Age-dependent antibody profiles to plasmodium antigens are differentially associated with two artemisinin combination therapy outcomes in high transmission setting.

The impact of pre-existing immunity on the efficacy of artemisinin combination therapy is largely unknown. We performed in-depth profiling of serological responses in a therapeutic efficacy study [comparing artesunate-mefloquine (ASMQ) and artemether-lumefantrine (AL)] using a proteomic microarray. Responses to over 200 Plasmodium antigens were significantly associated with ASMQ treatment outcome but not AL. We used machine learning to develop predictive models of treatment outcome based on the immunoprofile data. The models predict treatment outcome for ASMQ with high (72-85%) accuracy, but could not predict treatment outcome for AL. This divergent treatment outcome suggests that humoral immunity may synergize with the longer mefloquine half-life to provide a prophylactic effect at 28-42 days post-treatment, which was further supported by simulated pharmacokinetic profiling. Our computational approach and modeling revealed the synergistic effect of pre-existing immunity in patients with drug combination that has an extended efficacy on providing long term treatment efficacy of ASMQ.

Immunoprofiling Identifies Functional B and T Cell Subsets Induced by an Attenuated Whole Parasite Malaria Vaccine as Correlates of Sterile Immunity.

Immune correlates of protection remain elusive for most vaccines. An identified immune correlate would accelerate the down-selection of vaccine formulations by reducing the need for human pathogen challenge studies that are currently required to determine vaccine efficacy. Immunization via mosquito-delivered, radiation-attenuated P. falciparum sporozoites (IMRAS) is a well-established model for efficacious malaria vaccines, inducing greater than 90% sterile immunity. The current immunoprofiling study utilized samples from a clinical trial in which vaccine dosing was adjusted to achieve only 50% protection, thus enabling a comparison between protective and non-protective immune signatures. In-depth immunoprofiling was conducted by assessing a wide range of antigen-specific serological and cellular parameters and applying our newly developed computational tools, including machine learning. The computational component of the study pinpointed previously un-identified cellular T cell subsets (namely, TNFα-secreting CD8+CXCR3-CCR6- T cells, IFNγ-secreting CD8+CCR6+ T cells and TNFα/FNγ-secreting CD4+CXCR3-CCR6- T cells) and B cell subsets (i.e., CD19+CD24hiCD38hiCD69+ transitional B cells) as important factors predictive of protection (92% accuracy). Our study emphasizes the need for in-depth immunoprofiling and subsequent data integration with computational tools to identify immune correlates of protection. The described process of computational data analysis is applicable to other disease and vaccine models.

A cross-study analysis of drug response prediction in cancer cell lines.

To enable personalized cancer treatment, machine learning models have been developed to predict drug response as a function of tumor and drug features. However, most algorithm development efforts have relied on cross-validation within a single study to assess model accuracy. While an essential first step, cross-validation within a biological data set typically provides an overly optimistic estimate of the prediction performance on independent test sets. To provide a more rigorous assessment of model generalizability between different studies, we use machine learning to analyze five publicly available cell line-based data sets: National Cancer Institute 60, ancer Therapeutics Response Portal (CTRP), Genomics of Drug Sensitivity in Cancer, Cancer Cell Line Encyclopedia and Genentech Cell Line Screening Initiative (gCSI). Based on observed experimental variability across studies, we explore estimates of prediction upper bounds. We report performance results of a variety of machine learning models, with a multitasking deep neural network achieving the best cross-study generalizability. By multiple measures, models trained on CTRP yield the most accurate predictions on the remaining testing data, and gCSI is the most predictable among the cell line data sets included in this study. With these experiments and further simulations on partial data, two lessons emerge: (1) differences in viability assays can limit model generalizability across studies and (2) drug diversity, more than tumor diversity, is crucial for raising model generalizability in preclinical screening.

Immunoprofiling Correlates of Protection Against SHIV Infection in Adjuvanted HIV-1 Pox-Protein Vaccinated Rhesus Macaques.

