The Physiological Molecular Shape of Spectrin: A Compact Supercoil Resembling a Chinese Finger Trap

© 2015 Brown et al. The primary, secondary, and tertiary structures of spectrin are reasonably well defined, but the structural basis for the known dramatic molecular shape change, whereby the molecular length can increase three-fold, is not understood. In this study, we combine previously reported biochemical and high-resolution crystallographic data with structural mass spectroscopy and electron microscopic data to derive a detailed, experimentally-supported quaternary structure of the spectrin heterotetramer. In addition to explaining spectrin’s physiological resting length of ~55-65 nm, our model provides a mechanism by which spectrin is able to undergo a seamless three-fold extension while remaining a linear filament, an experimentally observed property. According to the proposed model, spectrin’s quaternary structure and mechanism of extension is similar to a Chinese Finger Trap: at shorter molecular lengths spectrin is a hollow cylinder that extends by increasing the pitch of each spectrin repeat, which decreases the internal diameter. We validated our model with electron microscopy, which demonstrated that, as predicted, spectrin is hollow at its biological resting length of ~55-65 nm. The model is further supported by zero-length chemical crosslink data indicative of an approximately 90 degree bend between adjacent spectrin repeats. The domain-domain interactions in our model are entirely consistent with those present in the prototypical linear antiparallel heterotetramer as well as recently reported inter-strand chemical crosslinks. The model is consistent with all known physical properties of spectrin, and upon full extension our Chinese Finger Trap Model reduces to the ~180-200 nm molecular model currently in common use

Systematic Analysis of Pleiotropy in C. elegans Early Embryogenesis

Pleiotropy refers to the phenomenon in which a single gene controls several distinct, and seemingly unrelated, phenotypic effects. We use C. elegans early embryogenesis as a model to conduct systematic studies of pleiotropy. We analyze high-throughput RNA interference (RNAi) data from C. elegans and identify “phenotypic signatures”, which are sets of cellular defects indicative of certain biological functions. By matching phenotypic profiles to our identified signatures, we assign genes with complex phenotypic profiles to multiple functional classes. Overall, we observe that pleiotropy occurs extensively among genes involved in early embryogenesis, and a small proportion of these genes are highly pleiotropic. We hypothesize that genes involved in early embryogenesis are organized into partially overlapping functional modules, and that pleiotropic genes represent “connectors” between these modules. In support of this hypothesis, we find that highly pleiotropic genes tend to reside in central positions in protein-protein interaction networks, suggesting that pleiotropic genes act as connecting points between different protein complexes or pathways

Systematic Analysis of Pleiotropy in C. elegans Early Embryogenesis

Pleiotropy refers to the phenomenon in which a single gene controls several distinct, and seemingly unrelated, phenotypic effects. We use C. elegans early embryogenesis as a model to conduct systematic studies of pleiotropy. We analyze high-throughput RNA interference (RNAi) data from C. elegans and identify “phenotypic signatures”, which are sets of cellular defects indicative of certain biological functions. By matching phenotypic profiles to our identified signatures, we assign genes with complex phenotypic profiles to multiple functional classes. Overall, we observe that pleiotropy occurs extensively among genes involved in early embryogenesis, and a small proportion of these genes are highly pleiotropic. We hypothesize that genes involved in early embryogenesis are organized into partially overlapping functional modules, and that pleiotropic genes represent “connectors” between these modules. In support of this hypothesis, we find that highly pleiotropic genes tend to reside in central positions in protein-protein interaction networks, suggesting that pleiotropic genes act as connecting points between different protein complexes or pathways

Computational modelling of NF-κB activation by IL-1RI and its co-receptor TILRR, predicts a role for Cytoskeletal Sequestration of IκBα in inflammatory signalling.

