Biology subjects

Nicholson, J. K.

Publications and source records attributed to Nicholson, J. K..

11 recordsLinked to original sources

bioRxiv2026

A dependency-free, streamable format and cross-language toolkit for scalable LC-MS feature detection: reading only what you need

Mass spectrometry generates data faster than it can be read, and the exchange standard, mzML, is text-based and must be parsed in full before any spectrum is accessible. Binary alternatives are smaller but depend on storage engines such as HDF5, so access is dictated by the engine, not the file. We present Ionic (.ion), an open-source, compact, streamable binary format, and Quant{middle dot}ion, a processing toolkit built on the former. Ionic stores spectra, chromatograms and metadata as independently compressed, indexed blocks, so a reader retrieves only the bytes it needs, even inside a web browser, and converts losslessly to and from mzML. Ionic was smaller than compressed mzMLb on every acquisition type tested, and extracting one compound took under 40 ms, 35 to 90 times faster than an mzML reader. Quant{middle dot}ion exposes one core to R, Python, and JavaScript with identical results, and recovered 97% of true features on a ground-truth benchmark.

bioinformatics↗

bioRxiv2026

Multi-Omic Dissection of Autism Reveals Dominant Effects of Family, Sex, and Host-Microbe Metabolic Interactions

Autism spectrum disorder (ASD) has been associated with gut microbiome and metabolic alterations, but reported biomarkers are inconsistent and often inadequately account for family and shared environment. We analysed 620 children, including 334 with ASD and 286 neurotypical siblings, with a median age of 6.0 years (IQR 4.0-8.0). Faecal and urine samples were collected monthly up to eight times. After quality control, 869 microbiome, 754 faecal metabolome and 787 urine metabolome samples were analysed using 16S rRNA sequencing, NMR and LC-MS metabolomics. Mixed models accounted for family, repeated sampling and biological sex. Family membership explained 46.1% of variation across microbiome and metabolome profiles, compared with 8.0% attributable to ASD. After family adjustment, ASD explained only 0.1% to 0.3% of microbiome beta diversity. No stable taxonomic biomarkers were identified, and microbial classification was poor (AUROC <0.75). Urinary metabolomics identified elevated 5-hydroxy-L-tryptophan and altered phenylalanine metabolism. Structural equation modelling found no direct associations between selected taxa and metabolites. These findings argue against a universal ASD microbiome and indicate that family context contributes more strongly than diagnosis to microbial and metabolic variation.

microbiology↗

bioRxiv2025

Dried Blood Spot Lipidomics in Diabetes

Comprehensive lipidomic profiling in diabetes has identified disease-associated lipid signatures that may support more personalised monitoring beyond routine glycaemic control. Dried blood spot (DBS) microsampling offers a minimally invasive method for collecting and storing small blood volumes (<50 {micro}L), enabling decentralised and longitudinal sampling. However, its application in lipidomics for cardiometabolic phenotyping remains underexplored. In this study, 34 participants (17 diabetics, 17 non-diabetic controls) self-collected DBS samples using advanced microsampling devices (10 {micro}L, Capitainer(R)B). Lipid extracts were analysed using a validated liquid chromatography-trapped ion mobility-time-of-flight mass spectrometry method optimised for semi-quantitative DBS lipidomics. Multivariate analysis revealed significant differences in lipidomic profiles (n = 432 lipids) between diabetic and non-diabetic individuals (R{superscript 2}Y = 0.82, Q{superscript 2}Y = 0.50, p = 0.05). Key discriminatory lipids (all VIP > 1.44), including HexCer(18:1;O2/24:0), HexCer(18:1;O2/22:0), LPC(16:0), PC(O-34:3), TG(18:0_18:1_20:3), and TG(18:1_18:1_22:6), were consistent with matched venous and capillary plasma samples. Lipid class and structural differences, including elevated long-chain triacylglycerols and reduced lysophosphatidylcholines reflected known dyslipidaemia in diabetes and demonstrate the capacity of DBS to capture biologically meaningful lipid perturbations. Participant-reported outcomes from Problem Areas In Diabetes (PAID-20), Patient Activation Measure (PAM-13), and microsampling perception questionnaires highlighted psychosocial and behavioural insights, and indicated strong support for DBS sampling over venepuncture, citing ease of use and reduced burden. These findings establish DBS lipidomics as a feasible and informative approach for stratifying cardiometabolic disease, supporting broader implementation in remote, personalised monitoring frameworks (Figure 1). O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=77 SRC="FIGDIR/small/656742v1_fig1.gif" ALT="Figure 1"> View larger version (13K): org.highwire.dtl.DTLVardef@c53b4org.highwire.dtl.DTLVardef@14b507forg.highwire.dtl.DTLVardef@191e563org.highwire.dtl.DTLVardef@16af172_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 1.C_FLOATNO Graphical abstract - Chapter 5. C_FIG

biochemistry↗

bioRxiv2023

Metabolic consequences of non-severe burn injury are associated with increased plasma markers of inflammation and cardiovascular disease risk.

