Author Correction: The evolving metabolic landscape of chromatin biology and epigenetics.

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The evolving metabolic landscape of chromatin biology and epigenetics.

Molecular inputs to chromatin via cellular metabolism are modifiers of the epigenome. These inputs - which include both nutrient availability as a result of diet and growth factor signalling - are implicated in linking the environment to the maintenance of cellular homeostasis and cell identity. Recent studies have demonstrated that these inputs are much broader than had previously been known, encompassing metabolism from a wide variety of sources, including alcohol and microbiotal metabolism. These factors modify DNA and histones and exert specific effects on cell biology, systemic physiology and pathology. In this Review, we discuss the nature of these molecular networks, highlight their role in mediating cellular responses and explore their modifiability through dietary and pharmacological interventions.

Dietary methionine influences therapy in mouse cancer models and alters human metabolism.

Nutrition exerts considerable effects on health, and dietary interventions are commonly used to treat diseases of metabolic aetiology. Although cancer has a substantial metabolic component1, the principles that define whether nutrition may be used to influence outcomes of cancer are unclear2. Nevertheless, it is established that targeting metabolic pathways with pharmacological agents or radiation can sometimes lead to controlled therapeutic outcomes. By contrast, whether specific dietary interventions can influence the metabolic pathways that are targeted in standard cancer therapies is not known. Here we show that dietary restriction of the essential amino acid methionine-the reduction of which has anti-ageing and anti-obesogenic properties-influences cancer outcome, through controlled and reproducible changes to one-carbon metabolism. This pathway metabolizes methionine and is the target of a variety of cancer interventions that involve chemotherapy and radiation. Methionine restriction produced therapeutic responses in two patient-derived xenograft models of chemotherapy-resistant RAS-driven colorectal cancer, and in a mouse model of autochthonous soft-tissue sarcoma driven by a G12D mutation in KRAS and knockout of p53 (KrasG12D/+;Trp53-/-) that is resistant to radiation. Metabolomics revealed that the therapeutic mechanisms operate via tumour-cell-autonomous effects on flux through one-carbon metabolism that affects redox and nucleotide metabolism-and thus interact with the antimetabolite or radiation intervention. In a controlled and tolerated feeding study in humans, methionine restriction resulted in effects on systemic metabolism that were similar to those obtained in mice. These findings provide evidence that a targeted dietary manipulation can specifically affect tumour-cell metabolism to mediate broad aspects of cancer outcome.

MCIBox: a toolkit for single-molecule multi-way chromatin interaction visualization and micro-domains identification.

The emerging ligation-free three-dimensional (3D) genome mapping technologies can identify multiplex chromatin interactions with single-molecule precision. These technologies not only offer new insight into high-dimensional chromatin organization and gene regulation, but also introduce new challenges in data visualization and analysis. To overcome these challenges, we developed MCIBox, a toolkit for multi-way chromatin interaction (MCI) analysis, including a visualization tool and a platform for identifying micro-domains with clustered single-molecule chromatin complexes. MCIBox is based on various clustering algorithms integrated with dimensionality reduction methods that can display multiplex chromatin interactions at single-molecule level, allowing users to explore chromatin extrusion patterns and super-enhancers regulation modes in transcription, and to identify single-molecule chromatin complexes that are clustered into micro-domains. Furthermore, MCIBox incorporates a two-dimensional kernel density estimation algorithm to identify micro-domains boundaries automatically. These micro-domains were stratified with distinctive signatures of transcription activity and contained different cell-cycle-associated genes. Taken together, MCIBox represents an invaluable tool for the study of multiple chromatin interactions and inaugurates a previously unappreciated view of 3D genome structure.

Serum 14-3-3η is a Marker that Complements Current Biomarkers for the Diagnosis of RA: Evidence from a Meta-analysis.

