Synthetic Lethal Pair Research Articles

Understanding flux switching in metabolic networks through an analysis of synthetic lethals

Biological systems are robust and redundant. The redundancy can manifest as alternative metabolic pathways. Synthetic double lethals are pairs of reactions that, when deleted simultaneously, abrogate cell growth. However, removing one reaction allows the rerouting of metabolites through alternative pathways. Little is known about these hidden linkages between pathways. Understanding them in the context of pathogens is useful for therapeutic innovations. We propose a constraint-based optimisation approach to identify inter-dependencies between metabolic pathways. It minimises rerouting between two reaction deletions, corresponding to a synthetic lethal pair, and outputs the set of reactions vital for metabolic rewiring, known as the synthetic lethal cluster. We depict the results for different pathogens and show that the reactions span across metabolic modules, illustrating the complexity of metabolism. Finally, we demonstrate how the two classes of synthetic lethals play a role in metabolic networks and influence the different properties of a synthetic lethal cluster.

Abstract PR003: Mechanisms of therapeutic vulnerability of mismatch repair defective cancers to RNA polymerase I inhibitors

Abstract The goal of this study was to identify cancer types with synthetic vulnerability to RNA polymerase I (Pol I) inhibitors and to resolve mechanistic underpinnings of this vulnerability. Specific and selective Pol I inhibitors, pioneered by our group, block the transcription of rRNA, which is the rate-limiting step in ribosome biogenesis and protein synthesis. We conducted unbiased large-scale cancer cell line screens with the Pol I inhibitors and performed correlative analyses using DepMap and Achilles databases to identify cancer types and features that sensitize cancer cells to these inhibitors. The most sensitive lineages were colorectal, endometrial, esophageal, leukemia, myeloma, and ovarian. Microsatellite instable (MSI) cancer cell lines were highly sensitive and significantly more sensitive than microsatellite stable lines. Comparison of the cancer cell line responses to the Achilles gene essentiality database showed high correlation of the sensitivity to genetic knock-out of three Pol I subunits. This unbiased finding strongly supports identification of Pol I as the target. Correlative analyses for vulnerability identified two ribosomal proteins, RPL22 and RPL22L1, and MDM4, a p53 transcriptional repressor as the top candidates. Notably, RPL22K15Rfs hotspot mutation, the top-ranking genetic correlate, is present in up 70% MSI cancer cell lines and tumors. RPL22 and RPL22L1 are a synthetic lethal paralog pair. RPL22 is a negative regulator of RPL22L1 and consequently, haploinsufficiency or loss of RPL22 induces RPL22L1. RPL22L1 was the top-ranking expression correlate. Lastly, MDM4 was the top protein marker and is highly expressed in MSI cancers. These findings were validated using genetic and mechanistic analyses. We show that either genetic or chemical inhibition of the Pol I enzyme decreases the expression of RPL22L1 and MDM4 and alters their splicing. RPL22L1 is spliced to an alternative 3’ splice site in intron 2 leading to an early truncation. MDM4 exon 6 skipping converts the full-length p53 repressive form to a short NMD-targeted form. Mechanistically, by using GoldCLIP-seq, rMATS and splicing analyses, we show that RPL22 is a master regulator of splicing by binding to intronic sequences and splice sites of RPL22L1 and MDM4. Finally, we show the efficacy and tolerance of the Pol I inhibitors in xenograft and patient-derived MSI models expressing these markers. In summary, this synthetic vulnerability screen using new Pol I inhibitors uncovered a connection between Pol I transcription and cellular splicing programs and identified perturbation of this connection in MSI cancers. We conclude that RPL22 mutation could serve as tumor-agnostic biomarker for targeting cancers by Pol I inhibitors. Furthermore, this work identifies a new Pol I transcription-dependent ribosomal protein-governed pathway that converges on the regulation of p53. Citation Format: Marikki Laiho, Wenjun Fan, Hester Liu, Gregory C. Stachelek, Asma Begum, Catherine E. Davis, Tony Dorado, Pablo de Leon, Rajeshkumar N.V., James C. Barrow. Mechanisms of therapeutic vulnerability of mismatch repair defective cancers to RNA polymerase I inhibitors [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Expanding and Translating Cancer Synthetic Vulnerabilities; 2024 Jun 10-13; Montreal, Quebec, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(6 Suppl):Abstract nr PR003.

