VH2+ Antigen-Experienced B Cells in the Cerebrospinal Fluid Are Expanded and Enriched in Pediatric Anti-NMDA Receptor Encephalitis.

Pediatric and adult autoimmune encephalitis (AE) are often associated with Abs to the NR1 subunit of the N-methyl-d-aspartate (NMDA) receptor (NMDAR). Very little is known regarding the cerebrospinal fluid humoral immune profile and Ab genetics associated with pediatric anti-NMDAR-AE. Using a combination of cellular, molecular, and immunogenetics tools, we collected cerebrospinal fluid from pediatric subjects and generated 1) flow cytometry data to calculate the frequency of B cell subtypes in the cerebrospinal fluid of pediatric subjects with anti-NMDAR-AE and controls, 2) a panel of recombinant human Abs from a pediatric case of anti-NMDAR-AE that was refractory to treatment, and 3) a detailed analysis of the Ab genes that bound the NR1 subunit of the NMDAR. Ag-experienced B cells including memory cells, plasmablasts, and Ab-secreting cells were expanded in the pediatric anti-NMDAR-AE cohort, but not in the controls. These Ag-experienced B cells in the cerebrospinal fluid of a pediatric case of NMDAR-AE that was refractory to treatment had expanded use of variable H chain family 2 (VH2) genes with high somatic hypermutation that all bound to the NR1 subunit of the NMDAR. A CDR3 motif was identified in this refractory case that likely drove early stage activation and expansion of naive B cells to Ab-secreting cells, facilitating autoimmunity associated with pediatric anti-NMDAR-AE through the production of Abs that bind NR1. These features of humoral immune responses in the cerebrospinal fluid of pediatric anti-NMDAR-AE patients may be relevant for clinical diagnosis and treatment.

Outcome and Immune Correlates of a Phase II Trial of High-Dose Interleukin-2 and Stereotactic Ablative Radiotherapy for Metastatic Renal Cell Carcinoma.

PURPOSE: This phase II clinical trial evaluated whether the addition of stereotactic ablative radiotherapy (SAbR), which may promote tumor antigen presentation, improves the overall response rate (ORR) to high-dose IL2 (HD IL2) in metastatic renal cell carcinoma (mRCC). PATIENTS AND METHODS: Patients with pathologic evidence of clear cell renal cell carcinoma (RCC) and radiographic evidence of metastasis were enrolled in this single-arm trial and were treated with SAbR, followed by HD IL2. ORR was assessed based on nonirradiated metastases. Secondary endpoints included overall survival (OS), progression-free survival (PFS), toxicity, and treatment-related tumor-specific immune response. Correlative studies involved whole-exome and transcriptome sequencing, T-cell receptor sequencing, cytokine analysis, and mass cytometry on patient samples. RESULTS: Thirty ethnically diverse mRCC patients were enrolled. A median of two metastases were treated with SAbR. Among 25 patients evaluable by RECIST v1.1, ORR was 16% with 8% complete responses. Median OS was 37 months. Treatment-related adverse events (AE) included 22 grade ≥3 events that were not dissimilar from HD IL2 alone. There were no grade 5 AEs. A correlation was observed between SAbR to lung metastases and improved PFS (P = 0.0165). Clinical benefit correlated with frameshift mutational load, mast cell tumor infiltration, decreased circulating tumor-associated T-cell clones, and T-cell clonal expansion. Higher regulatory/CD8+ T-cell ratios at baseline in the tumor and periphery correlated with no clinical benefit. CONCLUSIONS: Adding SAbR did not improve the response rate to HD IL2 in patients with mRCC in this study. Tissue analyses suggest a possible correlation between frameshift mutation load as well as tumor immune infiltrates and clinical outcomes.

T Cell Receptor Repertoires Acquired via Routine Pap Testing May Help Refine Cervical Cancer and Precancer Risk Estimates.

