Integration of Transcriptome and Metabolome Provides Unique Insights to Pathways Associated With Obese Breast Cancer Patients

A critical appraisal of the role of metabolomics in breast cancer research and diagnostics

Breast cancer (BC) remains the most prevalent cancer among women worldwide, despite significant advancements in its prevention and treatment. The escalating incidence of BC globally necessitates continued research into novel diagnostic and therapeutic strategies. Metabolomics, a burgeoning field, offers a comprehensive analysis of all metabolites within a cell, tissue, system, or organism, providing crucial insights into the dynamic changes occurring during cancer development and progression. This review focuses on the metabolic alterations associated with BC, highlighting the potential of metabolomics in identifying biomarkers for early detection, diagnosis, treatment and prognosis. Metabolomics studies have revealed distinct metabolic signatures in BC, including alterations in lipid metabolism, amino acid metabolism, and energy metabolism. These metabolic changes not only support the rapid proliferation of cancer cells but also influence the tumour microenvironment and therapeutic response. Furthermore, metabolomics holds great promise in personalized medicine, facilitating the development of tailored treatment strategies based on an individual's metabolic profile. By providing a holistic view of the metabolic changes in BC, metabolomics has the potential to revolutionize our understanding of the disease and improve patient outcomes.

Understanding pulmonary hypertension: the need for an integrative metabolomics and transcriptomics approach

Pulmonary hypertension (PH), characterised by mean pulmonary arterial pressure (mPAP) >20 mm Hg at rest, is a complex pathophysiological disorder associated with multiple clinical conditions. The high prevalence of the disease along with increased mortality and morbidity makes it a global health burden. Despite major advances in understanding the disease pathophysiology, much of the underlying complex molecular mechanism remains to be elucidated. Lack of a robust diagnostic test and specific therapeutic targets also poses major challenges. This review provides a comprehensive update on the dysregulated pathways and promising candidate markers identified in PH patients using the transcriptomics and metabolomics approach. The review also highlights the need of using an integrative multi-omics approach for obtaining insight into the disease at a molecular level. The integrative multi-omics/pan-omics approach envisaged to help in bridging the gap from genotype to phenotype is outlined. Finally, the challenges commonly encountered while conducting omics-driven studies are also discussed.

Konozy E, Abu Zeid IM, Ahmad A, Altayb HN. Prediction of anticancer peptides derived from the true lectins of Phoenix dactylifera and their synergetic effect with mitotane

Background and aims: Cancer continues to be a significant source of both illness and death on a global scale, traditional medicinal plants continue to serve as a fundamental resource of natural bioactive compounds as an alternative source of remedies. Although there have been numerous studies on the therapeutic role of Phoenix dactylifera, the study of the role of peptides has not been thoroughly investigated. This study aimed to investigate the anticancer activity of lectin peptides from P. dactylifera using in silico and in vivo analysis. Methods: Different computational tools were used to extract and predict anticancer peptides from the true lectins of P. dactylifera. Nine peptides that are bioactive substances have been investigated for their anticancer activity against MCF-7 and T47D (two forms of breast cancer). To counteract the unfavorable effects of mitotane, the most potent peptides (U3 and U7) were combined with it and assessed for anticancer activity against MCF-7 and HepG2. Results: In silico analysis revealed that nine peptides were predicted with anticancer activity. In cell lines, the lowest IC50 values were measured in U3 and U7 against MCF-7 and T47D cells. U3 or U7 in combination with mitotane demonstrated the lowest IC50 against MCF-7 and HepG2. The maximum level of cell proliferation inhibition was 22% when U3 (500 µg/mL) and 25 µg/mL mitotane were combined, compared to 41% when 25 µg/mL mitotane was used alone. When mitotane and U3 or U7 were combined, it was shown that these bioactive substances worked synergistically with mitotane to lessen its negative effects. The combination of peptides and mitotane could be regarded as an efficient chemotherapeutic medication having these bioactive properties for treating a variety of tumors while enhancing the reduction of side effects.

A journey from omics to clinicomics in solid cancers: Success stories and challenges

The word 'cancer' encompasses a heterogenous group of distinct disease types characterized by a spectrum of pathological features, genetic alterations and response to therapies. According to the World Health Organization, cancer is the second leading cause of death worldwide, responsible for one in six deaths and hence imposes a significant burden on global healthcare systems. High-throughput omics technologies combined with advanced imaging tools, have revolutionized our ability to interrogate the molecular landscape of tumors and has provided unprecedented understanding of the disease. Yet, there is a gap between basic research discoveries and their translation into clinically meaningful therapies for improving patient care. To bridge this gap, there is a need to analyse the vast amounts of high dimensional datasets from multi-omics platforms. The integration of multi-omics data with clinical information like patient history, histological examination and imaging has led to the novel concept of clinicomics and may expedite the bench-to-bedside transition in cancer. The journey from omics to clinicomics has gained momentum with development of radiomics which involves extracting quantitative features from medical imaging data with the help of deep learning and artificial intelligence (AI) tools. These features capture detailed information about the tumor's shape, texture, intensity, and spatial distribution. Together, the related fields of multiomics, translational bioinformatics, radiomics and clinicomics may provide evidence-based recommendations tailored to the individual cancer patient's molecular profile and clinical characteristics. In this chapter, we summarize multiomics studies in solid cancers with a specific focus on breast cancer. We also review machine learning and AI based algorithms and their use in cancer diagnosis, subtyping, prognosis and predicting treatment resistance and relapse.

