The Novel DNA Binding Mechanism of Ridinilazole, a Precision Clostridiodes difficile Antibiotic.

Clostridioides difficile infection (CDI) causes substantial morbidity and mortality worldwide with limited antibiotic treatment options. Ridinilazole is a precision bisbenzimidazole antibiotic being developed to treat CDI and reduce unacceptably high rates of infection recurrence in patients. Although in late clinical development, the precise mechanism of action by which ridinilazole elicits its bactericidal activity has remained elusive. Here, we present conclusive biochemical and structural data to demonstrate that ridinilazole has a primary DNA binding mechanism, with a co-complex structure confirming binding to the DNA minor groove. Additional RNA-seq data indicated early pleiotropic changes to transcription, with broad effects on multiple C. difficile compartments and significant effects on energy generation pathways particularly. DNA binding and genomic localization was confirmed through confocal microscopy utilizing the intrinsic fluorescence of ridinilazole upon DNA binding. As such, ridinilazole has the potential to be the first antibiotic approved with a DNA minor groove binding mechanism of action.

Fibroblast growth factor signalling in multiple sclerosis: inhibition of myelination and induction of pro-inflammatory environment by FGF9.

Remyelination failure plays an important role in the pathophysiology of multiple sclerosis, but the underlying cellular and molecular mechanisms remain poorly understood. We now report actively demyelinating lesions in patients with multiple sclerosis are associated with increased glial expression of fibroblast growth factor 9 (FGF9), which we demonstrate inhibits myelination and remyelination in vitro. This inhibitory activity is associated with the appearance of multi-branched 'pre-myelinating' MBP+ / PLP+ oligodendrocytes that interact with axons but fail to assemble myelin sheaths; an oligodendrocyte phenotype described previously in chronically demyelinated multiple sclerosis lesions. This inhibitory activity is not due to a direct effect of FGF9 on cells of the oligodendrocyte lineage but is mediated by factors secreted by astrocytes. Transcriptional profiling and functional validation studies demonstrate that these include effects dependent on increased expression of tissue inhibitor of metalloproteinase-sensitive proteases, enzymes more commonly associated with extracellular matrix remodelling. Further, we found that FGF9 induces expression of Ccl2 and Ccl7, two pro-inflammatory chemokines that contribute to recruitment of microglia and macrophages into multiple sclerosis lesions. These data indicate glial expression of FGF9 can initiate a complex astrocyte-dependent response that contributes to two distinct pathogenic pathways involved in the development of multiple sclerosis lesions. Namely, induction of a pro-inflammatory environment and failure of remyelination; a combination of effects predicted to exacerbate axonal injury and loss in patients.

Elevated O-GlcNAc levels activate epigenetically repressed genes and delay mouse ESC differentiation without affecting naïve to primed cell transition.

The differentiation of mouse embryonic stem cells (ESCs) is controlled by the interaction of multiple signaling pathways, typically mediated by post-translational protein modifications. The addition of O-linked N-acetylglucosamine (O-GlcNAc) to serine and threonine residues of nuclear and cytoplasmic proteins is one such modification (O-GlcNAcylation), whose function in ESCs is only now beginning to be elucidated. Here, we demonstrate that the specific inhibition of O-GlcNAc hydrolase (Oga) causes increased levels of protein O-GlcNAcylation and impairs differentiation of mouse ESCs both in serum-free monolayer and in embryoid bodies (EBs). Use of reporter cell lines demonstrates that Oga inhibition leads to a reduction in the number of Sox1-expressing neural progenitors generated following induction of neural differentiation as well as maintained expression of the ESC marker Oct4 (Pou5f1). In EBs, expression of mesodermal and endodermal markers is also delayed. However, the transition of naïve cells to primed pluripotency indicated by Rex1 (Zfp42), Nanog, Esrrb, and Dppa3 downregulation and Fgf5 upregulation remains unchanged. Finally, we demonstrate that increased O-GlcNAcylation results in upregulation of genes normally epigenetically silenced in ESCs, supporting the emerging role for this protein modification in the regulation of histone modifications and DNA methylation.

PGE(2) induces macrophage IL-10 production and a regulatory-like phenotype via a protein kinase A-SIK-CRTC3 pathway.

