Tumor-Selective Nano-Dispatcher Enforced Cancer Immunotherapeutic Effects via Regulating Lactate Metabolism and Activating Toll-Like Receptors.

The development of tumors relies on lactate metabolic reprogramming to facilitate their unchecked growth and evade immune surveillance. This poses a significant challenge to the efficacy of antitumor immunity. To address this, a tumor-selective nano-dispatcher, PIMDQ/Syro-RNP, to enforce the immunotherapeutic effect through regulation of lactate metabolism and activation of toll-like receptors is developed. By using the tumor-targeting properties of c-RGD, the system can effectively deliver monocarboxylate transporters 4 (MCT4) inhibitor (Syro) to inhibit lactate efflux in tumor cells, leading to decreased lactate levels in the tumor microenvironment (TME) and increased accumulation within tumor cells. The reduction of lactate in TME will reduce the nutritional support for regulatory T cells (Tregs) and promote the effector function of T cells. The accumulation of lactate in tumor cells will lead to tumor death due to cellular acidosis. In addition, it will also reduce the uptake of glucose by tumor cells, reduce nutrient plunder, and further weaken the inhibition of T cell function. Furthermore, the pH-responsive release of Toll-like receptors (TLR) 7/8 agonist IMDQ within the TME activates dendritic cells (DCs) and promotes the infiltration of T cells. These findings offer a promising approach for enhancing tumor immune response through targeted metabolic interventions.

A lactate metabolism-related gene signature to diagnose osteoarthritis based on machine learning combined with experimental validation.

BACKGROUND: Lactate is gradually proved as the essential regulator in intercellular signal transduction, energy metabolism reprogramming, and histone modification. This study aims to clarify the diagnosis value of lactate metabolism-related genes in osteoarthritis (OA). METHODS: Lactate metabolism-related genes were retrieved from the MSigDB. GSE51588 was downloaded from the Gene Expression Omnibus (GEO) as the training dataset. GSE114007, GSE117999, and GSE82107 datasets were adopted for external validation. Genomic difference detection, protein-protein interaction network analysis, LASSO, SVM-RFE, Boruta, and univariate logistic regression (LR) analyses were used for feature selection. Multivariate LR, Random Forest (RF), Support Vector Machine (SVM), and XGBoost (XGB) were used to develop the multiple-gene diagnosis models. 12 control and 12 OA samples were collected from the local hospital for re-verification. The transfection assays were conducted to explore the regulatory ability of the gene to the apoptosis and vitality of chondrocytes. RESULTS: Through the bioinformatical analyses and machine learning algorithms, SLC2A1 and NDUFB9 of the 273 lactate metabolism-related genes were identified as the significant diagnosis biomarkers. The LR, RF, SVM, and XGB models performed impressively in the cohorts (AUC > 0.7). The local clinical samples indicated that SLC2A1 and NDUFB9 were both down-regulated in the OA samples (both P < 0.05). The knockdown of NDUFB9 inhibited the viability and promoted the apoptosis of the CHON-001 cells treated with IL-1beta (both P < 0.05). CONCLUSIONS: A lactate metabolism-related gene signature was constructed to diagnose OA, which was validated in multiple independent cohorts, local clinical samples, and cellular functional experiments.

Lactate Metabolism Subtypes Analysis Reveals CCDC80 as a Novel Prognostic Biomarker in Gastric Cancer.

Lactate metabolism plays a vital role in tumor progression. Currently, gastric cancer (GC) has a poor prognosis. Therefore, our research aimed to investigate novel biomarkers related to lactate metabolism in patients. Patient data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database were divided into subtypes based on the expression of lactate metabolism-related genes (LMRGs). Based on the subtypes, we identified coiled-coil domain containing 80 (CCDC80) for further investigation. Univariate and multivariate Cox regression models were constructed to determine the prognostic value of CCDC80 in GC. We further explored the mechanism by which CCDC80 affects GC prognosis using gene set enrichment analysis (GSEA). Immune infiltration and drug sensitivity analyses were also performed. Finally, immunohistochemical staining was used to evaluate CCDC80 expression in normal and tumor tissues. We observed that CCDC80 was overexpressed in GC samples and was significantly associated with T and pathological stages. Multivariate Cox analysis identified high CCDC80 expression as an independent prognostic marker. GSEA indicated that the oxidative phosphorylation pathway was highly enriched in the low CCDC80 expression group. Moreover, CCDC80 was associated with immune cell infiltration, especially that of M2 macrophages. Patients with higher CCDC80 expression exhibited lower sensitivity to paclitaxel. In conclusion, our findings demonstrate that CCDC80 is a critical regulator in GC progression and immune response and is associated with lactate metabolism, and it could be used as a novel biomarker for prognostic and chemotherapy treatment purposes.