Profiling canine mammary tumors: A potential model for studying human breast cancer

Despite all clinical progress recorded in the last decades, human breast cancer (HBC) remains a major challenge worldwide both in terms of its incidence and its management. Canine mammary tumors (CMTs) share similarities with HBC and represent an alternative model for HBC. The utility of the canine model in studying HBC relies on their common features, include spontaneous development, subtype classification, mutational profile, alterations in gene expression profile, and incidence/prevalence. This review describes the similarities between CMTs and HBC regarding genomic landscape, microRNA expression alteration, methylation, and metabolomic changes occurring during mammary gland carcinogenesis. The primary purpose of this review is to highlight the advantages of using the canine model as a translational animal model for HBC research and to investigate the challenges and limitations of this approach.

Endocrine-disrupting compounds and metabolomic reprogramming in breast cancer

Endocrine-disrupting chemicals pose a growing threat to human health through their increasing presence in the environment and their potential interactions with the mammalian endocrine systems. Due to their structural similarity to hormones like estrogen, these chemicals can interfere with endocrine signaling, leading to many deleterious effects. Exposure to estrogenic endocrine-disrupting compounds (EDC) is a suggested risk factor for the development of breast cancer, one of the most frequently diagnosed cancers in women. However, the mechanisms through which EDCs contribute to breast cancer development remain elusive. To rapidly proliferate, cancer cells undertake distinct metabolic programs to utilize existing nutrients in the tumor microenvironment and synthesize macromolecules de novo. EDCs are known to dysregulate cell signaling pathways related to cellular metabolism, which may be an important mechanism through which they exert their cancer-promoting effects. These altered pathways can be studied via metabolomic analysis, a new advancement in -omics technologies that can interrogate molecular pathways that favor cancer development and progression. This review will summarize recent discoveries regarding EDCs and the metabolic reprogramming that they may induce to facilitate the development of breast cancer.

Metabolomics and transcriptomics based multi-omics integration reveals radiation-induced altered pathway networking and underlying mechanism

Recent advancement in integrated multi-omics has significantly contributed to many areas of the biomedical field. Radiation research has also grasped uprising omics technologies in biomarker identification to aid in triage management. Herein, we have used a combinatorial multi-omics approach based on transcriptomics together with metabolomics and lipidomics of blood from murine exposed to 1 Gy (LD) and 7.5 Gy (HD) of total-body irradiation (TBI) for a comprehensive understanding of biological processes through integrated pathways and networking. Both omics displayed demarcation of HD group from controls using multivariate analysis. Dysregulated amino acids, various PC, PE and carnitine were observed along with many dysregulated genes (Nos2, Hmgcs2, Oxct2a, etc.). Joint-Pathway Analysis and STITCH interaction showed radiation exposure resulted in changes in amino acid, carbohydrate, lipid, nucleotide, and fatty acid metabolism. Elicited immune response was also observed by Gene Ontology. BioPAN has predicted Elovl5, Elovl6 and Fads2 for fatty acid pathways, only in HD group. Collectively, the combined omics approach facilitated a better understanding of processes uncovering metabolic pathways. Presumably, this is the first in radiation metabolomics that utilized an integrated omics approach following TBI in mice. Our work showed that omics integration could be a valuable tool for better comprehending the mechanism as well as molecular interactions.

CPNE1 mediates glycolysis and metastasis of breast cancer through activation of PI3K/AKT/HIF-1α signaling

CPNE1 regulates multiple signaling pathways and can stimulate cell proliferation and differentiation by activating the AKT-mTOR signaling pathway. In addition, CPNE1 is associated with various cancers; however, its role in breast cancer, particularly in TNBC, has not been fully elucidated. Our study aimed to reveal the impact of the CPNE1/PI3K/AKT/HIF-1α axis on TNBC. We first measured the expression of CPNE1 in the tumor tissues of TNBC patients and examined its prognostic value. Subsequently, we used sh-CPNE1 and overexpression vectors to transfect TNBC cell lines and analyzed cell viability, migration, and invasive abilities using colony formation and CCK-8 assays. Metabolites were analyzed through metabolomics. We found that higher expression of CPNE1 predicted poor prognosis in TNBC patients. Knockdown of CPNE1 reduced the viability, migration, invasion, and proliferation capabilities of TNBC cells. Furthermore, metabolomics analysis showed that glucose metabolism was the most dominant pathway, and knockdown of CPNE1 significantly limited the glycolytic activity of TNBC cells. We verified these conclusions in mouse models. Additionally, we overexpressed CPNE1 and treated TNBC cell lines with a PI3K inhibitor (LY294002). The results indicated that CPNE1 promoted aerobic glycolysis in TNBC cells through the PI3K/AKT/HIF-1α signaling pathway. This suggests that CPNE1 regulates cell glycolysis and participates in the development of TNBC. Our study may provide a new therapeutic target for TNBC treatment.

Herpesvirus entry mediator as a potential biomarker in breast cancer compared with conventional cytotoxic T‑lymphocyte‑associated antigen 4

Breast cancer (BC) is the most common cancer in women worldwide, with 2.3 million cases recorded in 2020. Despite improvements in cancer treatment, patients with BC still succumb to the disease, due to regional and distant metastases when diagnosed at later stages. Several immune checkpoint inhibitors have been approved for BC treatment, based on their expression and role in maintaining immunosurveillance against tumors. The present study aimed to evaluate the expression of 12 immune checkpoints in patients with BC, and assess their role as diagnostic and therapeutic markers. Expression levels were measured using reverse transcription-quantitative polymerase chain reaction. Among the 12 immune markers, herpesvirus entry mediator (HVEM) was found to be significantly upregulated in patients with malignant BC compared to non-malignant controls, with a relative fold change (FC) of 1.46 and P=0.012. A similar finding was observed for cytotoxic T-lymphocyte-associated antigen 4 (CTLA4; FC=1.47 and P=0.035). In addition, receiver operating characteristic curve analysis revealed that HVEM expression allowed significant differentiation between groups, with an area under the curve of 0.74 (P=0.013). Upregulation in both HVEM and CTLA4 was revealed to be significantly associated with the human epidermal growth factor receptor-2 (HER2)-enriched phenotype (FC=3.53, P=0.009 and FC=5.98, P=0.002, respectively), while only HVEM was significantly associated with the triple-negative phenotype (FC=2.07, P=0.016). Furthermore, HVEM was significantly higher in patients with grade III tumors (FC=1.88, P=0.025) and negative vascular invasion (FC=1.67, P=0.046) compared with non-malignant controls. Serum protein levels were assessed by multiplex immunoassay, and a significant increase in HVEM was detected in patients with malignant BC compared with that in non-malignant controls (P=0.035). These data indicated that HVEM may serve as a potential biomarker and target for immunotherapy, especially for certain types of BC.

