Activation Of Metabolic Pathways Research Articles

Co-metabolism driven sulfaquinoxaline removal in microbial electrolysis cells: A mechanistic analysis based on DFT calculation, metabolic pathway and functional enzyme activity

Sulfaquinoxaline (SQX) is a vital sulfonamide antibiotic for the treatment of livestock infections. Although its degradation has become a research focus in the last few years, its biodegradation process and biometabolic mechanisms were rarely studied. In this research, microbial electrolytic cells (MECs) were used to remove SQX using sodium acetate as a co-substrate. The findings indicated that the maximum removal rate of SQX for a 5-day period in co-substrate reached 94.2%, and SQX exhibited a lower resistance (45.64 Ω) and had a larger double-layer area than a single substrate (95.03 Ω). LCMS/MS analysis suggested that SQX could eventually degrade to 17 byproducts through hydrolysis, hydroxylation, N rearrangement and sulfur reduction. Density flooding theory (DFT) calculations showed that sodium acetate addition weakened the N-C bond and facilitated hydroxylation. Meanwhile, the fluorescence composition of fDOM shifted from protein-like to humic acid-like with co-substrate supplementation. Microbial community analysis showed that Actinobacteria, Proteobacteria, Bacteroidota and Chloroflexi were highly correlated with SQX removal. Analysis of the metabolic pathways showed that key enzymes such as NAT, CAT, DHP and NAD+ were involved in the degradation of SQX. Biotoxicity tests indicated that weak electrical stimulation was beneficial in reducing biotoxicity. These results provide theoretical basis and new insights into the bioelectrochemical degradation of SQX.

Metabolomic landscape of macrophage discloses an anabolic signature of dengue virus infection and antibody-dependent enhancement of viral infection.

Dengue virus (DENV) infection causes dengue fever, the most prevalent arthropod-transmitted viral disease worldwide. Viruses are acellular parasites and obligately rely on host cell machinery for reproduction. Previous studies have indicated metabolomic changes in endothelial cell models and sera of animal models and patients with dengue fever. To probe the immunometabolic mechanism of DENV infection, here, we report the metabolomic landscape of a human macrophage cell model of DENV infection and its antibody-dependent enhancement. DENV infection of THP-1-derived macrophages caused 202 metabolic variants, of which amino acids occupied 23.7%, fatty acids 21.78%, carbohydrates 10.4%, organic acids 13.37%, and carnitines 10.4%. These metabolomic changes indicated an overall anabolic signature, which was characterized by the global exhaustion of amino acids, increases of cellular fatty acids, carbohydrates and pentoses, but decreases of acylcarnitine. Significant activation of metabolic pathways of glycolysis, pentose phosphate, amino acid metabolism, and tricarboxylic acid cycle collectively support the overall anabolism to meet metabolic demands of DENV replication and immune activation by viral infection. Totally 88 of 202 metabolic variants were significantly changed by DENV infection, 36 of which met the statistical standard (P<0.05, VIP>1.5) of differentially expressed metabolites, which were the predominantly decreased variants of acylcarnitine and the increased variants of fatty acids and carbohydrates. Remarkably, 11 differentially expressed metabolites were significantly distinct between DENV only infection and antibody-dependent enhancement of viral infection. Our data suggested that the anabolic activation by DENV infection integrates the viral replication and anti-viral immune activation.

Long chain monomethyl branched-chain fatty acid levels in human milk vary with gestational weight gain

Breastfeeding is an important determinant of infant health and there is immense interest in understanding its metabolite composition so that key beneficial components can be identified. The aim of this research was to measure the fatty acid composition of human milk in an Irish cohort where we examined changes depending on lactation stage and gestational weight gain trajectory. Utilizing a chromatography approach optimal for isomer separation, we identified 44 individual fatty acid species via GCMS and showed that monomethyl branched-chain fatty acids(mmBCFA's), C15:0 and C16:1 are lower in women with excess gestational weight gain versus low gestational weight gain. To further explore the potential contribution of the activity of endogenous metabolic pathways to levels of these fatty acids in milk, we administered D2O to C57BL/6J dams fed a purified lard based high fat diet (HFD) or low-fat diet during gestation and quantified the total and de novo synthesized levels of fatty acids in their milk. We found that de novo synthesis over three days can account for between 10 and 50 % of mmBCFAs in milk from dams on the low-fat diet dependent on the branched-chain fatty acid species. However, HFD fed mice had significantly decreased de novo synthesized fatty acids in milk resulting in lower total mmBCFAs and medium chain fatty acid levels. Overall, our findings highlight the diverse fatty acid composition of human milk and that human milk mmBCFA levels differ between gestational weight gain phenotypes. In addition, our data indicates that de novo synthesis contributes to mmBCFA levels in mice milk and thus may also be a contributory factor to mmBCFA levels in human milk. Given emerging data indicating mmBCFAs may be beneficial components of milk, this study contributes to our knowledge around the phenotypic factors that may impact their levels.

