Biology subjects

Rogers, S.

Publications and source records attributed to Rogers, S..

45 records · Page 3Linked to original sources

bioRxiv2020

A novel multi-platform model of human red blood cell homeostasis applied to investigate the mechanisms behind the multiple effects of elevated i

We introduce here a new multiplatform JAVA-based mathematical-computational model of RBC homeostasis for investigating the dynamics of changes in RBC homeostasis in health and disease. We provide a brief overview on the homeostasis of human RBCs and on the general biophysical principles guiding the modelling design. By way of a detailed tutorial we apply the model to investigate in depth the multiple effects associated with RBC dehydration induced by potassium permeabilization, a necessary preliminary for understanding the pathophysiology of a wide group of inherited haemolytic anaemias, a subject of intense current research and clinical interest. Using the red cell model (RCM), we design and run in silico representations of experimental protocols to study global RBC responses to calcium and potassium permeabilization covering a wide range of experimental, physiological and pathological conditions. Model outputs report the evolution in time of all the homeostatic variables in the system allowing, for the first time, a detailed and comprehensive account of the complex processes shaping global cell responses. Analysis of the results explains the mechanisms by which the entangled operation of all the RBC components link cell dehydration and protein crowding to cell acidification and to the generation of hypertonic, alkaline effluents. Open access to the RCM in a GitHub repository, together with the tutorial primed for a specific investigation pave the way for researchers and clinicians to apply the model on many different aspects of RBC physiology and pathology.

physiology↗

bioRxiv2020

Changes in the homeostasis of human red blood cells during capillary transits

In this and the next paper of this series we apply the red cell model introduced in the previous paper to investigate the changes in RBC homeostasis during capillary transits and over the full circulatory lifespan of the cells. These are topics inaccessible to direct experimentation but rendered mature for a modelling approach by recent findings and by a large body of apparently unrelated early results which robustly constrain the parameter space offering the opportunity for an in depth study of the mechanisms involved. Capillary transit times vary between 0.5 and 1.5s during which the red blood cells squeeze and deform in the capillary stream transiently opening stress-gated PIEZO1 channels, creating minuscule quantal changes in RBC ion contents and volume. Widely accepted early views originally based on results from experimentally shear-stressed red cells suggested that quantal changes generated during capillary transits add up over time to generate the documented changes in RBC density during their long circulatory lifespan, the quantal hypothesis. Applying the new PIEZO1 extension of the RBC model (RCM) introduced in the previous paper we investigated in detail the changes in homeostatic variables that may be expected during single capillary transits resulting from transient PIEZO1 channel activation. The predicted quantal volume changes were infinitesimal in magnitude, biphasic in nature, and essentially irreversible within inter-transit periods. A sub-second transient PIEZO1 activation triggered a sharp swelling peak followed by a much slower recovery period towards lower-than-baseline volumes. The peak response was caused by net CaCl2 and fluid gain via PIEZO1 channels driven by the steep electrochemical inward Ca2+ gradient. The ensuing dehydration followed a complex time-course with sequential, but partially overlapping contributions by KCl loss via Ca2+-activated Gardos channels, PMCA mediated calcium extrusion and chloride efflux by the Jacobs-Steward mechanism. The change in relative cell volume predicted for single capillary transits was below 10-4, an infinitesimal volume change incompatible with a functional role in capillary flow. The biphasic response predicted by the RCM appears to conform to the quantal hypothesis, but whether its cumulative effects could account for the documented changes in density during RBC senescence required an investigation of the effects of myriad transits over the full period of circulatory lifespan, the subject of the next paper of this series.

physiology↗

bioRxiv2020

III. Unravelling the basis for the extended circulatory lifespan of human red blood cells and for the hyperdense collapse of irreversibly sickled cells

Human red blood cells (RBCs) have a circulatory lifespan of about four months. Under constant oxidative and mechanical stress, but devoid of organelles and deprived of biosynthetic capacity for protein renewal, RBCs undergo substantial homeostatic changes, progressive densification followed by late density reversal among others, changes assumed to have been harnessed by evolution to sustain the rheological competence of the RBCs for as long as possible. The unknown mechanisms by which this is achieved are the subject of this investigation. Each RBC traverses capillaries between 1000 and 2000 times per day, roughly one transit per minute, a total of about 2*105 transits during their lifespan. A dedicated Lifespan model of RBC homeostasis was developed as an extension of the RCM introduced in the first paper of this series to explore the cumulative patterns predicted for repetitive capillary transits over a standardized lifespan period of 120 days, using experimental data to constrain the parameter space. Capillary transits were simulated by periods of elevated cell/medium volume ratios and by transient deformation-induced permeability changes attributed to PIEZO1 channel mediation as outlined in the second paper of this series. The first unexpected finding was that quantal changes generated during single capillary transits cease accumulating after a few days and cannot account for the observed progressive densification of RBCs on their own, thus ruling out the quantal hypothesis. The second unexpected finding was that the documented patterns of RBC densification and late reversal could only be emulated by the implementation of a strict time-course of decay in the activities of the calcium and Na/K pumps, but only in addition to the quantal changes. These results showed that both quantal changes and pump-decay regimes were necessary to account for the documented lifespan pattern, neither sufficient on their own. They also suggested a strong selective component in the pump decay sequence. A third finding was that RBCs exposed to levels of calcium permeation above certain thresholds in the circulation could develop a pattern of late or early hyperdense collapse followed by delayed density reversal. When tested over much reduced lifespan periods the results emulated the known circulatory fate of irreversible sickle cells, the cell subpopulation responsible for vaso-occlusion and for most of the clinical manifestations of sickle cell disease. Analysis of the results provided an insightful new understanding of the mechanisms driving the changes in RBC homeostasis during circulatory aging in health and disease.