Human immunodeficiency virus type 1 (HIV-1) infection remains a major public health threat due to its incurable nature and the lack of a highly efficacious vaccine. The RV144 vaccine trial is the only clinical study to date that demonstrated significant but modest decrease in HIV infection risk. To improve HIV-1 vaccine immunogenicity and efficacy, we recently evaluated pox-protein vaccination using a next generation liposome-based adjuvant, Army Liposomal Formulation adsorbed to aluminum (ALFA), in rhesus monkeys and observed 90% efficacy against limiting dose mucosal SHIV challenge in male animals. Here, we analyzed binding antibody responses, as assessed by Fc array profiling using a broad range of HIV-1 envelope antigens and Fc features, to explore the mechanisms of ALFA-mediated protection by employing machine learning and Cox proportional hazards regression analyses. We found that Fcγ receptor 2a-related binding antibody responses were augmented by ALFA relative to aluminium hydroxide, and these responses were associated with reduced risk of infection in male animals. Our results highlight the application of systems serology to provide mechanistic insights to vaccine-elicited protection and support evidence that antibody effector responses protect against HIV-1 infection.

Glucocorticoids enhance the antileukemic activity of FLT3 inhibitors in FLT3-mutant acute myeloid leukemia.

FLT3 is a frequently mutated gene that is highly associated with a poor prognosis in acute myeloid leukemia (AML). Despite initially responding to FLT3 inhibitors, most patients eventually relapse with drug resistance. The mechanism by which resistance arises and the initial response to drug treatment that promotes cell survival is unknown. Recent studies show that a transiently maintained subpopulation of drug-sensitive cells, so-called drug-tolerant "persisters" (DTPs), can survive cytotoxic drug exposure despite lacking resistance-conferring mutations. Using RNA sequencing and drug screening, we find that treatment of FLT3 internal tandem duplication AML cells with quizartinib, a selective FLT3 inhibitor, upregulates inflammatory genes in DTPs and thereby confers susceptibility to anti-inflammatory glucocorticoids (GCs). Mechanistically, the combination of FLT3 inhibitors and GCs enhances cell death of FLT3 mutant, but not wild-type, cells through GC-receptor-dependent upregulation of the proapoptotic protein BIM and proteasomal degradation of the antiapoptotic protein MCL-1. Moreover, the enhanced antileukemic activity by quizartinib and dexamethasone combination has been validated using primary AML patient samples and xenograft mouse models. Collectively, our study indicates that the combination of FLT3 inhibitors and GCs has the potential to eliminate DTPs and therefore prevent minimal residual disease, mutational drug resistance, and relapse in FLT3-mutant AML.

A delayed fractionated dose RTS,S AS01 vaccine regimen mediates protection via improved T follicular helper and B cell responses.

Malaria-071, a controlled human malaria infection trial, demonstrated that administration of three doses of RTS,S/AS01 malaria vaccine given at one-month intervals was inferior to a delayed fractional dose (DFD) schedule (62.5% vs 86.7% protection, respectively). To investigate the underlying immunologic mechanism, we analyzed the B and T peripheral follicular helper cell (pTfh) responses. Here, we show that protection in both study arms was associated with early induction of functional IL-21-secreting circumsporozoite (CSP)-specific pTfh cells, together with induction of CSP-specific memory B cell responses after the second dose that persisted after the third dose. Data integration of key immunologic measures identified a subset of non-protected individuals in the standard (STD) vaccine arm who lost prior protective B cell responses after receiving the third vaccine dose. We conclude that the DFD regimen favors persistence of functional B cells after the third dose.

Advances on PPARγ Research in the Emerging Era of Precision Medicine.

BACKGROUND: Peroxisome proliferator-activated receptor gamma (PPARγ) is a member of the nuclear receptor superfamily that functions as a ligand-inducible transcription factor. It regulates glucose and lipid metabolism, immunity, and cellular growth and differentiation. Thiazolidinediones (TZDs) are potent insulin sensitizers that function by activating PPARs, with a high specificity for PPARγ. Due to their ability to preserve pancreatic beta cell function and reduce insulin resistance, TZDs have become one of the most prescribed classes of medications for type 2 diabetes (T2D) since their approval by the US Food and Drug Administration (FDA) and initial use in 1997. OBJECTIVE: However, adverse effects, including weight gain, bone loss, fluid retention, congestive heart failure, and risk to bladder cancer, have weakened the benefits of TZDs in T2D therapies. Therefore, there is an urgent need to have a deeper understanding of regulatory mechanisms of PPARγ expression and activity so that novel classes of PPARγ-modulating therapeutics with fewer or weaker side effects can be developed. CONCLUSION: This article systematically reviews PPARγ's mechanisms of action and multilayer regulations. In addition, novel classes of therapeutics modulating PPARγ and new direction of research on genetic variants that affect PPARγ function and antidiabetic drug response are highlighted, which sheds light on PPARγ as a promising target for developing safer and precision medicine based therapeutic strategies.

