Over the last two decades, two-dimensional electrophoresis (2-DE) gel has established itself as the de facto approach to separating proteins from cell and tissue samples. Due to the sheer volume of data and its experimental geometric and expression uncertainties, quantitative analysis of these data with image processing and modelling has become an actively pursued research topic. The results of these analyses include accurate protein quantification, isoelectric point and relative molecular mass estimation, and the detection of differential expression between samples run on different gels. Systematic errors such as current leakage and regional expression inhomogeneities are corrected for, followed by each protein spot in the gel being segmented and modelled for quantification. To assess differential expression of protein spots in different samples run on a series of two-dimensional gels, a number of image registration techniques for correcting geometric distortion have been proposed. This paper provides a comprehensive review of the computation techniques used in the analysis of 2-DE gels, together with a discussion of current and future trends in large scale analysis. We examine the pitfalls of existing techniques and highlight some of the key areas that need to be developed in the coming years, especially those related to statistical approaches based on multiple gel runs and image mining techniques through the use of parallel processing based on cluster computing and the grid technology.

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that currently affects 36 million people worldwide with no effective treatment available. Development of AD follows a distinctive pattern in the brain and is poorly modelled in animals. Therefore, it is vital to widen the spatial scope of the study of AD and prioritise the study of human brains. Here we show that functionally distinct human brain regions display varying and region-specific changes in protein expression. These changes provide insights into the progression of disease, novel AD-related pathways, the presence of a gradient of protein expression change from less to more affected regions and a possibly protective protein expression profile in the cerebellum. This spatial proteomics analysis provides a framework which can underpin current research and open new avenues to enhance molecular understanding of AD pathophysiology, provide new targets for intervention and broaden the conceptual frameworks for future AD research.

SUMMARYCoupled fluid-structure interaction (FSI) analysis of the human right coronary artery (RCA) has been carried out to investigate the effects of wall compliance on coronary hemodynamics. A 3-D model of a stenosed RCA was reconstructed based on multislice computerized tomography images. A velocity waveform in the proximal RCA and a pressure waveform in the distal RCA of a patient with a severe stenosis were acquired with a catheter delivered wire probe and applied as boundary conditions. The arterial wall was modeled as a Mooney-Rivlin hyperelastic material. The predicted maximum wall displacement (3.85 mm) was comparable with the vessel diameter (∼ 4mm), but the diameter variation was much smaller, 0.134 mm at the stenosis and 0.486 mm in the distal region. Comparison of the computational results between the FSI and rigid-wall models showed that the instantaneous wall shear stress (WSS) distributions were affected by diameter variation in the arterial wall; increasing systolic blood pressure dilated the vessel and consequently lowered WSS, whereas the opposite occurred when pressure started to decrease. However, the effects of wall compliance on time-averaged WSS (TAWSS) and oscillatory shear index (OSI) were insignificant (4.5 and 2.7% difference in maximum TAWSS and OSI, respectively).

The issue of antimicrobial resistance is of global concern across human and animal health. In 2016, the UK government committed to new targets for reducing antimicrobial use (AMU) in livestock. Although a number of metrics for quantifying AMU are defined in the literature, all give slightly different interpretations. This paper evaluates a selection of metrics for AMU in the dairy industry: total mg, total mg/kg, daily dose and daily course metrics. Although the focus is on their application to the dairy industry, the metrics and issues discussed are relevant across livestock sectors. In order to be used widely, a metric should be understandable and relevant to the veterinarians and farmers who are prescribing and using antimicrobials. This means that clear methods, assumptions (and possible biases), standardised values and exceptions should be published for all metrics. Particularly relevant are assumptions around the number and weight of cattle at risk of treatment and definitions of dose rates and course lengths; incorrect assumptions can mean metrics over-represent or under-represent AMU. The authors recommend that the UK dairy industry work towards the UK-specific metrics using the UK-specific medicine dose and course regimens as well as cattle weights in order to monitor trends nationally.

Impairment of brain-glucose uptake and brain-copper regulation occurs in Alzheimer’s disease (AD). Here we sought to further elucidate the processes that cause neurodegeneration in AD by measuring levels of metabolites and metals in brain regions that undergo different degrees of damage. We employed mass spectrometry (MS) to measure metabolites and metals in seven post-mortem brain regions of nine AD patients and nine controls, and plasma-glucose and plasma-copper levels in an ante-mortem case-control study. Glucose, sorbitol and fructose were markedly elevated in all AD brain regions, whereas copper was correspondingly deficient throughout (all P < 0.0001). In the ante-mortem case-control study, by contrast, plasma-glucose and plasma-copper levels did not differ between patients and controls. There were pervasive defects in regulation of glucose and copper in AD brain but no evidence for corresponding systemic abnormalities in plasma. Elevation of brain glucose and deficient brain copper potentially contribute to the pathogenesis of neurodegeneration in AD.