Cervical cancer is the fourth most common cancer and fourth leading cause of cancer death among women worldwide. In low Human Development Index settings, it ranks second. Screening and surveillance involve the cytology-based Papanicolaou (Pap) test and testing for high-risk human papillomavirus (hrHPV). The Pap test has low sensitivity to detect precursor lesions, while a single hrHPV test cannot distinguish a persistent infection from one that the immune system will naturally clear. Furthermore, among women who are hrHPV-positive and progress to high-grade cervical lesions, testing cannot identify the ~20% who would progress to cancer if not treated. Thus, reliable detection and treatment of cancers and precancers requires routine screening followed by frequent surveillance among those with past abnormal or positive results. The consequence is overtreatment, with its associated risks and complications, in screened populations and an increased risk of cancer in under-screened populations. Methods to improve cervical cancer risk assessment, particularly assays to predict regression of precursor lesions or clearance of hrHPV infection, would benefit both populations. Here we show that women who have lower risk results on follow-up testing relative to index testing have evidence of enhanced T cell clonal expansion in the index cervical cytology sample compared to women who persist with higher risk results from index to follow-up. We further show that a machine learning classifier based on the index sample T cells predicts this transition to lower risk with 95% accuracy (19/20) by leave-one-out cross-validation. Using T cell receptor deep sequencing and machine learning, we identified a biophysicochemical motif in the complementarity-determining region 3 of T cell receptor ß chains whose presence predicts this transition. While these results must still be tested on an independent cohort in a prospective study, they suggest that this approach could improve cervical cancer screening by helping distinguish women likely to spontaneously regress from those at elevated risk of progression to cancer. The advancement of such a strategy could reduce surveillance frequency and overtreatment in screened populations and improve the delivery of screening to under-screened populations.

TCRMatch: Predicting T-Cell Receptor Specificity Based on Sequence Similarity to Previously Characterized Receptors.

The adaptive immune system in vertebrates has evolved to recognize non-self antigens, such as proteins expressed by infectious agents and mutated cancer cells. T cells play an important role in antigen recognition by expressing a diverse repertoire of antigen-specific receptors, which bind epitopes to mount targeted immune responses. Recent advances in high-throughput sequencing have enabled the routine generation of T-cell receptor (TCR) repertoire data. Identifying the specific epitopes targeted by different TCRs in these data would be valuable. To accomplish that, we took advantage of the ever-increasing number of TCRs with known epitope specificity curated in the Immune Epitope Database (IEDB) since 2004. We compared seven metrics of sequence similarity to determine their power to predict if two TCRs have the same epitope specificity. We found that a comprehensive k-mer matching approach produced the best results, which we have implemented into TCRMatch, an openly accessible tool (http://tools.iedb.org/tcrmatch/) that takes TCR ß-chain CDR3 sequences as an input, identifies TCRs with a match in the IEDB, and reports the specificity of each match. We anticipate that this tool will provide new insights into T cell responses captured in receptor repertoire and single cell sequencing experiments and will facilitate the development of new strategies for monitoring and treatment of infectious, allergic, and autoimmune diseases, as well as cancer.

Comparative genetics of Enterococcus faecalis intestinal tissue isolates before and after surgery in a rat model of colon anastomosis.

We have recently demonstrated that collagenolytic Enterococcus faecalis plays a key and causative role in the pathogenesis of anastomotic leak, an uncommon but potentially lethal complication characterized by disruption of the intestinal wound following segmental removal of the colon (resection) and its reconnection (anastomosis). Here we hypothesized that comparative genetic analysis of E. faecalis isolates present at the anastomotic wound site before and after surgery would shed insight into the mechanisms by which collagenolytic strains are selected for and predominate at sites of anastomotic disruption. Whole genome optical mapping of four pairs of isolates from rat colonic tissue obtained following surgical resection (herein named "pre-op" isolates) and then 6 days later from the anastomotic site (herein named "post-op" isolates) demonstrated that the isolates with higher collagenolytic activity formed a distinct cluster. In order to perform analysis at a deeper level, a single pair of E. faecalis isolates (16A pre-op and 16A post-op) was selected for whole genome sequencing and assembled using a hybrid assembly algorithm. Comparative genomics demonstrated absence of multiple gene clusters, notably a pathogenicity island in the post-op isolate. No differences were found in the fsr-gelE-sprE genes (EF1817-1822) responsible for regulation and production of collagenolytic activity. Analysis of unique genes among the 16A pre-op and post-op isolates revealed the predominance of transporter systems-related genes in the pre-op isolate and phage-related and hydrolytic enzyme-encoding genes in the post-op isolate. Despite genetic differences observed between pre-op and post-op isolates, the precise genetic determinants responsible for their differential expression of collagenolytic activity remains unknown.

