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Catheter-associated urinary tract infections (CAUTI) are complex infections often involving multi-species bacteria. Escherichia coli is frequently an early coloniser. Subsequent colonisation by Pseudomonas aeruginosa and coexistence mechanisms between the two strains within urethral catheters is not yet fully understood. In this study, metabolic adaptations between co-isolated clinical E. coli and P. aeruginosa strains were investigated. It was found that P. aeruginosa outgrew E. coli in artificial urine medium (AUM), whereas E. coli dominated in culture broth such as Iso-sensitest. No evidence of direct antagonism was observed. Metabolite analyses revealed distinct metabolite patterns indicating cross-feeding and metabolic adaptations. In AUM, stress-response metabolites were elevated. Additionally, E. coli appeared to experience Fe-limitation in AUM, while the same was not observed for P. aeruginosa. The results highlight the influence of nutrient conditions on processes within mixed biofilms.

Introduction: Bacterial colonization of medical devices is promoting hospital-acquired infections leading to worsening patient outcomes and high costs for society. Sequential bacterial colonization of surfaces may provide altered conditions that benefit pathogens.

Methods: In this study we have investigated the interactions between two pairs of clinical isolates collected from patients that were on mechanical ventilation. Two patients were first colonized by Staphylococcus aureus and thereafter Pseudomonas aeruginosa settled. The two P. aeruginosa isolates were weak colonizers in monoculture. We investigated two hypotheses: (1) S. aureus preconditions material surfaces, facilitating adhesion of later colonizers. (2) S. aureus provides an altered nutrient environment promoting the growth and settlement of other bacteria.

Results: Surface preconditioning did not seem to enhance colonization of P. aeruginosa. However, bacterial growth, biofilm formation, ratio of colony forming units, and metabolic profiles were influenced by co-cultivation. The effects varied depending on nutrient content in the medium.

Discussion: In general, co-cultures appeared to benefit clinical isolates to a higher degree, compared to reference strains. The results indicate that differences in airway microenvironment between patients may have a large effect on the infection process and which pathogens that persist.

Cell culture bioprocess data are typically collected across many timepoints and batches, where numerous analytes covary with each other and, critically, with elapsed process time. This time dependence can inflate performance metrics and compromise the validity of multivariate models. We introduce time-adjusted performance evaluation (TAPE), a regression-agnostic validation technique that quantifies and separates time-driven from time-independent predictivity. TAPE pairs leave-one-group-out cross-validation with per-timepoint centering to decompose performance into between-timepoint (time-dependent) and within-timepoint (time-decoupled) parts by comparing predicted and observed deviations from each timepoint mean. Applying TAPE to orthogonal partial least squares models across five Chinese hamster ovary cell culture datasets (three Raman spectroscopy, one metabolomics, and one transcriptomics), several ostensibly strong models’ predictivity was largely explained by timepoint means alone. After removing between-timepoint variation, only models with sample–response relationships independent of time retained good predictivity. For Raman, only models for Raman-active analytes (glucose, lactate) remained predictive, whereas Raman-inactive ones (K+, NH4+) did not. In the omics studies, the models for titer, viable cell density, growth rate, and death rates were predominantly time-driven. By quantifying time’s contribution to model performance, TAPE helps prevent misleadingly good performance metrics and supports more reliable multivariate modeling of time-series bioprocess data.

In the analysis of metabolomics data, selecting the appropriate statistical approach is crucial for maximizing model interpretation, predictivity and reliability. This study evaluates the effectiveness of Orthogonal Partial Least Squares (OPLS) models, specifically comparing OPLS-DA (assuming sample independence) and OPLS-EP (assuming sample dependency) in datasets of bacterial samples under different experimental conditions. OPLS-EP consistently demonstrates superior predictive performance, evidenced by higher predictive ability by means of cross-validation (Q2) compared to OPLS-DA, indicating greater model significance. Our findings prove the advantages of the paired statistical approach. This approach ensures that treatment effects are accurately measured by minimizing inter-sample variation and enhancing signal detection. Previous research in metabolomics has demonstrated the benefits of this method for biomarker sensitivity, particularly in matched case–control studies. The present study extends this understanding by applying paired statistical approaches to bacterial isolate treatments, offering novel insights into their utility. Overall, the findings emphasize the importance of OPLS-EP in enhancing biomarker sensitivity and model reliability in metabolomics research.

