Consensus-phenotype integration of transcriptomic and metabolomic data implies a role for metabolism in the chemosensitivity of tumour cells

Multi-omics profiling combined with molecular docking reveals immune-inflammatory proteins as potential drug targets in colorectal cancer

Colorectal cancer is globally ranked as the third most common malignant tumor. Its development involves a complex biological process driven by various genetic and epigenetic alterations. To elucidate the biological significance of the extensive omics data, we conducted comparative multi-omics studies on colorectal cancer patients at different clinical stages. Bioinformatics methods were applied to analyze multi-omics datasets and explore the molecular landscape. Drug prediction and molecular docking also were conducted to assess potential therapeutic interventions. In vitro experiments were used to validate the inhibitory effect on the migration and proliferation of cell lines. The results indicate up-regulated proteins involved in immune-inflammatory related pathways, while biomarkers related to muscular contraction and cell adhesion are significantly down-regulated. Drug prediction, coupled with in vitro experiments, suggests that AZ-628 may act as a potential drug to inhibit the proliferation and migration of CRC cell lines HCT-116 and HT-29 by regulating the aforementioned key biological pathways or proteins. Complementing these findings, metabolomics analysis unveiled a down-regulation of key carbon metabolism pathways, alongside an up-regulation in amino acid metabolism, particularly proline metabolism. This metabolic shift may reflect an adaptive response in cancer cells, favoring specific amino acids to support their growth. Together, these integrated results provide valuable insights into the intricate landscape of tumor development, highlighting the crossroads of immune regulation, cellular structure, and metabolic reprogramming in the tumorigenic process and providing valuable insights into cancer pathology.

Design, Synthesis, Antitumor Activity and NMR-Based Metabolomics of Novel Amino Substituted Tetracyclic Imidazo[4,5-b]Pyridine Derivatives

Newly prepared tetracyclic imidazo[4,5-b]pyridine derivatives were synthesized to study their antiproliferative activity against human cancer cells. Additionally, the structure-activity was studied to confirm the impact of the N atom position in pyridine nuclei as well as the chosen amino side chains on antiproliferative activity. Targeted amino substituted regioisomers were prepared by using uncatalyzed amination from corresponding chloro substituted precursors. The most active compounds 6 a, 8 and 10 showed improved activity in comparison to standard drug etoposide with IC50 values in a nanomolar range of concentration (0.2-0.9 μM). NMR-based metabolomics is a powerful instrument to elucidate activity mechanism of new chemotherapeutics. Multivariate and univariate statistical analysis of metabolic profiles of non-small cell lung cancer cells before and after exposure to 6 a revealed significant changes in metabolism of essential amino acids, glycerophospholipids and oxidative defense. Insight into the changes of metabolic pathways that are heavily involved in cell proliferation and survival provide valuable guidelines for more detailed analysis of activity metabolism and possible targets of this class of bioactive compounds.

Sex Modifies the Impact of Type 2 Diabetes Mellitus on the Murine Whole Brain Metabolome

Type 2 diabetes mellitus (T2DM) leads to the development of cardiovascular diseases, cognitive impairment, and dementia. There are sex differences in the presentation of T2DM and its associated complications. We sought to determine the impact of sex and T2DM on the brain metabolome to gain insights into the underlying mechanisms of T2DM-associated cognitive complications. Untargeted metabolomic analysis was performed, using liquid chromatography-mass spectrometry, on whole brain tissue from adult male and female db/db mice (a T2DM model) compared to wild-type (WT) C57Bl6/J mice. Regardless of sex, T2DM increased free fatty acids and decreased acylcarnitines in the brain. Sex impacted the number (103 versus 65 in males and females, respectively), and types of metabolites shifted by T2DM. Many choline-containing phospholipids were decreased by T2DM in males. Female-specific T2DM effects included changes in neuromodulatory metabolites (γ-aminobutyric acid, 2-linoleoyl glycerol, N-methylaspartic acid, and taurine). Further, there were more significantly different metabolites between sexes in the T2DM condition as compared to the WT controls (54 vs. 15 in T2DM and WT, respectively). T2DM alters the murine brain metabolome in both sex-independent and sex-dependent manners. This work extends our understanding of brain metabolic sex differences in T2DM, cognitive implications, and potential sex-specific metabolic therapeutic targets.

Systemic Markers of Lung Function and Forced Expiratory Volume in 1 Second Decline across Diverse Cohorts

Rationale: Chronic obstructive pulmonary disease (COPD) is a complex disease characterized by airway obstruction and accelerated lung function decline. Our understanding of systemic protein biomarkers associated with COPD remains incomplete. Objectives: To determine what proteins and pathways are associated with impaired pulmonary function in a diverse population. Methods: We studied 6,722 participants across six cohort studies with both aptamer-based proteomic and spirometry data (4,566 predominantly White participants in a discovery analysis and 2,156 African American cohort participants in a validation). In linear regression models, we examined protein associations with baseline forced expiratory volume in 1 second (FEV1) and FEV1/forced vital capacity (FVC). In linear mixed effects models, we investigated the associations of baseline protein levels with rate of FEV1 decline (ml/yr) in 2,777 participants with up to 7 years of follow-up spirometry. Results: We identified 254 proteins associated with FEV1 in our discovery analyses, with 80 proteins validated in the Jackson Heart Study. Novel validated protein associations include kallistatin serine protease inhibitor, growth differentiation factor 2, and tumor necrosis factor-like weak inducer of apoptosis (discovery β = 0.0561, Q = 4.05 × 10-10; β  = 0.0421, Q = 1.12 × 10-3; and β = 0.0358, Q = 1.67 × 10-3, respectively). In longitudinal analyses within cohorts with follow-up spirometry, we identified 15 proteins associated with FEV1 decline (Q < 0.05), including elafin leukocyte elastase inhibitor and mucin-associated TFF2 (trefoil factor 2; β = -4.3 ml/yr, Q = 0.049; β = -6.1 ml/yr, Q = 0.032, respectively). Pathways and processes highlighted by our study include aberrant extracellular matrix remodeling, enhanced innate immune response, dysregulation of angiogenesis, and coagulation. Conclusions: In this study, we identify and validate novel biomarkers and pathways associated with lung function traits in a racially diverse population. In addition, we identify novel protein markers associated with FEV1 decline. Several protein findings are supported by previously reported genetic signals, highlighting the plausibility of certain biologic pathways. These novel proteins might represent markers for risk stratification, as well as novel molecular targets for treatment of COPD.

Metabolomics: Going Deeper, Going Broader, Going Further

Metabolomics is a continuously dynamic field of research that is driven by demanding research questions and technological advances alike. In this review we highlight selected recent and ongoing developments in the area of mass spectrometry-based metabolomics. The field of view that can be seen through the metabolomics lens can be broadened by adoption of separation techniques such as hydrophilic interaction chromatography and ion mobility mass spectrometry (going broader). For a given biospecimen, deeper metabolomic analysis can be achieved by resolving smaller entities such as rare cell populations or even single cells using nano-LC and spatially resolved metabolomics or by extracting more useful information through improved metabolite identification in untargeted metabolomic experiments (going deeper). Integration of metabolomics with other (omics) data allows researchers to further advance in the understanding of the complex metabolic and regulatory networks in cells and model organisms (going further). Taken together, diverse fields of research from mechanistic studies to clinics to biotechnology applications profit from these technological developments.