CNDP2: An Enzyme Linking Metabolism and Cardiovascular Diseases?

The heart requires a substantial amount of energy to function, utilising various substrates including lipids, glucose and lactate as energy sources. In times of increased stress, lactate becomes the primary energy source of the heart, but persistently elevated lactate levels are linked to poor patient outcomes and increased mortality. Recently, carnosine dipeptidase II (CNDP2) was discovered to catalyse the formation of Lac-Phe, an exercise-induced metabolite derived from lactate, which has been shown to suppress appetite in mice and reduce adipose tissue in humans. This review discusses CNDP2, including its role in lactate clearance, carnosine hydrolysis, oxidative stress regulation, and involvement in metabolite regulation. The association between CNDP2 and cardiometabolic and renal diseases is also explored, and knowledge gaps are highlighted. CNDP2 appears to be a complex participant in human physiological processes and disease, necessitating additional research to unveil its functions and potential therapeutic applications.

Friend or foe: Lactate in neurodegenerative diseases.

Lactate, a byproduct of glycolysis, was considered as a metabolic waste until identified by studies on the Warburg effect. Increasing evidence elucidates that lactate functions as energy fuel, signaling molecule, and donor for protein lactylation. Altered lactate utilization is a common metabolic feature of the onset and progression of neurodegenerative diseases, such as Alzheimer's disease, multiple sclerosis, amyotrophic lateral sclerosis, Parkinson's disease and Huntington's disease. This review offers an overview of lactate metabolism from the perspective of production, transportation and clearance, and the role of lactate in neurodegenerative progression, as well as a summary of protein lactylation and the signaling function of lactate in neurodegenerative diseases. Besides, this review delves into the dual roles of changed lactate metabolism during neurodegeneration and explores prospective therapeutic methods targeting lactate. We propose that elucidating the correlation between lactate and neurodegeneration is pivotal for exploring innovative therapeutic interventions for neurodegenerative diseases.

Modulation of brain energy metabolism in hepatic encephalopathy: impact of glucose metabolic dysfunction.

Cerebral function is linked to a high level of metabolic activity and relies on glucose as its primary energy source. Glucose aids in the maintenance of physiological brain activities; as a result, a disruption in metabolism has a significant impact on brain function, launching a chain of events that leads to neuronal death. This metabolic insufficiency has been observed in a variety of brain diseases and neuroexcitotoxicity disorders, including hepatic encephalopathy. It is a significant neurological complication that develops in people with liver disease, ranging from asymptomatic abnormalities to coma. Hyperammonemia is the main neurotoxic villain in the development of hepatic encephalopathy and induces a wide range of complications in the brain. The neurotoxic effects of ammonia on brain function are thought to be mediated by impaired glucose metabolism. Accordingly, in this review, we provide an understanding of deranged brain energy metabolism, emphasizing the role of glucose metabolic dysfunction in the pathogenesis of hepatic encephalopathy. We also highlighted the differential metabolic profiles of brain cells and the status of metabolic cooperation between them. The major metabolic pathways that have been explored are glycolysis, glycogen metabolism, lactate metabolism, the pentose phosphate pathway, and the Krebs cycle. Furthermore, the lack of efficacy in current hepatic encephalopathy treatment methods highlights the need to investigate potential therapeutic targets for hepatic encephalopathy, with regulating deficient bioenergetics being a viable alternative in this case. This review also demonstrates the importance of the development of glucose metabolism-focused disease diagnostics and treatments, which are now being pursued for many ailments.

Bulk-RNA and single-nuclei RNA seq analyses reveal the role of lactate metabolism-related genes in Alzheimer's disease.

