Generalized adaptive intelligent binning of multiway data

Central Role for Glycolysis and Fatty Acids in LH-responsive Progesterone Synthesis

Progesterone production by the corpus luteum is fundamental for establishing and maintaining pregnancy. The pituitary gonadotropin luteinizing hormone (LH) is recognized as the primary stimulus for luteal formation and progesterone synthesis, regardless of species. Previous studies demonstrated an elevation in abundance of genes related to glucose and lipid metabolism during the follicular to luteal transition. However, the metabolic phenotype of these highly steroidogenic cells has not been studied. Herein, we determined acute metabolic changes induced by LH in primary luteal cells and defined pathways required for progesterone synthesis. Untargeted metabolomics analysis revealed that LH induces rapid changes in vital metabolic pathways, including glycolysis, tricarboxylic acid (TCA) cycle, pentose phosphate pathway, de novo lipogenesis, and hydrolysis of phospholipids. LH stimulated glucose uptake, enhanced glycolysis, and flux of [U- 13 C 6 ]-labeled glucose-derived carbons into metabolic branches associated with adenosine 5'-triphosphate (ATP) and NADH/NADPH production, synthesis of nucleotides, proteins, and lipids, glycosylation of proteins or lipids, and redox homeostasis. Selective use of small molecule inhibitors targeting the most significantly changed pathways, such as glycolysis, TCA cycle, and lipogenesis, uncovered cellular metabolic routes required for LH-stimulated steroidogenesis. Furthermore, LH via the protein kinase A (PKA) pathway triggered post- translational modification of acetyl-CoA carboxylase alpha (ACACA) and ATP citrate lyase (ACLY), enzymes involved in de novo synthesis of fatty acids. Inhibition of ACLY and fatty acid transport into mitochondria reduced LH-stimulated ATP, cAMP production, PKA activation, and progesterone synthesis. Taken together, these findings reveal novel hormone-sensitive metabolic pathways essential for maintaining LHCGR/PKA signaling and steroidogenesis in ovarian luteal cells.

Significance

The establishment and maintenance of pregnancy require a well-developed corpus luteum, an endocrine gland within the ovary that produces progesterone. Although there is increased awareness of intracellular signaling events initiating the massive production of progesterone during the reproductive cycle and pregnancy, there are critical gaps in our knowledge of the metabolic and lipidomic pathways required for initiating and maintaining luteal progesterone synthesis. Here, we describe rapid, hormonally triggered metabolic pathways, and define metabolic targets crucial for progesterone synthesis by ovarian steroidogenic cells. Understanding hormonal control of metabolic pathways may help elucidate approaches for improving ovarian function and successful reproduction or identifying metabolic targets for developing nonhormonal contraceptives.

NMR Fingerprints of Formyl Phloroglucinol Meroterpenoids and Their Application to the Investigation of Eucalyptus gittinsii subsp. gittinsii

NMR fingerprints provide powerful tools to identify natural products in complex mixtures. Principal component analysis and machine learning using ¹H and ¹³C NMR data, alongside structural information from 180 published formyl phloroglucinols, have generated diagnostic NMR fingerprints to categorize subclasses within this group. This resulted in the reassignment of 167 NMR chemical shifts ascribed to 44 compounds. Three pyrano-diformyl phloroglucinols, euglobal In-1 and psiguadiols E and G, contained ¹H and ¹³C NMR data inconsistent with their predicted phloroglucinol subclass. Subsequent reinterpretation of their 2D NMR data combined with DFT ¹³C NMR chemical shift and ECD calculations led to their structure revisions. Direct covariance processing of HMBC data permitted ¹H resonances for individual compounds in mixtures to be associated, and analysis of their ¹H/¹³C HMBC correlations using the fingerprint tool further classified components into phloroglucinol subclasses. NMR fingerprinting HMBC data obtained for six eucalypt flower extracts identified three subclasses of pyrano-acyl-formyl phloroglucinols from Eucalyptus gittinsii subsp. gittinsii. New, eucalteretial F and (+)-eucalteretial B, and known, (-)-euglobal VII and eucalrobusone C, compounds, each belonging to predicted subclasses, were isolated and characterized. Staphylococcus aureus and Plasmodium falciparum screening revealed eucalrobusone C as the most potent antiplasmodial formyl phloroglucinol to date.