Metabolomics and Lipidomics Screening Reveal Reprogrammed Signaling Pathways toward Cancer Development in Non-Alcoholic Steatohepatitis

With the rising incidence of hepatocellular carcinoma (HCC) from non-alcoholic steatohepatitis (NASH), identifying new metabolic readouts that function in metabolic pathway perpetuation is still a demand. The study aimed to compare the metabolic signature between NASH and NASH-HCC patients to explore novel reprogrammed metabolic pathways that might modulate cancer progression in NASH patients. NASH and NASH-HCC patients were recruited and screened for metabolomics, and isotope-labeled lipidomics were targeted and profiled using the EXION-LCTM system equipped with a Triple-TOFTM 5600+ system. Results demonstrated significantly (p ≤ 0.05) higher levels of triacylglycerol, AFP, AST, and cancer antigen 19-9 in NASH-HCC than in NASH patients, while prothrombin time, platelet count, and total leukocyte count were decreased significantly (p ≤ 0.05). Serum metabolic profiling showed a panel of twenty metabolites with 10% FDR and p ≤ 0.05 in both targeted and non-targeted analysis that could segregate NASH-HCC from NASH patients. Pathway analysis revealed that the metabolites are implicated in the down-regulation of necroptosis, amino acid metabolism, and regulation of lipid metabolism by PPAR-α, biogenic amine synthesis, fatty acid metabolism, and the mTOR signaling pathway. Cholesterol metabolism, DNA repair, methylation pathway, bile acid, and salts metabolism were significantly upregulated in NASH-HCC compared to the NASH group. Metabolite-protein interactions network analysis clarified a set of well-known protein encoding genes that play crucial roles in cancer, including PEMT, IL4I1, BAAT, TAT, CDKAL1, NNMT, PNP, NOS1, and AHCYL. Taken together, reliable metabolite fingerprints are presented and illustrated in a detailed map for the most predominant reprogrammed metabolic pathways that target HCC development from NASH.

An activation to memory differentiation trajectory of tumor-infiltrating lymphocytes informs metastatic melanoma outcomes

There is a need for better classification and understanding of tumor-infiltrating lymphocytes (TILs). Here, we applied advanced functional genomics to interrogate 9,000 human tumors and multiple single-cell sequencing sets using benchmarked T cell states, comprehensive T cell differentiation trajectories, human and mouse vaccine responses, and other human TILs. Compared with other T cell states, enrichment of T memory/resident memory programs was observed across solid tumors. Trajectory analysis of single-cell melanoma CD8+ TILs also identified a high fraction of memory/resident memory-scoring TILs in anti-PD-1 responders, which expanded post therapy. In contrast, TILs scoring highly for early T cell activation, but not exhaustion, associated with non-response. Late/persistent, but not early activation signatures, prognosticate melanoma survival, and co-express with dendritic cell and IFN-γ response programs. These data identify an activation-like state associated to poor response and suggest successful memory conversion, above resuscitation of exhaustion, is an under-appreciated aspect of successful anti-tumoral immunity.

The altered serum lipidome and its diagnostic potential for Non-Alcoholic Fatty Liver (NAFL)-associated hepatocellular carcinoma

BACKGROUND

Non-alcoholic fatty liver disease (NAFLD) is affecting more people globally. Indeed, NAFLD is a spectrum of metabolic dysfunctions that can progress to hepatocellular carcinoma (NAFLD-HCC). This development can occur in a non-cirrhotic liver and thus, often lack clinical surveillance. The aim of this study was to develop non-invasive surveillance method for NAFLD-HCC.

METHODS

Using comprehensive ultra-high-performance liquid chromatography mass-spectrometry, we investigated 1,295 metabolites in serum from 249 patients. Area under the receiver operating characteristic curve was calculated for all detected metabolites and used to establish their diagnostic potential. Logistic regression analysis was used to establish the diagnostic score.

FINDINGS

We show that NAFLD-HCC is characterised by a complete rearrangement of the serum lipidome, which distinguishes NAFLD-HCC from non-cancerous individuals and other HCC patients. We used machine learning to build a diagnostic model for NAFLD-HCC. We quantified predictive metabolites and developed the NAFLD-HCC Diagnostic Score (NHDS), presenting superior diagnostic potential compared to alpha-fetoprotein (AFP). Patients' metabolic landscapes show a progressive depletion in unsaturated fatty acids and acylcarnitines during transformation. Upregulation of fatty acid transporters in NAFLD-HCC tumours contribute to fatty acid depletion in the serum.

INTERPRETATION

NAFLD-HCC patients can be efficiently distinguished by serum metabolic alterations from the healthy population and from HCC patients related to other aetiologies (alcohol and viral hepatitis). Our model can be used for non-invasive surveillance of individuals with metabolic syndrome(s), allowing for early detection of NAFLD-HCC. Therefore, serum metabolomics may provide valuable insight to monitor patients at risk, including morbidly obese, diabetics, and NAFLD patients.

FUNDING

The funding sources for this study had no role in study design, data collection, data analyses, interpretation or writing of the report as it is presented herein.

Metabolic tumor constitution is superior to tumor regression grading for evaluating response to neoadjuvant therapy of esophageal adenocarcinoma patients

The response to neoadjuvant therapy can vary widely between individual patients. Histopathological tumor regression grading (TRG) is a strong factor for treatment response and survival prognosis of esophageal adenocarcinoma (EAC) patients following neoadjuvant treatment and surgery. However, TRG systems are usually based on the estimation of residual tumor but do not consider stromal or metabolic changes after treatment. Spatial metabolomics analysis is a powerful tool for molecular tissue phenotyping but has not been used so far in the context of neoadjuvant treatment of esophageal cancer. We used imaging mass spectrometry to assess the potential of spatial metabolomics on tumor and stroma tissue for evaluating therapy response of neoadjuvant-treated EAC patients. With an accuracy of 89.7%, the binary classifier trained on spatial tumor metabolite data proved to be superior for stratifying patients when compared to histopathological response assessment which had an accuracy of 70.5%. Sensitivities and specificities for the poor and favorable survival patient groups ranged from 84.9 to 93.3% using the metabolic classifier and from 62.2 to 78.1% using TRG. The tumor classifier was the only significant prognostic factor (HR 3.38, 95% CI = 1.40-8.12, P = 0.007) when adjusted for clinicopathological parameters such as TRG (HR 1.01, 95% CI = 0.67-1.53, P = 0.968) or stromal classifier (HR 1.856, 95% CI = 0.81-4.25, P = 0.143). The classifier even allowed to further stratify patients within the TRG1-3 categories. The underlying mechanisms of response to treatment has been figured out through network analysis. In summary, metabolic response evaluation outperformed histopathological response evaluation in our study with regard to prognostic stratification. This finding indicates that the metabolic constitution of tumor may have a greater impact on patient survival than the quantity of residual tumor cells or the stroma. This article is protected by copyright. All rights reserved.

Persistent DNA damage alters the neuronal transcriptome suggesting cell cycle dysregulation and altered mitochondrial function

Oxidative DNA damage induces changes in the neuronal cell cycle and activates a DNA damage response (DDR) to promote repair, but these processes may be altered under a chronic oxidative environment, leading to the accumulation of unrepaired DNA damage and continued activation of a DDR. Failure to repair DNA damage can lead to apoptosis or senescence, which is characterized by a permanent cell cycle arrest. Increased oxidative stress and accumulation of oxidative DNA damage are features of brain ageing and neurodegeneration, but the effects of persistent DNA damage in neurons are not well characterized. We developed a model of persistent oxidative DNA damage in immortalized post-mitotic neurons in vitro by exposing them to a sublethal concentration of hydrogen peroxide following a 'double stress' protocol and performed a detailed characterization of the neuronal transcriptome using microarray analysis. Persistent DNA damage significantly altered the expression of genes involved in cell cycle regulation, DDR and repair mechanisms, and mitochondrial function, suggesting an active DDR response to replication stress and alterations in mitochondrial electron transport chain. Quantitative polymerase chain reaction (qPCR) and functional validation experiments confirmed hyperactivation of mitochondrial Complex I in response to persistent DNA damage. These changes in response to persistent oxidative DNA damage may lead to further oxidative stress, contributing to neuronal dysfunction and ultimately neurodegeneration.

