Epidemiology of Escherichia coli bloodstream infection antimicrobial resistance trends across South West England during the first 2 years of the coronavirus disease 2019 pandemic response

OBJECTIVES

Between 2016 and 2019, the proportion of Escherichia coli bloodstream infection (BSI) with resistance to at least one antibiotic increased nationally. Public health interventions implemented in response to the COVID-19 pandemic changed population contact patterns and healthcare systems, with consequent effects on epidemiological trends of numerous pathogens. We investigated the impact of COVID-19 restrictions on epidemiological trends of E. coli BSI antimicrobial resistance (AMR) across South West England.

METHODS

We undertook a retrospective ecological analysis utilizing routine surveillance data of E. coli BSI cases reported to the UK Health Security Agency between 2016 and 2021. We analysed AMR trends for antimicrobial agents including amoxicillin-clavulanate, ciprofloxacin, piperacillin-tazobactam, gentamicin, third-generation cephalosporins and carbapenems before and after the implementation of COVID-19 restrictions (23 March 2020) using Bayesian segmented regression.

RESULTS

We identified 19 055 cases. A total of 50.2% were male. Median age was 76 (interquartile range, 65-85 years). Piperacillin-tazobactam (-2.90% [95% highest density interval {HDI} -4.51%, -0.48%]) and ciprofloxacin (-2.40% [95% HDI -4.35%, 0.48%]) resistance demonstrated immediate step changes at the implementation of COVID-19 restrictions. Gentamicin (odds ratio [OR] 0.92 [95% HDI 0.76, 1.12]) and third-generation cephalosporins (OR 0.95 [95% HDI 0.80, 1.14]) exhibited decreasing annual resistance trends after the implementation of COVID-19 restrictions, with moderate evidence for a lower OR after restrictions as compared to the period before (gentamicin Bayes Factor = 5.10, third-generation cephalosporins Bayes Factor = 6.67).

DISCUSSION

COVID-19 restrictions led to abrupt and longer term changes to E.coli BSI AMR. The immediate effects suggest altered transmission, whereas changes to resistant E. coli reservoirs may explain trend effects.

Five Inhibitory Receptors Display Distinct Vesicular Distributions in Murine T Cells

T cells can express multiple inhibitory receptors. Upon induction of T cell exhaustion in response to a persistent antigen, prominently in the anti-tumor immune response, many are expressed simultaneously. Key inhibitory receptors are CTLA-4, PD-1, LAG3, TIM3, and TIGIT, as investigated here. These receptors are important as central therapeutic targets in cancer immunotherapy. Inhibitory receptors are not constitutively expressed on the cell surface, but substantial fractions reside in intracellular vesicular structures. It remains unresolved to which extent the subcellular localization of different inhibitory receptors is distinct. Using quantitative imaging of subcellular distributions and plasma membrane insertion as complemented by proximity proteomics and biochemical analysis of the association of the inhibitory receptors with trafficking adaptors, the subcellular distributions of the five inhibitory receptors were discrete. The distribution of CTLA-4 was most distinct, with preferential association with lysosomal-derived vesicles and the sorting nexin 1/2/5/6 transport machinery. With a lack of evidence for the existence of specific vesicle subtypes to explain divergent inhibitory receptor distributions, we suggest that such distributions are driven by divergent trafficking through an overlapping joint set of vesicular structures. This extensive characterization of the subcellular localization of five inhibitory receptors in relation to each other lays the foundation for the molecular investigation of their trafficking and its therapeutic exploitation.

Five inhibitory receptors display distinct vesicular distributions in T cells

T cells can express multiple inhibitory receptors. Upon induction of T cell exhaustion in response to persistent antigen, prominently in the anti-tumor immune response, many are expressed simultaneously. Key inhibitory receptors are CTLA-4, PD-1, LAG3, TIM3 and TIGIT, as investigated here. These receptors are important as central therapeutic targets in cancer immunotherapy. Inhibitory receptors are not constitutively expressed on the cell surface, but substantial fractions reside in intracellular vesicular structures. It remains unresolved to which extent the subcellular localization of different inhibitory receptors is distinct. Using quantitative imaging of subcellular distributions and plasma membrane insertion as complemented by proximity proteomics and a biochemical analysis of the association of the inhibitory receptors with trafficking adaptors, the subcellular distributions of the five inhibitory receptors were discrete. The distribution of CTLA-4 was most distinct with preferential association with lysosomal-derived vesicles and the sorting nexin 1/2/5/6 transport machinery. With a lack of evidence for the existence of specific vesicle subtypes to explain divergent inhibitory receptor distributions, we suggest that such distributions are driven by divergent trafficking through an overlapping joint set of vesicular structures. This extensive characterization of the subcellular localization of five inhibitory receptors in relation to each other lays the foundation for the molecular investigation of their trafficking and its therapeutic exploitation.

