Placental Metabolomics of Fetal Growth Restriction.

Fetal growth restriction is an obstetrical pathological condition that causes high neonatal mortality and morbidity. The mechanisms of its onset are not completely understood. Metabolites were extracted from 493 placentas from non-complicated pregnancies in Hamilton Country, TN (USA), and analyzed by gas chromatography-mass spectrometry (GC-MS). Newborns were classified according to raw fetal weight (low birth weight (LBW; <2500 g) and non-low birth weight (Non-LBW; >2500 g)), and according to the calculated birth weight centile as it relates to gestational age (small for gestational age (SGA), large for gestational age (LGA), and adequate for gestational age (AGA)). Mothers of LBW infants had a lower pre-pregnancy weight (66.2 ± 17.9 kg vs. 73.4 ± 21.3 kg, p < 0.0001), a lower body mass index (BMI) (25.27 ± 6.58 vs. 27.73 ± 7.83, p < 0.001), and a shorter gestation age (246.4 ± 24.0 days vs. 267.2 ± 19.4 days p < 0.001) compared with non-LBW. Marital status, tobacco use, and fetus sex affected birth weight centile classification according to gestational age. Multivariate statistical comparisons of the extracted metabolomes revealed that asparagine, aspartic acid, deoxyribose, erythritol, glycerophosphocholine, tyrosine, isoleucine, serine, and lactic acid were higher in both SGA and LBW placentas, while taurine, ethanolamine, ß-hydroxybutyrate, and glycine were lower in both SGA and LBW. Several metabolic pathways are implicated in fetal growth restriction, including those related to the hypoxia response and amino-acid uptake and metabolism. Inflammatory pathways are also involved, suggesting that fetal growth restriction might share some mechanisms with preeclampsia.

Exogenous polyunsaturated fatty acids (PUFAs) influence permeability, antimicrobial peptide resistance, biofilm formation and membrane phospholipid structure in an A-layer and non-A-layer strain of Aeromonas salmonicida.

Aeromonas salmonicida is a Gram-negative bacterium that can infect a wide host range of fish populations, including salmonids and non-salmonids as well as freshwater and marine life. Some strains of A. salmonicida cause the disease furunculosis, which can cause lethargy, intestinal inflammation, ulcers, haemorrhaging and death. The infection is spread through fish-to-fish contact, and the presence of infection can have devastating effects on cultivated fish populations. The purpose of this study was to explore the ability of non-A-layer and A-layer A. salmonicida strains to incorporate polyunsaturated fatty acids (PUFAs) into their lipid profile and test the phenotypic effects thereof. Lipids were extracted from PUFA-exposed cultures and analysed for lipid modification by thin-layer chromatography and ultraperformance liquid chromatography-mass spectrometry, showing A. salmonicida, regardless of A-layer, capable of incorporating all seven of the PUFAs studied. Phenotypic effects were determined through the use of assays that tested for biofilm formation, membrane permeability and cyclic peptide susceptibility. Temperature-dependent effects on biofilm formation were observed, and PUFA exposure showed significant (p < .001) increases in membrane permeability as tested by the uptake of the hydrophobic compounds crystal violet and ethidium bromide. Additionally, some PUFAs elicited modest protection and vulnerability against the membrane-targeting cyclic peptides polymyxin B (PMB) and colistin. The diverse, strain-specific responses to exogenous PUFAs may allude to evolved adaptive strategies that enhance survival, persistence and virulence of non-pathogenic and pathogenic members of bacteria that oscillate between environmental and fish host niches.

A screening test proposal for congenital defects based on maternal serum metabolomics profile.

