Combinatorial CRISPR-Cas9 Metabolic Screens Reveal Critical Redox Control Points Dependent on the KEAP1-NRF2 Regulatory Axis

Molecular Targets of Oxidative Stress: Focus on Nuclear Factor Erythroid 2-Related Factor 2 Function in Leukemia and Other Cancers

Nuclear factor erythroid 2-related factor 2 (Nrf2) is a transcription factor that plays a central role in regulating cellular responses to oxidative stress. It governs the expression of a broad range of genes involved in antioxidant defense, detoxification, metabolism, and other cytoprotective pathways. In normal cells, the transient activation of Nrf2 serves as a protective mechanism to maintain redox homeostasis. However, the persistent or aberrant activation of Nrf2 in cancer cells has been implicated in tumor progression, metabolic reprogramming, and resistance to chemotherapy and radiotherapy. These dual roles underscore the complexity of Nrf2 signaling and its potential as a therapeutic target. A deeper understanding of Nrf2 regulation in both normal and malignant contexts is essential for the development of effective Nrf2-targeted therapies. This review provides a comprehensive overview of Nrf2 regulation and function, highlighting its unique features in cancer biology, particularly its role in metabolic adaptation and drug resistance. Special attention is given to the current knowledge of Nrf2's involvement in leukemia and emerging strategies for its therapeutic modulation.

Sensitive detection of synthetic response to cancer immunotherapy driven by gene paralog pairs

Immunotherapies, including checkpoint blockade and chimeric antigen receptor T cell (CAR-T) therapy, have revolutionized cancer treatment; however, many patients remain unresponsive to these treatments or relapse following treatment. CRISPR screenings have been used to identify novel single gene targets that can enhance immunotherapy effectiveness, but the identification of combinational targets remains a challenge. Here, we introduce a computational approach that uses sgRNA set enrichment analysis to identify cancer-intrinsic paralog pairs for enhancing immunotherapy using genome-wide screens. We have further developed an ensemble learning model that uses an XGBoost classifier and incorporates features to predict paralog gene pairs that influence immunotherapy efficacy. We experimentally validated the functional significance of these predicted paralog pairs using CRISPR double knockout (DKO). These data and analyses collectively provide a sensitive approach to identifying previously undetected paralog gene pairs that can significantly affect cancer immunotherapy response, even when individual genes within the pair have limited effect.

Strategic base modifications refine RNA function and reduce CRISPR-Cas9 off-targets

In contrast to traditional RNA regulatory approaches that modify the 2'-OH group, this study explores strategic base modifications using 5-carboxylcytosine (ca5C). We developed a technique where ca5C is transformed into dihydrouracil via treatment with borane-pyridine complex or 2-picoline borane complex, leading to base mutations that directly impact RNA functionality. This innovative strategy effectively manages CRISPR-Cas9 system activities, significantly minimizing off-target effects. Our approach not only demonstrates a significant advancement in RNA manipulation but also offers a new method for the precise control of gene editing technologies, showcasing its potential for broad application in chemical biology.

Redox control of NRF2 signaling in oocytes harnessing Porphyra derivatives as a toggle

This study investigated the potential of Porphyra derivatives (PD), including Porphyra334, to activate the nuclear factor erythroid 2-related factor 2 (NRF2) pathway in porcine oocytes to enhance oocyte competency and intracellular networks. Conventional methods for manipulating mitochondrial functions and antioxidant pathways often rely upon genetic modifications that are impractical for direct application in humans. We hypothesized that PD serves as a natural regulator of the NRF2 pathway without requiring genetic intervention. To test this hypothesis, brusatol (Bru), a direct NRF2 inhibitor, was used to evaluate the specific role of PD in NRF2-mediated processes. The results demonstrated that PD significantly improved oocyte maturation, blastocyst formation, and mitochondrial function, including subsequent lipid metabolism. PD activates NRF2 and its downstream antioxidant response elements (AREs), whereas Bru inhibits these effects. Co-treatment with PD and Bru resulted in the partial recovery of NRF2 activity. These findings suggest that PD functions as a toggle for NRF2 activation, potentially offering a non-genetic strategy for enhancing oocyte quality and embryo development by modulating antioxidant mechanisms and mitochondrial functions. This study provides new avenues for investigating natural compounds in the context of reproductive biology and assisted reproductive technologies (ARTs).

Metabolic Rewiring in the Face of Genomic Assault: Integrating DNA Damage Response and Cellular Metabolism

The DNA damage response (DDR) and cellular metabolism exhibit a complex, bidirectional relationship crucial for maintaining genomic integrity. Studies across multiple organisms, from yeast to humans, have revealed how cells rewire their metabolism in response to DNA damage, supporting repair processes and cellular homeostasis. We discuss immediate metabolic shifts upon damage detection and long-term reprogramming for sustained genomic stability, highlighting key signaling pathways and participating molecules. Importantly, we examine how DNA repair processes can conversely induce metabolic changes and oxidative stress through specific mechanisms, including the histone H2A variant X (H2AX)/ataxia telangiectasia mutated (ATM)/NADPH oxidase 1 (Nox1) pathway and repair-specific ROS signatures. The review covers organelle-specific responses and metabolic adaptations associated with different DNA repair mechanisms, with a primary focus on human cells. We explore the implications of this DDR-metabolism crosstalk in cancer, aging, and neurodegenerative diseases, and discuss emerging therapeutic opportunities. By integrating recent findings, this review provides a comprehensive overview of the intricate interplay between DDR and cellular metabolism, offering new perspectives on cellular resilience and potential avenues for therapeutic intervention.

A multilineage screen identifies actionable synthetic lethal interactions in human cancers

Cancers are driven by alterations in diverse genes, creating dependencies that can be therapeutically targeted. However, many genetic dependencies have proven inconsistent across tumors. Here we describe SCHEMATIC, a strategy to identify a core network of highly penetrant, actionable genetic interactions. First, fundamental cellular processes are perturbed by systematic combinatorial knockouts across tumor lineages, identifying 1,805 synthetic lethal interactions (95% unreported). Interactions are then analyzed by hierarchical pooling, revealing that half segregate reliably by tissue type or biomarker status (51%) and a substantial minority are penetrant across lineages (34%). Interactions converge on 49 multigene systems, including MAPK signaling and BAF transcriptional regulatory complexes, which become essential on disruption of polymerases. Some 266 interactions translate to robust biomarkers of drug sensitivity, including frequent genetic alterations in the KDM5C/6A histone demethylases, which sensitize to inhibition of TIPARP (PARP7). SCHEMATIC offers a context-aware, data-driven approach to match genetic alterations to targeted therapies.

The present and future of the Cancer Dependency Map

Despite tremendous progress in the past decade, the complex and heterogeneous nature of cancer complicates efforts to identify new therapies and therapeutic combinations that achieve durable responses in most patients. Further advances in cancer therapy will rely, in part, on the development of targeted therapeutics matched with the genetic and molecular characteristics of cancer. The Cancer Dependency Map (DepMap) is a large-scale data repository and research platform, aiming to systematically reveal the landscape of cancer vulnerabilities in thousands of genetically and molecularly annotated cancer models. DepMap is used routinely by cancer researchers and translational scientists and has facilitated the identification of several novel and selective therapeutic strategies for multiple cancer types that are being tested in the clinic. However, it is also clear that the current version of DepMap is not yet comprehensive. In this Perspective, we review (1) the impact and current uses of DepMap, (2) the opportunities to enhance DepMap to overcome its current limitations, and (3) the ongoing efforts to further improve and expand DepMap.

