184 research outputs found
Modifying the maternal microbiota alters the gut-brain metabolome and prevents emotional dysfunction in the adult offspring of obese dams [metabolomics dataset]
Get PDFData collected between 2019 and 2021. Data pertains to work published in Proceedings of the National Academy of Sciences of the United States of America. This dataset contains the metabolomics sum-normalised data used in the study. The name of each spreadsheet identifies the cohort the data corresponds to
Defamation, A Camouflage of Psychic Interests: The Beginning of a Behavioral Analysis
Get PDFDoes the law of defamation need to be reformed? The author thinks so. Professor Probert rejects the doctrine of libel per se and questions the courts\u27 understanding and use of the term reputation. It is his belief that plaintiffs on an individual basis should have increased benefit of the knowledge accumulated by the various social sciences in proving the harm done by the alleged defamation, with more liberalization in the requirements of pleading and proof than is now generally countenanced by the courts
Solution NMR analyses of the C-type carbohydrate recognition domain of DC-SIGNR protein reveal different binding modes for HIV-derived oligosaccharides and smaller glycan fragments
No full textBackground: DC-SIGNR, a C-type lectin which promotes infection of pathogens such as HIV, is a promising drug target.
Results: Carbohydrate recognition domain of DC-SIGNR is highly dynamic, displaying unique binding modes for individual glycans.
Conclusion: More complex, disease-associated glycans have different binding modes than smaller glycans previously studied.
Significance: Understanding ligand-binding properties and solution dynamics of DC-SIGNR will facilitate therapeutic desig
Investigating peripheral metabolites and lipids as potential biomarkers for Major Depressive Disorder
Get PDFMajor Depressive Disorder (MDD) poses significant clinical challenges due to its complicated and elusive pathophysiology. This thesis seeks to fill research gaps in understanding MDD by investigating peripheral metabolites and lipids as potential biomarkers which could be used to improve diagnosis and prognosis of this debilitating mental health condition. Small molecules, such as amino acids, that are involved in energy homeostasis are of particular interest to investigate alongside lipids because mood disorders are strongly associated with metabolic dysfunction. A systematic review and meta-analysis was first conducted on articles reporting metabolomic analyses on amino acids and demonstrated significantly elevated citrate, alanine, and glutamate levels in peripheral biofluids from depressed people compared to healthy individuals. The investigation analyzed metabolomic and lipidomic data from UK twin populations, focusing on individuals assessed using the Hospital Anxiety and Depression Scale (HADS). Participants meeting inclusion criteria had at least one HADS score and <15% missing data. With 1,532 entries, the primary emphasis was on a two-year database, revealing insights into twin health, including 43 Normal, 35 Borderline Abnormal, and 35 Abnormal samples. This comprehensive study illuminated connections between lifestyle factors, inflammatory markers, lipidomics, and MDD severity. Positive associations between smoking and alcohol consumption underscored gender-specific implications and negative correlations between exercise and HADS suggested a protective relationship against depression, aligning with established benefits for mental well-being. Pyruvate and tyrosine were found to be positively associated with depression, while alanine and acetate were negatively associated with depression. However, none of these associations were statistically significant. Multiple regression study found positive relationships between IDLFC, SLDLL, LDLC, and HADS levels, while others exhibited significant negative associations. SLDLCE and SLDLFC were found to have the highest impact on HADS scores, highlighting the complex nature of lipid metabolism in depression. Furthermore, the inverse connection between cholesterol and cholesterol esters in small and medium LDL particles and depression severity shows that cholesterol may be a more important biomarker for major depressive disorder (MDD), regardless of lipoprotein size or density. The OPLS-DA model effectively distinguished normal and abnormal mental health categories, highlighting the discriminative potential of metabolite biomarkers. Conspicuously, "AcAce" led with a VIP score of 2.24, followed by "BOHBut" (1.89) and "Ala" (1.75), emphasizing their impact. The lipid analysis illuminated complex shifts in cholesterol metabolism and lipid species, accentuating their crucial role in the pathophysiology of depression. "FreeC" stood out with a VIP score of 1.04, indicating its significant predictive influence, while "SM" closely followed with a score of 1.03. Additionally, "IDLFC" and "LLDLL" demonstrated remarkable importance, with VIP scores of 1.02 and 1.01, respectively, capturing essential data patterns. Comorbidity was found to influence lipid profiles, emphasizing the need to consider psychiatric conditions in research and clinical practice. Collectively, these findings contribute valuable insights into the multifaceted nature of MDD, proposing potential new biomarkers that may refine diagnostic accuracy and deepen our comprehension of this complex disorder
Metabolomics for biomarker discovery in neurological disorders
Get PDFBlood-borne metabolites can serve as readily accessible biomarkers reflecting biochemical alterations in neurological diseases, facilitating diagnostic, therapeutic and prognostic determination. In neurological disorders with overlapping symptoms, but with different aetiologies, timely diagnosis and appropriate treatment are crucial for optimal prognosis. This thesis employed nuclear magnetic resonance (NMR)-based metabolomics for biomarker discovery to improve diagnostics for antibody-mediated neurological disease.
