Modeling rectal cancer to advance neoadjuvant precision therapy.

Progress in rectal cancer therapy has been hindered by the lack of effective disease-specific preclinical models that account for the unique molecular profile and biology of rectal cancer. Thus, we developed complementary patient-derived xenograft (PDX) and subsequent in vitro tumor organoid (PDTO) platforms established from preneoadjuvant therapy rectal cancer specimens to advance personalized care for rectal cancer patients. Multiple endoscopic samples were obtained from 26 Stages 2 and 3 rectal cancer patients prior to receiving 5FU/RT and implanted subcutaneously into NSG mice to generate 15 subcutaneous PDXs. Second passaged xenografts demonstrated 100% correlation with the corresponding human cancer histology with maintained mutational profiles. Individual rectal cancer PDXs reproduced the 5FU/RT response observed in the corresponding human cancers. Similarly, rectal cancer PDTOs reproduced significant heterogeneity in cellular morphology and architecture. PDTO in vitro 5FU/RT treatment response replicated the clinical 5FU/RT neoadjuvant therapy pathologic response observed in the corresponding patient tumors (p < 0.05). The addition of cetuximab to the 5FU/RT regiment was significantly more sensitive in the rectal cancer PDX and PDTOs with wild-type KRAS compared to mutated KRAS (p < 0.05). Considering the close relationship between the patient's cancer and the corresponding PDX/PDTO, rectal cancer patient-derived research platforms represent powerful translational research resources as population-based tools for biomarker discovery and experimental therapy testing. In addition, our findings suggest that cetuximab may enhance RT effectiveness by improved patient selection based on mutational profile in addition to KRAS or by developing a protocol using PDTOs to identify sensitive patients.

miR-145 Antagonizes SNAI1-Mediated Stemness and Radiation Resistance in Colorectal Cancer.

Epithelial-to-mesenchymal transition (EMT) has been closely linked with therapy resistance and cancer stem cells (CSCs). However, EMT pathways have proven challenging to therapeutically target. MicroRNA 145 (miR-145) targets multiple stem cell transcription factors and its expression is inversely correlated with EMT. Therefore, we hypothesized that miR-145 represents a therapeutic target to reverse snail family transcriptional repressor 1 (SNAI1)-mediated stemness and radiation resistance (RT). Stable expression of SNAI1 in DLD1 and HCT116 cells (DLD1-SNAI1; HCT116-SNAI1) increased expression of Nanog and decreased miR-145 expression compared to control cells. Using a miR-145 luciferase reporter assay, we determined that ectopic SNAI1 expression significantly repressed the miR-145 promoter. DLD1-SNAI1 and HCT116-SNAI1 cells demonstrated decreased RT sensitivity and, conversely, miR-145 replacement significantly enhanced RT sensitivity. Of the five parental colon cancer cell lines, SW620 cells demonstrated relatively high endogenous SNAI1 and low miR-145 levels. In the SW620 cells, miR-145 replacement decreased CSC-related transcription factor expression, spheroid formation, and radiation resistance. In rectal cancer patient-derived xenografts, CSC identified by EpCAM+/aldehyde dehydrogenase (ALDH)+ demonstrated high expression of SNAI1, c-Myc, and Nanog compared with non-CSCs (EpCAM+/ALDH-). Conversely, patient-derived CSCs demonstrated low miR-145 expression levels relative to non-CSCs. These results suggest that the SNAI1:miR-145 pathway represents a novel therapeutic target in colorectal cancer to overcome RT resistance.

Enhancing the effectiveness of γδ T cells by mRNA transfection of chimeric antigen receptors or bispecific T cell engagers.

Adoptive cell therapy (ACT) utilizing γδ T cells is becoming a promising option for the treatment of cancer, because it offers an off-the-shelf allogeneic product that is safe, potent, and clinically effective. Approaches to engineer or enhance immune-competent cells for ACT, like expression of chimeric antigen receptors (CARs) or combination treatments with bispecific T cell engagers, have improved the specificity and cytotoxic potential of ACTs and have shown great promise in preclinical and clinical settings. Here, we test whether electroporation of γδ T cells with CAR or secreted bispecific T cell engager (sBite) mRNA is an effective approach to improve the cytotoxicity of γδ T cells. Using a CD19-specific CAR, approximately 60% of γδ T cells are modified after mRNA electroporation and these cells show potent anticancer activity in vitro and in vivo against two CD19-positive cancer cell lines. In addition, expression and secretion of a CD19 sBite enhances γδ T cell cytotoxicity, both in vitro and in vivo, and promotes killing of target cells by modified and unmodified γδ T cells. Taken together, we show that transient transfection of γδ T cells with CAR or sBite mRNA by electroporation can be an effective treatment platform as a cancer therapeutic.