Evaluating Metabolite-Based Biomarkers for Early Diagnosis of Pancreatic Cancer: A Systematic Review

Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancers, with five-year survival rates around 10%. The only curative option remains complete surgical resection, but due to the delay in diagnosis, less than 20% of patients are eligible for surgery. Therefore, discovering diagnostic biomarkers for early detection is crucial for improving clinical outcomes. Metabolomics has become a powerful technology for biomarker discovery, and several metabolomic-based panels have been proposed for PDAC diagnosis, but these advances have not yet been translated into the clinic. Therefore, this review focused on summarizing metabolites identified for the early diagnosis of PDAC in the last five years. Bibliographic searches were performed in the PubMed, Scopus and WOS databases, using the terms "Biomarkers, Tumor", "Pancreatic Neoplasms", "Early Diagnosis", "Metabolomics" and "Lipidome" (January 2018-March 2023), and resulted in the selection of fourteen original studies that compared PDAC patients with subjects with other pancreatic diseases. These investigations showed amino acid and lipid metabolic pathways as the most commonly altered, reflecting their potential for biomarker research. Furthermore, other relevant metabolites such as glucose and lactate were detected in the pancreas tissue and body fluids from PDAC patients. Our results suggest that the use of metabolomics remains a robust approach to improve the early diagnosis of PDAC. However, these studies showed heterogeneity with respect to the metabolomics techniques used and further studies will be needed to validate the clinical utility of these biomarkers.

Integrated Analysis of Transcriptome and Metabolome Provides Insight into Camellia oleifera Oil Alleviating Fat Accumulation in High-Fat Caenorhabditis elegans

Camellia oil (CO) is a high medicinal and nutritional value edible oil. However, its ability to alleviate fat accumulation in high-fat Caenorhabditis elegans has not been well elucidated. Therefore, this study aimed to investigate the effect of CO on fat accumulation in high-fat C. elegans via transcriptome and metabolome analysis. The results showed that CO significantly reduced fat accumulation in high-fat C. elegans by 10.34% (Oil Red O method) and 11.54% (TG content method), respectively. Furthermore, CO primarily altered the transcription levels of genes involved in longevity regulating pathway. Specifically, CO decreased lipid storage in high-fat C. elegans by inhibiting fat synthesis. In addition, CO supplementation modulated the abundance of metabolic biomarkers related to pyrimidine metabolism and riboflavin metabolism. The integrated transcriptome and metabolome analyses indicated that CO supplementation could alleviate fat accumulation in high-fat C. elegans by regulating retinol metabolism, drug metabolism-cytochrome P450, metabolism of xenobiotics by cytochrome P450, ascorbate and aldarate metabolism, and pentose and glucuronate interconversions. Overall, these findings highlight the potential health benefits of CO that could potentially be used as a functional edible oil.

Maintained imbalance of triglycerides, apolipoproteins, energy metabolites and cytokines in long-term COVID-19 syndrome patients

Background

Deep metabolomic, proteomic and immunologic phenotyping of patients suffering from an infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have matched a wide diversity of clinical symptoms with potential biomarkers for coronavirus disease 2019 (COVID-19). Several studies have described the role of small as well as complex molecules such as metabolites, cytokines, chemokines and lipoproteins during infection and in recovered patients. In fact, after an acute SARS-CoV-2 viral infection almost 10-20% of patients experience persistent symptoms post 12 weeks of recovery defined as long-term COVID-19 syndrome (LTCS) or long post-acute COVID-19 syndrome (PACS). Emerging evidence revealed that a dysregulated immune system and persisting inflammation could be one of the key drivers of LTCS. However, how these biomolecules altogether govern pathophysiology is largely underexplored. Thus, a clear understanding of how these parameters within an integrated fashion could predict the disease course would help to stratify LTCS patients from acute COVID-19 or recovered patients. This could even allow to elucidation of a potential mechanistic role of these biomolecules during the disease course.

Methods

This study comprised subjects with acute COVID-19 (n=7; longitudinal), LTCS (n=33), Recov (n=12), and no history of positive testing (n=73). ¹H-NMR-based metabolomics with IVDr standard operating procedures verified and phenotyped all blood samples by quantifying 38 metabolites and 112 lipoprotein properties. Univariate and multivariate statistics identified NMR-based and cytokine changes.

Results

Here, we report on an integrated analysis of serum/plasma by NMR spectroscopy and flow cytometry-based cytokines/chemokines quantification in LTCS patients. We identified that in LTCS patients lactate and pyruvate were significantly different from either healthy controls (HC) or acute COVID-19 patients. Subsequently, correlation analysis in LTCS group only among cytokines and amino acids revealed that histidine and glutamine were uniquely attributed mainly with pro-inflammatory cytokines. Of note, triglycerides and several lipoproteins (apolipoproteins Apo-A1 and A2) in LTCS patients demonstrate COVID-19-like alterations compared with HC. Interestingly, LTCS and acute COVID-19 samples were distinguished mostly by their phenylalanine, 3-hydroxybutyrate (3-HB) and glucose concentrations, illustrating an imbalanced energy metabolism. Most of the cytokines and chemokines were present at low levels in LTCS patients compared with HC except for IL-18 chemokine, which tended to be higher in LTCS patients.