Identification and validation of hub genes and molecular classifications associated with chronic myeloid leukemia.

Chronic myeloid leukemia (CML) is a kind of malignant blood tumor, which is prone to drug resistance and relapse. This study aimed to identify novel diagnostic and therapeutic targets for CML. Differentially expressed genes (DEGs) were obtained by differential analysis of the CML cohort in the GEO database. Weighted gene co-expression network analysis (WGCNA) was used to identify CML-related co-expressed genes. Least absolute shrinkage and selection operator (LASSO) regression analysis was used to screen hub genes and construct a risk score model based on hub genes. Consensus clustering algorithm was used for the identification of molecular subtypes. Clinical samples and in vitro experiments were used to verify the expression and biological function of hub genes. A total of 378 DEGs were identified by differential analysis. 369 CML-related genes were identified by WGCNA analysis, which were mainly enriched in metabolism-related signaling pathways. In addition, CML-related genes are mainly involved in immune regulation and anti-tumor immunity, suggesting that CML has some immunodeficiency. Immune infiltration analysis confirmed the reduced infiltration of immune killer cells such as CD8+ T cells in CML samples. 6 hub genes (LINC01268, NME8, DMXL2, CXXC5, SCD and FBN1) were identified by LASSO regression analysis. The receiver operating characteristic (ROC) curve confirmed the high diagnostic value of the hub genes in the analysis and validation cohorts, and the risk score model further improved the diagnostic accuracy. hub genes were also associated with cell proliferation, cycle, and metabolic pathway activity. Two molecular subtypes, Cluster A and Cluster B, were identified based on hub gene expression. Cluster B has a lower risk score, higher levels of CD8+ T cell and activated dendritic cell infiltration, and immune checkpoint expression, and is more sensitive to commonly used tyrosine kinase inhibitors. Finally, our clinical samples validated the expression and diagnostic efficacy of hub genes, and the knockdown of LINC01268 inhibited the proliferation of CML cells, and promoted apoptosis. Through WGCNA analysis and LASSO regression analysis, our study provides a new target for CML diagnosis and treatment, and provides a basis for further CML research.

Understanding microbiome dynamics and functional responses during acidogenic fermentation of sucrose and sugarcane vinasse through metatranscriptomic analysis

Improving anaerobic digestion of sugarcane vinasse – a high-strength wastewater from ethanol distillation – is a subject of great interest, in view of the reduction of the pollutants and recovery of methane and valuable metabolites as byproducts. Through metatranscriptomic analysis, this study evaluated the active microbiome and metabolic pathways in a continuous acidogenic reactor: Stage 1S (control): 100% sucrose-based substrate (SBS); Stage 2SV (acclimation): 50% SBS and 50% vinasse; Stage 3V: 100% vinasse. Metatranscriptome obtained from each Stage was subjected to taxonomic and functional annotations. Under SBS feeding, pH dropped to pH 2.7 and biohydrogen production was observed. As vinasse was added, pH increased to 4.1–4.5, resulting in community structure and metabolite changes. In Stage 3V, biohydrogen production ceased, and propionate and acetate prevailed among the volatile fatty acids. Release of homoacetogenesis enzymes by Clostridium ljungdahlii and of uptake hydrogenase (EC 1.12.99.6) by Pectinatus frisingensis were linked to hydrogen consumption in Stages 2SV and 3V. Metabolic pathways of vinasse compounds, such as carbohydrates, malate, oxalate, glycerol, sulfate and phenol, were investigated in detail. In pyruvate metabolism, gene transcripts of oadA (oxaloacetate decarboxylase) and mdh (malate dehydrogenase), were upregulated in Stage 3V, being mostly attributed to P. frisingensis. Acetate formation from vinasse degradation was mainly attributed to Megasphaera and Clostridium, and propionate formation to P. frisingensis. Glycerol removal from vinasse exceeded 99%, and gene transcripts encoding for glpF (glycerol uptake facilitator protein), glpK (glycerol kinase) and glpABC (glycerol-3-phosphate dehydrogenase) were expressed mostly by Pectinatus and Prevotella. mRNA profiling showed that active bacteria and gene expression greatly changed when vinasse replaced sucrose, and Pectinatus was the main active bacterium degrading the searched compounds from vinasse. The identification of the main metabolic routes and the associated microorganisms achieved in this work contributes with valuable information to support further optimization of fermentation towards the desired metabolites.

