A196 CROHN’S DISEASE PATIENT DERIVED MACROPHAGES ARE MORE SUSCEPTIBLE TO HYDROGEN PEROXIDE INDUCED CELL DEATH

Background

Crohn’s disease (CD) is characterized by intestinal inflammation due to the interplay between immunity, genetics, and environmental factors such as diet. Selenium (Se) deficiency is common in patients with CD due to malabsorption or high enteric losses. Selenium is used in the synthesis of selenoproteins that have antioxidant properties (e.g. glutathione peroxidases (GPx)) and are highly expressed in macrophages. However, how Se deficiency affects immune system function in patients with CD is unknown. We hypothesize that characterizing Se status, selenoprotein expression and subsequently macrophage function will advance knowledge of mucosal immunity and provide novel insight into CD.

Purpose

To determine if patients with active CD and healthy controls differ in Se dietary intake and status, oxidative stress, and macrophage cytotoxicity in response to oxidative stress.

Method

Blood was collected from healthy volunteers and patients diagnosed with ileal, ileocolonic or colonic CD (age ≥18 years, with mild or moderate endoscopic disease activity or fecal calprotectin ≥250 µg/g, and Harvey Bradshaw index <16, stable medications including biologics for at least 8-weeks prior to recruitment). Serum was analyzed for GPx activity, malondialdehyde (MDA) and C-reactive protein (CRP) concentrations. Monocytes were isolated by plastic adherence and treated with M-CSF (10 ng/ml, 7d) to derive macrophages. mRNA expression of GPx1, GPx4 and SelenoP was determined by qPCR. Lactate dehydrogenase release was measured in macrophages treated with 500 µM H₂O₂ for 2h.

Result(s)

Samples and/or dietary intake data were collected from 9 patients with CD (3 female, 6 male, mean age=36.8 years) and 13 controls (7 female, 6 male, mean age=27.7 years). Dietary Se intake did not differ between patients with CD and controls (126.1 ± 23.2 vs. 123.3 ± 19.8 µg/day). GPx activity was greater in the serum of patients with CD compared to controls (369 ± 49 vs. 169 ± 27 mU/mL, n=6-8, p<0.005). Patients with CD and controls did not differ in serum MDA concentration (7.80 ± 0.57 vs. 6.53 ± 1.1 µM). CRP levels correlated with serum MDA concentration in patients with CD (r=0.95, n=5, p<0.05) but not GPx activity. Macrophages from patients with CD (n=6) and controls (n=7) did not differ in expression of GPx1 and GPx4 mRNA, whereas SelenoP mRNA was ~200-fold lower in macrophages from patients with CD. Macrophages derived from patients with CD were more susceptible to H₂O₂-evoked cell death (10.3 ± 1.1 vs. 4.7 ± 0.7 % n=2-3 p<0.05).

Conclusion(s)

Despite adequate dietary Se intake our findings suggest altered Se metabolism in patients with active CD, with increases in serum GPx potentially indicative of the need for antioxidant activity to counter oxidative stress. The increased sensitivity of macrophages from patients with CD to H₂O₂ emphasizes the role of oxidative stress and redox balance in IBD. Defining how micronutrients, in this instance Se, impacts innate immunity may provide new approaches to the management of CD.

Disclosure of Interest

None Declared

Comparing Host Module Activation Patterns and Temporal Dynamics in Infection by Influenza H1N1 Viruses

Influenza is a serious global health threat that shows varying pathogenicity among different virus strains. Understanding similarities and differences among activated functional pathways in the host responses can help elucidate therapeutic targets responsible for pathogenesis. To compare the types and timing of functional modules activated in host cells by four influenza viruses of varying pathogenicity, we developed a new DYNAmic MOdule (DYNAMO) method that addresses the need to compare functional module utilization over time. This integrative approach overlays whole genome time series expression data onto an immune-specific functional network, and extracts conserved modules exhibiting either different temporal patterns or overall transcriptional activity. We identified a common core response to influenza virus infection that is temporally shifted for different viruses. We also identified differentially regulated functional modules that reveal unique elements of responses to different virus strains. Our work highlights the usefulness of combining time series gene expression data with a functional interaction map to capture temporal dynamics of the same cellular pathways under different conditions. Our results help elucidate conservation of the immune response both globally and at a granular level, and provide mechanistic insight into the differences in the host response to infection by influenza strains of varying pathogenicity.