The open XML format mzML, used for representation of MS data, is pivotal for the development of platform-independent MS analysis software. Although conversion from vendor formats to mzML must take place on a platform on which the vendor libraries are available (i.e. Windows), once mzML files have been generated, they can be used on any platform. However, the mzML format has turned out to be less efficient than vendor formats. In many cases, the naïve mzML representation is fourfold or even up to 18-fold larger compared with the original vendor file. In disk I/O limited setups, a larger data file also leads to longer processing times, which is a problem given the data production rates of modern mass spectrometers. In an attempt to reduce this problem, we here present a family of numerical compression algorithms called MS-Numpress, intended for efficient compression of MS data. To facilitate ease of adoption, the algorithms target the binary data in the mzML standard, and support in main proteomics tools is already available. Using a test set of 10 representative MS data files we demonstrate typical file size decreases of 90% when combined with traditional compression, as well as read time decreases of up to 50%. It is envisaged that these

High glucose levels in the peripheral nervous system (PNS) have been implicated in the pathogenesis of diabetic neuropathy (DN). However, our understanding of the molecular mechanisms that cause the marked distal pathology is incomplete. We performed a comprehensive, system-wide analysis of the PNS of a rodent model of DN. We integrated proteomics and metabolomics from the sciatic nerve (SN), the lumbar 4/5 dorsal root ganglia (DRG), and the trigeminal ganglia (TG) of streptozotocin-diabetic and healthy control rats. Even though all tissues showed a dramatic increase in glucose and polyol pathway intermediates in diabetes, a striking upregulation of mitochondrial oxidative phosphorylation and perturbation of lipid metabolism was found in the distal SN that was not present in the corresponding cell bodies of the DRG or the cranial TG. This finding suggests that the most severe molecular consequences of diabetes in the nervous system present in the SN, the region most affected by neuropathy. Such spatial metabolic dysfunction suggests a failure of energy homeostasis and/or oxidative stress, specifically in the distal axon/Schwann cell-rich SN. These data provide a detailed molecular description of the distinct compartmental effects of diabetes on the PNS that could underlie the distal-proximal distribution of pathology.Diabetic neuropathy (DN) will develop in ;30-50% of patients with diabetes. DN typically presents with sensory symptoms in a distal glove-and-stocking distribution (1,2). It is a poorly understood complication of diabetes, and currently, few treatments are available (3). Raised glucose has long been believed to instigate pathology in DN either through direct neurotoxicity or from the activation of secondary pathways (4,5). However, exactly how these pathways cause nerve conduction velocity deficits, neuropathic pain, distal axonopathy, and numbness in the extremities continues to elude us, and many clinical trials aimed at specific targets have failed due to lack of efficacy (3).Crossover exists between proposed pathogenic pathways in DN, but how these interact is unclear. This question can be approached by implementing extensive -omic technologies to measure many transcripts, proteins, or metabolites in parallel. Gene microarrays were among the first of these technologies to be used, and transcriptomic analyses have been performed on tissues such as the dorsal root ganglia (DRG) from streptozotocin (STZ)-diabetic rats compared with healthy controls (6), the sciatic nerve (SN) of db/db mice compared with those from db/+ mice (7), and sural nerve biopsy specimens from human patients whose neuropathy progressed over 1 year compared with those whose neuropathy did not (based on myelinated fiber density loss) (8). Common changes across these gene array studies highlight altered carbohydrate and lipid metabolism.Because gene transcript levels do not always correlate with protein expression due to varying transcriptional/translational

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that currently affects 36 million people worldwide with no effective treatment available. Development of AD follows a distinctive pattern in the brain and is poorly modelled in animals. Therefore, it is vital to widen both the spatial scope of the study of AD and prioritise the study of human brains. Here we show that functionally distinct human brain regions show varying and region-specific changes in protein expression. These changes provide novel insights into the progression of disease, novel AD-related pathways, the presence of a ‘gradient’ of protein expression change from less to more affected regions, and the presence of a ‘protective’ protein expression profile in the cerebellum. This spatial proteomics analysis provides a framework which can underpin current research and opens new avenues of interest to enhance our understanding of molecular pathophysiology of AD, provides new targets for intervention and broadens the conceptual frameworks for future AD research.