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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.

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 Phase 3 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.

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.

ObjectiveTo characterize changes in the plasma metabolic profile in newly diagnosed rheumatoid arthritis (RA) patients upon commencement of conventional disease modifying anti-rheumatic drug (cDMARD) therapy.MethodsPlasma samples collected in an early RA randomized 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 3 months post commencement of non-biologic cDMARD. Metabolites that changed in association with changes in the DAS44 score were identified at the 3 month timepoint.ResultsA total of ten metabolites exhibited a clear correlation with 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 CRP.ConclusioncDMARD 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 an inflammatory disease in humans. Quantitative metabolic biomarker based tests of clinical change in state are feasible and should be developed around the itaconate pathway.Key MessagesWhat is already known about this subject?Rheumatoid arthritis is associated with perturbations in metabolic activity, which have also been associated with response to certain treatments. In vitro work on immunometabolism has recently revealed itaconate as a key metabolite controlling macrophage activation.What does this study add?In newly diagnosed RA, commencement of csDMARD therapy is associated with changes in the levels of ten metabolites (especially itaconate and its derivatives) that correlate to a corresponding fall in disease activity Pathway analyses suggest these metabolites are associated with macrophage activation.How might this impact on clinical practice?Changes in metabolite levels in response to treatment provide additional new insights into RA pathogenesis that suggest a focus on macrophage activation state. The association of increased itaconate with decreased inflammation point to possible routes of intervention in RA.

Longitudinal proteomic analysis of bovine milk shows consistent changes over time across cows after intramammary challenge with Streptococcus uberis., 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.

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.

The failure of remyelination in multiple sclerosis is largely unexplained. Lindner et al. report that glial cells in demyelinating lesions show increased expression of fibroblast growth factor 9 (FGF9). This induces astrocyte-dependent responses that inhibit remyelination and stimulate expression of pro-inflammatory chemokines, supporting a feedback loop that amplifies disease activity., The failure of remyelination in multiple sclerosis is largely unexplained. Lindner et al. report that glial cells in demyelinating lesions show increased expression of fibroblast growth factor 9 (FGF9). This induces astrocyte-dependent responses that inhibit remyelination and stimulate expression of pro-inflammatory chemokines, supporting a feedback loop that amplifies disease activity, 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.

Mastitis, which is caused by infection of the mammary gland is the most important disease problem facing dairy farmers. While the disease has been studied for decades in order to determine better diagnosis and treatment, it is only recently that the full panoply of advanced biotechnological methodologies has been applied that are needed to bring a systems biology approach to investigations. Molecular investigations using analyte-specific immunoassays, such as for the acute phase proteins haptoglobin and mammary-associated serum amyloid A3 in milk have introduced possibilities for monitoring the host inflammatory response. The omics revolution in biology, with genomics being harnessed especially for identification of the causative pathogens of mastitis, has enhanced dissection of the mammary microbiome. The application of proteomics, peptidomics and metabolomics to the diagnosis and pathophysiology of mastitis, in contrast, is in its infancy though the potential of these advanced tools of biological research is clear as they are applied in a systems biology analysis of this major health problem of dairy cows.

Background: Proteomics-based approaches for biomarker discovery are promising strategies used in cancer research. We present state-of-art label-free quantitative proteomics method to assess proteome of renal cell carcinoma (RCC) compared with noncancer renal tissues. Methods: Fresh frozen tissue samples from eight primary RCC lesions and autologous adjacent normal renal tissues were obtained from surgically resected tumour-bearing kidneys. Proteins were extracted by complete solubilisation of tissues using filter-aided sample preparation (FASP) method. Trypsin digested proteins were analysed using quantitative label-free proteomics approach followed by data interpretation and pathways analysis. Results: A total of 1761 proteins were identified and quantified with high confidence (MASCOT ion score threshold of 35 and P-value

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, β-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.

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.

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-kappa'B) 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-kappa B 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-kappa B/p65 canonical pathway, indicating that canine lymphoma could be used as a model to study NF-kappa B-targeted therapeutics for human lymphoma. To validate this, tissue arrays were generated for canine and human NHL and immunohistochemistry was employed to assess NF-kappa B activation status. In addition, human and canine B-cell lymphoma lines were assessed for NF-kB activity and the effects of NF-kappa B inhibition.

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 PGE2, 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 PGE2 promote the phosphorylation of the transcription factor CREB on Ser133. However, although CREB regulates IL-10 transcription, the mutation of Ser133 to Ala in the endogenous CREB gene did not prevent the ability of PGE2 to promote IL-10 transcription. Instead, we demonstrate that protein kinase A regulates the phosphorylation of salt-inducible kinase 2 on Ser343, 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 PGE2 on IL-10 production.

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.