Integrative analysis of multimodal patient data identifies personalized predictors of tuberculosis treatment prognosis

Tuberculosis (TB) afflicted 10.6 million people in 2021, and its global burden is increasing due to multidrug-resistant TB (MDR-TB) and extensively resistant TB (XDR-TB). Here, we analyze multi-domain information from 5,060 TB patients spanning 10 countries with high burden of MDR-TB from the NIAID TB Portals database to determine predictors of TB treatment outcome. Our analysis revealed significant associations between radiological, microbiological, therapeutic, and demographic data modalities. Our machine learning model, built with 203 features across modalities outperforms models built using each modality alone in predicting treatment outcomes, with an accuracy of 83% and area under the curve of 0.84. Notably, our analysis revealed that the drug regimens Bedaquiline-Clofazimine-Cycloserine-Levofloxacin-Linezolid and Bedaquiline-Clofazimine-Linezolid-Moxifloxacin were associated with treatment success and failure, respectively, for MDR non-XDR-TB. Drug combinations predicted to be synergistic by the INDIGO algorithm performed better than antagonistic combinations. Our prioritized set of features predictive of treatment outcomes can ultimately guide the personalized clinical management of TB.

Leveraging metabolic modeling and machine learning to uncover modulators of quiescence depth

Quiescence, a temporary withdrawal from the cell cycle, plays a key role in tissue homeostasis and regeneration. Quiescence is increasingly viewed as a continuum between shallow and deep quiescence, reflecting different potentials to proliferate. The depth of quiescence is altered in a range of diseases and during aging. Here, we leveraged genome-scale metabolic modeling (GEM) to define the metabolic and epigenetic changes that take place with quiescence deepening. We discovered contrasting changes in lipid catabolism and anabolism and diverging trends in histone methylation and acetylation. We then built a multi-cell type machine learning model that accurately predicts quiescence depth in diverse biological contexts. Using both machine learning and genome-scale flux simulations, we performed high-throughput screening of chemical and genetic modulators of quiescence and identified novel small molecule and genetic modulators with relevance to cancer and aging.

Metabolic stratification of human breast tumors reveal subtypes of clinical and therapeutic relevance

Extensive metabolic heterogeneity in breast cancers has limited the deployment of metabolic therapies. To enable patient stratification, we studied the metabolic landscape in breast cancers (∼3000 patients combined) and identified three subtypes with increasing degrees of metabolic deregulation. Subtype M1 was found to be dependent on bile-acid biosynthesis, whereas M2 showed reliance on methionine pathway, and M3 engaged fatty-acid, nucleotide, and glucose metabolism. The extent of metabolic alterations correlated strongly with tumor aggressiveness and patient outcome. This pattern was reproducible in independent datasets and using in vivo tumor metabolite data. Using machine-learning, we identified robust and generalizable signatures of metabolic subtypes in tumors and cell lines. Experimental inhibition of metabolic pathways in cell lines representing metabolic subtypes revealed subtype-specific sensitivity, therapeutically relevant drugs, and promising combination therapies. Taken together, metabolic stratification of breast cancers can thus aid in predicting patient outcome and designing precision therapies.

Abstract 5263: Licochalcone A is a candidate for breast cancer prevention through its reprogramming of metabolic and antioxidant pathways

Background: Increased adiposity is a risk factor for postmenopausal breast cancer. It is accompanied by protumorigenic effects: chronic low-grade inflammation and elevated levels of reactive oxygen species. Breast cancer risk reducing drugs with proven efficacy have adverse side effects, significantly minimizing their uptake and impact. Effective alternative strategies with lower toxicity are needed. We have shown that licochalcone A (LicA) suppresses aromatase expression and activity, enhances the activity of detoxifying enzymes, and reduces estrogen genotoxic metabolism in cell lines and animal models. However, no previous data exist on the breast tissue of women at substantial risk of breast cancer. We hypothesize that LicA creates a tumor preventive environment in the breast by modulating antioxidant/anti-inflammatory responses in the breast and adipogenesis leading to decreased proliferation.

Methods: We prepared microstructures from the fresh tissue of contralateral unaffected mastectomy specimens of 6 postmenopausal women with incident unilateral breast cancer. After exposing them to DMSO (control) and LicA (5 µM), we performed total RNA sequencing. Differentially expressed genes were identified, and analyzed by gene ontology and pathway membership. The RNA-seq data was utilized also to conduct metabolism flux analysis. Combined enrichment scores > 4 and FDR < 0.05 was considered significant. The NanoString metabolism panel was employed in 6 additional subjects. We performed live cell imaging to monitor proliferation of pre-malignant DCIS.COM, DCIS.COM/ER+ PR+; and malignant MDA-MB-231 (ER- PR-), MCF-7 (ER+ PR+), MCF-7aro, and BRCA1 defective HCC-1937, and HCC3153 cells.

Results: We observed upregulation of antioxidant genes (up to 8-fold), consistent with upregulation of NRF2 and the thioredoxin system, the major regulators of antioxidant pathways. This was accompanied with the significant downregulation of RELA- and NF-kB1-dependent inflammatory pathways. In addition, we observed decreased expression of the pro-adipogenic transcription factors SREBF1 and SREBF2, which may explain the downregulation (4 to 32-fold) of cholesterol biosynthesis and transport, and lipid metabolism genes. Metabolism studies confirmed these data and demonstrated a robust increase in the pentose phosphate shunt and NAD(P)H generation without enhancing ribose 5 phosphate formation, suggesting an antioxidant and anti-proliferative environment. LicA also suppressed proliferation of pre-malignant and malignant cells, with sustained effects on aggressive cells at doses < 10 µM.

Conclusion: Our data suggest that LicA is a good candidate for breast cancer prevention through modulation of metabolic and antioxidant pathways leading to decreased proliferation. Our ongoing in vivo study will further demonstrate the efficacy of LicA for breast cancer prevention.

Citation Format: Atieh Hajirahimkhan, Elizabeth Bartom, Sriram Chandrasekaran, Xiaoling Xuei, Susan Clare, Seema Khan. Licochalcone A is a candidate for breast cancer prevention through its reprogramming of metabolic and antioxidant pathways. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5263.

