Multi-Omics Analyses Detail Metabolic Reprogramming in Lipids, Carnitines, and Use of Glycolytic Intermediates between Prostate Small Cell Neuroendocrine Carcinoma and Prostate Adenocarcinoma

From molecular mechanisms of prostate cancer to translational applications: based on multi-omics fusion analysis and intelligent medicine

Prostate cancer is the most common cancer in men worldwide and has a high mortality rate. The complex and heterogeneous development of prostate cancer has become a core obstacle in the treatment of prostate cancer. Simultaneously, the issues of overtreatment in early-stage diagnosis, oligometastasis and dormant tumor recognition, as well as personalized drug utilization, are also specific concerns that require attention in the clinical management of prostate cancer. Some typical genetic mutations have been proved to be associated with prostate cancer's initiation and progression. However, single-omic studies usually are not able to explain the causal relationship between molecular alterations and clinical phenotypes. Exploration from a systems genetics perspective is also lacking in this field, that is, the impact of gene network, the environmental factors, and even lifestyle behaviors on disease progression. At the meantime, current trend emphasizes the utilization of artificial intelligence (AI) and machine learning techniques to process extensive multidimensional data, including multi-omics. These technologies unveil the potential patterns, correlations, and insights related to diseases, thereby aiding the interpretable clinical decision making and applications, namely intelligent medicine. Therefore, there is a pressing need to integrate multidimensional data for identification of molecular subtypes, prediction of cancer progression and aggressiveness, along with perosonalized treatment performing. In this review, we systematically elaborated the landscape from molecular mechanism discovery of prostate cancer to clinical translational applications. We discussed the molecular profiles and clinical manifestations of prostate cancer heterogeneity, the identification of different states of prostate cancer, as well as corresponding precision medicine practices. Taking multi-omics fusion, systems genetics, and intelligence medicine as the main perspectives, the current research results and knowledge-driven research path of prostate cancer were summarized.

Fusarium graminearum spores disrupt gut microbiota and metabolome via the lung-gut axis in mice

Fusarium graminearum, the primary pathogen responsible for wheat Fusarium head blight, can induce pulmonary damage through its spores. However, the detailed mechanism by which these spores cause intestinal injury is not yet fully understood. This study aimed to investigate the impact of exposure to fungal spores on the intestinal microbiota using a mice model that mimics the effects of fusarium graminearum spores on the gut microbiota and its metabolic profile. The study utilized 16S rRNA sequencing and metabolomics methodologies to analyze the contents of the cecum and feces in mice. The results showed that exposure to fungal spores led to significant changes in the composition of the intestinal microbiota in mice, characterized by an increase in Akkermansia and Staphylococcus populations. A non-targeted metabolomics analysis identified 316 metabolites associated with various metabolic pathways, particularly galactose metabolism. Pre-exposure to antibiotics before fungal spore exposure resulted in a decrease in the metabolic capacity of the intestinal microbiota in mice. This research demonstrates that fusarium graminearum spores can disrupt the intestinal microbiota and metabolome via the lung-gut axis. These findings provide valuable insights into the intestinal damage caused by fungal spores and offer important support for the development of therapeutic strategies for intestinal diseases.

Metabolic Response to Androgen Deprivation Therapy of Prostate Cancer

Prostate cancer (PC) stands as the most frequently diagnosed non-skin cancer and ranks as the second highest cause of cancer-related deaths among men in the United States. For those facing non-metastatic PC necessitating intervention, solely local treatments may not suffice, leading to a possible transition toward systemic therapies, including androgen deprivation therapy (ADT), chemotherapy, and therapies targeting androgen. Yet, these systemic treatments often bring about considerable adverse effects. Additionally, it is observed that overweight men are at a higher risk of developing aggressive forms of PC, advancing to metastatic stages, and succumbing to the disease. Consequently, there is a pressing demand for new treatment options that carry fewer side effects and enhance the current standard treatments, particularly for the majority of American men who are overweight or obese. In this article, we will review the metabolic response to ADT and how lifestyle modulation can mitigate these ADT-associated metabolic responses with a particular focus on the two clinical trials, Carbohydrate and Prostate Study 1 (CAPS1) and Carbohydrate and Prostate Study 2 (CAPS2), which tested the effects of low-carbohydrate diets on the metabolic side effects of ADT and PC progression, respectively. Furthermore, we will summarize the findings of serum metabolomic studies to elucidate the potential mechanisms by which ADT and low-carbohydrate diets can affect the metabolic response to mitigate the metabolic side effects while maximizing therapeutic efficacy.