Using BEAN-counter to quantify genetic interactions from multiplexed barcode sequencing experiments

The construction of genome-wide mutant collections has enabled high-throughput, high-dimensional quantitative characterization of gene and chemical function, particularly via genetic and chemical-genetic interaction experiments. As the throughput of such experiments increases with improvements in sequencing technology and sample multiplexing, appropriate tools must be developed to handle the large volume of data produced. Here, we describe how to apply our approach to high-throughput, fitness-based profiling of pooled mutant yeast collections using the BEAN-counter software pipeline (Barcoded Experiment Analysis for Next-generation sequencing) for analysis. The software has also successfully processed data from Schizosaccharomyces pombe, Escherichia coli, and Zymomonas mobilis mutant collections. We provide general recommendations for the design of large-scale, multiplexed barcode sequencing experiments. The procedure outlined here was used to score interactions for ~4 million chemical-by-mutant combinations in our recently published chemical-genetic interaction screen of nearly 14,000 chemical compounds across seven diverse compound collections. Here we selected a representative subset of these data on which to demonstrate our analysis pipeline. BEAN-counter is open source, written in Python, and freely available for academic use. Users should be proficient at the command line; advanced users who wish to analyze larger datasets with hundreds or more conditions should also be familiar with concepts in analysis of high-throughput biological data. BEAN-counter encapsulates the knowledge we have accumulated from, and successfully applied to, our multiplexed, pooled barcode sequencing experiments. This protocol will be useful to those interested in generating their own high-dimensional, quantitative characterizations of gene or chemical function in a high-throughput manner.

Predicting bioprocess targets of chemical compounds through integration of chemical-genetic and genetic interactions

Chemical-genetic interactions–observed when the treatment of mutant cells with chemical compounds reveals unexpected phenotypes–contain rich functional information linking compounds to their cellular modes of action. To systematically identify these interactions, an array of mutants is challenged with a compound and monitored for fitness defects, generating a chemical-genetic interaction profile that provides a quantitative, unbiased description of the cellular function(s) perturbed by the compound. Genetic interactions, obtained from genome-wide double-mutant screens, provide a key for interpreting the functional information contained in chemical-genetic interaction profiles. Despite the utility of this approach, integrative analyses of genetic and chemical-genetic interaction networks have not been systematically evaluated. We developed a method, called CG-TARGET (Chemical Genetic Translation via A Reference Genetic nETwork), that integrates large-scale chemical-genetic interaction screening data with a genetic interaction network to predict the biological processes perturbed by compounds. In a recent publication, we applied CG-TARGET to a screen of nearly 14,000 chemical compounds in Saccharomyces cerevisiae, integrating this dataset with the global S. cerevisiae genetic interaction network to prioritize over 1500 compounds with high-confidence biological process predictions for further study. We present here a formal description and rigorous benchmarking of the CG-TARGET method, showing that, compared to alternative enrichment-based approaches, it achieves similar or better accuracy while substantially improving the ability to control the false discovery rate of biological process predictions. Additional investigation of the compatibility of chemical-genetic and genetic interaction profiles revealed that one-third of observed chemical-genetic interactions contributed to the highest-confidence biological process predictions and that negative chemical-genetic interactions overwhelmingly formed the basis of these predictions. We also present experimental validations of CG-TARGET-predicted tubulin polymerization and cell cycle progression inhibitors. Our approach successfully demonstrates the use of genetic interaction networks in the high-throughput functional annotation of compounds to biological processes.