1H NMR is used for metabolite measurement due to its high reproducibility, instrument stability, minimal sample preparation requirements and ability to detect a wide range of biologically significant metabolites. Here, the effectiveness of NMR-based metabolomics to distinguish between disease classes was explored using orthogonal partial least squares-discriminant analysis (OPLS-DA), with 10-fold cross-validation with repetition and permutation testing. Plasma samples from a cohort comprising patients with autoimmune encephalitis (AE) and those with drug-resistant epilepsy (DRE) were analysed. NMR-based metabolomics effectively distinguished AE from DRE, achieving a high predictive accuracy of 87.0 ± 3.1%. This method also enabled the stratification of AE subtypes (NMDAR-Ab, LGI1-Ab, CASPR2-Ab AE), with subtype-specific metabolite signatures observed. Therefore, NMR-based blood tests are able to complement current autoantibody assays and could provide a faster, cost-effective, and highly accurate adjunct for AE diagnosis. The same methodology was then applied to serum samples from psychosis patients with and without the neuronal surface antibodies in common with those found in AE. A distinct metabolic signature, including decreased lipoprotein fatty acids and increased branched-chain amino acids and glucose, was identified in psychosis patients with voltage-gated potassium channel (VGKC) or glycine receptor (GlyR) antibodies. These patients have more severe psychotic presentations, suggesting a unique inflammatory subtype.
To facilitate clinical translation, this thesis work then addressed preanalytical variability in blood samples by investigating the impact of pre-processing delays, post-processing delays, and different blood collection tubes on metabolite variability. A significant source of variability in blood was identified as the conversion of glucose to lactate owing to delays in blood pre-processing. However, it was discovered that the fluoride oxalate collection tubes effectively stabilise glucose and lactate levels for 24 hours at either 4 °C or room temperature, providing a viable alternative when rapid processing is not feasible.
Preanalytical variability can also arise in brain NMR metabolomics owing to suboptimal extractions. Investigations using rodent brain samples revealed that metabolites such as aspartate, acetate, N-acetyl aspartate, and glutamate exhibited instability as a consequence of inadequate protein precipitation with 50% acetonitrile or methanol. Conversely, a methanol/water/chloroform extraction method provides the necessary reproducibility and extraction efficiency while effectively preserving metabolite stability by sufficient protein precipitation. This method can, therefore, be recommended for untargeted brain NMR metabolomics, as it enables the reliable detection of metabolic alterations at the site of pathology, complementing histological analysis and enhancing diagnostic precision.
In conclusion, this thesis has demonstrated the potential of NMR-based metabolomics to be a powerful tool for distinguishing antibody-mediated neurological conditions, with the potential to enhance diagnostic precision, inform therapeutic strategies, and improve the understanding of disease mechanisms. By addressing preanalytical variability in both blood and brain metabolomics, this work has provided a blueprint to enhance the reliability and reproducibility of metabolomic analyses, leading to more accurate and consistent diagnostic and research outcomes
Metabolism in mood disorder across the gut-liver-brain axis
Get PDFBackground and Aims: Depression and nonalcoholic fatty liver disease (NAFLD) are common disorders that share a bidirectional relationship and continue to increase in prevalence. Both maternal depression and obesity during pregnancy increase the risk of neuropsychiatric disease in the offspring. Alterations to systemic immunity and gut microbiota composition are thought to contribute to these relationships, though the precise mechanisms are not known. In rodents, probiotic supplementation has been reported to have anti-inflammatory and antidepressant-like effects, yet it is not clear how modifying the microbiota of obese dams during the perinatal period may affect the behaviour, metabolism, and neuroplasticity gene expression of their offspring. Principally, this thesis explores the distinct effects of maternal perinatal probiotic intake and obesity (induced by chronic consumption of a high fat diet [HFD]) on the behaviour, brain-gut metabolism, and neuroplasticity gene expression of the offspring. Maternal diet-induced obesity was hypothesised to increase anxiety and depressive-like behaviour in the young offspring. Long-lasting behavioural changes were also expected in adult offspring. Additionally, I predicted that perinatal probiotic exposure would attenuate these behavioural changes and lead to changes in neuroplasticity gene expression alongside an increase in faecal