Pseudomonas aeruginosa, a common opportunistic pathogen, is known to cause infections in a variety of compromised human tissues. An emerging mechanism for microbial survival is the incorporation of exogenous fatty acids to alter the cell’s membrane phospholipid profile. With these findings, we show that exogenous fatty acid exposure leads to changes in bacterial membrane phospholipid structure, membrane permeability, virulence phenotypes and consequent stress responses that may influence survival and persistence of Pseudomonas aeruginosa. Thin-layer chromatography and ultra performance liquid chromatography / ESI-mass spectrometry indicated alteration of bacterial phospholipid profiles following growth in the presence of polyunsaturated fatty acids (PUFAs) (ranging in carbon length and unsaturation). The exogenously supplied fatty acids were incorporated into the major bacterial phospholipids phosphatidylethanolamine and phosphatidylglycerol. The incorporation of fatty acids increased membrane permeability as judged by both accumulation and exclusion of ethidium bromide. Individual fatty acids were identified as modifying resistance to the cyclic peptide antibiotics polymyxin B and colistin, but not the beta-lactam imipenem. Biofilm formation was increased by several PUFAs and significant fluctuations in swimming motility were observed. Our results emphasize the relevance and complexity of exogenous fatty acids in the membrane physiology and pathobiology of a medically important pathogen. P. aeruginosa exhibits versatility with regard to utilization of and response to exogenous fatty acids, perhaps revealing potential strategies for prevention and control of infection.

Background: The utilization of exogenous fatty acids by Gram-negative bacteria has been linked to many cellular processes, including fatty acid oxidation for metabolic gain, assimilation into membrane phospholipids, and control of phenotypes associated with virulence. The expanded fatty acid handling capabilities have been demonstrated in several bacteria of medical importance; however, a survey of the polyunsaturated fatty acid responses in the model organism Escherichia coli has not been performed. The current study examined the impacts of exogenous fatty acids on E. coli. Results: All PUFAs elicited higher overall growth, with several fatty acids supporting growth as sole carbon sources. Most PUFAs were incorporated into membrane phospholipids as determined by Ultra performance liquid chromatography-mass spectrometry, whereas membrane permeability was variably affected as measured by two separate dye uptake assays. Biofilm formation, swimming motility and antimicrobial peptide resistance were altered in the presence of PUFAs, with arachidonic and docosahexaenoic acids eliciting strong alteration to these phenotypes. Conclusions: The findings herein add E. coli to the growing list of Gram-negative bacteria with broader capabilities for utilizing and responding to exogenous fatty acids. Understanding bacterial responses to PUFAs may lead to microbial behavioral control regimens for disease prevention.

Endometrial cancer (EC) is the most common gynecological neoplasm in high-income countries. Five-year survival rates are related to stage at diagnosis, but currently, no validated screening tests are available in clinical practice. The metabolome offers an unprecedented overview of the molecules underlying EC. In this study, we aimed to validate a metabolomics signature as a screening test for EC on a large study population of symptomatic women. Serum samples collected from women scheduled for gynecological surgery (n = 691) were separated into training (n = 90), test (n = 38), and validation (n = 563) sets. The training set was used to train seven classification models. The best classification performance during the training phase was the PLS-DA model (96% accuracy). The subsequent screening test was based on an ensemble machine learning algorithm that summed all the voting results of the seven classification models, statistically weighted by each models’ classification accuracy and confidence. The efficiency and accuracy of these models were evaluated using serum samples taken from 871 women who underwent endometrial biopsies. The EC serum metabolomes were characterized by lower levels of serine, glutamic acid, phenylalanine, and glyceraldehyde 3-phosphate. Our results illustrate that the serum metabolome can be an inexpensive, non-invasive, and accurate EC screening test.