BACKGROUND: Historically, noninvasive techniques are only able to identify chromosomal anomalies that accounted for <50% of all congenital defects; the other congenital defects are diagnosed via ultrasound evaluations in the later stages of pregnancy. Metabolomic analysis may provide an important improvement, potentially addressing the need for novel noninvasive and multicomprehensive early prenatal screening tools. A growing body of evidence outlines notable metabolic alterations in different biofluids derived from pregnant women carrying fetuses with malformations, suggesting that such an approach may allow the discovery of biomarkers common to most fetal malformations. In addition, metabolomic investigations are inexpensive, fast, and risk-free and often generate high performance screening tests that may allow early detection of a given pathology. OBJECTIVE: This study aimed to evaluate the diagnostic accuracy of an ensemble machine learning model based on maternal serum metabolomic signatures for detecting fetal malformations, including both chromosomal anomalies and structural defects. STUDY DESIGN: This was a multicenter observational retrospective study that included 2 different arms. In the first arm, a total of 654 Italian pregnant women (334 cases with fetuses with malformations and 320 controls with normal developing fetuses) were enrolled and used to train an ensemble machine learning classification model based on serum metabolomics profiles. In the second arm, serum samples obtained from 1935 participants of the New Zealand Screening for Pregnancy Endpoints study were blindly analyzed and used as a validation cohort. Untargeted metabolomics analysis was performed via gas chromatography-mass spectrometry. Of note, 9 individual machine learning classification models were built and optimized via cross-validation (partial least squares-discriminant analysis, linear discriminant analysis, naïve Bayes, decision tree, random forest, k-nearest neighbor, artificial neural network, support vector machine, and logistic regression). An ensemble of the models was developed according to a voting scheme statistically weighted by the cross-validation accuracy and classification confidence of the individual models. This ensemble machine learning system was used to screen the validation cohort. RESULTS: Significant metabolic differences were detected in women carrying fetuses with malformations, who exhibited lower amounts of palmitic, myristic, and stearic acids; N-α-acetyllysine; glucose; L-acetylcarnitine; fructose; para-cresol; and xylose and higher levels of serine, alanine, urea, progesterone, and valine (P<.05), compared with controls. When applied to the validation cohort, the screening test showed a 99.4%±0.6% accuracy (specificity of 99.9%±0.1% [1892 of 1894 controls correctly identified] with a sensitivity of 78%±6% [32 of 41 fetal malformations correctly identified]). CONCLUSION: This study provided clinical validation of a metabolomics-based prenatal screening test to detect the presence of congenital defects. Further investigations are needed to enable the identification of the type of malformation and to confirm these findings on even larger study populations.

The Metabolomic Approach for the Screening of Endometrial Cancer: Validation from a Large Cohort of Women Scheduled for Gynecological Surgery.

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.

A Metabolomics-Based Screening Proposal for Colorectal Cancer.

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.

Polyunsaturated fatty acids cause physiological and behavioral changes in Vibrio alginolyticus and Vibrio fischeri.

Vibrio alginolyticus and Vibrio (Aliivibrio) fischeri are Gram-negative bacteria found globally in marine environments. During the past decade, studies have shown that certain Gram-negative bacteria, including Vibrio species (cholerae, parahaemolyticus, and vulnificus) are capable of using exogenous polyunsaturated fatty acids (PUFAs) to modify the phospholipids of their membrane. Moreover, exposure to exogenous PUFAs has been shown to affect certain phenotypes that are important factors of virulence. The purpose of this study was to investigate whether V. alginolyticus and V. fischeri are capable of responding to exogenous PUFAs by remodeling their membrane phospholipids and/or altering behaviors associated with virulence. Thin-layer chromatography (TLC) analyses and ultra-performance liquid chromatography-electrospray ionization mass spectrometry (UPLC/ESI-MS) confirmed incorporation of all PUFAs into membrane phosphatidylglycerol and phosphatidylethanolamine. Several growth phenotypes were identified when individual fatty acids were supplied in minimal media and as sole carbon sources. Interestingly, several PUFAs acids inhibited growth of V. fischeri. Significant alterations to membrane permeability were observed depending on fatty acid supplemented. Strikingly, arachidonic acid (20:4) reduced membrane permeability by approximately 35% in both V. alginolyticus and V. fischeri. Biofilm assays indicated that fatty acid influence was dependent on media composition and temperature. All fatty acids caused decreased swimming motility in V. alginolyticus, while only linoleic acid (18:2) significantly increased swimming motility in V. fischeri. In summary, exogenous fatty acids cause a variety of changes in V. alginolyticus and V. fischeri, thus adding these bacteria to a growing list of Gram-negatives that exhibit versatility in fatty acid utilization and highlighting the potential for environmental PUFAs to influence phenotypes associated with planktonic, beneficial, and pathogenic associations.

Advancements within Modern Machine Learning Methodology: Impacts and Prospects in Biomarker Discovery.

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.

Noninvasive screening for congenital heart defects using a serum metabolomics approach.