The polarization of macrophages into a regulatory-like phenotype and the production of IL-10 plays an important role in the resolution of inflammation. We show in this study that PGE(2), in combination with LPS, is able to promote an anti-inflammatory phenotype in macrophages characterized by high expression of IL-10 and the regulatory markers SPHK1 and LIGHT via a protein kinase A-dependent pathway. Both TLR agonists and PGE(2) promote the phosphorylation of the transcription factor CREB on Ser(133). However, although CREB regulates IL-10 transcription, the mutation of Ser(133) to Ala in the endogenous CREB gene did not prevent the ability of PGE(2) to promote IL-10 transcription. Instead, we demonstrate that protein kinase A regulates the phosphorylation of salt-inducible kinase 2 on Ser(343), inhibiting its ability to phosphorylate CREB-regulated transcription coactivator 3 in cells. This in turn allows CREB-regulated transcription coactivator 3 to translocate to the nucleus where it serves as a coactivator with the transcription factor CREB to induce IL-10 transcription. In line with this, we find that either genetic or pharmacological inhibition of salt-inducible kinases mimics the effect of PGE(2) on IL-10 production.

Changes in Plasma Itaconate Elevation in Early Rheumatoid Arthritis Patients Elucidates Disease Activity Associated Macrophage Activation.

Changes in the plasma metabolic profile were characterised in newly diagnosed rheumatoid arthritis (RA) patients upon commencement of conventional disease-modifying anti-rheumatic drug (cDMARD) therapy. Plasma samples collected in an early RA randomised strategy study (NCT00920478) that compared clinical (DAS) disease activity assessment with musculoskeletal ultrasound assessment (MSUS) to drive treatment decisions were subjected to untargeted metabolomic analysis. Metabolic profiles were collected at pre- and three months post-commencement of nonbiologic cDMARD. Metabolites that changed in association with changes in the DAS44 score were identified at the three-month timepoint. A total of nine metabolites exhibited a clear correlation with a reduction in DAS44 score following cDMARD commencement, particularly itaconate, its derived anhydride and a derivative of itaconate CoA. Increasing itaconate correlated with improved DAS44 score and decreasing levels of C-reactive protein (CRP). cDMARD treatment effects invoke consistent changes in plasma detectable metabolites, that in turn implicate clinical disease activity with macrophages. Such changes inform RA pathogenesis and reveal for the first time a link between itaconate production and resolution of inflammatory disease in humans. Quantitative metabolic biomarker-based tests of clinical change in state are feasible and should be developed around the itaconate pathway.

Common and unique transcriptional responses to dietary restriction and loss of insulin receptor substrate 1 (IRS1) in mice.

Dietary restriction (DR) is the most widely studied non-genetic intervention capable of extending lifespan across multiple taxa. Modulation of genes, primarily within the insulin/insulin-like growth factor signalling (IIS) and the mechanistic target of rapamycin (mTOR) signalling pathways also act to extend lifespan in model organisms. For example, mice lacking insulin receptor substrate-1 (IRS1) are long-lived and protected against several age-associated pathologies. However, it remains unclear how these particular interventions act mechanistically to produce their beneficial effects. Here, we investigated transcriptional responses in wild-type and IRS1 null mice fed an ad libitum diet (WTAL and KOAL) or fed a 30% DR diet (WTDR or KODR). Using an RNAseq approach we noted a high correlation coefficient of differentially expressed genes existed within the same tissue across WTDR and KOAL mice and many metabolic features were shared between these mice. Overall, we report that significant overlap exists in the tissue-specific transcriptional response between long-lived DR mice and IRS1 null mice. However, there was evidence of disconnect between transcriptional signatures and certain phenotypic measures between KOAL and KODR, in that additive effects on body mass were observed but at the transcriptional level DR induced a unique set of genes in these already long-lived mice.

Sterol 14α-demethylase mutation leads to amphotericin B resistance in Leishmania mexicana.

Amphotericin B has emerged as the therapy of choice for use against the leishmaniases. Administration of the drug in its liposomal formulation as a single injection is being promoted in a campaign to bring the leishmaniases under control. Understanding the risks and mechanisms of resistance is therefore of great importance. Here we select amphotericin B-resistant Leishmania mexicana parasites with relative ease. Metabolomic analysis demonstrated that ergosterol, the sterol known to bind the drug, is prevalent in wild-type cells, but diminished in the resistant line, where alternative sterols become prevalent. This indicates that the resistance phenotype is related to loss of drug binding. Comparing sequences of the parasites' genomes revealed a plethora of single nucleotide polymorphisms that distinguish wild-type and resistant cells, but only one of these was found to be homozygous and associated with a gene encoding an enzyme in the sterol biosynthetic pathway, sterol 14α-demethylase (CYP51). The mutation, N176I, is found outside of the enzyme's active site, consistent with the fact that the resistant line continues to produce the enzyme's product. Expression of wild-type sterol 14α-demethylase in the resistant cells caused reversion to drug sensitivity and a restoration of ergosterol synthesis, showing that the mutation is indeed responsible for resistance. The amphotericin B resistant parasites become hypersensitive to pentamidine and also agents that induce oxidative stress. This work reveals the power of combining polyomics approaches, to discover the mechanism underlying drug resistance as well as offering novel insights into the selection of resistance to amphotericin B itself.