Lanthionine Synthetase C-Like 2 Modulates Immune Responses to Influenza Virus Infection.

Broad-based, host-targeted therapeutics have the potential to ameliorate viral infections without inducing antiviral resistance. We identified lanthionine synthetase C-like 2 (LANCL2) as a new therapeutic target for immunoinflammatory diseases. To examine the therapeutic efficacy of oral NSC61610 administration on influenza, we infected C57BL/6 mice with influenza A H1N1pdm virus and evaluated influenza-related mortality, lung inflammatory profiles, and pulmonary histopathology. Oral treatment with NSC61610 ameliorates influenza virus infection by down-modulating pulmonary inflammation through the downregulation of TNF-α and MCP-1 and reduction in the infiltration of neutrophils. NSC61610 treatment increases IL10-producing CD8+ T cells and macrophages in the lungs during the resolution phase of disease. The loss of LANCL2 or neutralization of IL-10 in mice infected with influenza virus abrogates the ability of NSC61610 to accelerate recovery and induce IL-10-mediated regulatory responses. These studies validate that oral treatment with NSC61610 ameliorates morbidity and mortality and accelerates recovery during influenza virus infection through a mechanism mediated by activation of LANCL2 and subsequent induction of IL-10 responses by CD8+ T cells and macrophages in the lungs.

mutLBSgeneDB: mutated ligand binding site gene DataBase.

Mutations at the ligand binding sites (LBSs) can influence protein structure stability, binding affinity with small molecules, and drug resistance in cancer patients. Our recent analysis revealed that ligand binding residues had a significantly higher mutation rate than other parts of the protein. Here, we built mutLBSgeneDB (mutated Ligand Binding Site gene DataBase) available at http://zhaobioinfo.org/mutLBSgeneDB We collected and curated over 2300 genes (mutLBSgenes) having ∼12 000 somatic mutations at ∼10 000 LBSs across 16 cancer types and selected 744 drug targetable genes (targetable_mutLBSgenes) by incorporating kinases, transcription factors, pharmacological genes, and cancer driver genes. We analyzed LBS mutation information, differential gene expression network, drug response correlation with gene expression, and protein stability changes for all mutLBSgenes using integrated genetic, genomic, transcriptomic, proteomic, network and functional information. We calculated and compared the binding affinities of 20 carefully selected genes with their drugs in wild type and mutant forms. mutLBSgeneDB provides a user-friendly web interface for searching and browsing through seven categories of annotations: Gene summary, Mutated information, Protein structure related information, Differential gene expression and gene-gene network, Phenotype information, Pharmacological information, and Conservation information. mutLBSgeneDB provides a useful resource for functional genomics, protein structure, drug and disease research communities.

SZGR 2.0: a one-stop shop of schizophrenia candidate genes.

SZGR 2.0 is a comprehensive resource of candidate variants and genes for schizophrenia, covering genetic, epigenetic, transcriptomic, translational and many other types of evidence. By systematic review and curation of multiple lines of evidence, we included almost all variants and genes that have ever been reported to be associated with schizophrenia. In particular, we collected ∼4200 common variants reported in genome-wide association studies, ∼1000 de novo mutations discovered by large-scale sequencing of family samples, 215 genes spanning rare and replication copy number variations, 99 genes overlapping with linkage regions, 240 differentially expressed genes, 4651 differentially methylated genes and 49 genes as antipsychotic drug targets. To facilitate interpretation, we included various functional annotation data, especially brain eQTL, methylation QTL, brain expression featured in deep categorization of brain areas and developmental stages and brain-specific promoter and enhancer annotations. Furthermore, we conducted cross-study, cross-data type and integrative analyses of the multidimensional data deposited in SZGR 2.0, and made the data and results available through a user-friendly interface. In summary, SZGR 2.0 provides a one-stop shop of schizophrenia variants and genes and their function and regulation, providing an important resource in the schizophrenia and other mental disease community. SZGR 2.0 is available at https://bioinfo.uth.edu/SZGR/.