OBJECTIVE: To systematically evaluate the diagnostic value of 14-3-3η protein for rheumatoid arthritis (RA). METHOD: Searched PubMed, Web of Science, Embase and China Biology Medicine (CBM) databases comprehensively from inception to May 2020. The evaluation index were the pooled sensitivity, specificity, diagnosis odds ratio (DOR), positive likelihood ratio (PLR), negative likelihood ratio (NLR), as well as the area under the summary receiver operating characteristic (SROC) curves. Meta-Disc 1.4 and RevMan 5.3 were used to analyze all statistics. QUADAS-2 tool was applied to evaluate the quality of eligible studies. Subgroup analysis and meta-regression were used to explore the sources of heterogeneity. RESULTS: Nine articles containing eleven records were eligible for this meta-analysis. The pooled sensitivity of 14-3-3η was 0.63 (95% CI: 0.60 to 0.66), the pooled specificity was 0.90 (95% CI: 0.88 to 0.91). The pooled PLR and NLR was 6.10 (95% CI: 4.67 to 7.96) and 0.40 (95% CI: 0.33 to 0.48), respectively. The pooled DOR was 15.90 (95% CI: 11.15 to 22.68), and the area under the curve (AUC) was 0.8696. Compared with a single indicator (rheumatoid factor or anti-citrullinated protein antibodies), adding 14-3-3η can bring incremental benefits to the diagnosis of RA. The results of subgroup analysis and meta-regression suggested that the two factors (ethnicity, early vs established RA) we analyzed might not be the source of heterogeneity (P value were 0.0979 and 0.4298, respectively) and there was no publication bias among these articles (P = .42). CONCLUSION: Serum 14-3-3η protein is a supplementary biomarker in the diagnosis of RA.

Edaravone Ameliorates Cerebral Ischemia-Reperfusion Injury by Downregulating Ferroptosis via the Nrf2/FPN Pathway in Rats.

Edaravone, an antioxidant protective agent, has anti-cerebral ischemic reperfusion injury (CIRI) effects, but its anti-CIRI mechanism is unclear. The aim of this study is to investigate the anti-CIRI mechanism of edaravone based on the nuclear factor-E2-related factor 2 (Nrf2)/ferroportin (FPN) pathway that regulates ferroptosis-mediated cerebral ischemia-reperfusion injury. We evaluated the brain injury by constructing a middle cerebral artery occlusion and reperfusion (MCAO/R) model in rats. The results showed that cerebral infarct volume and neurological impairment scores were increased in cerebral ischemia-reperfusion rats, with impaired sensorimotor ability; furthermore, brain tissue glutathione (GSH) content was decreased, Fe2+, malondialdehyde (MDA) and lipide peroxide (LPO) content were increased, and the expression level of glutathione peroxidase 4 (GPX4), a key protein of ferroptosis, was also decreased. Meanwhile, the Nrf2 expression level was increased and the FPN expression level was decreased after cerebral ischemia-reperfusion, while the levels of interleukin (IL)-6, IL-1ß, tumor necrosis factor (TNF)-α, and myeloperoxidase (MPO) were increased. However, edaravone exhibited a protective effect on cerebral infarct and neurological and sensorimotor function in relevant tests. In addition, we also found that edaravone decreased the contents of Fe2+, MDA, and LPO in the brain tissue of MCAO/R rats and increased GSH content to inhibit ferroptosis. Furthermore, Western blot showed that after treatment with edaravone, the expression of Nrf2, GPX4, and FPN was up-regulated, the nuclear location of Nrf2 was increased, and the levels of inflammation-related indicators IL-6, IL-1ß, TNF-α, and MPO were lower than in the MCAO/R group. Our results demonstrated that edaravone inhibits ferroptosis to attenuate CIRI, probably through the activation of the Nrf2/FPN pathway.

Evolved resistance to partial GAPDH inhibition results in loss of the Warburg effect and in a different state of glycolysis.