Conclusion

The identification of these persisting plasma metabolites, lipoprotein and inflammation alterations will help to better stratify LTCS patients from other diseases and could help to predict ongoing severity of LTCS patients.

A Novel Phenazine Analog, CPUL1, Suppresses Autophagic Flux and Proliferation in Hepatocellular Carcinoma: Insight from Integrated Transcriptomic and Metabolomic Analysis

BACKGROUND

CPUL1, a phenazine analog, has demonstrated potent antitumor properties against hepatocellular carcinoma (HCC) and indicates a promising prospect in pharmaceutical development. However, the underlying mechanisms remain largely obscure.

METHODS

Multiple HCC cell lines were used to investigate the in vitro effects of CPUL1. The antineoplastic properties of CPUL1 were assessed in vivo by establishing a xenograft nude mice model. After that, metabolomics, transcriptomics, and bioinformatics were integrated to elucidate the mechanisms underlying the therapeutic efficacy of CPUL1, highlighting an unanticipated involvement of autophagy dysregulation.

RESULTS

CPUL1 suppressed HCC cell proliferation in vitro and in vivo, thereby endorsing the potential as a leading agent for HCC therapy. Integrative omics characterized a deteriorating scenario of metabolic debilitation with CPUL1, presenting an issue in the autophagy contribution of autophagy. Subsequent observations indicated that CPUL1 treatment could impede autophagic flow by suppressing autophagosome degradation rather than its formation, which supposedly exacerbated cellular damage triggered by metabolic impairment. Moreover, the observed late autophagosome degradation may be attributed to lysosome dysfunction, which is essential for the final stage of autophagy and cargo disposal.

CONCLUSIONS

Our study comprehensively profiled the anti-hepatoma characteristics and molecular mechanisms of CPUL1, highlighting the implications of progressive metabolic failure. This could partially be ascribed to autophagy blockage, which supposedly conveyed nutritional deprivation and intensified cellular vulnerability to stress.

Exploration of metabolic responses towards hypoxia mimetic DMOG in cancer cells by using untargeted metabolomics

Hypoxia is considered as one of the most crucial elements of tumor microenvironment. The hypoxia inducible transcription factors (HIF-1/2) are used by the cancer cells to adapt hypoxic microenvironment through regulating the expression of various target genes, including metabolic enzymes. Dimethyloxalylglycine (DMOG), a hypoxic mimetic used for HIF stabilisation in cell and animal models, also demonstrates multiple metabolic effects. In past, it was shown that in cancer cells, DMOG treatment alters mitochondrial ATP production, glycolysis, respiration etc. However, a global landscape of metabolic level alteration in cancer cells during DMOG treatment is still not established. In the current work, the metabolic landscape of cancer cells during DMOG treatment is explored by using untargeted metabolomics approach. Results showed that DMOG treatment primarily alters the one carbon and lipid metabolism. The levels of one-carbon metabolism related metabolites like serine, ornithine, and homomethionine levels significantly altered during DMOG treatment. Further, DMOG treatment reduces the global fatty acyls like palmitic acids, stearic acids, and arachidonic acid levels in cancer cell lines. Additionally, we found an alteration in glycolytic metabolites known to be regulated by hypoxia in cancer cell lines. Collectively, the results provided novel insights into the metabolic impact of DMOG on cancer cells and showed that the use of DMOG to induce hypoxia yields similar metabolic features relative to physiological hypoxia.

Peppers in Diet: Genome-Wide Transcriptome and Metabolome Changes in Drosophila melanogaster

The habanero pepper (Capsicum chinense) is an increasingly important spice and vegetable crop worldwide because of its high capsaicin content and pungent flavor. Diets supplemented with the phytochemicals found in habanero peppers might cause shifts in an organism’s metabolism and gene expression. Thus, understanding how these interactions occur can reveal the potential health effects associated with such changes. We performed transcriptomic and metabolomic analyses of Drosophila melanogaster adult flies reared on a habanero pepper diet. We found 539 genes/59 metabolites that were differentially expressed/accumulated in flies fed a pepper versus control diet. Transcriptome results indicated that olfactory sensitivity and behavioral responses to the pepper diet were mediated by olfactory and nutrient-related genes including gustatory receptors (Gr63a, Gr66a, and Gr89a), odorant receptors (Or23a, Or59a, Or82a, and Orco), and odorant-binding proteins (Obp28a, Obp83a, Obp83b, Obp93a, and Obp99a). Metabolome analysis revealed that campesterol, sitosterol, and sucrose were highly upregulated and azelaic acid, ethyl phosphoric acid, and citric acid were the major metabolites downregulated in response to the habanero pepper diet. Further investigation by integration analysis between transcriptome and metabolome data at gene pathway levels revealed six unique enriched pathways, including phenylalanine metabolism; insect hormone biosynthesis; pyrimidine metabolism; glyoxylate, and dicarboxylate metabolism; glycine, serine, threonine metabolism; and glycerolipid metabolism. In view of the transcriptome and metabolome findings, our comprehensive analysis of the response to a pepper diet in Drosophila have implications for exploring the molecular mechanism of pepper consumption.

Metabolomics of Breast Cancer: A Review

Breast cancer is the most commonly diagnosed cancer in women worldwide. Major advances have been made towards breast cancer prevention and treatment. Unfortunately, the incidence of breast cancer is still increasing globally. Metabolomics is the field of science which studies all the metabolites in a cell, tissue, system, or organism. Metabolomics can provide information on dynamic changes occurring during cancer development and progression. The metabolites identified using cutting-edge metabolomics techniques will result in the identification of biomarkers for the early detection, diagnosis, and treatment of cancers. This review briefly introduces the metabolic changes in cancer with particular focus on breast cancer.