Immune, metabolic landscapes of prognostic signatures for lung adenocarcinoma based on a novel deep learning framework

Lung adenocarcinoma (LUAD) is a malignant tumor with high lethality, and the aim of this study was to identify promising biomarkers for LUAD. Using the TCGA-LUAD dataset as a discovery cohort, a novel joint framework VAEjMLP based on variational autoencoder (VAE) and multilayer perceptron (MLP) was proposed. And the Shapley Additive Explanations (SHAP) method was introduced to evaluate the contribution of feature genes to the classification decision, which helped us to develop a biologically meaningful biomarker potential scoring algorithm. Nineteen potential biomarkers for LUAD were identified, which were involved in the regulation of immune and metabolic functions in LUAD. A prognostic risk model for LUAD was constructed by the biomarkers HLA-DRB1, SCGB1A1, and HLA-DRB5 screened by Cox regression analysis, dividing the patients into high-risk and low-risk groups. The prognostic risk model was validated with external datasets. The low-risk group was characterized by enrichment of immune pathways and higher immune infiltration compared to the high-risk group. While, the high-risk group was accompanied by an increase in metabolic pathway activity. There were significant differences between the high- and low-risk groups in metabolic reprogramming of aerobic glycolysis, amino acids, and lipids, as well as in angiogenic activity, epithelial-mesenchymal transition, tumorigenic cytokines, and inflammatory response. Furthermore, high-risk patients were more sensitive to Afatinib, Gefitinib, and Gemcitabine as predicted by the pRRophetic algorithm. This study provides prognostic signatures capable of revealing the immune and metabolic landscapes for LUAD, and may shed light on the identification of other cancer biomarkers.

EMPathways2: Estimation of Enzyme Expression and Metabolic Pathway Activity Using RNA-Seq Reads.

In this chapter, we outline an approach to analyzing metatranscriptomic data, focusing on the assessment of differential enzyme expression and metabolic pathway activities using a novel bioinformatics software tool, EMPathways2. The analysis pipeline commences with raw data originating from a sequencer and concludes with an output of enzyme expressions and an estimate of metabolic pathway activities. The initial step involves aligning specific transcriptomes assembled from RNA-Seq data using Bowtie2 and acquiring gene expression data with IsoEM2. Subsequently, the pipeline proceeds to quality assessment and preprocessing of the input data, ensuring accurate estimates of enzymes and their differential regulation. Upon completion of the preprocessing stage, EMPathways2 is employed to decipher the intricate relationships between genes, enzymes, and pathways. An online repository containing sample data has been made available, alongside custom Python scripts designed to modify the output of the programs within the pipeline for diverse downstream analyses. This chapter highlights the technical aspects and practical applications of using EMPathways2, which facilitates the advancement of transcriptome data analysis and contributes to a deeper understanding of the complex regulatory mechanisms underlying living systems.

Metabolic Heterogeneity of Tumor Cells and its Impact on Colon Cancer Metastasis: Insights from Single-Cell and Bulk Transcriptome Analyses.