Histidine Enhances the Anticancer Effect of Gemcitabine against Pancreatic Cancer via Disruption of Amino Acid Homeostasis and Oxidant-Antioxidant Balance

Due to the severe toxicity posed by chemotherapeutic drugs, adjuvant nutritional intervention has gained increased attention in the treatment of pancreatic cancer (PC). Amino acid (AA) metabolism is aberrantly regulated in PC and circulating histidine (His) levels are low in PC patients. We hypothesized that His uptake and/or metabolism is dysregulated in PC and that combining His with gemcitabine (Gem), a drug used in the treatment of PC, will enhance the anti-cancer effects of Gem. We performed in vitro and in vivo studies to determine the anticancer effect of the combination of His and Gem against lethal PC. We demonstrate that circulating His levels are low in both human subjects and genetically engineered mice exhibiting pancreatic tumors. Interestingly, the expression of histidine ammonia lyase, an enzyme involved in His catabolism, is higher in PC compared to normal subjects. His + Gem exerts a more potent cytotoxic effect in PC cells compared to individual treatments. His treatment results in a profound increase in His accumulation, accompanied by a depletion of a number of AAs, promoting cancer cell survival and/or glutathione (GSH) synthesis. His but not Gem increases hydrogen peroxide and depletes cellular GSH. Supplementation with GSH protects cells against His + Gem-induced cytotoxicity. Further, our in vivo studies demonstrate that His + Gem potently reduced tumor mass and improved mouse survival. Taken together, our data suggest that PC cells exhibit an aberrant His uptake/accumulation which, in turn, leads to oxidative stress and depletion of AA pool, thereby enhancing the anticancer effect of Gem.

Redox Regulation and Metabolic Dependency of Zika Virus Replication: Inhibition by Nrf2-Antioxidant Response and NAD(H) Antimetabolites

Viral infections alter host cell metabolism and homeostasis; however, the mechanisms that regulate these processes have only begun to be elucidated. We report here that Zika virus (ZIKV) infection activates the antioxidant nuclear factor erythroid 2-related factor 2 (Nrf2), which precedes oxidative stress. Downregulation of Nrf2 or inhibition of glutathione (GSH) synthesis resulted in significantly increased viral replication. Interestingly, 6-amino-nicotinamide (6-AN), a nicotinamide analog commonly used as an inhibitor of the pentose phosphate pathway (PPP), decreased viral replication by over 1,000-fold. This inhibition was neither recapitulated by the knockdown of PPP enzymes, glucose 6-phosphate dehydrogenase (G6PD), or 6-phosphogluconate dehydrogenase (6PGD), nor prevented by supplementation with ribose 5-phosphate. Instead, our metabolomics and metabolic phenotype studies support a mechanism in which 6-AN depletes cells of NAD(H) and impairs NAD(H)-dependent glycolytic steps resulting in inhibition of viral replication. The inhibitory effect of 6-AN was rescued with precursors of the salvage pathway but not with those of other NAD+ biosynthesis pathways. Inhibition of glycolysis reduced viral protein levels, which were recovered transiently. This transient recovery in viral protein synthesis was prevented when oxidative metabolism was inhibited by blockage of the mitochondrial pyruvate carrier, fatty acid oxidation, or glutaminolysis, demonstrating a compensatory role of mitochondrial metabolism in ZIKV replication. These results establish an antagonistic role for the host cell Nrf2/GSH/NADPH-dependent antioxidant response against ZIKV and demonstrate the dependency of ZIKV replication on NAD(H). Importantly, our work suggests the potential use of NAD(H) antimetabolite therapy against the viral infection. IMPORTANCE Zika virus (ZIKV) is a major public health concern of international proportions. While the incidence of ZIKV infections has declined substantially in recent years, the potential for the reemergence or reintroduction remains high. Although viral infection alters host cell metabolism and homeostasis to promote its replication, deciphering the mechanism(s) involved in these processes is important for identifying therapeutic targets. The present work reveals the complexities of host cell redox regulation and metabolic dependency of ZIKV replication. An antagonistic effect of the Nrf2/GSH/NADP(H)-dependent antioxidant response against ZIKV infection and an essential role of NAD(H) metabolism and glycolysis for viral replication are established for the first time. These findings highlight the potential use of NAD(H) antimetabolites to counter ZIKV infection and pathogenesis.