Biophysicochemical motifs in T cell receptor sequences as a potential biomarker for high-grade serous ovarian carcinoma.

We previously showed, in a pilot study with publicly available data, that T cell receptor (TCR) repertoires from tumor infiltrating lymphocytes (TILs) could be distinguished from adjacent healthy tissue repertoires by the presence of TCRs bearing specific, biophysicochemical motifs in their antigen binding regions. We hypothesized that such motifs might allow development of a novel approach to cancer detection. The motifs were cancer specific and achieved high classification accuracy: we found distinct motifs for breast versus colorectal cancer-associated repertoires, and the colorectal cancer motif achieved 93% accuracy, while the breast cancer motif achieved 94% accuracy. In the current study, we sought to determine whether such motifs exist for ovarian cancer, a cancer type for which detection methods are urgently needed. We made two significant advances over the prior work. First, the prior study used patient-matched TILs and healthy repertoires, collecting healthy tissue adjacent to the tumors. The current study collected TILs from patients with high-grade serous ovarian carcinoma (HGSOC) and healthy ovary repertoires from cancer-free women undergoing hysterectomy/salpingo-oophorectomy for benign disease. Thus, the classification task is distinguishing women with cancer from women without cancer. Second, in the prior study, classification accuracy was measured by patient-hold-out cross-validation on the training data. In the current study, classification accuracy was additionally assessed on an independent cohort not used during model development to establish the generalizability of the motif to unseen data. Classification accuracy was 95% by patient-hold-out cross-validation on the training set and 80% when the model was applied to the blinded test set. The results on the blinded test set demonstrate a biophysicochemical TCR motif found overwhelmingly in women with HGSOC but rarely in women with healthy ovaries, strengthening the proposal that cancer detection approaches might benefit from incorporation of TCR motif-based biomarkers. Furthermore, these results call for studies on large cohorts to establish higher classification accuracies, as well as for studies in other cancer types.

Inferred Allelic Variants of Immunoglobulin Receptor Genes: A System for Their Evaluation, Documentation, and Naming.

Immunoglobulins or antibodies are the main effector molecules of the B-cell lineage and are encoded by hundreds of variable (V), diversity (D), and joining (J) germline genes, which recombine to generate enormous IG diversity. Recently, high-throughput adaptive immune receptor repertoire sequencing (AIRR-seq) of recombined V-(D)-J genes has offered unprecedented insights into the dynamics of IG repertoires in health and disease. Faithful biological interpretation of AIRR-seq studies depends upon the annotation of raw AIRR-seq data, using reference germline gene databases to identify the germline genes within each rearrangement. Existing reference databases are incomplete, as shown by recent AIRR-seq studies that have inferred the existence of many previously unreported polymorphisms. Completing the documentation of genetic variation in germline gene databases is therefore of crucial importance. Lymphocyte receptor genes and alleles are currently assigned by the Immunoglobulins, T cell Receptors and Major Histocompatibility Nomenclature Subcommittee of the International Union of Immunological Societies (IUIS) and managed in IMGT®, the international ImMunoGeneTics information system® (IMGT). In 2017, the IMGT Group reached agreement with a group of AIRR-seq researchers on the principles of a streamlined process for identifying and naming inferred allelic sequences, for their incorporation into IMGT®. These researchers represented the AIRR Community, a network of over 300 researchers whose objective is to promote all aspects of immunoglobulin and T-cell receptor repertoire studies, including the standardization of experimental and computational aspects of AIRR-seq data generation and analysis. The Inferred Allele Review Committee (IARC) was established by the AIRR Community to devise policies, criteria, and procedures to perform this function. Formalized evaluations of novel inferred sequences have now begun and submissions are invited via a new dedicated portal (https://ogrdb.airr-community.org). Here, we summarize recommendations developed by the IARC-focusing, to begin with, on human IGHV genes-with the goal of facilitating the acceptance of inferred allelic variants of germline IGHV genes. We believe that this initiative will improve the quality of AIRR-seq studies by facilitating the description of human IG germline gene variation, and that in time, it will expand to the documentation of TR and IG genes in many vertebrate species.