Dysfunctional lactate metabolism in the brain has been implicated in neuroinflammation, Aß deposition, and cell disturbance, all of which play a significant role in the pathogenesis of Alzheimer's disease (AD). In this study, we aimed to investigate the lactate metabolism-related genes (LMRGs) in AD via an integrated bulk RNA and single-nuclei RNA sequencing (snRNA-seq) analysis, with a specific focus on microglia. We obtained 26 HC and 24 AD snRNA-seq samples originated from human prefrontal cortex in Gene Expression Omnibus (GEO) database and collected 873 LMRGs from three databases, namely MSigDB, The Human Protein Atlas and GeneCards. Bulk RNA was analyzed with LMRG characteristics in AD by using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), the protein-protein interaction (PPI), CytoHubba-MCC, Support Vector Machine (SVM) algorithms analyses. Then we conducted the Receiver Operating Characteristic (ROC) curve, correlation, and connection network analyses for biomarkers. Their differential expression validation was performed using AlzData database. The single-nuclei RNA analysis of microglia was applied to identify hub genes and pathways using cell-cell communication analysis and high dimensional Weighted Gene Co-Expression Network Analysis (hdWGCNA). Support Vector Machine (SVM) algorithm showed an AUC of 0.967, a sensitivity of 93.30% and a specificity of 100.00%. Our analysis identified biomarkers with LMRG characteristics, namely INSR, CDKL1, and PNISR. ROC analysis revealed that each of these biomarkers exhibited excellent diagnostic potential, as evidenced by their respective area under the curve (AUC) values: INSR (AUC: 0.679), CDKL1 (AUC: 0.788), and PNISR (AUC: 0.724). Correlation analysis showed that biomarkers exhibited a positive correlation with each other. Connection network illustrated their shared biological processes: aging, phosphorylation, metabolic process, and apoptosis. Cell-cell communication analysis revealed that GALECTIN signaling pathway was exclusively expressed in AD microglia, and only LGALS9 exhibited significant overexpression. HdWGCNA identified FTH1 as a hub gene enriched in ferroptosis and mineral absorption pathways within microglia. The roles of INSR, CDKL1, PNISR, LGALS9, and FTH1 should be taken into account to enhance our understanding of lactate metabolism in the context of AD.

A bright red fluorescent genetically encoded sensor for lactate imaging.

Lactate plays a crucial role in energy metabolism and greatly impacts protein activities, exerting diverse physiological and pathological effects. Therefore, convenient lactate assays for tracking spatiotemporal dynamics in living cells are desirable. In this paper, we engineered and optimized a red fluorescent protein sensor for l-lactate named FiLa-Red. This indicator exhibited a maximal fluorescence change of 730 % and an apparent dissociation constant (Kd) of approximately 460 µM. By utilizing FiLa-Red and other sensors, we monitored energy metabolism in a multiplex manner by simultaneously tracking lactate and NAD+/NADH abundance in the cytoplasm, nucleus, and mitochondria. The FiLa-Red sensor is expected to be a useful tool for performing metabolic analysis in vitro, in living cells and in vivo.

Identification of a combined hypoxia and lactate metabolism prognostic signature in lung adenocarcinoma : Author.