OBJECTIVE: Heart anomalies represent nearly one-third of all congenital anomalies. They are currently diagnosed using ultrasound. However, there is a strong need for a more accurate and less operator-dependent screening method. Here we report a metabolomics characterization of maternal serum in order to describe a metabolomic fingerprint representative of heart congenital anomalies. METHODS: Metabolomic profiles were obtained from serum of 350 mothers (280 controls and 70 cases). Nine classification models were built and optimized. An ensemble model was built based on the results from the individual models. RESULTS: The ensemble machine learning model correctly classified all cases and controls. Malonic, 3-hydroxybutyric and methyl glutaric acid, urea, androstenedione, fructose, tocopherol, leucine, and putrescine were determined as the most relevant metabolites in class separation. CONCLUSION: The metabolomic signature of second trimester maternal serum from pregnancies affected by a fetal heart anomaly is quantifiably different from that of a normal pregnancy. Maternal serum metabolomics is a promising tool for the accurate and sensitive screening of such congenital defects. Moreover, the revelation of the associated metabolites and their respective biochemical pathways allows a better understanding of the overall pathophysiology of affected pregnancies.

Exogenous polyunsaturated fatty acids (PUFAs) promote changes in growth, phospholipid composition, membrane permeability and virulence phenotypes in Escherichia coli.

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.

Development and Validation of a Serum Metabolomic Signature for Endometrial Cancer Screening in Postmenopausal Women.

Importance: Endometrial carcinoma (EC) is the most commonly diagnosed gynecologic cancer. Its early detection is advisable because 20% of women have advanced disease at the time of diagnosis. Objective: To clinically validate a metabolomics-based classification algorithm as a screening test for EC. Design, Setting, and Participants: This diagnostic study enrolled 2 cohorts. A multicenter prospective cohort, with 50 cases (postmenopausal women with EC; International Federation of Gynecology and Obstetrics stage I-III and grade G1-G3) and 70 controls (no EC but matched on age, years from menopause, tobacco use, and comorbidities), was used to train multiple classification models. The accuracy of each trained model was then used as a statistical weight to produce an ensemble machine learning algorithm for testing, which was validated with a subsequent prospective cohort of 1430 postmenopausal women. The study was conducted at the San Giovanni di Dio e Ruggi d'Aragona University Hospital of Salerno (Italy) and Lega Italiana per la Lotta contro i Tumori clinic in Avellino (Italy). Data collection was conducted from January 2018 to February 2019, and analysis was conducted from January to March 2019. Main Outcomes and Measures: The presence or absence of EC based on evaluation of the blood metabolome. Metabolites were extracted from dried blood samples from all participants and analyzed by gas chromatography-mass spectrometry. A confusion matrix was used to summarize test results. Performance indices included sensitivity, specificity, positive and negative predictive values, positive and negative likelihood ratios, and accuracy. Confirmation or exclusion of EC in women with a positive test result was by means of hysteroscopy. Participants with negative results were followed up 1 year after enrollment to investigate the appearance of EC signs. Results: The study population consisted of 1550 postmenopausal women. The mean (SD) age was 68.2 (11.7) years for participants with no EC in the training cohort, 69.4 (13.8) years for women with EC in the training cohort, and 59.7 (7.7) years for women in the validation cohort. Application of the ensemble machine learning to the validation cohort resulted in 16 true-positives, 2 false-positives, and 0 false-negatives, and it correctly classified more than 99% of samples. Disease prevalence was 1.12% (16 of 1430). Conclusions and Relevance: In this study, dried blood metabolomic profile was used to assess the presence or absence of EC in postmenopausal women not receiving hormonal therapy with greater than 99% accuracy.

Metabolomics in genetic testing.

Metabolomics is an intriguing field of study providing a new readout of the biochemical activities taking place at the moment of sampling within a subject's biofluid or tissue. Metabolite concentrations are influenced by several factors including disease, environment, drugs, diet and, importantly, genetics. Metabolomics signatures, which describe a subject's phenotype, are useful for disease diagnosis and prognosis, as well as for predicting and monitoring the effectiveness of treatments. Metabolomics is conventionally divided into targeted (i.e., the quantitative analysis of a predetermined group of metabolites) and untargeted studies (i.e., analysis of the complete set of small-molecule metabolites contained in a biofluid without a pre-imposed metabolites-selection). Both approaches have demonstrated high value in the investigation and understanding of several monogenic and multigenic conditions. Due to low costs per sample and relatively short analysis times, metabolomics can be a useful and robust complement to genetic sequencing.

Serum metallome in pregnant women and the relationship with congenital malformations of the central nervous system: a case-control study.