Mastitomics, the integrated omics of bovine milk in an experimental model of Streptococcus uberis mastitis: 1. High abundance proteins, acute phase proteins and peptidomics.

A peptidomic investigation of milk from an experimental model of Streptococcus uberis mastitis in dairy cows has incorporated a study of milk high abundance and acute phase (APP) proteins as well as analysis of low molecular weight peptide biomarkers. Intramammary infection (IMI) with S. uberis caused a shift in abundance from caseins, ß-lactoglobulin and α-lactalbumin to albumin, lactoferrin and IgG with the increase in lactoferrin occurring last. The APP response of haptoglobin, mammary associated serum amyloid A3 and C-reactive protein occurred between 30-48 hours post challenge with peak concentrations of APPs at 72-96 hours post challenge and declined thereafter at a rate resembling the fall in bacterial count rather than the somatic cell count. A peptide biomarker panel for IMI based on capillary electrophoresis and mass spectrometry was developed. It comprised 77 identified peptides (IMI77) composed mainly of casein derived peptides but also including peptides of glycosylation dependent cell adhesion molecule and serum amyloid A. The panel had a biomarker classification score that increased from 36 hour to 81 hour post challenge, significantly differentiating infected from non-infected milk, thus suggesting potential as a peptide biomarker panel of bovine mastitis and specifically that of S. uberis origin. The use of omic technology has shown a multifactorial cross system reaction in high and low abundance proteins and their peptide derivatives with changes of over a thousand fold in analyte levels in response to S. uberis infection.

Mastitomics, the integrated omics of bovine milk in an experimental model of Streptococcus uberis mastitis: 3. Untargeted metabolomics.

Intramammary infection leading to bovine mastitis is the leading disease problem affecting dairy cows and has marked effects on the milk produced by infected udder quarters. An experimental model of Streptococcus uberis mastitis has previously been investigated for clinical, immunological and pathophysiological alteration in milk, and has been the subject of peptidomic and quantitative proteomic investigation. The same sample set has now been investigated with a metabolomics approach using liquid chromatography and mass spectrometry. The analysis revealed over 3000 chromatographic peaks, of which 690 were putatively annotated with a metabolite. Hierarchical clustering analysis and principal component analysis demonstrated that metabolite changes due to S. uberis infection were maximal at 81 hours post challenge with metabolites in the milk from the resolution phase at 312 hours post challenge being closest to the pre-challenge samples. Metabolic pathway analysis revealed that the majority of the metabolites mapped to carbohydrate and nucleotide metabolism show a decreasing trend in concentration up to 81 hours post-challenge whereas an increasing trend was found in lipid metabolites and di-, tri- and tetra-peptides up to the same time point. The increase in these peptides coincides with an increase in larger peptides found in the previous peptidomic analysis and is likely to be due to protease degradation of milk proteins. Components of bile acid metabolism, linked to the FXR pathway regulating inflammation, were also increased. Metabolomic analysis of the response in milk during mastitis provides an essential component to the full understanding of the mammary gland's response to infection.

Mastitomics, the integrated omics of bovine milk in an experimental model of Streptococcus uberis mastitis: 2. Label-free relative quantitative proteomics.

Mastitis, inflammation of the mammary gland, is the most common and costly disease of dairy cattle in the western world. It is primarily caused by bacteria, with Streptococcus uberis as one of the most prevalent causative agents. To characterize the proteome during Streptococcus uberis mastitis, an experimentally induced model of intramammary infection was used. Milk whey samples obtained from 6 cows at 6 time points were processed using label-free relative quantitative proteomics. This proteomic analysis complements clinical, bacteriological and immunological studies as well as peptidomic and metabolomic analysis of the same challenge model. A total of 2552 non-redundant bovine peptides were identified, and from these, 570 bovine proteins were quantified. Hierarchical cluster analysis and principal component analysis showed clear clustering of results by stage of infection, with similarities between pre-infection and resolution stages (0 and 312 h post challenge), early infection stages (36 and 42 h post challenge) and late infection stages (57 and 81 h post challenge). Ingenuity pathway analysis identified upregulation of acute phase protein pathways over the course of infection, with dominance of different acute phase proteins at different time points based on differential expression analysis. Antimicrobial peptides, notably cathelicidins and peptidoglycan recognition protein, were upregulated at all time points post challenge and peaked at 57 h, which coincided with 10 000-fold decrease in average bacterial counts. The integration of clinical, bacteriological, immunological and quantitative proteomics and other-omic data provides a more detailed systems level view of the host response to mastitis than has been achieved previously.

