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.

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 Aß/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.

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 spectrometry­based 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 cell­specific 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 low­genetic 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.

Widespread severe cerebral elevations of haptoglobin and haemopexin in sporadic Alzheimer's disease: Evidence for a pervasive microvasculopathy.

Sporadic Alzheimer's disease (sAD) is the commonest cause of age-related neurodegeneration but there are no available treatments with demonstrated disease-modifying actions. It is therefore relevant to study hitherto-unknown aspects of brain structure and function to seek new disease-related mechanisms that might be targeted by novel disease-modifying interventions. During hypothesis-generating proteomic investigations in a case-control study of sAD, we observed widespread elevations of haptoglobin and haemopexin in all six brain-regions studied, which together represent much of the brain. Measured perturbations were significant, with the posterior probability of upregulation generally >95% and haptoglobin doubling in expression levels on average across deep brain structures (hippocampus, entorhinal cortex and cingulate gyrus) as well as sensory and motor cortices, and cerebellum. Haptoglobin and haemopexin are often regarded as circulating proteins whose main functions are to bind, respectively, the strongly pro-inflammatory extracellular haemoglobin and haeme molecules that form following haemolysis, thereby promoting their clearance and suppressing damage they might otherwise cause, for example, acute kidney injury. To our knowledge, elevations in neither cerebral haptoglobin nor haemopexin have previously been linked to the pathogenesis of sAD. Post-mortem examination of these cases showed no signs of macroscopic cerebral haemorrhage. These findings demonstrate pervasive cerebral elevation of haptoglobin and haemopexin, consistent with low-level intracerebral leakage of haemoglobin and consequent haeme formation throughout sAD brain. They point to a widespread underlying microvasculopathy that facilitates erythrocyte leakage, thereby triggering elevated tissue-free haemoglobin and driving the measured elevations in haptoglobin and haemopexin.

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.

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].

Rutin suppresses human-amylin/hIAPP misfolding and oligomer formation in-vitro, and ameliorates diabetes and its impacts in human-amylin/hIAPP transgenic mice.

Pancreatic islet ß-cells secrete the hormones insulin and amylin, and defective ß-cell function plays a central role in the pathogenesis of type-2 diabetes (T2D). Human amylin (hA, also termed hIAPP) misfolds and forms amyloid aggregates whereas orthologous mouse amylin does neither. Furthermore, hA elicits apoptosis in cultured ß-cells and ß-cell death in ex-vivo islets. In addition, hA-transgenic mice that selectively express hA in their ß-cells, manifest ß-cell apoptosis and progressive islet damage that leads to diabetes closely resembling that in patients with T2D. Aggregation of hA is thus linked to the causation of diabetes. We employed time-dependent thioflavin-T spectroscopy and ion-mobility mass spectrometry to screen potential suppressors of hA misfolding for anti-diabetic activity. We identified the dietary flavonol rutin as an inhibitor of hA-misfolding and measured its anti-diabetic efficacy in hA-transgenic mice. In vitro, rutin bound hA, suppressed misfolding, disaggregated oligomers and reverted hA-conformation towards the physiological. In hA-transgenic mice, measurements of glucose, fluid-intake, and body-weight showed that rutin-treatment slowed diabetes-progression by lowering of rates of elevation in blood glucose (P = 0.030), retarding deterioration from symptomatic diabetes to death (P = 0.014) and stabilizing body-weight (P < 0.0001). In conclusion, rutin treatment suppressed hA-aggregation in vitro and doubled the lifespan of diabetic mice (P = 0.011) by a median of 69 days compared with vehicle-treated control-diabetic hA-transgenic mice.

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.

Boosting drug named entity recognition using an aggregate classifier.

OBJECTIVE: Drug named entity recognition (NER) is a critical step for complex biomedical NLP tasks such as the extraction of pharmacogenomic, pharmacodynamic and pharmacokinetic parameters. Large quantities of high quality training data are almost always a prerequisite for employing supervised machine-learning techniques to achieve high classification performance. However, the human labour needed to produce and maintain such resources is a significant limitation. In this study, we improve the performance of drug NER without relying exclusively on manual annotations. METHODS: We perform drug NER using either a small gold-standard corpus (120 abstracts) or no corpus at all. In our approach, we develop a voting system to combine a number of heterogeneous models, based on dictionary knowledge, gold-standard corpora and silver annotations, to enhance performance. To improve recall, we employed genetic programming to evolve 11 regular-expression patterns that capture common drug suffixes and used them as an extra means for recognition. MATERIALS: Our approach uses a dictionary of drug names, i.e. DrugBank, a small manually annotated corpus, i.e. the pharmacokinetic corpus, and a part of the UKPMC database, as raw biomedical text. Gold-standard and silver annotated data are used to train maximum entropy and multinomial logistic regression classifiers. RESULTS: Aggregating drug NER methods, based on gold-standard annotations, dictionary knowledge and patterns, improved the performance on models trained on gold-standard annotations, only, achieving a maximum F-score of 95%. In addition, combining models trained on silver annotations, dictionary knowledge and patterns are shown to achieve comparable performance to models trained exclusively on gold-standard data. The main reason appears to be the morphological similarities shared among drug names. CONCLUSION: We conclude that gold-standard data are not a hard requirement for drug NER. Combining heterogeneous models build on dictionary knowledge can achieve similar or comparable classification performance with that of the best performing model trained on gold-standard annotations.

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.