Endogenous somatostatin is critical in regulating the acute effects of L-arginine on growth hormone and insulin release in mice

Metabolomic approaches to dissect dysregulated metabolism in the progression of pre-diabetes to T2DM

Many individuals with pre-diabetes eventually develop diabetes. Therefore, profiling of prediabetic metabolic disorders may be an effective targeted preventive measure. We aimed to elucidate the metabolic mechanism of progression of pre-diabetes to type 2 diabetes mellitus (T2DM) from a metabolic perspective. Four sets of plasma samples (20 subjects per group) collected according to fasting blood glucose (FBG) concentration were subjected to metabolomic analysis. An integrative approach of metabolome and WGCNA was employed to explore candidate metabolites. Compared with the healthy group (FBG < 5.6 mmol L-1), 113 metabolites were differentially expressed in the early stage of pre-diabetes (5.6 mmol L-1 ⩽ FBG < 6.1 mmol L-1), 237 in the late stage of pre-diabetes (6.1 mmol L-1 ⩽ FBG < 7.0 mmol L-1), and 245 in the T2DM group (FBG ⩾ 7.0 mmol L-1). A total of 27 differentially expressed metabolites (DEMs) were shared in all comparisons. Among them, L-norleucine was downregulated, whereas ethionamide, oxidized glutathione, 5-methylcytosine, and alpha-D-glucopyranoside beta-D-fructofuranosyl were increased with the rising levels of FBG. Surprisingly, 15 (11 lyso-phosphatidylcholines, L-norleucine, oxidized glutathione, arachidonic acid, and 5-oxoproline) of the 27 DEMs were ferroptosis-associated metabolites. WGCNA clustered all metabolites into 8 modules and the pathway enrichment analysis of DEMs showed a significant annotation to the insulin resistance-related pathway. Integrated analysis of DEMs, ROC and WGCNA modules determined 12 potential biomarkers for pre-diabetes and T2DM, including L-norleucine, 8 of which were L-arginine or its metabolites. L-Norleucine and L-arginine could serve as biomarkers for pre-diabetes. The inventory of metabolites provided by our plasma metabolome offers insights into T2DM physiology metabolism.

A novel strategy for L-arginine production in engineered Escherichia coli

BACKGROUND

L-arginine is an important amino acid with applications in diverse industrial and pharmaceutical fields. N-acetylglutamate, synthesized from L-glutamate and acetyl-CoA, is a precursor of the L-arginine biosynthetic branch in microorganisms. The enzyme that produces N-acetylglutamate, N-acetylglutamate synthase, is allosterically inhibited by L-arginine. L-glutamate, as a central metabolite, provides carbon backbone for diverse biological compounds besides L-arginine. When glucose is the sole carbon source, the theoretical maximum carbon yield towards L-arginine is 96.7%, but the experimental highest yield was 51%. The gap of L-arginine yield indicates the regulation complexity of carbon flux and energy during the L-arginine biosynthesis. Besides endogenous biosynthesis, N-acetylglutamate, the key precursor of L-arginine, can be obtained by chemical acylation of L-glutamate with a high yield of 98%. To achieve high-yield production of L-arginine, we demonstrated a novel approach by directly feeding precursor N-acetylglutamate to engineered Escherichia coli.

RESULTS

We reported a new approach for the high yield of L-arginine production in E. coli. Gene argA encoding N-acetylglutamate synthase was deleted to disable endogenous biosynthesis of N-acetylglutamate. The feasibility of external N-acetylglutamate towards L-arginine was verified via growth assay in argA- strain. To improve L-arginine production, astA encoding arginine N-succinyltransferase, speF encoding ornithine decarboxylase, speB encoding agmatinase, and argR encoding an arginine responsive repressor protein were disrupted. Based on overexpression of argDGI, argCBH operons, encoding enzymes of the L-arginine biosynthetic pathway, ~ 4 g/L L-arginine was produced in shake flask fermentation, resulting in a yield of 0.99 mol L-arginine/mol N-acetylglutamate. This strain was further engineered for the co-production of L-arginine and pyruvate by removing genes adhE, ldhA, poxB, pflB, and aceE, encoding enzymes involved in the conversion and degradation of pyruvate. The resulting strain was shown to produce 4 g/L L-arginine and 11.3 g/L pyruvate in shake flask fermentation.

CONCLUSIONS

Here, we developed a novel approach to avoid the strict regulation of L-arginine on ArgA and overcome the metabolism complexity in the L-arginine biosynthesis pathway. We achieve a high yield of L-arginine production from N-acetylglutamate in E. coli. Co-production pyruvate and L-arginine was used as an example to increase the utilization of input carbon sources.