physiology↗

bioRxiv2019

In-silico Optimisation of Mass Spectrometry Fragmentation Strategies in Metabolomics

Liquid-Chromatography (LC) coupled to tandem mass spectrometry (MS/MS) is widely used in identifying small molecules in untargeted metabolomics. Various strategies exist to acquire MS/MS fragmentation spectra; however, the development of new acquisition strategies is hampered by the lack of simulators that let researchers prototype, compare, and optimise strategies before validations on real machines. We introduce Virtual Metabolomics Mass Spectrometer (ViMMS), a modular metabolomics LC-MS/MS simulator framework that allows for scan-level control of the MS2 acquisition process in-silico. ViMMS can generate new LC-MS/MS data based on empirical data or virtually re-run a previous LC-MS/MS analysis using pre-existing data in-silico to allow the testing of different fragmentation strategies. It allows the comparison of different fragmentation strategies on real data, with the resulting scan results extractable as mzML files. To demonstrate its utility, we show how our proposed framework can be used to take the output of a real tandem mass spectrometry analysis and examine the effect of varying parameters in Top-N Data Dependent Acquisition protocol. We also demonstrate how ViMMS can be used to compare a recently published Data-set-Dependent Acquisition strategy with a standard Top-N strategy. We expect that ViMMS will save method development time by allowing for offline evaluation of novel fragmentation strategies and optimisation of fragmentation strategy for a particular experiment.

bioinformatics↗

bioRxiv2019

Towards development of a statistical framework to evaluate myotonic dystrophy type 1 mRNA biomarkers in the context of a clinical trial

Myotonic dystrophy type 1 (DM1) is a rare genetic disorder, characterised by muscular dystrophy, myotonia, and other symptoms. DM1 is caused by the expansion of a CTG repeat in the 3-untranslated region of DMPK. Longer CTG expansions are associated with greater symptom severity and earlier age at onset. The primary mechanism of pathogenesis is thought to be mediated by a gain of function of the CUG-containing RNA, that leads to trans-dysregulation of RNA metabolism of many other genes. Specifically, the alternative splicing (AS) and alternative polyadenylation (APA) of many genes is known to be disrupted. In the context of clinical trials of emerging DM1 treatments, it is important to be able to objectively quantify treatment efficacy at the level of molecular biomarkers. We show how previously described candidate mRNA biomarkers can be used to model an effective reduction in CTG length, using modern high-dimensional statistics (machine learning), and a blood and muscle mRNA microarray dataset. We show how this model could be used to detect treatment effects in the context of a clinical trial.

bioinformatics↗

bioRxiv2019

MolNetEnhancer: enhanced molecular networks by integrating metabolome mining and annotation tools

Metabolomics has started to embrace computational approaches for chemical interpretation of large data sets. Yet, metabolite annotation remains a key challenge. Recently, molecular networking and MS2LDA emerged as molecular mining tools that find molecular families and substructures in mass spectrometry fragmentation data. Moreover, in silico annotation tools obtain and rank candidate molecules for fragmentation spectra. Ideally, all structural information obtained and inferred from these computational tools could be combined to increase the resulting chemical insight one can obtain from a data set. However, integration is currently hampered as each tool has its own output format and efficient matching of data across these tools is lacking. Here, we introduce MolNetEnhancer, a workflow that combines the outputs from molecular networking, MS2LDA, in silico annotation tools (such as Network Annotation Propagation or DEREPLICATOR) and the automated chemical classification through ClassyFire to provide a more comprehensive chemical overview of metabolomics data whilst at the same time illuminating structural details for each fragmentation spectrum. We present examples from four plant and bacterial case studies and show how MolNetEnhancer enables the chemical annotation, visualization, and discovery of the subtle substructural diversity within molecular families. We conclude that MolNetEnhancer is a useful tool that greatly assists the metabolomics researcher in deciphering the metabolome through combination of multiple independent in silico pipelines.

