OBJECTIVE: Mutations in Tissue Inhibitor of Metalloproteinases 3 (TIMP3) cause Sorsby\'s Fundus Dystrophy (SFD), a dominantly inherited, rare form of macular degeneration that results in vision loss. TIMP3 is synthesized primarily by retinal pigment epithelial (RPE) cells, which constitute the outer blood-retinal barrier. One major function of RPE is the synthesis and transport of vital nutrients, such as glucose, to the retina. Recently, metabolic dysfunction in RPE cells has emerged as an important contributing factor in retinal degenerations. We set out to determine if RPE metabolic dysfunction was contributing to SFD pathogenesis.
METHODS: Quantitative proteomics was conducted on RPE of mice expressing the S179C variant of TIMP3, known to be causative of SFD in humans. Proteins found to be differentially expressed (P < 0.05) were analyzed using statistical overrepresentation analysis to determine enriched pathways, processes, and protein classes using g:profiler and PANTHER Gene Ontology. We examined the effects of mutant TIMP3 on RPE metabolism using human ARPE-19 cells expressing mutant S179C TIMP3 and patient-derived induced pluripotent stem cell-derived RPE (iRPE) carrying the S204C TIMP3 mutation. RPE metabolism was directly probed using isotopic tracing coupled with GC/MS analysis. Steady state [U-13C6] glucose isotopic tracing was preliminarily conducted on S179C ARPE-19 followed by [U-13C6] glucose and [U-13C5] glutamine isotopic tracing in SFD iRPE cells.
RESULTS: Quantitative proteomics and enrichment analysis conducted on RPE of mice expressing mutant S179C TIMP3 identified differentially expressed proteins that were enriched for metabolism-related pathways and processes. Notably these results highlighted dysregulated glycolysis and glucose metabolism. Stable isotope tracing experiments with [U-13C6] glucose demonstrated enhanced glucose utilization and glycolytic activity in S179C TIMP3 APRE-19 cells. Similarly, [U-13C6] glucose tracing in SFD iRPE revealed increased glucose contribution to glycolysis and the TCA cycle. Additionally, [U-13C5] glutamine tracing found evidence of altered malic enzyme activity.
CONCLUSIONS: This study provides important information on the dysregulation of RPE glucose metabolism in SFD and implicates a potential commonality with other retinal degenerative diseases, emphasizing RPE cellular metabolism as a therapeutic target.

The central carbon (C) metabolic network is responsible for most of the production of energy and biosynthesis in microorganisms and is therefore key to a mechanistic understanding of microbial life in soil communities. Many upland soil communities have shown a relatively high C flux through the pentose phosphate (PP) or the Entner-Doudoroff (ED) pathway, thought to be related to oxidative damage control. We tested the hypothesis that the metabolic organization of the central C metabolic network differed between two ecosystems, an anoxic marsh soil and oxic upland soil, and would be affected by altering oxygen concentrations. We expected there to be high PP/ED pathway activity under high oxygen concentrations and in oxic soils and low PP/ED activity in reduced oxygen concentrations and in marsh soil. Although we found high PP/ED activity in the upland soil and low activity in the marsh soil, lowering the oxygen concentration for the upland soil did not reduce the relative PP/ED pathway activity as hypothesized, nor did increasing the oxygen concentration in the marsh soil increase the PP/ED pathway activity. We speculate that the high PP/ED activity in the upland soil, even when exposed to low oxygen concentrations, was related to a high demand for NADPH for biosynthesis, thus reflecting higher microbial growth rates in C-rich soils than in C-poor sediments. Further studies are needed to explain the observed metabolic diversity among soil ecosystems and determine whether it is related to microbial growth rates.IMPORTANCEWe observed that the organization of the central carbon (C) metabolic processes differed between oxic and anoxic soil. However, we also found that the pentose phosphate pathway/Entner-Doudoroff (PP/ED) pathway activity remained high after reducing the oxygen concentration for the upland soil and did not increase in response to an increase in oxygen concentration in the marsh soil. These observations contradicted the hypothesis that oxidative stress is a main driver for high PP/ED activity in soil communities. We suggest that the high PP/ED activity and NADPH production reflect higher anabolic activities and growth rates in the upland soil compared to the anaerobic marsh soil. A greater understanding of the molecular and biochemical processes in soil communities is needed to develop a mechanistic perspective on microbial activities and their relationship to soil C and nutrient cycling. Such an increased mechanistic perspective is ecologically relevant, given that the central carbon metabolic network is intimately tied to the energy metabolism of microbes, the efficiency of new microbial biomass production, and soil organic matter formation.