Thermodynamic Genome-Scale Metabolic Modeling of Metallodrug Resistance in Colorectal Cancer

BACKGROUND

Mass spectrometry-based metabolomics approaches provide an immense opportunity to enhance our understanding of the mechanisms that underpin the cellular reprogramming of cancers. Accurate comparative metabolic profiling of heterogeneous conditions, however, is still a challenge.

METHODS

Measuring both intracellular and extracellular metabolite concentrations, we constrain four instances of a thermodynamic genome-scale metabolic model of the HCT116 colorectal carcinoma cell line to compare the metabolic flux profiles of cells that are either sensitive or resistant to ruthenium- or platinum-based treatments with BOLD-100/KP1339 and oxaliplatin, respectively.

RESULTS

Normalizing according to growth rate and normalizing resistant cells according to their respective sensitive controls, we are able to dissect metabolic responses specific to the drug and to the resistance states. We find the normalization steps to be crucial in the interpretation of the metabolomics data and show that the metabolic reprogramming in resistant cells is limited to a select number of pathways.

CONCLUSIONS

Here, we elucidate the key importance of normalization steps in the interpretation of metabolomics data, allowing us to uncover drug-specific metabolic reprogramming during acquired metal-drug resistance.

Integration of the Microbiome, Metabolome and Transcriptomics Data Identified Novel Metabolic Pathway Regulation in Colorectal Cancer

Integrative multiomics data analysis provides a unique opportunity for the mechanistic understanding of colorectal cancer (CRC) in addition to the identification of potential novel therapeutic targets. In this study, we used public omics data sets to investigate potential associations between microbiome, metabolome, bulk transcriptomics and single cell RNA sequencing datasets. We identified multiple potential interactions, for example 5-aminovalerate interacting with Adlercreutzia; cholesteryl ester interacting with bacterial genera Staphylococcus, Blautia and Roseburia. Using public single cell and bulk RNA sequencing, we identified 17 overlapping genes involved in epithelial cell pathways, with particular significance of the oxidative phosphorylation pathway and the ACAT1 gene that indirectly regulates the esterification of cholesterol. These findings demonstrate that the integration of multiomics data sets from diverse populations can help us in untangling the colorectal cancer pathogenesis as well as postulate the disease pathology mechanisms and therapeutic targets.

Mitochondrial structural alterations in ovarian cancer patient-derived xenografts resistant to cisplatin

Mitochondria have attracted attention in cancer research as organelles associated with tumor development and response to therapy. We recently reported acquisition of resistance to cisplatin (DDP) associated with a metabolic rewiring in ovarian cancer patient-derived xenografts (PDXs) models. DDP-resistant PDXs models were obtained mimicking the clinical setting, treating mice bearing sensitive-DDP tumors with multiple cycles of DDP until the development of resistance. To further characterize the metabolic rewiring, the present study focused on tumor mitochondria. We analysed by transmission electron microscopy the mitochondria structure in two models of DDP-resistant and the corresponding DDP-sensitive PDXs and evaluated tumor mDNA content, the expression of genes and proteins involved in mitochondria functionality, and mitochondria fitness-related processes, such as autophagy. We observed a decrease in the number of mitochondria paralleled by an increased volume in DDP-resistant versus DDP-sensitive PDXs. DDP-resistant PDXs presented a higher percentage of damaged mitochondria, in particular of type 2 (concave-shape), and type 3 (cristolysis) damage. We found no difference in the mDNA content, and the expression of genes involved in mitochondrial biogenesis was similar between the sensitive and resistant PDXs. An upregulation of some genes involved in mitochondrial fitness in DDP-R versus DDP-S PDXs was observed. At protein level, no difference in the expression of proteins involved in mitochondrial function and biogenesis, and in autophagy/mitophagy was found. We here reported that the acquisition of DDP resistance is associated with morphological alterations in mitochondria, even if we couldn't find any dysregulation in the studied genes/proteins that could explain the observed differences.

Approaches to Integrating Metabolomics and Multi-Omics Data: A Primer

Metabolomics deals with multiple and complex chemical reactions within living organisms and how these are influenced by external or internal perturbations. It lies at the heart of omics profiling technologies not only as the underlying biochemical layer that reflects information expressed by the genome, the transcriptome and the proteome, but also as the closest layer to the phenome. The combination of metabolomics data with the information available from genomics, transcriptomics, and proteomics offers unprecedented possibilities to enhance current understanding of biological functions, elucidate their underlying mechanisms and uncover hidden associations between omics variables. As a result, a vast array of computational tools have been developed to assist with integrative analysis of metabolomics data with different omics. Here, we review and propose five criteria-hypothesis, data types, strategies, study design and study focus- to classify statistical multi-omics data integration approaches into state-of-the-art classes under which all existing statistical methods fall. The purpose of this review is to look at various aspects that lead the choice of the statistical integrative analysis pipeline in terms of the different classes. We will draw particular attention to metabolomics and genomics data to assist those new to this field in the choice of the integrative analysis pipeline.

Integration of metabolomics and transcriptomics reveals convergent pathways driving radiation-induced salivary gland dysfunction

Radiation therapy for head and neck cancer causes damage to the surrounding salivary glands, resulting in salivary gland hypofunction and xerostomia. Current treatments do not provide lasting restoration of salivary gland function following radiation; therefore, a new mechanistic understanding of the radiation-induced damage response is necessary for identifying therapeutic targets. The purpose of the present study was to investigate the metabolic phenotype of radiation-induced damage in parotid salivary glands by integrating transcriptomic and metabolomic data. Integrated data were then analyzed to identify significant gene-metabolite interactions. Mice received a single 5 Gy dose of targeted head and neck radiation. Parotid tissue samples were collected 5 days following treatment for RNA sequencing and metabolomics analysis. Altered metabolites and transcripts significantly converged on a specific region in the metabolic reaction network. Both integrative pathway enrichment using rank-based statistics and network analysis highlighted significantly coordinated changes in glutathione metabolism, energy metabolism (TCA cycle and thermogenesis), peroxisomal lipid metabolism, and bile acid production with radiation. Integrated changes observed in energy metabolism suggest that radiation induces a mitochondrial dysfunction phenotype. These findings validated previous pathways involved in the radiation-damage response, such as altered energy metabolism, and identified robust signatures in salivary glands, such as reduced glutathione metabolism, that may be driving salivary gland dysfunction.

Gene-Metabolite Network Linked to Inhibited Bioenergetics in Association With Spaceflight-Induced Loss of Male Mouse Quadriceps Muscle

Prolonged residence of mice in spaceflight is a scientifically robust and ethically ratified model of muscle atrophy caused by continued unloading. Under the Rodent Research Program of the National Aeronautics and Space Administration (NASA), we assayed the large-scale mRNA and metabolomic perturbations in the quadriceps of C57BL/6j male mice that lived in spaceflight (FLT) or on the ground (control or CTR) for approximately 4 weeks. The wet weights of the quadriceps were significantly reduced in FLT mice. Next-generation sequencing and untargeted mass spectroscopic assays interrogated the gene-metabolite landscape of the quadriceps. A majority of top-ranked differentially suppressed genes in FLT encoded proteins from the myosin or troponin families, suggesting sarcomere alterations in space. Significantly enriched gene-metabolite networks were found linked to sarcomeric integrity, immune fitness, and oxidative stress response; all inhibited in space as per in silico prediction. A significant loss of mitochondrial DNA copy numbers in FLT mice underlined the energy deprivation associated with spaceflight-induced stress. This hypothesis was reinforced by the transcriptomic sequencing-metabolomics integrative analysis that showed inhibited networks related to protein, lipid, and carbohydrate metabolism, and adenosine triphosphate (ATP) synthesis and hydrolysis. Finally, we discovered important upstream regulators, which could be targeted for next-generation therapeutic intervention for chronic disuse of the musculoskeletal system. © 2020 The Authors. Journal of Bone and Mineral Research published by American Society for Bone and Mineral Research.