Understanding how chemical compounds affect biological systems is of paramount importance as pharmaceutical companies strive to develop life-saving medicines, governments seek to regulate the safety of consumer products and agrichemicals, and basic scientists continue to study the fundamental inner workings of biological organisms. One powerful approach to characterize the effects of chemical compounds in living cells is chemical-genetic interaction screening. Using this approach, a collection of cells–each with a different defined genetic perturbation–is tested for sensitivity or resistance to the presence of a compound, resulting in a quantitative profile describing the functional effects of that compound on the cells. The work presented here describes our efforts to integrate compounds’ chemical-genetic interaction profiles with reference genetic interaction profiles containing information on gene function to predict the cellular processes perturbed by the compounds. We focused on specifically developing a method that could scale to perform these functional predictions for large collections of thousands of screened compounds and robustly control the false discovery rate. With chemical-genetic and genetic interaction screens now underway in multiple species including human cells, the method described here can be generally applied to enable the characterization of compounds’ effects across the tree of life.

Systematic analysis of complex genetic interactions

To systematically explore complex genetic interactions, we constructed ~200,000 yeast triple mutants and scored negative trigenic interactions. We selected double-mutant query genes across a broad spectrum of biological processes, spanning a range of quantitative features of the global digenic interaction network and tested for a genetic interaction with a third mutation. Trigenic interactions often occurred among functionally related genes, and essential genes were hubs on the trigenic network. Despite their functional enrichment, trigenic interactions tended to link genes in distant bioprocesses and displayed a weaker magnitude than digenic interactions. We estimate that the global trigenic interaction network is ~100 times as large as the global digenic network, highlighting the potential for complex genetic interactions to affect the biology of inheritance, including the genotype-to-phenotype relationship.

Errata: Functional annotation of chemical libraries across diverse biological processes

This corrects the article DOI: 10.1038/nchembio.2436.

A global genetic interaction network maps a wiring diagram of cellular function

We generated a global genetic interaction network for Saccharomyces cerevisiae, constructing more than 23 million double mutants, identifying about 550,000 negative and about 350,000 positive genetic interactions. This comprehensive network maps genetic interactions for essential gene pairs, highlighting essential genes as densely connected hubs. Genetic interaction profiles enabled assembly of a hierarchical model of cell function, including modules corresponding to protein complexes and pathways, biological processes, and cellular compartments. Negative interactions connected functionally related genes, mapped core bioprocesses, and identified pleiotropic genes, whereas positive interactions often mapped general regulatory connections among gene pairs, rather than shared functionality. The global network illustrates how coherent sets of genetic interactions connect protein complex and pathway modules to map a functional wiring diagram of the cell.

Efficacy of C.E.R.A. in Routine Clinical Practice for Correction of Anaemia and Maintenance of the Haemoglobin Levels in CKD Patients not on Dialysis

INTRODUCTION

C.E.R.A. reported effective correction of anaemia and was well tolerated in International studies on CKD patients not on dialysis.

OBJECTIVE

The study aimed to describe the management of renal anaemia in CKD patients not on dialysis with C.E.R.A. in routine clinical practice in India.

METHODS

This was a prospective, single-arm, open-label, multi-centre, non-interventional, Phase IV study which followed 108 CKD Stage III-IV patients, not on dialysis with Hb < 10 g/dL for correction of anaemia with C.E.R.A.

RESULTS

Of the 108 patients with Hb < 10 g/dL at baseline, 83 (90.2%) patients achieved target Hb of 10-12 g/dL and the time taken to achieve correction of anaemia was 9.6 weeks ± 6.13 weeks in the Intent-to-treat population. Haemoglobin concentration increased from 8.59 ± 0.808 g/dL pre-therapy to 10.91 ± 0.634 g/dL post-therapy. The change in mean ± SD Hb value was 2.32 ± 0.174 g/dL. Maintenance of Hb levels within the target range of Hb 10 - 12 g/dL was observed in 78.2% of ITT and 80.8% of the PP population for mean duration of 16.69 weeks. Four patients (3.7%) experienced 5 AEs and 2 patients (1.9%) experienced 3 SAEs in the safety population. As per the treating physician none of the AEs or SAEs was considered related to study drug. There were no deaths reported.