Comparative gene expression profiling identifies common molecular signatures of NF-κB activation in canine and human diffuse large B cell lymphoma (DLBCL).

We present the first comparison of global transcriptional changes in canine and human diffuse large B-cell lymphoma (DLBCL), with particular reference to the nuclear factor-kappa B (NF-κB) pathway. Microarray data generated from canine DLBCL and normal lymph nodes were used for differential expression, co-expression and pathway analyses, and compared with analysis of microarray data from human healthy and DLBCL lymph nodes. The comparisons at gene level were performed by mapping the probesets in canine microarrays to orthologous genes in humans and vice versa. A considerable number of differentially expressed genes between canine lymphoma and healthy lymph node samples were also found differentially expressed between human DLBCL and healthy lymph node samples. Principal component analysis using a literature-derived NF-κB target gene set mapped to orthologous canine array probesets and human array probesets clearly separated the healthy and cancer samples in both datasets. The analysis demonstrated that for both human and canine DLBCL there is activation of the NF-κB/p65 canonical pathway, indicating that canine lymphoma could be used as a model to study NF-κB-targeted therapeutics for human lymphoma. To validate this, tissue arrays were generated for canine and human NHL and immunohistochemistry was employed to assess NF-κB activation status. In addition, human and canine B-cell lymphoma lines were assessed for NF-κB activity and the effects of NF-κB inhibition.

Simultaneous non-negative matrix factorization for multiple large scale gene expression datasets in toxicology.

Non-negative matrix factorization is a useful tool for reducing the dimension of large datasets. This work considers simultaneous non-negative matrix factorization of multiple sources of data. In particular, we perform the first study that involves more than two datasets. We discuss the algorithmic issues required to convert the approach into a practical computational tool and apply the technique to new gene expression data quantifying the molecular changes in four tissue types due to different dosages of an experimental panPPAR agonist in mouse. This study is of interest in toxicology because, whilst PPARs form potential therapeutic targets for diabetes, it is known that they can induce serious side-effects. Our results show that the practical simultaneous non-negative matrix factorization developed here can add value to the data analysis. In particular, we find that factorizing the data as a single object allows us to distinguish between the four tissue types, but does not correctly reproduce the known dosage level groups. Applying our new approach, which treats the four tissue types as providing distinct, but related, datasets, we find that the dosage level groups are respected. The new algorithm then provides separate gene list orderings that can be studied for each tissue type, and compared with the ordering arising from the single factorization. We find that many of our conclusions can be corroborated with known biological behaviour, and others offer new insights into the toxicological effects. Overall, the algorithm shows promise for early detection of toxicity in the drug discovery process.

Discretization provides a conceptually simple tool to build expression networks.

Biomarker identification, using network methods, depends on finding regular co-expression patterns; the overall connectivity is of greater importance than any single relationship. A second requirement is a simple algorithm for ranking patients on how relevant a gene-set is. For both of these requirements discretized data helps to first identify gene cliques, and then to stratify patients.We explore a biologically intuitive discretization technique which codes genes as up- or down-regulated, with values close to the mean set as unchanged; this allows a richer description of relationships between genes than can be achieved by positive and negative correlation. We find a close agreement between our results and the template gene-interactions used to build synthetic microarray-like data by SynTReN, which synthesizes "microarray" data using known relationships which are successfully identified by our method.We are able to split positive co-regulation into up-together and down-together and negative co-regulation is considered as directed up-down relationships. In some cases these exist in only one direction, with real data, but not with the synthetic data. We illustrate our approach using two studies on white blood cells and derived immortalized cell lines and compare the approach with standard correlation-based computations. No attempt is made to distinguish possible causal links as the search for biomarkers would be crippled by losing highly significant co-expression relationships. This contrasts with approaches like ARACNE and IRIS.The method is illustrated with an analysis of gene-expression for energy metabolism pathways. For each discovered relationship we are able to identify the samples on which this is based in the discretized sample-gene matrix, along with a simplified view of the patterns of gene expression; this helps to dissect the gene-sample relevant to a research topic--identifying sets of co-regulated and anti-regulated genes and the samples or patients in which this relationship occurs.