Abstract 3677: DNA damage signaling activates de novo GTP synthesis to promote chemoradiation resistance in glioblastoma

Glioblastoma (GBM) is uniformly fatal due to inherent radiation (RT) and chemotherapy resistance. We have found this therapeutic resistance is mediated by alterations in tumor cellular metabolic activity. Our group and others have found that metabolites can regulate DNA repair and RT resistance in brain tumors, but little is known about how DNA damage regulates metabolic pathway activity in cancer. Here, we show that DNA damage acutely increases guanine-containing purine metabolites in multiple in vitro and intracranial GBM models. By interrogating metabolic fluxes in vitro using a variety of stable isotope tracers, we confirmed RT-induced elevation in guanylates was due to increased de novo purine synthesis (DNPS) rather than activation of purine salvage. By developing and using novel stable isotope tracing methods to directly measure DNPS in awake, unrestrained mice, we confirmed that orthotopic GBMs have higher DNPS rates than adjacent cortical tissue that further increase after treatment with RT. Neither salvage synthesis of purines nor pyrimidine synthesis were impacted by RT in any intracranial tissues. With these findings, we opened a clinical study to directly measure purine synthesis in patients, and we found that human GBMs have similarly high purine synthesis rates compared to normal brain tissue. Because DNA damage activated DNPS without affecting purine salvage or pyrimidine synthesis, we reasoned that active signaling may be involved. Indeed, therapy-induced DNPS increases are lost in vitro and in vivo upon pharmacological or genomic inhibition of the DNA-damage sensing kinase DNA-PK. Moreover, RT and DNA-PK have direct influence over the spatial organization of DNPS enzymes, including IMPDH, the rate-limiting step in guanylate synthesis. Because purines can promote DNA repair, these findings suggest that DNA-PK signaling helps promote DNA repair in part by causing the spatial reorganization of DNPS enzymes, thereby activating purine synthesis. To determine if disrupting this regulation can augment GBM treatment efficacy, we combined an FDA-approved inhibitor of purine synthesis with chemoradiation in a variety of mouse models of GBM. Critically, targeting GTP synthesis improved the efficacy of both RT alone and chemoradiation in multiple patient-derived and syngeneic intracranial models, suggesting a potential therapeutic targeting opportunity in patients. In this study, we have developed novel methodology to directly measure purine synthesis in brain tumors in mice and humans. With these tools, we discovered that after DNA damage, DNA-PK mediates a novel pathway controlling the spatial reorganization of purine synthesis enzymes and subsequent DNPS increases. The resulting elevation of GTP levels promotes therapy resistance in tumors, and we are now directly measuring and inhibiting this molecular activity in patients with GBM in an effort to improve standard therapy.

Citation Format: Andrew J. Scott, Alexandra M. O'Brien, Weihua Zhou, Vidhi Pareek, Zhou Sha, Sravya Palavalasa, Ayesha U. Kothari, Kari Wilder-Romans, Li Zhang, Anthony C. Andren, Sriram Chandrasekaran, Jason Heth, Yoshie Umemura, Nathan Qi, John Woulfe, Sriram Venneti, Meredith A. Morgan, Theodore S. Lawrence, Wajd N. Al-Holou, Costas A. Lyssiotis, Daniel R. Wahl. DNA damage signaling activates de novo GTP synthesis to promote chemoradiation resistance in glioblastoma. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3677.

Abstract P2-08-01: Licochalcone A from licorice reprograms metabolic and antioxidant pathways in the breast leading to a tumor preventive environment

Background: Increased adiposity is a risk factor for postmenopausal breast cancer. It is often accompanied by chronic low-grade inflammation and elevated levels of reactive oxygen species, which drive breast tumorigenesis. Risk reducing drugs such as selective estrogen receptor modulators and aromatase inhibitors, which have demonstrated efficacy, have had a significantly low acceptance among women at high risk for breast cancer. This hesitancy is mainly due to the adverse side effects of these medications such as vasomotor symptoms, osteoporosis, thromboembolism, and uterine cancer. Therefore, alternative strategies with lower toxicity and greater acceptability are needed. We have previously shown that licochalcone A (LicA) from licorice (Glycyrrhiza inflata), which has osteogenic effects, suppresses aromatase expression and activity, enhances the activity of detoxifying enzymes, and reduces estrogen genotoxic metabolism in cell lines and animal models. However, its effects on the breast tissue of high-risk women are understudied. We hypothesize that LicA creates a tumor preventive environment in the breast by locally modulating adipogenesis and antioxidant/anti-inflammatory responses leading to decreased proliferation. Methods: We prepared microstructures from fresh tissue of contralateral unaffected mastectomy specimens of 6 postmenopausal women with incident unilateral breast cancer. We exposed these to DMSO (control) and LicA (5 µM) for 24 h. Employing total RNA sequencing, we examined differential gene expression between treated and control samples. Up-regulated and down-regulated genes were analyzed using Enrichr gene ontology (GO) pathway analysis. Enriched pathways with combined enrichment scores > 4 and FDR < 0.05 were considered statistically significant. Metabolism flux analysis was performed (FDR < 0.05). Live cell imaging to monitor proliferation of pre-malignant DCIS.COM, DCIS.COM/ER+ PR+; and malignant MDA-MB-231 (ER- PR-), MCF-7 (ER+ PR+), MCF-7aro, and BRCA1 defective HCC-1937, and HCC3153 cells was conducted using IncuCyte. Single versus repeated dosing of various concentrations of LicA were also evaluated. Results: We observed significant (P < 0.05) upregulation up to 8-fold of antioxidant genes, consistent with significant upregulation of NRF2, the major regulator of antioxidant pathways. This was accompanied by significant (P < 0.05) downregulation of NF-kB dependent inflammatory pathways. In addition, we observed the significant (P < 0.05) downregulation, ranging from 4 to 32-fold of cholesterol biosynthesis and transport, steroid hormone biosynthesis, as well as lipid metabolism genes, consistent with the profound downregulation of SREBF1 and SREBF2, which encode the master regulator of adipogenesis, SREBP. Metabolic flux results demonstrated a robust increase (FDR < 0.05) in the pentose phosphate shunt and NAD(P)H generation without enhancing ribose 5 phosphate formation, confirming an antioxidant and anti-proliferative environment. Likewise, LicA suppressed proliferation of pre-malignant and malignant cells dose- and time-dependently. Repeated dosing of lower concentrations of LicA (< 10 µM) demonstrated sustained antiproliferative effects even in the aggressive cancer cell lines. Conclusion: Our data suggest that LicA can generate a tumor-preventive breast microenvironment by reprogramming metabolic pathways involved in steroid and lipid homeostasis and antioxidant responses. These observations along with its low toxicity, suggest that LicA is a good candidate for further investigation as a breast cancer prevention agent.