Uncertainty-Aware Protein-Level Quantification and Differential Expression Analysis of Proteomics Data with seaMass

seaMass is an R package for protein-level quantification, normalization, and differential expression analysis of proteomics mass spectrometry data after peptide identification, protein grouping, and feature-level quantification. Using the concept of a blocked experimental design, seaMass can analyze all common discovery proteomics paradigms, including label-free (e.g., Waters Progenesis input), SILAC (e.g., MaxQuant input), isotope labelling (e.g., SCIEX ProteinPilot iTraq and Thermo ProteomeDiscoverer TMT input), and data-independent acquisition (e.g., OpenSWATH-PyProphet input), and is able to scale to study with hundreds of assays or more. By utilizing hierarchical Bayesian modelling, seaMass assesses the quantification reliability of each feature and peptide across assays so that only those in consensus influence the resulting protein group quantification strongly. Similarly, unexplained variation in each individual assay is captured, providing both a metric for quality control and automatic down-weighting of suspect assays. To achieve this, each protein group-level quantification outputted by seaMass is accompanied by the standard deviation of its posterior uncertainty. Moreover, seaMass integrates a flexible differential expression analysis subsystem with false discovery rate control based on the popular MCMCglmm package for Bayesian mixed-effects modelling, and also provides uncertainty-aware principal components analysis. We provide a description for using seaMass to perform an end-to-end analysis using a real dataset associated with a published clinical proteomics study.

Diagnostic MALDI-TOF MS can differentiate between high and low toxic Staphylococcus aureus bacteraemia isolates as a predictor of patient outcome

Staphylococcus aureus bacteraemia (SAB) is a major cause of blood-stream infection (BSI) in both healthcare and community settings. While the underlying comorbidities of a patient significantly contributes to their susceptibility to and outcome following SAB, recent studies show the importance of the level of cytolytic toxin production by the infecting bacterium. In this study we demonstrate that this cytotoxicity can be determined directly from the diagnostic MALDI-TOF mass spectrum generated in a routine diagnostic laboratory. With further development this information could be used to guide the management and improve the outcomes for SAB patients.

Mast cell infiltration of the choroid and protease release are early events in age-related macular degeneration associated with genetic risk at both chromosomes 1q32 and 10q26

Age-related macular degeneration (AMD) is a leading cause of visual loss. It has a strong genetic basis, and common haplotypes on chromosome (Chr) 1 (CFH Y402H variant) and on Chr10 (near HTRA1/ARMS2) contribute the most risk. Little is known about the early molecular and cellular processes in AMD, and we hypothesized that analyzing submacular tissue from older donors with genetic risk but without clinical features of AMD would provide biological insights. Therefore, we used mass spectrometry–based quantitative proteomics to compare the proteins in human submacular stromal tissue punches from donors who were homozygous for high-risk alleles at either Chr1 or Chr10 with those from donors who had protective haplotypes at these loci, all without clinical features of AMD. Additional comparisons were made with tissue from donors who were homozygous for high-risk Chr1 alleles and had early AMD. The Chr1 and Chr10 risk groups shared common changes compared with the low-risk group, particularly increased levels of mast cell–specific proteases, including tryptase, chymase, and carboxypeptidase A3. Histological analyses of submacular tissue from donors with genetic risk of AMD but without clinical features of AMD and from donors with Chr1 risk and AMD demonstrated increased mast cells, particularly the tryptase-positive/chymase-negative cells variety, along with increased levels of denatured collagen compared with tissue from low–genetic risk donors. We conclude that increased mast cell infiltration of the inner choroid, degranulation, and subsequent extracellular matrix remodeling are early events in AMD pathogenesis and represent a unifying mechanistic link between Chr1- and Chr10-mediated AMD.

Coenzyme A-Dependent Tricarboxylic Acid Cycle Enzymes Are Decreased in Alzheimer's Disease Consistent With Cerebral Pantothenate Deficiency

Sporadic Alzheimer's disease (sAD) is the commonest cause of age-related neurodegeneration and dementia globally, and a leading cause of premature disability and death. To date, the quest for a disease-modifying therapy for sAD has failed, probably reflecting our incomplete understanding of aetiology and pathogenesis. Drugs that target aggregated Aβ/tau are ineffective, and metabolic defects are now considered to play substantive roles in sAD pathobiology. We tested the hypothesis that the recently identified, pervasive cerebral deficiency of pantothenate (vitamin B5) in sAD, might undermine brain energy metabolism by impairing levels of tricarboxylic acid (TCA)-cycle enzymes and enzyme complexes, some of which require the pantothenate-derived cofactor, coenzyme A (CoA) for their normal functioning. We applied proteomics to measure levels of the multi-subunit TCA-cycle enzymes and their cytoplasmic homologues. We analysed six functionally distinct brain regions from nine sAD cases and nine controls, measuring 33 cerebral proteins that comprise the nine enzymes of the mitochondrial-TCA cycle. Remarkably, we found widespread perturbations affecting only two multi-subunit enzymes and two enzyme complexes, whose function is modulated, directly or indirectly by CoA: pyruvate dehydrogenase complex, isocitrate dehydrogenase, 2-oxoglutarate dehydrogenase complex, and succinyl-CoA synthetase. The sAD cases we studied here displayed widespread deficiency of pantothenate, the obligatory precursor of CoA. Therefore, deficient cerebral pantothenate can damage brain-energy metabolism in sAD, at least in part through impairing levels of these four mitochondrial-TCA-cycle enzymes.