biochemistry↗

bioRxiv2019

Drosophila Snazarus regulates a lipid droplet population at plasma membrane-droplet contacts in adipocytes

Adipocytes store nutrients as lipid droplets (LDs), but how they organize their LD stores to balance lipid uptake, storage, and mobilization remains poorly understood. Here, using Drosophila fat body (FB) adipocytes we characterize spatially distinct LD populations that are maintained by different lipid pools. We identify peripheral LDs (pLDs) that make close contact with the plasma membrane (PM) and are maintained by lipophorin-dependent lipid trafficking. pLDs are distinct from larger cytoplasmic medial LDs (mLDs) which are maintained by FASN1-dependent de novo lipogenesis. We find that sorting nexin CG1514/Snazarus (Snz) associates with pLDs and regulates LD homeostasis at ER-PM contact sites. Loss of SNZ perturbs pLD organization whereas Snz over-expression drives LD expansion, triacylglyceride production, starvation resistance, and lifespan extension through a DESAT1-dependent pathway. We propose that Drosophila adipocytes maintain spatially distinct LD populations and identify Snz as a novel regulator of LD organization and inter-organelle crosstalk.

cell biology↗

bioRxiv2019

A universal fluorescence-based toolkit for real-time quantification of DNA and RNA nuclease activity

DNA and RNA nucleases play a critical role in a growing number of cellular processes ranging from DNA repair to immune surveillance. Nevertheless, many nucleases have unknown or poorly characterized activities. Elucidating nuclease substrate specificities and co-factors can support a more definitive understanding of cellular mechanisms in physiology and disease. Using fluorescence-based methods, we present a quick, safe, cost-effective, and real-time versatile nuclease assay, which uniquely studies nuclease enzyme kinetics. In conjunction with a substrate library we can now analyse nuclease catalytic rates, directionality, and substrate preferences. The assay is sensitive enough to detect kinetics of repair enzymes when confronted with DNA mismatches or DNA methylation sites. We have also extended our analysis to study the kinetics of human single-strand DNA nuclease TREX2, DNA polymerases, RNA, and RNA:DNA nucleases. These nucleases are involved in DNA repair, immune regulation, and have been associated with various diseases, including cancer and immune disorders.

biochemistry↗

bioRxiv2018

Deciphering complex metabolite mixtures by unsupervised and supervised substructure discovery and semi-automated annotation from MS/MS spectra

O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=94 SRC=\"FIGDIR/small/491506_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (19K):\norg.highwire.dtl.DTLVardef@1382d8borg.highwire.dtl.DTLVardef@13636a4org.highwire.dtl.DTLVardef@3d7decorg.highwire.dtl.DTLVardef@1161d62_HPS_FORMAT_FIGEXP M_FIG Integration of MS2LDA substructure discovery with MAGMa spectral annotations and ClassyFire term predictions complemented with MotifDB significantly advance metabolite annotation.\n\nC_FIG Complex metabolite mixtures are challenging to unravel. Mass spectrometry (MS) is a widely used and sensitive technique to obtain structural information on complex mixtures. However, just knowing the molecular masses of the mixtures constituents is almost always insufficient for confident assignment of the associated chemical structures. Structural information can be augmented through MS fragmentation experiments whereby detected metabolites are fragmented giving rise to MS/MS spectra. However, how can we maximize the structural information we gain from fragmentation spectra?\n\nWe recently proposed a substructure-based strategy to enhance metabolite annotation for complex mixtures by considering metabolites as the sum of (bio)chemically relevant moieties that we can detect through mass spectrometry fragmentation approaches. Our MS2LDA tool allows us to discover - unsupervised - groups of mass fragments and/or neutral losses termed Mass2Motifs that often correspond to substructures. After manual annotation, these Mass2Motifs can be used in subsequent MS2LDA analyses of new datasets, thereby providing structural annotations for many molecules that are not present in spectral databases.\n\nHere, we describe how additional strategies, taking advantage of i) combinatorial in-silico matching of experimental mass features to substructures of candidate molecules, and ii) automated machine learning classification of molecules, can facilitate semi-automated annotation of substructures. We show how our approach accelerates the Mass2Motif annotation process and therefore broadens the chemical space spanned by characterized motifs. Our machine learning model used to classify fragmentation spectra learns the relationships between fragment spectra and chemical features. Classification prediction on these features can be aggregated for all molecules that contribute to a particular Mass2Motif and guide Mass2Motif annotations.\n\nTo make annotated Mass2Motifs available to the community, we also present motifDB: an open database of Mass2Motifs that can be browsed and accessed programmatically through an API. MotifDB is integrated within ms2lda.org, allowing users to efficiently search for characterized motifs in their own experiments. We expect that with an increasing number of Mass2Motif annotations available through a growing database we can more quickly gain insight in the constituents of complex mixtures. That will allow prioritization towards novel or unexpected chemistries and faster recognition of known biochemical building blocks.

bioinformatics↗