Background: High-risk prostate cancer is often treated with combined androgen deprivation therapy (ADT) and radiotherapy (RT). Blood biomarkers may enable treatments to be tailored to individual patients. Metabolomics, the study of small-molecule alterations in blood, is promising, and lipids are emerging as potential markers of poor prognosis. This study aims to investigate metabolic changes during prostate cancer treatment and their correlation to disease outcome.

Methods: This study included 136 blood plasma samples from 35 patients with high-risk prostate cancer treated with RT and ADT, recruited from the Uppsala/Umeå Comprehensive Cancer Consortium (U-CAN) project. Blood samples were collected before, during, and after treatment and analyzed at Metabolon Inc. (Durham, NC, USA). To study differences in metabolic levels during treatment, three different sampling time points were considered: before ADT, in-between ADT and RT, and after RT. Both multivariate (orthogonal projections to latent structures, OPLS) and univariate analyses were performed, where statistical significance in combination with a large fold change was considered indicative of a substantial change.

Results: Significant changes in metabolite levels were observed. Many of the significant metabolites for the whole course of treatment were also significant during ADT but not during RT, indicating that changes during ADT dominated the overall treatment. Changes were found to be especially common in steroids and fatty acids. Multivariate analysis revealed significant differences in metabolites between relapsing and non-relapsing patients. Among the significant metabolites were cholesterol and epiandrosterone.

Conclusions: Metabolomics can identify biomarkers for prostate cancer treatment response and relapse. Further studies are needed to identify patterns and individual metabolites to personalize treatment strategies for prostate cancer.

Biopharmaceuticals are medical compounds derived from biological sources and are often manufactured by living cells, primarily Chinese hamster ovary (CHO) cells. CHO cells display variation among cell clones, leading to growth and productivity differences that influence the product's quantity and quality. The biological and environmental factors behind these differences are not fully understood. To identify metabolites with a consistent relationship to productivity or cell death over time, we analyzed the extracellular metabolome of 11 CHO clones with different growth and productivity characteristics over 14 days. However, in bioreactor processes, metabolic profiles and process variables are both strongly time-dependent, confounding the metabolite-process variable relationship. To address this, we customized an existing hierarchical approach for handling time dependency to highlight metabolites with a consistent correlation to a process variable over a selected timeframe. We benchmarked this new method against conventional orthogonal partial least squares (OPLS) models. Our hierarchical method highlighted several metabolites consistently related to productivity or cell death that the conventional method missed. These metabolites were biologically relevant; most were known already, but some that had not been reported in CHO literature before, such as 3-methoxytyrosine and succinyladenosine, had ties to cell death in studies with other cell types. The metabolites showed an inverse relationship with the response variables: those positively correlated with productivity were typically negatively correlated with the death rate, or vice versa. For both productivity and cell death, the citrate cycle and adjacent pathways (pyruvate, glyoxylate, pantothenate) were among the most important. In summary, we have proposed a new method to analyze time-dependent omics data in bioprocess production. This approach allowed us to identify metabolites tied to cell death and productivity that were not detected with traditional models.

Gliomas are highly complex and metabolically active brain tumors associated with poor prognosis. Recent reports have found altered levels of blood metabolites during early tumor development, suggesting that tumor development could be detected several years before clinical manifestation. In this study, we performed metabolite analyses of blood samples collected from healthy controls and future glioma patients, up to eight years before glioma diagnosis, and on the day of glioma surgery. We discovered that metabolites related to early glioma development were associated with an increased energy turnover, as highlighted by elevated levels of TCA-related metabolites such as fumarate, malate, lactate and pyruvate in pre-diagnostic cases. We also found that metabolites related to glioma progression at surgery were primarily high levels of amino acids and metabolites of amino acid catabolism, with elevated levels of 11 amino acids and two branched-chain alpha-ketoacids, ketoleucine and ketoisoleucine. High amino acid turnover in glioma tumor tissue is currently utilized for PET imaging, diagnosis and delineation of tumor margins. By examining blood-based metabolic progression patterns towards disease onset, we demonstrate that this high amino acid turnover is also detectable in a simple blood sample. These findings provide additional insight of metabolic alterations during glioma development and progression.