Critical evaluation of an autologous peripheral blood mononuclear cell-based humanized cancer model.

The use of humanized mouse models for oncology is rapidly expanding. Autologous patient-derived systems are particularly attractive as they can model the human cancer's heterogeneity and immune microenvironment. In this study, we developed an autologous humanized mouse cancer model by engrafting NSG mice with patient-derived xenografts and infused matched peripheral blood mononuclear cells (PBMCs). We first defined the time course of xenogeneic graft-versus-host-disease (xGVHD) and determined that only minimal xGVHD was observed for up to 8 weeks. Next, colorectal and pancreatic cancer patient-derived xenograft bearing NSG mice were infused with 5x106 human PBMCS for development of the humanized cancer models (iPDX). Early after infusion of human PBMCs, iPDX mice demonstrated engraftment of human CD4+ and CD8+ T cells in the blood of both colorectal and pancreatic cancer patient-derived models that persisted for up to 8 weeks. At the end of the experiment, iPDX xenografts maintained the features of the primary human tumor including tumor grade and cell type. The iPDX tumors demonstrated infiltration of human CD3+ cells with high PD-1 expression although we observed significant intra and inter- model variability. In summary, the iPDX models reproduced key features of the corresponding human tumor. The observed variability and high PD-1 expression are important considerations that need to be addressed in order to develop a reproducible model system.

Non-signaling Chimeric Antigen Receptors Enhance Antigen-Directed Killing by γδ T Cells in Contrast to αß T Cells.

Chimeric antigen receptor (CAR)-modified T cells have demonstrated efficacy against B cell leukemias/lymphomas. However, redirecting CAR T cells to malignant T cells is more challenging due to product-specific cis- and trans-activation causing fratricide. Other challenges include the potential for product contamination and T cell aplasia. We expressed non-signaling CARs (NSCARs) in γδ T cells since donor-derived γδ T cells can be used to prevent product contamination, and NSCARs lack signaling/activation domains, but retain antigen-specific tumor cell-targeting capability. As a result, NSCAR targeting requires an alternative cytotoxic mechanism, which can be achieved through utilization of γδ T cells that possess major histocompatibility complex (MHC)-independent cytotoxicity. We designed two distinct NSCARs and demonstrated that they do not enhance tumor-killing by αß T cells, as predicted. However, both CD5-NSCAR- and CD19-NSCAR-modified γδ T cells enhanced cytotoxicity against T and B cell acute lymphoblastic leukemia (T-ALL and B-ALL) cell lines, respectively. CD5-NSCAR expression in γδ T cells resulted in a 60% increase in cytotoxicity of CD5-expressing T-ALL cell lines. CD19-NSCAR-modified γδ T cells exhibited a 350% increase in cytotoxicity against a CD19-expressing B-ALL cell line compared to the cytotoxicity of naive cells. NSCARs may provide a mechanism to enhance antigen-directed anti-tumor cytotoxicity of γδ T cells through the introduction of a high-affinity interaction while avoiding self-activation.

Phased Diploid Genome Sequence for the Fast-Growing Microalga Picochlorum celeri.

Picochlorum celeri is a fast-growing marine microalga with high biomass productivity. Here, we report the use of PacBio sequencing to assemble the phased diploid genome of P. celeri.

Context-specific metabolic networks are consistent with experiments.

