Integrative omics for health and disease

Personalized Metabolomics

The human metabolome is the cumulative product of ingested metabolites and those produced by the body and its microbiota. Together these metabolites can dynamically report on the health and disease state of an individual, as well as their response to drug treatments and other external perturbations. Profiling metabolites in human body fluids provides an opportunity to identify biomarkers and stratify patients for personalized treatments but requires the development of high-throughput approaches compatible with large cohort and longitudinal studies. Here we review in detail sample preparation and analytical liquid chromatography-mass spectrometry (LC-MS) methods to measure the broad chemical diversity of metabolites found in human plasma and urine.

Cancer biomarker discovery and translation: proteomics and beyond

INTRODUCTION

Cancer is often diagnosed at late stages when the chance of cure is relatively low and although research initiatives in oncology discover many potential cancer biomarkers, few transition to clinical applications. This review addresses the current landscape of cancer biomarker discovery and translation with a focus on proteomics and beyond. Areas covered: The review examines proteomic and genomic techniques for cancer biomarker detection and outlines advantages and challenges of integrating multiple omics approaches to achieve optimal sensitivity and address tumor heterogeneity. This discussion is based on a systematic literature review and direct participation in translational studies. Expert commentary: Identifying aggressive cancers early on requires improved sensitivity and implementation of biomarkers representative of tumor heterogeneity. During the last decade of genomic and proteomic research, significant advancements have been made in next generation sequencing and mass spectrometry techniques. This in turn has led to a dramatic increase in identification of potential genomic and proteomic cancer biomarkers. However, limited successes have been shown with translation of these discoveries into clinical practice. We believe that the integration of these omics approaches is the most promising molecular tool for comprehensive cancer evaluation, early detection and transition to Precision Medicine in oncology.

An integrative approach for building personalized gene regulatory networks for precision medicine

Only a small fraction of patients respond to the drug prescribed to treat their disease, which means that most are at risk of unnecessary exposure to side effects through ineffective drugs. This inter-individual variation in drug response is driven by differences in gene interactions caused by each patient's genetic background, environmental exposures, and the proportions of specific cell types involved in disease. These gene interactions can now be captured by building gene regulatory networks, by taking advantage of RNA velocity (the time derivative of the gene expression state), the ability to study hundreds of thousands of cells simultaneously, and the falling price of single-cell sequencing. Here, we propose an integrative approach that leverages these recent advances in single-cell data with the sensitivity of bulk data to enable the reconstruction of personalized, cell-type- and context-specific gene regulatory networks. We expect this approach will allow the prioritization of key driver genes for specific diseases and will provide knowledge that opens new avenues towards improved personalized healthcare.

Metabolic Signature of Dietary Iron Overload in a Mouse Model

Iron is an essential co-factor for several metabolic processes, including the Krebs cycle and mitochondrial oxidative phosphorylation. Therefore, maintaining an appropriate iron balance is essential to ensure sufficient energy production and to avoid excessive reactive oxygen species formation. Iron overload impairs mitochondrial fitness; however, little is known about the associated metabolic changes. Here we aimed to characterize the metabolic signature triggered by dietary iron overload over time in a mouse model, where mice received either a standard or a high-iron diet. Metabolic profiling was assessed in blood, plasma and liver tissue. Peripheral blood was collected by means of volumetric absorptive microsampling (VAMS). Extracted blood and tissue metabolites were analyzed by liquid chromatography combined to high resolution mass spectrometry. Upon dietary iron loading we found increased glucose, aspartic acid and 2-/3-hydroxybutyric acid levels but low lactate and malate levels in peripheral blood and plasma, pointing to a re-programming of glucose homeostasis and the Krebs cycle. Further, iron loading resulted in the stimulation of the urea cycle in the liver. In addition, oxidative stress was enhanced in circulation and coincided with increased liver glutathione and systemic cysteine synthesis. Overall, iron supplementation affected several central metabolic circuits over time. Hence, in vivo investigation of metabolic signatures represents a novel and useful tool for getting deeper insights into iron-dependent regulatory circuits and for monitoring of patients with primary and secondary iron overload, and those ones receiving iron supplementation therapy.

