Transforming omics data into context: Bioinformatics on genomics and proteomics raw data

Emerging Diagnostic and Therapeutic Potentials of Human Hair Proteomics

The use of noninvasive human substrates to interrogate pathophysiological conditions has become essential in the post- Human Genome Project era. Due to its high turnover rate, and its long term capability to incorporate exogenous and endogenous substances from the circulation, hair testing is emerging as a key player in monitoring long term drug compliance, chronic alcohol abuse, forensic toxicology, and biomarker discovery, among other things. Novel high-throughput 'omics based approaches like proteomics have been underutilized globally in comprehending human hair morphology and its evolving use as a diagnostic testing substrate in the era of precision medicine. There is paucity of scientific evidence that evaluates the difference in drug incorporation into hair based on lipid content, and very few studies have addressed hair growth rates, hair forms, and the biological consequences of hair grooming or bleaching. It is apparent that protein-based identification using the human hair proteome would play a major role in understanding these parameters akin to DNA single nucleotide polymorphism profiling, up to single amino acid polymorphism resolution. Hence, this work seeks to identify and discuss the progress made thus far in the field of molecular hair testing using proteomic approaches, and identify ways in which proteomics would improve the field of hair research, considering that the human hair is mostly composed of proteins. Gaps in hair proteomics research are identified and the potential of hair proteomics in establishing a historic medical repository of normal and disease-specific proteome is also discussed.

Omics-based molecular techniques in oral pathology centred cancer: prospect and challenges in Africa

BACKGROUND

The completion of the human genome project and the accomplished milestones in the human proteome project; as well as the progress made so far in computational bioinformatics and "big data" processing have contributed immensely to individualized/personalized medicine in the developed world.

MAIN BODY

At the dawn of precision medicine, various omics-based therapies and bioengineering can now be applied accurately for the diagnosis, prognosis, treatment, and risk stratification of cancer in a manner that was hitherto not thought possible. The widespread introduction of genomics and other omics-based approaches into the postgraduate training curriculum of diverse medical and dental specialties, including pathology has improved the proficiency of practitioners in the use of novel molecular signatures in patient management. In addition, intricate details about disease disparity among different human populations are beginning to emerge. This would facilitate the use of tailor-made novel theranostic methods based on emerging molecular evidences.

CONCLUSION

In this review, we examined the challenges and prospects of using currently available omics-based technologies vis-à-vis oral pathology as well as prompt cancer diagnosis and treatment in a resource limited setting.

Vascular endothelial growth factor A as predictive marker for mTOR inhibition in relapsing high-grade serous ovarian cancer

Background

Development of resistance against first line drug therapy including cisplatin and paclitaxel in high-grade serous ovarian cancer (HGSOC) presents a major challenge. Identifying drug candidates breaking resistance, ideally combined with predictive biomarkers allowing precision use are needed for prolonging progression free survival of ovarian cancer patients.

Modeling of molecular processes driving drug resistance in tumor tissue further combined with mechanism of action of drugs provides a strategy for identification of candidate drugs and associated predictive biomarkers.

Results

Consolidation of transcriptomics profiles and biomedical literature mining results provides 1242 proteins linked with ovarian cancer drug resistance. Integrating this set on a protein interaction network followed by graph segmentation results in a molecular process model representation of drug resistant HGSOC embedding 409 proteins in 24 molecular processes. Utilizing independent transcriptomics profiles with follow-up data on progression free survival allows deriving molecular biomarker-based classifiers for predicting recurrence under first line therapy. Biomarkers of specific relevance are identified in a molecular process encapsulating TGF-beta, mTOR, Jak-STAT and Neurotrophin signaling. Mechanism of action molecular model representations of cisplatin and paclitaxel embed the very same signaling components, and specifically proteins afflicted with the activation status of the mTOR pathway become evident, including VEGFA. Analyzing mechanism of action interference of the mTOR inhibitor sirolimus shows specific impact on the drug resistance signature imposed by cisplatin and paclitaxel, further holding evidence for a synthetic lethal interaction to paclitaxel mechanism of action involving cyclin D1.

Conclusions

Stratifying drug resistant high grade serous ovarian cancer via VEGFA, and specifically treating with mTOR inhibitors in case of activation of the pathway may allow adding precision for overcoming resistance to first line therapy.