Citation Format: Atieh Hajirahimkhan, Elizabeth T. Bartom, Sriram Chandrasekaran, Susan Clare, Seema Khan. Licochalcone A from licorice reprograms metabolic and antioxidant pathways in the breast leading to a tumor preventive environment. [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P2-08-01.

Application of an electro elution system for direct purification of linear covalently closed DNA fragments

Gene therapy is a powerful treatment modality. Non-viral gene therapy vectors power one arm of this important approach, due to their enhanced safety profile compared to their viral counterparts. New non-viral approaches continue to be developed, but purification can bottleneck the scaleup and cost-effectiveness and quality of some of these advanced vectors. We require more advanced purification and separation techniques compared to conventional methods to maximize resolution in a scalable manner. The Prep Cell system is a continuous electro elution system that contains a circular gel casting tube where DNA mixtures can be run through and subsequently migrate into an elution chamber, to be eluted by a peristaltic pump. This DNA separation and purification process confers advantages over other conventional methods, including i) the elimination of multiple downstream purification process requirements; ii) its ability to be applied in mid-scale settings, and iii), its high-resolution power. In this study, we assessed the ability of this Prep Cell Model 491 system to purify a novel type of non-viral linear covalently closed (LCC) DNA minivector (ministring DNA) from its precursor parent plasmid DNA and process by-product DNA species by analyzing for effective separation via agarose gel electrophoresis, recovery yield, single enzyme digestion, and quality control assessments. Overall, effective separation and resolution of mini-DNA vectors was obtained using the Prep Cell system, conferring its potential to be applied towards mid-scale purification of DNA vectors for a variety of research, and eventually, clinical applications.

Development of a numerical model for sector-average plume gamma dose and its validation with dose rate measurements at Kalpakkam NPP site, India

A Gaussian Plume based simple numerical model, named DIFFUSE is developed to simulate the long-term sector-average plume gamma dose due to radioactive plume released during normal operation of nuclear facilities. DIFFUSE calculates site specific joint frequency distributions of wind speed, wind direction and atmospheric stability using micrometeorological observations. It performs the finite sector-average dose integration for any stack height and gamma energy using Simpson's 1/3rd method with sufficient computational efficiency within the site boundary up to 2 km. Plume dose contribution to the main plume sector from nearest and next nearest side plume sectors is also calculated. DIFFUSE is validated with a 3-month long, starting from February 2021 to April 2021, dose rate observation data during operational releases from 100 m stack of Madras Atomic Power Station, Kalpakkam, India. Meteorological data from onsite 50 m tower and continuous dose rate observation from two sets of Autonomous Gamma Dose Logger (AGDL) detectors, namely n-AGDLs and r-AGDLs, placed in two different configurations along the geometric arcs of wind sectors around the stack are used. Simulated doses are compared with look-up table based dose estimates by Hukkoo et al. (1988). Linear spatial averaging of cumulative AGDL doses on a sector arc is used as measured sector-average dose for model validation. Simulations performed for both n-AGDL and r-AGDL configurations show DIFFUSE estimated doses are ∼37% lower and Hukkoo estimated doses are at least ∼50% lower than the measured doses. Statistical analysis of DIFFUSE simulated doses shows a statistical correlation of R2∼98.3%, slope of the fit ∼1.36 for n-AGDL setup and R2∼75.3%, slope of the fit ∼1.57 for r-AGDL setup. Overall, DIFFUSE produces conservative doses compared to look-up table based doses as required by regulatory bodies.

Single-cell RNA-sequencing identifies anti-cancer immune phenotypes in the early lung metastatic niche during breast cancer

Microenvironmental changes in the early metastatic niche may be exploited to identify therapeutic targets to inhibit secondary tumor formation and improve disease outcomes. We dissected the developing lung metastatic niche in a model of metastatic, triple-negative breast cancer using single-cell RNA-sequencing. Lungs were extracted from mice at 7-, 14-, or 21 days after tumor inoculation corresponding to the pre-metastatic, micro-metastatic, and metastatic niche, respectively. The progression of the metastatic niche was marked by an increase in neutrophil infiltration (5% of cells at day 0 to 81% of cells at day 21) and signaling pathways corresponding to the hallmarks of cancer. Importantly, the pre-metastatic and early metastatic niche were composed of immune cells with an anti-cancer phenotype not traditionally associated with metastatic disease. As expected, the metastatic niche exhibited pro-cancer phenotypes. The transition from anti-cancer to pro-cancer phenotypes was directly associated with neutrophil and monocyte behaviors at these time points. Predicted metabolic, transcription factor, and receptor-ligand signaling suggested that changes in the neutrophils likely induced the transitions in the other immune cells. Conditioned medium generated by cells extracted from the pre-metastatic niche successfully inhibited tumor cell proliferation and migration in vitro and the in vivo depletion of pre-metastatic neutrophils and monocytes worsened survival outcomes, thus validating the anti-cancer phenotype of the developing niche. Genes associated with the early anti-cancer response could act as biomarkers that could serve as targets for the treatment of early metastatic disease. Such therapies have the potential to revolutionize clinical outcomes in metastatic breast cancer.

Integrative analysis of clinical health records, imaging and pathogen genomics identifies personalized predictors of disease prognosis in tuberculosis

Tuberculosis (TB) afflicts over 10 million people every year and its global burden is projected to increase dramatically due to multidrug-resistant TB (MDR-TB). The Covid-19 pandemic has resulted in reduced access to TB diagnosis and treatment, reversing decades of progress in disease management globally. It is thus crucial to analyze real-world multi-domain information from patient health records to determine personalized predictors of TB treatment outcome and drug resistance. We conduct a retrospective analysis on electronic health records of 5060 TB patients spanning 10 countries with high burden of MDR-TB including Ukraine, Moldova, Belarus and India available on the NIAID-TB portals database. We analyze over 200 features across multiple host and pathogen modalities representing patient social demographics, disease presentations as seen in cChest X rays and CT scans, and genomic records with drug susceptibility features of the pathogen strain from each patient. Our machine learning model, built with diverse data modalities outperforms models built using each modality alone in predicting treatment outcomes, with an accuracy of 81% and AUC of 0.768. We determine robust predictors across countries that are associated with unsuccessful treatmentclinical outcomes, and validate our predictions on new patient data from TB Portals. Our analysis of drug regimens and drug interactions suggests that synergistic drug combinations and those containing the drugs Bedaquiline, Levofloxacin, Clofazimine and Amoxicillin see more success in treating MDR and XDR TB. Features identified via chest imaging such as percentage of abnormal volume, size of lung cavitation and bronchial obstruction are associated significantly with pathogen genomic attributes of drug resistance. Increased disease severity was also observed in patients with lower BMI and with comorbidities. Our integrated multi-modal analysis thus revealed significant associations between radiological, microbiological, therapeutic, and demographic data modalities, providing a deeper understanding of personalized responses to aid in the clinical management of TB.