Genetic and non-genetic determinants of clinical phenotypes in cardiomyopathy

Cardiomyopathy, a leading cause of death worldwide, is etiologically and phenotypically heterogeneous and is caused by a combination of genetic and non-genetic factors. Major genomic determinants of dilated cardiomyopathy (DCM) are titin truncating mutations and lamin A/C mutations. Patients with these two genotypes show critically different phenotypes, including penetrance, coexistence with a conduction system abnormality, cardiac prognosis, and treatment response. The transcriptomic and epigenomic characteristics of DCM include activation of the DNA damage response, metabolic reprogramming, and dedifferentiation. The proteomic and metabolomic signatures of the DCM heart include a rigorous dependency for free fatty acids, activation of the stress response, and metabolic reprogramming. Proteomic and metabolomic analyses of blood show a distinct immune response and an unexpected link with pathology-specific microbiota in DCM. The direct integration of multi-omics data will not only elucidate inter-omics associations but also enable omics-based patient stratification, which will lead to a deeper understanding of cardiomyopathy and the development of precision medicine in cardiology.

Advances in multiple omics of natural-killer/T cell lymphoma

Natural-killer/T cell lymphoma (NKTCL) represents the most common subtype of extranodal lymphoma with aggressive clinical behavior. Prevalent in Asians and South Americans, the pathogenesis of NKTCL remains to be fully elucidated. Using system biology techniques including genomics, transcriptomics, epigenomics, and metabolomics, novel biomarkers and therapeutic targets have been revealed in NKTCL. Whole-exome sequencing studies identify recurrent somatic gene mutations, involving RNA helicases, tumor suppressors, JAK-STAT pathway molecules, and epigenetic modifiers. Another genome-wide association study reports that single nucleotide polymorphisms mapping to the class II MHC region on chromosome 6 contribute to lymphomagenesis. Alterations of oncogenic signaling pathways janus kinase-signal transducer and activator of transcription (JAK-STAT), nuclear factor-κB (NF-κB), mitogen-activated protein kinase (MAPK), WNT, and NOTCH, as well as epigenetic dysregulation of microRNA and long non-coding RNAs, are also frequently observed in NKTCL. As for metabolomic profiling, abnormal amino acids metabolism plays an important role on disease progression of NKTCL. Of note, through targeting multiple omics aberrations, clinical outcome of NKTCL patients has been significantly improved by asparaginase-based regimens, immune checkpoints inhibitors, and histone deacetylation inhibitors. Future investigations will be emphasized on molecular classification of NKTCL using integrated analysis of system biology, so as to optimize targeted therapeutic strategies of NKTCL in the era of precision medicine.

integRATE: a desirability-based data integration framework for the prioritization of candidate genes across heterogeneous omics and its application to preterm birth

BACKGROUND

The integration of high-quality, genome-wide analyses offers a robust approach to elucidating genetic factors involved in complex human diseases. Even though several methods exist to integrate heterogeneous omics data, most biologists still manually select candidate genes by examining the intersection of lists of candidates stemming from analyses of different types of omics data that have been generated by imposing hard (strict) thresholds on quantitative variables, such as P-values and fold changes, increasing the chance of missing potentially important candidates.

METHODS

To better facilitate the unbiased integration of heterogeneous omics data collected from diverse platforms and samples, we propose a desirability function framework for identifying candidate genes with strong evidence across data types as targets for follow-up functional analysis. Our approach is targeted towards disease systems with sparse, heterogeneous omics data, so we tested it on one such pathology: spontaneous preterm birth (sPTB).

RESULTS

We developed the software integRATE, which uses desirability functions to rank genes both within and across studies, identifying well-supported candidate genes according to the cumulative weight of biological evidence rather than based on imposition of hard thresholds of key variables. Integrating 10 sPTB omics studies identified both genes in pathways previously suspected to be involved in sPTB as well as novel genes never before linked to this syndrome. integRATE is available as an R package on GitHub ( https://github.com/haleyeidem/integRATE ).

CONCLUSIONS

Desirability-based data integration is a solution most applicable in biological research areas where omics data is especially heterogeneous and sparse, allowing for the prioritization of candidate genes that can be used to inform more targeted downstream functional analyses.