Synthetic lethality in human bladder cancer cells by curcumin via concurrent Aurora A inhibition and autophagy induction

Combination therapies to induce mixed-type cell death and synthetic lethality have the potential to overcome drug resistance in cancer. In this study, we demonstrated that the curcumin-enhanced cytotoxicity of cisplatin/carboplatin in combination with gemcitabine was associated with Aurora A suppression-mediated G2/M arrest, and thus apoptosis, as well as MEK/ERK-mediated autophagy in human bladder cancer cells. Animal study data confirmed that curcumin combined with cisplatin/gemcitabine reduced tumorigenesis of xenograft in mice and this phenomenon was associated with elevated expressions of p-ERK and reduced p-Aurora A in tumors. Gene analyses using data repositories further revealed that reduced Aurora A expression alone did not significantly elevate the sensitivity of human bladder carcinoma cells to these anticancer drugs. Unlike other major cancer types, human bladder urothelial carcinoma tissue coexpressed higher AURKA and lower MAP1LC3B than normal tissue, and reduced Aurora A and induction of autophagy have been clinically associated with a better prognosis in patients with early but not advanced stage bladder cancer. Therefore, our results suggest that treatment strategies can utilize the synthetic lethal pair to concurrently suppress oncogenic Aurora A and induce autophagy by coadministrating curcumin with anticancer drugs for early-stage bladder cancer with high expression of Aurora A.

NOTCH pathway inactivation reprograms stem-like oral cancer cells to JAK-STAT dependent state and provides the opportunity of synthetic lethality

BackgroundWe have recently provided the evidence of interconvertible cellular states, driving non-genetic heterogeneity among stem-like oral cancer cells (oral-SLCCs). Here, NOTCH pathway-activity status is explored as one of the possible mechanisms behind this stochastic plasticity. MethodsOral-SLCCs were enriched in 3D-spheroids. Constitutively-active and inactive status of NOTCH pathway was achieved by genetic or pharmacological approaches. RNA sequencing and real-time PCR was performed for gene expression studies. in vitro cytotoxicity assessments were performed by AlamarBlue assay and in vivo effects were studied by xenograft growth in zebrafish embryo. ResultsWe have observed stochastic plasticity in oral-SLCCs, spontaneously maintaining both NOTCH-active and inactive states. While cisplatin refraction was associated with post-treatment adaptation to the active-state of NOTCH pathway, oral-SLCCs with inactive NOTCH pathway status showed aggressive tumor growth and poor prognosis. RNAseq analysis clearly suggested the upregulation of JAK-STAT pathway in NOTCH pathway-inactive subset. The 3D-spheroids with lower NOTCH-activity status displayed significantly higher sensitivity to JAK-selective drugs, Ruxolitinib or Tofacitinib or siRNA mediated downregulation of tested partners STAT3/4. Oral-SLCCs were programmed to adapt the inactive status of NOTCH pathway by exposing to γ-secretase inhibitors, LY411575 or RO4929097, followed by targeting with JAK-inhibitors, Ruxolitinib or Tofacitinib. This approach resulted in a very significant inhibition in viability of 3D-spheroids as well as xenograft initiation in Zebrafish embryos. ConclusionStudy revealed for the first time that NOTCH pathway-inactive state exhibit activation of JAK-STAT pathways, as synthetic lethal pair. Therefore, co-inhibition of these pathway may serve as novel therapeutic strategy against aggressive oral cancer.

Abstract P2-17-01: Targeted CRISPR screen to identify synthetic lethal combinations between APOBEC3B and DNA repair

