19 results found
Demichev V, Tober-Lau P, Nazarenko T, et al., 2022, A proteomic survival predictor for COVID-19 patients in intensive care, PLOS Digital Health, Vol: 1, Pages: e0000007-e0000007
<jats:p>Global healthcare systems are challenged by the COVID-19 pandemic. There is a need to optimize allocation of treatment and resources in intensive care, as clinically established risk assessments such as SOFA and APACHE II scores show only limited performance for predicting the survival of severely ill COVID-19 patients. Additional tools are also needed to monitor treatment, including experimental therapies in clinical trials. Comprehensively capturing human physiology, we speculated that proteomics in combination with new data-driven analysis strategies could produce a new generation of prognostic discriminators. We studied two independent cohorts of patients with severe COVID-19 who required intensive care and invasive mechanical ventilation. SOFA score, Charlson comorbidity index, and APACHE II score showed limited performance in predicting the COVID-19 outcome. Instead, the quantification of 321 plasma protein groups at 349 timepoints in 50 critically ill patients receiving invasive mechanical ventilation revealed 14 proteins that showed trajectories different between survivors and non-survivors. A predictor trained on proteomic measurements obtained at the first time point at maximum treatment level (i.e. WHO grade 7), which was weeks before the outcome, achieved accurate classification of survivors (AUROC 0.81). We tested the established predictor on an independent validation cohort (AUROC 1.0). The majority of proteins with high relevance in the prediction model belong to the coagulation system and complement cascade. Our study demonstrates that plasma proteomics can give rise to prognostic predictors substantially outperforming current prognostic markers in intensive care.</jats:p>
Nené NR, Ney A, Nazarenko T, et al., 2021, Early detection of pancreatic ductal adenocarcinomas with an ensemble learning model based on a panel of protein serum biomarkers
<jats:title>Abstract</jats:title><jats:p>Earlier detection of pancreatic ductal adenocarcinoma (PDAC) is key to improving patient outcomes, as it is mostly detected at advanced stages which are associated with poor survival. Developing non-invasive blood tests for early detection would be an important breakthrough. The primary objective of the work presented here was to use a unique dataset, that is both large and prospectively collected, to quantify a set of 96 cancer-associated proteins and construct multi-marker models with the capacity to accurately predict PDAC years before diagnosis. The data is part of a nested case control study within UK Collaborative Trial of Ovarian Cancer Screening and is comprised of 219 samples, collected from a total of 143 post-menopausal women who were diagnosed with pancreatic cancer within 70 months after sample collection, and 248 matched non-cancer controls. We developed a stacked ensemble modelling technique to achieve robustness in predictions and, therefore, improve performance in newly collected datasets. With a pool of 10 base-learners and a Bayesian averaging meta-learner, we can predict PDAC status with an AUC of 0.91 (95% CI 0.75 - 1.0), sensitivity of 92% (95% CI 0.54 - 1.0) at 90% specificity, up to 1 year to diagnosis, and at an AUC of 0.85 (95% CI 0.74 - 0.93) up to 2 years to diagnosis (sensitivity of 61%, 95 % CI 0.17 - 0.83, at 90% specificity). These models also use clinical covariates such as hormone replacement therapy use (at randomization), oral contraceptive pill use (ever) and diabetes and outperform biomarker combinations cited in the literature.</jats:p>
Parenclitic networks provide a powerful and relatively new way to coerce multidimensional data into a graph form, enabling the application of graph theory to evaluate features. Different algorithms have been published for constructing parenclitic networks, leading to the question-which algorithm should be chosen? Initially, it was suggested to calculate the weight of an edge between two nodes of the network as a deviation from a linear regression, calculated for a dependence of one of these features on the other. This method works well, but not when features do not have a linear relationship. To overcome this, it was suggested to calculate edge weights as the distance from the area of most probable values by using a kernel density estimation. In these two approaches only one class (typically controls or healthy population) is used to construct a model. To take account of a second class, we have introduced synolytic networks, using a boundary between two classes on the feature-feature plane to estimate the weight of the edge between these features. Common to all these approaches is that topological indices can be used to evaluate the structure represented by the graphs. To compare these network approaches alongside more traditional machine-learning algorithms, we performed a substantial analysis using both synthetic data with a priori known structure and publicly available datasets used for the benchmarking of ML-algorithms. Such a comparison has shown that the main advantage of parenclitic and synolytic networks is their resistance to over-fitting (occurring when the number of features is greater than the number of subjects) compared to other ML approaches. Secondly, the capability to visualise data in a structured form, even when this structure is not a priori available allows for visual inspection and the application of well-established graph theory to their interpretation/application, eliminating the "black-box" nature of other ML approaches.
