Immune recognition of self nucleic acids driven by endogenous antimicrobial peptides: role in autoimmunity

Innate immune recognition of extracellular host-derived self-DNA and self-RNA is prevented by endosomal seclusion of the Toll-like receptors (TLRs) in the dendritic cells (DCs). However, in psoriasis plasmacytoid dendritic cells have been found to be able to sense self-DNA molecules in complex with the endogenous cationic antimicrobial peptide LL37, which are internalized into the endosomal compartments and thus can access TLR9. We investigated whether this endogenous peptide can also interact with extracellular self-RNA and lead to DC activation. We found that LL37 binds self-RNA as well as self-DNA going into an electrostatic interaction; forms micro-aggregates of nano-scale particles protected from enzymatic degradation and transport it into the endosomal compartments of both plasmacytoid and myeloid dendritic cells. In the plasmacytoid DCs, the self-RNA-LL37 complexes activate TLR7 and like the self-DNA-LL37 complexes, trigger the production of IFN-α in the absence of induction of maturation or production of IL-6 and TNF-α. In contrast to the self-DNA-LL37 complexes, the self-RNA-LL37 complexes are also internalized into the endosomal compartments of myeloid dendritic cells and trigger activation through TLR8, leading to the production of TNF-α and IL-6, and the maturation of the myeloid DCs. Furthermore, we found that these self nucleic acid-LL37 complexes can be found in vivo in the skin lesions of the cutaneous autoimmune disease psoriasis, where they are associated with mature mDCs in situ. On the other hand, in the systemic autoimmune disease systemic lupus erythematosus, self-DNA-LL37 complexes were found to be a constituent of the circulating immune complexes isolated from patient sera. This interaction between the endogenous peptide with the self nucleic acid molecules present in the immune complexes was found to be electrostatic and it confers resistance to enzymatic degradation of the nucleic acid molecules in the immune complexes. Moreover, autoantibodies to these endogenous peptides were found to trigger neutrophil activation and release of neutrophil extracellular traps composed of DNA, which are potential sources of the self nucleic acid-LL37 complexes present in SLE immune complexes. Our results demonstrate that the cationic antimicrobial peptide LL37 drives the innate immune recognition of self nucleic acid molecules through toll-like receptors in human dendritic cells, thus elucidating a pathway for innate sensing of host cell death. This pathway of autoreactivity was found to be pathologically relevant in human autoimmune diseases psoriasis and SLE, and thus this study provides new insights into the mechanisms autoimmune diseases.

Ontology driven integration platform for clinical and translational research

Semantic Web technologies offer a promising framework for integration of disparate biomedical data. In this paper we present the semantic information integration platform under development at the Center for Clinical and Translational Sciences (CCTS) at the University of Texas Health Science Center at Houston (UTHSC-H) as part of our Clinical and Translational Science Award (CTSA) program. We utilize the Semantic Web technologies not only for integrating, repurposing and classification of multi-source clinical data, but also to construct a distributed environment for information sharing, and collaboration online. Service Oriented Architecture (SOA) is used to modularize and distribute reusable services in a dynamic and distributed environment. Components of the semantic solution and its overall architecture are described.

MODELING SPORADIC TUMOR FORMATION DRIVEN BY TELOMERE DYSFUNCTION IN THE GASTROINTESTINAL TRACT

Colorectal cancer is a complex disease that is thought to arise when cells accumulate mutations that allow for uncontrolled growth. There are several recognized mechanisms for generating such mutations in sporadic colon cancer; one of which is chromosomal instability (CIN). One hypothesized driver of CIN in cancer is the improper repair of dysfunctional telomeres. Telomeres comprise the linear ends of chromosomes and play a dual role in cancer. Its length is maintained by the ribonucleoprotein, telomerase, which is not a normally expressed in somatic cells and as cells divide, telomeres continuously shorten. Critically shortened telomeres are considered dysfunctional as they are recognized as sites of DNA damage and cells respond by entering into replicative senescence or apoptosis, a process that is p53-dependent and the mechanism for telomere-induced tumor suppression. Loss of this checkpoint and improper repair of dysfunctional telomeres can initiate a cycle of fusion, bridge and breakage that can lead to chromosomal changes and genomic instability, a process that can lead to transformation of normal cells to cancer cells. Mouse models of telomere dysfunction are currently based on knocking out the telomerase protein or RNA component; however, the naturally long telomeres of mice require multiple generational crosses of telomerase null mice to achieve critically short telomeres. Shelterin is a complex of six core proteins that bind to telomeres specifically. Pot1a is a highly conserved member of this complex that specifically binds to the telomeric single-stranded 3’ G-rich overhang. Previous work in our lab has shown that Pot1a is essential for chromosomal end protection as deletion of Pot1a in murine embryonic fibroblasts (MEFs) leads to open telomere ends that initiate a DNA damage response mediated by ATR, resulting in p53-dependent cellular senescence. Loss of Pot1a in the background of p53 deficiency results in increased aberrant homologous recombination at telomeres and elevated genomic instability, which allows Pot1a-/-, p53-/- MEFs to form tumors when injected into SCID mice. These phenotypes are similar to those seen in cells with critically shortened telomeres. In this work, we created a mouse model of telomere ysfunction in the gastrointestinal tract through the conditional deletion of Pot1a that recapitulates the microscopic features seen in severe telomere attrition. Combined intestinal loss of Pot1a and p53 lead to formation of invasive adenocarcinomas in the small and large intestines. The tumors formed with long latency, low multiplicity and had complex genomes due to chromosomal instability, features similar to those seen in sporadic human colorectal cancers. Taken together, we have developed a novel mouse model of intestinal tumorigenesis based on genomic instability driven by telomere dysfunction.