Abstract APOBEC-catalyzed deamination of cytosine bases is the largest enzymatic and second largest overall source of mutation in cancer. One member from the APOBEC family of enzymes, APOBEC3B (A3B) is overexpressed and dysregulated in many different cancer types. In addition to hallmark C-to-T transitions and C-to-G transversions, APOBEC-catalyzed uracil lesions can be processed into single- and double-strand DNA breaks. Therefore, A3B-positive tumors are under continual stress to repair DNA breaks and may be vulnerable to DNA repair inhibition. Previous results from the Harris lab have identified UNG2, DNA uracil glycosylase 2 (UNG2), initiator of the base excision repair pathway, as synthetic lethal pair with A3B. The genetic disruption of UNG2 combined with high expression of A3B, causes cell death. This provides the rational to hypothesize that also other DNA repair proteins could be putative synthetic lethal pairs with A3B, when its expression and activity are high. To test this hypothesis, CRISPR guide RNA library targeting 237 DNA damage repair and response genes was used in doxycycline-inducible TREX-293-A3Bi-eGFP cell line. Cells expressing or not A3B were harvested for DNA extraction and sequencing at different time points. Comparison of guide RNA abundance between doxycycline and H2O treated cells revealed the dropout guides that disrupt genes and create putative synthetic lethal combinations with A3B. The screen design and overall results will be presented Citation Format: Bojana Stefanovska, Kevin Lin, Benjamin Troness, Chad Myers, Reuben Harris. Targeted CRISPR screen to identify synthetic lethal combinations between APOBEC3B and DNA repair [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 P2-17-01.

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

Abstract 168: Computational model for prediction of actionable drug combinations in cancer

Abstract Synthetic essentiality presents an effective approach towards finding connected genetic targets in cancer by identifying genes that produce an inviable phenotype upon joint inactivation. However, it is less studied how synthetic lethal gene pairs may be contextualized with respect to deregulation of different cell functions and how these pairs act in different cell types and tissues. To study cancer-specific synthetic essentiality, we devised an integrated network-based algorithm to analyze relationships between tumor gene expression and patient survival in The Cancer Genome Atlas (TCGA) and expression data from the Genotype-Tissue Expression (GTEx) project. In particular, we identify gene pairs in each cancer cohort that exhibit upregulation in tumors but are associated with improved survival for patients whose tumors feature reduced expression of the gene pair. Our findings reveal variability in synthetic lethal pair activity across different cancer types, and the identified pairs connect distinct signaling and metabolic cellular pathways to different cancer types. Altogether, our findings present a pan-cancer reference for gene expression-based synthetic lethal gene pairs as well as a nuanced perspective on changes in synthetic essentiality across different cellular and tissue contexts, pointing to a new avenue for drug combination discovery oriented on targeting key pathways based on the cancer type. Citation Format: Sairahul Pentaparthi, Brandon Burgman, S. Stephen Yi. Computational model for prediction of actionable drug combinations in cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 168.

Mapping the Landscape of Synthetic Lethal Interactions in Liver Cancer

Background: Current therapies against liver cancer all follow the “one fits all” principle, without the capacity to provide individualized treatment regimens, which may be responsible for the fact that almost all of them fail to bring about satisfactory survival benefit to liver cancer patients. Synthetic lethality, a concept that simultaneous losses of two genes is lethal to a cell while a single loss is non-lethal, can be utilized to selectively eliminate tumors with genetic aberrations through targeting synthetic lethal (SL) partners of those aberrations, offering an alternate route towards precision therapy. Methods: To infer liver cancer-specific SL interactions, we proposed a computational pipeline, termed SiLi (statistical inference-based synthetic lethality identification), that incorporated four inference procedures. Four sequencing datasets of liver cancer with both expression and mutation profiles available were integrated into a large-scale metadata set that was then taken as the basis for the identification of liver cancer-specific SL interactions. Findings: Through SiLi analysis, a total of 272 SL pairs were discovered, which included 209 unique target candidates. Among these, PLK1 was considered to have considerable therapeutic potential. Further computational and experimental validation of the corresponding SL pair, TP53-PLK1, demonstrated that inhibiting PLK1 could be a novel therapeutic strategy selectively targeting those patients with TP53-mutant liver tumors. Interpretation: Generally, our findings in this study may open new possibilities as well as provide a potential treasure trove for patient-tailored therapeutic interventions in liver cancer. Funding Statement: National Key Sci-Tech Special Projects of Infectious Diseases of China (2018ZX10732202- 002-003); the National Natural Science Foundation of China (81874229); Shanghai Natural Science Foundation (19ZR1452700). Declaration of Interests: The authors declare no conflict of interest.

