotools for discovery of the hidden therapeutic potential of medicinal plants.

Nitrogen biobank for cardiovascular research

Biobanks play a crucial role in "-Omics" research providing well-annotated samples to study major diseases, their pathways and mechanisms. Accordingly, there are major efforts worldwide to professionalize biobanks in order to provide high quality preservation and storage of biological samples with potentially greater scientific impact. Biobanks are an important resource to elucidate relevant disease mechanisms as well as to improve the diagnosis, prognosis, and treatment of both pediatric and adult cardiovascular disease. High-quality biological sample collections housed in specialized bio-repositories are needed to discover new genetic factors and molecular mechanisms of congenital heart disease and inherited cardiomyopathies in order to prevent the potential risk of having a fatal cardiac condition as well as to facilitate rational drug design around molecular diseases (personalized medicine). Biological samples are also required to improve the understanding the environmental mechanisms of heart disease (environmental cardiology). The goal of this paper is to focus on preanalytical issues (informed consent, sample type, time of collection, temperature and processing procedure) related to collection of biological samples for research purposes. In addition, the paper provides an overview of the efforts made recently by our Institute in designing and implementing a high-security liquid nitrogen storage system (-196°C). We described the implementations of reliable preservation technologies and appropriate quality control (the right temperature, the right environment, fully traceable with all possible back-up systems) in order to ensure maximum security for personnel as well as the quality and suitability of the stored samples.

Applications of metabolomics in cancer research

The first discovery of metabolic changes in cancer occurred almost a century ago. While the genetic underpinnings of cancer have dominated its study since then, altered metabolism has recently been acknowledged as a key hallmark of cancer and metabolism-focused research has received renewed attention. The emerging field of metabolomics – which attempts to profile all metabolites within a cell or biological system – is now being used to analyze cancer metabolism on a system-wide scale, painting a broad picture of the altered pathways and their interactions with each other. While a large fraction of cancer metabolomics research is focused on finding diagnostic biomarkers, metabolomics is also being used to obtain more fundamental mechanistic insight into cancer and carcinogenesis. Applications of metabolomics are also emerging in areas such as tumor staging and assessment of treatment efficacy. This review summarizes contributions that metabolomics has made in cancer research and presents the current challenges and potential future directions within the field.

Translational physiology: from molecules to public health

The term 'translational research' was coined 20 years ago and has become a guiding influence in biomedical research. It refers to a process by which the findings of basic research are extended to the clinical research setting (bench to bedside) and then to clinical practice and eventually health policy (bedside to community). It is a dynamic, multidisciplinary research approach. The concept of translational physiology applies the translational research model to the physiological sciences. It differs from the traditional areas of integrative and clinical physiology by its broad investigative scope of basic research to community health. Translational physiology offers exciting opportunities, but presently is under-developed and -utilized. A key challenge will be to expand physiological research by extending investigations to communities of patients and healthy (or at risk) individuals. This will allow bidirectional physiological investigation throughout the translational continuum: basic research observations can be studied up to the population level, and mechanisms can be assessed by 'reverse translation' in clinical research settings and preclinical models based on initial observations made in populations. Examples of translational physiology questions, experimental approaches, roadblocks and strategies for promotion are discussed. Translational physiology provides a novel framework for physiology programs and an investigational platform for physiologists to study function from molecular events to public health. It holds promise for enhancing the completeness and societal impact of our work, while further solidifying the critical role of physiology in the biomedical research enterprise.

Protein-Protein Interactions: Gene Acronym Redundancies and Current Limitations Precluding Automated Data Integration

Understanding protein interaction networks and their dynamic changes is a major challenge in modern biology. Currently, several experimental and in silico approaches allow the screening of protein interactors in a large-scale manner. Therefore, the bulk of information on protein interactions deposited in databases and peer-reviewed published literature is constantly growing. Multiple databases interfaced from user-friendly web tools recently emerged to facilitate the task of protein interaction data retrieval and data integration. Nevertheless, as we evidence in this report, despite the current efforts towards data integration, the quality of the information on protein interactions retrieved by in silico approaches is frequently incomplete and may even list false interactions. Here we point to some obstacles precluding confident data integration, with special emphasis on protein interactions, which include gene acronym redundancies and protein synonyms. Three human proteins (choline kinase, PPIase and uromodulin) and three different web-based data search engines focused on protein interaction data retrieval (PSICQUIC, DASMI and BIPS) were used to explain the potential occurrence of undesired errors that should be considered by researchers in the field. We demonstrate that, despite the recent initiatives towards data standardization, manual curation of protein interaction networks based on literature searches are still required to remove potential false positives. A three-step workflow consisting of: (i) data retrieval from multiple databases, (ii) peer-reviewed literature searches, and (iii) data curation and integration, is proposed as the best strategy to gather updated information on protein interactions. Finally, this strategy was applied to compile bona fide information on human DREAM protein interactome, which constitutes liable training datasets that can be used to improve computational predictions.