short-chain fatty acids acetate, butyrate, and propionate. It was then determined whether a serum metabolomic profile of depressive symptoms could be detected in the context of metabolic dysfunction in humans, whereby I expected to observe an additive effect of depression on serum markers of dyslipidaemia and inflammation in NAFLD. Methods: Female CD-1 mice were fed either a HFD or control diet for 6 weeks prior to mating until three weeks postpartum (PND21). At the time of conception through to PND21, a multispecies probiotic supplement (or vehicle control) was administered through the drinking water. At the time of weaning, dams and half the juvenile offspring were assessed for anxiety and depressive-like behaviours, and the faecal, liver, blood, and brain metabolome were quantified using nuclear magnetic resonance (NMR) spectroscopy. In the offspring prefrontal cortex, mRNA expression of genes relating to neuroplasticity, astrocyte metabolism, and inflammation were quantified. The remainder of the offspring were weaned onto regular chow and at 4-months age were profiled similarly to the juvenile offspring. In translational work, a human cohort with NAFLD where >90% of individuals were obese, serum NMR metabolite profiles were compared between patients with depressive symptoms in the last 12-months (n = 81), lifetime depression (n = 30) and no history of depressive symptoms (n = 107) using multivariate statistics. Results: Prolonged high-fat diet feeding reduced maternal gut SCFA abundance, increased markers of peripheral inflammation, and decreased the abundance of neuroactive metabolites in maternal milk during nursing. Both the juvenile and adult offspring of obese dams exhibited increased anxiety-like behaviour, which were prevented by perinatal probiotic exposure. Maternal probiotic treatment increased gut butyrate and brain lactate in the juvenile and adult offspring and increased the expression of prefrontal cortex PFKFB3, a marker of glycolytic metabolism in astrocytes. PFKFB3 expression correlated with the increase in gut butyrate in the juvenile and adult offspring. The resilience of juvenile and adult offspring to anxiety-like behaviour was most prominently associated with increased brain lactate abundance. Individuals with NAFLD and recent depressive symptoms, but not lifetime depression, had a distinct serum metabolomic profile compared to those with NAFLD without depression. This signature was found to be independent of age, sex, medication, and disease severity. Serum triglycerides, VLDL, isoleucine, and the inflammatory biomarker of glycoprotein acetylation were key metabolites increased in patients with recent depressive symptoms. Conclusions: Taken together, the data in this thesis demonstrate a clear relationship between maternal diet-induced obesity and emotional dysfunction in the offspring. I also indicate that the gut microbiome may act as a key modifiable (and therefore treatable) feature of the relationship between maternal obesity and offspring brain function and behaviour. Lastly, I found that depressive symptoms in humans with liver disease, independent of demographic factors and liver disease severity, are associated with altered circulating metabolite concentrations such as increased serum isoleucine, triglycerides, and glycoprotein acetylation
A solution state NMR study of the structure and ligand binding properties of the human C-type lectin DC-SIGNR
Get PDFThe protein DC-SIGNR (Dendritic-cell specific ICAM3 grabbing non-integrin
related) is a C-type (calcium-dependent) lectin, which binds highly-branched
mannose oligosaccharides. DC-SIGNR interacts with a range of deadly diseases via
surface glycans on pathogenic glycoproteins, and the ability of DC-SIGNR to
increase the rate of infection of viruses including human immunodeficiency virus
(HIV) and hepatitis C virus (HCV) makes the study of DC-SIGNR/oligosaccharide
interactions very attractive. The research described in this thesis sought to gain
insight into the calcium and ligand binding properties of the DC-SIGNR
carbohydrate recognition domain (CRD) in solution by utilising solution state
nuclear magnetic resonance spectroscopy (NMR).
A protocol for the production of uniformly 15N /13C labelled DC-SIGNR CRD
was developed, allowing the acquisition of heteronuclear NMR experiments and
the first assignment of the calcium-bound (holo) DC-SIGNR CRD to be reported.
The assignment has allowed investigation of calcium and glycan binding, as well
as the pH dependence of the DC-SIGNR CRD.
The data presented in this thesis reveal that the DC-SIGNR CRD is highly
dynamic in the calcium-free state, with the addition of calcium resulting in global
conformational and dynamic changes throughout the CRD. While calcium binding
hinders the protein dynamics (particularly in the calcium binding regions), a large
degree of mobility remains. The evidence that ligands are released at low pH
suggests that DC-SIGNR may act as an endocytic receptor.