Abstract 4385: Proteogenomics-driven synthetic lethality discovery to predict targetable protein dependencies induced by somatic deletions

Abstract Computational prediction of synthetic lethality has been shown as a promising approach to identify therapeutic vulnerabilities in many cancer types but has been limited to using genomics and transcriptomics data for discovery. Recent advancements on integrating mass spectrometry (MS)-based proteomics with genomics and transcriptomics in cancer research have produced proteogenomics datasets on large tumor cohorts, enabling the integration of proteomics data into synthetic lethality discovery. This is particularly attractive for tumors driven by loss-of-function alterations such as somatic deletions. Although deletions are challenging to target themselves, they may induce targetable protein dependencies, and this relationship can be identified through computational prediction of synthetic lethality. Here, we performed an unbiased discovery of such synthetic lethal interactions by identifying protein targets of drugs for which under-expression is observed significantly less than expected by chance with gene deletions using proteogenomics data from a breast cancer cohort with 105 tumors. We found many significant pairs uniquely driven by either protein under-expression (18,897 pairs) or mRNA under-expression (42,754 pairs) while only 5% (3,042 pairs) were supported by both protein and mRNA measurements. To systematically evaluate candidate synthetic lethal pairs, an AUROC analysis was performed using publicly available drug response data from breast cancer cells. Results showed synthetic lethal relationships supported by both protein and mRNA-based analyses were substantially more predictive of drug response (AUC = 81%) than protein alone (AUC = 65%) or mRNA alone (AUC = 61%). Despite having comparable AUCs, protein-based analysis showed much higher sensitivity than mRNA-based analysis when limiting false positive rate to less than 10%. An example of a synthetic lethal pair supported by both protein and mRNA-based analyses include tumors with low AURKA protein/mRNA and PARP2 deletions, which were significantly under-represented in the proteogenomics dataset (p = 0.003). Interestingly, these tumors were enriched for triple-negative breast tumors (p = 0.001), a type of breast cancer that lacks biomarkers to guide clinical treatment. This finding suggests AURKA as a putative dependency in triple-negative tumors with PARP2 deletion as a biomarker. Taken together, these results demonstrate that a multi-omics approach using proteomics to complement transcriptomics along with genomic changes may better predict therapeutic vulnerabilities in breast cancer with driver gene deletions than using proteomics or transcriptomics alone. As genomic testing has become more widely implemented as part of precision oncology programs, our approach to identifying protein dependencies induced by loss-of-function genomic alterations may provide new treatment opportunities for many patients with breast cancer or other tumor types. Citation Format: Jonathan T. Lei, Eric Jaehnig, Bing Zhang. Proteogenomics-driven synthetic lethality discovery to predict targetable protein dependencies induced by somatic deletions [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 4385.

Multi-omics analysis delineates the distinct functions of sub-cellular acetyl-CoA pools in Toxoplasma gondii

BackgroundAcetyl-CoA is a key molecule in all organisms, implicated in several metabolic pathways as well as in transcriptional regulation and post-translational modification. The human pathogen Toxoplasma gondii possesses at least four enzymes which generate acetyl-CoA in the nucleo-cytosol (acetyl-CoA synthetase (ACS); ATP citrate lyase (ACL)), mitochondrion (branched-chain α-keto acid dehydrogenase-complex (BCKDH)) and apicoplast (pyruvate dehydrogenase complex (PDH)). Given the diverse functions of acetyl-CoA, we know very little about the role of sub-cellular acetyl-CoA pools in parasite physiology.ResultsTo assess the importance and functions of sub-cellular acetyl-CoA-pools, we measured the acetylome, transcriptome, proteome and metabolome of parasites lacking ACL/ACS or BCKDH. We demonstrate that ACL/ACS constitute a synthetic lethal pair. Loss of both enzymes causes a halt in fatty acid elongation, hypo-acetylation of nucleo-cytosolic and secretory proteins and broad changes in gene expression. In contrast, loss of BCKDH results in an altered TCA cycle, hypo-acetylation of mitochondrial proteins and few specific changes in gene expression. We provide evidence that changes in the acetylome, transcriptome and proteome of cells lacking BCKDH enable the metabolic adaptations and thus the survival of these parasites.ConclusionsUsing multi-omics and molecular tools, we obtain a global and integrative picture of the role of distinct acetyl-CoA pools in T. gondii physiology. Cytosolic acetyl-CoA is essential and is required for the synthesis of parasite-specific fatty acids. In contrast, loss of mitochondrial acetyl-CoA can be compensated for through metabolic adaptations implemented at the transcriptional, translational and post-translational level.