CONCLUSIONS

This study demonstrated successful correction of anaemia in Indian patients with C.E.R.A. treatment as well as maintenance of Hb levels within the target range. C.E.R.A. was well tolerated with no new safety concerns specific to the Indian population. The less frequent up to monthly dosing schedule of C.E.R.A. may offer clinicians and patients a simplified regimen of anaemia management as compared to traditional frequently administered (thrice weekly to once weekly) ESAs.

Plant-derived antifungal agent poacic acid targets β-1,3-glucan

A rise in resistance to current antifungals necessitates strategies to identify alternative sources of effective fungicides. We report the discovery of poacic acid, a potent antifungal compound found in lignocellulosic hydrolysates of grasses. Chemical genomics using Saccharomyces cerevisiae showed that loss of cell wall synthesis and maintenance genes conferred increased sensitivity to poacic acid. Morphological analysis revealed that cells treated with poacic acid behaved similarly to cells treated with other cell wall-targeting drugs and mutants with deletions in genes involved in processes related to cell wall biogenesis. Poacic acid causes rapid cell lysis and is synergistic with caspofungin and fluconazole. The cellular target was identified; poacic acid localized to the cell wall and inhibited β-1,3-glucan synthesis in vivo and in vitro, apparently by directly binding β-1,3-glucan. Through its activity on the glucan layer, poacic acid inhibits growth of the fungi Sclerotinia sclerotiorum and Alternaria solani as well as the oomycete Phytophthora sojae. A single application of poacic acid to leaves infected with the broad-range fungal pathogen S. sclerotiorum substantially reduced lesion development. The discovery of poacic acid as a natural antifungal agent targeting β-1,3-glucan highlights the potential side use of products generated in the processing of renewable biomass toward biofuels as a source of valuable bioactive compounds and further clarifies the nature and mechanism of fermentation inhibitors found in lignocellulosic hydrolysates.

Chemical genomic profiling via barcode sequencing to predict compound mode of action

Chemical genomics is an unbiased, whole-cell approach to characterizing novel compounds to determine mode of action and cellular target. Our version of this technique is built upon barcoded deletion mutants of Saccharomyces cerevisiae and has been adapted to a high-throughput methodology using next-generation sequencing. Here we describe the steps to generate a chemical genomic profile from a compound of interest, and how to use this information to predict molecular mechanism and targets of bioactive compounds.

Forazoline A: marine-derived polyketide with antifungal in vivo efficacy

Forazoline A, a novel antifungal polyketide with in vivo efficacy against Candida albicans, was discovered using LCMS-based metabolomics to investigate marine-invertebrate-associated bacteria. Forazoline A had a highly unusual and unprecedented skeleton. Acquisition of (13)C-(13)C gCOSY and (13)C-(15)N HMQC NMR data provided the direct carbon-carbon and carbon-nitrogen connectivity, respectively. This approach represents the first example of determining direct (13)C-(15)N connectivity for a natural product. Using yeast chemical genomics, we propose that forazoline A operated through a new mechanism of action with a phenotypic outcome of disrupting membrane integrity.

Unbiased screening of marine sponge extracts for anti-inflammatory agents combined with chemical genomics identifies girolline as an inhibitor of protein synthesis

Toll-like receptors (TLRs) play a critical role in innate immunity, but activation of TLR signaling pathways is also associated with many harmful inflammatory diseases. Identification of novel anti-inflammatory molecules targeting TLR signaling pathways is central to the development of new treatment approaches for acute and chronic inflammation. We performed high-throughput screening from crude marine sponge extracts on TLR5 signaling and identified girolline. We demonstrated that girolline inhibits signaling through both MyD88-dependent and -independent TLRs (i.e., TLR2, 3, 4, 5, and 7) and reduces cytokine (IL-6 and IL-8) production in human peripheral blood mononuclear cells and macrophages. Using a chemical genomics approach, we identified Elongation Factor 2 as the molecular target of girolline, which inhibits protein synthesis at the elongation step. Together these data identify the sponge natural product girolline as a potential anti-inflammatory agent acting through inhibition of protein synthesis.