Sex-Specific Effects of Polystyrene Microplastic and Lead(II) Co-Exposure on the Gut Microbiome and Fecal Metabolome in C57BL/6 Mice

The wide spread of microplastics has fueled growing public health concern globally. Due to their porous structure and large surface area, microplastics can serve as carriers for other environmental pollutants, including heavy metals. Although the toxic effects of microplastics or heavy metals have been reported previously, investigations into the sex-differential health effects of combined exposure to microplastics and heavy metals are lacking. In the present study, the effects of polystyrene microplastics and lead(II) co-exposure on the gut microbiome, intestinal permeability, and fecal metabolome were examined in both male and female mice. Combined exposure of polystyrene microplastics and lead(II) increased intestinal permeability in both male and female mice. Sex-specific responses to the co-exposure were found in gut bacteria, fungi, microbial metabolic pathways, microbial genes encoding antibiotic resistance and virulence factors, as well as fecal metabolic profiles. In particular, Shannon and Simpson indices of gut bacteria were reduced by the co-exposure only in female mice. A total of 34 and 13 fecal metabolites were altered in the co-exposure group in female and male mice, respectively, among which only three metabolites were shared by both sexes. These sex-specific responses to the co-exposure need to be taken into consideration when investigating the combined toxic effects of microplastics and heavy metals on the gut microbiota.

Long-term chronic food-derived arsenic exposure induce the urinary system metabolic dysfunction in mice

The consumption of rice contaminated with arsenic on a long-term basis has emerged as a pressing public health issue of global significance. Arsenic-induced urinary injury, particularly kidney damage, has received widespread attention. In this study, mice model under long-term arsenic exposure was established, mouse were exposed to rice arsenic (30 mg/kg) for 14 months. Changes of related metabolites were observed based on kidney metabolomics and lipidomics, and major biomarkers were screened by urine metabolomics. The results showed that phosphatidylethanolamine (PE) was significantly increased and phosphatidycholine (PC) and phosphatidylglycerol (PG) were significantly reduced after arsenic exposure, leading to related downstream lipid metabolism disorders. The metabolic pathways for amino acid and energy were observed to be impacted. In addition, metabolic disorders due to arsenic exposure may be associated with inherited neurometabolic disorders, such as D-2-hydroxyglutaric aciduria (D-2-HGA), and pyruvate carboxylase deficiency (PCD), which is predicted based on significant difference biomarkers (2-oxoglutarate, malic acid, and succinic acid) screened for urine. This study elucidates the mechanism of toxicity in the urinary system induced by arsenic exposure at nearly half life cycle, which furnishes crucial scientific evidence pertaining to the toxicity and risk evaluation associated with chronic exposure to the arsenic.

Effects of Perfluorooctanoic Acid on Gut Microbiota and Microbial Metabolites in C57BL/6J Mice

Perfluorooctanoic acid (PFOA) represents an increasing public health concern due to its persistence in the environment and its toxic effects. The gut microbiota is known to produce various metabolites that assist the host to maintain metabolic homeostasis. However, few studies have explored the effects of PFOA on gut-microbiota-related metabolites. In the present study, male C57BL/6J mice were exposed to 1 ppm of PFOA in drinking water for four weeks and integrative analysis of the gut microbiome and metabolome was performed to reveal the health effects of PFOA. Our results showed that PFOA disturbed both the gut microbiota composition and the metabolic profiles of the feces, serum, and liver in mice. A correlation was found between Lachnospiraceae UCG004, Turicibacter, Ruminococcaceae, and different fecal metabolites. Significant alterations of gut-microbiota-related metabolites were induced by PFOA exposure, including bile acids and tryptophan metabolites such as 3-indoleacrylic acid and 3-indoleacetic acid. The findings of this study are helpful to improve the understanding of the health effects of PFOA, which might be mediated through the gut microbiota and its related metabolites.

A new tumorgraft panel to accelerate precision medicine in prostate cancer

Background

Despite the significant advances in the management of advanced prostate cancer (PCa), metastatic PCa is currently considered incurable. For further investigations in precision treatment, the development of preclinical models representing the complex prostate tumor heterogeneity are mandatory. Accordingly, we aimed to establish a resource of patient-derived xenograft (PDX) models that exemplify each phase of this multistage disease for accurate and rapid evaluation of candidate therapies.

Methods

Fresh tumor samples along with normal corresponding tissues were obtained directly from patients at surgery. To ensure that the established models reproduce the main features of patient's tumor, both PDX tumors at multiple passages and patient's primary tumors, were processed for histological characteristics. STR profile analyses were also performed to confirm patient identity. Finally, the responses of the PDX models to androgen deprivation, PARP inhibitors and chemotherapy were also evaluated.