Nazarenko T, Blyuss O, Whitwell H, et al., 2021, Ensemble of correlation, parenclitic and synolitic graphs as a tool to detect universal changes in complex biological systems, Physics of Life Reviews, Vol: 38, Pages: 120-123, ISSN: 1571-0645
Demichev V, Tober-Lau P, Lemke O, et al., 2021, A time-resolved proteomic and prognostic map of COVID-19, Cell Systems, Vol: 12, Pages: 780-794.e7, ISSN: 2405-4712
COVID-19 is highly variable in its clinical presentation, ranging from asymptomatic infection to severe organ damage and death. We characterized the time-dependent progression of the disease in 139 COVID-19 inpatients by measuring 86 accredited diagnostic parameters, such as blood cell counts and enzyme activities, as well as untargeted plasma proteomes at 687 sampling points. We report an initial spike in a systemic inflammatory response, which is gradually alleviated and followed by a protein signature indicative of tissue repair, metabolic reconstitution, and immunomodulation. We identify prognostic marker signatures for devising risk-adapted treatment strategies and use machine learning to classify therapeutic needs. We show that the machine learning models based on the proteome are transferable to an independent cohort. Our study presents a map linking routinely used clinical diagnostic parameters to plasma proteomes and their dynamics in an infectious disease.
Shinde P, Whitwell HJ, Verma RK, et al., 2021, Impact of modular mitochondrial epistatic interactions on the evolution of human subpopulations, MITOCHONDRION, Vol: 58, Pages: 111-122, ISSN: 1567-7249
Di Blasi R, Blyuss O, Timms JF, et al., 2021, Non-histone protein methylation: biological significance and bioengineering potential, ACS Chemical Biology, Vol: 16, Pages: 238-250, ISSN: 1554-8929
Protein methylation is a key post-translational modification whose effects on gene expression have been intensively studied over the last two decades. Recently, renewed interest in non-histone protein methylation has gained momentum for its role in regulating important cellular processes and the activity of many proteins, including transcription factors, enzymes, and structural complexes. The extensive and dynamic role that protein methylation plays within the cell also highlights its potential for bioengineering applications. Indeed, while synthetic histone protein methylation has been extensively used to engineer gene expression, engineering of non-histone protein methylation has not been fully explored yet. Here, we report the latest findings, highlighting how non-histone protein methylation is fundamental for certain cellular functions and is implicated in disease, and review recent efforts in the engineering of protein methylation.
Watson A, Srensen GL, Holmskov U, et al., 2020, Generation of novel trimeric fragments of human SP-A and SP-D after recombinant soluble expression in E. coli, Immunobiology, Vol: 225, Pages: 1-7, ISSN: 0171-2985
Surfactant treatment for neonatal respiratory distress syndrome has dramatically improved survival of preterm infants. However, this has resulted in a markedly increased incidence of sequelae such as neonatal chronic inflammatory lung disease. The current surfactant preparations in clinical use lack the natural lung defence proteins surfactant proteins (SP)-A and D. These are known to have anti-inflammatory and anti-infective properties essential for maintaining healthy non-inflamed lungs.Supplementation of currently available animal derived surfactant therapeutics with these anti-inflammatory proteins in the first few days of life could prevent the development of inflammatory lung disease in premature babies. However, current systems for production of recombinant versions of SP-A and SP-D require a complex solubilisation and refolding protocol limiting expression at scale for drug development.Using a novel solubility tag, we describe the expression and purification of recombinant fragments of human (rfh) SP-A and SP-D using Escherichia coli without the need for refolding. We obtained a mean (± SD) of 23.3 (± 5.4) mg and 86 mg (± 3.5) per litre yield of rfhSP-A and rfhSP-D, respectively. rfhSP-D was trimeric and 68% bound to a ManNAc-affinity column, giving a final yield of 57.5 mg/litre of highly pure protein, substantially higher than the 3.3 mg/litre obtained through the standard refolding protocol. Further optimisation of this novel lab based method could potentially make rfhSP-A and rfhSP-D production more commercially feasible to enable development of novel therapeutics for the treatment of lung infection and inflammation.
