JNK1 phosphorylation of SCG10 determines microtubule dynamics and axodendritic length

c-Jun NH2-terminal kinases (JNKs) are essential during brain development, when they regulate morphogenic changes involving cell movement and migration. In the adult, JNK determines neuronal cytoarchitecture. To help uncover the molecular effectors for JNKs in these events, we affinity purified JNK-interacting proteins from brain. This revealed that the stathmin family microtubule-destabilizing proteins SCG10, SCLIP, RB3, and RB3′ interact tightly with JNK. Furthermore, SCG10 is also phosphorylated by JNK in vivo on sites that regulate its microtubule depolymerizing activity, serines 62 and 73. SCG10-S73 phosphorylation is significantly decreased in JNK1−/− cortex, indicating that JNK1 phosphorylates SCG10 in developing forebrain. JNK phosphorylation of SCG10 determines axodendritic length in cerebrocortical cultures, and JNK site–phosphorylated SCG10 colocalizes with active JNK in embryonic brain regions undergoing neurite elongation and migration. We demonstrate that inhibition of cytoplasmic JNK and expression of SCG10-62A/73A both inhibited fluorescent tubulin recovery after photobleaching. These data suggest that JNK1 is responsible for regulation of SCG10 depolymerizing activity and neurite elongation during brain development

Pathogenic huntingtin inhibits fast axonal transport by activating JNK3 and phosphorylating kinesin

Author Posting. © The Author(s), 2009. This is the author's version of the work. It is posted here by permission of Nature America for personal use, not for redistribution. The definitive version was published in Nature Neuroscience 12 (2009): 864-871, doi:10.1038/nn.2346.Selected vulnerability of neurons in Huntington’s disease (HD) suggests alterations in a cellular process particularly critical for neuronal function. Supporting this idea, pathogenic Htt (polyQ-Htt) inhibits fast axonal transport (FAT) in various cellular and animal HD models (mouse and squid), but the molecular basis of this effect remains unknown. Here we show that polyQ-Htt inhibits FAT through a mechanism involving activation of axonal JNK. Accordingly, increased activation of JNK was observed in vivo in cellular and animal HD models. Additional experiments indicate that polyQ-Htt effects on FAT are mediated by the neuron-specific JNK3, and not ubiquitously expressed JNK1, providing a molecular basis for neuron-specific pathology in HD. Mass spectrometry identified a residue in the kinesin-1 motor domain phosphorylated by JNK3, and this modification reduces kinesin-1 binding to microtubules. These data identify JNK3 as a critical mediator of polyQ-Htt toxicity and provides a molecular basis for polyQ-Htt-induced inhibition of FAT.This work was supported by 2007/2008 MBL summer fellowship to GM; an HDSA grant to GM; NIH grants MH066179 to GB; and ALSA, Muscular Dystrophy Association, and NIH (NS23868, NS23320, NS41170) grants to STB

Robusta biomarkörer för prediktion av risk och sjukdom : en utvärdering av reproducerbarheten hos de stora kommersiella omik-plattformarna