Results

In this study, we described the development and characterization of 5 new PDX models of PCa. Within this collection, hormone-naïve, androgen-sensitive and castration-resistant (CRPC) primary tumors as well as prostate carcinoma with neuroendocrine differentiation (CRPC-NE) were represented. Interestingly, the comprehensive genomic characterization of the models identified recurrent cancer driver alterations in androgen signaling, DNA repair and PI3K, among others. Results were supported by expression patterns highlighting new potential targets among gene drivers and the metabolic pathway. In addition, in vivo results showed heterogeneity of response to androgen deprivation and chemotherapy, like the responses of patients to these treatments. Importantly, the neuroendocrine model has been shown to be responsive to PARP inhibitor.

Conclusion

We have developed a biobank of 5 PDX models from hormone-naïve, androgen-sensitive to CRPC primary tumors and CRPC-NE. Increased copy-number alterations and accumulation of mutations within cancer driver genes as well as the metabolism shift are consistent with the increased resistance mechanisms to treatment. The pharmacological characterization suggested that the CRPC-NE could benefit from the PARP inhibitor treatment. Given the difficulties in developing such models, this relevant panel of PDX models of PCa will provide the scientific community with an additional resource for the further development of PDAC research.

RaMP-DB 2.0: a renovated knowledgebase for deriving biological and chemical insight from metabolites, proteins, and genes

MOTIVATION

Functional interpretation of high-throughput metabolomic and transcriptomic results is a crucial step in generating insight from experimental data. However, pathway and functional information for genes and metabolites are distributed among many siloed resources, limiting the scope of analyses that rely on a single knowledge source.

RESULTS

RaMP-DB 2.0 is a web interface, relational database, API and R package designed for straightforward and comprehensive functional interpretation of metabolomic and multi-omic data. RaMP-DB 2.0 has been upgraded with an expanded breadth and depth of functional and chemical annotations (ClassyFire, LIPID MAPS, SMILES, InChIs, etc.), with new data types related to metabolites and lipids incorporated. To streamline entity resolution across multiple source databases, we have implemented a new semi-automated process, thereby lessening the burden of harmonization and supporting more frequent updates. The associated RaMP-DB 2.0 R package now supports queries on pathways, common reactions (e.g. metabolite-enzyme relationship), chemical functional ontologies, chemical classes and chemical structures, as well as enrichment analyses on pathways (multi-omic) and chemical classes. Lastly, the RaMP-DB web interface has been completely redesigned using the Angular framework.

AVAILABILITY AND IMPLEMENTATION

The code used to build all components of RaMP-DB 2.0 are freely available on GitHub at https://github.com/ncats/ramp-db, https://github.com/ncats/RaMP-Client/ and https://github.com/ncats/RaMP-Backend. The RaMP-DB web application can be accessed at https://rampdb.nih.gov/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Perfluorooctanoic Acid-Disturbed Serum and Liver Lipidome in C57BL/6 Mice

Perfluorooctanoic acid is a manufactured material extensively utilized in industrial and consumer products. As a persistent organic pollutant, perfluorooctanoic acid has raised increasing public health concerns recently. Although perfluorooctanoic acid is known to induce lipid accumulation in the liver, the impact of perfluorooctanoic acid on different lipid classes has not been fully evaluated. In this study, we performed untargeted lipidomics analysis to investigate the impact of perfluorooctanoic acid on the lipid homeostasis in C57BL/6 male mice. Perfluorooctanoic acid disturbed the lipid profiles in serum and liver, with a variety of lipid classes significantly altered. Greater impacts were observed in the liver lipidome than the serum lipidome. In particular, some lipid clusters in the liver were altered by both high- and low-dose perfluorooctanoic acid exposure, including the increase of unsaturated triglycerides and the decrease of sphingomyelins, saturated phosphatidylcholines, saturated lysophosphatidylcholines, and phospholipid ethers. In parallel with an increase in the liver, a decrease of saturated phosphatidylcholines was found in the serum of high-dose perfluorooctanoic acid-treated mice. The findings from this study are helpful to improve the understanding of perfluorooctanoic acid-induced dysregulation of lipid metabolism and perfluorooctanoic acid-associated health effects in liver.

Prostate cancer in omics era

Recent advances in omics technology have prompted extraordinary attempts to define the molecular changes underlying the onset and progression of a variety of complex human diseases, including cancer. Since the advent of sequencing technology, cancer biology has become increasingly reliant on the generation and integration of data generated at these levels. The availability of multi-omic data has transformed medicine and biology by enabling integrated systems-level approaches. Multivariate signatures are expected to play a role in cancer detection, screening, patient classification, assessment of treatment response, and biomarker identification. This review reports current findings and highlights a number of studies that are both novel and groundbreaking in their application of multi Omics to prostate cancer.