Whitwell HJ, Bacalini MG, Blyuss O, et al., 2020, The human body as a super network: digital methods to analyze the propagation of aging, Frontiers in Aging Neuroscience, Vol: 12, ISSN: 1663-4365
Biological aging is a complex process involving multiple biological processes. These can be understood theoretically though considering them as individual networks—e.g., epigenetic networks, cell-cell networks (such as astroglial networks), and population genetics. Mathematical modeling allows the combination of such networks so that they may be studied in unison, to better understand how the so-called “seven pillars of aging” combine and to generate hypothesis for treating aging as a condition at relatively early biological ages. In this review, we consider how recent progression in mathematical modeling can be utilized to investigate aging, particularly in, but not exclusive to, the context of degenerative neuronal disease. We also consider how the latest techniques for generating biomarker models for disease prediction, such as longitudinal analysis and parenclitic analysis can be applied to as both biomarker platforms for aging, as well as to better understand the inescapable condition. This review is written by a highly diverse and multi-disciplinary team of scientists from across the globe and calls for greater collaboration between diverse fields of research.
Whitwell H, DiMaggio P, 2020, HighLight-PTM: An online application to aid matching peptide pairs with isotopically labelled PTMs, Bioinformatics, Vol: 36, Pages: 938-939, ISSN: 1367-4803
MotivationDatabase searching of isotopically labelled PTMs can be problematic and we frequently find that only one, or neither in a heavy/light pair are assigned. In such cases, having a pair of MS/MS spectra that differ due to an isotopic label can assist in identifying the relevant m/z values that support the correct peptide annotation or can be used for de novo sequencing.ResultsWe have developed an online application that identifies matching peaks and peaks differing by the appropriate mass shift (difference between heavy and light PTM) between two MS/MS spectra. Furthermore, the application predicts, from the exact-match peaks, the mass of their complementary ions and highlights these as high confidence matches between the two spectra. The result is a tool to visually compare two spectra, and downloadable peaks lists that can be used to support de novo sequencing.AvailabilityHiLight-PTM is released using shinyapps.io by RStudio, and can be accessed from any internet browser at https://harrywhitwell.shinyapps.io/hilight-ptm/Supplementary informationSupplementary data are available at Bioinformatics online.
Whitwell HJ, Worthington J, Blyuss O, et al., 2020, Improved early detection of ovarian cancer using longitudinal multimarker models, British Journal of Cancer, Vol: 122, Pages: 847-856, ISSN: 0007-0920
BackgroundOvarian cancer has a poor survival rate due to late diagnosis and improved methods are needed for its early detection. Our primary objective was to identify and incorporate additional biomarkers into longitudinal models to improve on the performance of CA125 as a first-line screening test for ovarian cancer.MethodsThis case–control study nested within UKCTOCS used 490 serial serum samples from 49 women later diagnosed with ovarian cancer and 31 control women who were cancer-free. Proteomics-based biomarker discovery was carried out using pooled samples and selected candidates, including those from the literature, assayed in all serial samples. Multimarker longitudinal models were derived and tested against CA125 for early detection of ovarian cancer.ResultsThe best performing models, incorporating CA125, HE4, CHI3L1, PEBP4 and/or AGR2, provided 85.7% sensitivity at 95.4% specificity up to 1 year before diagnosis, significantly improving on CA125 alone. For Type II cases (mostly high-grade serous), models achieved 95.5% sensitivity at 95.4% specificity. Predictive values were elevated earlier than CA125, showing the potential of models to improve lead time.ConclusionsWe have identified candidate biomarkers and tested longitudinal multimarker models that significantly improve on CA125 for early detection of ovarian cancer. These models now warrant independent validation.
