系統樹の比較によるタンパク質相互作用パートナー予測法の開発

Tohoku University木下賢

Identifying plasma proteomic signatures from health to heart failure, across the ejection fraction spectrum

Circulating proteins may provide insights into the varying biological mechanisms involved in heart failure (HF) with preserved ejection fraction (HFpEF) and reduced ejection fraction (HFrEF). We aimed to identify specific proteomic patterns for HF, by comparing proteomic profiles across the ejection fraction spectrum. We investigated 4210 circulating proteins in 739 patients with normal (Stage A/Healthy) or elevated (Stage B) filling pressures, HFpEF, or ischemic HFrEF (iHFrEF). We found 2122 differentially expressed proteins between iHFrEF-Stage A/Healthy, 1462 between iHFrEF-HFpEF and 52 between HFpEF-Stage A/Healthy. Of these 52 proteins, 50 were also found in iHFrEF vs. Stage A/Healthy, leaving SLITRK6 and NELL2 expressed in lower levels only in HFpEF. Moreover, 108 proteins, linked to regulation of cell fate commitment, differed only between iHFrEF-HFpEF. Proteomics across the HF spectrum reveals overlap in differentially expressed proteins compared to stage A/Healthy. Multiple proteins are unique for distinguishing iHFrEF from HFpEF, supporting the capacity of proteomics to discern between these conditions.</p

Identifying plasma proteomic signatures from health to heart failure, across the ejection fraction spectrum

Circulating proteins may provide insights into the varying biological mechanisms involved in heart failure (HF) with preserved ejection fraction (HFpEF) and reduced ejection fraction (HFrEF). We aimed to identify specific proteomic patterns for HF, by comparing proteomic profiles across the ejection fraction spectrum. We investigated 4210 circulating proteins in 739 patients with normal (Stage A/Healthy) or elevated (Stage B) filling pressures, HFpEF, or ischemic HFrEF (iHFrEF). We found 2122 differentially expressed proteins between iHFrEF-Stage A/Healthy, 1462 between iHFrEF-HFpEF and 52 between HFpEF-Stage A/Healthy. Of these 52 proteins, 50 were also found in iHFrEF vs. Stage A/Healthy, leaving SLITRK6 and NELL2 expressed in lower levels only in HFpEF. Moreover, 108 proteins, linked to regulation of cell fate commitment, differed only between iHFrEF-HFpEF. Proteomics across the HF spectrum reveals overlap in differentially expressed proteins compared to stage A/Healthy. Multiple proteins are unique for distinguishing iHFrEF from HFpEF, supporting the capacity of proteomics to discern between these conditions

Models and Algorithms in Biological Network Evolution with Modularity

Networks are commonly used to represent key processes in biology; examples include transcriptional regulatory networks, protein-protein interaction (PPI) networks, metabolic networks, etc. Databases store many such networks, as graphs, observed or inferred. Generative models for these networks have been proposed. For PPI networks, current models are based on duplication and divergence (D&D): a node (gene) is duplicated and inherits some subset of the connections of the original node. An early finding about biological networks is modularity: a higher-level structure is prevalent consisting of well connected subgraphs with less substantial connectivity to other such subgraphs. While D&D models spontaneously generate modular structures, neither have these structures been compared with those in the databases nor are D&D models known to maintain and evolve them. Given that the preferred generative models being based on D&D, the network inference models are also based on the same principle. We describe NEMo (Network Evolution with Modularity), a new model that embodies modularity. It consists of two layers: the lower layer is a derivation of the D&D process thus node-and-edge based, while the upper layer is module-aware. NEMo allows modules to appear and disappear, to fission and to merge, all driven by the underlying edge-level events using a duplication-based process. We also introduce measures to compare biological networks in terms of their modular structure. We present an extensive study of six model organisms across six public databases aimed at uncovering commonalities in network structure. We then use these commonalities as reference against which to compare the networks generated by D&D models and by our module-aware model NEMo. We find that, by restricting our data to high-confidence interactions, a number of shared structural features can be identified among the six species and six databases. When comparing these characteristics with those extracted from the networks produced by D&D models and our NEMo model, we further find that the networks generated by NEMo exhibit structural characteristics much closer to those of the PPI networks of the model organisms. We conclude that modularity in PPI networks takes a particular form, one that is better approximated by the module-aware NEMo model than by other current models. Finally, we draft the ideas for a module-aware network inference model that uses an altered form of our module-aware NEMo as the core component, from a parsimony perspective

Diseño y desarrollo de una plataforma bioinformática para la integración, gestión y visualización de redes de interacción de proteínas e interactomas

[ES] El trabajo de investigación que se expone en esta tesis doctoral se centra en el ámbito de las interacciones entre proteínas y la definición global de los conjuntos de interacciones presentes en cada organismo, o interactomas, en forma de redes biomoleculares. Partiendo de las diferentes bases de datos públicas sobre interacciones entre proteínas se construye un sistema de integración de dichas interacciones y se generan interactomas con diferentes niveles de calidad en función del soporte experimental de las interacciones que contienen. Toda esta información se pone a disposición de la comunidad científica a través de una aplicación diseñada a tal efecto que, entre otras cosas, posibilita la visualización y anotación funcional de las redes de interacción generadas por el propio investigador. Dicha aplicación se ha denominado APID Interactomes y está libremente accesible en la URL http://apid.dep.usal.es

STING at the nuclear envelope: novel partners contribute to innate immune responses

The innate immune response (IIR) is the first line of defence against pathogen infection and relies on the recognition of pathogen associated molecules by host cell sensors. STING (STimulator of INterferon Genes) is the essential adaptor protein in IIRs triggered by the recognition of cytoplasmic double-stranded DNA, a potent signal of pathogen infection or host cell DNA damage. Most STING resides in the endoplasmic reticulum and propagates IIR signalling cascades upon binding the second messenger, cGAMP, produced by the upstream cytosolic DNA sensor, cGAS. However, STING was initially identified as a nuclear envelope transmembrane protein and yet the function of STING within the nuclear envelope has been relatively understudied. Therefore, in this study I sought to investigate STING localisation and functions within the nuclear envelope. The nuclear envelope is a double membrane system comprising inner and outer nuclear membranes and, in this thesis, I present work showing for the first time that STING is present in both the inner and outer membranes by immunogold electron microscopy. Moreover, live-cell microscopy of GFP-tagged STING reveals that it increases mobility and redistributes to the outer nuclear membrane upon IIR stimulation by transfected dsDNA or the dsRNA mimic poly(I:C). Previously, immunoprecipitation of STING from isolated nuclear envelopes coupled with mass spectrometry identified a nuclear envelope-STING proteome consisting of known nuclear membrane proteins and enriched in DNA- and RNA-binding proteins. Seventeen of these nuclear envelope STING partners are known to bind direct interactors of the immune transcription factors, IRF3/7, and so it was hypothesised that these proteins could contribute to IIR through STING at the nuclear envelope. Therefore, I interrogated a subset of these for a role in IIR, finding that STING partners SYNCRIP, MEN1, DDX5, SNRNP70, RPS27A, and AATF are novel modulators of dsDNA triggered IIR. Moreover, through siRNA-mediated knockdown and CRISPR/Cas9 gene editing I found that SYNCRIP is a novel antagonist of the RNA virus, influenza A virus, potentially shedding light on reports of STING-mediated inhibition of RNA viruses. Thus, the work presented in this thesis expands our knowledge of STING’s role in IIR and adds to a growing literature which shows that STING’s functions are more extensive than its role in the cytoplasmic DNA sensing pathway