Chromosome length ratio as a biomarker of DNA damage in cells exposed to high dose ionizing radiation

The premature chromosome condensation (PCC) assay is considered as complementary bio-dosimetry tool for chromosome aberration assay and the PCC assay can be used to estimate high dose exposure. Though the PCC ring is considered as prospective biomarker, chromosome length ratio (ratio of longest and shortest chromosome length in PCC spreads) of chemically induced PCC is shown to be very good indicator of ionizing radiation. In view of this, an in-vitro study has been performed using PCC assay to suggest chromosome length ratio (LR) as potential bio-dosimeter induced by high dose ionizing radiation. Blood samples were collected from healthy subjects (n = 3) after prior consent and irradiated to ten different doses ranging between 0 and 20 Gy using 6 MV LINAC X-rays with dose rate of 5.6 Gy/min. Irradiated lymphocytes were cultured and calyculin induced PCC spreads were prepared. PCC spreads were captured using image analysis system and chromosome lengths were measured using open-source ImageJ software. For each dose, LR for 50 chromosome spreads were computed and mean LR value was calculated. LR varies between 6.0 ± 0.08 and 23.6 ± 0.55 for the dose range between 2 and 20 Gy. The dose response curve for LR was observed to be linear with y = 1.02x + 3.36, R² = 0.97. Linear dose response relationship obtained in the present study confirms the prospective use of LR measurement. This study is first of its kind to examine chromosome length ratio as a biomarker of DNA damage in cells exposed to high dose X-ray exposure.

A flux-based machine learning model to simulate the impact of pathogen metabolic heterogeneity on drug interactions

Drug combinations are a promising strategy to counter antibiotic resistance. However, current experimental and computational approaches do not account for the entire complexity involved in combination therapy design, such as the effect of pathogen metabolic heterogeneity, changes in the growth environment, drug treatment order, and time interval. To address these limitations, we present a comprehensive approach that uses genome-scale metabolic modeling and machine learning to guide combination therapy design. Our mechanistic approach (a) accommodates diverse data types, (b) accounts for time- and order-specific interactions, and (c) accurately predicts drug interactions in various growth conditions and their robustness to pathogen metabolic heterogeneity. Our approach achieved high accuracy (area under the receiver operating curve (AUROC) = 0.83 for synergy, AUROC = 0.98 for antagonism) in predicting drug interactions for Escherichia coli cultured in 57 metabolic conditions based on experimental validation. The entropy in bacterial metabolic response was predictive of combination therapy outcomes across time scales and growth conditions. Simulation of metabolic heterogeneity using population FBA identified two subpopulations of E. coli cells defined by the levels of three proteins (eno, fadB, and fabD) in glycolysis and lipid metabolism that influence cell tolerance to a broad range of antibiotic combinations. Analysis of the vast landscape of condition-specific drug interactions revealed a set of 24 robustly synergistic drug combinations with potential for clinical use.

Acetyl-CoA metabolism drives epigenome change and contributes to carcinogenesis risk in fatty liver disease

BACKGROUND

The incidence of non-alcoholic fatty liver disease (NAFLD)-associated hepatocellular carcinoma (HCC) is increasing worldwide, but the steps in precancerous hepatocytes which lead to HCC driver mutations are not well understood. Here we provide evidence that metabolically driven histone hyperacetylation in steatotic hepatocytes can increase DNA damage to initiate carcinogenesis.

METHODS

Global epigenetic state was assessed in liver samples from high-fat diet or high-fructose diet rodent models, as well as in cultured immortalized human hepatocytes (IHH cells). The mechanisms linking steatosis, histone acetylation and DNA damage were investigated by computational metabolic modelling as well as through manipulation of IHH cells with metabolic and epigenetic inhibitors. Chromatin immunoprecipitation and next-generation sequencing (ChIP-seq) and transcriptome (RNA-seq) analyses were performed on IHH cells. Mutation locations and patterns were compared between the IHH cell model and genome sequence data from preneoplastic fatty liver samples from patients with alcohol-related liver disease and NAFLD.

RESULTS

Genome-wide histone acetylation was increased in steatotic livers of rodents fed high-fructose or high-fat diet. In vitro, steatosis relaxed chromatin and increased DNA damage marker γH2AX, which was reversed by inhibiting acetyl-CoA production. Steatosis-associated acetylation and γH2AX were enriched at gene clusters in telomere-proximal regions which contained HCC tumour suppressors in hepatocytes and human fatty livers. Regions of metabolically driven epigenetic change also had increased levels of DNA mutation in non-cancerous tissue from NAFLD and alcohol-related liver disease patients. Finally, genome-scale network modelling indicated that redox balance could be a key contributor to this mechanism.

CONCLUSIONS

Abnormal histone hyperacetylation facilitates DNA damage in steatotic hepatocytes and is a potential initiating event in hepatocellular carcinogenesis.

Lipid exposure activates gene expression changes associated with estrogen receptor negative breast cancer

Improved understanding of local breast biology that favors the development of estrogen receptor negative (ER-) breast cancer (BC) would foster better prevention strategies. We have previously shown that overexpression of specific lipid metabolism genes is associated with the development of ER- BC. We now report results of exposure of MCF-10A and MCF-12A cells, and mammary organoids to representative medium- and long-chain polyunsaturated fatty acids. This exposure caused a dynamic and profound change in gene expression, accompanied by changes in chromatin packing density, chromatin accessibility, and histone posttranslational modifications (PTMs). We identified 38 metabolic reactions that showed significantly increased activity, including reactions related to one-carbon metabolism. Among these reactions are those that produce S-adenosyl-L-methionine for histone PTMs. Utilizing both an in-vitro model and samples from women at high risk for ER- BC, we show that lipid exposure engenders gene expression, signaling pathway activation, and histone marks associated with the development of ER- BC.

