Detection of network motifs using three-way ANOVA

Corticosterone and chlorpyrifos oxon exposure elicits spatiotemporal MAPK phosphoprotein signaling in a mouse brain

Chlorpyrifos (CPF) is one of the most widely-used pesticides globally for agricultural purposes. Certain occupations (e.g., farmers, military) are at an increased risk for high-dose exposure to CPF, which can lead to seizures and irreversible brain injury. Workers with the highest risk of exposure typically experience increased circulating cortisol levels, which is related to physiological stress. To better represent this exposure scenario, a mouse model utilized exogenous administration of corticosterone (CORT; high physiologic stress mimic) in combination with chlorpyrifos oxon (CPO; oxon metabolite of CPF); this combination increases neuroinflammation post-exposure. In the present study adult male C57BL/6J mice were given CORT (200 μg/mL) in drinking water for seven days followed by a single intraperitoneal injection of CPO (8.0 mg/kg) on day eight, and euthanized 0.5, 2, and 24 h post-injection. Ten post-translationally modified proteins were measured in the frontal cortex and striatum to evaluate brain region-specific effects. The spatiotemporal response to CORT + CPO sequentially activated phosphoproteins (p-ERK1/2, p-MEK1/2, p-JNK) involved in mitogen-activated protein kinase (MAPK) signaling. Observed p-ZAP70 responses further integrated MAPK signaling and provided a spatiotemporal connection between protein phosphorylation and neuroinflammation. This study provides insight into the spatiotemporal cellular signaling cascade following CORT + CPO exposure that represent these vulnerable populations.

Differential phosphoprotein signaling in the cortex in mouse models of Gulf War Illness using corticosterone and acetylcholinesterase inhibitors

Aims

Veterans from the 1990–91 Gulf War were exposed to acetylcholinesterase inhibitors (AChEIs), and, following service, an estimated one-third began suffering from a medically unexplained, multi-symptom illness termed Gulf War Illness (GWI). Previous research has developed validated rodent models that include exposure to exogenous corticosterone (CORT) and AChEIs to simulate high stress and chemical exposures encountered in theater. This combination of exposures in mice resulted in a marked increase in neuroinflammation, which is a common symptom of veterans suffering from GWI. To further elucidate the mechanisms associated with these mouse models of GWI, an investigation into intracellular responses in the cortex were performed to characterize the early cellular signaling changes associated with this exposure-initiated neuroinflammation.

Main methods

Adult male C57BL/6J mice were exposed to CORT in the drinking water (200 μg/mL) for 7 days followed by a single intraperitoneal injection of diisopropyl fluorophosphate (DFP; 4.0 mg/kg) or chlorpyrifos oxon (CPO; 8.0 mg/kg), on day 8 and euthanized 0.5, 2, and 24 h post-injection. Eleven post-translationally modified protein targets were measured using a multiplexed ELISA.

Key findings

Phosphoprotein responses were found to be exposure specific following AChEI insult, with and without CORT. Specifically, CORT + CPO exposure was found to sequentially activate several phosphoproteins involved in mitogen activated protein kinase signaling (p-MEK1/2, p-ERK1/2, and p-JNK). DFP alone similarly increased proteins in this pathway (p-RPS6, and p-JNK), but the addition of CORT ameliorated these affects.

Significance

The results of this study provide insight into differentially activated pathways depending on AChEI in these GWI models.

Identification of Sub-Golgi protein localization by use of deep representation learning features

Motivation

The Golgi apparatus has a key functional role in protein biosynthesis within the eukaryotic cell with malfunction resulting in various neurodegenerative diseases. For a better understanding of the Golgi apparatus, it is essential to identification of sub-Golgi protein localization. Although some machine learning methods have been used to identify sub-Golgi localization proteins by sequence representation fusion, more accurate sub-Golgi protein identification is still challenging by existing methodology.

Results

we developed a protein sub-Golgi localization identification protocol using deep representation learning features with 107 dimensions. By this protocol, we demonstrated that instead of multi-type protein sequence feature representation fusion as in previous state-of-the-art sub-Golgi-protein localization classifiers, it is sufficient to exploit only one type of feature representation for more accurately identification of sub-Golgi proteins. Compared with independent testing results for benchmark datasets, our protocol is able to perform generally, reliably and robustly for sub-Golgi protein localization prediction.

Availabilityand implementation

A use-friendly webserver is freely accessible at http://isGP-DRLF.aibiochem.net and the prediction code is accessible at https://github.com/zhibinlv/isGP-DRLF.

Supplementary information

Supplementary data are available at Bioinformatics online.