The transcription factor NF-κB (nuclear factor kappa B) is activated by Toll-like receptors and controlled by mechanotransduction and changes in the cytoskeleton. In this study we combine 3-D predictive protein modelling and in vitro experiments with in silico simulations to determine the role of the cytoskeleton in regulation of NF-κB. Simulations used a comprehensive agent-based model of the NF-κB pathway, which includes the type 1 IL-1 receptor (IL-1R1) complex and signalling intermediates, as well as cytoskeletal components. Agent based modelling relies on in silico reproductions of systems through the interactions of its components, and provides a reliable tool in investigations of biological processes, which require spatial considerations and involve complex formation and translocation of regulatory components. We show that our model faithfully reproduces the multiple steps comprising the NF-κB pathway, and provides a framework from which we can explore novel aspects of the system. The analysis, using 3-D predictive protein modelling and in vitro assays, demonstrated that the NF-κB inhibitor, IκBα is sequestered to the actin/spectrin complex within the cytoskeleton of the resting cell, and released during IL-1 stimulation, through a process controlled by the IL-1RI co-receptor TILRR (Toll-like and IL-1 receptor regulator). In silico simulations using the agent-based model predict that the cytoskeletal pool of IκBα is released to adjust signal amplification in relation to input levels. The results suggest that the process provides a mechanism for signal calibration and enables efficient, activation-sensitive regulation of NF-κB and inflammatory responses

Supervised machine learning to decipher the complex associations between neuro-immune biomarkers and quality of life in schizophrenia

Supervised machine learning to decipher the complex associations between neuro-immune biomarkers and quality of life in schizophreni

Episodic memory and delayed recall are significantly more impaired in younger patients with deficit schizophrenia than in elderly patients with amnestic mild cognitive impairment

Background Both amnestic mild cognitive impairment (aMCI) and schizophrenia, in particular deficit schizophrenia, are accompanied by cognitive impairments. The aim of the present study was to examine the cognitive differences between aMCI and (non)deficit schizophrenia. Methods Towards this end we recruited 60 participants with aMCI, 40 with deficit and 40 with nondeficit schizophrenia and 103 normal volunteers. Cognitive measures were assessed with the Consortium to Establish a Registry for Alzheimer’s disease (CERAD) using the Verbal Fluency Test (VFT), Boston Naming Test (BNT), Mini-Mental State Examination (MMSE), Word list memory (WLM), Word list recall (WLRecall) and Word list recognition (WLRecognition). Data were analyzed using multivariate analyses and machine learning techniques. Results BNT scores were significantly lower in aMCI as compared with nondeficit schizophrenia. Patients with deficit schizophrenia had significantly lower MMSE, WLM, WL True Recall and WL Recognition than aMCI patients, while WL False Recall was significantly higher in deficit schizophrenia than in aMCI. Neural network importance charts show that deficit and nondeficit schizophrenia are best separated from aMCI using total BNT score, while WLM and WL false Recall follow at a distance. Conclusions Patients with schizophrenia and aMCI have a significantly different neurocognitive profile. Memory impairments, especially in episodic memory, are significantly worse in younger patients with deficit schizophrenia as compared with elderly patients with aMCI, while the latter show more dysnomia than patients with schizophrenia

Development of a Novel Neuro-immune and Opioid-Associated Fingerprint with a Cross-Validated Ability to Identify and Authenticate Unknown Patients with Major Depression: Far Beyond Differentiation, Discrimination, and Classification

Development of a Novel Neuro-immune and Opioid-Associated Fingerprint with a Cross-Validated Ability to Identify and Authenticate Unknown Patients with Major Depression: Far Beyond Differentiation, Discrimination, and Classificatio

Deficit schizophrenia is a discrete diagnostic category defined by neuro-immune and neurocognitive features: results of supervised machine learning

Deficit schizophrenia is a discrete diagnostic category defined by neuro-immune and neurocognitive features: results of supervised machine learnin

Towards a new classification of stable phase schizophrenia into major and simple neuro-cognitive psychosis: Results of unsupervised machine learning analysis

Towards a new classification of stable phase schizophrenia into major and simple neuro-cognitive psychosis: Results of unsupervised machine learning analysi