A comparison of diagnostic imaging modalities for colorectal liver metastases

AIMS

The aims of this study were to compare the diagnostic performance of CT scan, MR liver, PET-CT and intra-operative ultrasound (IOUS) for the detection of liver metastases against the histopathological findings, and to compare PET-CT with CT for the detection of distant disease in metastatic colorectal cancer patients eligible for surgical treatment.

METHODS

A prospective study was performed that measured concordance between the number and stage of metastatic lesions identified with various preoperative imaging modalities and histology of patients undergoing surgical treatment for CRLM.

RESULTS

Compared with histopathology, concordance for the number of metastatic liver lesions was moderate for CT scan (K = 0.477, 95% CI: 0.28-0.66), moderate for MR scan (K = 0.574, 95% CI: 0.39-0.75), good for FDG PET-CT (K = 0.703, 95% CI: 0.52-0.87) and very good for IOUS (K = 0.904, 95% CI: 0.81-0.99). Additional CRLM were identified intraoperatively in six patients (9.1%) with IOUS and in 7.5% of the cases surgical strategy was changed according to the new intraoperative findings. The diagnosis of intra abdominal lymph node metastatic disease was made with PET-CT only in nine patients (13.6%)

DISCUSSION

Our study supports the recent recommendations of the Oncosurg Multidisciplinary International Consensus regarding the importance of high quality CT and MR in the staging of CRLM but provides further evidence for the added value of PET-CT, especially in detecting extrahepatic intra-abdominal metastatic disease that may be amenable to potentially curative resection. Despite these advances in preoperative staging, there still remains a role for IOUS in detecting additional metastases at the time of surgery.

Measurement of fetal fat in utero in normal and diabetic pregnancies using magnetic resonance imaging

OBJECTIVES

To assess the reliability of magnetic resonance imaging (MRI) to measure fetal fat volume in utero, and to study fetal growth in women with and without diabetes in view of the increased prevalence of macrosomia in the former.

METHODS

We studied 26 pregnant women, 14 with pre-gestational diabetes and 12 non-diabetic controls. Fetal assessment took place at 24 weeks' gestation and again at 34 weeks by standard ultrasound biometry followed by MRI at 1.5 T. Fetal fat volume was determined from T1-weighted water-suppressed images using a semi-automated approach based on pixel intensity and taking into account partial volume effects. Fetal volume was also determined from the MRI images. Fetal weight was calculated using published fat and lean tissue densities.

RESULTS

There was little fetal fat at 24 weeks' gestation, but at 34 weeks the fetal fat content was considerably higher in the women with diabetes, with a mean fat content of 1090 ± 417 cm(3) compared with 541 ± 348 cm(3) in the controls (P = 0.006). Measurements of fetal fat volume showed low intra- and interobserver variability at 34 weeks, with intraclass correlation coefficients consistently above 0.99. Birth-weight centile correlated with fetal fat volume (R(2)  = 0.496, P < 0.001), percentage of fetal fat (R(2)  = 0.362, P = 0.008) and calculated fetal weight (R(2)  = 0.492, P < 0.001) at 34 weeks.

CONCLUSIONS

MRI appears to be a promising tool for the determination of fetal fat, body composition and weight in utero during the third trimester of pregnancy.