I och med utveckling inom storskalig analys av blodprover har man idag insett nyttan av att omvandla biobanker med lagrade humanprover till data-banker där forskare snabbt kan få tillgång till data för att svara på forsknings-frågor. Problemet är att många av teknikerna för att skapa storskaliga data är semikvantitativa, värdena går inte att relatera till en absolut koncentration och är därmed svåra att slå samman och jämföra över tid. Randomisering, det vill säga att proverna analyseras i slumpvis inbördes ordning, är en av de viktigas-te aspekterna för att skapa data som går att slå samman och återanvända för många forskningsfrågor. Detta underlättar korrigering av oönskade analysva-riationer över tid. Utöver detta kan man använda sig av bryggningsprover, QC-prov (kvalitetskontrollprov) eller ankarprover, som analyseras upprepat både inom och mellan analystillfällen, vilket underlättar att lägga samman dataset som analyseras vid olika tillfällen. Många kommersiella analysplattformar inkluderar ett eget QC-prov i analysen och vissa delar med sig av data för dessa prover. Det vore värdefullt om alla plattformar delade dessa data för kvalitetsutvärdering och eventuell korrige-ring av analysvariationer över tid. För alla semikvantitativa plattformar som undersöktes (Olink, Somalogic, Metabolon och Biocrates) var den tekniska variabiliteten mellan QC-proverna betydligt lägre än variabiliteten mellan ana-lyserade plasmaprover. Detta var tydligast för proteomikplattformarna, vilket antyder att förutsättningarna att upptäcka biologiska skillnader är bättre i pro-teomikdata. Undantaget från detta är en femte plattform, Nightingale, en kvan-titativ men smalare metabololmikmetod som anses generera stabila mätningar. Vid all utveckling av biomarkörpaneler för att prediktera sjukdom behöver man göra upptäcktsanalyser, sedan valideringsstudier och därefter tester i den situation man tänker att testet ska fungera. De breda omikplattformarna läm-par sig för upptäckt och eventuellt validering, men för det faktiska kliniska tes-tet behövs en kvantitativ analys för att verkligen utvärdera att de proteiner eller metaboliter man vill använda är stabilt uppmätbara och fungerar för att pre-diktera sjukdom eller risk för sjukdom

Identification of Pre-Diagnostic Metabolic Patterns for Glioma Using Subset Analysis of Matched Repeated Time Points

Simple Summary: Reprogramming of cellular metabolism is a major hallmark of cancer cells, and play an important role in tumor initiation and progression. The aim of our study is to discover circulating early metabolic markers of brain tumors, as discovery and development of reliable predictive molecular markers are needed for precision oncology applications. We use a study design tailored to minimize confounding factors and a novel machine learning and visualization approach (SMART) to identify a panel of 15 interlinked metabolites related to glioma development. The presented SMART strategy facilitates early molecular marker discovery and can be used for many types of molecular data. Abstract: Here, we present a strategy for early molecular marker pattern detection-Subset analysis of Matched Repeated Time points (SMART)-used in a mass-spectrometry-based metabolomics study of repeated blood samples from future glioma patients and their matched controls. The outcome from SMART is a predictive time span when disease-related changes are detectable, defined by time to diagnosis and time between longitudinal sampling, and visualization of molecular marker patterns related to future disease. For glioma, we detect significant changes in metabolite levels as early as eight years before diagnosis, with longitudinal follow up within seven years. Elevated blood plasma levels of myo-inositol, cysteine, N-acetylglucosamine, creatinine, glycine, proline, erythronic-, 4-hydroxyphenylacetic-, uric-, and aceturic acid were particularly evident in glioma cases. We use data simulation to ensure non-random events and a separate data set for biomarker validation. The latent biomarker, consisting of 15 interlinked and significantly altered metabolites, shows a strong correlation to oxidative metabolism, glutathione biosynthesis and monosaccharide metabolism, linked to known early events in tumor development. This study highlights the benefits of progression pattern analysis and provide a tool for the discovery of early markers of disease

All JNKs Can Kill, but Nuclear Localization Is Critical for Neuronal Death

JNKs are implicated in a range of brain pathologies and receive considerable attention as potential therapeutic targets. However, JNKs also regulate physiological and homeostatic processes. An attractive hypothesis from the drug development perspective is that distinct JNK isoforms mediate “physiological” and “pathological” responses. However, this lacks experimental evaluation. Here we investigate the isoforms, subcellular pools, and c-Jun/ATF2 targets of JNK in death of central nervous system neurons following withdrawal of trophic support. We use gene knockouts, gene silencing, subcellularly targeted dominant negative constructs, and pharmacological inhibitors. Combined small interfering RNA knockdown of all JNKs 1, 2, and 3, provides substantial neuroprotection. In contrast, knockdown or knock-out of individual JNKs or two JNKs together does not protect. This explains why the evidence for JNK in neuronal death has to date been largely pharmacological. Complete knockdown of c-Jun and ATF2 using small interfering RNA also fails to protect, casting doubt on c-Jun as a critical effector of JNK in neuronal death. Nonetheless, the death requires nuclear but not cytosolic JNK activity as nuclear dominant negative inhibitors of JNK protect, whereas cytosolic inhibitors only block physiological JNK function. Thus any one of the three JNKs is capable of mediating apoptosis and inhibition of nuclear JNK is protective