GIPMA: Global Intensity-Guided Peak Matching and Alignment for 2D 1H-13C HSQC-Based Metabolomics

Two-dimensional (2D) ¹H-¹³C heteronuclear single quantum coherence (HSQC) has been increasingly applied to metabolomics studies because it can greatly improve the resolving capability compared with one-dimensional (1D) ¹H NMR. However, preprocessing methods such as peak matching and alignment tools for 2D NMR-based metabolomics have lagged behind similar methods for 1D ¹H NMR-based metabolomics. Correct matching and alignment of 2D NMR spectral features across multiple samples are particularly important for subsequent multivariate data analysis. Considering different intensity dynamic ranges of a variety of metabolites and the chemical shift variation across the spectra of multiple samples, here, we developed an efficient peak matching and alignment algorithm for 2D ¹H-¹³C HSQC-based metabolomics, called global intensity-guided peak matching and alignment (GIPMA). In GIPMA, peaks identified in all spectra are pooled together and sorted by intensity. Chemical shift of a stronger peak is regarded to be more accurate and reliable than that of a weaker peak. The strongest undesignated peak is chosen as the reference of a new cluster if it is not located within the chemical shift tolerance of any existing peak cluster (PC), or otherwise it is matched to an existing PC and the aligned chemical shift of the PC is updated as the intensity-weighted average of the chemical shifts of all peaks in the cluster. Setting an optimum chemical shift tolerance (Δδ_o) is critical for the peak matching and alignment across multiple samples. GIPMA dynamically searches for and intelligently selects the Δδ_o for peak matching to maximize the number of valid peak clusters (vPC), that is, spectral features, among multiple samples. By GIPMA, fully automatic peakwise matching and alignment do not require any spectrum as initial reference, while the chemical shift of each PC is updated as the intensity-weighted average of the chemical shifts of all peaks in the same PC, which is warranted to be statistically more accurate. Accurate chemical shifts for each representative spectral feature will facilitate subsequent peak assignment and are essential for correct metabolite identification and result interpretation. The proposed method was demonstrated successfully on the spectra of six model mixtures consisting of seven typical metabolites, yielding correct matching of all known spectral features. The performance of GIPMA was also demonstrated on 2D ¹H-¹³C HSQC spectra of 87 real extracts of 29 samples of five Dendrobium species. Hierarchical cluster analysis (HCA) and principal component analysis (PCA) of the 87 matched and aligned spectra by GIPMA generates correct classification of the 29 samples into five groups. In summary, the proposed algorithm of GIPMA provided a practical peak matching and alignment method to facilitate 2D NMR-based metabolomics studies.