The clinical relevance of cancer cell lines

Although advances in genomics during the last decade have opened new avenues for translational research and allowed the direct evaluation of clinical samples, there is still a need for reliable preclinical models to test therapeutic strategies. Human cancer-derived cell lines are the most widely used models to study the biology of cancer and to test hypotheses to improve the efficacy of cancer treatment. Since the development of the first cancer cell line, the clinical relevance of these models has been continuously questioned. Based upon recent studies that have fueled the debate, we review the major events in the development of the in vitro models and the emergence of new technologies that have revealed important issues and limitations concerning human cancer cell lines as models. All cancer cell lines do not have equal value as tumor models. Some have been successful, whereas others have failed. However, the success stories should not obscure the growing body of data that motivates us to develop new in vitro preclinical models that would substantially increase the success rate of new in vitro-assessed cancer treatments.

"Drivers" of translational cancer epidemiology in the 21st century: needs and opportunities

Cancer epidemiology is at the cusp of a paradigm shift--propelled by an urgent need to accelerate the pace of translating scientific discoveries into health care and population health benefits. As part of a strategic planning process for cancer epidemiologic research, the Epidemiology and Genomics Research Program (EGRP) at the National Cancer Institute (NCI) is leading a "longitudinal" meeting with members of the research community to engage in an on-going dialogue to help shape and invigorate the field. Here, we review a translational framework influenced by "drivers" that we believe have begun guiding cancer epidemiology toward translation in the past few years and are most likely to drive the field further in the next decade. The drivers include: (i) collaboration and team science, (ii) technology, (iii) multilevel analyses and interventions, and (iv) knowledge integration from basic, clinical, and population sciences. Using the global prevention of cervical cancer as an example of a public health endeavor to anchor the conversation, we discuss how these drivers can guide epidemiology from discovery to population health impact, along the translational research continuum.

Systems biology of lung development and regeneration: current knowledge and recommendations for future research

The lung begins as a simple outpouching of the foregut and develops by stages into a highly complex organ, the proper function of which is essential to life for terrestrial mammals. Interruption of normal lung development can result in death or chronic disease. Conversely, repair after lung injury, as well as many acquired diseases, involves recapitulation, often aberrant, of developmental pathways. The principal paradigms in lung development are branching morphogenesis and alveolar septation, but others, such as vasculogenesis, are critical. These are partially understood at the level of cellular differentiation and molecular signaling, but a true systems biology analysis of lung development and lung repair/regeneration, including bioinformatics analysis and integration of data from unbiased and complementary '-omics' level studies, is still lacking. The past decade has seen increasing numbers of genomic, proteomic, metabolomics, and epigenomic studies of lung development and lung remodeling. In many cases, these studies have confirmed the importance of pathways uncovered painstakingly through single-molecule approaches, but they have also uncovered novel and unexpected pathways and new paradigms such as noncoding RNA. Future studies will need to combine data from multiple repositories and apply novel mathematical and computational models in order to establish a systems-level understanding of this remarkable organ.

Pathway-directed weighted testing procedures for the integrative analysis of gene expression and metabolomic data

We explore the utility of p-value weighting for enhancing the power to detect differential metabolites in a two-sample setting. Related gene expression information is used to assign an a priori importance level to each metabolite being tested. We map the gene expression to a metabolite through pathways and then gene expression information is summarized per-pathway using gene set enrichment tests. Through simulation we explore four styles of enrichment tests and four weight functions to convert the gene information into a meaningful p-value weight. We implement the p-value weighting on a prostate cancer metabolomic dataset. Gene expression on matched samples is used to construct the weights. Under certain regulatory conditions, the use of weighted p-values does not inflate the type I error above what we see for the un-weighted tests except in high correlation situations. The power to detect differential metabolites is notably increased in situations with disjoint pathways and shows moderate improvement, relative to the proportion of enriched pathways, when pathway membership overlaps.