Genetic and metabolic changes in tissue and blood are reported to occur several years before glioma diagnosis. Since gliomas are currently detected late, a liquid biopsy for early detection could affect the quality of life and prognosis of patients. Here, we present a nested case-control study of 550 prediagnostic glioma cases and 550 healthy controls from the Northern Sweden Health and Disease study (NSHDS) and the European Prospective Investigation into Cancer and Nutrition (EPIC) study. We identified 93 significantly altered metabolites related to glioma development up to 8 years before diagnosis. Out of these metabolites, a panel of 20 selected metabolites showed strong disease correlation and a consistent progression pattern toward diagnosis in both the NSHDS and EPIC cohorts, and they separated future cases from controls independently of biological sex. The blood metabolite panel also successfully separated both lower-grade glioma and glioblastoma cases from controls, up to 8 years before diagnosis in patients within the NSHDS cohort and up to 2 years before diagnosis in EPIC. Pathway enrichment analysis detected metabolites related to the TCA cycle, Warburg effect, gluconeogenesis, and cysteine, pyruvate, and tyrosine metabolism as the most affected.

Pancreatic ductal adenocarcinoma (PDAC) is a major cause of cancer death that typically presents at an advanced stage. No reliable markers for early detection presently exist. The prominent tumor stroma represents a source of circulating biomarkers for use together with cancer cell-derived biomarkers for earlier PDAC diagnosis. CA19-9 and CEA (cancer cell-derived biomarkers), together with endostatin and collagen IV (stroma-derived) were examined alone, or together, by multivariable modelling, using pre-diagnostic plasma samples (n = 259 samples) from the Northern Sweden Health and Disease Study biobank. Serial samples were available for a subgroup of future patients. Marker efficacy for future PDAC case prediction (n = 154 future cases) was examined by both cross-sectional (ROC analysis) and longitudinal analyses. CA19-9 performed well at, and within, six months to diagnosis and multivariable modelling was not superior to CA19-9 alone in cross-sectional analysis. Within six months to diagnosis, CA19-9 (AUC = 0.92) outperformed the multivariable model (AUC = 0.81) at a cross-sectional level. At diagnosis, CA19-9 (AUC = 0.995) and the model (AUC = 0.977) performed similarly. Longitudinal analysis revealed increases in CA19-9 up to two years to diagnosis which indicates a window of opportunity for early detection of PDAC.

BACKGROUND: Gliomas are complex tumors with several genetic aberrations and diverse metabolic programs contributing to their aggressive phenotypes and poor prognoses. This study defines key metabolic features that can be used to differentiate between glioma subtypes, with potential for improved diagnostics and subtype targeted therapy.

METHODS: Cross-platform global metabolomic profiling coupled with clinical, genetic, and pathological analysis of glioma tissue from 224 tumors - oligodendroglioma (n=31), astrocytoma (n=31) and glioblastoma (n=162) - were performed. Identified metabolic phenotypes were evaluated in accordance with the WHO classification, IDH-mutation, 1p/19q-codeletion, WHO-grading 2-4, and MGMT promoter methylation.

RESULTS: Distinct metabolic phenotypes separate all six analyzed glioma subtypes. IDH-mutated subtypes, expressing 2-hydroxyglutaric acid, were clearly distinguished from IDH-wildtype subtypes. Considerable metabolic heterogeneity outside of the mutated IDH pathway were also evident, with key metabolites being high expression of glycerophosphates, inositols, monosaccharides and sugar alcohols and low levels of sphingosine and lysoglycerophospholipids in IDH-mutants. Among the IDH-mutated subtypes, we observed high levels of amino acids, especially glycine and 2-aminoadipic acid, in grade 4 glioma, and N-acetyl aspartic acid in low-grade astrocytoma and oligodendroglioma. Both IDH-wildtype and mutated oligodendroglioma and glioblastoma were characterized by high levels of acylcarnitines, likely driven by rapid cell growth and hypoxic features. We found elevated levels of 5-HIAA in gliosarcoma and a subtype of oligodendroglioma not yet defined as a specific entity, indicating a previously not described role for the serotonin pathway linked to glioma with bimorphic tissue.

CONCLUSION: Key metabolic differences exist across adult glioma subtypes.