Peter James

Exposure to the environmental toxin rotenone increases risk for Parkinson's disease (PD). However, protein phosphorylation changes induced by rotenone in neural-derived cells have not been reported. We examined the effect of rotenone on the proteome and phosphoproteome of cortex-derived cultures from wild-type and Lrrk2-/- mouse brains. We also analyzed the phosphoproteome of the PD cadaver brain using a previously unanalyzed dataset. Rotenone alters phosphorylation at 904 sites, most of which are unchanged in Lrrk2-/- cultures. Common targets include proteins that control microtubule stability, vesicular transport, and protein clearance via the autophagosomal/lysosomal pathway. Analysis of phosphosites with known function indicate that rotenone activates histone deacetylase-1 and represses the pro-survival transcriptional regulators MEF2C and MEF2D, while inhibiting translation by phosphorylating EIF2b5. These effects do not occur in Lrrk2-/- cultures. This study shows that Lrrk2 is required for the earliest rotenone-triggered phospho-signalling events.

JNKs mediate neuronal damage in neurodegenerative disease and inhibitors of JNK1 have shown anxiolytic and anti-depressive effects in mice. Here, we analyze the phosphoproteomes of hippocampus and nucleus accumbens from DJNKI-1 (JNK inhibitor)-infused mice. We correlate phospho-site changes with anxiety-like behaviours in the elevated plus maze and light-dark test and identify unique changes in responder mice. Among the DJNKI-1 regulated phosphosites, several lie within GSK3 motifs and are exclusively down-regulated. Consistent with this, GSK3β is inhibited and AKT activated. Importantly, we detect multilevel regulation of glucose metabolism enzymes including increased PDPK1-S241 phosphorylation, and a 5-fold increase in pyruvate dehydrogenase (PDHA1)-S-293 phosphorylation, signifying its inhibition. This suggests that JNK inhibitor drives a metabolic transformation to the neuronal Warburg response. This and a range of identified synaptic and cytoskeletal protein phosphosite changes are discussed in the context of JNK-regulated anxiety responses. The annotated hippocampal and nucleus accumbens phosphoproteomes described here will support a mechanistic understanding of the JNK pathway for future studies of brain disorders.

This is a correction to: Ye Hong, Dani Flinkman, Tomi Suomi, Sami Pietilä, Peter James, Eleanor Coffey, Laura L Elo, PhosPiR: an automated phosphoproteomic pipeline in R, Briefings in Bioinformatics, Volume 23, Issue 1, January 2022, bbab510, https://doi.org/10.1093/bib/bbab510.

Parkinson’s disease (PD) is characterized by heterogeneous motor and non-motor symptoms. The majority of PD cases are sporadic, whereas the leucine-rich repeat kinase 2 (LRRK2) G2019S mutation is the most common monogenic cause of familial PD and symptomatically indistinguishable from the sporadic form. Deficits in protein synthesis have been reported in PD. However, it is not known which proteins are affected or if there are synthesis differences between patients with sporadic and LRRK2 G2019S PD. Here we used non-canonical amino acid labeling and mass-spectrometry based proteomics to perform a quantitative comparison of nascent proteins in fibroblasts from sporadic and G2019S mutation PD patients compared to healthy individuals. We found that several proteins were under-represented among nascent proteins in cells from PD patients. Among these were regulators endo-lysosomal sorting, mRNA processing and translation itself. We further validated which of these proteins were dysregulated at the static proteome level using targeted proteomics. Key downregulated proteins included the autophagy regulator ATG9A and translational stability regulator YTHDF3. Notably, 68% of the identified proteins were downregulated in both sporadic and LRRK2-G2019S forms of PD. We identify translational repression of key regulators of proteostasis in patient fibroblasts that are common to both sporadic and LRRK2-G2019S PD.

Large-scale phosphoproteome profiling using mass spectrometry (MS) provides functional insight that is crucial for disease biology and drug discovery. However, extracting biological understanding from these data is an arduous task requiring multiple analysis platforms that are not adapted for automated high-dimensional data analysis. Here, we introduce an integrated pipeline that combines several R packages to extract high-level biological understanding from large-scale phosphoproteomic data by seamless integration with existing databases and knowledge resources. In a single run, PhosPiR provides data clean-up, fast data overview, multiple statistical testing, differential expression analysis, phosphosite annotation and translation across species, multilevel enrichment analyses, proteome-wide kinase activity and substrate mapping and network hub analysis. Data output includes graphical formats such as heatmap, box-, volcano- and circos-plots. This resource is designed to assist proteome-wide data mining of pathophysiological mechanism without a need for programming knowledge.