The Integration of Metabolomics with Other Omics: Insights into Understanding Prostate Cancer

Our understanding of prostate cancer (PCa) has shifted from solely caused by a few genetic aberrations to a combination of complex biochemical dysregulations with the prostate metabolome at its core. The role of metabolomics in analyzing the pathophysiology of PCa is indispensable. However, to fully elucidate real-time complex dysregulation in prostate cells, an integrated approach based on metabolomics and other omics is warranted. Individually, genomics, transcriptomics, and proteomics are robust, but they are not enough to achieve a holistic view of PCa tumorigenesis. This review is the first of its kind to focus solely on the integration of metabolomics with multi-omic platforms in PCa research, including a detailed emphasis on the metabolomic profile of PCa. The authors intend to provide researchers in the field with a comprehensive knowledge base in PCa metabolomics and offer perspectives on overcoming limitations of the tool to guide future point-of-care applications.

Lipidomics for the Prediction of Progressive Liver Disease in Patients with Alcohol Use Disorder

Alcohol-related liver disease is a public health care burden globally. Only 10-20% of patients with alcohol use disorder have progressive liver disease. This study aimed to identify lipid biomarkers for the early identification of progressive alcohol-related liver disease, which is a key step for early intervention. We performed untargeted lipidomics analysis in serum and fecal samples for a cohort of 49 subjects, including 17 non-alcoholic controls, 16 patients with non-progressive alcohol-related liver disease, and 16 patients with progressive alcohol-related liver disease. The serum and fecal lipidome profiles in the two patient groups were different from that in the controls. Nine lipid biomarkers were identified that were significantly different between patients with progressive liver disease and patients with non-progressive liver disease in both serum and fecal samples. We further built a random forest model to predict progressive alcohol-related liver disease using nine lipid biomarkers. Fecal lipids performed better (Area Under the Curve, AUC = 0.90) than serum lipids (AUC = 0.79). The lipid biomarkers identified are promising candidates for the early identification of progressive alcohol-related liver disease.

Unravelling Prostate Cancer Heterogeneity Using Spatial Approaches to Lipidomics and Transcriptomics

Simple Summary

Prostate cancer is a heterogenous disease in terms of disease aggressiveness and therapy response, leading to dilemmas in treatment decisions. This heterogeneity reflects the multifocal nature of prostate cancer and its diversity in cellular and molecular composition, necessitating spatial molecular approaches. Here in view of the emerging importance of rewired lipid metabolism as a source of biomarkers and therapeutic targets for prostate cancer, we highlight recent advancements in technologies that enable the spatial mapping of lipids and related metabolic pathways associated with prostate cancer development and progression. We also evaluate their potential for future implementation in treatment decision-making in the clinical management of prostate cancer.

Abstract

Due to advances in the detection and management of prostate cancer over the past 20 years, most cases of localised disease are now potentially curable by surgery or radiotherapy, or amenable to active surveillance without treatment. However, this has given rise to a new dilemma for disease management; the inability to distinguish indolent from lethal, aggressive forms of prostate cancer, leading to substantial overtreatment of some patients and delayed intervention for others. Driving this uncertainty is the critical deficit of novel targets for systemic therapy and of validated biomarkers that can inform treatment decision-making and to select and monitor therapy. In part, this lack of progress reflects the inherent challenge of undertaking target and biomarker discovery in clinical prostate tumours, which are cellularly heterogeneous and multifocal, necessitating the use of spatial analytical approaches. In this review, the principles of mass spectrometry-based lipid imaging and complementary gene-based spatial omics technologies, their application to prostate cancer and recent advancements in these technologies are considered. We put in perspective studies that describe spatially-resolved lipid maps and metabolic genes that are associated with prostate tumours compared to benign tissue and increased risk of disease progression, with the aim of evaluating the future implementation of spatial lipidomics and complementary transcriptomics for prognostication, target identification and treatment decision-making for prostate cancer.

Metabolic Phenotyping in Prostate Cancer Using Multi-Omics Approaches

Prostate cancer (PCa), one of the most frequently diagnosed cancers among men worldwide, is characterized by a diverse biological heterogeneity. It is well known that PCa cells rewire their cellular metabolism to meet the higher demands required for survival, proliferation, and invasion. In this context, a deeper understanding of metabolic reprogramming, an emerging hallmark of cancer, could provide novel opportunities for cancer diagnosis, prognosis, and treatment. In this setting, multi-omics data integration approaches, including genomics, epigenomics, transcriptomics, proteomics, lipidomics, and metabolomics, could offer unprecedented opportunities for uncovering the molecular changes underlying metabolic rewiring in complex diseases, such as PCa. Recent studies, focused on the integrated analysis of multi-omics data derived from PCa patients, have in fact revealed new insights into specific metabolic reprogramming events and vulnerabilities that have the potential to better guide therapy and improve outcomes for patients. This review aims to provide an up-to-date summary of multi-omics studies focused on the characterization of the metabolomic phenotype of PCa, as well as an in-depth analysis of the correlation between changes identified in the multi-omics studies and the metabolic profile of PCa tumors.