Integrated Transcriptome and Metabolomic Analysis Reveal Anti-Angiogenic Properties of Disarib, a Novel Bcl2-Specific Inhibitor

Transcriptomic profiling of several drugs in cancer cell lines has been utilised to obtain drug-specific signatures and guided combination therapy to combat drug resistance and toxicity. Global metabolomics reflects changes due to altered activity of enzymes, environmental factors, etc. Integrating transcriptomics and metabolomics can provide genotype-phenotype correlation, providing meaningful insights into alterations in gene expression and its outcome to understand differential metabolism and guide therapy. This study uses a multi-omics approach to understand the global gene expression and metabolite changes induced by Disarib, a novel Bcl2-specific inhibitor in the Ehrlich adenocarcinoma (EAC) breast cancer mouse model. RNAseq analysis was performed on EAC mouse tumours treated with Disarib and compared to the controls. The expression of 6 oncogenes and 101 tumour suppressor genes interacting with Bcl2 and Bak were modulated upon Disarib treatment. Cancer hallmark pathways like DNA repair, Cell cycle, angiogenesis, and mitochondrial metabolism were downregulated, and programmed cell death platelet-related pathways were upregulated. Global metabolomic profiling using LC-MS revealed that Oncometabolites like carnitine, oleic acid, glycine, and arginine were elevated in tumour mice compared to normal and were downregulated upon Disarib treatment. Integrated transcriptomic and metabolomic profiles identified arginine metabolism, histidine, and purine metabolism to be altered upon Disarib treatment. Pro-angiogenic metabolites, arginine, palmitic acid, oleic acid, and myristoleic acid were downregulated in Disarib-treated mice. We further validated the effect of Disarib on angiogenesis by qRT-PCR analysis of genes in the VEGF pathway. Disarib treatment led to the downregulation of pro-angiogenic markers. Furthermore, the chorioallantoic membrane assay displayed a reduction in the formation of the number of secondary blood vessels upon Disarib treatment. Disarib reduces tumours by reducing oncometabolite and activating apoptosis and downregulating angiogenesis.

Profiling the Effect of Targeting Wild Isocitrate Dehydrogenase 1 (IDH1) on the Cellular Metabolome of Leukemic Cells

Leukemia is one of the most common primary malignancies of the hematologic system in both children and adults and remains a largely incurable or relapsing disease. The elucidation of disease subtypes based on mutational profiling has not improved clinical outcomes. IDH1/2 are critical enzymes of the TCA cycle that produces α-ketoglutarate (αKG). However, their mutated version is well reported in various cancer types, including leukemia, which produces D-2 hydroxyglutarate (D-2HG), an oncometabolite. Recently, some studies have shown that wild-type IDH1 is highly expressed in non-small cell lung carcinoma (NSCLC), primary glioblastomas (GBM), and several hematological malignancies and is correlated with disease progression. This work shows that the treatment of wild-type IDH1 leukemia cells with a specific IDH1 inhibitor shifted leukemic cells toward glycolysis from the oxidative phosphorylation (OXPHOS) phenotype. We also noticed a reduction in αKG in treated cells, possibly suggesting the inhibition of IDH1 enzymatic activity. Furthermore, we found that IDH1 inhibition reduced the metabolites related to one-carbon metabolism, which is essential for maintaining global methylation in leukemic cells. Finally, we observed that metabolic alteration in IDH1 inhibitor-treated leukemic cells promoted reactive oxygen species (ROS) formation and the loss of mitochondrial membrane potential, leading to apoptosis in leukemic cells. We showed that targeting wild-type IDH1 leukemic cells promotes metabolic alterations that can be exploited for combination therapies for a better outcome.

Fluctuations in Metabolites and Bone Markers Across the Menstrual Cycle in Eumenorrheic Women and Oral Contraceptive Users

CONTEXT

Little is known about changes in circulating metabolites during the menstrual cycle and how use of oral contraceptives (OCs) affects these changes.

OBJECTIVES

To study fluctuations in circulating metabolite and bone marker levels during the menstrual/pill cycle in eumenorrheic women and OC users.

METHODS

Plasma samples were collected from 28 eumenorrheic women and 10 OC users at 7 to 9 time points across a menstrual/pill cycle. Longitudinal and cross-sectional analyses were performed to examine the cycle- and OC-induced variations in the plasma metabolite and bone turnover marker levels.

RESULTS

In eumenorrheic women, plasma levels of alanine, glutamine, threonine, and tyrosine varied significantly across the menstrual cycle, and all dropped to the lowest level around day 21 of the menstrual cycle. These amino acid concentrations were negatively correlated with fluctuations in progesterone and/or estrogen levels. A between-group analysis showed that plasma levels of alanine, glutamine, glycine, proline, and tyrosine were lower in OC users than in nonusers. Concomitantly, plasma C-terminal telopeptide of type I collagen (CTX) and N-terminal propeptide of type I procollagen (PINP) levels were lower in OC users. Intriguingly, when all data were pooled, variations in CTX and PINP levels were positively correlated with fluctuations in proline and glycine concentrations (r > 0.5 or 0.3 < r < 0.5, P < 0.05).

CONCLUSIONS

The menstrual cycle and the use of OCs alter plasma levels of metabolites and bone turnover markers in young women. While the impact of these findings remains to be established, the lower glycine level among OC users and the accompanying lower CTX level supports that the use of OCs lowers collagen turnover in young women and may thereby have long-term implications for bone health among OC users.