Cobra snakes, including Naja mossambica and Naja nigricincta nigricincta, are one of the major groups of snakes responsible for snakebites in southern Africa, producing significant cytotoxicity and tissue damage. The venom of N. mossambica has been briefly characterised, but that of N. n. nigricincta is not reported. The current study identifies the venom proteins of N. mossambica and N. n. nigricincta. This is achieved using sodium dodecyl sulphate (SDS)-polyacrylamide gel eletrophroresis (PAGE), followed by high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS). Most of the proteins were less than 17 kDa in both snakes. N. mossambica was found to have 75 proteins in total (from 16 protein families), whereas N.n. nigricincta had 73 (from 16 protein families). Of these identified proteins, 57 were common in both snakes. The proteins identified belonged to various families, including the three-finger toxins (3FTx), Cysteine-rich secretory proteins (CRiSP), Phospholipase A2 (PLA₂) and Venom metalloproteinase M12B (SVMP). The current study contributes to the profile knowledge of snake venom compositions, which is of fundamental value in understanding the proteins that play a major role in envenomation.

Diabetic nephropathy (DN) is a late complication in both type 1 diabetes mellitus (T1DM) and T2DM. Already at an early stage of DN morphological changes occur at the cell surface and in the extracellular matrix where the majority of the proteins carry N-linked glycosylations. These glycosylated proteins are highly important in cell adhesion and cell–matrix processes but not much is known about how they change in DN or whether the distinct etiology of T1DM and T2DM could have an effect on their abundances. We enriched for the N-glycosylated kidney proteome in db/db mice dosed with insulin or vehicle, in streptozotocin-induced (STZ) diabetic mice and healthy control mice dosed with vehicle. Glycopeptides were analyzed with label-free shotgun mass spectrometry and differential protein abundances identified in both mouse models were compared using multivariate analyses. The majority of the N-glycosylated proteins were similarly regulated in both mouse models. However, distinct differences between the two mouse models were for example seen for integrin-β1, a protein expressed mainly in the glomeruli which abundance was increased in the STZ diabetic mice while decreased in the db/db mice and for the sodium/glucose cotransporter-1, mainly expressed in the proximal tubules which abundance was increased in the db/db mice but decreased in the STZ diabetic mice. Insulin had an effect on the level of both glomerular and tubular proteins in the db/db mice. It decreased the abundance of G-protein coupled receptor-116 and of tyrosine-protein phosphatase non-receptor type substrate-1 away from the level in the healthy control mice. Our finding of differences in the N-glycosylation protein profiles in the db/db and STZ mouse models suggest that the etiology of DN could give rise to variations in the cell adhesion and cell–matrix composition in T1DM and T2DM. Thus, N-glycosylated protein differences could be a clue to dissimilarities in T1DM and T2DM at later stages of DN. Furthermore, we observed insulin specific regulation of N-glycosylated proteins both in the direction of and away from the abundances in healthy control mice.

Breast cancer is a complex and heterogeneous disease that is usually characterized by histological parameters such as tumor size, cellular arrangements/rearrangments, necrosis, nuclear grade and the mitotic index, leading to a set of around twenty subtypes. Together with clinical markers such as hormone receptor status, this classification has considerable prognostic value but there is a large variation in patient response to therapy. Gene expression profiling has provided molecular profiles characteristic of distinct subtypes of breast cancer that reflect the divergent cellular origins and degree of progression. Here we present a large-scale proteomic and transcriptomic profiling study of 477 sporadic and hereditary breast cancer tumors with matching mRNA expression analysis. Unsupervised hierarchal clustering was performed and selected proteins from large-scale tandem mass spectrometry (MS/MS) analysis were transferred into a highly multiplexed targeted selected reaction monitoring assay to classify tumors using a hierarchal cluster and support vector machine with leave one out cross-validation. The subgroups formed upon unsupervised clustering agree very well with groups found at transcriptional level; however, the classifiers (genes or their respective protein products) differ almost entirely between the two datasets. In-depth analysis shows clear differences in pathways unique to each type, which may lie behind their different clinical outcomes. Targeted mass spectrometry analysis and supervised clustering correlate very well with subgroups determined by RNA classification and show convincing agreement with clinical parameters. This work demonstrates the merits of protein expression profiling for breast cancer stratification. These findings have important implications for the use of genomics and expression analysis for the prediction of protein expression, such as receptor status and drug target expression. The highly multiplexed MS assay is easily implemented in standard clinical chemistry practice, allowing rapid and cheap characterization of tumor tissue suitable for directing the choice of treatment.

Breast-cancer-derived cell lines are an important sample source for cancer proteomics and can be classified on the basis of transcriptomic analysis into subgroups corresponding to the molecular subtypes observed in mammary tumors. This study describes a tridimensional fractionation method that allows high sequence coverage and proteome-wide estimation of protein expression levels. This workflow has been used to conduct an in-depth quantitative proteomic survey of five breast cancer cell lines matching all major cancer subgroups and shows that despite their different classification, these cell lines display a very high level of similarity. A proteome-wide comparison with the RNA levels observed in the same samples showed very little to no correlation. Finally, we demonstrate that the proteomes of in vitro models of breast cancer display surprisingly little overlap with those of clinical samples.