Machine Learning Applied to Omics Datasets Predicts Mortality in Patients with Alcoholic Hepatitis

Alcoholic hepatitis is a major health care burden in the United States due to significant morbidity and mortality. Early identification of patients with alcoholic hepatitis at greatest risk of death is extremely important for proper treatments and interventions to be instituted. In this study, we used gradient boosting, random forest, support vector machine and logistic regression analysis of laboratory parameters, fecal bacterial microbiota, fecal mycobiota, fecal virome, serum metabolome and serum lipidome to predict mortality in patients with alcoholic hepatitis. Gradient boosting achieved the highest AUC of 0.87 for both 30-day mortality prediction using the bacteria and metabolic pathways dataset and 90-day mortality prediction using the fungi dataset, which showed better performance than the currently used model for end-stage liver disease (MELD) score.

PCK1 regulates neuroendocrine differentiation in a positive feedback loop of LIF/ZBTB46 signalling in castration-resistant prostate cancer

BACKGROUND

Castration-resistant prostate cancer (CRPC) patients frequently develop neuroendocrine differentiation, with high mortality and no effective treatment. However, the regulatory mechanism that connects neuroendocrine differentiation and metabolic adaptation in response to therapeutic resistance of prostate cancer remain to be unravelled.

METHODS

By unbiased cross-correlation between RNA-sequencing, database signatures, and ChIP analysis, combining in vitro cell lines and in vivo animal models, we identified that PCK1 is a pivotal regulator in therapy-induced neuroendocrine differentiation of prostate cancer through a LIF/ZBTB46-driven glucose metabolism pathway.

RESULTS

Upregulation of PCK1 supports cell proliferation and reciprocally increases ZBTB46 levels to promote the expression of neuroendocrine markers that are conducive to the development of neuroendocrine characteristic CRPC. PCK1 and neuroendocrine marker expressions are regulated by the ZBTB46 transcription factor upon activation of LIF signalling. Targeting PCK1 can reduce the neuroendocrine phenotype and decrease the growth of prostate cancer cells in vitro and in vivo.

CONCLUSION

Our study uncovers LIF/ZBTB46 signalling activation as a key mechanism for upregulating PCK1-driven glucose metabolism and neuroendocrine differentiation of CRPC, which may yield significant improvements in prostate cancer treatment after ADT using PCK1 inhibitors.

Integrative Analysis of Metabolome and Microbiome in Patients with Progressive Alcohol-Associated Liver Disease

Alcohol-associated liver disease is one of the most prevalent diseases around the world, with 10–20% of patients developing progressive liver disease. To identify the complex and correlated nature of metabolic and microbial data types in progressive liver disease, we performed an integrated analysis of the fecal and serum metabolomes with the gut microbiome in a cohort of 38 subjects, including 15 patients with progressive liver disease, 16 patients with non-progressive liver disease, and 7 control subjects. We found that although patients were generally clustered in three groups according to disease status, metabolites showed better separation than microbial species. Furthermore, eight serum metabolites were correlated with two microbial species, among which seven metabolites were decreased in patients with progressive liver disease. Five fecal metabolites were correlated with three microbial species, among which four metabolites were decreased in patients with progressive liver disease. When predicting progressive liver disease from non-progressive liver disease using correlated metabolic and microbial signatures with the random forest model, correlated serum metabolites and microbial species showed great predictive power, with the area under the receiver operating characteristic curve achieving 0.91. The multi-omics signatures identified in this study are helpful for the early identification of patients with progressive alcohol-associated liver disease, which is a key step for therapeutic intervention.

Contribution of upregulated aminoacyl-tRNA biosynthesis to metabolic dysregulation in gastric cancer

BACKGROUND AND AIM

Metabolic reprogramming is characterized by dysregulated levels of metabolites and metabolic enzymes. Integrated metabolomic and transcriptomic data analysis can help to elucidate changes in the levels of metabolites and metabolic enzymes, screen the core metabolic pathways, and develop novel therapeutic strategies for cancer.

METHODS

Here, the metabolome of gastric cancer tissues was determined using liquid chromatography-mass spectrometry. The transcriptome data from The Cancer Genome Atlas dataset were integrated with the liquid chromatography-mass spectrometry data to identify the common dysregulated gastric cancer-specific metabolic pathways. Additionally, the protein expression and clinical significance of key metabolic enzymes were examined using a gastric cancer tissue array.

