Project description:Multiple Affinity Removal Spin Cartridge Human 14 kit (Agilent) was used to remove 14 major proteins from plasma. Samples were denatured with an equivalent volume of trifluoroethanol and reduced with dithiothreitol. Free cysteine residues were alkylated with iodoacetamide. Samples were incubated with trypsin and desalted with C18 ZipTip. Desalted samples were rehydrated in 0.1% formic acid (FA) and were analyzed by LC-MS using a nanoLC Eksigent 400 system (Eksigent, AB Sciex), coupled online to an TripleTOF6600 mass spectrometer (AB Sciex). Peptide separation was performed using liquid chromatography with a trap and elution conﬁguration using a nano trap column (350 μm × 0.5 mm, 3 μm, 120 Å, AB Sciex) and a nano ChromXP C18 reverse phase column (75 μm × 15 cm, 3 μm, 120 Å, AB Sciex) at 300 nl/min with a 90 min linear gradient of 8-30% acetonitrile in 0.1% FA, and then, with a 10 min linear gradient of 30% to 40% acetonitrile in 0.1% FA. The mass spectrometer was operated in information-dependent acquisition (IDA) mode, scanning full spectra (400–1500 m/z) for 250 ms, followed by up to 30 MS/MS scans (100–1800 m/z for 50 ms each), for a cycle time of 1.8 s. Candidate ions with a charge state between +2 and + 5 and counts above a minimum threshold of 125 counts per second were isolated for fragmentation, and one MS/MS spectrum was collected before adding those ions to the exclusion list for 12 s. Rolling collision energy was used with a collision energy spread of 15. The mass spectrometer was operated using the Analyst TF 1.7.1 software program (AB Sciex). For data dependent acquisition (DDA, SWATH acquisition), the parameters were set as follows: 100 ms TOF MS scan, followed by 200 variable SWATH windows each at 50 ms accumulation time for m/z 400–1250. MS/MS SWATH scans were set at 5 Da window overlapping by 1 Da for m/z 400–1250 and varied on each side of the mass range. The total cycle time was 9.6 s. A rolling collision energy (CE) parameters script was used to automatically control the CE. Acquired spectra were searched against the UniProt reviewed database using the Paragon algorithm embedded in the ProteinPilot 5.0.1 software program (AB Sciex), with the following search parameters: (i) sample type: identiﬁcation, (ii) Cys alkylation: iodoacetamide, (iii) digestion: trypsin, (iv) instrument: TripleTOF 6600, (v) species: Homo sapiens, (vi) ID focus: biological modiﬁcations, (vii) detected protein threshold: > 0.05 (10% conﬁdence). The detected protein threshold was set to the minimum level to enhance the number of wrong answers to enable the curve ﬁtting by an independent FDR analysis. This was carried out by the target-decoy approach provided with the ProteinPilot software program, which was used to assess the quality of the identiﬁcations. Positive identiﬁcations were considered when identiﬁed proteins and peptides reached a 1% local FDR. The resulting group file was loaded into Peakview (v2.2.0, AB Sciex) and peaks from SWATH runs were extracted with a peptide confidence threshold of 99% and a false discovery rate <1%. The SWATH ﬁles were then exported to the MarkerView software program (version 1.3.0.1; AB Sciex) and the peak areas of individual peptides were normalized to the sum of the peak areas of all detected peptides.

Project description:BackgroundProgression of mild cognitive impairment (MCI) to Alzheimer's disease (AD) dementia can be predicted by clinical features and a combination of biomarkers may increase the predictive power. In the present study, we investigated whether the combination of olfactory function and plasma neuronal-derived exosome (NDE) Aβ1-42 can best predict progression to AD dementia.Methods87 MCI patients were enrolled and received the cognitive assessment at 2-year and 3-year follow-up to reevaluate cognition. In the meanwhile, 80 healthy controls and 88 AD dementia patients were enrolled at baseline as well to evaluate the diagnose value in cross-section. Olfactory function was evaluated with the sniffin sticks (SS-16) and Aβ1-42 levels in NDEs were determined by ELISA. Logistic regression was performed to evaluate the risk factors for cognitive decline in MCI at 2-year and 3-year revisits.ResultsIn the cross cohort, lower SS-16 scores and higher Aβ1-42 levels in NDEs were found in MCI and AD dementia compared to healthy controls. For the longitudinal set, 8 MCI individuals developed AD dementia within 2 years, and 16 MCI individuals developed AD dementia within 3 years. The two parameter-combination of SS-16 scores and Aβ1-42 level in NDEs showed better prediction in the conversion of MCI to AD dementia at 2-year and 3-year revisit. Moreover, after a 3-year follow-up, SS-16 scores also significantly predicted the conversion to AD dementia, where lower scores were associated with a 10-fold increased risk of developing AD dementia (p = 0.006). Similarly, higher Aβ1-42 levels in NDEs in patients with MCI increased the risk of developing AD dementia by 8.5-fold (p = 0.002).ConclusionA combination of two biomarkers of NDEs (Aβ1-42) and SS-16 predicted the conversion of MCI to AD dementia more accurately in combination. These findings have critical implications for understanding the pathophysiology of AD dementia and for developing preventative treatments for cognitive decline.

Dataset Information

Increased plasma brain-derived neurotrophic factor (BDNF) as a potential biomarker for and compensatory mechanism in mild cognitive impairment: a case-control study.

Background

Methods

Results

Conclusions

Publications

Increased plasma brain-derived neurotrophic factor (BDNF) as a potential biomarker for and compensatory mechanism in mild cognitive impairment: a case-control study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets