Treatment with the vascular endothelial growth factor-A antibody, bevacizumab, has sex-specific effects in a rat model of mild traumatic brain injury.

Mild traumatic brain injury (mTBI) involves damage to the cerebrovascular system. Vascular endothelial growth factor-A (VEGF-A) is an important modulator of vascular health and VEGF-A promotes the brain's ability to recover after more severe forms of brain injury; however, the role of VEGF-A in mTBI remains poorly understood. Bevacizumab (BEV) is a monoclonal antibody that binds to VEGF-A and neutralises its actions. To better understand the role of VEGF-A in mTBI recovery, this study examined how BEV treatment affected outcomes in rats given a mTBI. Adult Sprague-Dawley rats were assigned to sham-injury + vehicle treatment (VEH), sham-injury + BEV treatment, mTBI + VEH treatment, mTBI + BEV treatment groups. Treatment was administered intracerebroventricularly via a cannula beginning at the time of injury and continuing until the end of the study. Rats underwent behavioral testing after injury and were euthanized on day 11. In both females and males, BEV had a negative impact on cognitive function. mTBI and BEV treatment increased the expression of inflammatory markers in females. In males, BEV treatment altered markers related to hypoxia and vascular health. These novel findings of sex-specific responses to BEV and mTBI provide important insights into the role of VEGF-A in mTBI.

Treatment with vascular endothelial growth factor-A worsens cognitive recovery in a rat model of mild traumatic brain injury.

Mild traumatic brain injury (mTBI) is a common and unmet clinical issue, with limited treatments available to improve recovery. The cerebrovascular system is vital to provide oxygen and nutrition to the brain, and a growing body of research indicates that cerebrovascular injury contributes to mTBI symptomatology. Vascular endothelial growth factor-A (VEGF-A) is a potent promoter of angiogenesis and an important modulator of vascular health. While indirect evidence suggests that increased bioavailability of VEGF-A may be beneficial after mTBI, the direct therapeutic effects of VEGF-A in this context remains unknown. This study therefore aimed to determine whether intracerebroventricular administration of recombinant VEGF-A could improve recovery from mTBI in a rat model. Male and female Sprague-Dawley rats were assigned to four groups: sham + vehicle (VEH), sham + VEGF-A, mTBI + VEH, mTBI + VEGF-A. The mTBI was induced using the lateral impact model, and treatment began at the time of the injury and continued until the end of the study. Rats underwent behavioral testing between days 1 and 10 post-injury, and were euthanized on day 11 for post-mortem analysis. In males, the mTBI + VEGF-A group had significantly worse cognitive recovery in the water maze than all other groups. In females, the VEGF treatment worsened cognitive performance in the water maze regardless of mTBI or sham injury. Analysis of hippocampal tissue found that these cognitive deficits occurred in the presence of gene expression changes related to neuroinflammation and hypoxia in both male and female rats. These findings indicate that the VEGF-A treatment paradigm tested in this study failed to improve mTBI outcomes in either male or female rats.

Prediction of secondary structure population and intrinsic disorder of proteins using multitask deep learning.

Recent research in predicting protein secondary structure populations (SSP) based on Nuclear Magnetic Resonance (NMR) chemical shifts has helped quantitatively characterise the structural conformational properties of intrinsically disordered proteins and regions (IDP/IDR). Different from protein secondary structure (SS) prediction, the SSP prediction assumes a dynamic assignment of secondary structures that seem correlate with disordered states. In this study, we designed a single-task deep learning framework to predict IDP/IDR and SSP respectively; and multitask deep learning frameworks to allow quantitative predictions of IDP/IDR evidenced by the simultaneously predicted SSP. According to independent test results, single-task deep learning models improve the prediction performance of shallow models for SSP and IDP/IDR. Also, the prediction performance was further improved for IDP/IDR prediction when SSP prediction was simultaneously predicted in multitask models. With p53 as a use case, we demonstrate how predicted SSP is used to explain the IDP/IDR predictions for each functional region.

PhosContext2vec: a distributed representation of residue-level sequence contexts and its application to general and kinase-specific phosphorylation site prediction.

Phosphorylation is the most important type of protein post-translational modification. Accordingly, reliable identification of kinase-mediated phosphorylation has important implications for functional annotation of phosphorylated substrates and characterization of cellular signalling pathways. The local sequence context surrounding potential phosphorylation sites is considered to harbour the most relevant information for phosphorylation site prediction models. However, currently there is a lack of condensed vector representation for this important contextual information, despite the presence of varying residue-level features that can be constructed from sequence homology profiles, structural information, and physicochemical properties. To address this issue, we present PhosContext2vec which is a distributed representation of residue-level sequence contexts for potential phosphorylation sites and demonstrate its application in both general and kinase-specific phosphorylation site predictions. Benchmarking experiments indicate that PhosContext2vec could achieve promising predictive performance compared with several other existing methods for phosphorylation site prediction. We envisage that PhosContext2vec, as a new sequence context representation, can be used in combination with other informative residue-level features to improve the classification performance in a number of related bioinformatics tasks that require appropriate residue-level feature vector representation and extraction. The web server of PhosContext2vec is publicly available at http://phoscontext2vec.erc.monash.edu/.