BACKGROUND: In the tumor microenvironment (TME), a bidirectional relationship exists between hypoxia and lactate metabolism, with each component exerting a reciprocal influence on the other, forming an inextricable link. The aim of the present investigation was to develop a prognostic model by amalgamating genes associated with hypoxia and lactate metabolism. This model is intended to serve as a tool for predicting patient outcomes, including survival rates, the status of the immune microenvironment, and responsiveness to therapy in patients with lung adenocarcinoma (LUAD). METHODS: Transcriptomic sequencing data and patient clinical information specific to LUAD were obtained from comprehensive repositories of The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). A compendium of genes implicated in hypoxia and lactate metabolism was assembled from an array of accessible datasets. Univariate and multivariate Cox regression analyses were employed. Additional investigative procedures, including tumor mutational load (TMB), microsatellite instability (MSI), functional enrichment assessments and the ESTIMATE, CIBERSORT, and TIDE algorithms, were used to evaluate drug sensitivity and predict the efficacy of immune-based therapies. RESULTS: A novel prognostic signature comprising five lactate and hypoxia-related genes (LHRGs), PKFP, SLC2A1, BCAN, CDKN3, and ANLN, was established. This model demonstrated that LUAD patients with elevated LHRG-related risk scores exhibited significantly reduced survival rates. Both univariate and multivariate Cox analyses confirmed that the risk score was a robust prognostic indicator of overall survival. Immunophenotyping revealed increased infiltration of memory CD4 + T cells, dendritic cells and NK cells in patients classified within the high-risk category compared to their low-risk counterparts. Higher probability of mutations in lung adenocarcinoma driver genes in high-risk groups, and the MSI was associated with the risk-score. Functional enrichment analyses indicated a predominance of cell cycle-related pathways in the high-risk group, whereas metabolic pathways were more prevalent in the low-risk group. Moreover, drug sensitivity analyses revealed increased sensitivity to a variety of drugs in the high-risk group, especially inhibitors of the PI3K-AKT, EGFR, and ELK pathways. CONCLUSIONS: This prognostic model integrates lactate metabolism and hypoxia parameters, offering predictive insights regarding survival, immune cell infiltration and functionality, as well as therapeutic responsiveness in LUAD patients. This model may facilitate personalized treatment strategies, tailoring interventions to the unique molecular profile of each patient's disease.

Unravelling the metabolic landscape of cutaneous melanoma: Insights from single-cell sequencing analysis and machine learning for prognostic assessment of lactate metabolism.

This manuscript presents a comprehensive investigation into the role of lactate metabolism-related genes as potential prognostic markers in skin cutaneous melanoma (SKCM). Bulk-transcriptome data from The Cancer Genome Atlas (TCGA) and GSE19234, GSE22153, and GSE65904 cohorts from GEO database were processed and harmonized to mitigate batch effects. Lactate metabolism scores were assigned to individual cells using the 'AUCell' package. Weighted Co-expression Network Analysis (WGCNA) was employed to identify gene modules correlated with lactate metabolism. Machine learning algorithms were applied to construct a prognostic model, and its performance was evaluated in multiple cohorts. Immune correlation, mutation analysis, and enrichment analysis were conducted to further characterize the prognostic model's biological implications. Finally, the function of key gene NDUFS7 was verified by cell experiments. Machine learning resulted in an optimal prognostic model, demonstrating significant prognostic value across various cohorts. In the different cohorts, the high-risk group showed a poor prognosis. Immune analysis indicated differences in immune cell infiltration and checkpoint gene expression between risk groups. Mutation analysis identified genes with high mutation loads in SKCM. Enrichment analysis unveiled enriched pathways and biological processes in high-risk SKCM patients. NDUFS7 was found to be a hub gene in the protein-protein interaction network. After the expression of NDUFS7 was reduced by siRNA knockdown, CCK-8, colony formation, transwell and wound healing tests showed that the activity, proliferation and migration of A375 and WM115 cell lines were significantly decreased. This study offers insights into the prognostic significance of lactate metabolism-related genes in SKCM.

Sonocatalytic oncolysis microbiota curb intrinsic microbiota lactate metabolism and blockade CD24-Siglec10 immune escape to revitalize immunological surveillance.

Intrinsic lactate retention of chemically- or genetically-engineered bacteria therapy aggravates tumor immunosuppression, which will collaborate with immune escape to cause immunological surveillance failure. To address them, sonocatalytic oncolysis Escherichia coli (E.coli) that chemically chelated anti-CD24 and TiO1+x have been engineered to blockade CD24-siglec10 interaction, regulate microbiota colonization and curb its lactate metabolism, which are leveraged to revitalize immunological surveillance and repress breast cancer. The chemically-engineered E.coli inherited their parent genetic information and expansion function. Therefore, their intrinsic hypoxia tropism and CD24 targeting allow them to specifically accumulate and colonize in solid breast cancer to lyse tumor cells. The conjugated CD24 antibody is allowed to blockade CD24-Siglec10 signaling axis and revitalize immunological surveillance. More significantly, the chelated TiO1+x sonosensitizers produce ROS to render bacteria expansion controllable and curb immunosuppression-associated lactate birth that are usually neglected. Systematic experiments successfully vlaidate hypoxia-objective active targeting, sonocatalytic therapy, microbiota expansion-enabled oncolysis, CD24-Siglec10 communication blockade and precise microbiota abundance & lactate metabolism attenuations. These actions contribute to the potentiated anti-tumor immunity and activated anti-metastasis immune memory against breast cancer development. Our pioneering work provide a route to sonocatalytic cancer immunotherapy.