Preclinical studies on metal based anticancer drugs as enabled by integrated metallomics and metabolomics

Resistance development is a major obstacle for platinum-based chemotherapy, with the anticancer drug oxaliplatin being no exception. Acquired resistance is often associated with altered drug accumulation. In this work we introduce a novel -omics workflow enabling the parallel study of platinum drug uptake and its distribution between nucleus/protein and small molecule fraction along with metabolic changes after different treatment time points. This integrated metallomics/metabolomics approach is facilitated by a tailored sample preparation workflow suitable for preclinical studies on adherent cancer cell models. Inductively coupled plasma mass spectrometry monitors the platinum drug, while the metabolomics tool-set is provided by hydrophilic interaction liquid chromatography combined with high-resolution Orbitrap mass spectrometry. The implemented method covers biochemical key pathways of cancer cell metabolism as shown by a panel of >130 metabolite standards. Furthermore, the addition of yeast-based 13C-enriched internal standards upon extraction enabled a novel targeted/untargeted analysis strategy. In this study we used our method to compare an oxaliplatin sensitive human colon cancer cell line (HCT116) and its corresponding resistant model. In the acquired oxaliplatin resistant cells distinct differences in oxaliplatin accumulation correlated with differences in metabolomic rearrangements. Using this multi-omics approach for platinum-treated samples facilitates the generation of novel hypotheses regarding the susceptibility and resistance towards oxaliplatin.

Integrative Analysis of Fecal Metagenomics and Metabolomics in Colorectal Cancer

Although colorectal cancer (CRC) is the second leading cause of death in developed countries, current diagnostic tests for early disease stages are suboptimal. We have performed a combination of UHPLC-MS metabolomics and 16S microbiome analyses on 224 feces samples in order to identify early biomarkers for both advanced adenomas (AD) and CRC. We report differences in fecal levels of cholesteryl esters and sphingolipids in CRC. We identified Fusobacterium, Parvimonas and Staphylococcus to be increased in CRC patients and Lachnospiraceae family to be reduced. We finally described Adlercreutzia to be more abundant in AD patients' feces. Integration of metabolomics and microbiome data revealed tight interactions between bacteria and host and performed better than FOB test for CRC diagnosis. This study identifies potential early biomarkers that outperform current diagnostic tools and frame them into the stablished gut microbiota role in CRC pathogenesis.

Computational models applied to metabolomics data hints at the relevance of glutamine metabolism in breast cancer

Background

Metabolomics has a great potential in the development of new biomarkers in cancer and it has experiment recent technical advances.

Methods

In this study, metabolomics and gene expression data from 67 localized (stage I to IIIB) breast cancer tumor samples were analyzed, using (1) probabilistic graphical models to define associations using quantitative data without other a priori information; and (2) Flux Balance Analysis and flux activities to characterize differences in metabolic pathways.

Results

On the one hand, both analyses highlighted the importance of glutamine in breast cancer. Moreover, cell experiments showed that treating breast cancer cells with drugs targeting glutamine metabolism significantly affects cell viability. On the other hand, these computational methods suggested some hypotheses and have demonstrated their utility in the analysis of metabolomics data and in associating metabolomics with patient’s clinical outcome.

Conclusions

Computational analyses applied to metabolomics data suggested that glutamine metabolism is a relevant process in breast cancer. Cell experiments confirmed this hypothesis. In addition, these computational analyses allow associating metabolomics data with patient prognosis.

Heterogeneous Domain Adaptation for IHC Classification of Breast Cancer Subtypes

Increasingly, multiple parallel omics datasets are collected from biological samples. Integrating these datasets for classification is an open area of research. Additionally, whilst multiple datasets may be available for the training samples, future samples may only be measured by a single technology requiring methods which do not rely on the presence of all datasets for sample prediction. This enables us to directly compare the protein and the gene profiles. New samples with just one set of measurements (e.g., just protein) can then be mapped to this latent common space where classification is performed. Using this approach, we achieved an improvement of up to 12 percent in accuracy when classifying samples based on their protein measurements compared with baseline methods which were trained on the protein data alone. We illustrate that the additional inclusion of the gene expression or protein expression in the training process enabled the separation between the classes to become clearer.

Single Spheroid Metabolomics: Optimizing Sample Preparation of Three-Dimensional Multicellular Tumor Spheroids

Tumor spheroids are important model systems due to the capability of capturing in vivo tumor complexity. In this work, the experimental design of metabolomics workflows using three-dimensional multicellular tumor spheroid (3D MTS) models is addressed. Non-scaffold based cultures of the HCT116 colon carcinoma cell line delivered highly reproducible MTSs with regard to size and other key parameters (such as protein content and fraction of viable cells) as a prerequisite. Carefully optimizing the multiple steps of sample preparation, the developed procedure enabled us to probe the metabolome of single MTSs (diameter range 790 ± 22 µm) in a highly repeatable manner at a considerable throughput. The final protocol consisted of rapid washing of the spheroids on the cultivation plate, followed by cold methanol extraction. ¹³C enriched internal standards, added upon extraction, were key to obtaining the excellent analytical figures of merit. Targeted metabolomics provided absolute concentrations with average biological repeatabilities of <20% probing MTSs individually. In a proof of principle study, MTSs were exposed to two metal-based anticancer drugs, oxaliplatin and the investigational anticancer drug KP1339 (sodium trans-[tetrachloridobis(1H-indazole)ruthenate(III)]), which exhibit distinctly different modes of action. This difference could be recapitulated in individual metabolic shifts observed from replicate single MTSs. Therefore, biological variation among single spheroids can be assessed using the presented analytical strategy, applicable for in-depth anticancer drug metabolite profiling.

CMIP is a negative regulator of T cell signaling

Upon their interaction with cognate antigen, T cells integrate different extracellular and intracellular signals involving basal and induced protein–protein interactions, as well as the binding of proteins to lipids, which can lead to either cell activation or inhibition. Here, we show that the selective T cell expression of CMIP, a new adapter protein, by targeted transgenesis drives T cells toward a naïve phenotype. We found that CMIP inhibits activation of the Src kinases Fyn and Lck after CD3/CD28 costimulation and the subsequent localization of Fyn and Lck to LRs. Video microscopy analysis showed that CMIP blocks the recruitment of LAT and the lipid raft marker cholera toxin B at the site of TCR engagement. Proteomic analysis identified several protein clusters differentially modulated by CMIP and, notably, Cofilin-1, which is inactivated in CMIP-expressing T cells. Moreover, transgenic T cells exhibited the downregulation of GM3 synthase, a key enzyme involved in the biosynthesis of gangliosides. These results suggest that CMIP negatively impacts proximal signaling and cytoskeletal rearrangement and defines a new mechanism for the negative regulation of T cells that could be a therapeutic target.

Inhibition of early response genes prevents changes in global joint metabolomic profiles in mouse post-traumatic osteoarthritis

OBJECTIVE

Although joint injury itself damages joint tissues, a substantial amount of secondary damage is mediated by the cellular responses to the injury. Cellular responses include the production and activation of proteases (MMPs, ADAMTSs, Cathepsins), and the production of inflammatory cytokines. The trajectory of cellular responses is driven by the transcriptional activation of early response genes, which requires Cdk9-dependent RNA Polymerase II phosphorylation. Our objective was to determine whether inhibition of cdk9-dependent early response gene activation affects changes in the joint metabolome.

DESIGN

To model post-traumatic osteoarthritis, we subjected mice to non-invasive Anterior Cruciate Ligament (ACL)-rupture joint injury. Following injury, mice were treated with flavopiridol - a potent and selective inhibitor of Cdk9 kinase activity - to inhibit Cdk9-dependent transcriptional activation, or vehicle control. Global joint metabolomics were analyzed 1 h after injury.

RESULTS

We found that injury induced metabolomic changes, including increases in Vitamin D3 metabolism, anandamide, and others. Inhibition of primary response gene activation immediately after injury largely prevented the global changes in the metabolomics profiles. Cluster analysis of joint metabolomes identified groups of injury-induced and drug-responsive metabolites.

