43 research outputs found
Temporal proteomic analysis of IGF-1R signalling in MCF-7 breast adenocarcinoma cells
No full textDysregulation of the insulin‐like growth factor 1 receptor signalling network is implicated in tumour growth and resistance to chemotherapy. We explored proteomic changes resulting from insulin‐like growth factor 1 stimulation of MCF‐7 adenocarcinoma cells as a function of time. Quantitative analysis using iTRAQ™ reagents and 2‐D LC‐MS/MS analysis of three biological replicates resulted in the identification of 899 proteins (p≤0.05) with an estimated mean false‐positive rate of 2.6%. Quantitative protein expression was obtained from 681 proteins. Further analysis by supervised k‐means clustering identified five temporal clusters, which were submitted to the FuncAssociate server to assign overrepresented gene ontology terms. Proteins associated with vesicle transport were significantly overrepresented. We further analyzed our data set for proteins showing temporal significance using the software, extraction and analysis of differential gene expression, resulting in 20 significantly and temporally changing proteins (p≤0.1). These significant proteins play roles in, among others, altered glucose metabolism (lactate dehydrogenase A and pyruvate kinase M1/M2) and cellular stress (nascent polypeptide‐associated complex subunit α and heat shock (HSC70) proteins). We used multiple reaction monitoring to validate these interesting proteins and have revealed several differences in relative peptide expression corresponding to protein isoforms and variants
Targeted proteomic analysis of glycolysis in cancer cells
No full textAltered expression of glycolysis proteins is an important yet poorly understood characteristic of cancer. To better understand the glycolytic changes during tumorigenesis, we designed a liquid chromatography multiple reaction monitoring (LC−MRM) assay targeting the “glycolysis proteome” in MCF-7 breast cancer cells, using isotope-coded dimethylation of peptides for relative quantification. In silico, dimethyl labeled tryptic peptides [M + 2H]2+ (of length n) and their yn-1 fragment ions were determined based on UniprotKB database sequence entries for glycolysis proteins, related branching pathways, and reference proteins. Using predicted transitions ([M + 2H]2+ → yn-1), MRM-initiated detection and sequencing (MIDAS) was performed on a dimethyl-labeled, tryptic digest from MCF-7 cells, using two-dimensional liquid chromatography mass spectrometry analysis. Three transitions for each peptide were selected from identified spectra and assessed using 1D-LC−MRM-MS. Collision energy (CE) and dwell times were optimized and matching transitions for “heavy” isotope-coded dimethylated peptides were calculated. Resulting LC−MRM transitions were then used to measure changes in the glycolytic proteome in insulin-like growth factor-1 (IGF-1)-stimulated MCF-7 cells and other breast cell lines. Increases in the expression of glycolysis proteins leading to lactic acid production were observed common to IGF-1-stimulated MCF-7 cells and the invasive MDA-MB-231 cell line. Preliminary analysis of lung tumors with varied states of differentiation demonstrated the clinical applicability of LC−MRM and showed decreased levels of PGK1 in poorly differentiated tumors
Relative quantitative proteomic analysis reveals wound response proteins correlated with after-cooking darkening
No full textMany common potato tuber defects are difficult to elicidate because of the degree of genetic complexity involved, making systems biology approaches necessary. Interaction between chlorogenic acid and iron is responsible for the darkening of potato tuber tissues upon heating – termed after‐cooking darkening (ACD). To explore mechanisms of darkening severity in tuber tissues, we have employed relative quantitative proteomics to discover differentially expressed proteins involved in ACD. Tuber tissue samples were collected from a family of diploid clones which possess a highly segregated degree of the darkening. Exploiting this segregation, as well as the observation that darkening is more prevalent in the stem end of the tuber than the apical end, three sample groups were formed: (i) stem ends of three high‐ACD clones, (ii) stem ends of three low‐ACD clones, and (iii) apical ends of three low‐ACD clones. Protein samples were digested and differentially labeled using isotopic reductive methylation, allowing for an orthogonal two‐way comparison of protein profiles of the sample groups using 2‐D‐LC‐MS/MS. Using a cutoff fold change of 2 between the high‐ and the low‐ACD sample groups, 30 proteins showed a correlation with tissue darkening. Overall, we observed changes in relative protein abundance that showed an enhanced wound‐response program in high‐ACD tissues. Among these proteins, five proteins were further validated at the transcript level using qRT‐PCR. These proteins may be incorporated into design strategies to create potato cultivars with low levels of ACD
Cerebral atrophy in mild cognitive impairment and Alzheimer disease: rates and acceleration.
