Utilising proteomics to understand and define hypertension: where are we and where do we go?

Introduction: Hypertension is a complex and multifactorial cardiovascular disorder. With different mechanisms contributing to a different extent to an individual’s blood pressure the discovery of novel pathogenetic principles of hypertension is challenging. However, there is an urgent and unmet clinical need to improve prevention, detection and therapy of hypertension in order to reduce the global burden associated with hypertension-related cardiovascular diseases. Areas covered: Proteomic techniques have been applied in reductionist experimental models including angiotensin II infusion models in rodents and the spontaneously hypertensive rat in order to unravel mechanisms involved in blood pressure control and end organ damage. In humans proteomic studies mainly focus on prediction and detection of organ damage, particularly of heart failure and renal disease. Whilst there are only few proteomic studies specifically addressing human primary hypertension there are more data available in hypertensive disorders in pregnancy such as preeclampsia. We will review these studies and discuss implications of proteomics on precision medicine approaches. Expert commentary: Despite the potential of proteomic studies in hypertension there has been moderate progress in this area of research. Standardised large-scale studies are required in order to make best use of the potential that proteomics offers in hypertension and other cardiovascular diseases

Urinary biomarkers for renal tract malformations

Introduction: Renal tract malformations (RTMs) are congenital anomalies of the kidneys and urinary tract, which are the major cause of end-stage renal disease in children. Using immunoassay-based approaches (ELISA, western blot), individual urinary proteins including transforming growth factor β, tumor necrosis factor and monocyte attractant proteins 1 were found to be associated to RTMs. However, only mass spectrometry (MS) based methods leading to the identification of panels of protein-based markers composed of fragments of the extracellular matrix allowed the prediction of progression of RTMs and its complications. Areas covered: In this review, we summarized relevant studies identified in “Pubmed” using the keywords “urinary biomarkers” and “proteomics” and “renal tract malformations” or “hydronephrosis” or “ureteropelvic junction obstruction” or “posterior urethral valves” or “vesicoureteral reflux”. These publications represent studies on potential protein-based biomarkers, either individually or combined in panels, of RTMs in human and animal models. Expert commentary: Successful use in the clinic of these protein-based biomarkers will need to involve larger scale studies to reach sufficient power. Improved performance will potentially come from combining immunoassay- and MS-based markers

Separation of rhodium from iridium through synergistic solvent extraction

There are currently few effective processes for the solvent extraction of rhodium from hydrochloric acid streams, and none that allow rhodium to be selectively extracted over iridium. Realizing this goal could allow rhodium to be recovered earlier in a typical platinum group metal (PGM) refining flowsheet and reduce the environmental impact of PGM refining. In this work, we show that a synergistic combination of a tert-alkyl primary amine LA and various inner-sphere ligands L can be used to recover rhodium via the complex [RhCl5L].HLA2. Although we show that rhodium is extracted by several extractant combinations, it is only readily stripped from the amine/amide synergistic mixture. As this extraction relies on the inner-sphere coordination of the amide to the metal, this process also demonstrates a route to obtain preferential extraction of rhodium over more inert iridium chloridometalates under industrially relevant conditions.</p

Polarized trout epithelial cells regulate transepithelial electrical resistance, gene expression, and the phosphoproteome in response to viral infection

The burden of disease is a major challenge in aquaculture production. The fish gill characterised with a large surface area and short route to the bloodstream is a major environmental interface and a significant portal of entry for pathogens. To investigate gill responses to viral infection the salmonid gill cell line RTgill-W1 was stimulated with synthetic dsRNA and the salmonid alphavirus subtype 2 (SAV-2). Epithelial integrity in polarized cells measured as transepithelial electrical resistance (TER) immediately increased after stimulation with the synthetic dsRNA, polyinosinic:polycytidylic acid (poly(I:C)). In parallel, tight junction and gene expression of innate immune activation markers was modulated in response to poly(I:C). The SAV-2 virus was found to replicate at a low level in RTgill-W1 cells where TER was disturbed at an early stage of infection, however, gene expression related to tight junction regulation was not modulated. A strong poly(I:C)-driven antiviral response was observed including increases of Rig-like receptors (RLRs) and interferon stimulating genes (ISGs) mRNAs. At the level of signal transduction, poly(I:C) stimulation was accompanied by the phosphorylation of 671 proteins, of which 390 were activated solely in response to the presence of poly(I:C). According to motif analysis, kinases in this group included MAPKs, Ca2+/calmodulin-dependent kinase (CaMK) and cAMP-dependent protein kinase (PKA), all reported to be activated in response to viral infection in mammals. Results also highlighted an activation of the cytoskeletal organisation that could be mediated by members of the integrin family. While further work is needed to validate these results, our data indicate that salmonid gill epithelia mount a significant response to viral infection that is likely crucial to disease progression. In vitro cell culture can facilitate both a deeper understanding of the anti-viral response in fish and open novel therapeutic avenues for fish health management in aquaculture

Comparison of urine and plasma peptidome indicates selectivity in renal peptide handling

Purpose: Urine is considered to be produced predominantly as a result of plasma filtration in the kidney. However, the origin of the native peptides present in urine has never been investigated in detail. Therefore, we aimed to obtain a first insight into the origin of urinary peptides based on a side‐by‐side comprehensive analysis of the plasma and urine peptidome. Methods: Twenty‐two matched urine and plasma samples were analyzed for their peptidome using capillary electrophoresis coupled to mass spectrometry (CE‐MS; for relative quantification) and CE‐ or LC coupled to tandem mass spectrometry (CE‐ or LC‐ MS/MS; for peptide identification). The overlap and association of abundance of the different peptides present in these two body fluids were evaluated. Results: We were able to identify 561 plasma and 1461 urinary endogenous peptides. Only 90 peptides were detectable in both urine and plasma. No significant correlation was found when comparing the abundance of these common peptides, with the exception of collagen fragments. This observation was also supported when comparing published peptidome data from both plasma and urine. Conclusions and clinical relevance: Most of the plasma peptides are not detectable in urine, possibly due to tubular reabsorption. The majority of urinary peptides may in fact originate in the kidney. The notable exception is collagen fragments, which indicates potential selective exclusion of these peptides from tubular reabsorption. Experimental verification of this hypothesis is warranted

Identifying molecules as biosignatures with assembly theory and mass spectrometry

The search for alien life is hard because we do not know what signatures are unique to life. We show why complex molecules found in high abundance are universal biosignatures and demonstrate the first intrinsic experimentally tractable measure of molecular complexity, called the molecular assembly index (MA). To do this we calculate the complexity of several million molecules and validate that their complexity can be experimentally determined by mass spectrometry. This approach allows us to identify molecular biosignatures from a set of diverse samples from around the world, outer space, and the laboratory, demonstrating it is possible to build a life detection experiment based on MA that could be deployed to extraterrestrial locations, and used as a complexity scale to quantify constraints needed to direct prebiotically plausible processes in the laboratory. Such an approach is vital for finding life elsewhere in the universe or creating de-novo life in the lab

Dual targeting of p53 and c-MYC selectively eliminates leukaemic stem cells

e Glasgow and Manchester Experimental Cancer Medicine Centres (ECMC), which are funded by CR-UK and the Chief Scientist’s Office (Scotland). We acknowledge the funders who have contributed to this work: MRC stratified medicine infrastructure award (A.D.W.), CR-UK C11074/A11008 (F.P., L.E.M.H., T.L.H., A.D.W.); LLR08071 (S.A.A., E.C.); LLR11017 (M.C.); SCD/04 (M.C.); LLR13035 (S.A.A., K.D., A.D.W., and A.P.); LLR14005 (M.T.S., D.V.); KKL690 (L.E.P.); KKL698 (P.B.); LLR08004 (A.D.W., A.P. and A.J.W.); MRC CiC (M.E.D.); The Howat Foundation (FACS support); Friends of Paul O’Gorman (K.D. and FACS support); ELF 67954 (S.A.A.); BSH start up fund (S.A.A.); MR/K014854/1 (K.D.)