A comparative genomic approach for identifying synthetic lethal interactions in human cancer

Synthetic lethal interactions enable a novel approach for discovering specific genetic vulnerabilities in cancer cells that can be exploited for the development of therapeutics. Despite successes in model organisms such as yeast, discovering synthetic lethal interactions on a large scale in human cells remains a significant challenge. We describe a comparative genomic strategy for identifying cancer-relevant synthetic lethal interactions whereby candidate interactions are prioritized on the basis of genetic interaction data available in yeast, followed by targeted testing of candidate interactions in human cell lines. As a proof of principle, we describe two novel synthetic lethal interactions in human cells discovered by this approach, one between the tumor suppressor gene SMARCB1 and PSMA4, and another between alveolar soft-part sarcoma-associated ASPSCR1 and PSMC2. These results suggest therapeutic targets for cancers harboring mutations in SMARCB1 or ASPSCR1 and highlight the potential of a targeted, cross-species strategy for identifying synthetic lethal interactions relevant to human cancer.

Comparison of profile similarity measures for genetic interaction networks

Analysis of genetic interaction networks often involves identifying genes with similar profiles, which is typically indicative of a common function. While several profile similarity measures have been applied in this context, they have never been systematically benchmarked. We compared a diverse set of correlation measures, including measures commonly used by the genetic interaction community as well as several other candidate measures, by assessing their utility in extracting functional information from genetic interaction data. We find that the dot product, one of the simplest vector operations, outperforms most other measures over a large range of gene pairs. More generally, linear similarity measures such as the dot product, Pearson correlation or cosine similarity perform better than set overlap measures such as Jaccard coefficient. Similarity measures that involve L2-normalization of the profiles tend to perform better for the top-most similar pairs but perform less favorably when a larger set of gene pairs is considered or when the genetic interaction data is thresholded. Such measures are also less robust to the presence of noise and batch effects in the genetic interaction data. Overall, the dot product measure performs consistently among the best measures under a variety of different conditions and genetic interaction datasets.

Rarer in a rare

Though moya moya disease is a disease of Asian origin, it is one of the very rare causes of stroke in India. It is a rare disease mainly characterized by progressive cerebrovascular episode due to the slowly progressive stenosis of supraclinoid segment of bilateral internal carotid arteries, the anterior and the middle cerebral arteries, and very rarely, posterior cerebral arteries. We hereby report a case of a young female who presented to us with the psychiatric complaints and refractory headache since her childhood. Therefore, we are reporting rarer (headache and neuropsychiatric) manifestations in the rare (moya moya) disease.

Abstract LB-234: A comparative genomic approach for identifying synthetic lethal interactions in human cancer

Synthetic lethal interactions enable a novel approach for discovering specific genetic vulnerabilities in cancer cells that can be exploited for the development of therapeutics. This concept has recently been exploited in the development of PARP inhibitors as novel chemotherapeutics for breast cancer. While PARP is not essential in normal cells, BRCA mutant cells are dependent on it for their survival. This led to the successful treatment of BRCA mutant tumors of the breast, ovary, and prostate with the oral PARP inhibitor, olaparib. Therapeutic strategies based on such synthetic-lethal (SL) interactions can enable drug targeting of cancer-specific alterations for otherwise undruggable tumor suppressors. Large-scale analyses of genetic interaction networks in model organisms like yeast suggest that disease-specific networks should not only give rise to destructive phenotypes (e.g. uncontrolled cell growth), but will also present unique vulnerabilities that can be exploited for novel therapies. Despite successes in model organisms such as yeast, discovering synthetic lethal interactions on a large scale in human cells remains a significant challenge. We have used a combination of computational and experimental approach whereby yeast interactions between human orthologs are filtered by cancer association and prioritized by genomic features, and candidates from the prioritized list are then validated in human cell lines using an RNA interference approach. As a proof of principle, we utilized this approach to discover two previously unknown synthetic lethal/sick interactions, one between SMARCB1 (yeast SNF5) and PSMA4 (yeast PRE9), and another between ASPSCR1 (yeast UBX4) and PSMC2 (yeast RPT1) based on shRNA double knock-down in normal human fibroblast cells. These interactions suggest potentially new therapeutic targets for SMARCB1 and ASPSCR1 mutated cancers, and more broadly, illustrate the potential of this cross-species approach.