Metabolic reprogramming and biosensor-assisted mutagenesis screening for high-level production of L-arginine in Escherichia coli

L-arginine is a value-added amino acid with promising applications in the pharmaceutical and nutraceutical industries. Further unleashing the potential of microbial cell factories to make L-arginine production more competitive remains challenging due to the sophisticated intracellular interaction networks and the insufficient knowledge of global metabolic regulation. Here, we combined multilevel rational metabolic engineering with biosensor-assisted mutagenesis screening to exploit the L-arginine production potential of Escherichia coli. First, multiple metabolic pathways were systematically reprogrammed to redirect the metabolic flux into L-arginine synthesis, including the L-arginine biosynthesis, TCA cycle, and L-arginine export. Specifically, a toggle switch responding to special cellular physiological conditions was designed to dynamically control the expression of sucA and pull more carbon flux from the TCA cycle toward L-arginine biosynthesis. Subsequently, a biosensor-assisted high-throughput screening platform was designed and applied to further exploit the L-arginine production potential. The best-engineered ARG28 strain produced 132 g/L L-arginine in a 5-L bioreactor with a yield of 0.51 g/g glucose and productivity of 2.75 g/(L ⋅ h), which were the highest values reported so far. Through whole genome sequencing and reverse engineering, Frc frameshift mutant, PqiB A78P mutant, and RpoB P564T mutant were revealed for enhancing the L-arginine biosynthesis. Our study exhibited the power of coupling rational metabolic reprogramming and biosensor-assisted mutagenesis screening to unleash the cellular potential for value-added metabolite production.

Recognition of early and late stages of bladder cancer using metabolites and machine learning

INTRODUCTION

Bladder cancer (BCa) is one of the most common and aggressive cancers. It is the sixth most frequently occurring cancer in men and its rate of occurrence increases with age. The current method of BCa diagnosis includes a cystoscopy and biopsy. This process is expensive, unpleasant, and may have severe side effects. Recent growth in the power and accessibility of machine-learning software has allowed for the development of new, non-invasive diagnostic methods whose accuracy and sensitivity are uncompromising to function.

OBJECTIVES

The goal of this research was to elucidate the biomarkers including metabolites and corresponding genes for different stages of BCa, show their distinguishing and common features, and create a machine-learning model for classification of stages of BCa.

METHODS

Sets of metabolites for early and late stages, as well as common for both stages were analyzed using MetaboAnalyst and Ingenuity^® Pathway Analysis (IPA^®) software. Machine-learning methods were utilized in the development of a binary classifier for early- and late-stage metabolites of BCa. Metabolites were quantitatively characterized using EDragon 1.0 software. The two modeling methods used are Multilayer Perceptron (MLP) and Stochastic Gradient Descent (SGD) with a logistic regression loss function.

RESULTS

We explored metabolic pathways related to early-stage BCa (Galactose metabolism and Starch and sucrose metabolism) and to late-stage BCa (Glycine, serine, and threonine metabolism, Arginine and proline metabolism, Glycerophospholipid metabolism, and Galactose metabolism) as well as those common to both stages pathways. The central metabolite impacting the most cancerogenic genes (AKT, EGFR, MAPK3) in early stage is D-glucose, while late-stage BCa is characterized by significant fold changes in several metabolites: glycerol, choline, 13(S)-hydroxyoctadecadienoic acid, 2'-fucosyllactose. Insulin was also seen to play an important role in late stages of BCa. The best performing model was able to predict metabolite class with an accuracy of 82.54% and the area under precision-recall curve (PRC) of 0.84 on the training set. The same model was applied to three separate sets of metabolites obtained from public sources, one set of the late-stage metabolites and two sets of the early-stage metabolites. The model was better at predicting early-stage metabolites with accuracies of 72% (18/25) and 95% (19/20) on the early sets, and an accuracy of 65.45% (36/55) on the late-stage metabolite set.

CONCLUSION

By examining the biomarkers present in the urine samples of BCa patients as compared with normal patients, the biomarkers associated with this cancer can be pinpointed and lead to the elucidation of affected metabolic pathways that are specific to different stages of cancer. Development of machine-learning model including metabolites and their chemical descriptors made it possible to achieve considerable accuracy of prediction of stages of BCa.

Growth hormone receptor polymorphisms and growth hormone response to stimulation test: a pilot study

BACKGROUND

No gold standard pharmacological stimulation test exists for the diagnosis of growth hormone deficiency (GHD). In addition, the genetic factors that influence growth hormone (GH) responses remain unclear. This study aimed to determine whether polymorphisms in exon 6 of the GH receptor gene influence responses to the L-arginine GH stimulation test.

METHODS

This study included 27 prepubertal patients with confirmed GHD. GHD was defined as a peak GH level <8 ng/mL in response to pharmacological stimulation. The mean GH peak after L-arginine stimulation was 2.9±2.9 ng/mL.

RESULTS

The included patients had the following genotypes at the third position of codon 168: AA (N.=1), AG (N.=15) and GG (N.=11). Patients carrying the AA and AG genotypes exhibited stronger responses to arginine than patients with the GG genotype (3.1±2.7 vs. 1.5±1.3 ng/mL, P=0.01).

CONCLUSIONS

The approach employed in this study could elucidate GH profiles under physiological and pathological conditions, facilitating improved interpretation of pharmacological stimulation tests.