Benchmarking machine learning methods for synthetic lethality prediction in cancer

Synthetic lethality (SL) is a gold mine of anticancer drug targets, exposing cancer-specific dependencies of cellular survival. To complement resource-intensive experimental screening, many machine learning methods for SL prediction have emerged recently. However, a comprehensive benchmarking is lacking. This study systematically benchmarks 12 recent machine learning methods for SL prediction, assessing their performance across diverse data splitting scenarios, negative sample ratios, and negative sampling techniques, on both classification and ranking tasks. We observe that all the methods can perform significantly better by improving data quality, e.g., excluding computationally derived SLs from training and sampling negative labels based on gene expression. Among the methods, SLMGAE performs the best. Furthermore, the methods have limitations in realistic scenarios such as cold-start independent tests and context-specific SLs. These results, together with source code and datasets made freely available, provide guidance for selecting suitable methods and developing more powerful techniques for SL virtual screening.

Sensitive detection of synthetic response to cancer immunotherapy driven by gene paralog pairs

Emerging immunotherapies such as immune checkpoint blockade (ICB) and chimeric antigen receptor T-cell (CAR-T) therapy have revolutionized cancer treatment and have improved the survival of patients with multiple cancer types. Despite this success many patients are unresponsive to these treatments or relapse following treatment. CRISPR activation and knockout (KO) screens have been used to identify novel single gene targets that can enhance effector T cell function and promote immune cell targeting and eradication of tumors. However, cancer cells often employ multiple genes to promote an immunosuppressive pathway and thus modulating individual genes often has a limited effect. Paralogs are genes that originate from common ancestors and retain similar functions. They often have complex effects on a particular phenotype depending on factors like gene family similarity, each individual gene's expression and the physiological or pathological context. Some paralogs exhibit synthetic lethal interactions in cancer cell survival; however, a thorough investigation of paralog pairs that could enhance the efficacy of cancer immunotherapy is lacking. Here we introduce a sensitive computational approach that uses sgRNA sets enrichment analysis to identify cancer-intrinsic paralog pairs which have the potential to synergistically enhance T cell-mediated tumor destruction. We have further developed an ensemble learning model that uses an XGBoost classifier and incorporates features such as gene characteristics, sequence and structural similarities, protein-protein interaction (PPI) networks, and gene coevolution data to predict paralog pairs that are likely to enhance immunotherapy efficacy. We experimentally validated the functional significance of these predicted paralog pairs using double knockout (DKO) of identified paralog gene pairs as compared to single gene knockouts (SKOs). These data and analyses collectively provide a sensitive approach to identify previously undetected paralog pairs that can enhance cancer immunotherapy even when individual genes within the pair has a limited effect.

HyCas9-12aGEP: an efficient genome editing platform for Corynebacterium glutamicum

Corynebacterium glutamicum plays a crucial role as a significant industrial producer of metabolites. Despite the successful development of CRISPR-Cas9 and CRISPR-Cas12a-assisted genome editing technologies in C. glutamicum, their editing resolution and efficiency are hampered by the diverse on-target activities of guide RNAs (gRNAs). To address this problem, a hybrid CRISPR-Cas9-Cas12a genome editing platform (HyCas9-12aGEP) was developed in C. glutamicum in this study to co-express sgRNA (corresponding to SpCas9 guide RNA), crRNA (corresponding to FnCas12a guide RNA), or hfgRNA (formed by the fusion of sgRNA and crRNA). HyCas9-12aGEP improves the efficiency of mapping active gRNAs and outperforms both CRISPR-Cas9 and CRISPR-Cas12a in genome editing resolution and efficiency. In the experiment involving the deletion of the cg0697-0740 gene segment, an unexpected phenotype was observed, and HyCas9-12aGEP efficiently identified the responsible genotype from more than 40 genes. Here, HyCas9-12aGEP greatly improve our capability in terms of genome reprogramming in C. glutamicum.

SLKB: synthetic lethality knowledge base

Emerging CRISPR-Cas9 technology permits synthetic lethality (SL) screening of large number of gene pairs from gene combination double knockout (CDKO) experiments. However, the poor integration and annotation of CDKO SL data in current SL databases limit their utility, and diverse methods of calculating SL scores prohibit their comparison. To overcome these shortcomings, we have developed SL knowledge base (SLKB) that incorporates data of 11 CDKO experiments in 22 cell lines, 16,059 SL gene pairs and 264,424 non-SL gene pairs. Additionally, within SLKB, we have implemented five SL calculation methods: median score with and without background control normalization (Median-B/NB), sgRNA-derived score (sgRNA-B/NB), Horlbeck score, GEMINI score and MAGeCK score. The five scores have demonstrated a mere 1.21% overlap among their top 10% SL gene pairs, reflecting high diversity. Users can browse SL networks and assess the impact of scoring methods using Venn diagrams. The SL network generated from all data in SLKB shows a greater likelihood of SL gene pair connectivity with other SL gene pairs than non-SL pairs. Comparison of SL networks between two cell lines demonstrated greater likelihood to share SL hub genes than SL gene pairs. SLKB website and pipeline can be freely accessed at https://slkb.osubmi.org and https://slkb.docs.osubmi.org/, respectively.

Pathway-driven analysis of synthetic lethal interactions in cancer using perturbation screens

Synthetic lethality offers a promising approach for developing effective therapeutic interventions in cancer when direct targeting of driver genes is impractical. In this study, we comprehensively analyzed large-scale CRISPR, shRNA, and PRISM screens to identify potential synthetic lethal (SL) interactions in pan-cancer and 12 individual cancer types, using a new computational framework that leverages the biological function and signaling pathway information of key driver genes to mitigate the confounding effects of background genetic alterations in different cancer cell lines. This approach has successfully identified several putative SL interactions, including KRAS-MAP3K2 and APC-TCF7L2 in pan cancer, and CCND1-METTL1, TP53-FRS3, SMO-MDM2, and CCNE1-MTOR in liver, blood, skin, and gastric cancers, respectively. In addition, we proposed several FDA-approved cancer-targeted drugs for various cancer types through PRISM drug screens, such as cabazitaxel for VHL-mutated kidney cancer and alectinib for lung cancer with NRAS or KRAS mutations. Leveraging pathway information can enhance the concordance of shRNA and CRISPR screens and provide clinically relevant findings such as the potential efficacy of dasatinib, an inhibitor of SRC, for colorectal cancer patients with mutations in the WNT signaling pathway. These analyses revealed that taking signaling pathway information into account results in the identification of more promising SL interactions.