The Utilization of Autoantibodies in Approaches to Precision Health

Precision health (PH) applied to autoimmune disease will need paradigm shifts in the use and application of autoantibodies and other biomarkers. For example, autoantibodies combined with other multi-analyte “omic” profiles will form the basis of disease prediction allowing for earlier intervention linked to disease prevention strategies, as well as earlier, effective and personalized interventions for established disease. As medical intervention moves to disease prediction and a model of “intent to PREVENT,” diagnostics will include an early symptom/risk-based, as opposed to a disease-based approach. Newer diagnostic platforms that utilize emerging megatrends such as deep learning and artificial intelligence and close the gaps in autoantibody diagnostics will benefit from paradigm shifts thereby facilitating the PH agenda.

Application of metabolomics part II: Focus on fatty acids and their metabolites in healthy adults

Fatty acids (FAs) play critical roles in health and disease. The detection of FA imbalances through metabolomics can provide an overview of an individual’s health status, particularly as regards chronic inflammatory disorders. In this study, we aimed to establish sensitive reference value ranges for targeted plasma FAs in a well-defined population of healthy adults. Plasma samples were collected from 159 participants admitted as outpatients. A total of 24 FAs were analyzed using gas chromatography-mass spectrometry, and physiological values and 95% reference intervals were calculated using an approximate method of analysis. The differences among the age groups for the relative levels of stearic acid (P=0.005), the omega-6/omega-3 ratio (P=0.027), the arachidonic acid/eicosapentaenoic acid ratio (P<0.001) and the linoleic acid-produced dihomo-gamma-linolenic acid (P=0.046) were statistically significant. The majority of relative FA levels were higher in males than in females. The levels of myristic acid (P=0.0170) and docosahexaenoic acid (P=0.033) were signifi-cantly different between the sexes. The reference values for the FAs examined in this study represent a baseline for further studies examining the reproducibility of this methodology and sensitivities for nutrient deficiency detection and investigating the biochemical background of pathological conditions. The application of these values to clinical practice will allow for the discrimination between health and disease and contribute to early prevention and treatment.

From Single Level Analysis to Multi-Omics Integrative Approaches: A Powerful Strategy towards the Precision Oncology

Integration of multi-omics data from different molecular levels with clinical data, as well as epidemiologic risk factors, represents an accurate and promising methodology to understand the complexity of biological systems of human diseases, including cancer. By the extensive use of novel technologic platforms, a large number of multidimensional data can be derived from analysis of health and disease systems. Comprehensive analysis of multi-omics data in an integrated framework, which includes cumulative effects in the context of biological pathways, is therefore eagerly awaited. This strategy could allow the identification of pathway-addiction of cancer cells that may be amenable to therapeutic intervention. However, translation into clinical settings requires an optimized integration of omics data with clinical vision to fully exploit precision cancer medicine. We will discuss the available technical approach and more recent developments in the specific field.

Integrative workflows for network analysis

Due to genetic heterogeneity across patients, the identification of effective disease signatures and therapeutic targets is challenging. Addressing this challenge, we have previously developed a network-based approach, which integrates heterogeneous sources of biological information to identify disease specific core-regulatory networks. In particular, our workflow uses a multi-objective optimization function to calculate a ranking score for network components (e.g. feedback/feedforward loops) based on network properties, biomedical and high-throughput expression data. High ranked network components are merged to identify the core-regulatory network(s) that is then subjected to dynamical analysis using stimulus–response and in silico perturbation experiments for the identification of disease gene signatures and therapeutic targets. In a case study, we implemented our workflow to identify bladder and breast cancer specific core-regulatory networks underlying epithelial–mesenchymal transition from the E2F1 molecular interaction map.

In this study, we review our workflow and described how it has developed over time to understand the mechanisms underlying disease progression and prediction of signatures for clinical decision making.

Network and Pathway Analysis of Toxicogenomics Data

Toxicogenomics is the study of the molecular effects of chemical, biological and physical agents in biological systems, with the aim of elucidating toxicological mechanisms, building predictive models and improving diagnostics. The vast majority of toxicogenomics data has been generated at the transcriptome level, including RNA-seq and microarrays, and large quantities of drug-treatment data have been made publicly available through databases and repositories. Besides the identification of differentially expressed genes (DEGs) from case-control studies or drug treatment time series studies, bioinformatics methods have emerged that infer gene expression data at the molecular network and pathway level in order to reveal mechanistic information. In this work we describe different resources and tools that have been developed by us and others that relate gene expression measurements with known pathway information such as over-representation and gene set enrichment analyses. Furthermore, we highlight approaches that integrate gene expression data with molecular interaction networks in order to derive network modules related to drug toxicity. We describe the two main parts of the approach, i.e., the construction of a suitable molecular interaction network as well as the conduction of network propagation of the experimental data through the interaction network. In all cases we apply methods and tools to publicly available rat in vivo data on anthracyclines, an important class of anti-cancer drugs that are known to induce severe cardiotoxicity in patients. We report the results and functional implications achieved for four anthracyclines (doxorubicin, epirubicin, idarubicin, and daunorubicin) and compare the information content inherent in the different computational approaches.