CONCLUSIONS

Metabolomic profiling provides an instantaneous snapshot of biochemical activity representing cellular responses. We identified two sets of metabolites that change acutely after joint injury: those that require transcription of primary response genes, and those that do not. These data demonstrate the potential for inhibition of early response genes to alter the trajectory of cell-mediated degenerative changes following joint injury, which may offer novel targets for cell-mediated secondary joint damage.

Leveraging Multilayered "Omics" Data for Atopic Dermatitis: A Road Map to Precision Medicine

Atopic dermatitis (AD) is a complex multifactorial inflammatory skin disease that affects ~280 million people worldwide. About 85% of AD cases begin in childhood, a significant portion of which can persist into adulthood. Moreover, a typical progression of children with AD to food allergy, asthma or allergic rhinitis has been reported (“allergic march” or “atopic march”). AD comprises highly heterogeneous sub-phenotypes/endotypes resulting from complex interplay between intrinsic and extrinsic factors, such as environmental stimuli, and genetic factors regulating cutaneous functions (impaired barrier function, epidermal lipid, and protease abnormalities), immune functions and the microbiome. Though the roles of high-throughput “omics” integrations in defining endotypes are recognized, current analyses are primarily based on individual omics data and using binary clinical outcomes. Although individual omics analysis, such as genome-wide association studies (GWAS), can effectively map variants correlated with AD, the majority of the heritability and the functional relevance of discovered variants are not explained or known by the identified variants. The limited success of singular approaches underscores the need for holistic and integrated approaches to investigate complex phenotypes using trans-omics data integration strategies. Integrating omics layers (e.g., genome, epigenome, transcriptome, proteome, metabolome, lipidome, exposome, microbiome), which often have complementary and synergistic effects, might provide the opportunity to capture the flow of information underlying AD disease manifestation. Overlapping genes/candidates derived from multiple omics types include FLG, SPINK5, S100A8, and SERPINB3 in AD pathogenesis. Overlapping pathways include macrophage, endothelial cell and fibroblast activation pathways, in addition to well-known Th1/Th2 and NFkB activation pathways. Interestingly, there was more multi-omics overlap at the pathway level than gene level. Further analysis of multi-omics overlap at the tissue level showed that among 30 tissue types from the GTEx database, skin and esophagus were significantly enriched, indicating the biological interconnection between AD and food allergy. The present work explores multi-omics integration and provides new biological insights to better define the biological basis of AD etiology and confirm previously reported AD genes/pathways. In this context, we also discuss opportunities and challenges introduced by “big omics data” and their integration.

Metabolomic Markers of Phthalate Exposure in Plasma and Urine of Pregnant Women

Phthalates are known endocrine disruptors and found in almost all people with several associated adverse health outcomes reported in humans and animal models. Limited data are available on the relationship between exposure to endocrine disrupting chemicals and the human metabolome. We examined the relationship of metabolomic profiles in plasma and urine of 115 pregnant women with eleven urine phthalate metabolites measured at 26 weeks of gestation to identify potential biomarkers and relevant pathways. Targeted metabolomics was performed by selected reaction monitoring liquid chromatography and triple quadrupole mass spectrometry to measure 415 metabolites in plasma and 151 metabolites in urine samples. We have chosen metabolites with the best defined peaks for more detailed analysis (138 in plasma and 40 in urine). Relationship between urine phthalate metabolites and concurrent metabolomic markers in plasma and urine suggested potential involvement of diverse pathways including lipid, steroid, and nucleic acid metabolism and enhanced inflammatory response. Most of the correlations were positive for both urine and plasma, and further confirmed by regression and PCA analysis. However, after the FDR adjustment for multiple comparisons, only 9 urine associations remained statistically significant (q-values 0.0001–0.0451), including Nicotinamide mononucleotide, Cysteine T2, Cystine, and L-Aspartic acid. Additionally, we found negative associations of maternal pre-pregnancy body mass index (BMI) with more than 20 metabolomic markers related to lipid and amino-acid metabolism and inflammation pathways in plasma (p = 0.01–0.0004), while Mevalonic acid was positively associated (p = 0.009). Nicotinic acid, the only significant metabolite in urine, had a positive association with maternal BMI (p = 0.002). In summary, when evaluated in the context of metabolic pathways, the findings suggest enhanced lipid biogenesis, inflammation and altered nucleic acid metabolism in association with higher phthalate levels. These results provide new insights into the relationship between phthalates, common in most human populations, and metabolomics, a novel approach to exposure and health biomonitoring.

SPARC Inhibits Metabolic Plasticity in Ovarian Cancer

The tropism of ovarian cancer (OvCa) to the peritoneal cavity is implicated in widespread dissemination, suboptimal surgery, and poor prognosis. This tropism is influenced by stromal factors that are not only critical for the oncogenic and metastatic cascades, but also in the modulation of cancer cell metabolic plasticity to fulfill their high energy demands. In this respect, we investigated the role of Secreted Protein Acidic and Rich in Cysteine (SPARC) in metabolic plasticity of OvCa. We used a syngeneic model of OvCa in Sparc-deficient and proficient mice to gain comprehensive insight into the paracrine effect of stromal-SPARC in metabolic programming of OvCa in the peritoneal milieu. Metabolomic and transcriptomic profiling of micro-dissected syngeneic peritoneal tumors revealed that the absence of stromal-Sparc led to significant upregulation of the enzymes involved in glycolysis, TCA cycle, and mitochondrial electron transport chain (ETC), and their metabolic intermediates. Absence of stromal-Sparc increased reactive oxygen species and perturbed redox homeostasis. Recombinant SPARC exerted a dose-dependent inhibitory effect on glycolysis, mitochondrial respiration, ATP production and ROS generation. Comparative analysis with human tumors revealed that SPARC-regulated ETC-signature inversely correlated with SPARC transcripts. Targeting mitochondrial ETC by phenformin treatment of tumor-bearing Sparc-deficient and proficient mice mitigated the effect of SPARC-deficiency and significantly reduced tumor burden, ROS, and oxidative tissue damage in syngeneic tumors. In summary, our findings provide novel insights into the role of SPARC in regulating metabolic plasticity and bioenergetics in OvCa, and shines light on its potential therapeutic efficacy.

Application of global metabolomic profiling of synovial fluid for osteoarthritis biomarkers

Osteoarthritis affects over 250 million individuals worldwide. Currently, there are no options for early diagnosis of osteoarthritis, demonstrating the need for biomarker discovery. To find biomarkers of osteoarthritis in human synovial fluid, we used high performance liquid-chromatography mass spectrometry for global metabolomic profiling. Metabolites were extracted from human osteoarthritic (n = 5), rheumatoid arthritic (n = 3), and healthy (n = 5) synovial fluid, and a total of 1233 metabolites were detected. Principal components analysis clearly distinguished the metabolomic profiles of diseased from healthy synovial fluid. Synovial fluid from rheumatoid arthritis patients contained expected metabolites consistent with the inflammatory nature of the disease. Similarly, unsupervised clustering analysis found that each disease state was associated with distinct metabolomic profiles and clusters of co-regulated metabolites. For osteoarthritis, co-regulated metabolites that were upregulated compared to healthy synovial fluid mapped to known disease processes including chondroitin sulfate degradation, arginine and proline metabolism, and nitric oxide metabolism. We utilized receiver operating characteristic analysis to determine the diagnostic value of each metabolite and identified 35 metabolites as potential biomarkers of osteoarthritis, with an area under the receiver operating characteristic curve >0.9. These metabolites included phosphatidylcholine, lysophosphatidylcholine, ceramides, myristate derivatives, and carnitine derivatives. This pilot study provides strong justification for a larger cohort-based study of human osteoarthritic synovial fluid using global metabolomics. The significance of these data is the demonstration that metabolomic profiling of synovial fluid can identify relevant biomarkers of joint disease.