Biophysicochemical Motifs in T-cell Receptor Sequences Distinguish Repertoires from Tumor-Infiltrating Lymphocyte and Adjacent Healthy Tissue.

Immune repertoire deep sequencing allows comprehensive characterization of antigen receptor-encoding genes in a lymphocyte population. We hypothesized that this method could enable a novel approach to diagnose disease by identifying antigen receptor sequence patterns associated with clinical phenotypes. In this study, we developed statistical classifiers of T-cell receptor (TCR) repertoires that distinguish tumor tissue from patient-matched healthy tissue of the same organ. The basis of both classifiers was a biophysicochemical motif in the complementarity determining region 3 (CDR3) of TCRß chains. To develop each classifier, we extracted 4-mers from every TCRß CDR3 and represented each 4-mer using biophysicochemical features of its amino acid sequence combined with quantification of 4-mer (or receptor) abundance. This representation was scored using a logistic regression model. Unlike typical logistic regression, the classifier is fitted and validated under the requirement that at least 1 positively labeled 4-mer appears in every tumor repertoire and no positively labeled 4-mers appear in healthy tissue repertoires. We applied our method to publicly available data in which tumor and adjacent healthy tissue were collected from each patient. Using a patient-holdout cross-validation, our method achieved classification accuracy of 93% and 94% for colorectal and breast cancer, respectively. The parameter values for each classifier revealed distinct biophysicochemical properties for tumor-associated 4-mers within each cancer type. We propose that such motifs might be used to develop novel immune-based cancer screening assays. SIGNIFICANCE: This study presents a novel computational approach to identify T-cell repertoire differences between normal and tumor tissue.See related commentary by Zoete and Coukos, p. 1299.

VDJServer: A Cloud-Based Analysis Portal and Data Commons for Immune Repertoire Sequences and Rearrangements.

Background: Recent technological advances in immune repertoire sequencing have created tremendous potential for advancing our understanding of adaptive immune response dynamics in various states of health and disease. Immune repertoire sequencing produces large, highly complex data sets, however, which require specialized methods and software tools for their effective analysis and interpretation. Results: VDJServer is a cloud-based analysis portal for immune repertoire sequence data that provide access to a suite of tools for a complete analysis workflow, including modules for preprocessing and quality control of sequence reads, V(D)J gene segment assignment, repertoire characterization, and repertoire comparison. VDJServer also provides sophisticated visualizations for exploratory analysis. It is accessible through a standard web browser via a graphical user interface designed for use by immunologists, clinicians, and bioinformatics researchers. VDJServer provides a data commons for public sharing of repertoire sequencing data, as well as private sharing of data between users. We describe the main functionality and architecture of VDJServer and demonstrate its capabilities with use cases from cancer immunology and autoimmunity. Conclusion: VDJServer provides a complete analysis suite for human and mouse T-cell and B-cell receptor repertoire sequencing data. The combination of its user-friendly interface and high-performance computing allows large immune repertoire sequencing projects to be analyzed with no programming or software installation required. VDJServer is a web-accessible cloud platform that provides access through a graphical user interface to a data management infrastructure, a collection of analysis tools covering all steps in an analysis, and an infrastructure for sharing data along with workflows, results, and computational provenance. VDJServer is a free, publicly available, and open-source licensed resource.

iReceptor: A platform for querying and analyzing antibody/B-cell and T-cell receptor repertoire data across federated repositories.