Modeling the Role of Lanthionine Synthetase C-Like 2 (LANCL2) in the Modulation of Immune Responses to Helicobacter pylori Infection.

Immune responses to Helicobacter pylori are orchestrated through complex balances of host-bacterial interactions, including inflammatory and regulatory immune responses across scales that can lead to the development of the gastric disease or the promotion of beneficial systemic effects. While inflammation in response to the bacterium has been reasonably characterized, the regulatory pathways that contribute to preventing inflammatory events during H. pylori infection are incompletely understood. To aid in this effort, we have generated a computational model incorporating recent developments in the understanding of H. pylori-host interactions. Sensitivity analysis of this model reveals that a regulatory macrophage population is critical in maintaining high H. pylori colonization without the generation of an inflammatory response. To address how this myeloid cell subset arises, we developed a second model describing an intracellular signaling network for the differentiation of macrophages. Modeling studies predicted that LANCL2 is a central regulator of inflammatory and effector pathways and its activation promotes regulatory responses characterized by IL-10 production while suppressing effector responses. The predicted impairment of regulatory macrophage differentiation by the loss of LANCL2 was simulated based on multiscale linkages between the tissue-level gastric mucosa and the intracellular models. The simulated deletion of LANCL2 resulted in a greater clearance of H. pylori, but also greater IFNγ responses and damage to the epithelium. The model predictions were validated within a mouse model of H. pylori colonization in wild-type (WT), LANCL2 whole body KO and myeloid-specific LANCL2-/- (LANCL2Myeloid) mice, which displayed similar decreases in H. pylori burden, CX3CR1+ IL-10-producing macrophages, and type 1 regulatory (Tr1) T cells. This study shows the importance of LANCL2 in the induction of regulatory responses in macrophages and T cells during H. pylori infection.

Modeling-Enabled Characterization of Novel NLRX1 Ligands.

Nucleotide-binding domain and leucine-rich repeat containing (NLR) family are intracellular sentinels of cytosolic homeostasis that orchestrate immune and inflammatory responses in infectious and immune-mediated diseases. NLRX1 is a mitochondrial-associated NOD-like receptor involved in the modulation of immune and metabolic responses. This study utilizes molecular docking approaches to investigate the structure of NLRX1 and experimentally assesses binding to naturally occurring compounds from several natural product and lipid databases. Screening of compound libraries predicts targeting of NLRX1 by conjugated trienes, polyketides, prenol lipids, sterol lipids, and coenzyme A-containing fatty acids for activating the NLRX1 pathway. The ligands of NLRX1 were identified by docking punicic acid (PUA), eleostearic acid (ESA), and docosahexaenoic acid (DHA) to the C-terminal fragment of the human NLRX1 (cNLRX1). Their binding and that of positive control RNA to cNLRX1 were experimentally determined by surface plasmon resonance (SPR) spectroscopy. In addition, the ligand binding sites of cNLRX1 were predicted in silico and validated experimentally. Target mutagenesis studies demonstrate that mutation of 4 critical residues ASP677, PHE680, PHE681, and GLU684 to alanine resulted in diminished affinity of PUA, ESA, and DHA to NLRX1. Consistent with the regulatory actions of NLRX1 on the NF-κB pathway, treatment of bone marrow derived macrophages (BMDM)s with PUA and DHA suppressed NF-κB activity in a NLRX1 dependent mechanism. In addition, a series of pre-clinical efficacy studies were performed using a mouse model of dextran sodium sulfate (DSS)-induced colitis. Our findings showed that the regulatory function of PUA on colitis is NLRX1 dependent. Thus, we identified novel small molecules that bind to NLRX1 and exert anti-inflammatory actions.

Multiscale modeling of mucosal immune responses.