RESULTS

Metabolomic analysis of 16 gastric cancer tissues revealed that among the 15 dysregulated metabolomic pathways, the aminoacyl-tRNA biosynthesis pathway in the gastric tissues was markedly upregulated relative to that in the adjacent noncancerous tissues, which was consistent with the results of transcriptome analysis. Bioinformatic analysis revealed that among the key regulators in the aminoacyl-tRNA biosynthesis pathway, the expression levels of threonyl-tRNA synthetase (TARS) and phenylalanyl-tRNA synthetase (FARSB) were correlated with tumor grade and poor survival, respectively. Additionally, gastric tissue array data analysis indicated that TARS and FARSB were upregulated in gastric cancer tissues and were correlated with poor prognosis and tumor metastasis.

CONCLUSIONS

This study demonstrated that the aminoacyl-tRNA biosynthesis pathway is upregulated in gastric cancer and both TARS and FARSB play key roles in the progression of gastric cancer. Additionally, a novel therapeutic strategy for gastric cancer was proposed that involves targeting the aminoacyl-tRNA biosynthesis pathway.

The influence of low-carbohydrate diets on the metabolic response to androgen-deprivation therapy in prostate cancer

BACKGROUND

Prostate cancer (PC) is the second most lethal cancer for men. For metastatic PC, standard first-line treatment is androgen deprivation therapy (ADT). While effective, ADT has many metabolic side effects. Previously, we found in serum metabolome analysis that ADT reduced androsterone sulfate, 3-hydroxybutyric acid, acyl-carnitines but increased serum glucose. Since ADT reduced ketogenesis, we speculate that low-carbohydrate diets (LCD) may reverse many ADT-induced metabolic abnormalities in animals and humans.

METHODS

In a multicenter trial of patients with PC initiating ADT randomized to no diet change (control) or LCD, we previously showed that LCD intervention led to significant weight loss, reduced fat mass, improved insulin resistance, and lipid profiles. To determine whether and how LCD affects ADT-induced metabolic changes, we analyzed serum metabolites after 3-, and 6-months of ADT on LCD versus control.

RESULTS

We found androsterone sulfate was most consistently reduced by ADT and was slightly further reduced in the LCD arm. Contrastingly, LCD intervention increased 3-hydroxybutyric acid and various acyl-carnitines, counteracting their reduction during ADT. LCD also reversed the ADT-reduced lactic acid, alanine, and S-adenosyl methionine (SAM), elevating glycolysis metabolites and alanine. While the degree of androsterone reduction by ADT was strongly correlated with glucose and indole-3-carboxaldehyde, LCD disrupted such correlations.

CONCLUSIONS

Together, LCD intervention significantly reversed many ADT-induced metabolic changes while slightly enhancing androgen reduction. Future research is needed to confirm these findings and determine whether LCD can mitigate ADT-linked comorbidities and possibly delaying disease progression by further lowering androgens.

Comprehensive Plasma Metabolomic Profile of Patients with Advanced Neuroendocrine Tumors (NETs). Diagnostic and Biological Relevance

Simple Summary

Metabolic flexibility is one of the key hallmarks of cancer and metabolites are the final products of this adaptation, reflecting the aberrant changes of tumors. However, the metabolic plasticity of each cancer type is still unknown, and specifically to date, there are no data on metabolic profile in neuroendocrine tumors. The aim of our retrospective study was to assess the metabolomic profile of NET patients to understand metabolic deregulation in these tumors and identify novel biomarkers with clinical potential. We provided, for the first time, a comprehensive metabolic profile of NET patients and identifies a distinctive metabolic signature in plasma of potential clinical use, selecting a reduced set of metabolites of high diagnostic accuracy. We have identified 32 novel enriched metabolic pathways in NETs related with the TCA cycle, and with arginine, pyruvate or glutathione metabolism, which have distinct implications in oncogenesis and may open innovative avenues of clinical research.

Abstract

Purpose: High-throughput “-omic” technologies have enabled the detailed analysis of metabolic networks in several cancers, but NETs have not been explored to date. We aim to assess the metabolomic profile of NET patients to understand metabolic deregulation in these tumors and identify novel biomarkers with clinical potential. Methods: Plasma samples from 77 NETs and 68 controls were profiled by GC−MS, CE−MS and LC−MS untargeted metabolomics. OPLS-DA was performed to evaluate metabolomic differences. Related pathways were explored using Metaboanalyst 4.0. Finally, ROC and OPLS-DA analyses were performed to select metabolites with biomarker potential. Results: We identified 155 differential compounds between NETs and controls. We have detected an increase of bile acids, sugars, oxidized lipids and oxidized products from arachidonic acid and a decrease of carnitine levels in NETs. MPA/MSEA identified 32 enriched metabolic pathways in NETs related with the TCA cycle and amino acid metabolism. Finally, OPLS-DA and ROC analysis revealed 48 metabolites with diagnostic potential. Conclusions: This study provides, for the first time, a comprehensive metabolic profile of NET patients and identifies a distinctive metabolic signature in plasma of potential clinical use. A reduced set of metabolites of high diagnostic accuracy has been identified. Additionally, new enriched metabolic pathways annotated may open innovative avenues of clinical research.

