GDNF family ligands and neural stem cells in Parkinson's disease

Parkinson's disease (PD) is a very common neurodegenerative disorder, characterized by a progressive degeneration of mainly nigrostriatal and mesolimbic dopaminergic neurons leading to tremor, rigidity and hypokinesia, the classical symptoms of the disease. Degeneration of noradrenergic neurons of the locus coeruleus are also involved in the progression of the disease, causing dementia and depression. The cause of the selective degeneration of specific populations of neurons in P1) is unknown, but it has been suggested that an increased oxidative stress is involved in the pathophysiology of the disease. The current treatment is based on the substitution of a dopamine precursor, L- DOPA, which was introduced more than 30 years ago. However, there is currently no way of halting the disease progress. New therapeutic strategies are aiming at halting the neurodegenerative process or restoring the system by transplantation of fetal dopamine neurons. We have investigated the potential of the neurotrophic factors Glial cell linederived neurotrophic factor (GDN-F), Neurturin (NTN) and Persephin (PSP), belonging to the GDNF family of ligands, to exert neuroprotection and neuritogenesis on nigral dopamine and locus noradrenaline neurons. We saw both overlapping and differential effects of these ligands in their neurotrophic effects. The pronounced survival effects they all have make them good therapeutic candidates in the treatment of PD. The inability of neurotrophic factors to pass the blood-brain barrier and their potent effects on other cell populations, causing severe side effects by systemic administration, makes it essential to achieve a localized sustained delivery of these proteins to the target cells. We have accomplished this in an ex vivo gene therapy approach by overexpressing GDNF family ligands in neural stem cells (NSCs). The NSCs differentiate upon grafting and adopt local phenotypes. Grafting of these NSCs in the target of the projecting dopaminergic neurons, the striatum, in a lesion model of PD protected many of the dopaminergic cells from degeneration and improved behavioral parameters in the treated animals. In addition to the neuroprotective strategy in treatment of PD, as a replacement strategy we have managed to induce a dopaminergic fate in NSCs. We first overexpressed the transcription factor Nurr1 (required for the dopamine neurons in the substantia nigra) in proliferating NSCs and thereafter incubated the cells with soluble factors from type 1 astrocytes derived from the ventral mesencephalon. This resulted in an induction of more than 80% dopamine producing cells. These cells survived grafting to a low extent, but further improvement in survival must be achieved for these cells to become a realistic alternative to fetal dopaminergic cells

The association of second trimester biomarkers in amniotic fluid and fetal outcome.

Objective: To identify the level of amniotic fluid lactate (AFL), placental growth factor (PLGF), and vascular endothelial growth factor (VEGF) at second trimester amniocentesis, and to compare levels in normal pregnancies with pregnancies ending in a miscarriage, an intrauterine growth restricted fetus (IUGR) or decreased fetal movements. Study design: A prospective cohort study. Amniotic fluid was consecutively collected at amniocentesis in 106 pregnancies. Fetal wellbeing at delivery was evaluated from medical files and compared with the levels of AFL, VEGF, and PLGF at the time of amniocentesis. Results: The median level of AFL was 6.9 mmol/l, VEGF 0.088 pg/ml, and PLGF 0.208 pg/ml. The median levels of AFL in pregnancies ended in miscarriage were significantly higher (10.7 mmol/l) compared to those with a live new-born (6.9 mmol/L, p = .02). The levels of VEGF (p = .2) and PLGF (p = .7) were not affected. In pregnancies with an IUGR, the median level of AFL was higher compared to those with normal fetal growth (p = .003). No differences VEGF (p = .5), but significant lower PLGF were found in IUGR pregnancies (p = .03). Conclusions: Pregnancies ending in a miscarriage or with IUGR had significantly higher median values of AFL but lower values of PLGF in the amniotic fluid at the time of second trimester amniocentesis compared to normal pregnancies

Data from: High sensitivity isoelectric focusing to establish a signaling biomarker for the diagnosis of human colorectal cancer

Background: The progression of colorectal cancer (CRC) involves recurrent amplifications/mutations in the epidermal growth factor receptor (EGFR) and downstream signal transducers of the Ras pathway, KRAS and BRAF. Whether genetic events predicted to result in increased and constitutive signaling indeed lead to enhanced biological activity is often unclear and, due to technical challenges, unexplored. Here, we investigated proliferative signaling in CRC using a highly sensitive method for protein detection. The aim of the study was to determine whether multiple changes in proliferative signaling in CRC could be combined and exploited as a “complex biomarker” for diagnostic purposes. Methods: We used robotized capillary isoelectric focusing as well as conventional immunoblotting for the comprehensive analysis of epidermal growth factor receptor signaling pathways converging on extracellular regulated kinase 1/2 (ERK1/2), AKT, phospholipase Cγ1 (PLCγ1) and c-SRC in normal mucosa compared with CRC stage II and IV. Computational analyses were used to test different activity patterns for the analyzed signal transducers. Results: Signaling pathways implicated in cell proliferation were differently dysregulated in CRC and, unexpectedly, several were downregulated in disease. Thus, levels of activated ERK1 (pERK1), but not pERK2, decreased in stage II and IV while total ERK1/2 expression remained unaffected. In addition, c-SRC expression was lower in CRC compared with normal tissues and phosphorylation on the activating residue Y418 was not detected. In contrast, PLCγ1 and AKT expression levels were elevated in disease. Immunoblotting of the different signal transducers, run in parallel to capillary isoelectric focusing, showed higher variability and lower sensitivity and resolution. Computational analyses showed that, while individual signaling changes lacked predictive power, using the combination of changes in three signaling components to create a “complex biomarker” allowed with very high accuracy, the correct diagnosis of tissues as either normal or cancerous. Conclusions: We present techniques that allow rapid and sensitive determination of cancer signaling that can be used to differentiate colorectal cancer from normal tissue