Next-generation sequencing allows the characterization of the adaptive immune receptor repertoire (AIRR) in exquisite detail. These large-scale AIRR-seq data sets have rapidly become critical to vaccine development, understanding the immune response in autoimmune and infectious disease, and monitoring novel therapeutics against cancer. However, at present there is no easy way to compare these AIRR-seq data sets across studies and institutions. The ability to combine and compare information for different disease conditions will greatly enhance the value of AIRR-seq data for improving biomedical research and patient care. The iReceptor Data Integration Platform (gateway.ireceptor.org) provides one implementation of the AIRR Data Commons envisioned by the AIRR Community (airr-community.org), an initiative that is developing protocols to facilitate sharing and comparing AIRR-seq data. The iReceptor Scientific Gateway links distributed (federated) AIRR-seq repositories, allowing sequence searches or metadata queries across multiple studies at multiple institutions, returning sets of sequences fulfilling specific criteria. We present a review of the development of iReceptor, and how it fits in with the general trend toward sharing genomic and health data, and the development of standards for describing and reporting AIRR-seq data. Researchers interested in integrating their repositories of AIRR-seq data into the iReceptor Platform are invited to contact support@ireceptor.org.

Morphine Promotes Colonization of Anastomotic Tissues with Collagenase - Producing Enterococcus faecalis and Causes Leak.

BACKGROUND: Despite ever more powerful antibiotics, newer surgical techniques, and enhanced recovery programs, anastomotic leaks remain a clear and present danger to patients. Previous work from our laboratory suggests that anastomotic leakage may be caused by Enterococcus faecalis strains that express a high collagenase phenotype (i.e., collagenolytic). Yet the mechanisms by which the practice of surgery shifts or selects for collagenolytic phenotypes to colonize anastomotic tissues remain unknown. METHODS: Here, we hypothesized that morphine, an analgesic agent universally used in gastrointestinal surgery, promotes tissue colonization with collagenolytic E. faecalis and causes anastomotic leak. To test this, rats were administered morphine in a chronic release form as would occur during routine surgery or vehicle. Rats were observed for 6 days and then underwent exploratory laparotomy for anastomotic inspection and tissue harvest for microbial analysis. These results provide further rationale to enhanced recovery after surgery (i.e., ERAS) programs that suggest limiting or avoiding the use of opioids in gastrointestinal surgery. RESULTS: Results demonstrated that compared to placebo-treated rats, morphine-treated rats demonstrated markedly impaired anastomotic healing and gross leaks that correlated with the presence of high collagenase-producing E. faecalis adherent to anastomotic tissues. To determine the direct role of morphine on this response, various isolates of E. faecalis from the rats were exposed to morphine and their collagenase activity and adherence capacity determined in vitro. Morphine increased both the adhesiveness and collagenase production of four strains of E. faecalis harvested from anastomotic tissues, two that were low collagenase producers at baseline, and two that were high collagenase producers at baseline. CONCLUSION: These results provide further rationale to enhanced recovery after surgery (i.e., ERAS) programs that suggest limiting or avoiding the use of opioids in gastrointestinal surgery.

Pseudomonas aeruginosa wound infection involves activation of its iron acquisition system in response to fascial contact.