Interaction analysis of gene variants related to one-carbon metabolism with chronic hepatitis B infection in Chinese patients

BACKGROUND

The risk of chronic hepatitis B (CHB) infection is influenced by aberrant DNA methylation and altered nucleotide synthesis and repair, possibly caused by polymorphic variants in one-carbon metabolism genes. In the present study, we investigated the relationship between polymorphisms belonging to the one-carbon metabolic pathway and CHB infection.

METHODS

A case-control study using 230 CHB patients and 234 unrelated healthy controls was carried out to assess the genetic association of 24 single nucleotide polymorphisins (SNPs) determined by mass spectrometry.

RESULTS

Three SNPs, comprising rs10717122 and rs2229717 in serine hydroxymethyltransferase1/2 (SHMT2) and rs585800 in betaine-homocysteine S-methyltransferase (BHMT), were associated with the risk of CHB. Patients with DEL allele, DEL.DEL and DEL.T genotypes of rs10717122 had a 1.40-, 2.00- and 1.83-fold increased risk for CHB, respectively. Cases inheriting TA genotype of rs585800 had a 2.19-fold risk for CHB infection. The T allele of rs2229717 was less represented in the CHB cases (odds ratio = 0.66, 95% confidence interval = 0.48-0.92). The T allele of rs2229717 was less in patients with a low hepatitis B virus-DNA level compared to the control group (odds ratio = 0.49, 95% confidence interval = 0.25-0.97) and TT genotype of rs2229717 had a significant correlation with hepatitis B surface antigen level (p = 0.0195). Further gene-gene interaction analysis showed that subjects carrying the rs10717122 DEL.DEL/DEL.T and rs585800 TT/TA genotypes had a 2.74-fold increased risk of CHB.

CONCLUSIONS

The results of the present study suggest that rs10717122, rs585800 and rs2229717 and gene-gene interactions of rs10717122 and rs585800 affect the outcome of CHB infection, at the same time as indicating their usefulness as a predictive and diagnostic biomarker of CHB infection.

The Microbiome and p-Inulin in Hemodialysis: A Feasibility Study

Background

The intestinal microbiome is an appealing target for interventions in ESKD because of its likely contribution to uremic toxicity. Before conducting clinical trials of microbiome-altering treatments, it is necessary to understand the within-person and between-person variability in the composition and function of the gut microbiome in patients with ESKD.

Methods

We conducted a multicenter, nonrandomized, crossover feasibility study of patients on maintenance hemodialysis consisting of three phases: pretreatment (8 weeks); treatment, during which the prebiotic, p-inulin, was administered at a dosage of 8 g twice daily (12 weeks); and post-treatment (8 weeks). Stool samples were collected 1-2 times per week and blood was collected weekly for 28 weeks. The gut microbiome was characterized using 16S ribosomal-RNA sequencing and metabolomic profiling.

Results

A total of 11 of the 13 participants completed the 28-week study. Interparticipant variability was greater than intraparticipant variability for microbiome composition (P<0.001 by UniFrac distances) and metabolomic composition (P<0.001 by Euclidean distances). p-Inulin was well tolerated by 12 of 13 participants. Adherence to the frequent sample collection and self-aliquoting of stool samples were both 96%. A change in the microbiome composition from pretreatment to post-treatment was evident by the overall shifts in weighted UniFrac distances (P=0.004) and a progressive decrease in prevalence of high intraclass correlations, indicating an increase in intraparticipant microbiome diversity during and after p-inulin treatment. An effect of p-inulin on the metabolomic profile was not evident.

Conclusions

The intraparticipant stability of the gut microbiome under no-treatment conditions, the tolerability of p-inulin, the signals of increased diversity of the microbiome with p-inulin treatment, and the willingness of participants to provide stool samples all support the feasibility of a larger trial to investigate interventions targeting the gut microbiome in patients with ESKD. Whether or not p-inulin has sufficient efficacy as an intervention requires evaluation in larger studies.

Clinical Trial registry name and registration number

Gut Microbiome and p-Inulin in Hemodialysis, NCT02572882.