Aerobic glycolysis or the Warburg effect (WE) is characterized by increased glucose uptake and incomplete oxidation to lactate. Although the WE is ubiquitous, its biological role remains controversial, and whether glucose metabolism is functionally different during fully oxidative glycolysis or during the WE is unknown. To investigate this question, here we evolved resistance to koningic acid (KA), a natural product that specifically inhibits glyceraldehyde-3-phosphate dehydrogenase (GAPDH), a rate-controlling glycolytic enzyme, during the WE. We found that KA-resistant cells lose the WE but continue to conduct glycolysis and surprisingly remain dependent on glucose as a carbon source and also on central carbon metabolism. Consequently, this altered state of glycolysis led to differential metabolic activity and requirements, including emergent activities in and dependences on fatty acid metabolism. These findings reveal that aerobic glycolysis is a process functionally distinct from conventional glucose metabolism and leads to distinct metabolic requirements and biological functions.

Urinary Tumor Necrosis Factor-Like Weak Inducer of Apoptosis as a Biomarker for Diagnosis and Evaluating Activity in Lupus Nephritis: A Meta-analysis.

OBJECTIVE: Urinary tumor necrosis factor-like weak inducer of apoptosis (uTWEAK) has been identified as a candidate biomarker for lupus nephritis (LN). However, its diagnostic value remains unclear. This meta-analysis was conducted to comprehensively evaluate the value of uTWEAK for diagnosis and evaluating activity in LN. METHODS: Medline, Web of Science, Chinese Biomedical Medical, and Chinese National Knowledge Infrastructure databases were searched to acquire eligible studies published before September 30, 2019. The quality of the studies was evaluated by Quality Assessment of Diagnostic Accuracy Studies-2. Summary receiver operating characteristic curve and area under the curve were applied to summarize the overall diagnostic performances. The pooled sensitivity, specificity, and diagnostic odds ratio (DOR) were calculated with the fixed-effects model. RevMan 5.3, Stata 12.0, and Meta-disc 1.4 software were used. RESULTS: A total of 7 studies were included. Of these, 4 studies were available for comparison between SLE with and without LN, and 3 studies were for active and inactive LN. The total area under the curve was 0.8640, and DOR was 14.89 (95% confidence interval [CI], 7.95-27.86). For LN diagnosis, the pooled sensitivity, specificity, and DOR were 0.55 (95% CI, 0.47-0.63), 0.92 (95% CI, 0.86-0.96), and 16.54 (95% CI, 7.57-36.15), respectively. For assessing LN activity, the pooled sensitivity, specificity, and DOR were 0.91 (95% CI, 0.82-0.96), 0.70 (95% CI, 0.58-0.81), and 18.45 (95% CI, 7.45-45.87), respectively. CONCLUSIONS: This meta-analysis indicated that uTWEAK has relatively moderate sensitivity and specificity for diagnosis and evaluating activity in LN, suggesting that uTWEAK can serve as a helpful biomarker for LN.

Thermodynamic constraints on the regulation of metabolic fluxes.

Nutrition and metabolism are fundamental to cellular function. Metabolic activity (i.e. rates of flow, most commonly referred to as flux) is constrained by thermodynamics and regulated by the activity of enzymes. The general principles that relate biological and physical variables to metabolic control are incompletely understood. Using metabolic control analysis and computer simulations in several models of simplified metabolic pathways, we derive analytical expressions that define relationships between thermodynamics, enzyme activity, and flux control. The relationships are further analyzed in a mathematical model of glycolysis as an example of a complex biochemical pathway. We show that metabolic pathways that are very far from equilibrium are controlled by the activity of upstream enzymes. However, in general, regulation of metabolic fluxes by an enzyme has a more adaptable pattern, which relies more on distribution of free energy among reaction steps in the pathway than on the thermodynamic properties of the given enzyme. These findings show how the control of metabolic pathways is shaped by thermodynamic constraints of the given pathway.

Identification of Cancer-associated metabolic vulnerabilities by modeling multi-objective optimality in metabolism.