Deep Learning Accurately Predicts Estrogen Receptor Status in Breast Cancer Metabolomics Data

Metabolomics holds the promise as a new technology to diagnose highly heterogeneous diseases. Conventionally, metabolomics data analysis for diagnosis is done using various statistical and machine learning based classification methods. However, it remains unknown if deep neural network, a class of increasingly popular machine learning methods, is suitable to classify metabolomics data. Here we use a cohort of 271 breast cancer tissues, 204 positive estrogen receptor (ER+), and 67 negative estrogen receptor (ER-) to test the accuracies of feed-forward networks, a deep learning (DL) framework, as well as six widely used machine learning models, namely random forest (RF), support vector machines (SVM), recursive partitioning and regression trees (RPART), linear discriminant analysis (LDA), prediction analysis for microarrays (PAM), and generalized boosted models (GBM). DL framework has the highest area under the curve (AUC) of 0.93 in classifying ER+/ER- patients, compared to the other six machine learning algorithms. Furthermore, the biological interpretation of the first hidden layer reveals eight commonly enriched significant metabolomics pathways (adjusted P-value <0.05) that cannot be discovered by other machine learning methods. Among them, protein digestion and absorption and ATP-binding cassette (ABC) transporters pathways are also confirmed in integrated analysis between metabolomics and gene expression data in these samples. In summary, deep learning method shows advantages for metabolomics based breast cancer ER status classification, with both the highest prediction accuracy (AUC = 0.93) and better revelation of disease biology. We encourage the adoption of feed-forward networks based deep learning method in the metabolomics research community for classification.

New Strategies and Challenges in Lung Proteomics and Metabolomics. An Official American Thoracic Society Workshop Report

This document presents the proceedings from the workshop entitled, "New Strategies and Challenges in Lung Proteomics and Metabolomics" held February 4th-5th, 2016, in Denver, Colorado. It was sponsored by the National Heart Lung Blood Institute, the American Thoracic Society, the Colorado Biological Mass Spectrometry Society, and National Jewish Health. The goal of this workshop was to convene, for the first time, relevant experts in lung proteomics and metabolomics to discuss and overcome specific challenges in these fields that are unique to the lung. The main objectives of this workshop were to identify, review, and/or understand: (1) emerging technologies in metabolomics and proteomics as applied to the study of the lung; (2) the unique composition and challenges of lung-specific biological specimens for metabolomic and proteomic analysis; (3) the diverse informatics approaches and databases unique to metabolomics and proteomics, with special emphasis on the lung; (4) integrative platforms across genetic and genomic databases that can be applied to lung-related metabolomic and proteomic studies; and (5) the clinical applications of proteomics and metabolomics. The major findings and conclusions of this workshop are summarized at the end of the report, and outline the progress and challenges that face these rapidly advancing fields.

Combined transcriptome and metabolome analyses of metformin effects reveal novel links between metabolic networks in steroidogenic systems

Metformin is an antidiabetic drug, which inhibits mitochondrial respiratory-chain-complex I and thereby seems to affect the cellular metabolism in many ways. It is also used for the treatment of the polycystic ovary syndrome (PCOS), the most common endocrine disorder in women. In addition, metformin possesses antineoplastic properties. Although metformin promotes insulin-sensitivity and ameliorates reproductive abnormalities in PCOS, its exact mechanisms of action remain elusive. Therefore, we studied the transcriptome and the metabolome of metformin in human adrenal H295R cells. Microarray analysis revealed changes in 693 genes after metformin treatment. Using high resolution magic angle spinning nuclear magnetic resonance spectroscopy (HR-MAS-NMR), we determined 38 intracellular metabolites. With bioinformatic tools we created an integrated pathway analysis to understand different intracellular processes targeted by metformin. Combined metabolomics and transcriptomics data analysis showed that metformin affects a broad range of cellular processes centered on the mitochondrium. Data confirmed several known effects of metformin on glucose and androgen metabolism, which had been identified in clinical and basic studies previously. But more importantly, novel links between the energy metabolism, sex steroid biosynthesis, the cell cycle and the immune system were identified. These omics studies shed light on a complex interplay between metabolic pathways in steroidogenic systems.

Decreased glutathione biosynthesis contributes to EGFR T790M-driven erlotinib resistance in non-small cell lung cancer

Epidermal growth factor receptor (EGFR) inhibitors such as erlotinib are novel effective agents in the treatment of EGFR-driven lung cancer, but their clinical impact is often impaired by acquired drug resistance through the secondary T790M EGFR mutation. To overcome this problem, we analysed the metabonomic differences between two independent pairs of erlotinib-sensitive/resistant cells and discovered that glutathione (GSH) levels were significantly reduced in T790M EGFR cells. We also found that increasing GSH levels in erlotinib-resistant cells re-sensitised them, whereas reducing GSH levels in erlotinib-sensitive cells made them resistant. Decreased transcription of the GSH-synthesising enzymes (GCLC and GSS) due to the inhibition of NRF2 was responsible for low GSH levels in resistant cells that was directly linked to the T790M mutation. T790M EGFR clinical samples also showed decreased expression of these key enzymes; increasing intra-tumoural GSH levels with a small-molecule GST inhibitor re-sensitised resistant tumours to erlotinib in mice. Thus, we identified a new resistance pathway controlled by EGFR T790M and a therapeutic strategy to tackle this problem in the clinic.

Identification of Altered Metabolomic Profiles Following a Panchakarma-based Ayurvedic Intervention in Healthy Subjects: The Self-Directed Biological Transformation Initiative (SBTI)

The effects of integrative medicine practices such as meditation and Ayurveda on human physiology are not fully understood. The aim of this study was to identify altered metabolomic profiles following an Ayurveda-based intervention. In the experimental group, 65 healthy male and female subjects participated in a 6-day Panchakarma-based Ayurvedic intervention which included herbs, vegetarian diet, meditation, yoga, and massage. A set of 12 plasma phosphatidylcholines decreased (adjusted p < 0.01) post-intervention in the experimental (n = 65) compared to control group (n = 54) after Bonferroni correction for multiple testing; within these compounds, the phosphatidylcholine with the greatest decrease in abundance was PC ae C36:4 (delta = −0.34). Application of a 10% FDR revealed an additional 57 metabolites that were differentially abundant between groups. Pathway analysis suggests that the intervention results in changes in metabolites across many pathways such as phospholipid biosynthesis, choline metabolism, and lipoprotein metabolism. The observed plasma metabolomic alterations may reflect a Panchakarma-induced modulation of metabotypes. Panchakarma promoted statistically significant changes in plasma levels of phosphatidylcholines, sphingomyelins and others in just 6 days. Forthcoming studies that integrate metabolomics with genomic, microbiome and physiological parameters may facilitate a broader systems-level understanding and mechanistic insights into these integrative practices that are employed to promote health and well-being.

Integrated metabolomic and transcriptome analyses reveal finishing forage affects metabolic pathways related to beef quality and animal welfare

Beef represents a major dietary component and source of protein in many countries. With an increasing demand for beef, the industry is currently undergoing changes towards naturally produced beef. However, the true differences between the feeding systems, especially the biochemical and nutritional aspects, are still unclear. Using transcriptome and metabolome profiles, we identified biological pathways related to the differences between grass- and grain-fed Angus steers. In the latissimus dorsi muscle, we have recognized 241 differentially expressed genes (FDR < 0.1). The metabolome examinations of muscle and blood revealed 163 and 179 altered compounds in each tissue (P < 0.05), respectively. Accordingly, alterations in glucose metabolism, divergences in free fatty acids and carnitine conjugated lipid levels, and altered β-oxidation have been observed. The anti-inflammatory n3 polyunsaturated fatty acids are enriched in grass finished beef, while higher levels of n6 PUFAs in grain finished animals may promote inflammation and oxidative stress. Furthermore, grass-fed animals produce tender beef with lower total fat and a higher omega3/omega6 ratio than grain-fed ones, which could potentially benefit consumer health. Most importantly, blood cortisol levels strongly indicate that grass-fed animals may experience less stress than the grain-fed individuals. These results will provide deeper insights into the merits and mechanisms of muscle development.