Whitwell HJ, Blyuss O, Menon U, et al., 2018, Parenclitic networks for predicting ovarian cancer, Oncotarget, Vol: 9, Pages: 22717-22726, ISSN: 1949-2553
Prediction and diagnosis of complex disease may not always be possible with a small number of biomarkers. Modern ‘omics’ technologies make it possible to cheaply and quantitatively assay hundreds of molecules generating large amounts of data from individual samples. In this study, we describe a parenclitic network-based approach to disease classification using a synthetic data set modelled on data from the United Kingdom Collaborative Trial of Ovarian Cancer Screening (UKCTOCS) and serological assay data from a nested set of samples from the same study. This approach allows us to integrate quantitative proteomic and categorical metadata into a single network, and then use network topologies to construct logistic regression models for disease classification. In this study of ovarian cancer, comprising of 30 controls and cases with samples taken <14 months to diagnosis (n = 30) and/or >34 months to diagnosis (n = 29), we were able to classify cases with a sensitivity of 80.3% within 14 months of diagnosis and 18.9% in samples exceeding 34 months to diagnosis at a specificity of 98%. Furthermore, we use the networks to make observations about proteins within the cohort and identify GZMH and FGFBP1 as changing in cases (in relation to controls) at time points most distal to diagnosis. We conclude that network-based approaches may offer a solution to the problem of complex disease classification that can be used in personalised medicine and to describe the underlying biology of cancer progression at a system level.
Cuenco J, Wehnert N, Blyuss O, et al., 2018, Identification of a serum biomarker panel for the differential diagnosis of cholangiocarcinoma and primary sclerosing cholagnitis, Oncotarget, Vol: 9, Pages: 17430-17442, ISSN: 1949-2553
The non-invasive differentiation of malignant and benign biliary disease is a clinical challenge. Carbohydrate antigen 19-9 (CA19-9), leucine-rich α2-glycoprotein (LRG1), interleukin 6 (IL6), pyruvate kinase M2 (PKM2), cytokeratin 19 fragment (CYFRA21.1) and mucin 5AC (MUC5AC) have reported utility for differentiating cholangiocarcinoma (CCA) from benign biliary disease. Herein, serum levels of these markers were tested in 66 cases of CCA and 62 cases of primary sclerosing cholangitis (PSC) and compared with markers of liver function and inflammation. Markers panels were assessed for their ability to discriminate malignant and benign disease. Several of the markers were also assessed in pre-diagnosis biliary tract cancer (BTC) samples with performances evaluated at different times prior to diagnosis. We show that LRG1 and IL6 were unable to accurately distinguish CCA from PSC, whereas CA19-9, PKM2, CYFRA21.1 and MUC5AC were significantly elevated in malignancy. Area under the receiver operating characteristic curves for these individual markers ranged from 0.73–0.84, with the best single marker (PKM2) providing 61% sensitivity at 90% specificity. A panel combining PKM2, CYFRA21.1 and MUC5AC gave 76% sensitivity at 90% specificity, which increased to 82% sensitivity by adding gamma-glutamyltransferase (GGT). In the pre-diagnosis setting, LRG1, IL6 and PKM2 were poor predictors of BTC, whilst CA19-9 and C-reactive protein were elevated up to 2 years before diagnosis. In conclusion, LRG1, IL6 and PKM2 were not useful for early detection of BTC, whilst a model combining PKM2, CYFRA21.1, MUC5AC and GGT was beneficial in differentiating malignant from benign biliary disease, warranting validation in a prospective trial.