BACKGROUND: Cancer cells undergo global reprogramming of cellular metabolism to satisfy demands of energy and biomass during proliferation and metastasis. Computational modeling of genome-scale metabolic models is an effective approach for designing new therapeutics targeting dysregulated cancer metabolism by identifying metabolic enzymes crucial for satisfying metabolic goals of cancer cells, but nearly all previous studies neglect the existence of metabolic demands other than biomass synthesis and trade-offs between these contradicting metabolic demands. It is thus necessary to develop computational models covering multiple metabolic objectives to study cancer metabolism and identify novel metabolic targets. METHODS: We developed a multi-objective optimization model for cancer cell metabolism at genome-scale and an integrated, data-driven workflow for analyzing the Pareto optimality of this model in achieving multiple metabolic goals and identifying metabolic enzymes crucial for maintaining cancer-associated metabolic phenotypes. Using this workflow, we constructed cell line-specific models for a panel of cancer cell lines and identified lists of metabolic targets promoting or suppressing cancer cell proliferation or the Warburg Effect. The targets were then validated using knockdown and over-expression experiments in cultured cancer cell lines. RESULTS: We found that the multi-objective optimization model correctly predicted phenotypes including cell growth rates, essentiality of metabolic genes and cell line specific sensitivities to metabolic perturbations. To our surprise, metabolic enzymes promoting proliferation substantially overlapped with those suppressing the Warburg Effect, suggesting that simply targeting the overlapping enzymes may lead to complicated outcomes. We also identified lists of metabolic enzymes important for maintaining rapid proliferation or high Warburg Effect while having little effect on the other. The importance of these enzymes in cancer metabolism predicted by the model was validated by their association with cancer patient survival and knockdown and overexpression experiments in a variety of cancer cell lines. CONCLUSIONS: These results confirm this multi-objective optimization model as a novel and effective approach for studying trade-off between metabolic demands of cancer cells and identifying cancer-associated metabolic vulnerabilities, and suggest novel metabolic targets for cancer treatment.

Risk of gestational diabetes mellitus in systemic lupus erythematosus pregnancy: a systematic review and meta-analysis.

BACKGROUND: It is well established that the risks of insulin resistance and diabetes mellitus are elevated in systemic lupus erythematosus (SLE) patients. However, the relationship between SLE pregnancy and gestational diabetes mellitus (GDM) is still obscure. We perform the present systematic review and meta-analysis to determine the relationship between GDM and SLE pregnancy. METHODS: According to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement, relevant studies were carefully retrieved through PubMed, Cochrane library and Web of Science, China National Knowledge Infrastructure, Wanfang database and China Biology Medicine database from inception till 30 August 2018. GDM risk ratio (RR) of pregnant SLE patients versus controls was calculated to evaluate the association between GDM and SLE. Pooled RRs and 95% confidence intervals (CIs) were calculated using random effects model by R software. RESULTS: The literature retrieval identified 339 potential studies in total, and five studies containing 3432 pregnant participants with 248 GDM events were included finally. Pooled analysis found that the risk of GDM were not significant increased in SLE patients compared to controls (RR = 1.08, 95% CI = 0.49 to 2.41, Z = 0.19 and P = 0.848). Nevertheless, meta-regression identified that glucocorticoids use and anti-double stranded DNA antibodies positive of SLE patients were positively associated with the risk of GDM. CONCLUSIONS: Our meta-analysis demonstrated that SLE pregnancy may not increase the risk of GDM, but the steroid use during pregnancy was associated with increased risk of GDM. Further large prospective and basic immunologic studies should be implemented for exploring the mechanism underlying glucocorticoids use and GDM.

Molecular mechanism of 15-lipoxygenase allosteric activation and inhibition.