Integrated metabolomic and transcriptome analyses reveal finishing forage affects metabolic pathways related to beef quality and animal welfare

Beef represents a major dietary component and source of protein in many countries. With an increasing demand for beef, the industry is currently undergoing changes towards naturally produced beef. However, the true differences between the feeding systems, especially the biochemical and nutritional aspects, are still unclear. Using transcriptome and metabolome profiles, we identified biological pathways related to the differences between grass- and grain-fed Angus steers. In the latissimus dorsi muscle, we have recognized 241 differentially expressed genes (FDR < 0.1). The metabolome examinations of muscle and blood revealed 163 and 179 altered compounds in each tissue (P < 0.05), respectively. Accordingly, alterations in glucose metabolism, divergences in free fatty acids and carnitine conjugated lipid levels, and altered β-oxidation have been observed. The anti-inflammatory n3 polyunsaturated fatty acids are enriched in grass finished beef, while higher levels of n6 PUFAs in grain finished animals may promote inflammation and oxidative stress. Furthermore, grass-fed animals produce tender beef with lower total fat and a higher omega3/omega6 ratio than grain-fed ones, which could potentially benefit consumer health. Most importantly, blood cortisol levels strongly indicate that grass-fed animals may experience less stress than the grain-fed individuals. These results will provide deeper insights into the merits and mechanisms of muscle development.

Metabolomic network analysis of estrogen-stimulated MCF-7 cells: a comparison of overrepresentation analysis, quantitative enrichment analysis and pathway analysis versus metabolite network analysis

In the context of the Human Toxome project, mass spectroscopy-based metabolomics characterization of estrogen-stimulated MCF-7 cells was studied in order to support the untargeted deduction of pathways of toxicity. A targeted and untargeted approach using overrepresentation analysis (ORA), quantitative enrichment analysis (QEA) and pathway analysis (PA) and a metabolite network approach were compared. Any untargeted approach necessarily has some noise in the data owing to artifacts, outliers and misidentified metabolites. Depending on the chemical analytical choices (sample extraction, chromatography, instrument and settings, etc.), only a partial representation of all metabolites will be achieved, biased by both the analytical methods and the database used to identify the metabolites. Here, we show on the one hand that using a data analysis approach based exclusively on pathway annotations has the potential to miss much that is of interest and, in the case of misidentified metabolites, can produce perturbed pathways that are statistically significant yet uninformative for the biological sample at hand. On the other hand, a targeted approach, by narrowing its focus and minimizing (but not eliminating) misidentifications, renders the likelihood of a spurious pathway much smaller, but the limited number of metabolites also makes statistical significance harder to achieve. To avoid an analysis dependent on pathways, we built a de novo network using all metabolites that were different at 24 h with and without estrogen with a p value <0.01 (53) in the STITCH database, which links metabolites based on known reactions in the main metabolic network pathways but also based on experimental evidence and text mining. The resulting network contained a "connected component" of 43 metabolites and helped identify non-endogenous metabolites as well as pathways not visible by annotation-based approaches. Moreover, the most highly connected metabolites (energy metabolites such as pyruvate and alpha-ketoglutarate, as well as amino acids) showed only a modest change between proliferation with and without estrogen. Here, we demonstrate that estrogen has subtle but potentially phenotypically important alterations in the acyl-carnitine fatty acids, acetyl-putrescine and succinoadenosine, in addition to likely subtle changes in key energy metabolites that, however, could not be verified consistently given the technical limitations of this approach. Finally, we show that a network-based approach combined with text mining identifies pathways that would otherwise neither be considered statistically significant on their own nor be identified via ORA, QEA, or PA.

Integration of omics: more than the sum of its parts

Genome scale data on biological systems has increasingly become available by sequencing of DNA and RNA, and by mass spectrometric quantification of proteins and metabolites. The cellular components from which these -omics regimes are derived act as one integrated system in vivo; thus, there is a natural instinct to integrate -omics data types. Statistical analyses, the use of previous knowledge in the form of networks, and the use of time-resolved measurements are three key design elements for life scientists to consider in planning integrated -omics studies. These design elements are reviewed in the context of multiple recent systems biology studies that leverage data from different types of -omics analyses. While most of these studies rely on well-established model organisms, the concepts for integrating -omics data that were developed in these studies can help to enable systems research in the field of cancer biology.

A Systems Oncology Approach Identifies NT5E as a Key Metabolic Regulator in Tumor Cells and Modulator of Platinum Sensitivity

Altered metabolism in tumor cells is required for rapid proliferation but also can influence other phenotypes that affect clinical outcomes such as metastasis and sensitivity to chemotherapy. Here, a genome-wide association study (GWAS)-guided integration of NCI-60 transcriptome and metabolome data identified ecto-5'-nucleotidase (NT5E or CD73) as a major determinant of metabolic phenotypes in cancer cells. NT5E expression and associated metabolome variations were also correlated with sensitivity to several chemotherapeutics including platinum-based treatment. NT5E mRNA levels were observed to be elevated in cells upon in vitro and in vivo acquisition of platinum resistance in ovarian cancer cells, and specific targeting of NT5E increased tumor cell sensitivity to platinum. We observed that tumor NT5E levels were prognostic for outcomes in ovarian cancer and were elevated after treatment with platinum, supporting the translational relevance of our findings. In this work, we integrated and analyzed a plethora of public data, demonstating the merit of such a systems oncology approach for the discovery of novel players in cancer biology and therapy. We experimentally validated the main findings of the NT5E gene being involved in both intrinsic and acquired resistance to platinum-based drugs. We propose that the efficacy of conventional chemotherapy could be improved by NT5E inhibition and that NT5E expression may be a useful prognostic and predictive clinical biomarker.

Mechanotransduction in primary human osteoarthritic chondrocytes is mediated by metabolism of energy, lipids, and amino acids

Chondrocytes are the sole cell type found in articular cartilage and are repeatedly subjected to mechanical loading in vivo. We hypothesized that physiological dynamic compression results in changes in energy metabolism to produce proteins for maintenance of the pericellular and extracellular matrices. The objective of this study was to develop an in-depth understanding for the short term (<30min) chondrocyte response to sub-injurious, physiological compression by analyzing metabolomic profiles for human chondrocytes harvested from femoral heads of osteoarthritic donors. Cell-seeded agarose constructs were randomly assigned to experimental groups, and dynamic compression was applied for 0, 15, or 30min. Following dynamic compression, metabolites were extracted and detected by HPLC-MS. Untargeted analyzes examined changes in global metabolomics profiles and targeted analysis examined the expression of specific metabolites related to central energy metabolism. We identified hundreds of metabolites that were regulated by applied compression, and we report the detection of 16 molecules not found in existing metabolite databases. We observed patient-specific mechanotransduction with aging dependence. Targeted studies found a transient increase in the ratio of NADP+ to NADPH and an initial decrease in the ratio of GDP to GTP, suggesting a flux of energy into the TCA cycle. By characterizing metabolomics profiles of primary chondrocytes in response to applied dynamic compression, this study provides insight into how OA chondrocytes respond to mechanical load. These results are consistent with increases in glycolytic energy utilization by mechanically induced signaling, and add substantial new data to a complex picture of how chondrocytes transduce mechanical loads.

Transcriptomic and metabolomic data integration

Many studies now produce parallel data sets from different omics technologies; however, the task of interpreting the acquired data in an integrated fashion is not trivial. This review covers those methods that have been used over the past decade to statistically integrate and interpret metabolomics and transcriptomic data sets. It defines four categories of approaches, correlation-based integration, concatenation-based integration, multivariate-based integration and pathway-based integration, into which all existing statistical methods fit. It also explores the choices in study design for generating samples for analysis by these omics technologies and the impact that these technical decisions have on the subsequent data analysis options.