What's Stopping Knowledge Synthesis? A Systematic Review of Recent Practices in Research on Smallholder Diversity

A systematic review of recent publications was conducted to assess the extent to which contemporary micro-level research on smallholders facilitates data re-use and knowledge synthesis. Following PRISMA standards for systematic review, 1,182 articles were identified (published between 2018 and 2020), and 261 articles were selected for review in full. The themes investigated were: (i) data management, including data source, variables collected, granularity, and availability of the data; (ii) the statistical methods used, including analytical approach and reproducibility; and (iii) the interpretation of results, including the scope and objectives of the study, development issues addressed, scale of recommendations made relative to the scale of the sample, and the audience for recommendations. It was observed that household surveys were the most common data source and tended to be representative at the local (community) level. There was little harmonization of the variables collected between studies. Over three quarters of the studies (77%) drew on data which was not in the public domain, 14% published newly open data, and 9% drew on datasets which were already open. Other than descriptive statistics, linear and logistic regression methods were the most common analytical method used (64% of articles). In the vast majority of those articles, regression was used as an explanatory tool, as opposed to a predictive tool. More than half of the articles (59%) made claims or recommendations which extended beyond the coverage of their datasets. In combination these two common practices may lead to erroneous understanding: the tendency to rely upon simple regressions to explain context-specific and complex associations; and the tendency to generalize beyond the remit of the data collected. We make four key recommendations: (1) increased data sharing and variable harmonization would enable data to be re-used between studies; (2) providing detailed meta-data on sampling frames and study-context would enable more powerful meta-analyses; (3) methodological openness and predictive modeling could help test the transferability of approaches; (4) more precise language in study conclusions could help decision makers understand the relevance of findings for policy planning. Following these practices could leverage greater benefits from the substantial investment already made in data collection on smallholder farms.

mzMLb: A Future-Proof Raw Mass Spectrometry Data Format Based on Standards-Compliant mzML and Optimized for Speed and Storage Requirements

With ever-increasing amounts of data produced by mass spectrometry (MS) proteomics and metabolomics, and the sheer volume of samples now analyzed, the need for a common open format possessing both file size efficiency and faster read/write speeds has become paramount to drive the next generation of data analysis pipelines. The Proteomics Standards Initiative (PSI) has established a clear and precise extensible markup language (XML) representation for data interchange, mzML, receiving substantial uptake; nevertheless, storage and file access efficiency has not been the main focus. We propose an HDF5 file format "mzMLb" that is optimized for both read/write speed and storage of the raw mass spectrometry data. We provide an extensive validation of the write speed, random read speed, and storage size, demonstrating a flexible format that with or without compression is faster than all existing approaches in virtually all cases, while with compression is comparable in size to proprietary vendor file formats. Since our approach uniquely preserves the XML encoding of the metadata, the format implicitly supports future versions of mzML and is straightforward to implement: mzMLb's design adheres to both HDF5 and NetCDF4 standard implementations, which allows it to be easily utilized by third parties due to their widespread programming language support. A reference implementation within the established ProteoWizard toolkit is provided.

Cognitive dysfunction in diabetic rats is prevented by pyridoxamine treatment. A multidisciplinary investigation

OBJECTIVE

The impact of diabetes mellitus on the central nervous system is less widely studied than in the peripheral nervous system, but there is increasing evidence that it elevates the risk of developing cognitive deficits. The aim of this study was to characterize the impact of experimental diabetes on the proteome and metabolome of the hippocampus. We tested the hypothesis that the vitamin B6 isoform pyridoxamine is protective against functional and molecular changes in diabetes.

METHODS

We tested recognition memory using the novel object recognition (NOR) test in streptozotocin (STZ)-induced diabetic, age-matched control, and pyridoxamine- or insulin-treated diabetic male Wistar rats. Comprehensive untargeted metabolomic and proteomic analyses, using gas chromatography-mass spectrometry and iTRAQ-enabled protein quantitation respectively, were utilized to characterize the molecular changes in the hippocampus in diabetes.

RESULTS

We demonstrated diabetes-specific, long-term (but not short-term) recognition memory impairment and that this deficit was prevented by insulin or pyridoxamine treatment. Metabolomic analysis showed diabetes-associated changes in 13/82 identified metabolites including polyol pathway intermediates glucose (9.2-fold), fructose (4.9-fold) and sorbitol (5.2-fold). We identified and quantified 4807 hippocampal proteins; 806 were significantly altered in diabetes. Pathway analysis revealed significant alterations in cytoskeletal components associated with synaptic plasticity, glutamatergic signaling, oxidative stress, DNA damage and FXR/RXR activation pathways in the diabetic rat hippocampus.