Interleukin-1 receptor-mediated inflammation impairs the heat shock response of human mesothelial cells

Bioincompatibility of peritoneal dialysis fluids (PDF) limits their use in renal replacement therapy. PDF exposure harms mesothelial cells but induces heat shock proteins (HSP), which are essential for repair and cytoprotection. We searched for cellular pathways that impair the heat shock response in mesothelial cells after PDF-exposure. In a dose-response experiment, increasing PDF-exposure times resulted in rapidly increasing mesothelial cell damage but decreasing HSP expression, confirming impaired heat shock response. Using proteomics and bioinformatics, simultaneously activated apoptosis-related and inflammation-related pathways were identified as candidate mechanisms. Testing the role of sterile inflammation, addition of necrotic cell material to mesothelial cells increased, whereas addition of the interleukin-1 receptor (IL-1R) antagonist anakinra to PDF decreased release of inflammatory cytokines. Addition of anakinra during PDF exposure resulted in cytoprotection and increased chaperone expression. Thus, activation of the IL-1R plays a pivotal role in impairment of the heat shock response of mesothelial cells to PDF. Danger signals from injured cells lead to an elevated level of cytokine release associated with sterile inflammation, which reduces expression of HSP and other cytoprotective chaperones and exacerbates PDF damage. Blocking the IL-1R pathway might be useful in limiting damage during peritoneal dialysis.

Comparing geographical genetic differentiation between candidate and noncandidate loci for adaptation strengthens support for parallel ecological divergence in the marine snail Littorina saxatilis

The Galician sympatric ecotypes of Littorina saxatilis have been proposed as a model system for studying parallel ecological speciation. Such a model system makes a clear prediction: candidate loci (for divergent adaptation) should present a higher level of geographical differentiation than noncandidate (neutral) loci. We used 2356 amplified fragment length polymorphisms (AFLPs) and four microsatellite loci to identify candidate loci for ecological adaptation using the F(ST) outlier method. Three per cent of the studied AFLP loci were identified as candidate loci associated with adaptation, after multitest adjustments, thus contributing to ecotype differentiation (candidate loci were not detected within ecotypes). Candidate and noncandidate loci were analysed separately at four different F(ST) partitions: differences between ecotypes (overall and local), differences between localities and micro-geographical differences within ecotypes. The magnitude of F(ST) differed between candidate and noncandidate loci for all partitions except in the case of micro-geographical differentiation within ecotypes, and the microsatellites (putatively neutral) showed an identical pattern to noncandidate loci. Thus, variation in candidate loci is determined partially independent by divergent natural selection (in addition to stochastic forces) at each locality, while noncandidate loci are exclusively driven by stochastic forces. These results support the evolutionary history described for these particular populations, considered to be a clear example of incomplete sympatric ecological speciation.

Integrative analysis of -omics data and histologic scoring in renal disease and transplantation: renal histogenomics

The histologic scoring of renal biopsies is still the gold standard for renal disease classification. The Banff classification scheme and the chronic allograft damage index are histopathologic scoring schemes widely used in renal transplantation. The determination of genome-wide gene expression profiles in human renal biopsies has the potential to serve as independent validation data sets and also provide a more precise evaluation of the functional status behind the visible morphologic alterations. It is expected that results from high-throughput-omics experiments will lead to improved classification schemes in the near future as also discussed at recent Banff meetings. In this review we give an overview on-omics studies, focusing on the association of molecular changes on the transcript as well as on the protein level and morphologic scoring schemes in renal disease and transplantation.

Computational analysis workflows for Omics data interpretation

Progress in experimental procedures has led to rapid availability of Omics profiles. Various open-access as well as commercial tools have been developed for storage, analysis, and interpretation of transcriptomics, proteomics, and metabolomics data. Generally, major analysis steps include data storage, retrieval, preprocessing, and normalization, followed by identification of differentially expressed features, functional annotation on the level of biological processes and molecular pathways, as well as interpretation of gene lists in the context of protein-protein interaction networks. In this chapter, we discuss a sequential transcriptomics data analysis workflow utilizing open-source tools, specifically exemplified on a gene expression dataset on familial hypercholesterolemia.