BACKGROUND: An imbalance in energy metabolism serves as a causal factor for type 2 diabetes (T2D). Although metformin has been known to ameliorate the overall energy metabolism imbalance, but the direct correlation between metformin and central carbon metabolism (CCM) has not been thoroughly investigated. In this study, we employed a high-performance ion chromatography-tandem mass spectrometry (HPIC-MS/MS) technique to examine the alterations and significance of CCM both before and after metformin treatment for T2D.
METHODS: We recruited 29 participants, comprising 10 individuals recently diagnosed with T2D (T2D group). Among these, 10 patients underwent a 4-6-week treatment with metformin (MET group). Additionally, we included 9 healthy subjects (CON group). Employing HPIC-MS/MS, we quantitatively analyzed 56 metabolites across 18 biologically relevant metabolic pathways associated with CCM. Univariate and multivariate statistical analyses were utilized to identify differential metabolites. Subsequently, correlation analyses and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted on the identified differential metabolites.
RESULTS: We identified seven distinct metabolites in individuals with T2D (p < 0.05). Notably, cyclic 3\',5\'-Adenosine MonoPhosphate (AMP), Glucose 6-phosphate, L-lactic acid, Maleic acid, and Malic acid exhibited a reversal to normal levels following metformin treatment. Furthermore, Malic acid demonstrated a positive correlation with L-lactic acid (r = 0.94, p < 0.05), as did succinic acid with malic acid (r = 0.81, p < 0.05), L-lactic acid with succinic acid (r = 0.78, p < 0.05), and L-lactic acid with glucose-6-phosphate (r = 0.72, p < 0.05). These metabolites were notably enriched in pyruvate metabolism (p = 0.005), tricarboxylic acid cycle (TCA) (p = 0.007), propanoate metabolism (p = 0.007), and glycolysis or gluconeogenesis (p = 0.009), respectively.
CONCLUSIONS: We employed HPIC-MS/MS to uncover alterations in CCM among individuals recently diagnosed with T2D before and after metformin treatment. The findings suggest that metformin may ameliorate the energy metabolism imbalance in T2D by reducing intermediates within the CCM pathway.

The intracellular [ATP]/[ADP] ratio is crucial for Escherichia coli\'s cellular functions, impacting transport, phosphorylation, signaling, and stress responses. Overexpression of F1-ATPase genes in E. coli increases glucose consumption, lowers energy levels, and triggers transcriptional responses in central carbon metabolism genes, particularly glycolytic ones, enhancing carbon flux. In this contribution, we report the impact of the perturbation of the energetic level in a PTS- mutant of E. coli by modifying the [ATP]/[ADP] ratio by uncoupling the cytoplasmic activity of the F1 subunit of the ATP synthase. The disruption of [ATP]/[ADP] ratio in the evolved strain of E. coli PB12 (PTS-) was achieved by the expression of the atpAGD operon encoding the soluble portion of ATP synthase F1-ATPase (strain PB12AGD+). The analysis of the physiological and metabolic response of the PTS- strain to the ATP disruption was determined using RT-qPCR of 96 genes involved in glucose and acetate transport, glycolysis and gluconeogenesis, pentose phosphate pathway (PPP), TCA cycle and glyoxylate shunt, several anaplerotic, respiratory chain, and fermentative pathways genes, sigma factors, and global regulators. The apt mutant exhibited reduced growth despite increased glucose transport due to decreased energy levels. It heightened stress response capabilities under glucose-induced energetic starvation, suggesting that the carbon flux from glycolysis is distributed toward the pentose phosphate and the Entner-Duodoroff pathway with the concomitant. Increase acetate transport, production, and utilization in response to the reduction in the [ATP]/[ADP] ratio. Upregulation of several genes encoding the TCA cycle and the glyoxylate shunt as several respiratory genes indicates increased respiratory capabilities, coupled possibly with increased availability of electron donor compounds from the TCA cycle, as this mutant increased respiratory capability by 240% more than in the PB12. The reduction in the intracellular concentration of cAMP in the atp mutant resulted in a reduced number of upregulated genes compared to PB12, suggesting that the mutant remains a robust genetic background despite the severe disruption in its energetic level.

Saccharomyces cerevisiae is a major actor in winemaking that converts sugars from the grape must into ethanol and CO2 with outstanding efficiency. Primary metabolites produced during fermentation have a great importance in wine. While ethanol content contributes to the overall profile, other metabolites like glycerol, succinate, acetate or lactate also have significant impacts, even when present in lower concentrations. S. cerevisiae is known for its great genetic diversity that is related to its natural or technological environment. However, the variation range of metabolic diversity which can be exploited to enhance wine quality depends on the pathway considered. Our experiment assessed the diversity of primary metabolites production in a set of 51 S. cerevisiae strains from various genetic backgrounds. Results pointed out great yield differences depending on the metabolite considered, with ethanol having the lowest variation. A negative correlation between ethanol and glycerol was observed, confirming glycerol synthesis as a suitable lever to reduce ethanol yield. Genetic groups were linked to specific yields, such as the wine group and high α-ketoglutarate and low acetate yields. This research highlights the potential of using natural yeast diversity in winemaking. It also provides a detailed data set on production of well known (ethanol, glycerol, acetate) or little-known (lactate) primary metabolites.