CRISPR/Cas9-mediated mutagenesis to validate the synergy between PARP1 inhibition and chemotherapy in BRCA1-mutated breast cancer cells.

For patients carrying BRCA1 mutations, at least one‐third develop triple negative breast cancer (TNBC). Not only is TNBC difficult to treat due to the lack of molecular target receptors, but BRCA1 mutations (BRCA1m) also result in chemotherapeutic resistance, making disease recurrence more likely. Although BRCA1m are highly heterogeneous and therefore difficult to target, BRCA1 gene's synthetic lethal pair, PARP1, is conserved in BRCA1m cancer cells. Therefore, we hypothesize that targeting PARP1 might be a fruitful direction to sensitize BRCA1m cancer cells to chemotherapy. We used CRISPR/Cas9 technology to generate PARP1 deficiency in two TNBC cell lines, MDA‐MB‐231 (BRCA1 wild‐type) and MDA‐MB‐436 (BRCA1m). We explored whether this PARP1 disruption (PARP1m) could significantly lower the chemotherapeutic dose necessary to achieve therapeutic efficacy in both a 2D and 3D tumor‐on‐a‐chip model. With both BRCA1m and PARP1m, the TNBC cells were more sensitive to three representative chemotherapeutic breast cancer drugs, doxorubicin, gemcitabine and docetaxel, compared with the PARP1 wild‐type counterpart in the 2D culture environment. However, PARP1m did not result in this synergy in the 3D tumor‐on‐a‐chip model, suggesting that drug dosing in the tumor microenvironment may influence the synergy. Taken together, our results highlight a discrepancy in the efficacy of the combination of PARP1 inhibition and chemotherapy for TNBC treatment, which should be clarified to justify further clinical testing.

Construction of Breast Cancer-Related LncSLGP Models for Systematically Deciphering Their Functions and Improving Prognosis

With the rapid development of systematic biological approaches, researchers pay more attention to uncover the complex interplaying mechanisms of different molecular models constructed by multiple coding or non-coding RNAs in cancers, such as synthetic lethal gene pairs (SLGPs) or competing endogenous RNAs (ceRNAs). To further investigate more complex framework, we attempted to construct a LncRNA Synthetic Lethal Gene Pair (LncSLGP) model with the combination of one lncRNA and two synthetic lethal genes in health and disease conditions. We established LncSLGPs in breast cancer and expected the LncSLGPs may play more crucial roles during the development of cancers than individual RNA molecules. Through the integration of analysis of RNA-Seq, copy number variant (CNV), somatic mutation, and DNA methylation data of 411 BC samples and 83 normal samples in the TCGA database, 119 LncSLGP models containing 97 lncRNAs, 71 genes (87 SLGPs) were generated. We found the majority of lncRNAs and genes in these LncSLGP models were disease related. Function enrichment analysis showed that genes in LncSLGPs were enriched in 'IL2 STAT5 SIGNALING', 'WNT BETA-CATENIN SIGNALING', 'MYC TARGETS', and 'PI3K AKT MTOR SIGNALING' pathways. Survival analysis results of three candidate LncSLGPs, including 'LINC01133/CDC6-KRAS', 'RP11-626G11.1/KRAS-MAP3K7' and 'RP11-483H20.4/TOP1-KRAS' (log-rank pvalues: 0.002, 0.009 and 0.007, respectively) showed that they were significantly related to poor prognosis of BC patients. We developed a systematic approach to construct and identify LncSLGP models, which could be served as predictive factors for improving prognosis and guiding the clinical applications for BC patients. Funding Statement: This work was supported in part by the National Natural Science Foundation of China [Grant Nos. 31871336, 61573122, 31701159]. Wu lien-teh youth science fund project of Harbin medical university [Grant Nos. WLD-QN1407]. The Health Department Science Foundation of Heilongjiang Province (Grant Nos. 2013128), the Education Department Science Foundation of Heilongjiang Province (Grant Nos. 12541415), the Postdoctoral project of Heilongjiang Province (Grant Nos. LBH-Z14130). Declaration of Interests: The authors declare no conflict of interest. Ethics Approval Statement: Not needed.

Synthetic lethality guiding selection of drug combinations in ovarian cancer.