Serum Acylcarnitines Associated with High Short-Term Mortality in Patients with Alcoholic Hepatitis

Alcohol-related liver disease is one of the most prevalent liver diseases in the United States. Early stages of alcohol-related liver disease are characterized by accumulation of triglycerides in hepatocytes. Alcoholic hepatitis is a severe form of alcohol-related liver disease associated with significant morbidity and mortality. We sought to identify patients who are at greatest risk of death using serum lipids. First, we performed lipidomics analysis on serum samples collected from 118 patients with alcoholic hepatitis to identify lipid markers that are associated with high risk of death. Next, we performed gene set enrichment analysis on liver transcriptomics data to identify dysregulated lipid metabolism in patients who received liver transplantation. Finally, we built a random forest model to predict 30-day mortality using serum lipids. A total of 277 lipids were annotated in the serum of patients with alcoholic hepatitis, among which 25 were significantly different between patients in the deceased and alive groups. Five chemical clusters were significantly altered between the two groups. In particular, acylcarnitine cluster was enriched in the deceased group. Several hepatic lipid metabolism pathways were dysregulated in patients with alcoholic hepatitis who received liver transplantation. The mRNA expression of genes involved in the fatty acid transport into mitochondria and β-oxidation were also dysregulated. When predicting 30-day mortality in alcoholic hepatitis patients using serum lipids, we found that the area under the curve achieved 0.95. Serum lipids such as acylcarnitines may serve as biomarkers to identify alcoholic hepatitis patients at the greatest risk of death.

Metabolic regulation of prostate cancer heterogeneity and plasticity

Metabolic reprogramming is one of the main hallmarks of cancer cells. It refers to the metabolic adaptations of tumor cells in response to nutrient deficiency, microenvironmental insults, and anti-cancer therapies. Metabolic transformation during tumor development plays a critical role in the continued tumor growth and progression and is driven by a complex interplay between the tumor mutational landscape, epigenetic modifications, and microenvironmental influences. Understanding the tumor metabolic vulnerabilities might open novel diagnostic and therapeutic approaches with the potential to improve the efficacy of current tumor treatments. Prostate cancer is a highly heterogeneous disease harboring different mutations and tumor cell phenotypes. While the increase of intra-tumor genetic and epigenetic heterogeneity is associated with tumor progression, less is known about metabolic regulation of prostate cancer cell heterogeneity and plasticity. This review summarizes the central metabolic adaptations in prostate tumors, state-of-the-art technologies for metabolic analysis, and the perspectives for metabolic targeting and diagnostic implications.

An overview of advances in multi-omics analysis in prostate cancer

Prostate cancer (PCa) is a deadly disease for men, and studies of all types of omics data are necessary to promote precision medicine. The maturity of sequencing technology, the improvements of computer processing power, and the progress achieved in omics analysis methods have improved research efficiency and saved research costs. The occurrence and development of PCa is due to multisystem and multilevel pathological changes. Although omics research at a single level is important, this approach often has limitations. In contrast, the combined analysis of multiple types of omics data can better analyze PCa changes as a whole, thus ensuring the validity of research results to the greatest extent. This paper introduces the applications of single omics in PCa and then summarizes research progress in the combined analysis of two or more types of omics data, so as to systematically and comprehensively analyze the necessity of combined analysis of multiple omics data in PCa.

Metabolomic effects of androgen deprivation therapy treatment for prostate cancer

Androgen deprivation therapy (ADT) is the main treatment strategy for men with metastatic prostate cancer (PC). However, ADT is associated with various metabolic disturbances, including impaired glucose tolerance, insulin resistance and weight gain, increasing risk of diabetes and cardiovascular death. Much remains unknown about the metabolic pathways and disturbances altered by ADT and the mechanisms. We assessed the metabolomic effects of ADT in the serum of 20 men receiving ADT. Sera collected before (baseline), 3 and 6 months after initiation of ADT was used for the metabolomics and lipidomics analyses. The ADT‐associated metabolic changes were identified by univariable and multivariable statistical analysis, ANOVA, and Pearson correlation. We found multiple key changes. First, ADT treatments reduced the steroid synthesis as reflected by the lower androgen sulfate and other steroid hormones. Greater androgen reduction was correlated with higher serum glucose levels, supporting the diabetogenic role of ADT. Second, ADT consistently decreased the 3‐hydroxybutyric acid and ketogenesis. Third, many acyl‐carnitines were reduced, indicating the effects on the fatty acid metabolism. Fourth, ADT was associated with a corresponding reduction in 3‐formyl indole (a.k.a. indole‐3‐carboxaldehyde), a microbiota‐derived metabolite from the dietary tryptophan. Indole‐3‐carboxaldehyde is an agonist for the aryl hydrocarbon receptor and regulates the mucosal reactivity and inflammation. Together, these ADT‐associated metabolomic analyses identified reduction in steroid synthesis and ketogenesis as prominent features, suggesting therapeutic potential of restricted ketogenic diets, though this requires formal testing. ADT may also impact the microbial production of indoles related to the immune pathways. Future research is needed to determine the functional impact and underlying mechanisms to prevent ADT‐linked comorbidities and diabetes risk.