Proteome databases and other online resources for chloroplast research in Arabidopsis

Proteomics aimed at addressing sub cellular fractions, such as chloroplasts, are a complex challenge. In the past few years, several studies in different laboratories have identified and, more recently, quantified, thousands of proteins within whole chloroplasts or chloroplast fractions. A considerable number of these studies are available for querying, using online resources, such as databases containing the proteins identified, encoding genes, acquired spectra, and phosphopeptides. The main purpose of this review is to identity and highlight useful features of these online resourses, mainly focused in proteomics databases related to chloroplast research in Arabidopsis thaliana. Several web sites were consulted. Among them, 11 were selected and discussed herein. The databases were classified into Plastid Databases, General Organelle Proteome Databases, and General Arabidopsis Proteome Databases. Special care was taken to present information regarding protein identification, protein quantification, and data integration. A selected list of online resources is presented in two tables. The databases analyzed are a useful source of information for researchers in the plastid organelle and plant proteomics fields.

In silico models of cancer

Cancer is a complex disease that involves multiple types of biological interactions across diverse physical, temporal, and biological scales. This complexity presents substantial challenges for the characterization of cancer biology, and motivates the study of cancer in the context of molecular, cellular, and physiological systems. Computational models of cancer are being developed to aid both biological discovery and clinical medicine. The development of these in silico models is facilitated by rapidly advancing experimental and analytical tools that generate information-rich, high-throughput biological data. Statistical models of cancer at the genomic, transcriptomic, and pathway levels have proven effective in developing diagnostic and prognostic molecular signatures, as well as in identifying perturbed pathways. Statistically inferred network models can prove useful in settings where data overfitting can be avoided, and provide an important means for biological discovery. Mechanistically based signaling and metabolic models that apply a priori knowledge of biochemical processes derived from experiments can also be reconstructed where data are available, and can provide insight and predictive ability regarding the behavior of these systems. At longer length scales, continuum and agent-based models of the tumor microenvironment and other tissue-level interactions enable modeling of cancer cell populations and tumor progression. Even though cancer has been among the most-studied human diseases using systems approaches, significant challenges remain before the enormous potential of in silico cancer biology can be fully realized.

Biomarkers in renal transplantation ischemia reperfusion injury

Ischemia reperfusion injury (IRI) is a choreographed process leading to delayed graft function (DGF) and reduced long-term patency of the transplanted organ. Early identification of recipients of grafts at risk would allow modification of the posttransplant management, and thereby potentially improve short- and long-term outcomes. The recently emerged "omics" technologies together with bioinformatics workup have allowed the integration and analysis of IRI-associated molecular profiles in the context of DGF. Such a systems biological approach promises qualitative information about interdependencies of complex processes such as IRI regulation, rather than offering descriptive tables of differentially regulated features on a transcriptome, proteome, or metabolome level leaking the functional, biological framework. In deceased-donor kidney transplantation as the primary causative factor resulting in IRI and DGF, a distinct signature and choreography of molecular events in the graft before harvesting seems to be associated with subsequent DGF. A systems biological assessment of these molecular changes suggests that processes along inflammation are of pivotal importance for the early stage of IRI. The causal proof of this association has been tested by a double-blinded, randomized, controlled trial of steroid or placebo infusion into deceased donors before the organs were harvested. Thorough systems biological analysis revealed a panel of biomarkers with excellent discrimination. In summary, integrated analysis of omics data has brought forward biomarker candidates and candidate panels that promise early assessment of IRI. However, the clinical utility of these markers still needs to be established in prospective trials in independent patient populations.

In silico models of cancer

Cancer is a complex disease that involves multiple types of biological interactions across diverse physical, temporal, and biological scales. This complexity presents substantial challenges for the characterization of cancer biology, and motivates the study of cancer in the context of molecular, cellular, and physiological systems. Computational models of cancer are being developed to aid both biological discovery and clinical medicine. The development of these in silico models is facilitated by rapidly advancing experimental and analytical tools that generate information-rich, high-throughput biological data. Statistical models of cancer at the genomic, transcriptomic, and pathway levels have proven effective in developing diagnostic and prognostic molecular signatures, as well as in identifying perturbed pathways. Statistically inferred network models can prove useful in settings where data overfitting can be avoided, and provide an important means for biological discovery. Mechanistically based signaling and metabolic models that apply a priori knowledge of biochemical processes derived from experiments can also be reconstructed where data are available, and can provide insight and predictive ability regarding the behavior of these systems. At longer length scales, continuum and agent-based models of the tumor microenvironment and other tissue-level interactions enable modeling of cancer cell populations and tumor progression. Even though cancer has been among the most-studied human diseases using systems approaches, significant challenges remain before the enormous potential of in silico cancer biology can be fully realized.