BACKGROUND: Computational techniques are becoming increasingly powerful and modeling tools for biological systems are of greater needs. Biological systems are inherently multiscale, from molecules to tissues and from nano-seconds to a lifespan of several years or decades. ENISI MSM integrates multiple modeling technologies to understand immunological processes from signaling pathways within cells to lesion formation at the tissue level. This paper examines and summarizes the technical details of ENISI, from its initial version to its latest cutting-edge implementation. IMPLEMENTATION: Object-oriented programming approach is adopted to develop a suite of tools based on ENISI. Multiple modeling technologies are integrated to visualize tissues, cells as well as proteins; furthermore, performance matching between the scales is addressed. CONCLUSION: We used ENISI MSM for developing predictive multiscale models of the mucosal immune system during gut inflammation. Our modeling predictions dissect the mechanisms by which effector CD4+ T cell responses contribute to tissue damage in the gut mucosa following immune dysregulation.Computational modeling techniques are playing increasingly important roles in advancing a systems-level mechanistic understanding of biological processes. Computer simulations guide and underpin experimental and clinical efforts. This study presents ENteric Immune Simulator (ENISI), a multiscale modeling tool for modeling the mucosal immune responses. ENISI's modeling environment can simulate in silico experiments from molecular signaling pathways to tissue level events such as tissue lesion formation. ENISI's architecture integrates multiple modeling technologies including ABM (agent-based modeling), ODE (ordinary differential equations), SDE (stochastic modeling equations), and PDE (partial differential equations). This paper focuses on the implementation and developmental challenges of ENISI. A multiscale model of mucosal immune responses during colonic inflammation, including CD4+ T cell differentiation and tissue level cell-cell interactions was developed to illustrate the capabilities, power and scope of ENISI MSM.

Supervised learning methods in modeling of CD4+ T cell heterogeneity.

BACKGROUND: Modeling of the immune system - a highly non-linear and complex system - requires practical and efficient data analytic approaches. The immune system is composed of heterogeneous cell populations and hundreds of cell types, such as neutrophils, eosinophils, macrophages, dendritic cells, T cells, and B cells. Each cell type is highly diverse and can be further differentiated into subsets with unique and overlapping functions. For example, CD4+ T cells can be differentiated into Th1, Th2, Th17, Th9, Th22, Treg, Tfh, as well as Tr1. Each subset plays different roles in the immune system. To study molecular mechanisms of cell differentiation, computational systems biology approaches can be used to represent these processes; however, the latter often requires building complex intracellular signaling models with a large number of equations to accurately represent intracellular pathways and biochemical reactions. Furthermore, studying the immune system entails integration of complex processes which occur at different time and space scales. METHODS: This study presents and compares four supervised learning methods for modeling CD4+ T cell differentiation: Artificial Neural Networks (ANN), Random Forest (RF), Support Vector Machines (SVM), and Linear Regression (LR). Application of supervised learning methods could reduce the complexity of Ordinary Differential Equations (ODEs)-based intracellular models by only focusing on the input and output cytokine concentrations. In addition, this modeling framework can be efficiently integrated into multiscale models. RESULTS: Our results demonstrate that ANN and RF outperform the other two methods. Furthermore, ANN and RF have comparable performance when applied to in silico data with and without added noise. The trained models were also able to reproduce dynamic behavior when applied to experimental data; in four out of five cases, model predictions based on ANN and RF correctly predicted the outcome of the system. Finally, the running time of different methods was compared, which confirms that ANN is considerably faster than RF. CONCLUSIONS: Using machine learning as opposed to ODE-based method reduces the computational complexity of the system and allows one to gain a deeper understanding of the complex interplay between the different related entities.

Lanthionine synthetase component C-like protein 2: a new drug target for inflammatory diseases and diabetes.

Lanthionine synthetase component C-like protein 2 (LANCL2) is a member of the LANCL protein family, which is broadly expressed throughout the body. LANCL2 is the molecular target of abscisic acid (ABA), a compound with insulin-sensitizing and immune modulatory actions. LANCL2 is required for membrane binding and signaling of ABA in immune cells. Direct binding of ABA to LANCL2 was predicted in silico using molecular modeling approaches and validated experimentally using ligand-binding assays and kinetic surface plasmon resonance studies. The therapeutic potential of the LANCL2 pathway ranges from increasing cellular sensitivity to anticancer drugs, insulin-sensitizing effects and modulating immune and inflammatory responses in the context of immune-mediated and infectious diseases. A case for LANCL2-based drug discovery and development is also illustrated by the anti-inflammatory activity of novel LANCL2 ligands such as NSC61610 against inflammatory bowel disease and influenza-driven inflammation in mice. This review discusses the value of LANCL2 as a novel therapeutic target for the discovery and development of new classes of orally active drugs against chronic metabolic, immune-mediated and infectious diseases.

