RESUMO
HIV Dependency Factors (HDFs) are a class of human proteins that are essential for HIV replication, but are not lethal to the host cell when silenced. Three previous genome-wide RNAi experiments identified HDF sets with little overlap. We combine data from these three studies with a human protein interaction network to predict new HDFs, using an intuitive algorithm called SinkSource and four other algorithms published in the literature. Our algorithm achieves high precision and recall upon cross validation, as do the other methods. A number of HDFs that we predict are known to interact with HIV proteins. They belong to multiple protein complexes and biological processes that are known to be manipulated by HIV. We also demonstrate that many predicted HDF genes show significantly different programs of expression in early response to SIV infection in two non-human primate species that differ in AIDS progression. Our results suggest that many HDFs are yet to be discovered and that they have potential value as prognostic markers to determine pathological outcome and the likelihood of AIDS development. More generally, if multiple genome-wide gene-level studies have been performed at independent labs to study the same biological system or phenomenon, our methodology is applicable to interpret these studies simultaneously in the context of molecular interaction networks and to ask if they reinforce or contradict each other.
Assuntos
Bases de Dados de Proteínas , Infecções por HIV/metabolismo , Interações Hospedeiro-Patógeno/fisiologia , Modelos Estatísticos , Mapeamento de Interação de Proteínas/métodos , Proteínas/química , Algoritmos , Animais , Chlorocebus aethiops , Análise por Conglomerados , Progressão da Doença , HIV/fisiologia , Proteínas do Vírus da Imunodeficiência Humana/química , Proteínas do Vírus da Imunodeficiência Humana/metabolismo , Humanos , Macaca nemestrina , Proteínas/metabolismo , Proteômica , Interferência de RNA , Reprodutibilidade dos Testes , Síndrome de Imunodeficiência Adquirida dos Símios/metabolismo , Vírus da Imunodeficiência Símia/fisiologia , Replicação ViralRESUMO
HYD1 is an all D-amino acid linear 10-mer peptide that was discovered by one-bead-one-compound screening. HYD1 has five hydrophobic amino acids flanked by polar amino acids. Alanine scanning studies showed that alternating hydrophobic amino acid residues and N- and C-terminal lysine side chains were contributors to the biological activity of the linear 10-mer analogs. This observation led us to hypothesize that display of the hydrophobic pentapeptide sequence of HYD1 in a cyclic beta-hairpin-like scaffold could lead to better bioavailability and biological activity. An amphipathic pentapeptide sequence was used to form an antiparallel strand and those strands were linked via dipeptide-like sequences selected to promote ß-turns. Early cyclic analogs were more active but otherwise mimicked the biological activity of the linear HYD1 peptide. The cyclic peptidomimetics were synthesized using standard Fmoc solid phase synthesis to form linear peptides, followed by solution phase or on-resin cyclization. SAR studies were carried out with an aim to increase the potency of these drug candidates for the killing of multiple myeloma cells in vitro. The solution structures of 1, 5, and 10 were elucidated using NMR spectroscopy. 1H NMR and 2D TOCSY studies of these peptides revealed a downfield Hα proton chemical shift and 2D NOE spectral analysis consistent with a ß-hairpin-like structure.
RESUMO
Perineural spread (PNS) of tumor is a recognized pattern of metastasis occurring in the head and neck. Imaging plays a critical role in identifying PNS for adequate staging and treatment planning. Understanding the major branches and pathways of cranial nerves V and VII, key anatomic landmarks, interconnections between these nerves, and pearls and pitfalls of PNS imaging can aid in early detection, appropriate therapy, and the best possible chance for cure.
Assuntos
Neoplasias dos Nervos Cranianos/patologia , Doenças do Nervo Facial/patologia , Neoplasias de Cabeça e Pescoço/patologia , Imageamento por Ressonância Magnética/métodos , Base do Crânio/patologia , Doenças do Nervo Trigêmeo/patologia , Neoplasias dos Nervos Cranianos/diagnóstico por imagem , Diagnóstico Diferencial , Nervo Facial , Doenças do Nervo Facial/diagnóstico por imagem , Neoplasias de Cabeça e Pescoço/diagnóstico por imagem , Humanos , Invasividade Neoplásica/diagnóstico por imagem , Invasividade Neoplásica/patologia , Neuroimagem/métodos , Base do Crânio/diagnóstico por imagem , Nervo Trigêmeo/diagnóstico por imagem , Nervo Trigêmeo/patologiaRESUMO
Analysis of molecular interaction networks is pervasive in systems biology. This research relies almost entirely on graphs for modeling interactions. However, edges in graphs cannot represent multiway interactions among molecules, which occur very often within cells. Hypergraphs may be better representations for networks having such interactions, since hyperedges can naturally represent relationships among multiple molecules. Here, we propose using hypergraphs to capture the uncertainty inherent in reverse engineering gene-gene networks. Some subsets of nodes may induce highly varying subgraphs across an ensemble of networks inferred by a reverse engineering algorithm. We provide a novel formulation of hyperedges to capture this uncertainty in network topology. We propose a clustering-based approach to discover hyperedges. We show that our approach can recover hyperedges planted in synthetic data sets with high precision and recall, even for moderate amount of noise. We apply our techniques to a data set of pathways inferred from genetic interaction data in S. cerevisiae related to the unfolded protein response. Our approach discovers several hyperedges that capture the uncertain connectivity of genes in relevant protein complexes, suggesting that further experiments may be required to precisely discern their interaction patterns. We also show that these complexes are not discovered by an algorithm that computes frequent and dense subgraphs.
Assuntos
Algoritmos , Modelos Biológicos , Mapeamento de Interação de Proteínas/métodos , Proteínas de Saccharomyces cerevisiae/metabolismo , Saccharomyces cerevisiae/metabolismo , Transdução de Sinais/fisiologia , Simulação por ComputadorAssuntos
Congressos como Assunto/organização & administração , Erros de Diagnóstico/prevenção & controle , Educação Médica Continuada/organização & administração , Serviço Hospitalar de Radiologia/normas , Radiologia/educação , Tomografia Computadorizada por Raios X , Humanos , Objetivos Organizacionais , Melhoria de Qualidade , Estados UnidosRESUMO
OBJECTIVE: To determine sensitivity and specificity of the physical examination (PE) for identifying synovitis in the knee and ankle joints of children with juvenile idiopathic arthritis (JIA), and to identify cases in which ultrasound (US) screening augments the PE. METHODS: Nineteen patients with JIA were referred for US. Both knees and ankles were examined using US with and without power Doppler. Active arthritis on PE was defined as (1) non-bony swelling or (2) limitation of motion with either pain on motion or tenderness to palpation. Active arthritis on US was defined as synovial hyperplasia, effusion, or increased vascularity on power Doppler scan. RESULTS: There was agreement between US and PE in 75% of cases. PE was 64% sensitive and 86% specific for identifying active arthritis. PE was 100% specific if (1) the patient was positive for both PE criteria or (2) if arthritis was present on PE in the knees. When the PE was negative and the US was positive, 21.4% developed active disease on PE within 6 months. In cases where the PE was positive and US was negative, the joint involved was most often the ankle and frequently the subtalar joint. CONCLUSION: PE is neither highly sensitive nor specific for identifying active synovitis when compared to US, and screening with US can identify subclinical disease. In joints with both non-bony swelling and limitation of motion with pain on motion or tenderness, and in the knee joint, little additional information is gained by US. This has implications for classification and treatment of JIA.