Novel hypoxia- and lactate metabolism-related molecular subtyping and prognostic signature for colorectal cancer.

BACKGROUND: Colorectal cancer (CRC) is a serious global health burden because of its high morbidity and mortality rates. Hypoxia and massive lactate production are hallmarks of the CRC microenvironment. However, the effects of hypoxia and lactate metabolism on CRC have not been fully elucidated. This study aimed to develop a novel molecular subtyping based on hypoxia-related genes (HRGs) and lactate metabolism-related genes (LMRGs) and construct a signature to predict the prognosis of patients with CRC and treatment efficacy. METHODS: Bulk and single-cell RNA-sequencing and clinical data of CRC were downloaded from the TCGA and GEO databases. HRGs and LMRGs were obtained from the Molecular Signatures Database. The R software package DESeq2 was used to perform differential expression analysis. Molecular subtyping was performed using unsupervised clustering. A predictive signature was developed using univariate Cox regression, random forest model, LASSO, and multivariate Cox regression analyses. Finally, the sensitivity of tumor cells to chemotherapeutic agents before and after hypoxia was verified using in vitro experiments. RESULTS: We classified 575 patients with CRC into three molecular subtypes and were able to distinguish their prognoses clearly. The C1 subtype, which exhibits high levels of hypoxia, has a low proportion of CD8 + T cells and a high proportion of macrophages. The expression of immune checkpoint genes is generally elevated in C1 patients with severe immune dysfunction. Subsequently, we constructed a predictive model, the HLM score, which effectively predicts the prognosis of patients with CRC and the efficacy of immunotherapy. The HLM score was validated in GSE39582, GSE106584, GSE17536, and IMvigor210 datasets. Patients with high HLM scores exhibit high infiltration of CD8 + exhausted T cells (Tex), especially terminal Tex, and oxidative phosphorylation (OXPHOS)-Tex in the immune microenvironment. Finally, in vitro experiments confirmed that CRC cell lines were less sensitive to 5-fluorouracil, oxaliplatin, and irinotecan under hypoxic conditions. CONCLUSION: We constructed novel hypoxia- and lactate metabolism-related molecular subtypes and revealed their immunological and genetic characteristics. We also developed an HLM scoring system that could be used to predict the prognosis and efficacy of immunotherapy in patients with CRC.

Lactate supports cell-autonomous ECM production to sustain metastatic behavior in prostate cancer.

Extracellular matrix (ECM) is a major component of the tumor environment, promoting the establishment of a pro-invasive behavior. Such environment is supported by both tumor- and stromal-derived metabolites, particularly lactate. In prostate cancer (PCa), cancer-associated fibroblasts (CAFs) are major contributors of secreted lactate, able to impact on metabolic and transcriptional regulation in cancer cells. Here, we describe a mechanism by which CAF-secreted lactate promotes in PCa cells the expression of genes coding for the collagen family. Lactate-exploiting PCa cells rely on increased α-ketoglutarate (α-KG) which activates the α-KG-dependent collagen prolyl-4-hydroxylase (P4HA1) to support collagen hydroxylation. De novo synthetized collagen plays a signaling role by activating discoidin domain receptor 1 (DDR1), supporting stem-like and invasive features of PCa cells. Inhibition of lactate-induced collagen hydroxylation and DDR1 activation reduces the metastatic colonization of PCa cells. Overall, these results provide a new understanding of the link between collagen remodeling/signaling and the nutrient environment exploited by PCa.

Analysis of prognostic value of lactate metabolism-related genes in ovarian cancer based on bioinformatics.

