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Carrero J.A.,University of Washington | Calderon B.,University of Washington | Towfic F.,Immuneering Corporation | Artyomov M.N.,University of Washington | Unanue E.R.,University of Washington
PLoS ONE | Year: 2013

Our ability to successfully intervene in disease processes is dependent on definitive diagnosis. In the case of autoimmune disease, this is particularly challenging because progression of disease is lengthy and multifactorial. Here we show the first chronological compendium of transcriptional and cellular signatures of diabetes in the non-obese diabetic mouse. Our data relates the immunological environment of the islets of Langerhans with the transcriptional profile at discrete times. Based on these data, we have parsed diabetes into several discrete phases. First, there is a type I interferon signature that precedes T cell activation. Second, there is synchronous infiltration of all immunological cellular subsets and a period of control. Finally, there is the killing phase of the diabetogenic process that is correlated with an NF-kB signature. Our data provides a framework for future examination of autoimmune diabetes and its disease progression markers. © 2013 Carrero et al. Source


Dong J.,U.S. National Institutes of Health | Munoz A.,Johns Hopkins University | Munoz A.,U.S. National Institutes of Health | Kolitz S.E.,Johns Hopkins University | And 8 more authors.
Genes and Development | Year: 2014

Eukaryotic initiator tRNA (tRNAi) contains several highly conserved unique sequence features, but their importance in accurate start codon selection was unknown. Here we show that conserved bases throughout tRNAi, from the anticodon stem to acceptor stem, play key roles in ensuring the fidelity of start codon recognition in yeast cells. Substituting the conserved G31:C39 base pair in the anticodon stem with different pairs reduces accuracy (the Sui- [suppressor of initiation codon] phenotype), whereas eliminating base pairing increases accuracy (the Ssu- [suppressor of Sui-] phenotype). The latter defect is fully suppressed by a Sui- substitution of T-loop residue A54. These genetic data are paralleled by opposing effects of Sui- and Ssu- substitutions on the stability of methionylated tRNAi (Met-tRNAi) binding (in the ternary complex [TC] with eIF2-GTP) to reconstituted preinitiation complexes (PICs). Disrupting the C3:G70 base pair in the acceptor stem produces a Sui- phenotype and also reduces the rate of TC binding to 40S subunits in vitro and in vivo. Both defects are suppressed by an Ssu- substitution in eIF1A that stabilizes the open/POUT conformation of the PIC that exists prior to start codon recognition. Our data indicate that these signature sequences of tRNAi regulate accuracy by distinct mechanisms, promoting the open/POUT conformation of the PIC (for C3:G70) or destabilizing the closed/PIN state (for G31:C39 and A54) that is critical for start codon recognition. © 2014 Dong et al. Source


Liu L.,Bryn Mawr College | Tao J.,The College of New Jersey | Yang Z.,Bryn Mawr College | Towfic F.,Immuneering Corporation
Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015 | Year: 2015

Diseases that have different underlying genetic risk component(s) may share similar phenotypes. Traditionally, disease classifications have focused on characterizing diseases based on sets of related phenotypes. As an example, type I diabetes and type II diabetes are both classified as a type of diabetes based on patients having high blood sugar over long periods of time. However, as our understanding of genetic contributions to disease susceptibility and progression evolves, we start noticing that diseases with similar symptoms may have completely different causes. For example, type II diabetes is due to insulin resistance while type I diabetes is caused by immune cells attacking insulin producing cells. As genetic data becomes more highly available, it becomes possible to classify diseases based on their genetic causative drivers instead of their phenotypes. In this study, we have (1) explored the relationship between 10 autoimmune diseases along with type II diabetes based on their genetic susceptibility information and compared such classifications to existing disease categorizations based on disease symptoms/phenotypes from Human Phenotype Ontology, NCI-thesaurus and the Disease Ontology, and (2) developed automated scripts to compute similarities and cluster diseases based on the specified criteria. Categorization based on genetic susceptibility can help identify diseases that share similar drug targets and benefit from similar diagnosis technologies. We hope to further develop our system to apply it to more disease categories. © 2015 IEEE. Source


Methods for analyzing one or more elements, markers, ligands or other characteristics of the immune system of a body (e.g., of a human or other animal), and based on the analysis, determine a location of disease, identify immune system failure, and/or determine treatments based on disease location or immune system failure. All or a part of the immune system process may be analyzed to identify specific features of the immune system, e.g., which may indicate that the disease will evade the immune system and produce a negative outcome. Therapy may be employed to correct immune system failure rather than addressing the disease directly.


The disclosure relates to methods of predicting the effects of therapy, designing/conducting a clinical trial, selecting a subject for a clinical trial, selecting a subject for therapy, monitoring a subjects responsiveness to therapy, treating a subject, and predicting effects of anti-CD25 therapy.

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