Globally, burns are a significant cause of injury that can cause substantial acute trauma as well as lead to increased incidence of chronic co-morbidity and disease. To date, research has primarily focused on the systemic response to severe injury, with little in the literature reported on impact of non-severe injuries (<15% total burn surface area; TBSA). To elucidate the metabolic consequences of non-severe burn injury, longitudinal plasma was collected from adults (n=35) who presented at hospital with a non-severe burn injury at admission, and at 6 week follow up. A cross-sectional baseline sample was also collected from non-burn control participants (n=14). Samples underwent multiplatform metabolic phenotyping using 1H nuclear magnetic resonance spectroscopy and liquid chromatography-mass spectrometry to quantify 112 lipoprotein and glycoproteins signatures and 852 lipid species from across 20 subclasses. Multivariate data modelling (Orthogonal projection to latent structures-discriminate analysis) revealed alterations in lipoprotein and lipid metabolism when comparing baseline control to hospital admission samples, with the phenotypic signature found to be sustained at follow up. Univariate (Mann-Whitney U) testing and OPLS-DA indicated specific increases in GlycB (p-value <1.0e-4), low density lipoprotein-2 subfractions (Variable importance in projection score; VIP >6.83e-1) and monoacyglyceride (20:4)(p-value <1.0e-4) and decreases in circulating anti-inflammatory high-density lipoprotein-4 subfractions (VIP >7.75e-1), phosphatidylcholines, phosphatidylglycerols, phosphatidylinositols and phosphatidylserines. The results indicate a persistent systemic metabolic phenotype that occurs even in cases of non-severe burn injury. The phenotype is indicative of an acute inflammatory profile which continues to be sustained post-injury, suggesting an impact on systems health beyond the site of injury. The phenotypes contained metabolic signatures consistent with chronic inflammatory states reported to have elevated incidence post-burn injury. Such phenotypic signatures may provide patient stratification opportunities, to identify individual responses to injury, personalise intervention strategies and improve acute care, reducing risk of chronic co-morbidity.

systems biology↗

bioRxiv2019

Microbiome Determinants and Physiological Effects of the Benzoate-Hippurate Microbial-Host Co-Metabolic Pathway

ObjectiveGut microbial products are involved in type 2 diabetes, obesity and insulin resistance. In particular, hippurate, a hepatic phase 2 conjugation product of microbial benzoate metabolism, has been associated with a healthy phenotype. This study aims to identify metagenomic determinants and test protective effects of hippurate. DesignWe profiled the urine metabolome by 1H Nuclear Magnetic Resonance (NMR) spectroscopy to derive associations with metagenomic sequences in 271 middle-aged Danish individuals to identify dietary patterns in which urine hippurate levels were associated with health benefits. We follow up with benzoate and hippurate infusion in mice to demonstrate causality on clinical phenotypes. ResultsIn-depth analysis identifies that the urine hippurate concentration is associated with microbial gene richness, microbial functional redundancy as well as functional modules for microbial benzoate biosynthetic pathways across several enterotypes. Through dietary stratification, we identify a subset of study participants consuming a diet rich in saturated fat in which urine hippurate, independently of gene richness, accounts for links with metabolic health that we previously associated with gene richness. We then demonstrate causality in vivo through chronic subcutaneous infusions of hippurate or benzoate (20 nmol/day) resulting in improved glycemic control in mice fed a high-fat diet. Hippurate improved insulin secretion through increased {beta}-cell mass and reduced liver inflammation and fibrosis, whereas benzoate treatment resulted in liver inflammation. ConclusionOur translational study shows that the benzoate-hippurate pathway brings a range of metabolic improvements in the context of high-fat diets, highlighting the potential of hippurate as a mediator of metabolic health.

systems biology↗

bioRxiv2019

A network-based data-mining approach to investigate indole-related microbiota-host co-metabolism