Reconstructions of cellular metabolism are publicly available for a variety of different microorganisms and some mammalian genomes. To date, these reconstructions are "genome-scale" and strive to include all reactions implied by the genome annotation, as well as those with direct experimental evidence. Clearly, many of the reactions in a genome-scale reconstruction will not be active under particular conditions or in a particular cell type. Methods to tailor these comprehensive genome-scale reconstructions into context-specific networks will aid predictive in silico modeling for a particular situation. We present a method called Gene Inactivity Moderated by Metabolism and Expression (GIMME) to achieve this goal. The GIMME algorithm uses quantitative gene expression data and one or more presupposed metabolic objectives to produce the context-specific reconstruction that is most consistent with the available data. Furthermore, the algorithm provides a quantitative inconsistency score indicating how consistent a set of gene expression data is with a particular metabolic objective. We show that this algorithm produces results consistent with biological experiments and intuition for adaptive evolution of bacteria, rational design of metabolic engineering strains, and human skeletal muscle cells. This work represents progress towards producing constraint-based models of metabolism that are specific to the conditions where the expression profiling data is available.

Metabolite coupling in genome-scale metabolic networks.

BACKGROUND: Biochemically detailed stoichiometric matrices have now been reconstructed for various bacteria, yeast, and for the human cardiac mitochondrion based on genomic and proteomic data. These networks have been manually curated based on legacy data and elementally and charge balanced. Comparative analysis of these well curated networks is now possible. Pairs of metabolites often appear together in several network reactions, linking them topologically. This co-occurrence of pairs of metabolites in metabolic reactions is termed herein "metabolite coupling." These metabolite pairs can be directly computed from the stoichiometric matrix, S. Metabolite coupling is derived from the matrix ssT, whose off-diagonal elements indicate the number of reactions in which any two metabolites participate together, where s is the binary form of S. RESULTS: Metabolite coupling in the studied networks was found to be dominated by a relatively small group of highly interacting pairs of metabolites. As would be expected, metabolites with high individual metabolite connectivity also tended to be those with the highest metabolite coupling, as the most connected metabolites couple more often. For metabolite pairs that are not highly coupled, we show that the number of reactions a pair of metabolites shares across a metabolic network closely approximates a line on a log-log scale. We also show that the preferential coupling of two metabolites with each other is spread across the spectrum of metabolites and is not unique to the most connected metabolites. We provide a measure for determining which metabolite pairs couple more often than would be expected based on their individual connectivity in the network and show that these metabolites often derive their principal biological functions from existing in pairs. Thus, analysis of metabolite coupling provides information beyond that which is found from studying the individual connectivity of individual metabolites. CONCLUSION: The coupling of metabolites is an important topological property of metabolic networks. By computing coupling quantitatively for the first time in genome-scale metabolic networks, we provide insight into the basic structure of these networks.

Genome-scale reconstruction of the metabolic network in Staphylococcus aureus N315: an initial draft to the two-dimensional annotation.

BACKGROUND: Several strains of bacteria have sequenced and annotated genomes, which have been used in conjunction with biochemical and physiological data to reconstruct genome-scale metabolic networks. Such reconstruction amounts to a two-dimensional annotation of the genome. These networks have been analyzed with a constraint-based formalism and a variety of biologically meaningful results have emerged. Staphylococcus aureus is a pathogenic bacterium that has evolved resistance to many antibiotics, representing a significant health care concern. We present the first manually curated elementally and charge balanced genome-scale reconstruction and model of S. aureus' metabolic networks and compute some of its properties. RESULTS: We reconstructed a genome-scale metabolic network of S. aureus strain N315. This reconstruction, termed iSB619, consists of 619 genes that catalyze 640 metabolic reactions. For 91% of the reactions, open reading frames are explicitly linked to proteins and to the reaction. All but three of the metabolic reactions are both charge and elementally balanced. The reaction list is the most complete to date for this pathogen. When the capabilities of the reconstructed network were analyzed in the context of maximal growth, we formed hypotheses regarding growth requirements, the efficiency of growth on different carbon sources, and potential drug targets. These hypotheses can be tested experimentally and the data gathered can be used to improve subsequent versions of the reconstruction. CONCLUSION: iSB619 represents comprehensive biochemically and genetically structured information about the metabolism of S. aureus to date. The reconstructed metabolic network can be used to predict cellular phenotypes and thus advance our understanding of a troublesome pathogen.

Three factors underlying incorrect in silico predictions of essential metabolic genes.