BACKGROUND: Recent studies have provided evidence supporting the functional role and mechanism of lactate in suppressing anticancer immunity. However, there is no systematic analysis of lactate metabolism-related genes (LMRGs) and ovarian cancer (OV) prognosis. RESULTS: Six genes (CCL18, CCND1, MXRA5, NRBP2, OLFML2B and THY1) were selected as prognostic genes and a prognostic model was utilized. Kaplan-Meier (K-M) and Receiver Operating Characteristic (ROC) analyses were further performed and indicated that the prognostic model was effective. Subsequently, the neoplasm_cancer_status and RiskScore were determined as independent prognostic factors, and a nomogram was established with relatively accurate forecasting ability. Additionally, 2 types of immune cells (Central memory CD8 T cell and Immature B cell), 4 types of immune functions (APC co inhibition, DCs, Tfh and Th1 cells), 9 immune checkpoints (BTLA, CTLA4, IDO1, LAG3, VTCN1, CXCL10, CXCL9, IFNG, CD27) and tumor immune dysfunction and exclusion (TIDE) scores were significantly different between risk groups. The expression of 6 genes were verified by quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) and the expression of 6 genes were higher in the high-grade serous carcinoma (HGSC) samples. CONCLUSION: A prognostic model related to lactate metabolism was established for OV based on six genes (CCL18, CCND1, MXRA5, NRBP2, OLFML2B and THY1) that could provide new insights into therapy.

Emerging roles of lactate in acute and chronic inflammation.

Traditionally, lactate has been considered a 'waste product' of cellular metabolism. Recent findings have shown that lactate is a substance that plays an indispensable role in various physiological cellular functions and contributes to energy metabolism and signal transduction during immune and inflammatory responses. The discovery of lactylation further revealed the role of lactate in regulating inflammatory processes. In this review, we comprehensively summarize the paradoxical characteristics of lactate metabolism in the inflammatory microenvironment and highlight the pivotal roles of lactate homeostasis, the lactate shuttle, and lactylation ('lactate clock') in acute and chronic inflammatory responses from a molecular perspective. We especially focused on lactate and lactate receptors with either proinflammatory or anti-inflammatory effects on complex molecular biological signalling pathways and investigated the dynamic changes in inflammatory immune cells in the lactate-related inflammatory microenvironment. Moreover, we reviewed progress on the use of lactate as a therapeutic target for regulating the inflammatory response, which may provide a new perspective for treating inflammation-related diseases.

Role of lactate and lactate metabolism in liver diseases (Review).

Lactate is a byproduct of glycolysis, and before the Warburg effect was revealed (in which glucose can be fermented in the presence of oxygen to produce lactate) it was considered a metabolic waste product. At present, lactate is not only recognized as a metabolic substrate that provides energy, but also as a signaling molecule that regulates cellular functions under pathophysiological conditions. Lactylation, a post­translational modification, is involved in the development of various diseases, including inflammation and tumors. Liver disease is a major health challenge worldwide. In normal liver, there is a net lactate uptake caused by gluconeogenesis, exhibiting a higher net lactate clearance rate compared with any other organ. Therefore, abnormalities of lactate and lactate metabolism lead to the development of liver disease, and lactate and lactate metabolism­related genes can be used for predicting the prognosis of liver disease. Targeting lactate production, regulating lactate transport and modulating lactylation may be potential treatment approaches for liver disease. However, currently there is not a systematic review that summarizes the role of lactate and lactate metabolism in liver diseases. In the present review, the role of lactate and lactate metabolism in liver diseases including liver fibrosis, non­alcoholic fatty liver disease, acute liver failure and hepatocellular carcinoma was summarized with the aim to provide insights for future research.

Different lactate metabolism subtypes reveal heterogeneity in clinical outcomes and immunotherapy in lung adenocarcinoma patients.

Background: The excessive accumulation of lactate within the tumor microenvironment (TME) has been demonstrated to facilitate tumor advancement and evade the immune system. Nonetheless, the metabolic status of lactate in lung adenocarcinoma (LUAD) remains uncertain. Method: By analyzing the transcriptome profile of patients with LUAD, we created a lactate metabolism score (LMS) to predict survival. We then conducted a comprehensive examination of the biological functions and immune infiltration among different LMS patient groups. Moreover, we assessed the LMS predictive efficacy in chemotherapy and immunotherapy. Finally, we validated the detrimental phenotypic effects of SLC16A3 on LUAD cell lines (PC9 and A549) through in vitro experiments. We collected clinical samples to assess the prognostic impact of SLC16A3. Results: We constructed an LMS model with 6 lactate metabolism regulatory factors using LASSO regression. The high LMS model indicates worse clinical outcomes for LUAD patients. High LMS patients are associated with metabolic dysregulation and increased infiltration of M0 and M1 macrophages. Low LMS patients are related to upregulated citric acid metabolism pathways and memory immune cells. High LMS patients are suitable for traditional chemotherapy, while patients with low LMS are more likely to benefit from immunotherapy. Lastly, downregulating SLC16A3 significantly reduces the proliferative and invasive capabilities of LUAD cell lines. Clinical cohort shows that patients with high expression of SLC16A3 have a worse prognosis. Conclusions: The LMS model constructed based on the lactate metabolism pathway displays high effectiveness in predicting the outcome of patients with LUAD. LMS can offer direction regarding chemotherapy as well as immunotherapy in LUAD.