Integrative analysis of transcriptomics and clinical data uncovers the tumor-suppressive activity of MITF in prostate cancer

The dysregulation of gene expression is an enabling hallmark of cancer. Computational analysis of transcriptomics data from human cancer specimens, complemented with exhaustive clinical annotation, provides an opportunity to identify core regulators of the tumorigenic process. Here we exploit well-annotated clinical datasets of prostate cancer for the discovery of transcriptional regulators relevant to prostate cancer. Following this rationale, we identify Microphthalmia-associated transcription factor (MITF) as a prostate tumor suppressor among a subset of transcription factors. Importantly, we further interrogate transcriptomics and clinical data to refine MITF perturbation-based empirical assays and unveil Crystallin Alpha B (CRYAB) as an unprecedented direct target of the transcription factor that is, at least in part, responsible for its tumor-suppressive activity in prostate cancer. This evidence was supported by the enhanced prognostic potential of a signature based on the concomitant alteration of MITF and CRYAB in prostate cancer patients. In sum, our study provides proof-of-concept evidence of the potential of the bioinformatics screen of publicly available cancer patient databases as discovery platforms, and demonstrates that the MITF-CRYAB axis controls prostate cancer biology.

Integration of large-scale multi-omic datasets: a protein-centric view

Innovative mass spectrometry-based proteomics has enabled routine measurements of protein abundance, localization, interactions, and modifications, covering unique aspects of gene expression regulation and function. It is now time to move from isolated analyses of these datasets toward true integration of proteomics with other data types to gain insights from the interactions and interdependencies of biomolecules. When combined with genomic or transcriptomic data, proteomics expands genome annotation to identify variant or missing genes. Dynamic proteomic measurements can move analysis from predominantly concentration-based framework to that of synthesis and degradation of proteins. Proteomic data from thousands of cancer patients can foster identification of novel pathogenic mutations via detection of protein sequence changes that lead to dysregulated pathways in various tumors. Such comprehensive efforts can exploit the synergy arising from large and complex datasets to advance virtually every field of biology.

The growing role of precision and personalized medicine for cancer treatment

Cancer is a devastating disease that takes the lives of hundreds of thousands of people every year. Due to disease heterogeneity, standard treatments, such as chemotherapy or radiation, are effective in only a subset of the patient population. Tumors can have different underlying genetic causes and may express different proteins in one patient versus another. This inherent variability of cancer lends itself to the growing field of precision and personalized medicine (PPM). There are many ongoing efforts to acquire PPM data in order to characterize molecular differences between tumors. Some PPM products are already available to link these differences to an effective drug. It is clear that PPM cancer treatments can result in immense patient benefits, and companies and regulatory agencies have begun to recognize this. However, broader changes to the healthcare and insurance systems must be addressed if PPM is to become part of standard cancer care.

Corneocyte proteomics: Applications to skin biology and dermatology

Advances in mass spectrometry-based proteomics now permit analysis of complex cellular structures. Application to epidermis and its appendages (nail plate, hair shaft) has revealed a wealth of information about their protein profiles. The results confirm known site-specific differences in levels of certain keratins and add great depth to our knowledge of site specificity of scores of other proteins, thereby connecting anatomy and pathology. An example is the evident overlap in protein profiles of hair shaft and nail plate, helping rationalize their sharing of certain dystrophic syndromes distinct from epidermis. In addition, interindividual differences in protein level are manifest as would be expected. This approach permits characterization of altered profiles as a result of disease, where the magnitude of perturbation can be quantified and monitored during treatment. Proteomic analysis has also clarified the nature of the isopeptide cross-linked residual insoluble material after vigorous extraction with protein denaturants, nearly intractable to analysis without fragmentation. These structures, including the cross-linked envelope of epidermal corneocytes, are comprised of hundreds of protein constituents, evidence for strengthening the terminal structure complementary to disulphide bonding. Along with other developing technologies, proteomic analysis is anticipated to find use in disease risk stratification, detection, diagnosis and prognosis after the discovery phase and clinical validation.