CONCLUSIONS

Our data indicate a protective effect of pyridoxamine against diabetes-induced cognitive deficits, and our comprehensive 'omics datasets provide insight into the pathogenesis of cognitive dysfunction enabling development of further mechanistic and therapeutic studies.

Regional protein expression in human Alzheimer's brain correlates with disease severity

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.

Expanding the Use of Spectral Libraries in Proteomics

The 2017 Dagstuhl Seminar on Computational Proteomics provided an opportunity for a broad discussion on the current state and future directions of the generation and use of peptide tandem mass spectrometry spectral libraries. Their use in proteomics is growing slowly, but there are multiple challenges in the field that must be addressed to further increase the adoption of spectral libraries and related techniques. The primary bottlenecks are the paucity of high quality and comprehensive libraries and the general difficulty of adopting spectral library searching into existing workflows. There are several existing spectral library formats, but none captures a satisfactory level of metadata; therefore, a logical next improvement is to design a more advanced, Proteomics Standards Initiative-approved spectral library format that can encode all of the desired metadata. The group discussed a series of metadata requirements organized into three designations of completeness or quality, tentatively dubbed bronze, silver, and gold. The metadata can be organized at four different levels of granularity: at the collection (library) level, at the individual entry (peptide ion) level, at the peak (fragment ion) level, and at the peak annotation level. Strategies for encoding mass modifications in a consistent manner and the requirement for encoding high-quality and commonly seen but as-yet-unidentified spectra were discussed. The group also discussed related topics, including strategies for comparing two spectra, techniques for generating representative spectra for a library, approaches for selection of optimal signature ions for targeted workflows, and issues surrounding the merging of two or more libraries into one. We present here a review of this field and the challenges that the community must address in order to accelerate the adoption of spectral libraries in routine analysis of proteomics datasets.

The need for statistical contributions to bioinformatics at scale, with illustration to mass spectrometry

In their article, Morris and Baladandayuthapani clearly evidence the influence of statisticians in recent methodological advances throughout the bioinformatics pipeline and advocate for the expansion of this role. The latest acquisition platforms, such as next generation sequencing (genomics/transcriptomics) and hyphenated mass spectrometry (proteomics/metabolomics), output raw datasets in the order of gigabytes; it is not unusual to acquire a terabyte or more of data per study. The increasing computational burden this brings is a further impediment against the use of statistically rigorous methodology in the pre-processing stages of the bioinformatics pipeline. In this discussion I describe the mass spectrometry pipeline and use it as an example to show that beneath this challenge lies a two-fold opportunity: (a) Biological complexity and dynamic range is still well beyond what is captured by current processing methodology; hence, potential biomarkers and mechanistic insights are consistently missed; (b) Statistical science could play a larger role in optimizing the acquisition process itself. Data rates will continue to increase as routine clinical omics analysis moves to large-scale facilities with systematic, standardized protocols. Key inferential gains will be achieved by borrowing strength across the sum total of all analyzed studies, a task best underpinned by appropriate statistical modelling.

Quantitative data describing the impact of the flavonol rutin on in-vivo blood-glucose and fluid-intake profiles, and survival of human-amylin transgenic mice

Here we provide data describing the time-course of blood-glucose and fluid-intake profiles of diabetic hemizygous human-amylin (hA) transgenic mice orally treated with rutin, and matched control mice treated with water. We employed "parametric change-point regression analysis" for investigation of differences in time-course profiles between the control and rutin-treatment groups to extract, for each animal, baseline levels of blood glucose and fluid-intake, the change-point time at which blood glucose (diabetes-onset) and fluid-intake (polydipsia-onset) accelerated away from baseline, and the rate of this acceleration. The parametric change-point regression approach applied here allowed a much more accurate determination of the exact time of onset of diabetes than do the standard diagnostic criteria. These data are related to the article entitled "Rutin suppresses human-amylin/hIAPP misfolding and oligomer formation in-vitro, and ameliorates diabetes and its impacts in human-amylin/hIAPP transgenic mice" (J.F. Aitken, K.M. Loomes, I. Riba-Garcia, R.D. Unwin, G. Prijic, A.S. Phillips, A.R.J. Phillips, D. Wu, S.D. Poppitt, K. Ding, P.E. Barran, A.W. Dowsey, G.J.S. Cooper. 2016) [1].

Elevation of brain glucose and polyol-pathway intermediates with accompanying brain-copper deficiency in patients with Alzheimer's disease: metabolic basis for dementia

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.

Metabolic Dysfunction Is Restricted to the Sciatic Nerve in Experimental Diabetic Neuropathy

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.