High sensitivity isoelectric focusing to establish a signaling biomarker for the diagnosis of human colorectal cancer

Background: The progression of colorectal cancer (CRC) involves recurrent amplifications/mutations in the epidermal growth factor receptor (EGFR) and downstream signal transducers of the Ras pathway, KRAS and BRAF. Whether genetic events predicted to result in increased and constitutive signaling indeed lead to enhanced biological activity is often unclear and, due to technical challenges, unexplored. Here, we investigated proliferative signaling in CRC using a highly sensitive method for protein detection. The aim of the study was to determine whether multiple changes in proliferative signaling in CRC could be combined and exploited as a "complex biomarker" for diagnostic purposes. Methods: We used robotized capillary isoelectric focusing as well as conventional immunoblotting for the comprehensive analysis of epidermal growth factor receptor signaling pathways converging on extracellular regulated kinase 1/2 (ERK1/2), AKT, phospholipase C gamma 1 (PLC gamma 1) and c-SRC in normal mucosa compared with CRC stage II and IV. Computational analyses were used to test different activity patterns for the analyzed signal transducers. Results: Signaling pathways implicated in cell proliferation were differently dysregulated in CRC and, unexpectedly, several were downregulated in disease. Thus, levels of activated ERK1 (pERK1), but not pERK2, decreased in stage II and IV while total ERK1/2 expression remained unaffected. In addition, c-SRC expression was lower in CRC compared with normal tissues and phosphorylation on the activating residue Y418 was not detected. In contrast, PLC gamma 1 and AKT expression levels were elevated in disease. Immunoblotting of the different signal transducers, run in parallel to capillary isoelectric focusing, showed higher variability and lower sensitivity and resolution. Computational analyses showed that, while individual signaling changes lacked predictive power, using the combination of changes in three signaling components to create a "complex biomarker" allowed with very high accuracy, the correct diagnosis of tissues as either normal or cancerous. Conclusions: We present techniques that allow rapid and sensitive determination of cancer signaling that can be used to differentiate colorectal cancer from normal tissue

Raw Protein RPAs

The relative peak area (RPA), i.e. peak area value of the 23 different activity levels of the 7 signal transducers after normalization to the HSP70 level analyzed in parallel in each sample (columns 2-24). This file contains one sample per row and one protein per column with the sample name in the first column and the protein name in the first row. Columns 25-27 contain the result of the mutation analysis of KRAS and BRAF. One in column 25 (MutationKRAS) indicate that KRAS is mutated in the sample, one in column 26 (MutationBRAF) indicates that BRAF is mutated, while one in column 27 (Wildtype) indicate that neither KRAS nor BRAF is mutated. A one in the binary variables in column 28-31 indicate the classification of each sample as normal mucosa, colorectal cancer (CRC) stage II, CRC stage IV, or metastasis. NaN is used to indicate that no measurement was done

Padhan BMC Cancer 2016 Supplemental Results

File contains the test statistic (T) for each possible combination of 1-3 features, including the constructed features (column 5 and 6). One combination is shown per row with the name of the feature combination in the first column and the header explaining the value in each column in the first row

Raw Protein RPAs Constructed features replicate corrected

File shows the relative peak area (RPA), i.e. peak area value of the measured protein after normalization to the HSP70 level analyzed in parallel in each sample (columns 2-24, 28-42). This file contains one sample per row and one protein per column with the sample name in the first column and the protein name in the first row. Column 25-27 contain the result of the mutation analysis of KRAS and BRAF. One in column 25 (MutationKRAS) indicate that KRAS is mutated in the sample, one in column 26 (MutationBRAF) indicates that BRAF is mutated, while one in column 27 (Wildtype) indicate that neither KRAS nor BRAF is mutated. Columns 28 to 42 contain the RPA values of the constructed features, i.e. features that are calculated based on the 23 different activity levels of the 7 signal transducers in column 2-24. The four replicates of each constructed feature contains the minimum, maximum, mean, and median value based on all possible ways to combine the replicates of the proteins used to construct the feature. A one in the binary variables in column 43-46 indicate the classification of each sample as normal mucosa, colorectal cancer (CRC) stage II, CRC stage IV, or metastasis. In the last column the classification is 1 = normal mucosa, 2 = colorectal cancer (CRC) stage II, 3 = CRC stage IV, or 5 = metastasis. NaN is used to indicate that no measurement was done

Additional file 1: of High sensitivity isoelectric focusing to establish a signaling biomarker for the diagnosis of human colorectal cancer

Figure S1. Validation of antibodies used in the study by conventional immunoblotting. All antibodies showed immunoreactivity with the expected molecular species, in conventional immunoblotting on endothelial lysates. Figure S2. Detection of MEK1/2 protein by isoelectric focusing. There was no significant difference in MEK protein expression between normal, CRCII and CRCIV tissues. Detailed description of computational analyses; “Characterization of the data set and errors”. Figure S3. Distribution function for data subsets by Monte Carlo simulation. (DOCX 1215 kb