BACKGROUND: The indispensability of certain genes in an organism is important for studies of microorganism physiology, antibiotic targeting, and the engineering of minimal genomes. Time and resource intensive genome-wide experimental screens can be conducted to determine which genes are likely essential. For metabolic genes, a reconstructed metabolic network can be used to predict which genes are likely essential. The success rate of these predictions is less than desirable, especially with regard to comprehensively locating essential genes. RESULTS: We show that genes that are falsely predicted to be non-essential (for growth) share three characteristics across multiple organisms and growth media. First, these genes are on average connected to fewer reactions in the network than correctly predicted essential genes, suggesting incomplete knowledge of the functions of these genes. Second, they are more likely to be blocked (their associated reactions are prohibited from carrying flux in the given condition) than other genes, implying incomplete knowledge of metabolism surrounding these genes. Third, they are connected to less overcoupled metabolites. CONCLUSION: The results presented herein indicate genes that cannot be correctly predicted as essential have commonalities in different organisms. These elucidated failure modes can be used to better understand the biology of individual organisms and to improve future predictions.

Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox.

The manner in which microorganisms utilize their metabolic processes can be predicted using constraint-based analysis of genome-scale metabolic networks. Herein, we present the constraint-based reconstruction and analysis toolbox, a software package running in the Matlab environment, which allows for quantitative prediction of cellular behavior using a constraint-based approach. Specifically, this software allows predictive computations of both steady-state and dynamic optimal growth behavior, the effects of gene deletions, comprehensive robustness analyses, sampling the range of possible cellular metabolic states and the determination of network modules. Functions enabling these calculations are included in the toolbox, allowing a user to input a genome-scale metabolic model distributed in Systems Biology Markup Language format and perform these calculations with just a few lines of code. The results are predictions of cellular behavior that have been verified as accurate in a growing body of research. After software installation, calculation time is minimal, allowing the user to focus on the interpretation of the computational results.

Global reconstruction of the human metabolic network based on genomic and bibliomic data.

Metabolism is a vital cellular process, and its malfunction is a major contributor to human disease. Metabolic networks are complex and highly interconnected, and thus systems-level computational approaches are required to elucidate and understand metabolic genotype-phenotype relationships. We have manually reconstructed the global human metabolic network based on Build 35 of the genome annotation and a comprehensive evaluation of >50 years of legacy data (i.e., bibliomic data). Herein we describe the reconstruction process and demonstrate how the resulting genome-scale (or global) network can be used (i) for the discovery of missing information, (ii) for the formulation of an in silico model, and (iii) as a structured context for analyzing high-throughput biological data sets. Our comprehensive evaluation of the literature revealed many gaps in the current understanding of human metabolism that require future experimental investigation. Mathematical analysis of network structure elucidated the implications of intracellular compartmentalization and the potential use of correlated reaction sets for alternative drug target identification. Integrated analysis of high-throughput data sets within the context of the reconstruction enabled a global assessment of functional metabolic states. These results highlight some of the applications enabled by the reconstructed human metabolic network. The establishment of this network represents an important step toward genome-scale human systems biology.

Pancreatic trypsin increases matrix metalloproteinase-9 accumulation and activation during acute intestinal ischemia-reperfusion in the rat.

Ischemia-reperfusion of the intestine produces a set of inflammatory mediators, the origin of which has recently been shown to involve pancreatic digestive enzymes. Matrix metalloproteinase-9 (MMP-9) participates in a variety of inflammatory processes including myocardial, hepatic, and pancreatic ischemia-reperfusion. In the present study, we explore the role of neutrophil-derived MMP-9 in acute intestinal ischemia-reperfusion and its interaction with pancreatic trypsin. Male Sprague-Dawley rats were subjected to 45 minutes of superior mesenteric arterial occlusion followed by 90 minutes of reperfusion. In situ zymography of the proximal jejunum reveals increased gelatinase activity in the intestinal wall after ischemia-reperfusion. Gel electrophoresis zymography and immunofluorescence co-localization suggests that this gelatinase activity is derived from MMP-9 released from infiltrating neutrophils. The role of intraluminal trypsin in this process was investigated using an in vivo isolated jejunal loop model of intestinal ischemia-reperfusion. Trypsin increased the inflammatory response after reperfusion, with an augmented neutrophil infiltration of the intestinal wall. Furthermore, trypsin stimulated a rapid conversion of neutrophil-released proMMP-9 into the lower molecular weight enzymatically active MMP-9. This process represents a powerful in vivo pathophysiological mechanism for trypsin-induced MMP-9 activation and is likely to play a central role in the development of acute intestinal inflammation and shock.