Background: Metabolic reprogramming plays a crucial role in the development of colorectal cancer (CRC), influencing tumor heterogeneity, the tumor microenvironment, and metastasis. While the interaction between metabolism and CRC is critical for developing personalized treatments, gaps remain in understanding how tumor cell metabolism affects prognosis. Our study introduces novel insights by integrating single-cell and bulk transcriptome analyses to explore the metabolic landscape within CRC cells and its mechanisms influencing disease progression. This approach allows us to uncover metabolic heterogeneity and identify specific metabolic genes impacting metastasis, which have not been thoroughly examined in previous studies. Methods: We sourced microarray and single-cell RNA sequencing datasets from the Gene Expression Omnibus (GEO) and bulk sequencing data for CRC from The Cancer Genome Atlas (TCGA). We employed Gene Set Variation Analysis (GSVA) to assess metabolic pathway activity, consensus clustering to identify CRC-specific transcriptome subtypes in bulkseq, and rigorous quality controls, including the exclusion of cells with high mitochondrial gene expression in scRNA seq. Advanced analyses such as AUCcell, infercnvCNV, Non-negative Matrix Factorization (NMF), and CytoTRACE were utilized to dissect the cellular landscape and evaluate pathway activities and tumor cell stemness. The hdWGCNA algorithm helped identify prognosis-related hub genes, integrating these findings using a random forest machine learning model. Results: Kaplan-Meier survival curves identified 21 significant metabolic pathways linked to prognosis, with consensus clustering defining three CRC subtypes (C3, C2, C1) based on metabolic activity, which correlated with distinct clinical outcomes. The metabolic activity of the 13 cell subpopulations, particularly the epithelial cell subpopulation with active metabolic levels, was evaluated using AUCcell in scRNA seq. To further analyze tumor cells using infercnv, NMF disaggregated these cells into 10 cellular subpopulations. Among these, the C2 subpopulation exhibited higher stemness and tended to have a poorer prognosis compared to C6 and C0. Conversely, the C8, C3, and C1 subpopulations demonstrated a higher level of the five metabolic pathways, and the C3 and C8 subpopulations tended to have a more favorable prognosis. hdWGCNA identified 20 modules, from which we selected modules primarily expressed in high metabolic tumor subgroups and highly correlated with clinical information, including blue and cyan. By applying variable downscaling of RF to a total of 50 hub genes, seven gene signatures were obtained. Furthermore, molecules that were validated to be protective in GEO were screened alongside related molecules, resulting in the identification of prognostically relevant molecules such as UQCRFS1 and GRSF1. Additionally, the expression of GRSF1 was examined in colon cancer cell lines using qPCR and phenotypically verified by in vitro experiments. Conclusion: Our findings emphasize that high activity in specific metabolic pathways, including pyruvate metabolism and the tricarboxylic acid cycle, correlates with improved colon cancer outcomes, presenting new avenues for metabolic-based therapies. The identification of hub genes like GRSF1 and UQCRFS1 and their link to favorable metabolic profiles offers novel insights into tumor neovascularization and metastasis, with significant clinical implications for targeting metabolic pathways in CRC therapy.

The therapeutic and biomarker significance of ferroptosis in chronic myeloid leukemia.

The relationship between ferroptosis and the progression and treatment of hematological tumors has been extensively studied, although its precise association with chronic myeloid leukemia (CML) remains uncertain. Multi-transcriptome sequencing data were utilized to analyze the ferroptosis level of CML samples and its correlation with the tumor microenvironment, disease progression, and treatment response. Machine learning algorithms were employed to identify diagnostic ferroptosis-related genes (FRGs). The consensus clustering algorithm was applied to identify ferroptosis-related molecular subtypes. Clinical samples were collected for sequencing to validate the results obtained from bioinformatics analysis. Cell experiments were conducted to investigate the therapeutic efficacy of induced ferroptosis in drug-resistant CML. Ferroptosis scores were significantly lower in samples from patients with CML compared to normal samples, and these scores further decreased with disease progression and non-response to treatment. Most FRGs were downregulated in CML samples. A high ferroptosis score was also associated with greater immunosuppression and increased activity of metabolic pathways. Through support vector machine recursive feature elimination (SVM-RFE), least absolute shrinkage selection operator (LASSO), and random forest (RF) algorithms, we identified five FRGs (ACSL6, SLC11A2, HMOX1, SLC38A1, AKR1C3) that have high diagnostic value. The clinical diagnostic value of these five FRGs and their effectiveness in differentiating CML from other hematological malignancies were validated using additional validation cohorts and our real-world cohort. There are significant differences in immune landscape, chemosensitivity, and immunotherapy responsiveness between the two ferroptosis-related molecular subtypes. By conducting cellular experiments, we confirmed that CML-resistant cells are more sensitive to induction of ferroptosis and can enhance the sensitivity of imatinib treatment. Our study unveils the molecular signature of ferroptosis in samples from patients with CML. FRG identified by a variety of machine learning algorithms has reliable clinical diagnostic value. Furthermore, the characterization of different ferroptosis-related molecular subtypes provides valuable insights into individual patient characteristics and can guide clinical treatment strategies. Targeting and inducing ferroptosis holds great promise as a therapeutic approach for drug-resistant CML.

