Ergothioneine (ERG) is an unusual thio-histidine betaine amino acid that has potent antioxidant activities. It is synthesised by a variety of microbes, especially fungi (including in mushroom fruiting bodies) and actinobacteria, but is not synthesised by plants and animals who acquire it via the soil and their diet, respectively. Animals have evolved a highly selective transporter for it, known as solute carrier family 22, member 4 (SLC22A4) in humans, signifying its importance, and ERG may even have the status of a vitamin. ERG accumulates differentially in various tissues, according to their expression of SLC22A4, favouring those such as erythrocytes that may be subject to oxidative stress. Mushroom or ERG consumption seems to provide significant prevention against oxidative stress in a large variety of systems. ERG seems to have strong cytoprotective status, and its concentration is lowered in a number of chronic inflammatory diseases. It has been passed as safe by regulatory agencies, and may have value as a nutraceutical and antioxidant more generally.

The global burden of type 2 diabetes mellitus (T2DM), together with the presence of cardiovascular risk in this population, is reaching pandemic levels. A prominent feature of T2DM is chronic and systemic inflammation, with the accompanying presence of circulating and dysregulated inflammatory biomarkers; which in turn is associated with abnormal clot formation. Here, we investigate the correlation between abnormal blood clotting, using thromboelastography (TEG), clot ultrastructure using scanning electron microscopy (SEM) and the presence of a dysregulated inflammatory cytokine profile, by examining various circulating biomarkers. Our results show that many biomarkers, across TEG, cytokine and lipid groups, were greatly dysregulated in the T2DM sample. Furthermore, our T2DM sample’s coagulation profiles were significantly more hypercoagulable when compared to our heathy sample, and ultrastructural analysis confirmed a matted and denser clot structure in the T2DM sample. We suggest that dysregulated circulating molecules may in part be responsible for a hypercoagulable state and vascular dysfunction in the T2DM sample. We propose further that a personalized approach could be of great value when planning treatment and tracking the patient health status after embarking on a treatment regimes, and that looking to novel inflammatory and vascular biomarkers might be crucial.

Parkinson’s disease (PD) is a well-known neurodegenerative disease with a strong association established with systemic inflammation. Recently, the role of the gingipain protease group from Porphyromonas gingivalis was implicated in Alzheimer’s disease and here we present evidence, using a fluorescent antibody to detect gingipain R1 (RgpA), of its presence in a PD population. To further elucidate the action of this gingipain, as well as the action of the lipopolysaccharide (LPS) from P. gingivalis, low concentrations of recombinant RgpA and LPS were added to purified fluorescent fibrinogen. We also substantiate previous findings regarding PD by emphasizing the presence of systemic inflammation via multiplex cytokine analysis, and demonstrate hypercoagulation using thromboelastography (TEG), confocal and electron microscopy. Biomarker analysis confirmed significantly increased levels of circulating proinflammatory cytokines. In our PD and control blood analysis, our results show increased hypercoagulation, the presence of amyloid formation in plasma, and profound ultrastructural changes to platelets. Our laboratory analysis of purified fibrinogen with added RgpA, and/or LPS, showed preliminary data with regards to the actions of the protease and the bacterial membrane inflammagen on plasma proteins, to better understand the nature of established PD.

We address the problem of generating novel molecules with desired interaction properties as a multi-objective optimization problem. Interaction binding models are learned from binding data using graph convolution networks (GCNs). Since the experimentally obtained property scores are recognised as having potentially gross errors, we adopted a robust loss for the model. Combinations of these terms, including drug likeness and synthetic accessibility, are then optimized using reinforcement learning based on a graph convolution policy approach. Some of the molecules generated, while legitimate chemically, can have excellent drug-likeness scores but appear unusual. We provide an example based on the binding potency of small molecules to dopamine transporters. We extend our method successfully to use a multi-objective reward function, in this case for generating novel molecules that bind with dopamine transporters but not with those for norepinephrine. Our method should be generally applicable to the generation in silico of molecules with desirable properties.

Gut dysbiosis contributes to the development of a dysfunctional gut barrier, facilitating the translocation of bacteria and inflammagens, and is implicated in colorectal cancer (CRC) pathogenesis. Such ‘leaky gut’ conditions result in systemic inflammation, of which a hallmark is increased hypercoagulability. Fluorescence antibody confocal microscopy was used to determine circulating levels of lipopolysaccharide (LPS) in control and CRC populations. Here we showed that circulating levels of LPS are significantly elevated in the CRC population. We also showed that markers of inflammation and hypercoagulability are increased in this population. Furthermore, anomalous blood clotting and structural changes in blood components are presented. Importantly, the association between LPS levels, inflammation, and hematological dysfunction was analysed. Statistical regression models were applied to identify markers with strong association with CRC, and to investigate the correlation between markers. A core aim is enhanced biomarker discovery for CRC. We conclude that circulating LPS can promote systemic inflammation and contribute to the development of a pathological coagulation system, with resulting chronic inflammation and an activated coagulation system implicated in tumorigenesis. Blood-based screening tools are an emerging research area of interest for CRC screening. We propose the use of additional (novel) biomarkers to effectively screen for CRC.

SNAPPI-DB, a high performance database of Structures, iNterfaces and Alignments of Protein–Protein Interactions, and its associated Java Application Programming Interface (API) is described. SNAPPI-DB contains structural data, down to the level of atom co-ordinates, for each structure in the Protein Data Bank (PDB) together with associated data including SCOP, CATH, Pfam, SWISSPROT, InterPro, GO terms, Protein Quaternary Structures (PQS) and secondary structure information. Domain–domain interactions are stored for multiple domain definitions and are classified by their Superfamily/Family pair and interaction interface. Each set of classified domain–domain interactions has an associated multiple structure alignment for each partner. The API facilitates data access via PDB entries, domains and domain–domain interactions. Rapid development, fast database access and the ability to perform advanced queries without the requirement for complex SQL statements are provided via an object oriented database and the Java Data Objects (JDO) API. SNAPPI-DB contains many features which are not available in other databases of structural protein–protein interactions. It has been applied in three studies on the properties of protein–protein interactions and is currently being employed to train a protein–protein interaction predictor and a functional residue predictor. The database, API and manual are available for download at: Author Webpage.