Citation Format: Michael K. Asiedu, Raamesh Deshpande, Mitchell Klebig, Shari Sutor, Elena Kuzmin, Jeff Piotrowski, Seung Ho Shin, Michael Costanzo, Charles Boone, Dennis A. Wigle, Chad L Myers. A comparative genomic approach for identifying synthetic lethal interactions in human cancer. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr LB-234. doi:10.1158/1538-7445.AM2013-LB-234

Profiling bortezomib resistance identifies secondary therapies in a mouse myeloma model

Multiple myeloma is a hematologic malignancy characterized by the proliferation of neoplastic plasma cells in the bone marrow. Although the first-to-market proteasome inhibitor bortezomib (Velcade) has been successfully used to treat patients with myeloma, drug resistance remains an emerging problem. In this study, we identify signatures of bortezomib sensitivity and resistance by gene expression profiling (GEP) using pairs of bortezomib-sensitive (BzS) and bortezomib-resistant (BzR) cell lines created from the Bcl-XL/Myc double-transgenic mouse model of multiple myeloma. Notably, these BzR cell lines show cross-resistance to the next-generation proteasome inhibitors, MLN2238 and carfilzomib (Kyprolis) but not to other antimyeloma drugs. We further characterized the response to bortezomib using the Connectivity Map database, revealing a differential response between these cell lines to histone deacetylase (HDAC) inhibitors. Furthermore, in vivo experiments using the HDAC inhibitor panobinostat confirmed that the predicted responder showed increased sensitivity to HDAC inhibitors in the BzR line. These findings show that GEP may be used to document bortezomib resistance in myeloma cells and predict individual sensitivity to other drug classes. Finally, these data reveal complex heterogeneity within multiple myeloma and suggest that resistance to one drug class reprograms resistant clones for increased sensitivity to a distinct class of drugs. This study represents an important next step in translating pharmacogenomic profiling and may be useful for understanding personalized pharmacotherapy for patients with multiple myeloma.

Influence of γ-radiation on the structure and function of soybean trypsin inhibitor

Soybean trypsin inhibitor (STI) is a known antinutrient and food allergen present in soybean. γ-Radiation has the potential to inactivate the TI protein. However, a systematic study on the influence of different moisture levels during γ radiation on structure and function of the molecule has not been reported. Pure STI was irradiated up to 200 kGy, in dry state, with 50% moisture and in aqueous solution. The radiation damage in molecular structure was assessed using, SDS-PAGE, size exclusion chromatography, fluorescence measurement, and circular dichroism, while functional damage was assessed by the TI assay. In aqueous solution, both the structure and function of TI were almost destroyed at the 10 kGy dose. While with 50% moisture and in dry state, the loss in functional and structural attributes was discernible only at 30 and 100 kGy, respectively. The TI activity was found to be unaffected in dry and soaked seeds of soybean as well as other legumes up to irradiation doses of 100 and 50 kGy, respectively.

Conserved rules govern genetic interaction degree across species

Background

Synthetic genetic interactions have recently been mapped on a genome scale in the budding yeast Saccharomyces cerevisiae, providing a functional view of the central processes of eukaryotic life. Currently, comprehensive genetic interaction networks have not been determined for other species, and we therefore sought to model conserved aspects of genetic interaction networks in order to enable the transfer of knowledge between species.

Results

Using a combination of physiological and evolutionary properties of genes, we built models that successfully predicted the genetic interaction degree of S. cerevisiae genes. Importantly, a model trained on S. cerevisiae gene features and degree also accurately predicted interaction degree in the fission yeast Schizosaccharomyces pombe, suggesting that many of the predictive relationships discovered in S. cerevisiae also hold in this evolutionarily distant yeast. In both species, high single mutant fitness defect, protein disorder, pleiotropy, protein-protein interaction network degree, and low expression variation were significantly predictive of genetic interaction degree. A comparison of the predicted genetic interaction degrees of S. pombe genes to the degrees of S. cerevisiae orthologs revealed functional rewiring of specific biological processes that distinguish these two species. Finally, predicted differences in genetic interaction degree were independently supported by differences in co-expression relationships of the two species.