In addition to calcium binding, interactions of the DC-SIGNR CRD with a
range of ligands were investigated. In particular, interactions with the
oligosaccharide Man9GlcNAc (present on the HIV viral envelope) are described,
representing the first direct study of the CRD interacting with a diseaseassociated
ligand. The glycans employed in this study all bind to the primary
calcium binding site, supporting previous crystal data. However, each glycan
displays distinct patterns of chemical shift perturbations implying that they each
have different, extended binding modes. Particularly striking is the difference
between the disease-associated Man9GlcNAc ligand and the ligand present in a
previously published crystal structure, (GlcNAc)2Man3.
An investigation of the dynamics of the CRD in the holo form and bound
to the ligand Man5 shows that the CRD is highly dynamic and that glycan binding
further hinders, but does not abolish, the molecular motions. The dynamics data
also suggests that a ligand-induced conformational change may occur and
indicates potential new binding sites which are not present in any published
crystal structures. The dynamic nature of the DC-SIGNR CRD may explain the wide
range of ligand specificities and affinities of the C-type lectin scaffold and
suggests that the study of the ligand binding properties and dynamics of proteins
such as DC-SIGNR in solution is essential to further understanding of this class of
proteins
Estimation of NMR signals in the time domain: methodology, applications and software
Get PDFNuclear magnetic resonance (NMR) spectroscopy is an analytical technique employed in many scientific disciplines that is able to provide insights into the structures and dynamics of chemical species. To maximise the utility of NMR, appropriate data treatment and analysis is necessary. The conventional route to extracting quantitative information from the raw experimental data — the free induction decay (FID) — is to convert it to an NMR spectrum, through application of the Fourier transform (FT). NMR spectra provide a human-interpretable representation of data; trained practitioners are able to rationalise the appearance of a given spectrum by mapping its component peaks to chemical environments in the sample from which the dataset was acquired. However, the FT suffers from poor resolution, with peaks of similar frequencies exhibiting over- lap. Disentangling the information associated with such peaks is not feasible using typical methods such as integrating user-defined regions of the spectrum. As an alternative approach, parametric estimation techniques aim to provide a detailed description of each signal which contributes to the FID. These methods have been shown to perform effectively even in scenarios where significant spectral peak overlap exists.
This thesis focusses on the development of a parametric estimation method for the analysis of FIDs derived from solution-state NMR experiments. The guiding principle behind the method is that it should require as little user input as possible, while being able to provide accurate and reliable signal estimates. Beyond simply providing a breakdown of individual signal components, many useful applications may be realised when estimation techniques are employed. The initial motivation for this work was to develop a procedure for the generation of broadband homode- coupled (pure shift) NMR spectra with desirable properties from 2DJ datasets. Furthermore, a means of analysing datasets such as those from inversion recovery (Τ₁), Carr-Purcell-Meiboom- Gill (CPMG) (T₂), and diffusion experiments, in which each FID exhibits a variation in its amplitude, is presented. The last application described is a means of producing phased, ultra-broadband NMR spectra from an experiment comprising a single frequency-swept (chirp) excitation pulse.
The methods presented in this thesis are incorporated into a software package written in the Python programming language, called NMR estimation in Python (NMR-EsPy), of which more information can be found at https://github.com/foroozandehgroup/NMR-EsPy
Mechanistic and inhibition studies on a nucleophilic cysteine protease and transpeptidases
Get PDFEnzymes with a nucleophilic cysteine residue catalyse important reactions in biology, including proteolysis, redox reactions, and radical transfer reactions. Nucleophilic cysteine enzymes are validated medicinal chemistry targets, e.g. thioredoxin reductase and glutathione reductase. The work presented in this thesis describes biochemical and inhibition studies on viral and bacterial nucleophilic cysteine enzymes. Chapters 2-6 focus on inhibitors of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease (Mpro), which is a therapeutic target to treat coronavirus disease 2019 (COVID-19). Chapter 7 describes studies focusing on the mycobacterial L, D-transpeptidases, which are investigational targets for tuberculosis treatment.
Antiviral therapies to treat infections caused by SARS-CoV-2 were lacking at the onset of the COVID-19 pandemic in December 2019. In March 2020, I initiated a collaborative research program in the Schofield group to identify efficient and selective inhibitors of SARS-CoV-2 Mpro, which is essential for viral replication and is structurally distinct from human proteases, making it a potential target for antiviral therapies. Note that at the end of 2021, a small molecule Mpro inhibitor, i.e. nirmatrelvir, was approved for clinical use in humans.