Biophysical, Biochemical, and Cell Based Approaches Used to Decipher the Role of Carbonic Anhydrases in Cancer and to Evaluate the Potency of Targeted Inhibitors

Carbonic anhydrases (CAs) are thought to be important for regulating pH in the tumor microenvironment. A few of the CA isoforms are upregulated in cancer cells, with only limited expression in normal cells. For these reasons, there is interest in developing inhibitors that target these tumor-associated CA isoforms, with increased efficacy but limited nonspecific cytotoxicity. Here we present some of the biophysical, biochemical, and cell based techniques and approaches that can be used to evaluate the potency of CA targeted inhibitors and decipher the role of CAs in tumorigenesis, cancer progression, and metastatic processes. These techniques include esterase activity assays, stop flow kinetics, and mass inlet mass spectroscopy (MIMS), all of which measure enzymatic activity of purified protein, in the presence or absence of inhibitors. Also discussed is the application of X-ray crystallography and Cryo-EM as well as other structure-based techniques and thermal shift assays to the studies of CA structure and function. Further, large-scale genomic and proteomic analytical methods, as well as cell based techniques like those that measure cell growth, apoptosis, clonogenicity, and cell migration and invasion, are discussed. We conclude by reviewing approaches that test the metastatic potential of CAs and how the aforementioned techniques have contributed to the field of CA cancer research.

Using Omics to Understand and Treat Pulmonary Vascular Disease

Pulmonary arterial hypertension (PAH) is a devastating disease for which there is no cure. Presently this condition is differentiated from other diseases of the pulmonary vasculature by a practitioner's history, physical examination, and clinical studies with clinical markers of disease severity primarily guiding therapeutic choices. New technologies such as next generation DNA sequencing, high throughput RNA sequencing, metabolomics and proteomics have greatly enhanced the amount of data that can be studied efficiently in patients with PAH and other rare diseases. There is emerging data on the use of these “Omics” for pulmonary vascular disease classification and diagnosis and also new work that suggests molecular markers, including Omics, may be used to more efficiently match patients to their own most effective therapies. This review focuses on the state of knowledge on molecular classification and treatment of PAH. Strengths and weaknesses of current Omic technologies are discussed and how these new technologies can be used in the future to improve diagnosis of pulmonary vascular disease, more effectively treat patients with existing and future drugs, and generate new understanding of disease pathogenesis and mechanisms underlying treatment success or failure. Bioinformatic methods to analyze the large volumes of data are developing rapidly, but still present major challenges to interpretation of potential Omic findings in pulmonary vascular disease, with low numbers of patients studied and a potentially high false discovery rate. With more experience, precise and established drug response definitions, this field with move forward and will likely be a major component of the clinical care of PH patients in the future.

Integrating CNVs into meta-QTL identified GBP4 as positional candidate for adult cattle stature

Copy number variation (CNV) of DNA sequences, functionally significant but yet fully ascertained, is believed to confer considerable increments in unexplained heritability of quantitative traits. Identification of phenotype-associated CNVs (paCNVs) therefore is a pressing need in CNV studies to speed up their exploitation in cattle breeding programs. Here, we provided a new avenue to achieve this goal that is to project the published CNV data onto meta-quantitative trait loci (meta-QTL) map which connects causal genes with phenotypes. Any CNVs overlapping meta-QTL therefore will be potential paCNVs. This study reported potential paCNVs in Bos taurus autosome 3 (BTA3). Notably, overview indexes and CNVs both highlighted a narrower region (BTA3 54,500,000-55,000,000 bp, named BTA3_INQTL_6) within one constructed meta-QTL. Then, we ascertained guanylate-binding protein 4 (GBP4) among the nine positional candidate genes was significantly associated with adult cattle stature, including body weight (BW, P < 0.05) and withers height (WHT, P < 0.05), fitting GBP4 CNV either with three levels or with six levels in the model. Although higher copy number downregulated the mRNA levels of GBP2 (P < 0.05) and GBP4 (P < 0.05) in 1-Mb window (54.0-55.0 Mb) in muscle and adipose, additional analyses will be needed to clarify the causality behind the ascertained association.