Anthocyanin: A Natural Dye Extracted from Hibiscus Sabdariffa (L.) for Textile and Dye Industries

Environmental pollution is one of the major issues facing all countries throughout the world. Environmental degradation is occurring and creating crises in day-to-day life due to the increasing amount of chemicals used in industries, where even the effluents processed out after treatment also contain some trace elements. Hence the extraction of enzymes using natural methods is an alternative for the production of dye in order to reduce pollution, which in turn helps to nourish and protect the environment for future generations. Hibiscus sabdariffa (L.) is a rich source of anthocyanins that is further enhanced by callus formation and accumulated by increasing the sucrose concentration. Anthocyanin pigments were extracted using acidified ethanol. The dye obtained was screened by GC-MS analysis and its dyeing process used in the textile industry. The study showed certain properties affected the coloring nature depending on the cloth used. The color of anthocyanin pigment depends on the pH maintained and also shows adaptability to varied environmental conditions.

Metabolic signatures of regulation by phosphorylation and acetylation

Acetylation and phosphorylation are highly conserved posttranslational modifications (PTMs) that regulate cellular metabolism, yet how metabolic control is shared between these PTMs is unknown. Here we analyze transcriptome, proteome, acetylome, and phosphoproteome datasets in E. coli, S. cerevisiae, and mammalian cells across diverse conditions using CAROM, a new approach that uses genome-scale metabolic networks and machine learning to classify targets of PTMs. We built a single machine learning model that predicted targets of each PTM in a condition across all three organisms based on reaction attributes (AUC>0.8). Our model predicted phosphorylated enzymes during a mammalian cell-cycle, which we validate using phosphoproteomics. Interpreting the machine learning model using game theory uncovered enzyme properties including network connectivity, essentiality, and condition-specific factors such as maximum flux that differentiate targets of phosphorylation from acetylation. The conserved and predictable partitioning of metabolic regulation identified here between these PTMs may enable rational rewiring of regulatory circuits.

Distribution, annual committed effective dose, and health safety assessment of 210Po in marine biota from Kalpakkam coast, Bay of Bengal

Seafood, intertidal biota, beach sediment, and seawater from Kalpakkam coast, Bay of Bengal were analyzed for ²¹⁰Po to evaluate the internal exposure and other radiological safety aspects. Kalpakkam houses various nuclear power generation facilities on its coast. The activity concentration of ²¹⁰Po was more pronounced in the intertidal organisms. Pelagic planktivorous fishes have the highest activity of the non-technogenic radionuclide, followed by the detrital feeders, benthic planktivores, benthic carnivores, and pelagic carnivore fishes. The affinity of ²¹⁰Po to organic detrital matter and planktonic organisms has led to a higher accumulation of radionuclide in planktivorous fishes. Activity concentration of ²¹⁰Po in seafood ranged between 1.13 ± 0.3 and 96.71 ± 1.6 Bq kg^-1 (Becquerel/kilogram). In seaweeds and gastropods, it ranged from 2.09 ± 0.2 to 8.21 ± 0.6 and from 9.31 ± 0.7 to 21.58 ± 1.2 Bq kg^-1, respectively. The committed effective dose (CED) of ²¹⁰Po in seafood varied from 31.18 to 456.68 μSv yr^-1 (microSievert/year). Radiological hazard parameters, such as activity intake, CED in consumption, of the seafood from this coast are within the acceptable levels prescribed by the International Commission on Radiological Protection and US Environmental Protection Agency.

Metabolism, HDACs, and HDAC Inhibitors: A Systems Biology Perspective

Histone deacetylases (HDACs) are epigenetic enzymes that play a central role in gene regulation and are sensitive to the metabolic state of the cell. The cross talk between metabolism and histone acetylation impacts numerous biological processes including development and immune function. HDAC inhibitors are being explored for treating cancers, viral infections, inflammation, neurodegenerative diseases, and metabolic disorders. However, how HDAC inhibitors impact cellular metabolism and how metabolism influences their potency is unclear. Discussed herein are recent applications and future potential of systems biology methods such as high throughput drug screens, cancer cell line profiling, single cell sequencing, proteomics, metabolomics, and computational modeling to uncover the interplay between metabolism, HDACs, and HDAC inhibitors. The synthesis of new systems technologies can ultimately help identify epigenomic and metabolic biomarkers for patient stratification and the design of effective therapeutics.

Metabolic remodelling during early mouse embryo development

During early mammalian embryogenesis, changes in cell growth and proliferation depend on strict genetic and metabolic instructions. However, our understanding of metabolic reprogramming and its influence on epigenetic regulation in early embryo development remains elusive. Here we show a comprehensive metabolomics profiling of key stages in mouse early development and the two-cell and blastocyst embryos, and we reconstructed the metabolic landscape through the transition from totipotency to pluripotency. Our integrated metabolomics and transcriptomics analysis shows that while two-cell embryos favour methionine, polyamine and glutathione metabolism and stay in a more reductive state, blastocyst embryos have higher metabolites related to the mitochondrial tricarboxylic acid cycle, and present a more oxidative state. Moreover, we identify a reciprocal relationship between α-ketoglutarate (α-KG) and the competitive inhibitor of α-KG-dependent dioxygenases, L-2-hydroxyglutarate (L-2-HG), where two-cell embryos inherited from oocytes and one-cell zygotes display higher L-2-HG, whereas blastocysts show higher α-KG. Lastly, increasing 2-HG availability impedes erasure of global histone methylation markers after fertilization. Together, our data demonstrate dynamic and interconnected metabolic, transcriptional and epigenetic network remodelling during early mouse embryo development.

Next-Generation Genome-Scale Metabolic Modeling through Integration of Regulatory Mechanisms

Genome-scale metabolic models (GEMs) are powerful tools for understanding metabolism from a systems-level perspective. However, GEMs in their most basic form fail to account for cellular regulation. A diverse set of mechanisms regulate cellular metabolism, enabling organisms to respond to a wide range of conditions. This limitation of GEMs has prompted the development of new methods to integrate regulatory mechanisms, thereby enhancing the predictive capabilities and broadening the scope of GEMs. Here, we cover integrative models encompassing six types of regulatory mechanisms: transcriptional regulatory networks (TRNs), post-translational modifications (PTMs), epigenetics, protein-protein interactions and protein stability (PPIs/PS), allostery, and signaling networks. We discuss 22 integrative GEM modeling methods and how these have been used to simulate metabolic regulation during normal and pathological conditions. While these advances have been remarkable, there remains a need for comprehensive and widespread integration of regulatory constraints into GEMs. We conclude by discussing challenges in constructing GEMs with regulation and highlight areas that need to be addressed for the successful modeling of metabolic regulation. Next-generation integrative GEMs that incorporate multiple regulatory mechanisms and their crosstalk will be invaluable for discovering cell-type and disease-specific metabolic control mechanisms.