Initially, a mass spectrometry (MS)-based label-free SARS-CoV-2 Mpro assay was developed to investigate the effect of small molecules on catalysis and to identify efficient inhibitors. Screens of commercial small molecules and compounds available in the Schofield group revealed that β-lactams can be effective Mpro inhibitors, a finding of interest considering the established safety profiles of β-lactams. Structure-activity relationship (SAR) studies, in combination with MS and crystallographic studies, helped to improve the potency of the β-lactams as Mpro inhibitors and provided evidence for their covalent reaction with the active site cysteine. Apart from β-lactams, compounds containing reactive sulfur (e.g., ebsulfur derivatives), substrate-competitive linear peptides, and heterocyclic molecules (characterised as part of the COVID Moonshot Consortium) were identified as promising Mpro inhibitors.
Macrocyclic peptide Mpro inhibitors, which tightly bind the Mpro active site (e.g., TMGM4-05), were developed in collaboration with Prof. Suga’s group (University of Tokyo). SAR studies led to the identification of residues and intrapeptide interactions crucial for potent Mpro inhibition. Efforts to develop cell-permeable macrocyclic peptides are ongoing and are expected to enable their use as potential antivirals for the treatment of COVID-19.
In addition to the SARS-CoV-2 Mpro inhibition work, the research investigated the fragmentation of β-lactam antibiotics by mycobacterial L,D-transpeptidase nucleophilic cysteine enzymes, and analysed the extent to which these pathways resemble those catalysed by serine-β-lactamases and metallo-βlactamases. In the case of LdtMt2 and aminopenicillins, contrary to the expected hydrolytic mechanism, non-hydrolytic C5-C6 fragmentation and cyclisation via rearrangement to give a diketopiperazine were observed. Faropenem undergoes C5-C6 fragmentation on reaction with LdtMts, whereas an intact hydrolysed product is formed upon reaction of faropenem with metallo-β-lactamases (MBLs) and serine-β-lactamases. Interestingly, a degradation product in which the faropenem tetrahydrofuran ring is opened is observed with serine-β-lactamases; the THF ring remains closed in the cases of the metallo-β-lactamases, VIM-1 and VIM-2, but not NDM-1 and L1.
In summary, the results have identified new types of inhibitors of nucleophilic cysteine enzymes and have provided insight into their mechanism of action
Understanding the pathogenesis of hepatocellular carcinoma and how it may be leveraged for novel biomarker development
No full textBackground
Hepatocellular Carcinoma (HCC) represents a major global health problem. Curative treatment is possible in early disease; therefore, early-detection is an attractive strategy for improving outcomes. Novel biomarkers have the potential to improve early detection and offer personalised medicine. However, effective use will require a greater understanding of HCC pathogenesis, particularly in the early stages of development.
Methods
I performed a retrospective analysis of n=110 patients with HCC and examined factors driving HCC and influencing survival. To identify metabolic and immune microenvironmental differences that could be leveraged to develop HCC biomarkers, I prospectively recruited cohorts of patients with HCC or chronic liver disease for; plasma 1H-Nuclear Magnetic Resonance (NMR) Spectroscopy (n=74); and flow cytometric analysis of liver fine needle aspirates (FNA) from the background liver (n=10). Additionally, I developed an in vitro metabolomic model to examine the hypoxic metabolome in HCC and identify metabolites with the potential to detect hypoxia in vivo.
Results
HCC survival in Oxford (n=110) was; 1-year 71.2% (95%CI 63.2-80.3%) and 5-year 29.3% (95%CI 20.6-41.8%). However, current HCC surveillance did not improve survival in eligible patients HR 1.11 (0.55-2.22).
Plasma metabolomic analysis of HCC versus at-risk patients with alcohol and non-alcohol-related fatty liver disease identified HCC with accuracy 83.7%, specificity 77.9% and sensitivity 89.3%. In vitro metabolomic analysis identified 2,2-dimethylmalonic acid as secreted by HepG2 hepatocytes but not Calu3 pulmonary epithelial cells under hypoxic conditions.
Exploratory data from pan-immunophenotyping of Liver FNA samples indicates that T cell populations expressing CD69 and CD103 or CXCR6 may be depleted in HCC, however, further data is required.
Conclusions
Current HCC surveillance practice is of questionable efficacy yet, in a small cohort, a plasma metabolomic signature was able to accurately identify HCC patients. In vitro data also suggests the potential of 1H-NMR to identify hypoxic HCC tumours. Liver FNA was able to comprehensively profile the intrahepatic immune environment. However, further data is required to identify whether the observed immunological changes between HCC and at-risk patients are attributable to the HCC immunological microenvironment
- …