Metatranscriptomic insights into the microbial metabolic activities during an Ulva prolifera green tide in coastal Qingdao areas

Green tide, a typical marine environmental disaster that profoundly influenced the coastal areas, has been occurred consecutively in the South Yellow Sea of China since 2007. Herein, the active microbial community structure and metabolic pathways in Qingdao offshore during an Ulva prolifera green tide were investigated by using metatranscriptomic approach. The dominant active microbial taxa at the outbreak phase were primarily a functional group that can utilize organic matters derived from U. prolifera, such as Lentibacter, Polaribacter and Planktomarina. While the taxa involved in biogeochemical cycles, including Phaeobacter, Pseudomonas and Marinobacterium, dominated the active microbial communities at the decline phase. The expression level of enzymes involved in U. prolifera polysaccharides degradation was significantly higher at the outbreak phase compared to the decline phase. At the same time, the main players Glaciecola and Polarbacter showed similar trends, suggesting that the low competitiveness for nutrients of related microorganisms at this phase made them degrade more U. prolifera polysaccharides to meet their own nutrient needs, thereby accelerating the degradation of U. prolifera. According to KEGG annotation, the biogeochemical pathways including nitrogen cycle, sulfur cycle and methane oxidation altered during the green tide, with thiosulfate oxidation and methane oxidation probably being the crucial pathways at the outbreak and the decline phase respectively. The coupling of sulfide oxidation and denitrification was also observed in this study. Furthermore, the green tide in Qingdao offshore might impact the greenhouse effects induced by CH4 and N2O through influencing the related microbial processes.

Inferring secretory and metabolic pathway activity from omic data with secCellFie

Understanding protein secretion has considerable importance in biotechnology and important implications in a broad range of normal and pathological conditions including development, immunology, and tissue function. While great progress has been made in studying individual proteins in the secretory pathway, measuring and quantifying mechanistic changes in the pathway's activity remains challenging due to the complexity of the biomolecular systems involved. Systems biology has begun to address this issue with the development of algorithmic tools for analyzing biological pathways; however most of these tools remain accessible only to experts in systems biology with extensive computational experience. Here, we expand upon the user-friendly CellFie tool which quantifies metabolic activity from omic data to include secretory pathway functions, allowing any scientist to infer properties of protein secretion from omic data. We demonstrate how the secretory expansion of CellFie (secCellFie) can help predict metabolic and secretory functions across diverse immune cells, hepatokine secretion in a cell model of NAFLD, and antibody production in Chinese Hamster Ovary cells.

Effect of Air Drying on the Metabolic Profile of Fresh Wild and Artificial Cordyceps sinensis.

Fresh and dried Cordyceps sinensis are widely used by the public for medicinal and health purposes. However, the differences between them have not been examined. In this study, fresh wild and artificial C. sinensis (WFC and AFC) were dried to obtain dried wild and artificial C. sinensis (WDC and ADC). Non-targeted GC-MS was used to analyze the metabolic profile characteristics of the four groups of samples. The results showed that air drying significantly altered the composition and content of C. sinensis, mainly in the form of higher abundance of organic acids and derivatives and lower abundance of lipids and lipid-like molecules in fresh C. sinensis. Hierarchical cluster analysis (HCA) and quantitative analyses showed that air drying increased the abundance of Valine, Zinniol, Urocanate, Vulpinic acid, and Uridine 5'-diphosphate, and decreased Xanthotoxol, Vitexin-4-o-glucoside, Val-trp, and Wogonin. These differentially accumulated metabolites (DAMs) were also shown to be potential biomarkers for C. sinensis. KEGG enrichment analysis identified lysine biosynthesis as the most significantly enriched pathway. Annotation of these DAMs to lysine biosynthesis revealed that citrate cycle and pyruvate metabolism entered lysine biosynthesis via 2-oxohlutarate and Homocitrate, respectively, resulting in significant enrichment of L-saccharopine and L-lysine content was significantly higher. Alanine, aspartate, and Glutamate metabolism synthesized more L-aspartate to promote L-lysine synthesis. Thus, high levels of L-lysine result in lysine degradation and pymolysine, which are the most active metabolic pathways during the drying of fresh C. sinensis and indirectly lead to differences in metabolic profiles.