NMR Metabolomics in Serum Fingerprinting of Schizophrenia Patients in a Serbian Cohort

Schizophrenia is a widespread mental disorder that leads to significant functional impairments and premature death. The state of the art indicates gaps in the understanding and diagnosis of this disease, but also the need for personalized and precise approaches to patients through customized medical treatment and reliable monitoring of treatment response. In order to fulfill existing gaps, the establishment of a universal set of disorder biomarkers is a necessary step. Metabolomic investigations of serum samples of Serbian patients with schizophrenia (51) and healthy controls (39), based on NMR analyses associated with chemometrics, led to the identification of 26 metabolites/biomarkers for this disorder. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) models with prediction accuracies of 0.9718 and higher were accomplished during chemometric analysis. The established biomarker set includes aspartate/aspartic acid, lysine, 2-hydroxybutyric acid, and acylglycerols, which are identified for the first time in schizophrenia serum samples by NMR experiments. The other 22 identified metabolites in the Serbian samples are in accordance with the previously established NMR-based serum biomarker sets of Brazilian and/or Chinese patient samples. Thirteen metabolites (lactate/lactic acid, threonine, leucine, isoleucine, valine, glutamine, asparagine, alanine, gamma-aminobutyric acid, choline, glucose, glycine and tyrosine) that are common for three different ethnic and geographic origins (Serbia, Brazil and China) could be a good start point for the setup of a universal NMR serum biomarker set for schizophrenia.

Deep Learning-Based Method for Compound Identification in NMR Spectra of Mixtures

Nuclear magnetic resonance (NMR) spectroscopy is highly unbiased and reproducible, which provides us a powerful tool to analyze mixtures consisting of small molecules. However, the compound identification in NMR spectra of mixtures is highly challenging because of chemical shift variations of the same compound in different mixtures and peak overlapping among molecules. Here, we present a pseudo-Siamese convolutional neural network method (pSCNN) to identify compounds in mixtures for NMR spectroscopy. A data augmentation method was implemented for the superposition of several NMR spectra sampled from a spectral database with random noises. The augmented dataset was split and used to train, validate and test the pSCNN model. Two experimental NMR datasets (flavor mixtures and additional flavor mixture) were acquired to benchmark its performance in real applications. The results show that the proposed method can achieve good performances in the augmented test set (ACC = 99.80%, TPR = 99.70% and FPR = 0.10%), the flavor mixtures dataset (ACC = 97.62%, TPR = 96.44% and FPR = 2.29%) and the additional flavor mixture dataset (ACC = 91.67%, TPR = 100.00% and FPR = 10.53%). We have demonstrated that the translational invariance of convolutional neural networks can solve the chemical shift variation problem in NMR spectra. In summary, pSCNN is an off-the-shelf method to identify compounds in mixtures for NMR spectroscopy because of its accuracy in compound identification and robustness to chemical shift variation.

NMR in Metabolomics: From Conventional Statistics to Machine Learning and Neural Network Approaches

NMR measurements combined with chemometrics allow achieving a great amount of information for the identification of potential biomarkers responsible for a precise metabolic pathway. These kinds of data are useful in different fields, ranging from food to biomedical fields, including health science. The investigation of the whole set of metabolites in a sample, representing its fingerprint in the considered condition, is known as metabolomics and may take advantage of different statistical tools. The new frontier is to adopt self-learning techniques to enhance clustering or classification actions that can improve the predictive power over large amounts of data. Although machine learning is already employed in metabolomics, deep learning and artificial neural networks approaches were only recently successfully applied. In this work, we give an overview of the statistical approaches underlying the wide range of opportunities that machine learning and neural networks allow to perform with accurate metabolites assignment and quantification.Various actual challenges are discussed, such as proper metabolomics, deep learning architectures and model accuracy.

Multi-Omics-Based Identification and Functional Characterization of Gh_A06G1257 Proves Its Potential Role in Drought Stress Tolerance in Gossypium hirsutum