Identification of a lactate metabolism-related lncRNAs signature for predicting the prognosis in patients with kidney renal clear cell carcinoma.

Background: Lactate metabolism-related (LMR) long noncoding RNAs (lncRNAs) play significant roles in various cancers, but their impact on kidney renal clear cell carcinoma (KIRC) remains unclear. This study aimed to explore the value of LMR lncRNA and develop a risk model for KIRC. Methods: Data on KIRC patients were downloaded from The Cancer Genome Atlas (TCGA) database. LMR lncRNAs were identified by co-expression, univariate and multivariate analyses, and least absolute shrinkage selection operator (LASSO) regression analysis. Subsequently, a prognostic signature was constructed and its accuracy was verified. To predict the prognosis of KIRC effectively, we established a nomogram based on this information. Enrichment analysis, tumor mutational burden (TMB) analysis, immune status and the therapeutic sensitivities of KIRC patients were also investigated. Quantitative real-time polymerase chain reaction (qRT-PCR) was performed to detect the expression of lncRNAs. Results: We constructed and verified a predictive signature based on six LMR lncRNA (LINC00944, AC090772.3, Z83745.1, AP001267.3, AC092296.1, and AL162377.1) to assess the patient prognoses of KIRC. Survival analyses showed a more unfavorable outcome in high-risk patients (P<0.001). Enrichment analysis demonstrated that immune-related pathways were enriched in the high-risk group. Besides, patients classified by risk scores had distinguishable immune status, TMB, response to immunotherapy, and sensitivity to chemotherapy and targeted drugs. Conclusions: The LMR lncRNAs signature has significant implications for prognostic assessment and clinical treatment guidance in KIRC.

Lactate oxidation in Paracoccus denitrificans.

Paracoccus denitrificans has a classical cytochrome-dependent electron transport chain and two alternative oxidases. The classical transport chain is very similar to that in eukaryotic mitochondria. Thus, P. denitrificans can serve as a model of the mammalian mitochondrion that may be more tractable in elucidating mechanisms of regulation of energy production than are mitochondria. In a previous publication we reported detailed studies on respiration in P. denitrificans grown aerobically on glucose or malate. We noted that P. denitrificans has large stores of lactate under various growth conditions. This is surprising because P. denitrificans lacks an NAD+-dependent lactate dehydrogenase. The aim of this study was to investigate the mechanisms of lactate oxidation in P. denitrificans. We found that the bacterium grows well on either d-lactate or l-lactate. Growth on lactate supported a rate of maximum respiration that was equal to that of cells grown on glucose or malate. We report proteomic, metabolomic, and biochemical studies that establish that the metabolism of lactate by P. denitrificans is mediated by two non-NAD+-dependent lactate dehydrogenases. One prefers d-lactate over l-lactate (D-iLDH) and the other prefers l-lactate (L-iLDH). We cloned and produced the D-iLDH and characterized it. The Km for d-lactate was 34 µM, and for l-lactate it was 3.7 mM. Pyruvate was not a substrate, rendering the reaction unidirectional with lactate being converted to pyruvate for entry into the TCA cycle. The intracellular lactate was ∼14 mM such that both isomers could be metabolized by the enzyme. The enzyme has 1 FAD per molecule and utilizes a quinone rather than NAD + as an electron acceptor. D-iLDH provides a direct entry of lactate reducing equivalents into the cytochrome chain, potentially explaining the high respiratory capacity of P. denitrificans in the presence of lactate.