Human reticulocyte 15-lipoxygenase (15-LOX) plays an important role in inflammation resolution and is also involved in many cancer-related processes. Both an activator and an inhibitor will serve as research tools for understanding the biological functions of 15-LOX and provide opportunities for drug discovery. In a previous study, both allosteric activators and inhibitors of 15-LOX were discovered through a virtual screening based computational approach. However, why molecules binding to the same site causes different effects remains to be disclosed. In the present study, we used previously reported activator and inhibitor molecules as probes to elucidate the mechanism of allosteric regulation of 15-LOX. We measured the influences of the allosteric activator and inhibitor on the enzymatic reaction rate and found that the activator increases 15-LOX activity by preventing substrate inhibition instead of increasing the turnover number. The inhibitor can also prevent substrate inhibition but decreases the turnover number at the same time, resulting in inhibition. Molecular dynamics simulations were conducted to help explain the underlying mechanism of allostery. Both the activator and inhibitor were demonstrated to be able to prevent 15-LOX from transforming into potentially inactive conformations. Compared to the activator, the inhibitor molecule restrains the motions of residues around the substrate binding site and reduces the flexibility of 15-LOX. These results explained the different effects between the activator and the inhibitor and shed light on how to effectively design novel activator molecules.

Understanding metabolism with flux analysis: From theory to application.

Quantitative and qualitative knowledge of metabolic rates (i.e. fluxes) over a metabolic network and in specific cellular compartments gives insights into the regulation of metabolism and helps to understand the contribution of metabolic alterations to pathology. In this review we introduce methodology to resolve metabolic fluxes from stable isotope labeling and relevant techniques in model development, model simplification, flux uncertainty analysis and experimental design that together is termed metabolic flux analysis. Finally we discuss applications using metabolic flux analysis to elucidate mechanisms pertinent to tumor cell metabolism. We hope that this review gives the readers a brief introduction of how flux analysis is conducted, how technical issues related to it are addressed, and how its application has contributed to our knowledge of tumor cell metabolism.

A Flux Balance of Glucose Metabolism Clarifies the Requirements of the Warburg Effect.

The Warburg effect, or aerobic glycolysis, is marked by the increased metabolism of glucose to lactate in the presence of oxygen. Despite its widespread prevalence in physiology and cancer biology, the causes and consequences remain incompletely understood. Here, we show that a simple balance of interacting fluxes in glycolysis creates constraints that impose the necessary conditions for glycolytic flux to generate lactate as opposed to entering into the mitochondria. These conditions are determined by cellular redox and energy demands. By analyzing the constraints and sampling the feasible region of the model, we further study how cell proliferation rate and mitochondria-associated NADH oxidizing and ATP producing fluxes are interlinked. Together this analysis illustrates the simplicity of the origins of the Warburg effect by identifying the flux distributions that are necessary for its instantiation.

Deep Learning for Drug-Induced Liver Injury.

Drug-induced liver injury (DILI) has been the single most frequent cause of safety-related drug marketing withdrawals for the past 50 years. Recently, deep learning (DL) has been successfully applied in many fields due to its exceptional and automatic learning ability. In this study, DILI prediction models were developed using DL architectures, and the best model trained on 475 drugs predicted an external validation set of 198 drugs with an accuracy of 86.9%, sensitivity of 82.5%, specificity of 92.9%, and area under the curve of 0.955, which is better than the performance of previously described DILI prediction models. Furthermore, with deep analysis, we also identified important molecular features that are related to DILI. Such DL models could improve the prediction of DILI risk in humans. The DL DILI prediction models are freely available at http://www.repharma.cn/DILIserver/DILI_home.php.

Slow and bimolecular folding of a de novo designed monomeric protein DS119.

De novo protein design offers a unique means to test and advance our understanding of how proteins fold. However, most current design methods are native structure eccentric and folding kinetics has rarely been considered in the design process. Here, we show that a de novo designed mini-protein DS119, which folds into a ßαß structure, exhibits unusually slow and concentration-dependent folding kinetics. For example, the folding time for 50 µM of DS119 was estimated to be ~2 s. Stopped-flow fluorescence resonance energy transfer experiments further suggested that its folding was likely facilitated by a transient dimerization process. Taken together, these results highlight the need for consideration of the entire folding energy landscape in de novo protein design and provide evidence suggesting nonnative interactions can play a key role in protein folding.

Coupling of co-transcriptional splicing and 3' end Pol II pausing during termination in Arabidopsis.