Streaming visualisation of quantitative mass spectrometry data based on a novel raw signal decomposition method

As data rates rise, there is a danger that informatics for high-throughput LC-MS becomes more opaque and inaccessible to practitioners. It is therefore critical that efficient visualisation tools are available to facilitate quality control, verification, validation, interpretation, and sharing of raw MS data and the results of MS analyses. Currently, MS data is stored as contiguous spectra. Recall of individual spectra is quick but panoramas, zooming and panning across whole datasets necessitates processing/memory overheads impractical for interactive use. Moreover, visualisation is challenging if significant quantification data is missing due to data-dependent acquisition of MS/MS spectra. In order to tackle these issues, we leverage our seaMass technique for novel signal decomposition. LC-MS data is modelled as a 2D surface through selection of a sparse set of weighted B-spline basis functions from an over-complete dictionary. By ordering and spatially partitioning the weights with an R-tree data model, efficient streaming visualisations are achieved. In this paper, we describe the core MS1 visualisation engine and overlay of MS/MS annotations. This enables the mass spectrometrist to quickly inspect whole runs for ionisation/chromatographic issues, MS/MS precursors for coverage problems, or putative biomarkers for interferences, for example. The open-source software is available from http://seamass.net/viz/.

Age-dependent changes in heparan sulfate in human Bruch's membrane: implications for age-related macular degeneration

PURPOSE

Heparan sulfate (HS) has been implicated in age-related macular degeneration (AMD), since it is the major binding partner for complement factor H (CFH) in human Bruch's membrane (BrM), and CFH has a central role in inhibiting complement activation on extracellular matrices. The aim was to investigate potential aging changes in HS quantity and composition in human BrM.

METHODS

Postmortem human ocular tissue was obtained from donors without known retinal disease. The HS was purified from BrM and neurosensory retina, and after digestion to disaccharides, fluorescently labeled and analyzed by reverse-phase HPLC. The HS and heparanase-1 were detected by immunohistochemistry in macular tissue sections from young and old donors, and binding of exogenously applied recombinant CCP6-8 region of CFH (402Y and 402H variants) was compared.

RESULTS

Disaccharide analysis demonstrated that the mean quantity of HS in BrM was 50% lower (P = 0.006) in old versus young donors (average 82 vs. 32 years). In addition, there was a small, but significant decrease in HS sulfation in old BrM. Immunohistochemistry revealed approximately 50% (P = 0.02) less HS in macular BrM in old versus young donors, whereas heparanase-1 increased by 24% in old macular BrM (P = 0.56). In young donor tissue the AMD-associated 402H CCP6-8 bound relatively poorly to BrM, compared to the 402Y form. In BrM from old donors, this difference was significantly greater (P = 0.019).

CONCLUSIONS

The quantity of HS decreases substantially with age in human BrM, resulting in fewer binding sites for CFH and especially affecting the ability of the 402H variant of CFH to bind BrM.

Numerical compression schemes for proteomics mass spectrometry data

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 improvements will be beneficial for data handling within the MS community.

Network analysis: a new approach to study endocrine disorders

Systems biology is the study of the interactions that occur between the components of individual cells - including genes, proteins, transcription factors, small molecules, and metabolites, and their relationships to complex physiological and pathological processes. The application of systems biology to medicine promises rapid advances in both our understanding of disease and the development of novel treatment options. Network biology has emerged as the primary tool for studying systems biology as it utilises the mathematical analysis of the relationships between connected objects in a biological system and allows the integration of varied 'omic' datasets (including genomics, metabolomics, proteomics, etc.). Analysis of network biology generates interactome models to infer and assess function; to understand mechanisms, and to prioritise candidates for further investigation. This review provides an overview of network methods used to support this research and an insight into current applications of network analysis applied to endocrinology. A wide spectrum of endocrine disorders are included ranging from congenital hyperinsulinism in infancy, through childhood developmental and growth disorders, to the development of metabolic diseases in early and late adulthood, such as obesity and obesity-related pathologies. In addition to providing a deeper understanding of diseases processes, network biology is also central to the development of personalised treatment strategies which will integrate pharmacogenomics with systems biology of the individual.

Cardiovascular magnetic resonance tagging of the right ventricular free wall for the assessment of long axis myocardial function in congenital heart disease

BACKGROUND

Right ventricular ejection fraction (RV-EF) has traditionally been used to measure and compare RV function serially over time, but may be a relatively insensitive marker of change in RV myocardial contractile function. We developed a cardiovascular magnetic resonance (CMR) tagging-based technique with a view to rapid and reproducible measurement of RV long axis function and applied it in patients with congenital heart disease.

METHODS

We studied 84 patients: 56 with repaired Tetralogy of Fallot (rTOF); 28 with atrial septal defect (ASD): 13 with and 15 without pulmonary hypertension (RV pressure > 40 mmHG by echocardiography). For comparison, 20 healthy controls were studied. CMR acquisitions included an anatomically defined four chamber cine followed by a cine gradient echo-planar sequence in the same plane with a labelling pre-pulse giving a tag line across the basal myocardium. RV tag displacement was measured with automated registration and tracking of the tag line together with standard measurement of RV-EF.

RESULTS

Mean RV displacement was higher in the control (26 ± 3 mm) than in rTOF (16 ± 4 mm) and ASD with pulmonary hypertension (18 ± 3 mm) groups, but lower than in the ASD group without (30 ± 4 mm), P < 0.001. The technique was reproducible with inter-study bias ± 95% limits of agreement of 0.7 ± 2.7 mm. While RV-EF was lower in rTOF than in controls (49 ± 9% versus 57 ± 6%, P < 0.001), it did not differ between either ASD group and controls.