Wolhuter K, Whitwell HJ, Switzer CH, et al., 2018, Evidence against Stable Protein S-Nitrosylation as a Widespread Mechanism of Post-translational Regulation, MOLECULAR CELL, Vol: 69, Pages: 438-450.e5, ISSN: 1097-2765
S-nitrosation, commonly referred to as S-nitrosylation, is widely regarded as a ubiquitous, stable post-translational modification that directly regulates many proteins. Such a widespread role would appear to be incompatible with the inherent lability of the S-nitroso bond, especially its propensity to rapidly react with thiols to generate disulfide bonds. As anticipated, we observed robust and widespread protein S-nitrosation after exposing cells to nitrosocysteine or lipopolysaccharide. Proteins detected using the ascorbate-dependent biotin switch method are typically interpreted to be directly regulated by S-nitrosation. However, these S-nitrosated proteins are shown to predominantly comprise transient intermediates leading to disulfide bond formation. These disulfides are likely to be the dominant end effectors resulting from elevations in nitrosating cellular nitric oxide species. We propose that S-nitrosation primarily serves as a transient intermediate leading to disulfide formation. Overall, we conclude that the current widely held perception that stable S-nitrosation directly regulates the function of many proteins is significantly incorrect.
Krishnan S, Whitwell HJ, Cuenco J, et al., 2017, Evidence of Altered Glycosylation of Serum Proteins Prior to Pancreatic Cancer Diagnosis, International Journal of Molecular Sciences, Vol: 18, ISSN: 1422-0067
Biomarkers for the early detection of pancreatic cancer are urgently needed. The aim of this pilot study was to evaluate changes in serum N-glycoproteins and their glycosylation status prior to clinical presentation of pancreatic cancer that may be potential biomarkers. Prediagnosis serum samples pooled according to five time-to-diagnosis groups and a non-cancer control pool were digested with trypsin, labelled with mass tags, and subjected to titanium dioxide capture, deglycosylation, and 2D-LC-MS/MS profiling. Unbound peptides were profiled in parallel. Across the sample groups, 703 proteins were quantified and 426 putative sites of N-glycosylation were identified with evidence of several novel sites. Altered proteins with biomarker potential were predominantly abundant inflammatory response, coagulation, and immune-related proteins. Whilst glycopeptide profiles largely paralleled those of their parent proteins, there was evidence of altered N-glycosylation site occupancy or sialic acid content prior to diagnosis for some proteins, most notably of immunoglobulin gamma chains. α-1-Antitrypsin was tested as a biomarker, but found not to complement carbohydrate antigen 19-9 (CA19-9) in early detection of cancer. In conclusion, we provide preliminary evidence of altered glycosylation of several serum proteins prior to pancreatic cancer diagnosis, warranting further investigation of these proteins as early biomarkers. These changes may be largely driven by inflammatory processes that occur in response to tumour formation and progression.
Whitwell H, Mackay RM, Elgy C, et al., 2016, Nanoparticles in the lung and their protein corona: the few proteins that count., Nanotoxicology, Vol: 10, Pages: 1385-1394, ISSN: 1743-5404
The formation of protein coronae on nanoparticles (NPs) has been investigated almost exclusively in serum, despite the prevailing route of exposure being inhalation of airborne particles. In addition, an increasing number of nanomedicines, that exploit the airways as the site of delivery, are undergoing medical trials. An understanding of the effects of NPs on the airways is therefore required. To further this field, we have described the corona formed on polystyrene (PS) particles with different surface modifications and on titanium dioxide particles when incubated in human bronchoalveolar lavage fluid (BALF) from patients with pulmonary alveolar proteinosis (PAP). We show, using high-resolution quantitative mass spectrometry (MS(E)), that a large number of proteins bind with low copy numbers but that a few "core" proteins bind to all particles tested with high fidelity, averaging the surface properties of the different particles independent of the surface properties of the specific particle. The averaging effect at the particle surface means that differing cellular effects may not be due to the protein corona but due to the surface properties of the nanoparticle once inside the cell. Finally, the adherence of surfactant associated proteins (SP-A, B and D) suggests that there may be interactions with lipids and pulmonary surfactant (PSf), which could have potential in vivo health effects for people with chronic airway diseases such as asthma and chronic obstructive pulmonary disease (COPD), or those who have increased susceptibility toward other respiratory diseases.