Multi-Omics Approach Points to the Importance of Oxylipins Metabolism in Early-Stage Breast Cancer

The involvement of oxylipins, metabolites of polyunsaturated fatty acids, in cancer pathogenesis was known long ago, but only the development of the high-throughput methods get the opportunity to study oxylipins on a system level. The study aimed to elucidate alterations in oxylipin metabolism as characteristics of breast cancer patients. We compared the ultra-high-performance liquid chromatography-mass spectrometry (UPLC-MS/MS) oxylipin profile signatures in the blood plasma of 152 healthy volunteers (HC) and 169 patients with different stages of breast cancer (BC). To integrate lipidomics, transcriptomics, and genomics data, we analyzed a transcriptome of 10 open database datasets obtained from tissues and blood cells of BC patients and SNP data for 33 genes related to oxylipin metabolism. We identified 18 oxylipins, metabolites of omega-3 or omega-6 polyunsaturated fatty acids, that were differentially expressed between BCvsHC patients, including anandamide, prostaglandins and hydroxydocosahexaenoic acids. DEGs analysis of tissue and blood samples from BC patients revealed that 19 genes for oxylipin biosynthesis change their expression level, with CYP2C19, PTGS2, HPGD, and FAAH included in the list of DEGs in the analysis of transcriptomes and the list of SNPs associated with BC. Results allow us to suppose that oxylipin signatures reflect the organism's level of response to the disease. Our data regarding changes in oxylipins at the system level show that oxylipin profiles can be used to evaluate the early stages of breast cancer.

INTEGRATE: Model-based multi-omics data integration to characterize multi-level metabolic regulation

Metabolism is directly and indirectly fine-tuned by a complex web of interacting regulatory mechanisms that fall into two major classes. On the one hand, the expression level of the catalyzing enzyme sets the maximal theoretical flux level (i.e., the net rate of the reaction) for each enzyme-controlled reaction. On the other hand, metabolic regulation controls the metabolic flux through the interactions of metabolites (substrates, cofactors, allosteric modulators) with the responsible enzyme. High-throughput data, such as metabolomics and transcriptomics data, if analyzed separately, do not accurately characterize the hierarchical regulation of metabolism outlined above. They must be integrated to disassemble the interdependence between different regulatory layers controlling metabolism. To this aim, we propose INTEGRATE, a computational pipeline that integrates metabolomics and transcriptomics data, using constraint-based stoichiometric metabolic models as a scaffold. We compute differential reaction expression from transcriptomics data and use constraint-based modeling to predict if the differential expression of metabolic enzymes directly originates differences in metabolic fluxes. In parallel, we use metabolomics to predict how differences in substrate availability translate into differences in metabolic fluxes. We discriminate fluxes regulated at the metabolic and/or gene expression level by intersecting these two output datasets. We demonstrate the pipeline using a set of immortalized normal and cancer breast cell lines. In a clinical setting, knowing the regulatory level at which a given metabolic reaction is controlled will be valuable to inform targeted, truly personalized therapies in cancer patients.

Metabolomics Strategy Assisted by Transcriptomics Analysis to Identify Potential Biomarkers Associated with Tuberculosis

Purpose

To investigate the dysregulated pathways and identify reliable diagnostic biomarkers for tuberculosis using integrated analysis of metabolomics and transcriptomics.

Methods

Three groups of samples, untargeted metabolomics analysis of healthy controls (HC), latent tuberculosis infection patients (LTBI), and active tuberculosis patients (TB), were analyzed using gas chromatography time-of-flight mass spectrometry (GC-TOF MS) and ultra-high performance liquid chromatography-quantitative mass spectrometry (UHPLC-QE-MS). Both univariate and multivariate and statistical analyses were used to select differential metabolites (DMs) among group comparison, and LASSO regression analysis was employed to discover potential diagnostic biomarkers. Metabolite set enrichment analysis was performed to identify the altered metabolic pathways specifically in patients with TB. Meanwhile, a transcriptomic dataset GSEG4992 was downloaded from the GEO database to explore the differentially expressed genes (DEGs) between TB and HC identified in significantly enriched pathways. Finally, an integrative analysis of DMs and DEGs was performed to investigate the possible molecular mechanisms of TB.

Results

Thirty-three specific metabolites were significantly different between TB and HC, of which 7 (5-hydroxyindoleacetic acid, isoleucyl-isoleucine, heptadecanoic acid, indole acetaldehyde, 5-ethyl-2,4-dimethyloxazole, and 2-hydroxycaproic acid, unknown 71) were chosen as combinational potential biomarkers for TB. The area under the curve (AUC) value of these biomarkers was 0.97 (95% CI: 0.92–1.00). Metabolites set enrichment analysis (MSEA) displayed that there were 3 significantly enriched pathways among all. The genes in 3 significantly enriched pathways were further analyzed, of which 9(ALDH3B1, BCAT1, BCAT2, GLYAT, GOT1, IL4I1, MIF, SDS, SDSL) were expressed differentially. The area under the curve (AUC) values of these DEGs enriched in pathways mostly were greater than 0.8. As a result, a connected network of metabolites and genes in the pathways were established, which provides insights into the credibility of selected metabolites.

Conclusion

The newly identified metabolic biomarkers display a high potential to be developed into a promising tool for TB screening, diagnosis, and therapeutic effect monitoring.