Prediagnostic biomarkers for early detection of glioma : using case-control studies from cohorts as study approach

Background: Understanding the trajectory and development of disease is important and the knowledge can be used to find novel targets for therapy and new diagnostic tools for early diagnosis. Methods: Large cohorts from different parts of the world are unique assets for research as they have systematically collected plasma and DNA over long-time periods in healthy individuals, sometimes even with repeated samples. Over time, the population in the cohort are diagnosed with many different diseases, including brain tumors. Results: Recent studies have detected genetic variants that are associated with increased risk of glioblastoma and lower grade gliomas specifically. The impact for genetic markers to predict disease in a healthy population has been deemed low, and a relevant question is if the genetic variants for glioma are associated with risk of disease or partly consist of genes associated to survival. Both metabolite and protein spectra are currently being explored for early detection of cancer. Conclusions: We here present a focused review of studies of genetic variants, metabolomics, and proteomics studied in prediagnostic glioma samples and discuss their potential in early diagnostics

Pre-diagnostic levels of sVEGFR2, sTNFR2, sIL-2Rα and sIL-6R are associated with glioma risk : A nested case–control study of repeated samples

No strong aetiological factors have been established for glioma aside from genetic mutations and variants, ionising radiation and an inverse relationship with asthmas and allergies. Our aim was to investigate the association between pre-diagnostic immune protein levels and glioma risk. We conducted a case–control study nested in the Northern Sweden Health and Disease Study cohort. We analysed 133 glioma cases and 133 control subjects matched by age, sex and date of blood donation. ELISA or Luminex bead-based multiplex assays were used to measure plasma levels of 19 proteins. Conditional logistic regression models were used to estimate the odds ratios and 95% CIs. To further model the protein trajectories over time, the linear mixed-effects models were conducted. We found that the levels of sVEGFR2, sTNFR2, sIL-2Rα and sIL-6R were associated with glioma risk. After adjusting for the time between blood sample collection and glioma diagnosis, the odds ratios were 1.72 (95% CI = 1.01–2.93), 1.48 (95% CI = 1.01–2.16) and 1.90 (95% CI = 1.14–3.17) for sTNFR2, sIL-2Rα and sIL-6R, respectively. The trajectory of sVEGFR2 concentrations over time was different between cases and controls (p-value = 0.031), increasing for cases (0.8% per year) and constant for controls. Our findings suggest these proteins play important roles in gliomagenesis

Metabolic response patterns in brain microdialysis fluids and serum during interstitial cisplatin treatment of high-grade glioma

BACKGROUND: High-grade gliomas are associated with poor prognosis. Tumour heterogeneity and invasiveness create challenges for effective treatment and use of systemically administrated drugs. Furthermore, lack of functional predictive response-assays based on drug efficacy complicates evaluation of early treatment responses. METHODS: We used microdialysis to deliver cisplatin into the tumour and to monitor levels of metabolic compounds present in the tumour and non-malignant brain tissue adjacent to tumour, before and during treatment. In parallel, we collected serum samples and used multivariate statistics to analyse the metabolic effects. RESULTS: We found distinct metabolic patterns in the extracellular fluids from tumour compared to non-malignant brain tissue, including high concentrations of a wide range of amino acids, amino acid derivatives and reduced levels of monosaccharides and purine nucleosides. We found that locoregional cisplatin delivery had a strong metabolic effect at the tumour site, resulting in substantial release of glutamic acid, phosphate, and spermidine and a reduction of cysteine levels. In addition, patients with long-time survival displayed different treatment response patterns in both tumour and serum. Longer survival was associated with low tumour levels of lactic acid, glyceric acid, ketoses, creatinine and cysteine. Patients with longer survival displayed lower serum levels of ketohexoses, fatty acid methyl esters, glycerol-3-phosphate and alpha-tocopherol, while elevated phosphate levels were seen in both tumour and serum during treatment. CONCLUSION: We highlight distinct metabolic patterns associated with high-grade tumour metabolism, and responses to cytotoxic cisplatin treatment