Cotton is one of the most important fiber crops globally. Despite this, various abiotic stresses, including drought, cause yield losses. We used transcriptome profiles to investigate the co-expression patterns of gene networks associated with drought stress tolerance. We identified three gene modules containing 3,567 genes highly associated with drought stress tolerance. Within these modules, we identified 13 hub genes based on intramodular significance, for further validation. The yellow module has five hub genes (Gh_A07G0563, Gh_D05G0221, Gh_A05G3716, Gh_D12G1438, and Gh_D05G0697), the brown module contains three hub genes belonging to the aldehyde dehydrogenase (ALDH) gene family (Gh_A06G1257, Gh_A06G1256, and Gh_D06G1578), and the pink module has five hub genes (Gh_A02G1616, Gh_D12G2599, Gh_D07G2232, Gh_A02G0527, and Gh_D07G0629). Based on RT-qPCR results, the Gh_A06G1257 gene has the highest expression under drought stress in different plant tissues and it might be the true candidate gene linked to drought stress tolerance in cotton. Silencing of Gh_A06G1257 in cotton leaves conferred significant sensitivity in response to drought stress treatments. Overexpression of Gh_A06G1257 in Arabidopsis also confirms its role in drought stress tolerance. L-valine, Glutaric acid, L-proline, L-Glutamic acid, and L-Tryptophan were found to be the most significant metabolites playing roles in drought stress tolerance. These findings add significantly to existing knowledge of drought stress tolerance mechanisms in cotton.

DNAJA1 Dysregulates Metabolism Promoting an Antiapoptotic Phenotype in Pancreatic Ductal Adenocarcinoma

The cochaperone protein DNAJA1 (HSP40) is downregulated four-fold in pancreatic cancer cells. The impact of DNAJA1 expression on pancreatic ductal adenocarcinoma (PDAC) progression remains unclear. The metabolic impacts of increased DNAJA1 expression were evaluated using a combination of untargeted metabolomics, stable isotope-resolved metabolomics (SIRM), confocal microscopy, flow cytometry, and cell-based assays. Differential Warburg glycolysis, an increase in redox currency, and alterations in amino acid levels were observed in both overexpression cell lines. DNAJA1 overexpression also led to mitochondrial fusion, an increase in the expression of Bcl-2, a modest protection from redox-induced cell death, a loss of structural integrity due to the loss of actin fibers, and an increase in cell invasiveness in BxPC-3. These differences were more pronounced in BxPC-3, which contains a loss-of-function mutation in the tumor-suppressing gene SMAD4. These findings suggest a proto-oncogenic role of DNAJA1 in PDAC progression and suggest DNAJA1 may function synergistically with other proteins with altered activities in pancreatic cancer cell lines.

Combination of two analytical techniques improves wine classification by Vineyard, Region, and vintage

Three important wine parameters: vineyard, region, and vintage year, were evaluated using fifteen Vitis vinifera L. 'Pinot noir' wines derived from the same scion clone (Pinot noir 667). These wines were produced from two vintage years (2015 and 2016) and eight different regions along the Pacific Coast of the United States. We successfully improved the classification of the selected Pinot noir wines by combining an untargeted 1D ¹H NMR analysis with a targeted peptide based differential sensing array. NMR spectroscopy was used to evaluate the chemical fingerprint of the wines, whereas the peptide-based sensing array is known to mimic the senses of taste, smell, and palate texture by characterizing the phenolic profile. Multivariate and univariate statistical analyses of the combined NMR and differential sensing array dataset classified the genetically identical Pinot noir wines on the basis of distinctive metabolic signatures associated with the region of growth, vineyard, and vintage year.

Study protocol to investigate biomolecular muscle profile as predictors of long-term urinary incontinence in women with gestational diabetes mellitus

BACKGROUND

Pelvic floor muscles (PFM) and rectus abdominis muscles (RAM) of pregnant diabetic rats exhibit atrophy, co-localization of fast and slow fibers and an increased collagen type I/III ratio. However, the role of similar PFM or RAM hyperglycemic-related myopathy in women with gestational diabetes mellitus (GDM) remains poorly investigated. This study aims to assess the frequency of pelvic floor muscle disorders and pregnancy-specific urinary incontinence (PS-UI) 12 months after the Cesarean (C) section in women with GDM. Specifically, differences in PFM/RAM hyperglycemic myopathy will be evaluated.