Stress responses and conditioning effects in mesothelial cells exposed to peritoneal dialysis fluid

Renal replacement therapy by peritoneal dialysis is frequently complicated by technical failure. Peritoneal dialysis fluids (PDF) cause injury to the peritoneal mesothelial cell layer due to their cytotoxicity. As only isolated elements of the involved cellular processes have been studied before, we aimed at a global assessment of the mesothelial stress response to PDF. Following single or repeated exposure to PDF or control medium, proteomics and bioinformatics techniques were combined to study effects in mesothelial cells (MeT-5A). Protein expression was assessed by two-dimensional gel electrophoresis, and significantly altered spots were identified by MALDI-TOF MS and MS2 techniques. The lists of experimentally derived candidate proteins were expanded by a next neighbor approach and analyzed for significantly enriched biological processes. To address the problem of an unknown portion of false positive spots in 2DGE, only proteins showing significant p-values on both levels were further interpreted. Single PDF exposure resulted in reduction of biological processes in favor of reparative responses, including protein metabolism, modification and folding, with chaperones as a major subgroup. The observed biological processes triggered by this acute PDF exposure mainly contained functionally interwoven multitasking proteins contributing as well to cytoskeletal reorganization and defense mechanisms. Repeated PDF exposure resulted in attenuated protein regulation, reflecting inhibition of stress responses by high levels of preinduced chaperones. The identified proteins were less attributable to acute cellular injury but rather to specialized functions with a reduced number of involved multitasking proteins. This finding agrees well with the concept of conditioning effects and cytoprotection. In conclusion, this study describes the reprogrammed proteome of mesothelial cells during recovery from PDF exposure and adaption to repetitive stress. A broad stress response with a number of highly overlapping processes and multitasking proteins shifts toward a more specific response of only few less overlapping processes.

A new multitest correction (SGoF) that increases its statistical power when increasing the number of tests

BACKGROUND

The detection of true significant cases under multiple testing is becoming a fundamental issue when analyzing high-dimensional biological data. Unfortunately, known multitest adjustments reduce their statistical power as the number of tests increase. We propose a new multitest adjustment, based on a sequential goodness of fit metatest (SGoF), which increases its statistical power with the number of tests. The method is compared with Bonferroni and FDR-based alternatives by simulating a multitest context via two different kinds of tests: 1) one-sample t-test, and 2) homogeneity G-test.

RESULTS

It is shown that SGoF behaves especially well with small sample sizes when 1) the alternative hypothesis is weakly to moderately deviated from the null model, 2) there are widespread effects through the family of tests, and 3) the number of tests is large.

CONCLUSION

Therefore, SGoF should become an important tool for multitest adjustment when working with high-dimensional biological data.

Assessment of circulating Dickkopf-1 with a new two-site immunoassay in healthy subjects and women with breast cancer and bone metastases

The aim of our study was to investigate the sex- and age-related changes of serum Dickkopf-1 (Dkk-1), a soluble inhibitor of the Wnt signaling pathway, in healthy individuals and in patients with breast cancer (BC) and bone metastases (BM) using a new ELISA. Association of serum Dkk-1 with markers of bone turnover was also investigated. Serum Dkk-1 measurements were performed using a commercial sandwich ELISA in 150 healthy men, 175 healthy pre- and postmenopausal women (20-65 years), 22 women with BC and BM (mean age 63 years), and 16 women with BC and metastases at sites other than bone (mean age 53 years). Intra- and interassay coefficients of variation were below 7% and 12%, respectively. The detection limit was determined to be 0.018 microg/L. In healthy women and men, Dkk-1 did not change with age. Serum Dkk-1 modestly correlated with serum bone alkaline phosphatase (r = 0.19, P = 0.013) and serum C-terminal cross-linking telopeptide of type I collagen (r = 0.19, P = 0.014) in women but not in men. Dkk-1 levels were higher in women with BC and BM (5.57 +/- 5.50 microg/L) than in healthy age-matched controls (3.47 +/- 1.47 microg/L, P < 0.0001) and women with metastases at sites other than bone (3.57 +/- 1.66 microg/L, P = 0.0003). In conclusion, serum Dkk-1 is stable with age in healthy women and men and increases in patients with BC and BM. Measurements of circulating Dkk-1 with this new ELISA may be useful for the clinical investigation of patients with malignant bone diseases.