CONCLUSIONS

Measurements of RV long axis displacement by CMR tagging showed more differences between the groups studied than did RV-EF, and was reproducible, quick and easy to apply. Further work is needed to assess its potential use for the detection of longitudinal changes in RV myocardial function.

Image analysis tools and emerging algorithms for expression proteomics

Since their origins in academic endeavours in the 1970s, computational analysis tools have matured into a number of established commercial packages that underpin research in expression proteomics. In this paper we describe the image analysis pipeline for the established 2-DE technique of protein separation, and by first covering signal analysis for MS, we also explain the current image analysis workflow for the emerging high-throughput 'shotgun' proteomics platform of LC coupled to MS (LC/MS). The bioinformatics challenges for both methods are illustrated and compared, whereas existing commercial and academic packages and their workflows are described from both a user's and a technical perspective. Attention is given to the importance of sound statistical treatment of the resultant quantifications in the search for differential expression. Despite wide availability of proteomics software, a number of challenges have yet to be overcome regarding algorithm accuracy, objectivity and automation, generally due to deterministic spot-centric approaches that discard information early in the pipeline, propagating errors. We review recent advances in signal and image analysis algorithms in 2-DE, MS, LC/MS and Imaging MS. Particular attention is given to wavelet techniques, automated image-based alignment and differential analysis in 2-DE, Bayesian peak mixture models, and functional mixed modelling in MS, and group-wise consensus alignment methods for LC/MS.

The effect of dynamic vessel motion on haemodynamic parameters in the right coronary artery: a combined MR and CFD study

Human right coronary artery (RCA) haemodynamics is investigated using computational fluid dynamics (CFD) based on subject-specific information from magnetic resonance (MR) acquisitions. The dynamically varying vascular geometry is reconstructed from MR images, incorporated in CFD in conjunction with pulsatile flow conditions obtained from MR velocity mapping performed on the same subject. The effects of dynamic vessel motion on instantaneous and cycle-averaged haemodynamic parameters, such as wall shear stress (WSS), time-averaged WSS (TAWSS) and oscillatory shear index (OSI), are examined by comparing an RCA model with a time-varying geometry and those with a static geometry, corresponding to nine different time-points in the cardiac cycle. The results show that the TAWSS is similar for the dynamic and static wall models, both qualitatively and quantitatively (correlation coefficient 0.89-0.95). Conversely, the OSI shows much poorer correlations (correlation coefficient 0.38-0.60), with the best correspondence being observed with the static models constructed from images acquired in late diastole (at t = 0 and 800 ms, the cardiac cycle is 900 ms). These findings suggest that neglecting dynamic motion of the RCA is acceptable if TAWSS is the primary focus but may result in underestimation of haemodynamic parameters related to the oscillatory nature of the blood flow.

MR image-based geometric and hemodynamic investigation of the right coronary artery with dynamic vessel motion

The aim of this study was to develop a fully subject-specific model of the right coronary artery (RCA), including dynamic vessel motion, for computational analysis to assess the effects of cardiac-induced motion on hemodynamics and resulting wall shear stress (WSS). Vascular geometries were acquired in the right coronary artery (RCA) of a healthy volunteer using a navigator-gated interleaved spiral sequence at 14 time points during the cardiac cycle. A high temporal resolution velocity waveform was also acquired in the proximal region. Cardiac-induced dynamic vessel motion was calculated by interpolating the geometries with an active contour model and a computational fluid dynamic (CFD) simulation with fully subject-specific information was carried out using this model. The results showed the expected variation of vessel radius and curvature throughout the cardiac cycle, and also revealed that dynamic motion of the right coronary artery consequent to cardiac motion had significant effects on instantaneous WSS and oscillatory shear index. Subject-specific MRI-based CFD is feasible and, if scan duration could be shortened, this method may have potential as a non-invasive tool to investigate the physiological and pathological role of hemodynamics in human coronary arteries.

Informatics and statistics for analyzing 2-d gel electrophoresis images

Despite recent progress in "shotgun" peptide separation by integrated liquid chromatography and mass spectrometry (LC/MS), proteome coverage and reproducibility are still limited with this approach and obtaining enough replicate runs for biomarker discovery is a challenge. For these reasons, recent research demonstrates that there is a continuing need for protein separation by two-dimensional gel electrophoresis (2-DE). However, with traditional 2-DE informatics, the digitized images are reduced to symbolic data through spot detection and quantification before proteins are compared for differential expression by spot matching. Recently, a more robust and automated paradigm has emerged where gels are directly aligned in the image domain before spots are detected across the whole image set as a whole. In this chapter, we describe the methodology for both approaches and discuss the pitfalls present when reasoning statistically about the differential protein expression discovered.