METHODS

The Diamater is an ongoing cohort study of four groups of 59 pregnant women each from the Perinatal Diabetes Research Centre (PDRC), Botucatu Medical School (FMB)-UNESP (São Paulo State University), Brazil. Diagnosis of GDM and PS-UI will be made at 24-26 weeks, with a follow-up at 34-38 weeks of gestation. Inclusion in the study will occur at the time of C-section, and patients will be followed at 24-48 h, 6 weeks and 6 and 12 months postpartum. Study groups will be classified as (1) GDM plus PS-UI; (2) GDM without PS-UI; (3) Non-GDM plus PS-UI; and (4) Non-GDM without PS-UI. We will analyze relationships between GDM, PS-UI and hyperglycemic myopathy at 12 months after C-section. The mediator variables to be evaluated include digital palpation, vaginal squeeze pressure, 3D pelvic floor ultrasound, and 3D RAM ultrasound. RAM samples obtained during C-section will be analyzed for ex-vivo contractility, morphological, molecular and OMICS profiles to further characterize the hyperglycemic myopathy. Additional variables to be evaluated include maternal age, socioeconomic status, educational level, ethnicity, body mass index, weight gain during pregnancy, quality of glycemic control and insulin therapy.

DISCUSSION

To our knowledge, this will be the first study to provide data on the prevalence of PS-UI and RAM and PFM physical and biomolecular muscle profiles after C-section in mothers with GDM. The longitudinal design allows for the assessment of cause-effect relationships between GDM, PS-UI, and PFMs and RAMs myopathy. The findings may reveal previously undetermined consequences of GDM.

1D "Spikelet" Projections from Heteronuclear 2D NMR Data-Permitting 1D Chemometrics While Preserving 2D Dispersion

Nuclear magnetic resonance (NMR) spectroscopy is a powerful tool for the non-targeted metabolomics of intact biofluids and even living organisms. However, spectral overlap can limit the information that can be obtained from 1D 1H NMR. For example, magnetic susceptibility broadening in living organisms prevents any metabolic information being extracted from solution-state 1D 1H NMR. Conversely, the additional spectral dispersion afforded by 2D 1H-13C NMR allows a wide range of metabolites to be assigned in-vivo in 13C enriched organisms, as well as a greater depth of information for biofluids in general. As such, 2D 1H-13C NMR is becoming more and more popular for routine metabolic screening of very complex samples. Despite this, there are only a very limited number of statistical software packages that can handle 2D NMR datasets for chemometric analysis. In comparison, a wide range of commercial and free tools are available for analysis of 1D NMR datasets. Overtime, it is likely more software solutions will evolve that can handle 2D NMR directly. In the meantime, this application note offers a simple alternative solution that converts 2D 1H-13C Heteronuclear Single Quantum Correlation (HSQC) data into a 1D “spikelet” format that preserves not only the 2D spectral information, but also the 2D dispersion. The approach allows 2D NMR data to be converted into a standard 1D Bruker format that can be read by software packages that can only handle 1D NMR data. This application note uses data from Daphnia magna (water fleas) in-vivo to demonstrate how to generate and interpret the converted 1D spikelet data from 2D datasets, including the code to perform the conversion on Bruker spectrometers.

Nuclear Magnetic Resonance (NMR) Spectroscopy in Food Science: A Comprehensive Review

Nuclear magnetic resonance (NMR) spectroscopy is a robust method, which can rapidly analyze mixtures at the molecular level without requiring separation and/or purification steps, making it ideal for applications in food science. Despite its increasing popularity among food scientists, NMR is still an underutilized methodology in this area, mainly due to its high cost, relatively low sensitivity, and the lack of NMR expertise by many food scientists. The aim of this review is to help bridge the knowledge gap that may exist when attempting to apply NMR methodologies to the field of food science. We begin by covering the basic principles required to apply NMR to the study of foods and nutrients. A description of the discipline of chemometrics is provided, as the combination of NMR with multivariate statistical analysis is a powerful approach for addressing modern challenges in food science. Furthermore, a comprehensive overview of recent and key applications in the areas of compositional analysis, food authentication, quality control, and human nutrition is provided. In addition to standard NMR techniques, more sophisticated NMR applications are also presented, although limitations, gaps, and potentials are discussed. We hope this review will help scientists gain some of the knowledge required to apply the powerful methodology of NMR to the rich and diverse field of food science.