MotivationIndoles have been shown to play a significant role in cardiometabolic disorders. While some individual bacterial species are known to produce indole-adducts, to our best knowledge no studies have made use of publicly available genome data to identify prokaryotes, specifically those associated with the human gut microbiota, contributing to the indole metabolic network.\n\nResultsHere, we propose a computational strategy, comprising the integration of KEGG and BLAST, to identify prokaryote-specific metabolic reactions relevant for the production of indoles, as well as to predict new members of the human gut microbiota potentially involved in these reactions. By identifying relevant prokaryotic species for further validation studies in vitro, this strategy represents a useful approach for those interrogating the metabolism of other gut-derived microbial metabolites relevant to human health.\n\nAvailabilityAll R scripts and files (gut microbial dataset, FASTA protein sequences, BLASTP output files) are available from https://github.com/AndreaRMICL/Microbial_networks.\n\nContactARM: andrea.rodriguez-martinez13@imperial.ac.uk; LH: lesley.hoyles@ntu.ac.uk.

bioinformatics↗

bioRxiv2018

The Microbial Metabolite 4-Cresol Improves Glucose Homeostasis and Enhances β-Cell Function

Gut microbiota changes are associated with increased risk of Type 2 diabetes (T2D) and obesity. Through serum metabolome profiling in patients with cardiometabolic disease (CMD) we identified significant inverse correlation between the microbial metabolite 4-cresol and T2D. Chronic administration of non toxic dose of 4-cresol in two complementary preclinical models of CMD reduced adiposity, glucose intolerance and liver triglycerides, and enhanced insulin secretion in vivo, which may be explained by markedly increased pancreas weight, augmented islet density and size, and enhanced vascularisation suggesting activated islet neogenesis. Incubation of isolated islets with 4-cresol enhanced insulin secretion, insulin content and cell proliferation. In both CMD models 4-cresol treatment in vivo was associated with altered expression of SIRT1 and the kinase DYRK1A, which may contribute to mediate its biological effects. Our findings identify 4-cresol as an effective regulator of {beta}-cell function and T2D endophenotypes, which opens therapeutic perspectives in syndromes of insulin deficiency.

physiology↗

bioRxiv2018

Microbiome inhibition of IRAK-4 by trimethylamine mediates metabolic and immune benefits in high-fat-diet-induced insulin resistance

The global type 2 diabetes epidemic is a major health crisis and there is a critical need for innovative strategies to fight it. Although the microbiome plays important roles in the onset of insulin resistance (IR) and low-grade inflammation, the microbial compounds regulating these phenomena remain to be discovered. Here, we reveal that the microbiome inhibits a central kinase, eliciting immune and metabolic benefits. Through a series of in vivo experiments based on choline supplementation, blocking trimethylamine (TMA) production then administering TMA, we demonstrate that TMA decouples inflammation and IR from obesity in the context of high-fat diet (HFD) feeding. Through in vitro kinome screens, we reveal TMA specifically inhibits Interleukin-1 Receptor-associated Kinase 4 (IRAK4), a central kinase integrating signals from various toll-like receptors and cytokine receptors. TMA blunts TLR4 signalling in primary human hepatocytes and peripheral blood monocytic cells, and improves mouse survival after a lipopolysaccharide-induced septic shock. Consistent with this, genetic deletion and chemical inhibition of IRAK4 result in similar metabolic and immune improvements in HFD. In summary, TMA appears to be a key microbial compound inhibiting IRAK4 and mediating metabolic and immune effects with benefits upon HFD. Thereby we highlight the critical contribution of the microbial signalling metabolome in homeostatic regulation of host disease and the emerging role of the kinome in microbial-mammalian chemical crosstalk.

systems biology↗

bioRxiv2017

Metabolic retroconversion of trimethylamine N-oxide and the gut microbiota

BACKGROUNDThe dietary methylamines choline, carnitine and phosphatidylcholine are used by the gut microbiota to produce a range of metabolites, including trimethylamine (TMA). However, little is known about the use of trimethylamine N-oxide (TMAO) by this consortium of microbes.\n\nRESULTSA feeding study using deuterated TMAO in C57BL6/J mice demonstrated microbial conversion of TMAO to TMA, with uptake of TMA into the bloodstream and its conversion to TMAO. Microbial activity necessary to convert TMAO to TMA was suppressed in antibiotic-treated mice, with deuterated TMAO being taken up directly into the bloodstream. In batch-culture fermentation systems inoculated with human faeces, growth of Enterobacteriaceae was stimulated in the presence of TMAO. Human-derived faecal and caecal bacteria (n = 66 isolates) were screened on solid and liquid media for their ability to use TMAO, with metabolites in spent media analysed by 1H-NMR. As with the in vitro fermentation experiments, TMAO stimulated the growth of Enterobacteriaceae; these bacteria produced most TMA from TMAO. Caecal/small intestinal isolates of Escherichia coli produced more TMA from TMAO than their faecal counterparts. Lactic acid bacteria produced increased amounts of lactate when grown in the presence of TMAO, but did not produce large amounts of TMA. Clostridia (sensu stricto), bifidobacteria and coriobacteria were significantly correlated with TMA production in the mixed fermentation system but did not produce notable quantities of TMA from TMAO in pure culture.\n\nCONCLUSIONSReduction of TMAO by the gut microbiota (predominantly Enterobacteriaceae) to TMA followed by host uptake of TMA into the bloodstream from the intestine and its conversion back to TMAO by host hepatic enzymes is an example of metabolic retroconversion. TMAO influences microbial metabolism depending on isolation source and taxon of gut bacterium.\n\nCorrelation of metabolomic and abundance data from mixed microbiota fermentation systems did not give a true picture of which members of the gut microbiota were responsible for converting TMAO to TMA; only by supplementing the study with pure culture work and additional metabolomics was it possible to increase our understanding of TMAO bioconversions by the human gut microbiota.