Cellular dynamic simulator: an event driven molecular simulation environment for cellular physiology.

In this paper, we present the Cellular Dynamic Simulator (CDS) for simulating diffusion and chemical reactions within crowded molecular environments. CDS is based on a novel event driven algorithm specifically designed for precise calculation of the timing of collisions, reactions and other events for each individual molecule in the environment. Generic mesh based compartments allow the creation / importation of very simple or detailed cellular structures that exist in a 3D environment. Multiple levels of compartments and static obstacles can be used to create a dense environment to mimic cellular boundaries and the intracellular space. The CDS algorithm takes into account volume exclusion and molecular crowding that may impact signaling cascades in small sub-cellular compartments such as dendritic spines. With the CDS, we can simulate simple enzyme reactions; aggregation, channel transport, as well as highly complicated chemical reaction networks of both freely diffusing and membrane bound multi-protein complexes. Components of the CDS are generally defined such that the simulator can be applied to a wide range of environments in terms of scale and level of detail. Through an initialization GUI, a simple simulation environment can be created and populated within minutes yet is powerful enough to design complex 3D cellular architecture. The initialization tool allows visual confirmation of the environment construction prior to execution by the simulator. This paper describes the CDS algorithm, design implementation, and provides an overview of the types of features available and the utility of those features are highlighted in demonstrations.

Symmetric inverse consistent nonlinear registration driven by mutual information.

A nonlinear viscoelastic image registration algorithm based on the demons paradigm and incorporating inverse consistent constraint (ICC) is implemented. An inverse consistent and symmetric cost function using mutual information (MI) as a similarity measure is employed. The cost function also includes regularization of transformation and inverse consistent error (ICE). The uncertainties in balancing various terms in the cost function are avoided by alternatively minimizing the similarity measure, the regularization of the transformation, and the ICE terms. The diffeomorphism of registration for preventing folding and/or tearing in the deformation is achieved by the composition scheme. The quality of image registration is first demonstrated by constructing brain atlas from 20 adult brains (age range 30-60). It is shown that with this registration technique: (1) the Jacobian determinant is positive for all voxels and (2) the average ICE is around 0.004 voxels with a maximum value below 0.1 voxels. Further, the deformation-based segmentation on Internet Brain Segmentation Repository, a publicly available dataset, has yielded high Dice similarity index (DSI) of 94.7% for the cerebellum and 74.7% for the hippocampus, attesting to the quality of our registration method.

THE ROLE OF CYCLIN E IN PKC IOTA-DRIVEN EARLY OVARIAN TUMORIGENESIS

Among the gynecologic malignancies, epithelial ovarian tumors are the leading cause of death. For the past few decades, the only treatment has involved surgical resection of the tumor and/or general chemotherapies. In an attempt to improve treatment options, we have shown that several oncogenes that are overexpressed in ovarian cancer, PI3K, PKCiota, and cyclin E, all of which have been shown to lead to a poor prognosis and decreased survival, converge into a single pathway that could potentially be targeted therapeutically. Because of the ability of either PKCiota or cyclin E overexpression to independently induce anchorage-independent growth, a hallmark of cancer, we hypothesized that targeting PKCiota expression in ovarian cancer cells could induce a reversion of the transformed phenotype through down regulation of cyclin E. To test this hypothesis, we first established a correlation between PKCiota and cyclin E in a panel of 20 ovarian cancer cell lines. To show that PKCiota is upstream of cyclin E, PKCiota was stably knocked down using RNAi in IGROV cells (epithelial ovarian cancer cell line of serous histology). The silencing of PKCiota resulted in decreased expression of cell cycle drivers, such as cyclin D1/E and CDK2/4, and an increase in p27. These alteration in the regulators of the cell cycle resulted in a decrease in both proliferation and anchorage-independent growth, which was specifically through cyclin E, as determined by a rescue experiment. We also found that the mechanism of cyclin E regulation by PKCiota was at the level of degradation rather than transcription. Using two inhibitors to PI3K, we found that both the active form of PKCiota and total cyclin E levels decreased, implying that the PKCiota/cyclin E pathway is downstream from PI3K. In conclusion, we have identified a novel pathway in epithelial ovarian tumorigenesis (PI3K à PKCiota à Cyclin E à anchorage-independent growth), which could potentially be targeted therapeutically.

Pathway Semantics: An Algebraic Data Driven Algorithm to Generate Hypotheses about Molecular Patterns Underlying Disease Progression

The overarching goal of the Pathway Semantics Algorithm (PSA) is to improve the in silico identification of clinically useful hypotheses about molecular patterns in disease progression. By framing biomedical questions within a variety of matrix representations, PSA has the flexibility to analyze combined quantitative and qualitative data over a wide range of stratifications. The resulting hypothetical answers can then move to in vitro and in vivo verification, research assay optimization, clinical validation, and commercialization. Herein PSA is shown to generate novel hypotheses about the significant biological pathways in two disease domains: shock / trauma and hemophilia A, and validated experimentally in the latter. The PSA matrix algebra approach identified differential molecular patterns in biological networks over time and outcome that would not be easily found through direct assays, literature or database searches. In this dissertation, Chapter 1 provides a broad overview of the background and motivation for the study, followed by Chapter 2 with a literature review of relevant computational methods. Chapters 3 and 4 describe PSA for node and edge analysis respectively, and apply the method to disease progression in shock / trauma. Chapter 5 demonstrates the application of PSA to hemophilia A and the validation with experimental results. The work is summarized in Chapter 6, followed by extensive references and an Appendix with additional material.