Preventive and prophylactic mechanisms of action of pomegranate bioactive constituents.

Pomegranate fruit presents strong anti-inflammatory, antioxidant, antiobesity, and antitumoral properties, thus leading to an increased popularity as a functional food and nutraceutical source since ancient times. It can be divided into three parts: seeds, peel, and juice, all of which seem to have medicinal benefits. Several studies investigate its bioactive components as a means to associate them with a specific beneficial effect and develop future products and therapeutic applications. Many beneficial effects are related to the presence of ellagic acid, ellagitannins (including punicalagins), punicic acid and other fatty acids, flavonoids, anthocyanidins, anthocyanins, estrogenic flavonols, and flavones, which seem to be its most therapeutically beneficial components. However, the synergistic action of the pomegranate constituents appears to be superior when compared to individual constituents. Promising results have been obtained for the treatment of certain diseases including obesity, insulin resistance, intestinal inflammation, and cancer. Although moderate consumption of pomegranate does not result in adverse effects, future studies are needed to assess safety and potential interactions with drugs that may alter the bioavailability of bioactive constituents of pomegranate as well as drugs. The aim of this review is to summarize the health effects and mechanisms of action of pomegranate extracts in chronic inflammatory diseases.

Systems modeling of molecular mechanisms controlling cytokine-driven CD4+ T cell differentiation and phenotype plasticity.

Differentiation of CD4+ T cells into effector or regulatory phenotypes is tightly controlled by the cytokine milieu, complex intracellular signaling networks and numerous transcriptional regulators. We combined experimental approaches and computational modeling to investigate the mechanisms controlling differentiation and plasticity of CD4+ T cells in the gut of mice. Our computational model encompasses the major intracellular pathways involved in CD4+ T cell differentiation into T helper 1 (Th1), Th2, Th17 and induced regulatory T cells (iTreg). Our modeling efforts predicted a critical role for peroxisome proliferator-activated receptor gamma (PPARγ) in modulating plasticity between Th17 and iTreg cells. PPARγ regulates differentiation, activation and cytokine production, thereby controlling the induction of effector and regulatory responses, and is a promising therapeutic target for dysregulated immune responses and inflammation. Our modeling efforts predict that following PPARγ activation, Th17 cells undergo phenotype switch and become iTreg cells. This prediction was validated by results of adoptive transfer studies showing an increase of colonic iTreg and a decrease of Th17 cells in the gut mucosa of mice with colitis following pharmacological activation of PPARγ. Deletion of PPARγ in CD4+ T cells impaired mucosal iTreg and enhanced colitogenic Th17 responses in mice with CD4+ T cell-induced colitis. Thus, for the first time we provide novel molecular evidence in vivo demonstrating that PPARγ in addition to regulating CD4+ T cell differentiation also plays a major role controlling Th17 and iTreg plasticity in the gut mucosa.

Nutritional protective mechanisms against gut inflammation.

Inflammatory bowel disease (IBD) is a debilitating and widespread immune-mediated illness characterized by excessive inflammatory and effector mucosal responses leading to tissue destruction at the gastrointestinal tract. Interactions among the immune system, the commensal microbiota and the host genotype are thought to underlie the pathogenesis of IBD. However, the precise etiology of IBD remains unknown. Diet-induced changes in the composition of the gut microbiome can modulate the induction of regulatory versus effector immune responses at the gut mucosa and improve health outcomes. Therefore, manipulation of gut microbiota composition and the local production of microbial-derived metabolites by using prebiotics, probiotics and dietary fibers is being explored as a promising avenue of prophylactic and therapeutic intervention against gut inflammation. Prebiotics and fiber carbohydrates are fermented by resident microflora into short chain fatty acids (SCFAs) in the colon. SCFAs then activate peroxisome proliferator-activated receptor (PPAR)γ, a nuclear transcription factor with widely demonstrated anti-inflammatory efficacy in experimental IBD. The activation of PPARγ by naturally ocurring compounds such as conjugated linoleic acid, pomegranate seed oil-derived punicic acid, eleostearic acid and abscisic acid has been explored as nutritional interventions that suppress colitis by directly modulating the host immune response. The aim of this review is to summarize the status of innovative nutritional interventions against gastrointestinal inflammation, their proposed mechanisms of action, preclinical and clinical efficacy as well as bioinformatics and computational modeling approaches that accelerate discovery in nutritional and mucosal immunology research.