Anthocyanin: A Natural Dye Extracted From Hibiscus Sabdariffa (L.) For Textile and Dye Industries.. [DOI: 10.21203/rs.3.rs-772122/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Environmental pollution is one of the major issues faced by all the countries throughout the world. To prevent the environment scarcity and crisis faced in day-to-day life due the increasing chemical industries, usage of chemicals and the effluents processed out after the treatment also consists of some trace elements in them. Hence the extraction of enzymes on natural basis forms an alternative criteria for the production of dye in order to reduce pollution which in turn helps to nourish and protect the environment for future generations. Hibiscus sabdariffa (L) has a rich source of anthocyanins which is further enhanced by callus production and synthesized by increasing the sucrose concentration. Anthocyanin pigments were extracted using acidified ethanol and the dye obtained was screened for GL-MS analysis and its dyeing process in textile industry. The study showed significance properties along with coloring nature on the clothes used. Color of anthocyanin pigment depends on pH maintained and also shows the adaptability towards the nature with varied environmental conditions

The deacylase SIRT5 supports melanoma viability by influencing chromatin dynamics

Cutaneous melanoma remains the most lethal skin cancer, and ranks third among all malignancies in terms of years of life lost. Despite the advent of immune checkpoint and targeted therapies, only roughly half of patients with advanced melanoma achieve a durable remission. Sirtuin 5 (SIRT5) is a member of the sirtuin family of protein deacylases that regulates metabolism and other biological processes. Germline Sirt5 deficiency is associated with mild phenotypes in mice. Here we showed that SIRT5 was required for proliferation and survival across all cutaneous melanoma genotypes tested, as well as uveal melanoma, a genetically distinct melanoma subtype that arises in the eye and is incurable once metastatic. Likewise, SIRT5 was required for efficient tumor formation by melanoma xenografts and in an autochthonous mouse Braf Pten-driven melanoma model. Via metabolite and transcriptomic analyses, we found that SIRT5 was required to maintain histone acetylation and methylation levels in melanoma cells, thereby promoting proper gene expression. SIRT5-dependent genes notably included MITF, a key lineage-specific survival oncogene in melanoma, and the c-MYC proto-oncogene. SIRT5 may represent a druggable genotype-independent addiction in melanoma.

The Axes of Life: A roadmap for understanding dynamic multiscale systems

The biological challenges facing humanity are complex, multi-factorial, and are intimately tied to the future of our health, welfare, and stewardship of the Earth. Tackling problems in diverse areas, such as agriculture, ecology, and health care require linking vast data sets that encompass numerous components and spatio-temporal scales. Here, we provide a new framework and a road map for using experiments and computation to understand dynamic biological systems that span multiple scales. We discuss theories that can help understand complex biological systems and highlight the limitations of existing methodologies and recommend data generation practices. The advent of new technologies such as big data analytics and artificial intelligence can help bridge different scales and data types. We recommend ways to make such models transparent, compatible with existing theories of biological function, and to make biological data sets readable by advanced machine learning algorithms. Overall, the barriers for tackling pressing biological challenges are not only technological, but also sociological. Hence, we also provide recommendations for promoting interdisciplinary interactions between scientists.

Deep Learning for Reintegrating Biology

The goal of this vision paper is to investigate the possible role that advanced machine learning techniques, especially deep learning, could play in the reintegration of various biological disciplines. To achieve this goal, a series of operational, but admittedly very simplistic, conceptualizations have been introduced: Life has been taken as a multidimensional phenomenon that inhabits three physical dimensions (time, space, and scale) and biological research as establishing connection between different points in the domain of life. Each of these points hence denotes a position in time, space, and scale at which a life phenomenon of interest takes place. Using these conceptualizations, fragmentation of biology can be seen as the result of too few and especially too short-ranged connections. Reintegrating biology could then be accomplished by establishing more, longer ranged connections. Deep learning methods appear to be very well suited for addressing this particular need at this particular time. Not withstanding the numerous unsubstantiated claims regarding the capabilities of AI, deep learning networks represent a major advance in the ability to find complex relationships inside large data sets that would have not been accessible with traditional data analytic methods or to a human observer. In addition, ongoing advances in the automation of taking measurements from phenomena on all levels of biological organization, continue to increase the number of large quantitative data sets that are available. These increasingly common data sets could serve as anchor points for making long-range connections by virtue of deep learning. However, connections within the domain of life are likely to be structured in a highly nonuniform fashion and hence it is necessary to develop methods, e.g., theoretical, computational, and experimental, to determine linkage of biological data sets most likely to provide useful insights on a biological problem using deep learning. Finally, specific deep learning approaches and architectures should be developed to match the needs of reintegrating biology.

Construction of dose response curve for 6 MV LINAC X-rays using Premature Chromosome Condensation assay for radiation dosimetry

Quantification of chromosomal aberrations in the exposed personnel blood samples is considered as a 'gold standard' and sensitive biomarker in biological dosimetry. Despite technological developments, culture of cells for 48-52 h remains an unmet need in case of triage biodosimetry. Moreover, it is difficult to get sufficient number of metaphase spreads for scoring after high doses of exposures. The technique which causes condensation of chromatin before mitosis using biological or chemical agent is named as Premature Chromosome Condensation (PCC) assay. This assay is considered as an alternative to chromosome aberration assay, particularly at high acute doses of low and high LET radiation. To establish the PCC assay, blood samples were collected from healthy non-smoking individuals (n = 3) and exposed to various doses (0-20 Gy) of 6 MV X-rays at a dose rate of 5.6 Gy/min, using a high energy Linear accelerator (LINAC). Irradiated blood samples were subjected to Calyculin-A induced PCC. About 500 cells or more than 100 Ring Chromosomes (RC) were scored at each dose. Dicentric chromosomes (DC) and acentric fragments were also scored at each dose; the number of chromosomal aberrations in G1, M, G2/M and M/A phase of cell cycle were recorded and the frequency was used to construct the dose response curve. A dose dependent increase in RC and DC frequency were observed with a slope of 0.049 ± 0.002 and 0.30 ± 0.02 respectively. This study is first of its kind to construct a dose response curve for LINAC X-rays using a PCC assay.