BackgroundSynthetic lethality describes a relationship between two genes where single loss of either gene does not trigger significant impact on cell viability, but simultaneous loss of both gene functions results in lethality. Targeting synthetic lethal interactions with drug combinations promises increased efficacy in tumor therapy.Materials and methodsWe established a set of synthetic lethal interactions using publicly available data from yeast screens which were mapped to their respective human orthologs using information from orthology databases. This set of experimental synthetic lethal interactions was complemented by a set of predicted synthetic lethal interactions based on a set of protein meta-data like e.g. molecular pathway assignment. Based on the combined set, we evaluated drug combinations used in late stage clinical development (clinical phase III and IV trials) or already in clinical use for ovarian cancer with respect to their effect on synthetic lethal interactions. We furthermore identified a set of drug combinations currently not being tested in late stage ovarian cancer clinical trials that however have impact on synthetic lethal interactions thus being worth of further investigations regarding their therapeutic potential in ovarian cancer.ResultsTwelve of the tested drug combinations addressed a synthetic lethal interaction with the anti-VEGF inhibitor bevacizumab in combination with paclitaxel being the most studied drug combination addressing the synthetic lethal pair between VEGFA and BCL2. The set of 84 predicted drug combinations for example holds the combination of the PARP inhibitor olaparib and paclitaxel, which showed efficacy in phase II clinical studies.ConclusionA set of drug combinations currently not tested in late stage ovarian cancer clinical trials was identified having impact on synthetic lethal interactions thus being worth of further investigations regarding their therapeutic potential in ovarian cancer.

The Essential UPP Phosphatase Pair BcrC and UppP Connects Cell Wall Homeostasis during Growth and Sporulation with Cell Envelope Stress Response in Bacillus subtilis.

The bacterial cell wall separates the cell from its surrounding and protects it from environmental stressors. Its integrity is maintained by a highly regulated process of cell wall biosynthesis. The membrane-located lipid II cycle provides cell wall building blocks that are assembled inside the cytoplasm to the outside for incorporation. Its carrier molecule, undecaprenyl phosphate (UP), is then recycled by dephosphorylation from undecaprenyl pyrophosphate (UPP). In Bacillus subtilis, this indispensable reaction is catalyzed by the UPP phosphatases BcrC and UppP. Here, we study the physiological function of both phosphatases with respect to morphology, cell wall homeostasis and the resulting cell envelope stress response (CESR). We demonstrate that uppP and bcrC represent a synthetic lethal gene pair, which encodes an essential physiological function. Accordingly, cell growth and morphology were severely impaired during exponential growth if the overall UPP phosphatase level was limiting. UppP, but not BcrC, was crucial for normal sporulation. Expression of bcrC, but not uppP, was upregulated in the presence of cell envelope stress conditions caused by bacitracin if UPP phosphatase levels were limited. This homeostatic feedback renders BcrC more important during growth than UppP, particularly in defense against cell envelope stress.

Abstract 1246: Development of STAT3 dual-targeting strategies for the treatment of pancreatic cancer