A CRISPR-drug perturbational map for identifying compounds to combine with commonly used chemotherapeutics

Combination chemotherapy is crucial for successfully treating cancer. However, the enormous number of possible drug combinations means discovering safe and effective combinations remains a significant challenge. To improve this process, we conduct large-scale targeted CRISPR knockout screens in drug-treated cells, creating a genetic map of druggable genes that sensitize cells to commonly used chemotherapeutics. We prioritize neuroblastoma, the most common extracranial pediatric solid tumor, where ~50% of high-risk patients do not survive. Our screen examines all druggable gene knockouts in 18 cell lines (10 neuroblastoma, 8 others) treated with 8 widely used drugs, resulting in 94,320 unique combination-cell line perturbations, which is comparable to the largest existing drug combination screens. Using dense drug-drug rescreening, we find that the top CRISPR-nominated drug combinations are more synergistic than standard-of-care combinations, suggesting existing combinations could be improved. As proof of principle, we discover that inhibition of PRKDC, a component of the non-homologous end-joining pathway, sensitizes high-risk neuroblastoma cells to the standard-of-care drug doxorubicin in vitro and in vivo using patient-derived xenograft (PDX) models. Our findings provide a valuable resource and demonstrate the feasibility of using targeted CRISPR knockout to discover combinations with common chemotherapeutics, a methodology with application across all cancers.

Mining cancer genomes for change-of-metabolic-function mutations

Enzymes with novel functions are needed to enable new organic synthesis techniques. Drawing inspiration from gain-of-function cancer mutations that functionally alter proteins and affect cellular metabolism, we developed METIS (Mutated Enzymes from Tumors In silico Screen). METIS identifies metabolism-altering cancer mutations using mutation recurrence rates and protein structure. We used METIS to screen 298,517 cancer mutations and identify 48 candidate mutations, including those previously identified to alter enzymatic function. Unbiased metabolomic profiling of cells exogenously expressing a candidate mutant (OGDHLp.A400T) supports an altered phenotype that boosts in vitro production of xanthosine, a pharmacologically useful chemical that is currently produced using unsustainable, water-intensive methods. We then applied METIS to 49 million cancer mutations, yielding a refined set of candidates that may impart novel enzymatic functions or contribute to tumor progression. Thus, METIS can be used to identify and catalog potentially-useful cancer mutations for green chemistry and therapeutic applications.

Using graph-based model to identify cell specific synthetic lethal effects

Synthetic lethal (SL) pairs are pairs of genes whose simultaneous loss-of-function results in cell death, while a damaging mutation of either gene alone does not affect the cell's survival. This makes SL pairs attractive targets for precision cancer therapies, as targeting the unimpaired gene of the SL pair can selectively kill cancer cells that already harbor the impaired gene. Limited by the difficulty of finding true SL pairs, especially on specific cell types, current computational approaches provide only limited insights because of overlooking the crucial aspects of cellular context dependency and mechanistic understanding of SL pairs. As a result, the identification of SL targets still relies on expensive, time-consuming experimental approaches. In this work, we applied cell-line specific multi-omics data to a specially designed deep learning model to predict cell-line specific SL pairs. Through incorporating multiple types of cell-specific omics data with a self-attention module, we represent gene relationships as graphs. Our approach achieves the prediction of SL pairs in a cell-specific manner and demonstrates the potential to facilitate the discovery of cell-specific SL targets for cancer therapeutics, providing a tool to unearth mechanisms underlying the origin of SL in cancer biology. The code and data of our approach can be found at https://github.com/promethiume/SLwise.

How to deal with frenemy NRF2: Targeting NRF2 for chemoprevention and cancer therapy

Induction of antioxidant proteins and phase 2 detoxifying enzymes that neutralize reactive electrophiles are important mechanisms for protection against carcinogenesis. Normal cells provide multifaceted pathways to tightly control NF-E2-related factor 2 (NRF2)-mediated gene expression in response to an assault by a range of endogenous and exogenous oncogenic molecules. Transient activation of NRF2 by its activators is able to induce ARE-mediated cytoprotective proteins which are essential for protection against various toxic and oxidative damages, and NRF2 activators thereby have efficacy in cancer chemoprevention. Because NRF2 has a cytoprotective function, it can protect normal cells from carcinogens like an angel, but when the protective effect acts on cancer cells, it will give rise to invincible cancer cells and play a devilish role in tumor progression. Indeed, aberrant activation of NRF2 has been found in a variety of cancers that create a favorable environment for the proliferation and survival of cancer cells and leads to drug resistance, ultimately leading to the poor clinical prognosis of patients. Therefore, pharmacological inhibition of NRF2 signaling has emerged as a promising approach for cancer therapy. This review aims to compile the regulatory mechanisms of NRF2 and its double-edged role in cancer. In addition, we also summarize the research progress of NRF2 modulators, especially phytochemicals, in chemoprevention and cancer therapy.

The pentose phosphate pathway in health and disease

The pentose phosphate pathway (PPP) is a glucose-oxidizing pathway that runs in parallel to upper glycolysis to produce ribose 5-phosphate and nicotinamide adenine dinucleotide phosphate (NADPH). Ribose 5-phosphate is used for nucleotide synthesis, while NADPH is involved in redox homoeostasis as well as in promoting biosynthetic processes, such as the synthesis of tetrahydrofolate, deoxyribonucleotides, proline, fatty acids and cholesterol. Through NADPH, the PPP plays a critical role in suppressing oxidative stress, including in certain cancers, in which PPP inhibition may be therapeutically useful. Conversely, PPP-derived NADPH also supports purposeful cellular generation of reactive oxygen species (ROS) and reactive nitrogen species (RNS) for signalling and pathogen killing. Genetic deficiencies in the PPP occur relatively commonly in the committed pathway enzyme glucose-6-phosphate dehydrogenase (G6PD). G6PD deficiency typically manifests as haemolytic anaemia due to red cell oxidative damage but, in severe cases, also results in infections due to lack of leucocyte oxidative burst, highlighting the dual redox roles of the pathway in free radical production and detoxification. This Review discusses the PPP in mammals, covering its roles in biochemistry, physiology and disease.

Comparison of the Strengths and Weaknesses of Machine Learning Algorithms and Feature Selection on KEGG Database Microbial Gene Pathway Annotation and Its Effects on Reconstructed Network Topology

The development of tools for the annotation of genes from newly sequenced species has not evolved much from homologous alignment to prior annotated species. While the quality of gene annotations continues to decline as we sequence and assemble more evolutionary distant gut microbiome species, machine learning presents a high quality alternative to traditional techniques. In this study, we investigate the relative performance of common classical and nonclassical machine learning algorithms in the problem of gene annotation using human microbiome-associated species genes from the KEGG database. The majority of the ensemble, clustering, and deep learning algorithms that we investigated showed higher prediction accuracy than CD-Hit in predicting partial KEGG function. Motif-based, machine-learning methods of annotation in new species were faster and had higher precision-recall than methods of homologous alignment or orthologous gene clustering. Gradient boosted ensemble methods and neural networks also predicted higher connectivity in reconstructed KEGG pathways, finding twice as many new pathway interactions than blast alignment. The use of motif-based, machine-learning algorithms in annotation software will allow researchers to develop powerful tools to interact with bacterial microbiomes in ways previously unachievable through homologous sequence alignment alone.