BACKGROUND: Wound infections are traditionally thought to occur when microbial burden exceeds the innate clearance capacity of host immune system. Here, we introduce the idea that the wound environment itself plays a significant contributory role to wound infection. METHODS: We developed a clinically relevant murine model of soft tissue infection to explore the role of activation of microbial virulence in response to tissue factors as a mechanism by which pathogenic bacteria cause wound infections. Mice underwent abdominal skin incision and light muscle injury with a crushing forceps versus skin incision alone followed by topical inoculation of Pseudomonas aeruginosa. Mice were sacrificed on postoperative Day 6, and abdominal tissues were analyzed for clinical signs of wound infection. To determine if specific wound tissue components induce bacterial virulence, P. aeruginosa was exposed to the skin, fascia, and muscle. RESULTS: Gross wound infection caused by P. aeruginosa was observed to be significantly increased in injured tissues versus noninjured (80% vs.10%) tissues (n = 20 per group, p < 0.0001). Exposure of P. aeruginosa to individual tissue components demonstrated that fascia significantly induced bacterial virulence as judged by the production of pyocyanin, a redox-active phenazine compound known to kill immune cells. Whole-genome transcriptional profiling of P. aeruginosa exposed to the fascia demonstrated activation of multiple genes responsible for the synthesis of the iron scavenging molecule pyochelin. CONCLUSION: We conclude that wound elements, in particular fascia, may play a significant role in enhancing the virulence of P. aeruginosa and may contribute to the pathogenesis of clinical wound infection.

Towards anatomic scale agent-based modeling with a massively parallel spatially explicit general-purpose model of enteric tissue (SEGMEnT_HPC).

Perhaps the greatest challenge currently facing the biomedical research community is the ability to integrate highly detailed cellular and molecular mechanisms to represent clinical disease states as a pathway to engineer effective therapeutics. This is particularly evident in the representation of organ-level pathophysiology in terms of abnormal tissue structure, which, through histology, remains a mainstay in disease diagnosis and staging. As such, being able to generate anatomic scale simulations is a highly desirable goal. While computational limitations have previously constrained the size and scope of multi-scale computational models, advances in the capacity and availability of high-performance computing (HPC) resources have greatly expanded the ability of computational models of biological systems to achieve anatomic, clinically relevant scale. Diseases of the intestinal tract are exemplary examples of pathophysiological processes that manifest at multiple scales of spatial resolution, with structural abnormalities present at the microscopic, macroscopic and organ-levels. In this paper, we describe a novel, massively parallel computational model of the gut, the Spatially Explicitly General-purpose Model of Enteric Tissue_HPC (SEGMEnT_HPC), which extends an existing model of the gut epithelium, SEGMEnT, in order to create cell-for-cell anatomic scale simulations. We present an example implementation of SEGMEnT_HPC that simulates the pathogenesis of ileal pouchitis, and important clinical entity that affects patients following remedial surgery for ulcerative colitis.

Intestinal anastomotic injury alters spatially defined microbiome composition and function.

BACKGROUND: When diseased intestine (i.e., from colon cancer, diverticulitis) requires resection, its reconnection (termed anastomosis) can be complicated by non-healing of the newly joined intestine resulting in spillage of intestinal contents into the abdominal cavity (termed anastomotic leakage). While it is suspected that the intestinal microbiota have the capacity to both accelerate and complicate anastomotic healing, the associated genotypes and functions have not been characterized. RESULTS: Using 16S rRNA amplicon sequencing of samples collected on the day of surgery (postoperative day 0 (POD0)) and the 6th day following surgery (postoperative day 0 (POD6)), we analyzed the changes in luminal versus tissue-associated microbiota at anastomotic sites created in the colon of rats. Results indicated that anastomotic injury induced significant changes in the anastomotic tissue-associated microbiota with minimal differences in the luminal microbiota. The most striking difference was a 500-fold and 200-fold increase in the relative abundance of Enterococcus and Escherichia/Shigella, respectively. Functional profiling predicted the predominance of bacterial virulence-associated pathways in post-anastomotic tissues, including production of hemolysin, cytolethal toxins, fimbriae, invasins, cytotoxic necrotizing factors, and coccolysin. CONCLUSION: Taken together, our results suggest that compositional and functional changes accompany anastomotic tissues and may potentially accelerate or complicate anastomotic healing.

Investigation of inflammation and tissue patterning in the gut using a Spatially Explicit General-purpose Model of Enteric Tissue (SEGMEnT).