BACKGROUND: Congenital malformations of the central nervous system (CNS) consist of a wide range of birth defects of multifactorial origin. METHODS: Concentrations of 44 metals were determined by Inductively Coupled Plasma Mass Spectrometry in serum of 111 mothers in the second trimester of pregnancy who carried a malformed fetus and compared them with serum concentrations of the same metals in 90 mothers with a normally developed fetus at the same week of pregnancy. Data are reported as means ± standard deviations. RESULTS: We found a direct relationship between congenital defects of the CNS and maternal serum concentration of aluminum: it was statistically higher in women carrying a fetus with this class of malformation, compared both to mothers carrying a fetus with another class of malformation (6.45 ± 15.15 µg/L Vs 1.44 ± 4.21 µg/L, p < 0.0006) and to Controls (i.e. mothers carrying a normally-developed fetus) (6.45 ± 15.15 µg/L Vs 0.11 ± 0.51 µg/L, p < 0.0006). Moreover, Aluminum abundances were below the limit of detection in the majority of control samples. CONCLUSION: CAluminum may play a role in the onset of central nervous system malformations, although the exact Aluminum species and related specific type of malformation needs further elucidation.

Interpretation of long bones ballistic trauma.

In a forensic anthropology perspective, precise interpretation of long bone ballistic trauma could be the only way to determine features essential to understand the appearance of the wounds. The creation of a bone ballistic wound is a complex phenomenon that results from the action of a missile and the reaction of the bone tissue. Thus, it is often crucial to reconstruct the fracture site in order to analyze the fracture patterns. We have applied the previously established fundamentals of ballistic injury to cranial bones to the tubular structures on long bones (plug-and-spall bone fragments, radiating and concentric heaving fracture patterns). From interpretation of examples, we can conclude that the physics of ballistic penetration are constant and the material properties of cranial and tubular bone seem similar.

A comparative assessment of metals and phthalates in commercial tea infusions: A starting point to evaluate their tolerance limits.

Tea is one of the most consumed beverages in the word. Here we report the concentrations of metals and phthalates in 32 commercial tea packages. The data were used to estimate the average daily intake of metals and phthalates, and associated Hazard Quotients (HQ) were calculated in order to determine risk of non-cancerous health effects for adults consuming tea on a daily basis. Tea samples were chosen based on the sales network, the price, the marketing quality and the presence of filters in the packages. Relatively high median concentrations of Al (5240 µg/L), Ni (44 µg/L), and Mn (2919 µg/L) were detected. No metals or phthalates quantified in the tea infusions and soluble tea showed an HQ greater than 1, indicating no risk of non-cancerous health effects. The data presented herein may serve as a starting point to evaluate tolerance limits of metals and phthalate in the tea infusion.

Metabolomic change due to combined treatment with myo-inositol, D-chiro-inositol and glucomannan in polycystic ovarian syndrome patients: a pilot study.

BACKGROUND: 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. RESULTS: 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. CONCLUSION: 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. TRIAL REGISTRATION: ClinicalTial.gov, NCT03608813 . Registered 1st August 2018 - Retrospectively registered, .

Exogenous fatty acids alter phospholipid composition, membrane permeability, capacity for biofilm formation, and antimicrobial peptide susceptibility in Klebsiella pneumoniae.

Klebsiella pneumoniae represents a major threat to human health due to a combination of its nosocomial emergence and a propensity for acquiring antibiotic resistance. Dissemination of the bacteria from its native intestinal location creates severe, complicated infections that are particularly problematic in healthcare settings. Thus, there is an urgency for identifying novel treatment regimens as the incidence of highly antibiotic-resistant bacteria rises. Recent findings have highlighted the ability of some Gram-negative bacteria to utilize exogenous fatty acids in ways that modify membrane phospholipids and influence virulence phenotypes, such as biofilm formation and antibiotic resistance. This study explores the ability of K. pneumoniae to assimilate and respond to exogenous fatty acids. The combination of thin-layer chromatography liquid chromatography-mass spectrometry confirmed adoption of numerous exogenous polyunsaturated fatty acids (PUFAs) into the phospholipid species of K. pneumoniae. Membrane permeability was variably affected as determined by two dye uptake assays. Furthermore, the availability of many PUFAs lowered the MICs to the antimicrobial peptides polymyxin B and colistin. Biofilm formation was significantly affected depending upon the supplemented fatty acid.

Placental concentrations of essential, toxic, and understudied metals and relationships with birth outcomes in Chattanooga, TN.