No full textOBJECTIVE: To quantify the regional and global cerebral atrophy rates and assess acceleration rates in healthy controls, subjects with mild cognitive impairment (MCI), and subjects with mild Alzheimer disease (AD). METHODS: Using 0-, 6-, 12-, 18-, 24-, and 36-month MRI scans of controls and subjects with MCI and AD from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, we calculated volume change of whole brain, hippocampus, and ventricles between all pairs of scans using the boundary shift integral. RESULTS: We found no evidence of acceleration in whole-brain atrophy rates in any group. There was evidence that hippocampal atrophy rates in MCI subjects accelerate by 0.22%/year2 on average (p = 0.037). There was evidence of acceleration in rates of ventricular enlargement in subjects with MCI (p = 0.001) and AD (p < 0.001), with rates estimated to increase by 0.27 mL/year2 (95% confidence interval 0.12, 0.43) and 0.88 mL/year2 (95% confidence interval 0.47, 1.29), respectively. A post hoc analysis suggested that the acceleration of hippocampal loss in MCI subjects was mainly driven by the MCI subjects that were observed to progress to clinical AD within 3 years of baseline, with this group showing hippocampal atrophy rate acceleration of 0.50%/year2 (p = 0.003). CONCLUSIONS: The small acceleration rates suggest a long period of transition to the pathologic losses seen in clinical AD. The acceleration in hippocampal atrophy rates in MCI subjects in the ADNI seems to be driven by those MCI subjects who concurrently progressed to a clinical diagnosis of AD
For debate: substituting placebo controls in long-term Alzheimer's prevention trials
No full textINTRODUCTION: Novel compounds with potential to attenuate or stop the progression of Alzheimer's disease (AD) from its presymptomatic stage to dementia are being tested in man. The study design commonly used is the long-term randomized, placebo-controlled trial (RPCT), meaning that many patients will receive placebo for 18 months or longer. It is ethically problematic to expose presymptomatic AD patients, who by definition are at risk of developing dementia, to prolonged placebo treatment. As an alternative to long-term RPCTs we propose a novel clinical study design, termed the placebo group simulation approach (PGSA), using mathematical models to forecast outcomes of presymptomatic AD patients from their own baseline data. Forecasted outcomes are compared with outcomes observed on candidate drugs, thus replacing a concomitant placebo group. METHODS: First models were constructed using mild cognitive impairment (MCI) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. One outcome is the Alzheimer Disease Assessment Scale - cognitive subscale (ADAScog) score after 24 months, predicted in a linear regression model; the other is the trajectory over 36 months of a composite neuropsychological test score (Neuro-Psychological Battery (NP-Batt)), using a mixed model. Demographics and clinical, biological and neuropsychological baseline values were tested as potential predictors in both models. RESULTS: ADAScog scores after 24 months are predicted from gender, obesity, Functional Assessment Questionnaire (FAQ) and baseline scores of Mini-Mental State Examination, ADAScog and NP-Batt with an R2 of 0.63 and a residual standard deviation of 0.67, allowing reasonably precise estimates of sample means. The model of the NP-Batt trajectory has random intercepts and slopes and fixed effects for body mass index, time, apolipoprotein E4, age, FAQ, baseline scores of ADAScog and NP-Batt, and four interaction terms. Estimates of the residual standard deviation range from 0.3 to 0.5 on a standard normal scale. If novel drug candidates are expected to diminish the negative slope of scores with time, a change of 0.04 per year could be detected in samples of 400 with a power of about 80%. CONCLUSIONS: First PGSA models derived from ADNI MCI data allow prediction of cognitive endpoints and trajectories that correspond well with real observed values. Corroboration of these models with data from other observational studies is ongoing. It is suggested that the PGSA may complement RPCT designs in forthcoming long-term drug studies with presymptomatic AD individuals
A qualitative proteome investigation of the sediment portion of human urine: Implications in the biomarker discovery process
No full textInherent to the biomarker discovery process is a comparative analysis of physiological states. It is therefore critical that the proteome detection protocol does not bias the analysis. With urine, the sediment portion, obtained upon thawing frozen urine, is routinely discarded prior to proteome analysis. However, our results demonstrate that such a practice inadvertently induces bias, having significant implications in the biomarker discovery process. We present the first proteome investigation of human urinary sediments, identifying 60 proteins in this phase by MS. Many sediment proteins were also detected in the urinary supernatant, indicating that several proteins partition between the two phases. This partitioning is dependant on the pH of the sample, as well as the degree of sample agitation. As a consequence of discarding the sediment portion of urine, the concentration of potential candidate biomarkers in the supernatant phase will be altered or, in other instances, may be completely removed from the sample. To minimize this, the pH of all samples should first be normalized, and the samples vigorously vortexed prior to discarding the sediments. For more comprehensive biomarker investigations, we suggest that urinary sediments be analyzed along with the supernatant proteins