Variety-driven rhizosphere microbiome bestows differential salt tolerance to alfalfa for coping with salinity stress.

Soil salinization is a global environmental issue and a significant abiotic stress that threatens crop production. Root-associated rhizosphere microbiota play a pivotal role in enhancing plant tolerance to abiotic stresses. However, limited information is available concerning the specific variations in rhizosphere microbiota driven by different plant genotypes (varieties) in response to varying levels of salinity stress. In this study, we compared the growth performance of three alfalfa varieties with varying salt tolerance levels in soils with different degrees of salinization. High-throughput 16S rRNA and ITS sequencing were employed to analyze the rhizosphere microbial communities. Undoubtedly, the increasing salinity significantly inhibited alfalfa growth and reduced rhizosphere microbial diversity. However, intriguingly, salt-tolerant varieties exhibited relatively lower susceptibility to salinity, maintaining more stable rhizosphere bacterial community structure, whereas the reverse was observed for salt-sensitive varieties. Bacillus emerged as the dominant species in alfalfa's adaptation to salinity stress, constituting 21.20% of the shared bacterial genera among the three varieties. The higher abundance of Bacillus, Ensifer, and Pseudomonas in the rhizosphere of salt-tolerant alfalfa varieties is crucial in determining their elevated salt tolerance. As salinity levels increased, salt-sensitive varieties gradually accumulated a substantial population of pathogenic fungi, such as Fusarium and Rhizoctonia. Furthermore, rhizosphere bacteria of salt-tolerant varieties exhibited increased activity in various metabolic pathways, including biosynthesis of secondary metabolites, carbon metabolism, and biosynthesis of amino acids. It is suggested that salt-tolerant alfalfa varieties can provide more carbon sources to the rhizosphere, enriching more effective plant growth-promoting bacteria (PGPB) such as Pseudomonas to mitigate salinity stress. In conclusion, our results highlight the variety-mediated enrichment of rhizosphere microbiota in response to salinity stress, confirming that the high-abundance enrichment of specific dominant rhizosphere microbes and their vital roles play a significant role in conferring high salt adaptability to these varieties.

Detrimental impact of sulfide on the seagrass Zostera marina in dark hypoxia

Sulfide poisoning, hypoxia events, and reduced light availability pose threats to marine ecosystems such as seagrass meadows. These threats are projected to intensify globally, largely due to accelerating eutrophication of estuaries and coastal environments. Despite the urgency, our current comprehension of the metabolic pathways that underlie the deleterious effects of sulfide toxicity and hypoxia on seagrasses remains inadequate. To address this knowledge gap, I conducted metabolomic analyses to investigate the impact of sulfide poisoning under dark-hypoxia in vitro conditions on Zostera marina, a vital habitat-forming marine plant. During the initial 45 minutes of dark-hypoxia exposure, I detected an acclimation phase characterized by the activation of anaerobic metabolic pathways and specific biochemical routes that mitigated hypoxia and sulfide toxicity. These pathways served to offset energy imbalances, cytosolic acidosis, and sulfide toxicity. Notably, one such route facilitated the transformation of toxic sulfide into non-toxic organic sulfur compounds, including cysteine and glutathione. However, this sulfide tolerance mechanism exhibited exhaustion post the initial 45-minute acclimation phase. Consequently, after 60 minutes of continuous sulfide exposure, the sulfide toxicity began to inhibit the hypoxia-mitigating pathways, culminating in leaf senescence and tissue degradation. Utilizing metabolomic approaches, I elucidated the intricate metabolic responses of seagrasses to sulfide toxicity under in vitro dark-hypoxic conditions. My findings suggest that future increases in coastal eutrophication will compromise the resilience of seagrass ecosystems to hypoxia, primarily due to the exacerbating influence of sulfide.