Stress phase angle depicts differences in coronary artery hemodynamics due to changes in flow and geometry after percutaneous coronary intervention

The effects of changes in flow velocity waveform and arterial geometry before and after percutaneous coronary intervention (PCI) in the right coronary artery (RCA) were investigated using computational fluid dynamics. An RCA from a patient with a stenosis was reconstructed based on multislice computerized tomography images. A nonstenosed model, simulating the same RCA after PCI, was also constructed. The blood flows in the RCA models were simulated using pulsatile flow waveforms acquired with an intravascular ultrasound-Doppler probe in the RCA of a patient undergoing PCI. It was found that differences in the waveforms before and after PCI did not affect the time-averaged wall shear stress and oscillatory shear index, but the phase angle between pressure and wall shear stress on the endothelium, stress phase angle (SPA), differed markedly. The median SPA was -63.9 degrees (range, -204 degrees to -10.0 degrees ) for the pre-PCI state, whereas it was 10.4 degrees (range, -71.1 degrees to 25.4 degrees ) in the post-PCI state, i.e., more asynchronous in the pre-PCI state. SPA has been reported to influence the secretion of vasoactive molecules (e.g., nitric oxide, PGI(2), and endothelin-1), and asynchronous SPA ( approximately -180 degrees ) is proposed to be proatherogenic. Our results suggest that differences in the pulsatile flow waveform may have an important influence on atherogenesis, although associated with only minor changes in the time-averaged wall shear stress and oscillatory shear index. SPA may be a useful indicator in predicting sites prone to atherosclerosis.

Automated image alignment for 2D gel electrophoresis in a high-throughput proteomics pipeline

MOTIVATION

The quest for high-throughput proteomics has revealed a number of challenges in recent years. Whilst substantial improvements in automated protein separation with liquid chromatography and mass spectrometry (LC/MS), aka 'shotgun' proteomics, have been achieved, large-scale open initiatives such as the Human Proteome Organization (HUPO) Brain Proteome Project have shown that maximal proteome coverage is only possible when LC/MS is complemented by 2D gel electrophoresis (2-DE) studies. Moreover, both separation methods require automated alignment and differential analysis to relieve the bioinformatics bottleneck and so make high-throughput protein biomarker discovery a reality. The purpose of this article is to describe a fully automatic image alignment framework for the integration of 2-DE into a high-throughput differential expression proteomics pipeline.

RESULTS

The proposed method is based on robust automated image normalization (RAIN) to circumvent the drawbacks of traditional approaches. These use symbolic representation at the very early stages of the analysis, which introduces persistent errors due to inaccuracies in modelling and alignment. In RAIN, a third-order volume-invariant B-spline model is incorporated into a multi-resolution schema to correct for geometric and expression inhomogeneity at multiple scales. The normalized images can then be compared directly in the image domain for quantitative differential analysis. Through evaluation against an existing state-of-the-art method on real and synthetically warped 2D gels, the proposed analysis framework demonstrates substantial improvements in matching accuracy and differential sensitivity. High-throughput analysis is established through an accelerated GPGPU (general purpose computation on graphics cards) implementation.

AVAILABILITY

Supplementary material, software and images used in the validation are available at http://www.proteomegrid.org/rain/.

Motion-compensated MR valve imaging with COMB tag tracking and super-resolution enhancement

Understanding the morphology and function of heart valves is important to the study of underlying causes of heart failure. Existing techniques such as those based on echocardiography are limited by the relatively low signal-to-noise ratio (SNR), attenuation artefacts, and restricted access. The alternative of cardiovascular MR imaging offers versatility and accuracy in 3D localisation, but is hampered by large movements of the valves throughout the cardiac cycle. This paper presents a motion-compensated adaptive imaging approach for MR valve imaging. To illustrate its clinical potential, 3D motion of the aortic valve plane is first captured through a single breath-hold COMB tag pre-scan and then tracked in real-time with an automatic method based on multi-resolution image registration. Motion-compensated coverage of the aortic valve is then acquired prospectively, thus allowing its clear 3D reconstruction and visualisation. To provide isotropic voxel coverage of the imaging volume, retrospective projection onto convex sets (POCS) super-resolution enhancement is applied to the slice-select direction. In vivo results demonstrate the effectiveness of the proposed motion-compensation and super-resolution schemes for depicting the structure of the valve leaflets throughout the cardiac cycle. The proposed method fundamentally changes the way MR imaging is performed by transforming it from a spatially to materially localised imaging method. This also has important implications for quantifying blood flow and myocardial perfusion, as well as tracking anatomy and function of the heart.

Cardiac-motion compensated MR imaging and strain analysis of ventricular trabeculae

In conventional CMR, bulk cardiac motion causes target structures to move in and out of the static acquisition plane. Due to the partial volume effect, accurate localisation of subtle features through the cardiac cycle, such as the trabeculae and papillary muscles, is difficult. This problem is exacerbated by the short acquisition window necessary to avoid motion blur and ghosting, especially during early systole. This paper presents an adaptive imaging approach with COMB multi-tag tracking that follows true 3D motion of the myocardium so that the same tissue slice is imaged throughout the cine acquisition. The technique is demonstrated with motion-compensated multi-slice imaging of ventricles, which allows for tracked visualisation and analysis of the trabeculae and papillary muscles for the first time. This enables novel in-vivo measurement of circumferential and radial strain for trabeculation and papillary muscle contractility. These statistics will facilitate the evaluation of diseases such as mitral valve insufficiency and ischemic heart disease. The adaptive imaging technique will also have significant implications for CMR in general, including motion-compensated quantification of myocardial perfusion and blood flow, and motion-correction of sequences with long acquisition windows.