To metabolomics and beyond: a technological portfolio to investigate cancer metabolism

Tumour cells have exquisite flexibility in reprogramming their metabolism in order to support tumour initiation, progression, metastasis and resistance to therapies. These reprogrammed activities include a complete rewiring of the bioenergetic, biosynthetic and redox status to sustain the increased energetic demand of the cells. Over the last decades, the cancer metabolism field has seen an explosion of new biochemical technologies giving more tools than ever before to navigate this complexity. Within a cell or a tissue, the metabolites constitute the direct signature of the molecular phenotype and thus their profiling has concrete clinical applications in oncology. Metabolomics and fluxomics, are key technological approaches that mainly revolutionized the field enabling researchers to have both a qualitative and mechanistic model of the biochemical activities in cancer. Furthermore, the upgrade from bulk to single-cell analysis technologies provided unprecedented opportunity to investigate cancer biology at cellular resolution allowing an in depth quantitative analysis of complex and heterogenous diseases. More recently, the advent of functional genomic screening allowed the identification of molecular pathways, cellular processes, biomarkers and novel therapeutic targets that in concert with other technologies allow patient stratification and identification of new treatment regimens. This review is intended to be a guide for researchers to cancer metabolism, highlighting current and emerging technologies, emphasizing advantages, disadvantages and applications with the potential of leading the development of innovative anti-cancer therapies.

Benzo[a]pyrene treatment modulates Nrf2/Keap1 axis and changes the metabolic profile in rat lung cancer

Lung cancer is an aggressive malignancy and the leading cause of cancer-related deaths. Benzo[a]pyrene (B[a]P), a polycyclic hydrocarbon, plays a pivotal role in lung carcinogenesis. Uncovering the molecular mechanism underlying the pathophysiology of B[a]P induced malignancy is crucial. Male Sprague Dawley rats were induced with B[a]P to generate a lung cancer model. The B[a]P administered rats show increased body and lung weight, loss of normal pulmonary architecture, and decreased survival. This study demonstrated that B[a]P upregulates activating transcription factor-6 (ATF6) and C/EBP Homologous Protein (CHOP) and induces endoplasmic reticulum (ER) stress. B[a]P also dysregulated mitochondrial homeostasis by upregulating, PTEN-induced putative kinase-1 (PINK1) and Parkin. B[a]P affected the levels of superoxide dismutase (SOD), reduced glutathione (GSH), malondialdehyde (MDA), and increased oxidative stress. B[a]P exposure downregulated Kelch-like ECH-associated protein 1 (Keap1) and upregulated nuclear factor erythroid 2-related factor 2 (Nrf2) and Heme oxygenase-1(HO1). The metabolomic study identified that biosynthesis of nucleotide, amino acids, pentose phosphate pathway (PPP), tricarboxylic acid cycle (TCA), and glutathione metabolism were up-accumulated. On the other hand, lower accumulation of fatty acids e.g., palmitic acid, stearic acid, and oleic acid were reported in the B[a]P induced group. Overall, the results of this study indicate that B[a]P treatment affects several signaling and metabolic pathways, whose dysregulation might be involved in lung cancer induction.

Battles against aberrant KEAP1-NRF2 signaling in lung cancer: intertwined metabolic and immune networks

The Kelch-like ECH-associated protein 1/nuclear factor erythroid-derived 2-like 2 (KEAP1/NRF2) pathway is well recognized as a key regulator of redox homeostasis, protecting cells from oxidative stress and xenobiotics under physiological circumstances. Cancer cells often hijack this pathway during initiation and progression, with aberrant KEAP1-NRF2 activity predominantly observed in non-small cell lung cancer (NSCLC), suggesting that cell/tissue-of-origin is likely to influence the genetic selection during malignant transformation. Hyperactivation of NRF2 confers a multi-faceted role, and recently, increasing evidence shows that a close interplay between metabolic reprogramming and tumor immunity remodelling contributes to its aggressiveness, treatment resistance (radio-/chemo-/immune-therapy) and susceptibility to metastases. Here, we discuss in detail the special metabolic and immune fitness enabled by KEAP1-NRF2 aberration in NSCLC. Furthermore, we summarize the similarities and differences in the dysregulated KEAP1-NRF2 pathway between two major histo-subtypes of NSCLC, provide mechanistic insights on the poor response to immunotherapy despite their high immunogenicity, and outline evolving strategies to treat this recalcitrant cancer subset. Finally, we integrate bioinformatic analysis of publicly available datasets to illustrate the new partners/effectors in NRF2-addicted cancer cells, which may provide new insights into context-directed treatment.

Discovery of synthetic lethal interactions from large-scale pan-cancer perturbation screens

The development of cancer therapies is limited by the availability of suitable drug targets. Potential candidate drug targets can be identified based on the concept of synthetic lethality (SL), which refers to pairs of genes for which an aberration in either gene alone is non-lethal, but co-occurrence of the aberrations is lethal to the cell. Here, we present SLIdR (Synthetic Lethal Identification in R), a statistical framework for identifying SL pairs from large-scale perturbation screens. SLIdR successfully predicts SL pairs even with small sample sizes while minimizing the number of false positive targets. We apply SLIdR to Project DRIVE data and find both established and potential pan-cancer and cancer type-specific SL pairs consistent with findings from literature and drug response screening data. We experimentally validate two predicted SL interactions (ARID1A-TEAD1 and AXIN1-URI1) in hepatocellular carcinoma, thus corroborating the ability of SLIdR to identify potential drug targets.

Synthetic lethal gene pairs: Experimental approaches and predictive models

Synthetic lethality (SL) refers to a genetic interaction in which the simultaneous perturbation of two genes leads to cell or organism death, whereas viability is maintained when only one of the pair is altered. The experimental exploration of these pairs and predictive modeling in computational biology contribute to our understanding of cancer biology and the development of cancer therapies. We extensively reviewed experimental technologies, public data sources, and predictive models in the study of synthetic lethal gene pairs and herein detail biological assumptions, experimental data, statistical models, and computational schemes of various predictive models, speculate regarding their influence on individual sample- and population-based synthetic lethal interactions, discuss the pros and cons of existing SL data and models, and highlight potential research directions in SL discovery.

Erythritol synthesis is elevated in response to oxidative stress and regulated by the non-oxidative pentose phosphate pathway in A549 cells

Background

Erythritol is a predictive biomarker of cardiometabolic diseases and is produced from glucose metabolism through the pentose phosphate pathway (PPP). Little is known regarding the regulation of endogenous erythritol synthesis in humans.

Objective

In the present study, we investigated the stimuli that promote erythritol synthesis in human lung carcinoma cells and characterized potential points of regulation along the PPP.

Methods

Human A549 lung carcinoma cells were chosen for their known ability to synthesize erythritol. A549 cells were treated with potential substrates for erythritol production, including glucose, fructose, and glycerol. Using siRNA knockdown, we assessed the necessity of enzymes G6PD, TKT, TALDO, and SORD for erythritol synthesis. We also used position-specific ¹³C-glucose tracers to determine whether the carbons for erythritol synthesis are derived directly from glycolysis or through the oxidative PPP. Finally, we assessed if erythritol synthesis responds to oxidative stress using chemical and genetic models.

Results

Intracellular erythritol was directly associated with media glucose concentration. In addition, siRNA knockdown of TKT or SORD inhibited erythritol synthesis, whereas siG6PD did not. Both chemically induced oxidative stress and constitutive activation of the antioxidant response transcription factor NRF2 elevated intracellular erythritol.

Conclusion

Our findings indicate that in A549 cells, erythritol synthesis is proportional to flux through the PPP and is regulated by non-oxidative PPP enzymes.