Metabolic flux analysis (MFA) is a valuable tool for quantifying cellular phenotypes and to guide plant metabolic engineering. By introducing stable isotopic tracers and employing mathematical models, MFA can quantify the rates of metabolic reactions through biochemical pathways. Recent applications of isotopically nonstationary MFA (INST-MFA) to plants have elucidated nonintuitive metabolism in leaves under optimal and stress conditions, described coupled fluxes for fast-growing algae, and produced a synergistic multi-organ flux map that is a first in MFA for any biological system. These insights could not be elucidated through other approaches and show the potential of INST-MFA to correct an oversimplified understanding of plant metabolism.

The Warburg effect, also referred as aerobic glycolysis, is a common metabolic program during viral infection. Through targeted metabolomics combined with biochemical experiments and various cell models, we investigated the central carbon metabolism (CCM) profiles of cells infected with porcine deltacoronavirus (PDCoV), an emerging enteropathogenic coronavirus with zoonotic potential. We found that PDCoV infection required glycolysis but decreased glycolytic flux, exhibiting a non-Warburg effect characterized by pyruvic acid accumulation. Mechanistically, PDCoV enhanced pyruvate kinase activity to promote pyruvic acid anabolism, a process that generates pyruvic acid with concomitant ATP production. PDCoV also hijacked pyruvic acid catabolism to increase biosynthesis of non-essential amino acids (NEAAs), suggesting that pyruvic acid is an essential hub for PDCoV to scavenge host energy and metabolites. Furthermore, PDCoV facilitated glutaminolysis to promote the synthesis of NEAA and pyrimidines for optimal proliferation. Our work supports a novel CCM model after viral infection and provides potential anti-PDCoV drug targets.

Quorum sensing (QS), an integral component of bacterial communication, is essential in coordinating the collective response of diverse bacterial pathogens. Central carbon metabolism (CCM), serving as the primary metabolic hub for substances such as sugars, lipids, and amino acids, plays a crucial role in the life cycle of bacteria. Pathogenic bacteria often utilize CCM to regulate population metabolism and enhance the synthesis of specific cellular structures, thereby facilitating in adaptation to the host microecological environment and expediting infection. Research has demonstrated that QS can both directly or indirectly affect the CCM of numerous pathogenic bacteria, thus altering their virulence and pathogenicity. This article reviews the interplay between QS and CCM in Gram-positive pathogenic bacteria, details the molecular mechanisms by which QS modulates CCM, and lays the groundwork for investigating bacterial pathogenicity and developing innovative infection treatment drugs.

Many metabolomic studies are interested in both polar and nonpolar analyses. However, the available sample volume often precludes multiple separate extractions. Therefore, there are major advantages in performing a biphasic extraction and retaining both phases for subsequent separate analyses. To be successful, such approaches require the method to be robust and repeatable for both phases. Hence, we determined the performance of three extraction protocols, plus two variant versions, using 25 μL of commercially available mouse plasma. The preferred option for nonpolar lipids was a modified diluted version of a method employing methyl tert-butyl ether (MTBE) suggested by Matyash and colleagues due to its high repeatability for nonpolar compounds. For polar compounds, the Bligh-Dyer method performs best for sensitivity but with consequentially poorer lipid performance. Overall, the scaled-down version of the MTBE method gave the best overall performance, with high sensitivity for both polar and nonpolar compounds and good repeatability for polar compounds in particular.

BACKGROUND: Lung cancer causes a huge disease burden, and early detection of positive pulmonary nodules (PPNs) as an early sign of lung cancer is extremely important for effective intervention. It is necessary to develop PPNs risk recognizer based on machine learning algorithm combined with central carbon metabolomics.
METHODS: The study included 2248 participants at high risk for lung cancer from the Ma\'anshan Community Lung Cancer Screening cohort. The Least Absolute Shrinkage and Selection Operator (LASSO) was used to screen 18 central carbon-related metabolites in plasma, recursive feature elimination (RFE) was used to select all 42 features, followed by five machine learning algorithms for model development. The performance of the model was evaluated using area under the receiver operator characteristic curve (AUC), accuracy, precision, recall, and F1 scores. In addition, SHapley Additive exPlanations (SHAP) was performed to assess the interpretability of the final selected model and to gain insight into the impact of features on the predicted results.
RESULTS: Finally, the two prediction models based on the random forest (RF) algorithm performed best, with AUC values of 0.87 and 0.83, respectively, better than other models. We found that homogentisic acid, fumaric acid, maleic acid, hippuric acid, gluconic acid, and succinic acid played a significant role in both PPNs prediction model and NPNs vs PPNs model, while 2-oxadipic acid only played a role in the former model and phosphopyruvate only played a role in the NPNs vs PPNs model. This model demonstrates the potential of central carbon metabolism for PPNs risk prediction and identification.
CONCLUSIONS: We developed a series of predictive models for PPNs, which can help in the early detection of PPNs and thus reduce the risk of lung cancer.