DDRE-24. TARGETING PURINE METABOLISM TO OVERCOME GLIOBLASTOMA THERAPY RESISTANCE

Intratumoral genomic heterogeneity in glioblastoma (GBM) is a barrier to overcoming radiation (RT) resistance. To discover genotype-independent mediators of RT resistance, we correlated RT resistance with the concentration of approximately 700 metabolites across 23 GBM cell lines. Purine metabolites, especially those containing the base guanine, were most correlated with RT resistance. Similarly, increased abundance of tumor purines was associated with decreased survival in GBM patients treated with RT. This relationship is causal. Purine supplementation protected RT-sensitive GBMs from RT and promoted the repair of RT-induced double strand DNA breaks (DSBs). In vitro and in vivo stable isotope tracing confirmed that GBM cell lines and orthotopic patient-derived xenografts primarily generated purines through the de novo synthetic pathway. RT treatment further increased de novo purine synthesis in GBM through signaling via the DNA damage response. Inhibition of de novo GTP synthesis with mycophenolic acid (MPA) sensitized multiple GBM cell lines and neurospheres to RT by slowing the repair of RT-induced DSBs. MPA-induced radiosensitization was GTP-dependent as it was rescued by nucleoside supplementation. Modulating pyrimidine metabolism affected neither RT resistance nor DSB repair, suggesting these GTP-specific effects are due to active signaling rather than its ability to act as a physical substrate for DNA repair and candidate signaling molecules have been identified. These results were recapitulated in vivo with mycophenolate mofetil (MMF), the orally bioavailable FDA-approved prodrug of MPA. MMF potentiated RT efficacy, reduced tumor guanylates and slowed the repair of RT-induced DSBs across multiple models. Because de novo purine synthesis is activated by many of the oncogenic alterations that drive GBM, its inhibition is a promising genotype-independent strategy to overcome GBM RT resistance. We have now begun a clinical trial to determine whether combining MMF and RT is safe and potentially efficacious in patients with GBM.

A multi-scale pipeline linking drug transcriptomics with pharmacokinetics predicts in vivo interactions of tuberculosis drugs

Tuberculosis (TB) is the deadliest infectious disease worldwide. The design of new treatments for TB is hindered by the large number of candidate drugs, drug combinations, dosing choices, and complex pharmaco-kinetics/dynamics (PK/PD). Here we study the interplay of these factors in designing combination therapies by linking a machine-learning model, INDIGO-MTB, which predicts in vitro drug interactions using drug transcriptomics, with a multi-scale model of drug PK/PD and pathogen-immune interactions called GranSim. We calculate an in vivo drug interaction score (iDIS) from dynamics of drug diffusion, spatial distribution, and activity within lesions against various pathogen sub-populations. The iDIS of drug regimens evaluated against non-replicating bacteria significantly correlates with efficacy metrics from clinical trials. Our approach identifies mechanisms that can amplify synergistic or mitigate antagonistic drug interactions in vivo by modulating the relative distribution of drugs. Our mechanistic framework enables efficient evaluation of in vivo drug interactions and optimization of combination therapies.

Abstract PS17-40: Fatty acid metabolism at the connection of histone methylation and mammary cell plasticity

INTRODUCTION:Metabolic switching has been linked with cancer progression. We initially reported increased expression of lipids metabolism genes in the contralateral breasts of women with unilateral estrogen receptor negative (ER-) breast cancer. We then demonstrated chromatin remodeling in normal mammary epithelial cells following exposure to lipids. Metabolites from intermediate metabolism are substrates that generate chromatin modifications, connecting metabolism to epigenetics. Key to this crosstalk is the fact that the kinetic and thermodynamic properties of chromatin modification reactions are in line with the dynamic range of the physiological concentrations of the corresponding intermediates in metabolism. Since substrates for histone methylation and acetylation reactions often have cellular concentrations that are commensurate with enzyme Km values, and are sensitive and responsive to changes in metabolism, we now present evidence regarding changes in metabolic flux and cellular plasticity connecting lipid metabolism to ER- cancer progression.

Methods:MCF-10A cells were grown in the presence of medium chain fatty acid- octanoic acid (5mM) for 24 hours. RNA sequencing was performed on the treated and control cells using Illumina Next Seq 500 for 75bp single-read sequencing. Differential expression and gene set enrichment analyses were utilized to identify significantly enriched biological processes and molecular functions upon fatty acid treatment. Differential metabolic flux was determined using flux balance analysis. For proteomic analysis of post-translational histone modifications, histones were acid extracted from nuclei and LC-MS based mass spectrometry was used to quantitate the modifications and data is represented as the relative abundance in %.

Results:Transcriptome data from RNA sequencing, incorporated into the human metabolic network reconstruction, revealed a significant increase in methylation flux in octanoate treated MCF10-A cells. The treated cells showed an increase in the flux in fatty acid oxidation and one-carbon metabolism reactions. The proteomic histone modification profile showed an increase in histone methylation specifically at H3K9, H3K4, H3K27 and H3K36, which corroborates with the GSEA analysis showing a significant correlation of H3K27 methylation (NES = 2.47, q-value = 0.05), a marker of constitutive heterochromatin, in the octanoate treated phenotype. Further, RNA-Seq of octanoate treated MCF-10A cells revealed prominent upregulation of cell-fate commitment and cell differentiation pathways, specifically neural differentiation, and adenylate cyclase-activating adrenergic receptor signaling.

Conclusion:Lipids are a source of acetyl-CoA which serves as a donor for histone acetylation; our data shows that resultant increased flux through metabolic reactions produces s-adenosine methionine (SAM), an important methylating agent. These post-translational modifications have profound effects on chromatin structure, effecting changes that range from the expression of a single gene to a complete conversion of phenotype. We observe both an alteration of the molecular phenotype, as well as adenylate cyclase-activating adrenergic receptor signaling, which may be a clue to the development of ER- disease as GWAS have revealed that the adenylate cyclase-activating pathway is enriched in susceptibility to ER-negative disease.

Citation Format: Shivangi Yadav, Ranya KA Virk, Carolina H Chung, Sriram Chandrasekaran, Vadim Backman, Seema Ahsan Khan, Susan Elizabeth Clare. Fatty acid metabolism at the connection of histone methylation and mammary cell plasticity [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS17-40.