Generation of dual-gRNA library for combinatorial CRISPR screening of synthetic lethal gene pairs

Combinatorial CRISPR screening is useful for investigating synthetic lethality (SL) gene pairs. Here, we detail the steps for dual-gRNA library construction, with the introduction of two backbones, LentiGuide_DKO and LentiCRISPR_DKO. We describe steps for in vitro screening with 22Rv1-Cas9 and SaOS2-Cas9 cells followed by sequencing and data analysis. By introducing two backbones, we optimized the library construction process, facilitated standard pair-end sequencing, and provided options of screening on cells with or without modification of Cas9 expression.

A CRISPR screen identifies redox vulnerabilities for KEAP1/NRF2 mutant non-small cell lung cancer

The redox regulator NRF2 is hyperactivated in a large percentage of non-small cell lung cancer (NSCLC) cases, which is associated with chemotherapy and radiation resistance. To identify redox vulnerabilities for KEAP1/NRF2 mutant NSCLC, we conducted a CRISPR-Cas9-based negative selection screen for antioxidant enzyme genes whose loss sensitized cells to sub-lethal concentrations of the superoxide (O2•-) -generating drug β-Lapachone. While our screen identified expected hits in the pentose phosphate pathway, the thioredoxin-dependent antioxidant system, and glutathione reductase, we also identified the mitochondrial superoxide dismutase 2 (SOD2) as one of the top hits. Surprisingly, β-Lapachone did not generate mitochondrial O2•- but rather SOD2 loss enhanced the efficacy of β-Lapachone due to loss of iron-sulfur protein function, loss of mitochondrial ATP maintenance and deficient NADPH production. Importantly, inhibition of mitochondrial electron transport activity sensitized cells to β-Lapachone, demonstrating that these effects may be translated to increase ROS sensitivity therapeutically.

CRISPR/Cas9 system in breast cancer therapy: advancement, limitations and future scope

Cancer is one of the major causes of mortality worldwide, therefore it is considered a major health concern. Breast cancer is the most frequent type of cancer which affects women on a global scale. Various current treatment strategies have been implicated for breast cancer therapy that includes surgical removal, radiation therapy, hormonal therapy, chemotherapy, and targeted biological therapy. However, constant effort is being made to introduce novel therapies with minimal toxicity. Gene therapy is one of the promising tools, to rectify defective genes and cure various cancers. In recent years, a novel genome engineering technology, namely the clustered regularly interspaced short palindromic repeat (CRISPR)-associated protein-9 (Cas9) has emerged as a gene-editing tool and transformed genome-editing techniques in a wide range of biological domains including human cancer research and gene therapy. This could be attributed to its versatile characteristics such as high specificity, precision, time-saving and cost-effective methodologies with minimal risk. In the present review, we highlight the role of CRISPR/Cas9 as a targeted therapy to tackle drug resistance, improve immunotherapy for breast cancer.

High-throughput functional evaluation of human cancer-associated mutations using base editors

Comprehensive phenotypic characterization of the many mutations found in cancer tissues is one of the biggest challenges in cancer genomics. In this study, we evaluated the functional effects of 29,060 cancer-related transition mutations that result in protein variants on the survival and proliferation of non-tumorigenic lung cells using cytosine and adenine base editors and single guide RNA (sgRNA) libraries. By monitoring base editing efficiencies and outcomes using surrogate target sequences paired with sgRNA-encoding sequences on the lentiviral delivery construct, we identified sgRNAs that induced a single primary protein variant per sgRNA, enabling linking those mutations to the cellular phenotypes caused by base editing. The functions of the vast majority of the protein variants (28,458 variants, 98%) were classified as neutral or likely neutral; only 18 (0.06%) and 157 (0.5%) variants caused outgrowing and likely outgrowing phenotypes, respectively. We expect that our approach can be extended to more variants of unknown significance and other tumor types.

SynLethDB 2.0: a web-based knowledge graph database on synthetic lethality for novel anticancer drug discovery

Two genes are synthetic lethal if mutations in both genes result in impaired cell viability, while mutation of either gene does not affect the cell survival. The potential usage of synthetic lethality (SL) in anticancer therapeutics has attracted many researchers to identify synthetic lethal gene pairs. To include newly identified SLs and more related knowledge, we present a new version of the SynLethDB database to facilitate the discovery of clinically relevant SLs. We extended the first version of SynLethDB database significantly by including new SLs identified through Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) screening, a knowledge graph about human SLs, a new web interface, etc. Over 16 000 new SLs and 26 types of other relationships have been added, encompassing relationships among 14 100 genes, 53 cancers, 1898 drugs, etc. Moreover, a brand-new web interface has been developed to include modules such as SL query by disease or compound, SL partner gene set enrichment analysis and knowledge graph browsing through a dynamic graph viewer. The data can be downloaded directly from the website or through the RESTful Application Programming Interfaces (APIs). Database URL:  https://synlethdb.sist.shanghaitech.edu.cn/v2.

Functional genomics and the future of iPSCs in disease modeling

Induced pluripotent stem cells (iPSCs) are valuable in disease modeling because of their potential to expand and differentiate into virtually any cell type and recapitulate key aspects of human biology. Functional genomics are genome-wide studies that aim to discover genotype-phenotype relationships, thereby revealing the impact of human genetic diversity on normal and pathophysiology. In this review, we make the case that human iPSCs (hiPSCs) are a powerful tool for functional genomics, since they provide an in vitro platform for the study of population genetics. We describe cutting-edge tools and strategies now available to researchers, including multi-omics technologies, advances in hiPSC culture techniques, and innovations in drug development. Functional genomics approaches based on hiPSCs hold great promise for advancing drug discovery, disease etiology, and the impact of genetic variation on human biology.

Comparative optimization of combinatorial CRISPR screens

Combinatorial CRISPR technologies have emerged as a transformative approach to systematically probe genetic interactions and dependencies of redundant gene pairs. However, the performance of different functional genomic tools for multiplexing sgRNAs vary widely. Here, we generate and benchmark ten distinct pooled combinatorial CRISPR libraries targeting paralog pairs to optimize digenic knockout screens. Libraries composed of dual Streptococcus pyogenes Cas9 (spCas9), orthogonal spCas9 and Staphylococcus aureus (saCas9), and enhanced Cas12a from Acidaminococcus were evaluated. We demonstrate a combination of alternative tracrRNA sequences from spCas9 consistently show superior effect size and positional balance between the sgRNAs as a robust combinatorial approach to profile genetic interactions of multiple genes.

Computational methods, databases and tools for synthetic lethality prediction

Synthetic lethality (SL) occurs between two genes when the inactivation of either gene alone has no effect on cell survival but the inactivation of both genes results in cell death. SL-based therapy has become one of the most promising targeted cancer therapies in the last decade as PARP inhibitors achieve great success in the clinic. The key point to exploiting SL-based cancer therapy is the identification of robust SL pairs. Although many wet-lab-based methods have been developed to screen SL pairs, known SL pairs are less than 0.1% of all potential pairs due to large number of human gene combinations. Computational prediction methods complement wet-lab-based methods to effectively reduce the search space of SL pairs. In this paper, we review the recent applications of computational methods and commonly used databases for SL prediction. First, we introduce the concept of SL and its screening methods. Second, various SL-related data resources are summarized. Then, computational methods including statistical-based methods, network-based methods, classical machine learning methods and deep learning methods for SL prediction are summarized. In particular, we elaborate on the negative sampling methods applied in these models. Next, representative tools for SL prediction are introduced. Finally, the challenges and future work for SL prediction are discussed.