The mucosa of the intestinal tract represents a finely tuned system where tissue structure strongly influences, and is turn influenced by, its function as both an absorptive surface and a defensive barrier. Mucosal architecture and histology plays a key role in the diagnosis, characterization and pathophysiology of a host of gastrointestinal diseases. Inflammation is a significant factor in the pathogenesis in many gastrointestinal diseases, and is perhaps the most clinically significant control factor governing the maintenance of the mucosal architecture by morphogenic pathways. We propose that appropriate characterization of the role of inflammation as a controller of enteric mucosal tissue patterning requires understanding the underlying cellular and molecular dynamics that determine the epithelial crypt-villus architecture across a range of conditions from health to disease. Towards this end we have developed the Spatially Explicit General-purpose Model of Enteric Tissue (SEGMEnT) to dynamically represent existing knowledge of the behavior of enteric epithelial tissue as influenced by inflammation with the ability to generate a variety of pathophysiological processes within a common platform and from a common knowledge base. In addition to reproducing healthy ileal mucosal dynamics as well as a series of morphogen knock-out/inhibition experiments, SEGMEnT provides insight into a range of clinically relevant cellular-molecular mechanisms, such as a putative role for Phosphotase and tensin homolog/phosphoinositide 3-kinase (PTEN/PI3K) as a key point of crosstalk between inflammation and morphogenesis, the protective role of enterocyte sloughing in enteric ischemia-reperfusion and chronic low level inflammation as a driver for colonic metaplasia. These results suggest that SEGMEnT can serve as an integrating platform for the study of inflammation in gastrointestinal disease.

Integration of TGF-ß- and EGFR-based signaling pathways using an agent-based model of epithelial restitution.

Damage to an epithelial surface disrupts its mechanical and immunologic barrier function and exposes underlying tissues to a potentially hostile external environment. Epithelial restitution occurs quickly to reestablish the barrier and comprises a major part of the immediate host response to injured tissue. Pathways involving transforming growth factor beta and activation of epidermal growth factor receptor are both of critical importance, although cross-pathway interactions have been poorly characterized. Agent-based modeling has been showed to be useful in integrating disparate bodies of knowledge and showing the dynamic consequences of pathway structures and cellular population behavior and is used herein to create an in silico analog of an in vitro scratch assay. The In Vitro Scratch Agent-Based Model consists of agents representing individual epithelial cells in a simulated extracellular matrix. Agents sense signals from the damaged environment and produce effector molecules, leading to their healing behavior. The In Vitro Scratch Agent-Based Model qualitatively matched wound healing dynamics when compared against data from traditional experiments. Putative cross-talk mechanisms were then instantiated into the In Vitro Scratch Agent-Based Model and their relative plausibility examined, suggesting interaction at the receptor tyrosine kinase level. This highlights the utility of dynamic knowledge representation in the integration of pathways previously studied in separate contexts.

Addressing the translational dilemma: dynamic knowledge representation of inflammation using agent-based modeling.

Given the panoply of system-level diseases that result from disordered inflammation, such as sepsis, atherosclerosis, cancer, and autoimmune disorders, understanding and characterizing the inflammatory response is a key target of biomedical research. Untangling the complex behavioral configurations associated with a process as ubiquitous as inflammation represents a prototype of the translational dilemma: the ability to translate mechanistic knowledge into effective therapeutics. A critical failure point in the current research environment is a throughput bottleneck at the level of evaluating hypotheses of mechanistic causality; these hypotheses represent the key step toward the application of knowledge for therapy development and design. Addressing the translational dilemma will require utilizing the ever-increasing power of computers and computational modeling to increase the efficiency of the scientific method in the identification and evaluation of hypotheses of mechanistic causality. More specifically, development needs to focus on facilitating the ability of non-computer trained biomedical researchers to utilize and instantiate their knowledge in dynamic computational models. This is termed "dynamic knowledge representation." Agent-based modeling is an object-oriented, discrete-event, rule-based simulation method that is well suited for biomedical dynamic knowledge representation. Agent-based modeling has been used in the study of inflammation at multiple scales. The ability of agent-based modeling to encompass multiple scales of biological process as well as spatial considerations, coupled with an intuitive modeling paradigm, suggest that this modeling framework is well suited for addressing the translational dilemma. This review describes agent-based modeling, gives examples of its applications in the study of inflammation, and introduces a proposed general expansion of the use of modeling and simulation to augment the generation and evaluation of knowledge by the biomedical research community at large.