Examination of 2-DE in the Human Proteome Organisation Brain Proteome Project pilot studies with the new RAIN gel matching technique

The Human Proteome Organisation (HUPO) Brain Proteome Project (BPP) pilot studies have generated over 200 2-D gels from eight participating laboratories. This data includes 67 single-channel and 60 DIGE gels comparing 30 whole frozen C57/BL6 female mouse brains, ten each at embryonic day 16, postnatal day 7 (juvenile) and postnatal day 54-56 (adult); and ten single-channel and three DIGE gels comparing human epilepsy surgery of the temporal front lobe with a corresponding post-mortem specimen. The samples were generated centrally and distributed to the participating laboratories, but otherwise no restrictions were placed on sample preparation, running and staining protocols, nor on the 2-D gel analysis packages used. Spots were characterised by MS and the annotated gel images published on a ProteinScape web server. In order to examine the resultant differential expression and protein identifications, we have reprocessed a large subset of the gels using the newly developed RAIN (Robust Automated Image Normalisation) 2-D gel matching algorithm. Traditional approaches use symbolic representation of spots at the very early stages of the analysis, which introduces persistent errors due to inaccuracies in spot modelling and matching. With RAIN, image intensity distributions, rather than selected features, are used, where smooth geometric deformation and expression bias are modelled using multi-resolution image registration and bias-field correction. The method includes a new approach of volume-invariant warping which ensures the volume of protein expression under transformation is preserved. An image-based statistical expression analysis phase is then proposed, where small insignificant expression changes over one gel pair can be revealed when reinforced by the same consistent changes in others. Results of the proposed method as applied to the HUPO BPP data show significant intra-laboratory improvements in matching accuracy over a previous state-of-the-art technique, Multi-resolution Image Registration (MIR), and the commercial Progenesis PG240 package.

Motion-Compensated MR Valve Imaging with COMB Tag Tracking and Super-Resolution Enhancement

MR imaging of the heart valve leaflets is a challenging problem due to their large movements throughout the cardiac cycle. This paper presents a motion-compensated imaging approach with COMB tagging for valve imaging. It involves an automatic method for tracking the full 3D motion of the valve plane so as to provide a motion-tracked acquisition scheme. Super-resolution enhancement is then applied to the slice-select direction so that the partial volume effect is minimised. In vivo results have shown that in terms of slice positioning, the method has equivalent accuracy to that of a manual approach whilst being quicker and more consistent. The use of multiple parallel COMB tags will permit adaptive imaging that follows tissue motion. This will have significant implications for quantification of myocardial perfusion and tracking anatomy, functions that are traditionally difficult in MRI.

ProteomeGRID: towards a high-throughput proteomics pipeline through opportunistic cluster image computing for two-dimensional gel electrophoresis

The quest for high-throughput proteomics has revealed a number of critical issues. Whilst improved two-dimensional gel electrophoresis (2-DE) sample preparation, staining and imaging issues are being actively pursued by industry, reliable high-throughput spot matching and quantification remains a significant bottleneck in the bioinformatics pipeline, thus restricting the flow of data to mass spectrometry through robotic spot excision and protein digestion. To this end, it is important to establish a full multi-site Grid infrastructure for the processing, archival, standardisation and retrieval of proteomic data and metadata. Particular emphasis needs to be placed on large-scale image mining and statistical cross-validation for reliable, fully automated differential expression analysis, and the development of a statistical 2-DE object model and ontology that underpins the emerging HUPO PSI GPS (Human Proteome Organization Proteomics Standards Initiative General Proteomics Standards). The first step towards this goal is to overcome the computational and communications burden entailed by the image analysis of 2-DE gels with Grid enabled cluster computing. This paper presents the proTurbo framework as part of the ProteomeGRID, which utilises Condor cluster management combined with CORBA communications and JPEG-LS lossless image compression for task farming. A novel probabilistic eager scheduler has been developed to minimise make-span, where tasks are duplicated in response to the likelihood of the Condor machines' owners evicting them. A 60 gel experiment was pair-wise image registered (3540 tasks) on a 40 machine Linux cluster. Real-world performance and network overhead was gauged, and Poisson distributed worker evictions were simulated. Our results show a 4:1 lossless and 9:1 near lossless image compression ratio and so network overhead did not affect other users. With 40 workers a 32x speed-up was seen (80% resource efficiency), and the eager scheduler reduced the impact of evictions by 58%.

The role of bioinformatics in two-dimensional gel electrophoresis

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.