Lipid characteristics of lung tissue in silicosis rat model were studied based on lipid metabolomics

Silicosis is a common occupational disease caused by the long-term inhalation of large amounts of silica dust. Lipid metabolism plays an important role in the progression of silicosis, but its contributing mechanism remains unclear. The aim of this study was to investigate the differential lipid metabolites and active metabolic pathways in silicosis rat lung tissue. We first constructed a silicosis rat model, and randomly divided 24 male SD rats into control group (C), silicosis group for 1 week (S1W), silicosis group for 2 weeks (S2W) and silicosis group for 4 weeks (S4W) with 6 rats in each group. 1 mL SiO2 suspension (50 mg/mL) or normal saline were injected into the trachea, and the rats were killed at 1 week, 2 weeks and 4 weeks, respectively. The lung tissue pathology of the rats was observed by HE staining and VG staining, and the plasma TC and FC levels were detected by the kit. Western blot was used to detect the expression of lipid-related factors CD36, PGC1α and LXR. In addition, lipidomics analysis of lung tissue samples was performed using UPLC-IMS-QTOF mass spectrometer to screen out potential differential metabolites in silicosis models and analyze lipid enrichment, and verified the expression of differential gene CHPT1 in the metabolic pathway. HE and VG staining showed that the number of nodules and fibrosis increased in a time-dependent manner in the silicosis model group, and the levels of TC, FC and CE in silicosis plasma increased. Western blot results showed that PGC1α and LXR decreased in the silicosis model group, while CD36 expression increased. In addition, metabolomics screened out 28 differential metabolites in the S1W group, 32 in the S2W group, and 22 in the S4W group, and found that the differential metabolites were mainly enriched in metabolic pathways such as glycerophospholipid metabolism and ether lipid metabolism, and the expression of differential gene CHPT1 in the metabolic pathway was decreased in the silicosis model group. These results suggest that there are significant changes in lipid metabolites in lung tissue in silicosis rat models, and glycerophospholipid metabolism was significantly enriched, suggesting that glycerophospholipids play an important role in the progression of silicosis. The differential metabolites and pathways reported in this study may provide new ideas for the pathogenesis of silicosis.

WU-NK-101 (W-NK), a Memory-like (ML) NK Cell, Intrinsically Overcomes Factors Restricting Adoptive Cell Therapy (ACT) in Acute Myeloid Leukemia (AML)