Abstract Pancreatic cancer remains a largely incurable disease, with patients facing the worst 5-year survival rate of any cancer. The challenge is to identify the molecular effectors that regulate the survival of pancreatic ductal adenocarcinoma (PDAC) cells, to devise molecular-targeted strategies that are effective in the metastatic setting, and overcome the protective role of the tumor-associated fibrosis and stroma. Strategies targeting multiple molecular effectors in PDAC are likely going to make a bigger impact. Thus, we are identifying molecular targets or synthetic lethal pairs that regulate critical pro-survival or pro-invasive pathways in PDAC. Constitutively activated Signal Transducer and Activator of Transcription 3 (STAT3) protein has been found to be a key regulator of pancreatic cancer and a target for molecular therapeutic intervention. To better model the tumor and its microenvironment, we utilized ex vivo 3-Dimensional (3D) cultures of patient-derived pancreatic cancer cells in the absence and presence of cancer-associated fibroblasts (CAFs). We can quantitate the inhibitory effect on both the tumor and CAFs as they are labeled with different fluorescent markers. In this co-culture model, inhibition of tumor growth is maintained following STAT3 inhibition in the presence of CAFs. We screened several STAT3 small molecule inhibitors, derived from the SH-4-54 class of STAT3 inhibitors, and found inhibition of pancreatic cancer cell proliferation in the low μM range. Our inhibitors bind the STAT3 protein potently, as shown by SPR, and demonstrate no effect in a kinome screen. In vitro studies demonstrated potent cell killing as well as inhibition of STAT3 activation in the 3D co-culture model. We have previously reported that Ref-1 (redox factor-1) regulates STAT3 activity through its redox function and blocking STAT3 through phosphorylation and redox inhibition synergizes for PDAC cell killing. In our 3D co-culture system, Ref-1 inhibitor, APX3330 decreases tumor area and intensity in a dose-dependent manner. The addition of APX3330 to STAT3 pathway inhibition via Ruxolitinib (Rux, Jak 2 inhibitor) or direct STAT3 inhibitor potentiated the killing effect in the tumor. However, the combination treatment did not appear to sensitize CAF cells, suggesting that targeting of Ref-1 and the STAT3 pathway is more specifically targeting tumor cells. Utilizing APX3330, Rux, and our lead STAT3 inhibitors, we evaluated the effects of Ref-1/STAT3 inhibition in PDAC low passage patient-derived cell lines. The activity of STAT3 and specificity of lead compounds was assessed by immunoblotting for levels of phosphorylated proteins including STAT3 (Y705) and STAT5 (Y694) in 3D culture. These studies establish the rationale for the development of STAT3 dual-targeting strategies for the treatment of pancreatic cancer and suggest that Ref-1 and STAT3 may be a synthetic lethal pair. Citation Format: Melissa L. Fishel, Michelle L. Grimard, Mark R. Kelley, David A. Rosa, Andrew Shouksmith, Gary Tin, Ji Park, Patrick T. Gunning. Development of STAT3 dual-targeting strategies for the treatment of pancreatic cancer. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 1246.

Multi-omic measurement of mutually exclusive loss-of-function enriches for candidate synthetic lethal gene pairs.

BackgroundIdentification of synthetic lethal interactions in cancer cells could offer promising new therapeutic targets. Large-scale functional genomic screening presents an opportunity to test large numbers of cancer synthetic lethal hypotheses. Methods enriching for candidate synthetic lethal targets in molecularly defined cancer cell lines can steer effective design of screening efforts. Loss of one partner of a synthetic lethal gene pair creates a dependency on the other, thus synthetic lethal gene pairs should never show simultaneous loss-of-function. We have developed a computational approach to mine large multi-omic cancer data sets and identify gene pairs with mutually exclusive loss-of-function. Since loss-of-function may not always be genetic, we look for deleterious mutations, gene deletion and/or loss of mRNA expression by bimodality defined with a novel algorithm BiSEp.ResultsApplying this toolkit to both tumour cell line and patient data, we achieve statistically significant enrichment for experimentally validated tumour suppressor genes and synthetic lethal gene pairings. Notably non-reliance on genetic loss reveals a number of known synthetic lethal relationships otherwise missed, resulting in marked improvement over genetic-only predictions. We go on to establish biological rationale surrounding a number of novel candidate synthetic lethal gene pairs with demonstrated dependencies in published cancer cell line shRNA screens.ConclusionsThis work introduces a multi-omic approach to define gene loss-of-function, and enrich for candidate synthetic lethal gene pairs in cell lines testable through functional screens. In doing so, we offer an additional resource to generate new cancer drug target and combination hypotheses. Algorithms discussed are freely available in the BiSEp CRAN package at http://cran.r-project.org/web/packages/BiSEp/index.html.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-2375-1) contains supplementary material, which is available to authorized users.

Model-driven discovery of synergistic inhibitors against E. coli and S. enterica serovar Typhimurium targeting a novel synthetic lethal pair, aldA and prpC

Mathematical models of biochemical networks form a cornerstone of bacterial systems biology. Inconsistencies between simulation output and experimental data point to gaps in knowledge about the fundamental biology of the organism. One such inconsistency centers on the gene aldA in Escherichia coli: it is essential in a computational model of E. coli metabolism, but experimentally it is not. Here, we reconcile this disparity by providing evidence that aldA and prpC form a synthetic lethal pair, as the double knockout could only be created through complementation with a plasmid-borne copy of aldA. Moreover, virtual and biological screening against the two proteins led to a set of compounds that inhibited the growth of E. coli and Salmonella enterica serovar Typhimurium synergistically at 100–200 μM individual concentrations. These results highlight the power of metabolic models to drive basic biological discovery and their potential use to discover new combination antibiotics.