Individual differences in honey bee behavior enabled by plasticity in brain gene regulatory networks

Understanding the regulatory architecture of phenotypic variation is a fundamental goal in biology, but connections between gene regulatory network (GRN) activity and individual differences in behavior are poorly understood. We characterized the molecular basis of behavioral plasticity in queenless honey bee (Apis mellifera) colonies, where individuals engage in both reproductive and non-reproductive behaviors. Using high-throughput behavioral tracking, we discovered these colonies contain a continuum of phenotypes, with some individuals specialized for either egg-laying or foraging and 'generalists' that perform both. Brain gene expression and chromatin accessibility profiles were correlated with behavioral variation, with generalists intermediate in behavior and molecular profiles. Models of brain GRNs constructed for individuals revealed that transcription factor (TF) activity was highly predictive of behavior, and behavior-associated regulatory regions had more TF motifs. These results provide new insights into the important role played by brain GRN plasticity in the regulation of behavior, with implications for social evolution.

Brain α-Tocopherol Concentration and Stereoisomer Profile Alter Hippocampal Gene Expression in Weanling Mice

BACKGROUND

Alpha-tocopherol (αT), the bioactive constituent of vitamin E, is essential for fertility and neurological development. Synthetic αT (8 stereoisomers; all rac-αT) is added to infant formula at higher concentrations than natural αT (RRR-αT only) to adjust for bio-potency differences, but its effects on brain development are poorly understood.

OBJECTIVES

The objective was to determine the impact of bio-potency-adjusted dietary all rac-αT versus RRR-αT, fed to dams, on the hippocampal gene expression in weanling mice.

METHODS

Male/female pairs of C57BL/6J mice were fed AIN 93-G containing RRR-αT (NAT) or all rac-αT (SYN) at 37.5 or 75 IU/kg (n = 10/group) throughout gestation and lactation. Male pups were euthanized at 21 days. Half the brain was evaluated for the αT concentration and stereoisomer distribution. The hippocampus was dissected from the other half, and RNA was extracted and sequenced. Milk αT was analyzed in separate dams.

RESULTS

A total of 797 differentially expressed genes (DEGs) were identified in the hippocampi across the 4 dietary groups, at a false discovery rate of 10%. Comparing the NAT-37.5 group to the NAT-75 group or the SYN-37.5 group to the SYN-75 group, small differences in brain αT concentrations (10%; P < 0.05) led to subtle changes (<10%) in gene expression of 600 (NAT) or 487 genes (SYN), which were statistically significant. Marked differences in brain αT stereoisomer profiles (P < 0.0001) had a small effect on fewer genes (NAT-37.5 vs. SYN-37.5, 179; NAT-75 vs. SYN-75, 182). Most of the DEGs were involved in transcription regulation and synapse formation. A network analysis constructed around known vitamin E interacting proteins (VIPs) revealed a group of 32 DEGs between NAT-37.5 vs. SYN-37.5, explained by expression of the gene for the VIP, protein kinase C zeta (Pkcz).

CONCLUSIONS

In weanling mouse hippocampi, a network of genes involved in transcription regulation and synapse formation was differentially affected by dam diet αT concentration and source: all rac-αT or RRR-αT.

Fixed-dose combination in management of type 2 diabetes mellitus: Expert opinion from an international panel

Type 2 diabetes mellitus (T2DM) is a progressive disease with multifactorial etiology. The first-line therapy includes monotherapy (with metformin), which often fails to provide effective glycemic control, necessitating the addition of add-on therapy. In this regard, multiple single-dose agents formulated as a single-dose form called fixed-dose combinations (FDCs) have been evaluated for their safety, efficacy, and tolerability. The primary objective of this review is to develop practice-based expert group opinion on the current status and the causes of concern regarding the irrational use of FDCs, in Indian settings. After due discussions, the expert group analyzed the results from several clinical evidence in which various fixed combinations were used in T2DM management. The panel opined that FDCs (double or triple) improve patient adherence, reduce cost, and provide effective glycemic control and, thereby, play an important role in the management of T2DM. The expert group strongly recommended that the irrational metformin FDC's, banned by Indian government, should be stopped and could be achieved through active participation from the government, regulatory bodies, and health ministry, and through continuous education of primary care physicians and pharmacists. In T2DM management, FDCs play a crucial role in achieving glycemic targets effectively. However, understanding the difference between rational and irrational FDC combinations is necessary from the safety, efficacy, and tolerability perspective. In this regard, primary care physicians will have to use a multistep approach so that they can take informed decisions.

Purine metabolism regulates DNA repair and therapy resistance in glioblastoma

Intratumoral genomic heterogeneity in glioblastoma (GBM) is a barrier to overcoming therapy resistance. Treatments that are effective independent of genotype are urgently needed. By correlating intracellular metabolite levels with radiation resistance across dozens of genomically-distinct models of GBM, we find that purine metabolites, especially guanylates, strongly correlate with radiation resistance. Inhibiting GTP synthesis radiosensitizes GBM cells and patient-derived neurospheres by impairing DNA repair. Likewise, administration of exogenous purine nucleosides protects sensitive GBM models from radiation by promoting DNA repair. Neither modulating pyrimidine metabolism nor purine salvage has similar effects. An FDA-approved inhibitor of GTP synthesis potentiates the effects of radiation in flank and orthotopic patient-derived xenograft models of GBM. High expression of the rate-limiting enzyme of de novo GTP synthesis is associated with shorter survival in GBM patients. These findings indicate that inhibiting purine synthesis may be a promising strategy to overcome therapy resistance in this genomically heterogeneous disease.

Targeting genotype-independent abnormalities may overcome therapy resistance in glioblastoma despite intratumoral genomic heterogeneity. Here, the authors show that glioblastoma radiation resistance is promoted by purine metabolism and can be overcome by inhibitors of purine synthesis.

Nutrient Sensing by Histone Marks: Reading the Metabolic Histone Code Using Tracing, Omics, and Modeling

Several metabolites serve as substrates for histone modifications and communicate changes in the metabolic environment to the epigenome. Technologies such as metabolomics and proteomics have allowed us to reconstruct the interactions between metabolic pathways and histones. These technologies have shed light on how nutrient availability can have a dramatic effect on various histone modifications. This metabolism-epigenome cross talk plays a fundamental role in development, immune function, and diseases like cancer. Yet, major challenges remain in understanding the interactions between cellular metabolism and the epigenome. How the levels and fluxes of various metabolites impact epigenetic marks is still unclear. Discussed herein are recent applications and the potential of systems biology methods such as flux tracing and metabolic modeling to address these challenges and to uncover new metabolic-epigenetic interactions. These systems approaches can ultimately help elucidate how nutrients shape the epigenome of microbes and mammalian cells.