Conclusions

Our findings show that there are common relationships between gene properties and genetic interaction network topology in two evolutionarily distant species. This conservation allows use of the extensively mapped S. cerevisiae genetic interaction network as an orthology-independent reference to guide the study of more complex species.

P2-08-11: Correlation of Mammography, Ultrasound and MRI in Patients with Nipple Discharge

BACKGROUND: Breast cancer mainly originates in the epithelial lining of milk ducts or lobules. Once early pre-malignant or malignant changes occur in the ductal lining there is a long latent period before they manifest symptoms or are detected on imaging. Symptomatic nipple discharge (SND) is a common symptom noted in approximately 5% of patients. Although, usually benign it may be the earliest sign of cancer. The current standard evaluation of patients with nipple discharge includes clinical examination (CE), mammography (MMG) and ultrasound (US). Contrast enhanced MRI is considered the most sensitive imaging modality for the breast and reports suggest it may be of benefit in the evaluation of nipple discharge. We have combined MRI with standard evaluation in an ongoing prospective study to determine its utility in patients with SND.

PATIENTS AND METHODS: Between July 2004 and 31st May 2011, 75 women underwent surgery for SND. All were initially evaluated by CE, MMG and US following which a contrast enhanced breast MRI was requested. MRI was performed on a 1.5T MR unit using a dedicated breast coil. A STIR sequence in the coronal and sagittal planes along with an unenhanced fat-suppressed T1 W sequence was used to evaluate the ducts. The imaging findings were used to direct surgical excision. All specimens were oriented and evaluated by the pathologist by sectioning at 1 cm interval, from the ductal end to the periphery. The results of preoperative MMG, US and MRI findings were compared with pathology.

RESULTS: The incidence of cancer in this series was 29% and high risk lesions were noted in 43%. MMG and US were performed in 75 patients. MRI was requested in all 75 patients but could be performed in 64. MRI proved to be the most sensitive technique for detection of an abnormality in the breast (92%) compared to MMG 25%, and US 63%. MMG, US and MRI were suggestive of benign change in 64 (85%), 65 (87%) and 43 (67%) patients respectively, of which 14 (22%), 15 (23%) and 7 (16%) were malignant on pathology. MMG, US and MRI reported suspicious change in 11(15%), 10 (13%) and 21(33%) patients respectively of which 8 (73%), 7 (70%) and 9 (43%) patients had cancer on pathology. MRI had the highest sensitivity for malignancy (54%) compared to MMG (36%) and US (32%) but MRI had a low specificity (74%) (MMG 94%; US 94%). However, MRI had a high negative predictive value (86%) (MMG 78%; US 73%). Of the 22 patients with cancers, 12 were deemed to be normal on CE, MMG and US examination. Thus standard evaluation would have missed 54% of the cancers detected in this series.

CONCLUSIONS: Nipple discharge is associated with significant incidence of cancer and high risk changes. The majority of these are missed on standard evaluation (CE, MMG, US). MRI is the most sensitive method for the detection of an abnormality in these patients. It defines the location and extent of the abnormality and can guide surgical excision. However, MRI is unable to accurately differentiate malignant from benign lesions. Hence, excision of an identified abnormality and pathological evaluation remains essential.

Citation Information: Cancer Res 2011;71(24 Suppl):Abstract nr P2-08-11.

Padanamides A and B, highly modified linear tetrapeptides produced in culture by a Streptomyces sp. isolated from a marine sediment

Two highly modified linear tetrapeptides, padanamides A (1) and B (2), are produced by laboratory cultures of a Streptomyces sp. obtained from a marine sediment. Padanamide B is cytotoxic to Jurkat cells, and a chemical genomics analysis using Saccharomyces cerevisiae deletion mutants suggested that padanamide A inhibits cysteine and methionine biosynthesis or that these amino acids are involved in the yeast's response to the peptide.