BACKGROUND: In Arabidopsis, RNA Polymerase II (Pol II) often pauses within a few hundred base pairs downstream of the polyadenylation site, reflecting efficient transcriptional termination, but how such pausing is regulated remains largely elusive. RESULT: Here, we analyze Pol II dynamics at 3' ends by combining comprehensive experiments with mathematical modelling. We generate high-resolution serine 2 phosphorylated (Ser2P) Pol II positioning data specifically enriched at 3' ends and define a 3' end pause index (3'PI). The position but not the extent of the 3' end pause correlates with the termination window size. The 3'PI is not decreased but even mildly increased in the termination deficient mutant xrn3, indicating 3' end pause is a regulatory step early during the termination and before XRN3-mediated RNA decay that releases Pol II. Unexpectedly, 3'PI is closely associated with gene exon numbers and co-transcriptional splicing efficiency. Multiple exons genes often display stronger 3' end pauses and more efficient on-chromatin splicing than genes with fewer exons. Chemical inhibition of splicing strongly reduces the 3'PI and disrupts its correlation with exon numbers but does not globally impact 3' end readthrough levels. These results are further confirmed by fitting Pol II positioning data with a mathematical model, which enables the estimation of parameters that define Pol II dynamics. CONCLUSION: Our work highlights that the number of exons via co-transcriptional splicing is a major determinant of Pol II pausing levels at the 3' end of genes in plants.

USP39 promotes hepatocellular carcinogenesis through regulating alternative splicing in cooperation with SRSF6/HNRNPC.

Abnormal alternative splicing (AS) caused by alterations in spliceosomal factors is implicated in cancers. Standard models posit that splice site selection is mainly determined by early spliceosomal U1 and U2 snRNPs. Whether and how other mid/late-acting spliceosome components such as USP39 modulate tumorigenic splice site choice remains largely elusive. We observed that hepatocyte-specific overexpression of USP39 promoted hepatocarcinogenesis and potently regulated splice site selection in transgenic mice. In human liver cancer cells, USP39 promoted tumor proliferation in a spliceosome-dependent manner. USP39 depletion deregulated hundreds of AS events, including the oncogenic splice-switching of KANK2. Mechanistically, we developed a novel RBP-motif enrichment analysis and found that USP39 modulated exon inclusion/exclusion by interacting with SRSF6/HNRNPC in both humans and mice. Our data represented a paradigm for the control of splice site selection by mid/late-acting spliceosome proteins and their interacting RBPs. USP39 and possibly other mid/late-acting spliceosome proteins may represent potential prognostic biomarkers and targets for cancer therapy.

Pinpointing Cancer Sub-Type Specific Metabolic Tasks Facilitates Identification of Anti-cancer Targets.

Metabolic reprogramming is one of the hallmarks of tumorigenesis. Understanding the metabolic changes in cancer cells may provide attractive therapeutic targets and new strategies for cancer therapy. The metabolic states are not the same in different cancer types or subtypes, even within the same sample of solid tumors. In order to understand the heterogeneity of cancer cells, we used the Pareto tasks inference method to analyze the metabolic tasks of different cancers, including breast cancer, lung cancer, digestive organ cancer, digestive tract cancer, and reproductive cancer. We found that cancer subtypes haves different propensities toward metabolic tasks, and the biological significance of these metabolic tasks also varies greatly. Normal cells treat metabolic tasks uniformly, while different cancer cells focus on different pathways. We then integrated the metabolic tasks into the multi-objective genome-scale metabolic network model, which shows higher accuracy in the in silico prediction of cell states after gene knockout than the conventional biomass maximization model. The predicted potential single drug targets could potentially turn into biomarkers or drug design targets. We further implemented the multi-objective genome-scale metabolic network model to predict synthetic lethal target pairs of the Basal and Luminal B subtypes of breast cancer. By analyzing the predicted synthetic lethal targets, we found that mitochondrial enzymes are potential targets for drug combinations. Our study quantitatively analyzes the metabolic tasks of cancer and establishes cancer type-specific metabolic models, which opens a new window for the development of specific anti-cancer drugs and provides promising treatment plans for specific cancer subtypes.