Immature oxidative stress management as a unifying principle in the pathogenesis of necrotizing enterocolitis: insights from an agent-based model.

BACKGROUND: Necrotizing enterocolitis (NEC) is a complex disease involving prematurity, enteral feeding, and bacterial effects. We propose that the underlying initial condition in its pathogenesis is reduced ability of the neonatal gut epithelial cells (NGECs) to clear oxidative stress (OS), and that when such a NGEC population is exposed to enteral feeding, the increased metabolic OS tips the population toward apoptosis, inflammation, bacterial activation, and eventual necrosis. The multi-factorial complexity of NEC requires characterization with computational modeling, and herein, we used an agent-based model (ABM) to instantiate and examine our unifying hypothesis of the pathogenesis of NEC. METHODS: An ABM of the neonatal gut was created with NGEC computational agents incorporating rules for pathways for OS, p53, tight junctions, Toll-like receptor (TLR)-4, nitric oxide, and nuclear factor-kappa beta (NF-κB). The modeled bacteria activated TLR-4 on contact with NGECs. Simulations included parameter sweeps of OS response, response to feeding, addition of bacteria, and alterations in gut mucus production. RESULTS: The ABM reproduced baseline cellular respiration and clearance of OS. Reduction in OS clearance consistent with clinical NEC led to senescence, apoptosis, or inflammation, with disruption of tight junctions, but rarely to NGEC necrosis. An additional "hit" of bacteria activating TLR-4 potentiated a shift to NGEC necrosis across the entire population. The mucus layer was modeled to limit bacterial-NGEC interactions and reduce this effect, but concomitant apoptosis in the goblet cell population reduced the efficacy of the mucus layer and limited its protective effect in simulated experiments. This finding suggests a means by which increased apoptosis at the cellular population level can lead to a transition to the necrosis outcome. CONCLUSIONS: Our ABM incorporates known components of NEC and demonstrates that impaired OS management can lead to apoptosis and inflammation of NGECs, rendering the system susceptible to an additional insult involving regionalized mucus barrier failure and TLR-4 activation, which potentiates the necrosis outcome. This type of integrative dynamic knowledge representation can be a useful adjunct to help guide and contextualize research.

Patterns of mesenchymal condensation in a multiscale, discrete stochastic model.

Cells of the embryonic vertebrate limb in high-density culture undergo chondrogenic pattern formation, which results in the production of regularly spaced "islands" of cartilage similar to the cartilage primordia of the developing limb skeleton. The first step in this process, in vitro and in vivo, is the generation of "cell condensations," in which the precartilage cells become more tightly packed at the sites at which cartilage will form. In this paper we describe a discrete, stochastic model for the behavior of limb bud precartilage mesenchymal cells in vitro. The model uses a biologically motivated reaction-diffusion process and cell-matrix adhesion (haptotaxis) as the bases of chondrogenic pattern formation, whereby the biochemically distinct condensing cells, as well as the size, number, and arrangement of the multicellular condensations, are generated in a self-organizing fashion. Improving on an earlier lattice-gas representation of the same process, it is multiscale (i.e., cell and molecular dynamics occur on distinct scales), and the cells are represented as spatially extended objects that can change their shape. The authors calibrate the model using experimental data and study sensitivity to changes in key parameters. The simulations have disclosed two distinct dynamic regimes for pattern self-organization involving transient or stationary inductive patterns of morphogens. The authors discuss these modes of pattern formation in relation to available experimental evidence for the in vitro system, as well as their implications for understanding limb skeletal patterning during embryonic development.