Untargeted Metabolomics Reveals Predominant Alterations in Lipid Metabolism Following Light Exposure in Broccoli Sprouts

The consumption of vegetables belonging to the family Brassicaceae (e.g., broccoli and cauliflower) is linked to a reduced incidence of cancer and cardiovascular diseases. The molecular composition of such plants is strongly affected by growing conditions. Here we developed an unbiased metabolomics approach to investigate the effect of light and dark exposure on the metabolome of broccoli sprouts and we applied such an approach to provide a bird’s-eye view of the overall metabolic response after light exposure. Broccoli seeds were germinated and grown hydroponically for five days in total darkness or with a light/dark photoperiod (16 h light/8 h dark cycle). We used an ultra-performance liquid-chromatography system coupled to an ion-mobility, time-of-flight mass spectrometer to profile the large array of metabolites present in the sprouts. Differences at the metabolite level between groups were analyzed using multivariate statistical analyses, including principal component analysis and correlation analysis. Altered metabolites were identified by searching publicly available and in-house databases. Metabolite pathway analyses were used to support the identification of subtle but significant changes among groups of related metabolites that may have gone unnoticed with conventional approaches. Besides the chlorophyll pathway, light exposure activated the biosynthesis and metabolism of sterol lipids, prenol lipids, and polyunsaturated lipids, which are essential for the photosynthetic machinery. Our results also revealed that light exposure increased the levels of polyketides, including flavonoids, and oxylipins, which play essential roles in the plant’s developmental processes and defense mechanism against herbivores. This study highlights the significant contribution of light exposure to the ultimate metabolic phenotype, which might affect the cellular physiology and nutritional value of broccoli sprouts. Furthermore, this study highlights the potential of an unbiased omics approach for the comprehensive study of the metabolism.

Combining metabonomics and other -omics data

Combining molecular profiling data from multiple -omics platforms has the potential to provide a more comprehensive characterization of the biological system as well as improved prediction models for diagnostic applications compared to information derived from a single molecular profiling platform. In this chapter we outline analysis strategies for characterization of the genetic drivers of metabolism, joint pathway analysis in metabonomic and transcriptomic data and how metabonomic, and other -omics data can be combined to improve prediction models.

Redundancy: a critical obstacle to improving cancer therapy

A system characterized by redundancy has various elements that are able to act in the same biologic or dynamic manner, where the inhibition of one of those elements has no significant effect on the global biologic outcome or on the system's dynamic behavior. Methods that aim to predict the effectiveness of cancer therapies must include evolutionary and dynamic features that would change the static view that is widely accepted. Here, we explore several important issues about mechanisms of redundancy, heterogeneity, biologic importance, and drug resistance and describe methodologic challenges that, if overcome, would significantly contribute to cancer research.

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.

The human early-life exposome (HELIX): project rationale and design

BACKGROUND

Developmental periods in early life may be particularly vulnerable to impacts of environmental exposures. Human research on this topic has generally focused on single exposure-health effect relationships. The "exposome" concept encompasses the totality of exposures from conception onward, complementing the genome.

OBJECTIVES

The Human Early-Life Exposome (HELIX) project is a new collaborative research project that aims to implement novel exposure assessment and biomarker methods to characterize early-life exposure to multiple environmental factors and associate these with omics biomarkers and child health outcomes, thus characterizing the "early-life exposome." Here we describe the general design of the project.

METHODS

In six existing birth cohort studies in Europe, HELIX will estimate prenatal and postnatal exposure to a broad range of chemical and physical exposures. Exposure models will be developed for the full cohorts totaling 32,000 mother-child pairs, and biomarkers will be measured in a subset of 1,200 mother-child pairs. Nested repeat-sampling panel studies (n = 150) will collect data on biomarker variability, use smartphones to assess mobility and physical activity, and perform personal exposure monitoring. Omics techniques will determine molecular profiles (metabolome, proteome, transcriptome, epigenome) associated with exposures. Statistical methods for multiple exposures will provide exposure-response estimates for fetal and child growth, obesity, neurodevelopment, and respiratory outcomes. A health impact assessment exercise will evaluate risks and benefits of combined exposures.

CONCLUSIONS

HELIX is one of the first attempts to describe the early-life exposome of European populations and unravel its relation to omics markers and health in childhood. As proof of concept, it will form an important first step toward the life-course exposome.

Sky1 regulates the expression of sulfur metabolism genes in response to cisplatin

Cisplatin is commonly used in cancer therapy and yeast cells are also sensitive to this compound. We present a transcriptome analysis discriminating between RNA changes induced by cisplatin treatment, which are dependent on or independent of SKY1 function – a gene whose deletion increases resistance to the drug. Gene expression changes produced by addition of cisplatin to W303 and W303-Δsky1 cells were recorded using DNA microarrays. The data, validated by quantitative PCR, revealed 122 differentially expressed genes: 69 upregulated and 53 downregulated. Among the upregulated genes, those related to sulfur metabolism were over-represented and partially dependent on Sky1. Deletions of MET4 or other genes encoding co-regulators of the expression of sulfur-metabolism-related genes, with the exception of MET28, did not modify the cisplatin sensitivity of yeast cells. One of the genes with the highest cisplatin-induced upregulation was SEO1, encoding a putative permease of sulfur compounds. We also measured the platinum, sulfur and glutathione content in W303, W303-Δsky1 and W303-Δseo1 cells after cisplatin treatment, and integration of the data suggested that these transcriptional changes might represent a cellular response that allowed chelation of cisplatin with sulfur-containing amino acids and also helped DNA repair by stimulating purine biosynthesis. The transcription pattern of stimulation of sulfur-containing amino acids and purine synthesis decreased, or even disappeared, in the W303-Δsky1 strain.

The disparate twins: a comparative study of CXCR4 and CXCR7 in SDF-1α-induced gene expression, invasion and chemosensitivity of colon cancer

PURPOSE

In colorectal cancer, increased expression of the CXC chemokine receptor 4 (CXCR4) has been shown to provoke metastatic disease due to the interaction with its ligand stromal cell-derived factor-1 (SDF-1). Recently, a second SDF-1 receptor, CXCR7, was found to enhance tumor growth in solid tumors. Albeit signaling cascades via SDF-1/CXCR4 have been intensively studied, the significance of the SDF-1/CXCR7-induced intracellular communication triggering malignancy is still only marginally understood.

EXPERIMENTAL DESIGN

In tumor tissue of 52 patients with colorectal cancer, we observed that expression of CXCR7 and CXCR4 increased with tumor stage and tumor size. Asking whether activation of CXCR4 or CXCR7 might result in a similar expression pattern, we performed microarray expression analyses using lentivirally CXCR4- and/or CXCR7-overexpressing SW480 colon cancer cell lines with and without stimulation by SDF-1α.

RESULTS

Gene regulation via SDF-1α/CXCR4 and SDF-1α/CXCR7 was completely different and partly antidromic. Differentially regulated genes were assigned by gene ontology to migration, proliferation, and lipid metabolic processes. Expressions of AKR1C3, AXL, C5, IGFBP7, IL24, RRAS, and TNNC1 were confirmed by quantitative real-time PCR. Using the in silico gene set enrichment analysis, we showed that expressions of miR-217 and miR-218 were increased in CXCR4 and reduced in CXCR7 cells after stimulation with SDF-1α. Functionally, exposure to SDF-1α increased invasiveness of CXCR4 and CXCR7 cells, AXL knockdown hampered invasion. Compared with controls, CXCR4 cells showed increased sensitivity against 5-FU, whereas CXCR7 cells were more chemoresistant.

CONCLUSIONS

These opposing results for CXCR4- or CXCR7-overexpressing colon carcinoma cells demand an unexpected attention in the clinical application of chemokine receptor antagonists such as plerixafor.