Colorectal cancer (CRC) is a high incidence disease, characterized by high morbidity and mortality rates. Early diagnosis remains challenging because fecal occult blood screening tests have performed sub-optimally, especially due to hemorrhoidal, inflammatory, and vascular diseases, while colonoscopy is invasive and requires a medical setting to be performed. The objective of the present study was to determine if serum metabolomic profiles could be used to develop a novel screening approach for colorectal cancer. Furthermore, the study evaluated the metabolic alterations associated with the disease. Untargeted serum metabolomic profiles were collected from 100 CRC subjects, 50 healthy controls, and 50 individuals with benign colorectal disease. Different machine learning models, as well as an ensemble model based on a voting scheme, were built to discern CRC patients from CTRLs. The ensemble model correctly classified all CRC and CTRL subjects (accuracy = 100%) using a random subset of the cohort as a test set. Relevant metabolites were examined in a metabolite-set enrichment analysis, revealing differences in patients and controls primarily associated with cell glucose metabolism. These results support a potential use of the metabolomic signature as a non-invasive screening tool for CRC. Moreover, metabolic pathway analysis can provide valuable information to enhance understanding of the pathophysiological mechanisms underlying cancer. Further studies with larger cohorts, including blind trials, could potentially validate the reported results.

Background: The adoption of biomarkers as part of high-throughput, complex microarray or sequencing data has necessitated the discovery and validation of these data through machine learning. Machine learning has remained a fundamental and indispensable tool due to its efficacy and efficiency in both feature extraction of relevant biomarkers as well as the classification of samples as validation of the discovered biomarkers. Objectives: This review aims to present the impact and ability of various machine learning methodologies and models to process high-throughput, high-dimensionality data found within mass spectrometry, microarray, and DNA/RNA-sequence data; data that precluded biomarker discovery prior to the use of machine learning. Methods: A vast array of literature highlighting machine learning for biomarker discovery was reviewed, resulting in the eligibility of 21 machine learning algorithms/networks and 3 combinatory architectures, spanning 17 fields of study. This literature was screened to investigate the usage and development of machine learning within the framework of biomarker discovery. Results: Out of the 93 papers collected, a total of 62 biomarker studies were further reviewed across different subfields-49 of which employed machine learning algorithms, and 13 of which employed neural network-based models. Through the application, innovation, and creation of tools in biomarker-related machine learning methodologies, its use allowed for the discovery, accumulation, validation, and interpretation of biomarkers within varied data formats, sources, as well as fields of study. Conclusion: The use of machine learning methodologies for biomarker discovery is critical to the analysis of various types of data used for biomarker discovery, such as mass spectrometry, nucleotide and protein sequencing, and image (e.g. CT-scan) data. Further studies containing more standardized techniques for evaluation, and the use of cutting- edge machine learning architectures may lead to more accurate and specific results.

Polycystic ovarian syndrome (PCOS) is a highly variable syndrome and one of the most common female endocrine disorders. Although the association inositols-glucomannan may represent a good therapeutic strategy in the treatment of PCOS women with insulin resistance, the effect of inositols on the metabolomic profile of these women has not been described yet. Fifteen PCOS-patients and 15 controls were enrolled. Patients were treated with myo-inositol (1.75 g/day), D-chiro-inositol (0.25 g/day) and glucomannan (4 g/day) for 3 months. Blood concentrations of glucose, insulin, triglycerides and cholesterol, and ovary volumes and antral follicles count, as well as metabolomic profiles, were evaluated for control subjects and for cases before and after treatment. PCOS-patients had higher BMI compared with Controls, BMI decreased significantly after 3 months of treatment although it remained significantly higher compared to controls. 3-methyl-1-hydroxybutyl-thiamine-diphosphate, valine, phenylalanine, ketoisocapric, linoleic, lactic, glyceric, citric and palmitic acid, glucose, glutamine, creatinine, arginine, choline and tocopherol emerged as the most relevant metabolites for distinguishing cases from controls. Our pilot study has identified a complex network of serum molecules that appear to be correlated with PCOS, and with a combined treatment with inositols and glucomannan. ClinicalTial.gov, NCT03608813 . Registered 1st August 2018 - Retrospectively registered, .