Untargeted Metabolic Profiling of Extracellular Vesicles of SARS-CoV-2-Infected Patients Shows Presence of Potent Anti-Inflammatory Metabolites

Extracellular vesicles (EVs) carry important biomolecules, including metabolites, and contribute to the spread and pathogenesis of some viruses. However, to date, limited data are available on EV metabolite content that might play a crucial role during infection with the SARS-CoV-2 virus. Therefore, this study aimed to perform untargeted metabolomics to identify key metabolites and associated pathways that are present in EVs, isolated from the serum of COVID-19 patients. The results showed the presence of antivirals and antibiotics such as Foscarnet, Indinavir, and lymecycline in EVs from patients treated with these drugs. Moreover, increased levels of anti-inflammatory metabolites such as LysoPS, 7-α,25-Dihydroxycholesterol, and 15-d-PGJ2 were detected in EVs from COVID-19 patients when compared with controls. Further, we found decreased levels of metabolites associated with coagulation, such as thromboxane and elaidic acid, in EVs from COVID-19 patients. These findings suggest that EVs not only carry active drug molecules but also anti-inflammatory metabolites, clearly suggesting that exosomes might play a crucial role in negotiating with heightened inflammation during COVID-19 infection. These preliminary results could also pave the way for the identification of novel metabolites that might act as critical regulators of inflammatory pathways during viral infections.

Multi-Omic Approaches to Breast Cancer Metabolic Phenotyping: Applications in Diagnosis, Prognosis, and the Development of Novel Treatments

Breast cancer (BC) is characterized by high disease heterogeneity and represents the most frequently diagnosed cancer among women worldwide. Complex and subtype-specific gene expression alterations participate in disease development and progression, with BC cells known to rewire their cellular metabolism to survive, proliferate, and invade. Hence, as an emerging cancer hallmark, metabolic reprogramming holds great promise for cancer diagnosis, prognosis, and treatment. Multi-omics approaches (the combined analysis of various types of omics data) offer opportunities to advance our understanding of the molecular changes underlying metabolic rewiring in complex diseases such as BC. Recent studies focusing on the combined analysis of genomics, epigenomics, transcriptomics, proteomics, and/or metabolomics in different BC subtypes have provided novel insights into the specificities of metabolic rewiring and the vulnerabilities that may guide therapeutic development and improve patient outcomes. This review summarizes the findings of multi-omics studies focused on the characterization of the specific metabolic phenotypes of BC and discusses how they may improve clinical BC diagnosis, subtyping, and treatment.

Urolithin A and B Alter Cellular Metabolism and Induce Metabolites Associated with Apoptosis in Leukemic Cells

Leukemia is persistently a significant cause of illness and mortality worldwide. Urolithins, metabolites of ellagic acid and ellagitannins produced by gut microbiota, showed better bioactive compounds liable for the health benefits exerted by ellagic acid and ellagitannins containing pomegranate and walnuts. Here, we assessed the potential antileukemic activities of both urolithin A and urolithin B. Results showed that both urolithin A and B significantly inhibited the proliferation of leukemic cell lines Jurkat and K562, among which urolithin A showed the more prominent antiproliferative capability. Further, urolithin treatment alters leukemic cell metabolism, as evidenced by increased metabolic rate and notable changes in glutamine metabolism, one-carbon metabolism, and lipid metabolism. Next, we evidenced that both urolithins equally promoted apoptosis in leukemic cell lines. Based on these observations, we concluded that both urolithin A and B alter leukemic cell metabolome, resulting in a halt of proliferation, followed by apoptosis. The data can be used for designing new combinational therapies to eradicate leukemic cells.

Suppression of circulating AP001429.1 long non-coding RNA in obese patients with breast cancer

Long non-coding RNAs (lncRNAs), a type of cellular RNA, play a critical regulatory role in several physiological developments and pathological processes, such as tumorigenesis and tumor progression. Obesity is a risk factor for a number of serious health conditions, including breast cancer (BC). However, the underlying mechanisms behind the association between obesity and increased BC incidence and mortality remain unclear. Several studies have reported changes in lncRNA expression due to obesity and BC, independently encouraging further investigation of the relationship between the two in connection with lncRNAs. The present study was designed to first screen for the expression of 29 selected lncRNAs that showed a link to cancer or obesity in the blood of a selected cohort of 6 obese and 6 non-obese patients with BC. The expression levels of significantly expressed lncRNAs, AP001429.1, PCAT6, P5549, P19461 and P3134, were further investigated in a larger cohort of 69 patients with BC (36 obese and 33 non-obese), using reverse transcription-quantitative polymerase chain reaction. Results showed not only that AP001429.1 remained significantly downregulated in the larger cohort (P=0.002), but also that it was associated with several clinicopathological characteristics, such as negative HER2 status, negative E-cadherin expression, negative vascular invasion, negative margin invasion and LCIS. These findings suggest that obesity may have a role in inhibiting AP001429.1 expression, which may serve as a novel potential biomarker and therapeutic target for BC.

Identification of potential biomarkers and metabolic pathways based on integration of metabolomic and transcriptomic data in the development of breast cancer

OBJECTIVE

Breast cancer (BC) is the most common type of malignant tumor and the most common cause of cancer-related mortality among women. Metabolic reprogramming is considered a hallmark of cancer, and the study of BC metabolism may be the key to the development of new strategies for diagnosis and treatment. In this study, we aimed to explore the potential metabolites and gene biomarkers for BC through the integration of metabolomics and transcriptomic data, which could further understand BC tumor biology.

METHODS

Transcriptome dataset GSE139038 was downloaded to explore the differentially expressed genes (DEGs) between BC and normal control (NC) samples. Metabolomics dataset MTBLS326 was downloaded and preprocessed to obtain altered metabolites. Then, the principal component analysis (PCA) and linear models were used to reveal DEGs-metabolites relations. Finally, the pathway enrichment analysis of altered metabolites was performed.

RESULTS

A total of 280 DEGs and eight metabolites were explored between BC and NC samples. The liner module analysis investigated 28 DEGs-metabolites interactions including WASP family member 3 (WASF3)-lactate, ras-related protein Rab-7B (RAB7B)-lactate, and methyltransferase-like 7A (METTL7A)-pyruvate. Finally, pathways analysis showed that these metabolites (such as lactate and pyruvate) were mainly enriched in pathways like disorders of the Krebs cycle.