Biomarker candidates for cardiovascular disease and bone metabolism disorders in chronic kidney disease: a systems biology perspective

Patients with chronic kidney disease (CKD) show a panel of partially de-regulated serum markers indicative for bone metabolism disorders and cardiovascular diseases (CVDs). This review provides an overview of currently reported biomarker candidates at the interface of kidney disease, bone metabolism disorders and CVDs, and gives details on their functional interplay on the level of protein–protein interaction data. We retrieved 13 publications from 1999 to 2006 reporting 31 genes associated with CVDs, and 46 genes associated with bone metabolism disorders in patients with CKD. We identified these genes to be functionally involved in signal transduction processes, cell communication, immunity and defence, as well as skeletal development. On the basis of the given set of 77 genes further 276 interacting proteins were identified using reference data on known protein interactions. Their functional interplay was estimated by linking properties reflected by gene expression data characterizing CKD, gene ontology terms as provided by the gene ontology consortium and transcription factor binding site profiles. Highly connected sub-networks of proteins associated with CKD, CVDs or bone metabolism disorders were detected involving proteins like collagens (COL1A1, COL1A2), fibronectin, transforming growth factor-β₁, or components of fibrinogen (FG-α, FG-β, FG-γ). A systems biology approach provides a methodological framework for linking singular biomarker candidates towards deriving functional dependencies among clinically interlinked diseases.

Linking the ovarian cancer transcriptome and immunome

Background

Autoantigens have been reported in a variety of tumors, providing insight into the interplay between malignancies and the immune response, and also giving rise to novel diagnostic and therapeutic concepts. Why certain tumor-associated proteins induce an immune response remains largely elusive.

Results

This paper analyzes the proposed link between increased abundance of a protein in cancerous tissue and the increased potential of the protein for induction of a humoral immune response, using ovarian cancer as an example. Public domain data sources on differential gene expression and on autoantigens associated with this malignancy were extracted and compared, using bioinformatics analysis, on the levels of individual genes and proteins, transcriptional coregulation, joint functional pathways, and shared protein-protein interaction networks. Finally, a selected list of ovarian cancer-associated, differentially regulated proteins was tested experimentally for reactivity with antibodies prevalent in sera of ovarian cancer patients.

Genes reported as showing differential expression in ovarian cancer exhibited only minor overlap with the public domain list of ovarian cancer autoantigens. However, experimental screening for antibodies directed against antigenic determinants from ovarian cancer-associated proteins yielded clear reactions with sera.

Conclusion

A link between tumor protein abundance and the likelihood of induction of a humoral immune response in ovarian cancer appears evident.

Identification of eukaryotic secreted and cell surface proteins using the yeast secretion trap screen

Secreted and cell surface proteins play essential roles in numerous essential biological processes in eukaryotic organisms, but are often more difficult to isolate and identify than proteins that are localized in intracellular compartments. However, several high-throughput 'gene-trap' techniques have been developed to characterize these 'secretomes', including the yeast secretion trap (YST) screen. This method involves fusing cDNA libraries from the tissue or cell type of interest to a yeast (Saccharomyces cerevisiae) invertase reporter gene, transforming the resulting fusion library into an invertase-deficient yeast strain and plating the transformants on a medium containing sucrose as the sole carbon source. A yeast cell with a transgene encoding a secreted or cell surface protein can synthesize a secreted invertase fusion protein that can rescue the mutant, and the plasmid DNA can then be sequenced to identify the gene that encodes it. We describe a recently improved version of this screen, which allows the identification of genes encoding secreted proteins in 1-2 months.