Thawer S, Auret J, Schnoeller C, et al., 2016, Surfactant Protein-D Is Essential for Immunity to Helminth Infection., PLOS Pathogens, Vol: 12, ISSN: 1553-7366
Pulmonary epithelial cell responses can enhance type 2 immunity and contribute to control of nematode infections. An important epithelial product is the collectin Surfactant Protein D (SP-D). We found that SP-D concentrations increased in the lung following Nippostrongylus brasiliensis infection; this increase was dependent on key components of the type 2 immune response. We carried out loss and gain of function studies of SP-D to establish if SP-D was required for optimal immunity to the parasite. N. brasiliensis infection of SP-D-/- mice resulted in profound impairment of host innate immunity and ability to resolve infection. Raising pulmonary SP-D levels prior to infection enhanced parasite expulsion and type 2 immune responses, including increased numbers of IL-13 producing type 2 innate lymphoid cells (ILC2), elevated expression of markers of alternative activation by alveolar macrophages (alvM) and increased production of the type 2 cytokines IL-4 and IL-13. Adoptive transfer of alvM from SP-D-treated parasite infected mice into naïve recipients enhanced immunity to N. brasiliensis. Protection was associated with selective binding by the SP-D carbohydrate recognition domain (CRD) to L4 parasites to enhance their killing by alvM. These findings are the first demonstration that the collectin SP-D is an essential component of host innate immunity to helminths.
McKenzie Z, Kendall M, Mackay R-M, et al., 2015, Surfactant protein A (SP-A) inhibits agglomeration and macrophage uptake of toxic amine modified nanoparticles, Nanotoxicology, Vol: 9, Pages: 952-962, ISSN: 1743-5404
The lung provides the main route for nanomaterial exposure. Surfactant protein A (SP-A) is an important respiratory innate immune molecule with the ability to bind or opsonise pathogens to enhance phagocytic removal from the airways. We hypothesised that SP-A, like surfactant protein D, may interact with inhaled nanoparticulates, and that this interaction will be affected by nanoparticle (NP) surface characteristics. In this study, we characterise the interaction of SP-A with unmodified (U-PS) and amine-modified (A-PS) polystyrene particles of varying size and zeta potential using dynamic light scatter analysis. SP-A associated with both 100 nm U-PS and A-PS in a calcium-independent manner. SP-A induced significant calcium-dependent agglomeration of 100 nm U-PS NPs but resulted in calcium-independent inhibition of A-PS self agglomeration. SP-A enhanced uptake of 100 nm U-PS into macrophage-like RAW264.7 cells in a dose-dependent manner but in contrast inhibited A-PS uptake. Reduced association of A-PS particles in RAW264.7 cells following pre-incubation of SP-A was also observed with coherent anti-Stokes Raman spectroscopy. Consistent with these findings, alveolar macrophages (AMs) from SP-A−/− mice were more efficient at uptake of 100 nm A-PS compared with wild type C57Bl/6 macrophages. No difference in uptake was observed with 500 nm U-PS or A-PS particles. Pre-incubation with SP-A resulted in a significant decrease in uptake of 100 nm A-PS in macrophages isolated from both groups of mice. In contrast, increased uptake by AMs of U-PS was observed after pre-incubation with SP-A. Thus we have demonstrated that SP-A promotes uptake of non-toxic U-PS particles but inhibits the clearance of potentially toxic A-PS particles by blocking uptake into macrophages.
Kendall M, Hodges NJ, Whitwell H, et al., 2015, Nanoparticle growth and surface chemistry changes in cell-conditioned culture medium, PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES, Vol: 370, ISSN: 0962-8436
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