Qualitative and quantitative approaches to modelling tyrosine kinase inhibitor treatment and resistance in non-small cell lung cancer

Non-small cell lung cancer (NSCLC) affects 80-85% of lung cancer patients. Tyrosine kinase inhibitors (TKIs) are targeted therapies that bind to NSCLC cells with specific mutations and inhibit their growth. However, responses to these therapies generally fail due to the emergence of drug resistance and the onset of adverse events which can include; interstitial lung disease (ILD), edema, hypothyroidism, nausea and diarrhoea. Mathematical models can increase our understanding of the biological mechanisms that determine whether a tumour is likely to respond or become resistant to treatment. When combined with experimental studies, such models can provide mechanistic insight into factors which influence the evolution to resistance and how specific drugs act. They can also aid experimental design and generate testable hypotheses concerning, for example, optimal dose regimens, scheduling protocols and combination therapies. In this thesis, we develop mathematical models of tumour growth and treatment response, and use them to determine tumour-specific factors that influence drug resistance. Further, by combining the models with preclinical data, we infer the biological properties of different NSCLC cell lines and characterise their responses to different TKIs. In the first part of the thesis, we develop a mathematical model that describes the dynamics of four cell populations that differ in their levels of resistance to two TKIs. The model accounts for mutations, competition for resources and cellular interactions and is formulated as a system of time-dependent, ordinary differential equations. We use the model to show how tumour responses to TKI combinations depend on the rates at which the tumour cells mutate and proliferate. We then reformulate the model to account for stochastic effects that encapsulate the random nature of mutation events that occur with low probabilities. We find considerable variation in the predictions generated by the deterministic and stochastic modelling frameworks, highlighting the need for careful consideration of the modelling approach when predicting drug resistance and treatment response. The second part of the thesis focuses on integrating mathematical models with experimental data in order to uncover the mechanisms driving NSCLC cells’ responses to TKI treatment. We develop a suite of increasingly complex models that describe in vitro cell growth data. We use parameter estimation, information criteria/goodness-of-fit metrics and parameter identifiability analysis to investigate the identifiability of the model parameters given the preclinical data and to guide model selection. We extend the selected model to describe the response of NSCLC cells to TKI treatment. While this model is structurally identifiable, the existence of practically non-identifiable model parameters given the in vitro treatment data leads to uncertainty in the predictions of hidden variables. We use synthetic data to evaluate how experimental design (i.e. what variables are measured) affects certainty in parameter estimates and to generate hypotheses about the likely mechanisms of action of TKIs on NSCLC cells. To summarise, in this thesis, we develop a series of mathematical models that increase our understanding of the biological mechanisms that drive the evolution to resistant tumours and determine tumour response to treatment. Our results highlight the benefit of theoretical approaches for distinguishing the growth dynamics of different NSCLC cell lines, providing mechanistic insight into the effects of different drugs on different cell lines and suggesting strategies for improving experimental design

A Universal Sequencing System for Unknown Oligomers

No synthetic chemical system can produce complex oligomers with fidelities comparable to biological systems. To bridge this gap, chemists must be able to characterise synthetic oligomers. Currently there are no tools for identifying synthetic oligomers with sequence resolution. Herein, we present a system that allows us to do omics-level sequencing for synthetic oligomers and use this to explore unconstrained complex mixtures. The system, Oligomer-Soup-Sequencing (OLIGOSS), can sequence individual oligomers in heterogeneous and polydisperse mixtures from tandem mass spectrometry (MS/MS) data. Unlike existing software, OLIGOSS can sequence oligomers with different backbone chemistries. Using an input file format, OLIG, that formalizes the set of abstract properties, any MS/MS fragmentation pathway can be defined. This has been demonstrated on four model systems of linear oligomers. OLIGOSS can screen large sequence spaces, enabling reliable sequencing of synthetic oligomeric mixtures, with false discovery rates (FDRs) of 0-1.1%, providing sequence resolution comparable to bioinformatic tools.</p