Toward nanoscale genome sequencing

This article reports on the state-of-the-art technologies that sequence DNA using miniaturized devices. The article considers the miniaturization of existing technologies for sequencing DNA and the opportunities for cost reduction that 'on-chip' devices can deliver. The ability to construct nano-scale structures and perform measurements using novel nano-scale effects has provided new opportunities to identify nucleotides directly using physical, and not chemical, methods. The challenges that these technologies need to overcome to provide a US$1000-genome sequencing technology are also presented.

Characterization of protein-interaction networks in tumors

BACKGROUND

Analyzing differential-gene-expression data in the context of protein-interaction networks (PINs) yields information on the functional cellular status. PINs can be formally represented as graphs, and approximating PINs as undirected graphs allows the network properties to be characterized using well-established graph measures. This paper outlines features of PINs derived from 29 studies on differential gene expression in cancer. For each study the number of differentially regulated genes was determined and used as a basis for PIN construction utilizing the Online Predicted Human Interaction Database.

RESULTS

Graph measures calculated for the largest subgraph of a PIN for a given differential-gene-expression data set comprised properties reflecting the size, distribution, biological relevance, density, modularity, and cycles. The values of a distinct set of graph measures, namely Closeness Centrality, Graph Diameter, Index of Aggregation, Assortative Mixing Coefficient, Connectivity, Sum of the Wiener Number, modified Vertex Distance Number, and Eigenvalues differed clearly between PINs derived on the basis of differential gene expression data sets characterizing malignant tissue and PINs derived on the basis of randomly selected protein lists.

CONCLUSION

Cancer PINs representing differentially regulated genes are larger than those of randomly selected protein lists, indicating functional dependencies among protein lists that can be identified on the basis of transcriptomics experiments. However, the prevalence of hub proteins was not increased in the presence of cancer. Interpretation of such graphs in the context of robustness may yield novel therapies based on synthetic lethality that are more effective than focusing on single-action drugs for cancer treatment.

Biomarker discovery using high-dimensional lipid analysis

PURPOSE OF REVIEW

High-dimensional lipid analysis technologies (lipidomics) provide researchers with an opportunity to measure lipids on an unprecedented scale. They do not, however, guarantee a fast track to new knowledge. The vast amount of data produced by these platforms presents a major hurdle to assembling valid knowledge and to the discovery of mechanistic biomarkers. This review examines strategies for improving the quality of high-dimensional lipid data and streamlining data analysis to increase the value of lipidomics platforms to research and commercial applications.

RECENT FINDINGS

Recent articles focus on careful study design and data analysis protocols. Authors offer detailed descriptions of study populations, analytical methods and data analysis, and highlight the use of practical data preprocessing and the incorporation of biological knowledge into data analysis.

SUMMARY

The field is moving towards more methodical and structured approaches to biomarker identification. Experimental designs focusing on well-defined outcomes have a better chance of producing biologically relevant results. The high-dimensional lipid analysis techniques available are varied, have different strengths and weaknesses, and must be chosen carefully depending on the experimental design and application. Many techniques for data analysis are available, but the most successful are those incorporating existing biological knowledge into the statistical analysis.

Gene expression and biomarkers in renal transplant ischemia reperfusion injury

The incidence of postischemic acute renal allograft failure (ARF) occurs in roughly 25% of cadaveric donor kidney recipients. This high rate remained virtually unchanged over the last decades despite modification in recipient management and modern immunosuppressive strategies. It has recently been shown that among other reasons, the systemic inflammation in the brain death cadaveric organ donor contributes to subsequent ARF in the recipient. This review focuses on the consequences of ischemia and reperfusion on the cellular level and offers potential solutions for the reduction of ARF. Genome-wide gene expression analysis together with sophisticated biostatistical analysis made it possible to identify several candidate gene products and proteins that may act as specific and sensitive biomarker for renal inflammation and ischemia. These markers may be very helpful in the clinical management of patients with a high a priori risk of subsequent ARF such as recipients of marginal donor kidneys. Ongoing clinical trials will evaluate whether immunosuppression of the cadaveric organ donor before organ harvest will have the potential to reduce inflammation in the transplant kidney and subsequently lead to a reduction in the rate of ARF.