Helicobacter pylori colonization ameliorates glucose homeostasis in mice through a PPAR γ-dependent mechanism.

BACKGROUND: There is an inverse secular trend between the incidence of obesity and gastric colonization with Helicobacter pylori, a bacterium that can affect the secretion of gastric hormones that relate to energy homeostasis. H. pylori strains that carry the cag pathogenicity island (PAI) interact more intimately with gastric epithelial cells and trigger more extensive host responses than cag(-) strains. We hypothesized that gastric colonization with H. pylori strains differing in cag PAI status exert distinct effects on metabolic and inflammatory phenotypes. METHODOLOGY/PRINCIPAL FINDINGS: To test this hypothesis, we examined metabolic and inflammatory markers in db/db mice and mice with diet-induced obesity experimentally infected with isogenic forms of H. pylori strain 26695: the cag PAI wild-type and its cag PAI mutant strain 99-305. H. pylori colonization decreased fasting blood glucose levels, increased levels of leptin, improved glucose tolerance, and suppressed weight gain. A response found in both wild-type and mutant H. pylori strain-infected mice included decreased white adipose tissue macrophages (ATM) and increased adipose tissue regulatory T cells (Treg) cells. Gene expression analyses demonstrated upregulation of gastric PPAR γ-responsive genes (i.e., CD36 and FABP4) in H. pylori-infected mice. The loss of PPAR γ in immune and epithelial cells in mice impaired the ability of H. pylori to favorably modulate glucose homeostasis and ATM infiltration during high fat feeding. CONCLUSIONS/SIGNIFICANCE: Gastric infection with some commensal strains of H. pylori ameliorates glucose homeostasis in mice through a PPAR γ-dependent mechanism and modulates macrophage and Treg cell infiltration into the abdominal white adipose tissue.

Computational modeling-based discovery of novel classes of anti-inflammatory drugs that target lanthionine synthetase C-like protein 2.

BACKGROUND: Lanthionine synthetase component C-like protein 2 (LANCL2) is a member of the eukaryotic lanthionine synthetase component C-Like protein family involved in signal transduction and insulin sensitization. Recently, LANCL2 is a target for the binding and signaling of abscisic acid (ABA), a plant hormone with anti-diabetic and anti-inflammatory effects. METHODOLOGY/PRINCIPAL FINDINGS: The goal of this study was to determine the role of LANCL2 as a potential therapeutic target for developing novel drugs and nutraceuticals against inflammatory diseases. Previously, we performed homology modeling to construct a three-dimensional structure of LANCL2 using the crystal structure of lanthionine synthetase component C-like protein 1 (LANCL1) as a template. Using this model, structure-based virtual screening was performed using compounds from NCI (National Cancer Institute) Diversity Set II, ChemBridge, ZINC natural products, and FDA-approved drugs databases. Several potential ligands were identified using molecular docking. In order to validate the anti-inflammatory efficacy of the top ranked compound (NSC61610) in the NCI Diversity Set II, a series of in vitro and pre-clinical efficacy studies were performed using a mouse model of dextran sodium sulfate (DSS)-induced colitis. Our findings showed that the lead compound, NSC61610, activated peroxisome proliferator-activated receptor gamma in a LANCL2- and adenylate cyclase/cAMP dependent manner in vitro and ameliorated experimental colitis by down-modulating colonic inflammatory gene expression and favoring regulatory T cell responses. CONCLUSIONS/SIGNIFICANCE: LANCL2 is a novel therapeutic target for inflammatory diseases. High-throughput, structure-based virtual screening is an effective computational-based drug design method for discovering anti-inflammatory LANCL2-based drug candidates.