Tumor immunology CRISPR screening: present, past, and future

Recent advances in immunotherapy have fundamentally changed the landscape of cancer treatment by leveraging the specificity and selectivity of the adaptive immune system to kill cancer cells. These successes have ushered in a new wave of research aimed at understanding immune recognition with the hope of developing newer immunotherapies. The advent of clustered regularly interspaced short palindromic repeats (CRISPR) technologies and advancement of multiomics modalities have greatly accelerated the discovery process. Here, we review the current literature surrounding CRISPR screens within the context of tumor immunology, provide essential components needed to conduct immune-specific CRISPR screens, and present avenues for future research.

Metabolic regulation of somatic stem cells in vivo

Metabolism has been studied mainly in cultured cells or at the level of whole tissues or whole organisms in vivo. Consequently, our understanding of metabolic heterogeneity among cells within tissues is limited, particularly when it comes to rare cells with biologically distinct properties, such as stem cells. Stem cell function, tissue regeneration and cancer suppression are all metabolically regulated, although it is not yet clear whether there are metabolic mechanisms unique to stem cells that regulate their activity and function. Recent work has, however, provided evidence that stem cells do have a metabolic signature that is distinct from that of restricted progenitors and that metabolic changes influence tissue homeostasis and regeneration. Stem cell maintenance throughout life in many tissues depends upon minimizing anabolic pathway activation and cell division. Consequently, stem cell activation by tissue injury is associated with changes in mitochondrial function, lysosome activity and lipid metabolism, potentially at the cost of eroding self-renewal potential. Stem cell metabolism is also regulated by the environment: stem cells metabolically interact with other cells in their niches and are able to sense and adapt to dietary changes. The accelerating understanding of stem cell metabolism is revealing new aspects of tissue homeostasis with the potential to promote tissue regeneration and cancer suppression.

Functional buffering via cell-specific gene expression promotes tissue homeostasis and cancer robustness

Functional buffering that ensures biological robustness is critical for maintaining tissue homeostasis, organismal survival, and evolution of novelty. However, the mechanism underlying functional buffering, particularly in multicellular organisms, remains largely elusive. Here, we proposed that functional buffering can be mediated via expression of buffering genes in specific cells and tissues, by which we named Cell-specific Expression-BUffering (CEBU). We developed an inference index (C-score) for CEBU by computing C-scores across 684 human cell lines using genome-wide CRISPR screens and transcriptomic RNA-seq. We report that C-score-identified putative buffering gene pairs are enriched for members of the same duplicated gene family, pathway, and protein complex. Furthermore, CEBU is especially prevalent in tissues of low regenerative capacity (e.g., bone and neuronal tissues) and is weakest in highly regenerative blood cells, linking functional buffering to tissue regeneration. Clinically, the buffering capacity enabled by CEBU can help predict patient survival for multiple cancers. Our results suggest CEBU as a potential buffering mechanism contributing to tissue homeostasis and cancer robustness in humans.

Paralog knockout profiling identifies DUSP4 and DUSP6 as a digenic dependence in MAPK pathway-driven cancers

Although single-gene perturbation screens have revealed a number of new targets, vulnerabilities specific to frequently altered drivers have not been uncovered. An important question is whether the compensatory relationship between functionally redundant genes masks potential therapeutic targets in single-gene perturbation studies. To identify digenic dependencies, we developed a CRISPR paralog targeting library to investigate the viability effects of disrupting 3,284 genes, 5,065 paralog pairs and 815 paralog families. We identified that dual inactivation of DUSP4 and DUSP6 selectively impairs growth in NRAS and BRAF mutant cells through the hyperactivation of MAPK signaling. Furthermore, cells resistant to MAPK pathway therapeutics become cross-sensitized to DUSP4 and DUSP6 perturbations such that the mechanisms of resistance to the inhibitors reinforce this mechanism of vulnerability. Together, multigene perturbation technologies unveil previously unrecognized digenic vulnerabilities that may be leveraged as new therapeutic targets in cancer.

Discovery of putative tumor suppressors from CRISPR screens reveals rewired lipid metabolism in acute myeloid leukemia cells

CRISPR knockout fitness screens in cancer cell lines reveal many genes whose loss of function causes cell death or loss of fitness or, more rarely, the opposite phenotype of faster proliferation. Here we demonstrate a systematic approach to identify these proliferation suppressors, which are highly enriched for tumor suppressor genes, and define a network of 145 such genes in 22 modules. One module contains several elements of the glycerolipid biosynthesis pathway and operates exclusively in a subset of acute myeloid leukemia cell lines. The proliferation suppressor activity of genes involved in the synthesis of saturated fatty acids, coupled with a more severe loss of fitness phenotype for genes in the desaturation pathway, suggests that these cells operate at the limit of their carrying capacity for saturated fatty acids, which we confirm biochemically. Overexpression of this module is associated with a survival advantage in juvenile leukemias, suggesting a clinically relevant subtype.

CRISPR-based knockout screens in cancer cells have suggested the existence of proliferation suppressor genes (PSG). Here, the authors develop an approach to systematically identify them, and reveal a PSG module involved in fatty acid synthesis and tumour suppression in acute myeloid leukemia cell lines.

Application of CHyMErA Cas9-Cas12a combinatorial genome-editing platform for genetic interaction mapping and gene fragment deletion screening

CRISPR-based forward genetic screening represents a powerful approach for the systematic characterization of gene function. Recent efforts have been directed toward establishing CRISPR-based tools for the programmable delivery of combinatorial genetic perturbations, most of which are mediated by a single nuclease and the expression of structurally identical guide backbones from two promoters. In contrast, we have developed CHyMErA (Cas hybrid for multiplexed editing and screening applications), which is based on the co-expression of Cas9 and Cas12a nucleases in conjunction with a hybrid guide RNA (hgRNA) engineered by the fusion of Cas9 and Cas12a guides and expressed from a single U6 promoter. CHyMErA is suitable for the high-throughput deletion of genetic segments including the excision of individual exons. Furthermore, CHyMErA enables the concomitant targeting of two or more genes and can thus be used for the systematic mapping of genetic interactions in mammalian cells. CHyMErA can also be applied for the perturbation of paralogous gene pairs, thereby allowing the capturing of phenotypic roles that would otherwise be masked because of genetic redundancy. Here, we provide instructions for the cloning of hgRNA screening libraries and individual hgRNA constructs and offer guidelines for designing and performing combinatorial pooled genetic screens using CHyMErA. Starting with the generation of Cas9- and Cas12a-expressing cell lines, CHyMErA screening can be implemented within 15-20 weeks.

Metabolic reprograming of antioxidant defense: a precision medicine perspective for radiotherapy of lung cancer?