Metabolomics: A High-Throughput Platform for Metabolite Profile Exploration

Metabolomics aims to quantitatively measure small-molecule metabolites in biological samples, such as bodily fluids (e.g., urine, blood, and saliva), tissues, and breathe exhalation, which reflects metabolic responses of a living system to pathophysiological stimuli or genetic modification. In the past decade, metabolomics has made notable progresses in providing useful systematic insights into the underlying mechanisms and offering potential biomarkers of many diseases. Metabolomics is a complementary manner of genomics and transcriptomics, and bridges the gap between genotype and phenotype, which reflects the functional output of a biological system interplaying with environmental factors. Recently, the technology of metabolomics study has been developed quickly. This review will discuss the whole pipeline of metabolomics study, including experimental design, sample collection and preparation, sample detection and data analysis, as well as mechanism interpretation, which can help understand metabolic effects and metabolite function for living organism in system level.

Metabolic Mitigation of Staphylococcus aureus Vancomycin Intermediate-Level Susceptibility

Staphylococcus aureus is a major human pathogen whose infections are increasingly difficult to treat due to increased antibiotic resistance, including resistance to vancomycin. Vancomycin-intermediate S. aureus (VISA) strains develop resistance to vancomycin through adaptive changes that are incompletely understood. Central to this adaptation are metabolic changes that permit growth in the presence of vancomycin. To define the metabolic changes associated with adaptive resistance to vancomycin in S. aureus, the metabolomes of a vancomycin-sensitive and VISA strain pair isolated from the same patient shortly after vancomycin therapy began and following vancomycin treatment failure were analyzed. The metabolic adaptations included increases in acetogenesis, carbon flow through the pentose phosphate pathway, wall teichoic acid and peptidoglycan precursor biosynthesis, purine biosynthesis, and decreased tricarboxylic acid (TCA) cycle activity. The significance of these metabolic pathways for vancomycin-intermediate susceptibility was determined by assessing the synergistic potential of human-use-approved inhibitors of these pathways in combination with vancomycin against VISA strains. Importantly, inhibitors of amino sugar and purine biosynthesis acted synergistically with vancomycin to kill a diverse set of VISA strains, suggesting that combinatorial therapy could augment the efficacy of vancomycin even in patients infected with VISA strains.

Assessment of Metabolic Changes in Mycobacterium smegmatis Wild-Type and alr Mutant Strains: Evidence of a New Pathway of d-Alanine Biosynthesis

In mycobacteria, d-alanine is an essential precursor for peptidoglycan biosynthesis. The only confirmed enzymatic pathway to form d-alanine is through the racemization of l-alanine by alanine racemase (Alr, EC 5.1.1.1). Nevertheless, the essentiality of Alr in Mycobacterium tuberculosis and Mycobacterium smegmatis for cell survivability in the absence of d-alanine has been a point of controversy with contradictory results reported in the literature. To address this issue, we examined the effects of alr inactivation on the cellular metabolism of M. smegmatis. The M. smegmatis alr insertion mutant TAM23 exhibited essentially identical growth to wild-type mc²155 in the absence of d-alanine. NMR metabolomics revealed drastically distinct phenotypes between mc²155 and TAM23. A metabolic switch was observed for TAM23 as a function of supplemented d-alanine. In the absence of d-alanine, the metabolic response directed carbon through an unidentified transaminase to provide the essential d-alanine required for survival. The process is reversed when d-alanine is available, in which the d-alanine is directed to peptidoglycan biosynthesis. Our results provide further support for the hypothesis that Alr is not an essential function of M. smegmatis and that specific Alr inhibitors will have no bactericidal action.