BACKGROUND: Comprehensive examinations of placental metal concentrations and correlations with infant parameters are under-investigated. Chattanooga, Tennessee's consistently high incidence of low birth weight and potential for metal exposure provides an ideal opportunity to investigate potential correlations. OBJECTIVES: To investigate the associations between a wide variety of metals in placental tissue and multiple infant parameters. METHODS: A total of 31 elements were screened via ICP-MS in 374 individual placental samples. Of those, 14 were quantifiable in > 86% of the samples. We examined correlations between metal concentrations and infant parameters (birth weight, gestational age, birth weight centile, placental weight, birth length and head circumference). We fit multivariable regression models to estimate the covariate-adjusted associations of birth weight with ln-transformed concentrations of each of the 14 metals and used generalized additive models to examine nonlinear relationships. RESULTS: Some of the strongest relationships with infant parameters came from several lesser-studied metals. Placental rhodium concentrations were negatively correlated with almost all infant parameters. In the fully adjusted regression model, birth weight was significantly associated with several metals. On an IQR (25th to the 75th percentile) basis, estimated changes in birthweight were: for cobalt (82.5 g, IQR=6.05 µg/kg, p = 0.006), iron (-51.5 g, IQR = 171800 µg/kg, p = 0.030), manganese (-27.2 g, IQR=152.1 µg/kg, p = 0.017), lead (-72.7 g, IQR=16.55 µg/kg, p = 0.004) and rhodium (-1365.5 g, IQR = 0.33 µg/kg, p < 0.001). Finally, a generalized additive model showed significant nonlinear relationships between birth weight and concentrations of Co and Rh. CONCLUSIONS: Our comprehensive examination of placental metals illustrate many strong associations between lesser-studied metals and infant parameters. These data, in combination with our correlations of well-studied metals, illustrate a need to consider in utero exposure to a broad array of metals when considering fetal development.

Pseudomonas aeruginosa responds to exogenous polyunsaturated fatty acids (PUFAs) by modifying phospholipid composition, membrane permeability, and phenotypes associated with virulence.

BACKGROUND: 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. RESULTS: 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. CONCLUSIONS: 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.

Placental tissue metabolome analysis by GC-MS: Oven-drying is a viable sample preparation method.

Analysis of the human placenta metabolome has great potential to advance the understanding of complicated pregnancies and deleterious fetal outcomes in remote populations, but samples preparation can present unique challenges. Herein, we introduce oven-drying as a simple and widely available method of sample preparation that will facilitate investigations of the placental metabolome from remote and under-studied populations. Placentae from complicated and uncomplicated pregnancies were prepared in three ways (oven-dried at 60 °C, fresh, lyophilized) for metabolome analysis via gas chromatography-mass spectrometry (GC-MS). Multiple computer models (e.g. PLS-DA, ANN) were employed to classify and determine if there was a difference in placentae metabolome and a group of metabolites with high variable importance in projection scores across the three preparations and by complicated vs. control groups. The analyses used herein were shown to be thorough and sensitive. Indeed, significant differences were detected in metabolomes of complicated vs. uncomplicated pregnancies; however, there were no statistical differences in the metabolome of placentae prepared by oven-drying vs. lyophilization vs. fresh placentae. Oven-drying is a viable sample preparation method for placentae intended for use in metabolite analysis via GC-MS. These results open many possibilities for researching metabolome patterns associated with fetal outcomes in remote and resource-poor communities worldwide.

A metabolomics-based approach for non-invasive screening of fetal central nervous system anomalies.

BACKGROUND: Central nervous system anomalies represent a wide range of congenital birth defects, with an incidence of approximately 1% of all births. They are currently diagnosed using ultrasound evaluation. However, there is strong need for a more accurate and less operator-dependent screening method. OBJECTIVES: To perform a characterization of maternal serum in order to build a metabolomic fingerprint resulting from congenital anomalies of the central nervous system. METHODS: This is a case-control pilot study. Metabolomic profiles were obtained from serum of 168 mothers (98 controls and 70 cases), using gas chromatography coupled to mass spectrometry. Nine machine learning and classification models were built and optimized. An ensemble model was built based on results from the individual models. All samples were randomly divided into two groups. One was used as training set, the other one for diagnostic performance assessment. RESULTS: Ensemble machine learning model correctly classified all cases and controls. Propanoic, lactic, gluconic, benzoic, oxalic, 2-hydroxy-3-methylbutyric, acetic, lauric, myristic and stearic acid and myo-inositol and mannose were selected as the most relevant metabolites in class separation. CONCLUSION: The metabolomic signature of second trimester maternal serum from pregnancies affected by a fetal central nervous system anomaly is quantifiably different from that of a normal pregnancy. Maternal serum metabolomics is therefore a promising tool for the accurate and sensitive screening of such congenital defects. Moreover, the details of the most relevant metabolites and their respective biochemical pathways allow better understanding of the overall pathophysiology of affected pregnancies.