CONCLUSIONS

Combining with the transcriptomic and metabolomics data, we found that lactate, pyruvate, WASF3, RAB7B, and METTL7A might be used as novel biomarkers and potential therapeutic targets for BC. In addition, the disorders of the Krebs cycle pathway might affect the progression of BC.

Compound C, a Broad Kinase Inhibitor Alters Metabolic Fingerprinting of Extra Cellular Matrix Detached Cancer Cells

Most of the cancer related deaths are caused mainly by metastasis. Therefore, it is highly important to unfold the major mechanisms governing metastasis process in cancer. Throughout the metastatic cascade, cells need the ability to survive without attachment to neighboring cells and the original Extra Cellular Matrix (ECM). Recent reports showed that loss of ECM attachment shifts cancer cell metabolism towards glycolysis mostly through hypoxia. However, AMPK, a master metabolic regulator was also found to be upregulated under ECM detached conditions. Therefore, in this work we aimed to understand the consequences of targeting AMPK and other metabolic kinases by a broad kinase inhibitor namely Compound C in ECM detached cancer cells. Results showed that Compound C impacts glycolysis as evident by increased levels of pyruvate, but reduces its conversion to lactate thereby negatively regulating the Warburg effect. Simultaneously, Compound C induces block at multiple levels in TCA cycle as evident from accumulation of various TCA metabolites. Interestingly Compound C significantly reduces glutamine and reduced glutathione levels, suggesting loss of antioxidant potential of ECM detached cancer cells. Further, we found increased in metabolites associated with nucleotide synthesis, one carbon metabolism and PPP pathway during Compound C treatment of ECM detached cells. Finally, we also found induction in metabolites associated with DNA damage in ECM detached cancer cells during Compound C treatment, suggesting DNA damage regulatory role of metabolic kinases. Overall, our results showed that Compound C represses pyruvate to lactate conversion, reduces antioxidant potential and invokes DNA damage in ECM detached cancer cells. Our data provides a comprehensive metabolic map of ECM detached cancer cells that can be targeted with a broad kinase inhibitor, is Compound C. The data can be used for designing new combinational therapies to eradicate ECM detached cancer cells.

Does Proteomic Mirror Reflect Clinical Characteristics of Obesity?

Obesity is a frightening chronic disease, which has tripled since 1975. It is not expected to slow down staying one of the leading cases of preventable death and resulting in an increased clinical and economic burden. Poor lifestyle choices and excessive intake of “cheap calories” are major contributors to obesity, triggering type 2 diabetes, cardiovascular diseases, and other comorbidities. Understanding the molecular mechanisms responsible for development of obesity is essential as it might result in the introducing of anti-obesity targets and early-stage obesity biomarkers, allowing the distinction between metabolic syndromes. The complex nature of this disease, coupled with the phenomenon of metabolically healthy obesity, inspired us to perform data-centric, hypothesis-generating pilot research, aimed to find correlations between parameters of classic clinical blood tests and proteomic profiles of 104 lean and obese subjects. As the result, we assembled patterns of proteins, which presence or absence allows predicting the weight of the patient fairly well. We believe that such proteomic patterns with high prediction power should facilitate the translation of potential candidates into biomarkers of clinical use for early-stage stratification of obesity therapy.

Identification of novel cardiovascular disease associated metabolites using untargeted metabolomics

Cardiovascular disease (CVD) remains the leading cause of morbidity and mortality around the world. Early diagnosis of CVD could provide the opportunity for sensible management and better clinical outcome along with the prevention of further progression of the disease. In the current study, we used an untargeted metabolomic approach to identify possible metabolite(s) that associate well with the CVD and could serve either as therapeutic target or disease-associated metabolite. We identified 26 rationally adjusted unique metabolites that were differentially present in the serum of CVD patients compared with healthy individuals, among them 15 were found to be statistically significant. Out of these metabolites, we identified some novel metabolites like UDP-l-rhamnose and N1-acetylspermidine that have not been reported to be linked with CVD directly. Further, we also found that some metabolites like ethanolamide, solanidine, dimethylarginine, N-acetyl-l-tyrosine, can act as a discriminator of CVD. Metabolites integrating pathway enrichment analysis showed enrichment of various important metabolic pathways like histidine metabolism, methyl histidine metabolism, carnitine synthesis, along with arginine and proline metabolism in CVD patients. Our study provides a great opportunity to understand the pathophysiological role and impact of the identified unique metabolites and can be extrapolated as specific CVD specific metabolites.

Untargeted Metabolomics Identifies Key Metabolic Pathways Altered by Thymoquinone in Leukemic Cancer Cells

Thymoquinone (TQ), a naturally occurring anticancer compound extracted from Nigella sativa oil, has been extensively reported to possess potent anti-cancer properties. Experimental studies showed the anti-proliferative, pro-apoptotic, and anti-metastatic effects of TQ on different cancer cells. One of the possible mechanisms underlying these effects includes alteration in key metabolic pathways that are critical for cancer cell survival. However, an extensive landscape of the metabolites altered by TQ in cancer cells remains elusive. Here, we performed an untargeted metabolomics study using leukemic cancer cell lines during treatment with TQ and found alteration in approximately 335 metabolites. Pathway analysis showed alteration in key metabolic pathways like TCA cycle, amino acid metabolism, sphingolipid metabolism and nucleotide metabolism, which are critical for leukemic cell survival and death. We found a dramatic increase in metabolites like thymine glycol in TQ-treated cancer cells, a metabolite known to induce DNA damage and apoptosis. Similarly, we observed a sharp decline in cellular guanine levels, important for leukemic cancer cell survival. Overall, we provided an extensive metabolic landscape of leukemic cancer cells and identified the key metabolites and pathways altered, which could be critical and responsible for the anti-proliferative function of TQ.