Metabolic Needs and Capabilities of Toxoplasma gondii through Combined Computational and Experimental Analysis.

Toxoplasma gondii is a human pathogen prevalent worldwide that poses a challenging and unmet need for novel treatment of toxoplasmosis. Using a semi-automated reconstruction algorithm, we reconstructed a genome-scale metabolic model, ToxoNet1. The reconstruction process and flux-balance analysis of the model offer a systematic overview of the metabolic capabilities of this parasite. Using ToxoNet1 we have identified significant gaps in the current knowledge of Toxoplasma metabolic pathways and have clarified its minimal nutritional requirements for replication. By probing the model via metabolic tasks, we have further defined sets of alternative precursors necessary for parasite growth. Within a human host cell environment, ToxoNet1 predicts a minimal set of 53 enzyme-coding genes and 76 reactions to be essential for parasite replication. Double-gene-essentiality analysis identified 20 pairs of genes for which simultaneous deletion is deleterious. To validate several predictions of ToxoNet1 we have performed experimental analyses of cytosolic acetyl-CoA biosynthesis. ATP-citrate lyase and acetyl-CoA synthase were localised and their corresponding genes disrupted, establishing that each of these enzymes is dispensable for the growth of T. gondii, however together they make a synthetic lethal pair.

Predicting Human Genetic Interactions from Cancer Genome Evolution

Synthetic Lethal (SL) genetic interactions play a key role in various types of biological research, ranging from understanding genotype-phenotype relationships to identifying drug-targets against cancer. Despite recent advances in empirical measuring SL interactions in human cells, the human genetic interaction map is far from complete. Here, we present a novel approach to predict this map by exploiting patterns in cancer genome evolution. First, we show that empirically determined SL interactions are reflected in various gene presence, absence, and duplication patterns in hundreds of cancer genomes. The most evident pattern that we discovered is that when one member of an SL interaction gene pair is lost, the other gene tends not to be lost, i.e. the absence of co-loss. This observation is in line with expectation, because the loss of an SL interacting pair will be lethal to the cancer cell. SL interactions are also reflected in gene expression profiles, such as an under representation of cases where the genes in an SL pair are both under expressed, and an over representation of cases where one gene of an SL pair is under expressed, while the other one is over expressed. We integrated the various previously unknown cancer genome patterns and the gene expression patterns into a computational model to identify SL pairs. This simple, genome-wide model achieves a high prediction power (AUC = 0.75) for known genetic interactions. It allows us to present for the first time a comprehensive genome-wide list of SL interactions with a high estimated prediction precision, covering up to 591,000 gene pairs. This unique list can potentially be used in various application areas ranging from biotechnology to medical genetics.

MurJ and a novel lipid II flippase are required for cell wall biogenesis in Bacillus subtilis

Bacterial surface polysaccharides are synthesized from lipid-linked precursors at the inner surface of the cytoplasmic membrane before being translocated across the bilayer for envelope assembly. Transport of the cell wall precursor lipid II in Escherichia coli requires the broadly conserved and essential multidrug/oligosaccharidyl-lipid/polysaccharide (MOP) exporter superfamily member MurJ. Here, we show that Bacillus subtilis cells lacking all 10 MOP superfamily members are viable with only minor morphological defects, arguing for the existence of an alternate lipid II flippase. To identify this factor, we screened for synthetic lethal partners of MOP family members using transposon sequencing. We discovered that an uncharacterized gene amj (alternate to MurJ; ydaH) and B. subtilis MurJ (murJBs; formerly ytgP) are a synthetic lethal pair. Cells defective for both Amj and MurJBs exhibit cell shape defects and lyse. Furthermore, expression of Amj or MurJBs in E. coli supports lipid II flipping and viability in the absence of E. coli MurJ. Amj is present in a subset of gram-negative and gram-positive bacteria and is the founding member of a novel family of flippases. Finally, we show that Amj is expressed under the control of the cell envelope stress-response transcription factor σ(M) and cells lacking MurJBs increase amj transcription. These findings raise the possibility that antagonists of the canonical MurJ flippase trigger expression of an alternate translocase that can resist inhibition.