microbiology↗

bioRxiv2017

Network analysis of mass spectrometry imaging data from colorectal cancer identifies key metabolites common to metastatic development.

A deeper understanding of inter-tumor and intra-tumor heterogeneity is a critical factor for the advancement of next generation strategies against cancer. The heterogeneous morphology exhibited by solid tumors is mirrored by their metabolic heterogeneity. Defining the basic biological mechanisms that underlie tumor cell variability will be fundamental to the development of personalized cancer treatments. Variability in the molecular signatures found in local regions of cancer tissues can be captured through an untargeted analysis of their metabolic constituents. Here we demonstrate that DESI mass spectrometry imaging (MSI) combined with network analysis can provide detailed insight into the metabolic heterogeneity of colorectal cancer (CRC). We show that network modules capture signatures which differentiate tumor metabolism in the core and in the surrounding region. Moreover, module preservation analysis of network modules between patients with and without metastatic recurrence explains the inter-subject metabolic differences associated with diverse clinical outcomes such as metastatic recurrence.\n\nSignificanceNetwork analysis of DESI-MSI data from CRC human tissue reveals clinically relevant co-expression ion patterns associated with metastatic susceptibility. This delineates a more complex picture of tumor heterogeneity than conventional hard segmentation algorithms. Using tissue sections from central regions and at a distance from the tumor center, ion co-expression patterns reveal common features among patients who developed metastases (up of > 5 years) not preserved in patients who did not develop metastases. This offers insight into the nature of the complex molecular interactions associated with cancer recurrence. Presently, predicting CRC relapse is challenging, and histopathologically like-for-like cancers frequently manifest widely varying metastatic tendencies. Thus, the methodology introduced here more robustly defines the risk of metastases based on tumor biochemical heterogeneity.\n\nAuthor contributionsP.I., Z.T., R.C.G.: designed the study, developed the workflow, analyzed the data, interpreted the results, wrote the paper; N.S. collected the MS, performed the H...E staining, wrote the paper; L.D.: interpreted the results, wrote the paper; A.M.: collected the MS; A.S.: histological assessment; L.P.: collected the tissue specimens and clinical metadata; A.D.: collected the MS; H.K.: performed the H...E staining; R.M.: collected the tissue specimens and clinical metadata. R.G.: histological assessment; J.K.N: designed the study, edited the paper.

cancer biology↗

bioRxiv2017

Microbial-host metabolic systems interactions and the gut-brain axis: protective effects of microbial propionate upon the blood-brain barrier

Gut microbiome composition and function are symbiotically linked with host health, and altered in metabolic, inflammatory and neurodegenerative disorders. Three recognized mechanisms exist by which the microbiome influences the gut-brain axis: modification of autonomic/sensorimotor connections, immune activation, and neuroendocrine pathway regulation. We hypothesized interactions between circulating gut-derived microbial metabolites and the blood-brain barrier (BBB) also contribute to the gut-brain axis. Propionate, produced from dietary substrates by colonic bacteria, stimulates intestinal gluconeogenesis and is associated with reduced stress behaviours, but its potential endocrine role has not been addressed. After demonstrating expression of the propionate receptor FFAR3 on human brain endothelium, we examined the impact of a physiologically relevant propionate concentration (1 M) on BBB properties in vitro. Propionate inhibited pathways associated with non-specific microbial infections via a CD14-dependent mechanism, suppressed expression of LRP-1 and protected the BBB from oxidative stress via NRF2 (NFE2L2) signaling. Together, these results suggest gut-derived microbial metabolites interact with the BBB, representing a fourth facet of the gut-brain axis that warrants further attention.

neuroscience↗