Microscale mesoarrays created by dip-pen nanolithography for screening of protein-protein interactions

Using microarrays to probe protein-protein interactions is becoming increasingly attractive due to their compatibility with highly sensitive detection techniques, selectivity of interaction, robustness and capacity for examining multiple proteins simultaneously. The major drawback to using this approach is the relatively large volumes and high concentrations necessary. Reducing the protein array spot size should allow for smaller volumes and lower concentrations to be used as well as opening the way for combination with more sensitive detection technologies. Dip-Pen Nanolithography (DPN) is a recently developed technique for structure creation on the nano to microscale with the capacity to create biological architectures. Here we describe the creation of miniaturised microarrays, 'mesoarrays', using DPN with protein spots 400× smaller by area compared to conventional microarrays. The mesoarrays were then used to probe the ERK2-KSR binding event of the Ras/Raf/MEK/ERK signalling pathway at a physical scale below that previously reported. Whilst the overall assay efficiency was determined to be low, the mesoarrays could detect KSR binding to ERK2 repeatedly and with low non-specific binding. This study serves as a first step towards an approach that can be used for analysis of proteins at a concentration level comparable to that found in the cellular environment.

Dose-dependent modulation of the T cell proteome by ascorbic acid

To investigate the hypothesis that the micronutrient ascorbic acid can modulate the functional genome, T cells (CCRF-HSB2) were treated with ascorbic acid (up to 150 μM) for up to 24 h. Protein expression changes were assessed by two-dimensional electrophoresis. Forty-one protein spots which showed greater than two-fold expression changes were subject to identification by matrix-assisted laser desorption ionisation time of flight MS. The confirmed protein identifications were clustered into five groups; proteins were associated with signalling, carbohydrate metabolism, apoptosis, transcription and immune function. The increased expression of phosphatidylinositol transfer protein (promotes intracellular signalling) within 5 min of ascorbic acid treatment was confirmed by Western blotting. Together, these observations suggest that ascorbic acid modulates the T cell proteome in a time- and dose-dependent manner and identify molecular targets for study following antioxidant supplementation in vivo.

The anti-inflammatory action of tetracyclines

For six decades tetracyclines have been successfully used for their broad spectrum antibiotic effects. However, non-antibiotic effects of tetracyclines have been reported. The anti-inflammatory effects of tetracycline drugs have been investigated in the context of a range of inflammatory diseases including sepsis and a number of neurodegenerative diseases. This thesis investigates the effects of a range of clinically important tetracyclines (oxytetracycline, doxycycline, minocycline and tigecycline) on the ability of the J774.2 cell line to produce nitric oxide when stimulated with the bacterial cell wall component, LPS. The proteome of J774.2 cells was analysed in response to LPS stimulation (1 µg/ml) with and without prior treatment with minocycline (50µg/ml), this allows the unbiased analysis of the cellular proteome in response to minocycline and LPS, protein spots of interest were excised and identified by nano-electrospray ionisation-linear ion trap mass spectroscopy. All of the tetracyclines that were investigated inhibited LPS-induced nitric oxide production in a dose dependent manner and this was due to the inhibition of inducible nitric oxide synthase expression. This is the first report to show that tigecycline inhibits inducible nitric oxide expression and nitric oxide production. Using two-dimensional gel electrophoresis and total protein staining eleven proteins were identified as being modulated by LPS. Of these eleven proteins; expression of some, but not all was modulated when the cells received a prior treatment with minocycline suggesting that minocycline does not completely block LPS-induced macrophage activation but probably specifically acts on particular inflammatory signaling pathways in macrophages. Three protein spots with a similar molecular weight but different pI values identified in this proteomic study were identified as ATP synthase ß chain. These different protein spots probably correspond to different phosphorylation states of the protein, suggesting that minocycline affects the balance of protein kinase and protein phosphatase activity in the immune response.

CD4+ T cell surface alpha enolase is lower in older adults

To identify novel cell ageing markers in order to gain insight into ageing mechanisms, we adopted membrane enrichment and comparison of the CD4+ T cell membrane proteome (purified by cell surface labelling using Sulfo-NHS-SS-Biotin reagent) between healthy young (n=9, 20-25y) and older (n=10; 50-70y) male adults. Following two-dimensional gel electrophoresis (2DE) to separate pooled membrane proteins in triplicates, the identity of protein spots with age-dependent differences (p<0.05 and >1.4 fold difference) was determined using liquid chromatography-mass spectrometry (LC-MS/MS). Seventeen protein spot density differences (ten increased and seven decreased in the older adult group) were observed between young and older adults. From spot intensity analysis, CD4+ T cell surface α-enolase was decreased in expression by 1.5 fold in the older age group; this was verified by flow cytometry (n=22) and qPCR with significantly lower expression of cellular α-enolase mRNA and protein compared to young adult CD4+ T cells (p<0.05). In an independent age-matched case-control study, lower CD4+ T cell surface α-enolase expression was observed in age-matched patients with cardiovascular disease (p<0.05). An immune-modulatory role has been proposed for surface α-enolase and our findings of decreased expression suggest that deficits in surface α-enolase merit investigation in the context of immune dysfunction during ageing and vascular disease.

Accurate estimation of isoelectric point of protein and peptide based on amino acid sequences

Motivation: In any macromolecular polyprotic system - for example protein, DNA or RNA - the isoelectric point - commonly referred to as the pI - can be defined as the point of singularity in a titration curve, corresponding to the solution pH value at which the net overall surface charge - and thus the electrophoretic mobility - of the ampholyte sums to zero. Different modern analytical biochemistry and proteomics methods depend on the isoelectric point as a principal feature for protein and peptide characterization. Protein separation by isoelectric point is a critical part of 2-D gel electrophoresis, a key precursor of proteomics, where discrete spots can be digested in-gel, and proteins subsequently identified by analytical mass spectrometry. Peptide fractionation according to their pI is also widely used in current proteomics sample preparation procedures previous to the LC-MS/MS analysis. Therefore accurate theoretical prediction of pI would expedite such analysis. While such pI calculation is widely used, it remains largely untested, motivating our efforts to benchmark pI prediction methods. Results: Using data from the database PIP-DB and one publically available dataset as our reference gold standard, we have undertaken the benchmarking of pI calculation methods. We find that methods vary in their accuracy and are highly sensitive to the choice of basis set. The machine-learning algorithms, especially the SVM-based algorithm, showed a superior performance when studying peptide mixtures. In general, learning-based pI prediction methods (such as Cofactor, SVM and Branca) require a large training dataset and their resulting performance will strongly depend of the quality of that data. In contrast with Iterative methods, machine-learning algorithms have the advantage of being able to add new features to improve the accuracy of prediction. Contact: yperez@ebi.ac.uk Availability and Implementation: The software and data are freely available at https://github.com/ypriverol/pIR. Supplementary information: Supplementary data are available at Bioinformatics online.