Radiotherapy plays a key role in the management of lung cancer patients in curative and palliative settings. Traditionally, radiotherapy was either given alone or in combination with surgery, classical cytotoxic chemotherapy, or both. Technical and physical innovations achieved during the last two decades have helped to enhance the accuracy of radiotherapy dose delivery and have facilitated geometric radiotherapy individualization. Furthermore, multimodal combinations with molecularly tailored drugs or immunotherapy yielded promising survival benefits in selected patients. Yet high locoregional failure rates and frequent development of metastases still limit the patient outcome. One major obstacle to successful treatment is the high molecular heterogeneity observed in lung cancer. So far, clinical radiotherapy does not routinely use the knowledge on molecular subtypes with regard to therapy individualization and predictive biomarkers are missing. Herein, altered cancer metabolism has attracted novel attention during recent years as it promotes tumor growth and progression as well as resistance to anticancer therapies. The present perspective will exemplarily highlight how clinically relevant molecular subtypes defined by co-occurring somatic mutations in KRAS-driven lung cancer impact the metabolic phenotype of cancer cells, how the metabolic phenotype supports intrinsic radioresistance by the improved antioxidant defense, and also discuss potential subtype-specific actionable metabolic vulnerabilities. Understanding metabolic phenotypes of radioresistance and metabolic bottlenecks of cancer cells undergoing radiotherapy in a cancer-specific context will offer largely unexploited future avenues for biological individualization and optimization of radiotherapy. Transcriptional profiles will provide additional benefit in defining metabolic phenotypes associated with radioresistance, particularly in cases, where such dependencies cannot be identified by specific somatic mutations.

CRISPR screens in plants: approaches, guidelines, and future prospects

Clustered regularly interspaced short palindromic repeat (CRISPR)-associated systems have revolutionized genome engineering by facilitating a wide range of targeted DNA perturbations. These systems have resulted in the development of powerful new screens to test gene functions at the genomic scale. While there is tremendous potential to map and interrogate gene regulatory networks at unprecedented speed and scale using CRISPR screens, their implementation in plants remains in its infancy. Here we discuss the general concepts, tools, and workflows for establishing CRISPR screens in plants and analyze the handful of recent reports describing the use of this strategy to generate mutant knockout collections or to diversify DNA sequences. In addition, we provide insight into how to design CRISPR knockout screens in plants given the current challenges and limitations and examine multiple design options. Finally, we discuss the unique multiplexing capabilities of CRISPR screens to investigate redundant gene functions in highly duplicated plant genomes. Combinatorial mutant screens have the potential to routinely generate higher-order mutant collections and facilitate the characterization of gene networks. By integrating this approach with the numerous genomic profiles that have been generated over the past two decades, the implementation of CRISPR screens offers new opportunities to analyze plant genomes at deeper resolution and will lead to great advances in functional and synthetic biology.

CRISPR/Cas9 based genome editing for targeted transcriptional control in triple-negative breast cancer

Breast cancer (BC) is the most common type of cancer in women at the global level and the highest mortality rate has been observed with triple-negative breast cancer (TNBC). Accumulation of genetic lesions an aberrant gene expression and protein degradation are considered to underlie the onset of tumorigenesis and metastasis. Therefore, the challenge to identify the genes and molecules that could be potentially used as potent biomarkers for personalized medicine against TNBC with minimal or no associated side effects. Discovery of the clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR associated protein 9 (Cas9) arrangement and an increasing repertoire of its new variants has provided a much-needed fillip towards editing TNBC genomes. In this review, we discuss the CRISPR/Cas9 genome editing, CRISPR Technology for diagnosis of (Triple-negative breast cancer) TNBC, Drug Resistance, and potential applications of CRISPR/Cas9 and its variants in deciphering or engineering intricate molecular and epigenetic mechanisms associated with TNBC. Furthermore, we have also explored the TNBC and CRISPR/Cas9 genome editing potential for repairing, genetic modifications in TNBC.

Application of CRISPR screens to investigate mammalian cell competition

Cell competition is defined as the context-dependent elimination of cells that is mediated by intercellular communication, such as paracrine or contact-dependent cell signaling, and/or mechanical stresses. It is considered to be a quality control mechanism that facilitates the removal of suboptimal cells from both adult and embryonic tissues. Cell competition, however, can also be hijacked by transformed cells to acquire a 'super-competitor' status and outcompete the normal epithelium to establish a precancerous field. To date, many genetic drivers of cell competition have been identified predominately through studies in Drosophila. Especially during the last couple of years, ethylmethanesulfonate-based genetic screens have been instrumental to our understanding of the molecular regulators behind some of the most common competition mechanisms in Drosophila, namely competition due to impaired ribosomal function (or anabolism) and mechanical sensitivity. Despite recent findings in Drosophila and in mammalian models of cell competition, the drivers of mammalian cell competition remain largely elusive. Since the discovery of CRISPR/Cas9, its use in functional genomics has been indispensable to uncover novel cancer vulnerabilities. We envision that CRISPR/Cas9 screens will enable systematic, genome-scale probing of mammalian cell competition to discover novel mutations that not only trigger cell competition but also identify novel molecular components that are essential for the recognition and elimination of less fit cells. In this review, we summarize recent contributions that further our understanding of the molecular mechanisms of cell competition by genetic screening in Drosophila, and provide our perspective on how similar and novel screening strategies made possible by whole-genome CRISPR/Cas9 screening can advance our understanding of mammalian cell competition in the future.

The Taming of Nuclear Factor Erythroid-2-Related Factor-2 (Nrf2) Deglycation by Fructosamine-3-Kinase (FN3K)-Inhibitors-A Novel Strategy to Combat Cancers

Simple Summary

Aim of this review is to provide an overview on (a) Fructosamine-3-Kinase (FN3K) and its role in regulating Nuclear Factor Erythorid-2-Related Factor-2 (Nrf2); (b) the role of glycation and deglycation mechanisms in modulating the functional properties of proteins, in particular, the Nrf2; (c) the dual role of Nrf2 in the prevention and treatment of cancers. Since controlling the glycation of Nrf2 is one of the key mechanisms determining the fate of a cell; whether to get transformed into a cancerous one or to stay as a normal one, it is important to regulate Nrf2 and deglycating FN3K using pharmacological agents. Inhibitors of FN3K are being explored currently to modulate Nrf2 activity thereby control the cancers.

Abstract

Glycated stress is mediated by the advanced glycation end products (AGE) and the binding of AGEs to the receptors for advanced glycation end products (RAGEs) in cancer cells. RAGEs are involved in mediating tumorigenesis of multiple cancers through the modulation of several downstream signaling cascades. Glycated stress modulates various signaling pathways that include p38 mitogen-activated protein kinase (p38 MAPK), nuclear factor kappa–B (NF-κB), tumor necrosis factor (TNF)-α, etc., which further foster the uncontrolled proliferation, growth, metastasis, angiogenesis, drug resistance, and evasion of apoptosis in several cancers. In this review, a balanced overview on the role of glycation and deglycation in modulating several signaling cascades that are involved in the progression of cancers was discussed. Further, we have highlighted the functional role of deglycating enzyme fructosamine-3-kinase (FN3K) on Nrf2-driven cancers. The activity of FN3K is attributed to its ability to deglycate Nrf2, a master regulator of oxidative stress in cells. FN3K is a unique protein that mediates deglycation by phosphorylating basic amino acids lysine and arginine in various proteins such as Nrf2. Deglycated Nrf2 is stable and binds to small musculoaponeurotic fibrosarcoma (sMAF) proteins, thereby activating cellular antioxidant mechanisms to protect cells from oxidative stress. This cellular protection offered by Nrf2 activation, in one way, prevents the transformation of a normal cell into a cancer cell; however, in the other way, it helps a cancer cell not only to survive under hypoxic conditions but also, to stay protected from various chemo- and radio-therapeutic treatments. Therefore, the activation of Nrf2 is similar to a double-edged sword and, if not controlled properly, can lead to the development of many solid tumors. Hence, there is a need to develop novel small molecule modulators/phytochemicals that can regulate FN3K activity, thereby maintaining Nrf2 in a controlled activation state.

Deuterium Tracing to Interrogate Compartment-Specific NAD(P)H Metabolism in Cultured Mammalian Cells

Oxidation-reduction (redox) reactions are ubiquitous in biology and typically occur in specific subcellular compartments. In cells, the electron transfer between molecules and organelles is commonly facilitated by pyridine nucleotides such as nicotinamide adenine dinucleotide phosphate (NADPH) and nicotinamide adenine dinucleotide (NADH). While often taken for granted, these metabolic reactions are critically important for maintaining redox homeostasis and biochemical potentials across membranes. While ¹³C tracing and metabolic flux analysis (MFA) have emerged as powerful tools to study intracellular metabolism, this approach is limited when applied to pathways catalyzed in multiple cellular compartments. To address this issue, we and others have applied ²H (deuterium) tracers to observe transfer of labeled hydride anions, which accompanies electron transfer. Furthermore, we have developed a reporter system for indirectly quantifying NADPH enrichment in specific subcellular compartments. Here, we provide a detailed description of ²H tracing applications and the interrogation of mitochondrial versus cytosolic NAD(P)H metabolism in cultured mammalian cells. Specifically, we describe the generation of reporter cell lines that express epitope-tagged R132H-IDH1 or R172K-IDH2 and produce (D)2-hydroxyglutarate in a doxycycline-dependent manner. These tools and methods allow for quantitation of reducing equivalent turnover rates, the directionality of pathways present in multiple compartments, and the estimation of pathway contributions to NADPH pools.

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.

3D Culture Models with CRISPR Screens Reveal Hyperactive NRF2 as a Prerequisite for Spheroid Formation via Regulation of Proliferation and Ferroptosis

Cancer-associated mutations that stabilize NRF2, an oxidant defense transcription factor, are predicted to promote tumor development. Here, utilizing 3D cancer spheroid models coupled with CRISPR-Cas9 screens, we investigate the molecular pathogenesis mediated by NRF2 hyperactivation. NRF2 hyperactivation was necessary for proliferation and survival in lung tumor spheroids. Antioxidant treatment rescued survival but not proliferation, suggesting the presence of distinct mechanisms. CRISPR screens revealed that spheroids are differentially dependent on the mammalian target of rapamycin (mTOR) for proliferation and the lipid peroxidase GPX4 for protection from ferroptosis of inner, matrix-deprived cells. Ferroptosis inhibitors blocked death from NRF2 downregulation, demonstrating a critical role of NRF2 in protecting matrix-deprived cells from ferroptosis. Interestingly, proteomics analyses show global enrichment of selenoproteins, including GPX4, by NRF2 downregulation, and targeting NRF2 and GPX4 killed spheroids overall. These results illustrate the value of spheroid culture in revealing environmental or spatial differential dependencies on NRF2 and reveal exploitable vulnerabilities of NRF2-hyperactivated tumors.

In Vivo Imaging with Genetically Encoded Redox Biosensors

Redox reactions are of high fundamental and practical interest since they are involved in both normal physiology and the pathogenesis of various diseases. However, this area of research has always been a relatively problematic field in the context of analytical approaches, mostly because of the unstable nature of the compounds that are measured. Genetically encoded sensors allow for the registration of highly reactive molecules in real-time mode and, therefore, they began a new era in redox biology. Their strongest points manifest most brightly in in vivo experiments and pave the way for the non-invasive investigation of biochemical pathways that proceed in organisms from different systematic groups. In the first part of the review, we briefly describe the redox sensors that were used in vivo as well as summarize the model systems to which they were applied. Next, we thoroughly discuss the biological results obtained in these studies in regard to animals, plants, as well as unicellular eukaryotes and prokaryotes. We hope that this work reflects the amazing power of this technology and can serve as a useful guide for biologists and chemists who work in the field of redox processes.

Dissecting the Crosstalk between NRF2 Signaling and Metabolic Processes in Cancer

The transcription factor NRF2 (nuclear factor-erythroid 2 p45-related factor 2 or NFE2L2) plays a critical role in response to cellular stress. Following an oxidative insult, NRF2 orchestrates an antioxidant program, leading to increased glutathione levels and decreased reactive oxygen species (ROS). Mounting evidence now implicates the ability of NRF2 to modulate metabolic processes, particularly those at the interface between antioxidant processes and cellular proliferation. Notably, NRF2 regulates the pentose phosphate pathway, NADPH production, glutaminolysis, lipid and amino acid metabolism, many of which are hijacked by cancer cells to promote proliferation and survival. Moreover, deregulation of metabolic processes in both normal and cancer-based physiology can stabilize NRF2. We will discuss how perturbation of metabolic pathways, including the tricarboxylic acid (TCA) cycle, glycolysis, and autophagy can lead to NRF2 stabilization, and how NRF2-regulated metabolism helps cells deal with these metabolic stresses. Finally, we will discuss how the negative regulator of NRF2, Kelch-like ECH-associated protein 1 (KEAP1), may play a role in metabolism through NRF2 transcription-independent mechanisms. Collectively, this review will address the interplay between the NRF2/KEAP1 complex and metabolic processes.

Predicting synthetic lethal interactions using heterogeneous data sources

MOTIVATION

A synthetic lethal (SL) interaction is a relationship between two functional entities where the loss of either one of the entities is viable but the loss of both entities is lethal to the cell. Such pairs can be used as drug targets in targeted anticancer therapies, and so, many methods have been developed to identify potential candidate SL pairs. However, these methods use only a subset of available data from multiple platforms, at genomic, epigenomic and transcriptomic levels; and hence are limited in their ability to learn from complex associations in heterogeneous data sources.

RESULTS

In this article, we develop techniques that can seamlessly integrate multiple heterogeneous data sources to predict SL interactions. Our approach obtains latent representations by collective matrix factorization-based techniques, which in turn are used for prediction through matrix completion. Our experiments, on a variety of biological datasets, illustrate the efficacy and versatility of our approach, that outperforms state-of-the-art methods for predicting SL interactions and can be used with heterogeneous data sources with minimal feature engineering.

AVAILABILITY AND IMPLEMENTATION

Software available at https://github.com/lianyh.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Computational Methods for Analysis of Large-Scale CRISPR Screens

Large-scale CRISPR-Cas pooled screens have shown great promise to investigate functional links between genotype and phenotype at the genome-wide scale. In addition to technological advancement, there is a need to develop computational methods to analyze the large datasets obtained from high-throughput CRISPR screens. Many computational methods have been developed to identify reliable gene hits from various screens. In this review, we provide an overview of the technology development of CRISPR screening platforms, with a focus on recent advances in computational methods to identify and model gene effects using CRISPR screen datasets. We also discuss existing challenges and opportunities for future computational methods development.