ML-NK cells are generated from conventional (c) NK cells activated with cytokines IL-12/15/18 which results in enhanced fitness, in vivo persistence, and function against tumor targets. While effective in early phase clinical trials, a 1 donor to 1 recipient cell product hinders commercial development as a cellular therapeutic. Here, W-NK is a cytokine-reprogrammed, expanded, cryopreserved ML-NK product derived from healthy human peripheral blood (PBMC) cNK cells, with scalable manufacturability. W-NKs harness the benefits of ML-NKs, and further overcome challenges previously reported for adoptive cell therapy (ACT) in AML: antigen escape, limited tumor trafficking/infiltration, and restricted function in tumor microenvironment (TME). W-NK characterization was performed by scRNA-seq (10X Genomics) and multidimensional flow and mass cytometry.Functionality was evaluated by in vitro cytotoxicity assays against AML cell lines (THP-1, and HL-60) and bioenergetics assays (Seahorse XF), under different conditions: conventional (N) media (pH 7, glucose 11 mM), or two models for TME (TME-aligned media - pH 5.9, glucose 6 mM, immune suppressive agents, e.g., PGE2, TGFb1, and malignant ascites), and in hypoxia (0.1% O 2; AVATAR). In vivo efficacy was evaluated in NSG mice bearing THP-1 AML tumors. Proteomic data was acquired by tandem-mass spectrometry (Sciex Zeno TOF 7600). Cell trafficking was measured by fluorescent-labeled W-NK in NSG mice. W-NK cells are transcriptionally distinct from cNKs. Sc-RNA-seq analysis revealed important transcriptional and compositional differences compared to cNK cells, including novel NK clusters expressing metabolic ( GLUL), proliferation-associated ( Ki67) and IL-15-inducible genes ( TXNIP) known to enhance NK responses. Additionally, W-NKs express higher levels of activating receptors ( i.e. 2B4, DNAM-1, NKG2D, NKp30, etc.), cytotoxic effector proteins ( i.e. granzyme B), and lower levels of inhibitory receptors leading to increased potency in vitro against HL-60 cells (mEC50 1.7 and 5.2; p= 0.032; W-NK vs. cNK respectively) and demonstrated a dose dependent reduction of tumor burden in THP-1 xenografts ( p=0.015). W-NK also express high levels of CXCR4 (81.6% positive, MFI 5552), contributing to improved bone marrow (BM) homing (16.5 ± 2.4%) compared to data previously reported for IL-2-expanded NK cells (3%; Sato et al.CCR. 2020). Once in the TME, unlike cNK and T-cells, W-NK cytotoxic function is preserved (TME-media vs. N-media-; W-NK: 96.5% vs. 88.2%, p=0.45; cNK: 44.1% vs.13.5%, p=0.009; T-cell: 53.2 % vs. 25.2%, p=0.041, respectively). This is potentially due to W-NK resistance to hypoxia, and improved metabolic fitness/flexibility. W-NK cytotoxic activity was also preserved in hypoxic conditions (90% vs. 89.5%, p=0.2) compared to cNK (24% vs. 6.9%, p=0.02; normoxia vs. hypoxia). Metabolically, W-NK express higher levels of cell surface nutrient transporters, implying better adaptation to adverse TMEs ( i.e. upregulation of amino acids, lactate, and pyruvate). This might enable utilization of diverse macronutrients, activation of metabolic pathways, and optimal ATP manufacturing, and is supported by upregulation of REACTOME pathways capturing amino-acid and lipid metabolism, as well as mitochondrial function. Lastly, W-NK did not attack cells from healthy human tissues including PBMCs, indicating that W-NK are not indiscriminate killers. This observation would predict low myelosuppression and a favorable safety profile. In summary, W-NK are effective against AML cells, robustly home to the BM, overcome hypoxia, immunosuppression, and nutritional scarcity to survive/function within the leukemia TME. Moreover, they are not restricted to a single antigen target thereby mitigating antigen escape. These features, which overcome challenges for ACT, herald the promise of NK cell therapy and augur positively for patients being treated with W-NK in a Phase 1 study in R/R AML (NCT# 05470140).

Enrichment in Metabolic Pathway Activation Corresponds to Immunoglobulin Gene Diversity across B Cell Developmental Stages in B-Lymphoblastic Leukemia

Background: Intraclonal genetic variation in the immunoglobulin heavy chain variable gene (IGHV) has been widely documented in B-lymphoblastic leukemia (B-ALL), but its biologic significance as a presumed reflection of the B cell stage of leukemic transformation has not been systematically evaluated. Using ultradeep, targeted sequencing of the rearranged IgH gene in 22 patients with B-ALL, we previously demonstrated that there are subsets of patients with either extensive or minimal diversity in recombination activating gene (RAG)-mediated variable (V), diversity (D), and joining (J) gene rearrangements and that gene expression profiling (GEP) reveals enrichment of oxidative phosphorylation (OXPHOS), glycolysis, and fatty acid metabolism Hallmark pathways among cases with V-DJ rearrangement diversity (Fries et al. ASH 2022). We observed enrichment in these same metabolic signatures among leukemia cells with a pre-B cell phenotypic signature found to be predictive of relapse (Good et al. Nat Med 2018; Liu et al. ASH 2021). To understand the functional implications of this metabolic activation observed among cases with IgH diversity, we applied cytometry by time of flight (CyTOF) phenotyping to determine if IgH clonal composition reflects the metabolic and proliferative activation inherent to B cell stage of origin which may have relevance to disease behavior and outcome. Methods: Using viably-frozen, pre-treatment bone marrow (BM) and/or peripheral blood (PB) biospecimens from a cohort of 18 patients with B-ALL with known IgH clonal composition (7 with extensive V-DJ diversity, 8 without diversity, and 3 lacking any dominant clonal sequence), we performed deep phenotyping by mass cytometry and compared signaling pathway activation patterns between cohorts and compared to healthy controls. Samples were analyzed by mass cytometry with a 40-marker panel including B cell developmental phenotypic and metabolic proteins. Developmental classification was performed as previously described (Good et al. 2018). To test statistical significance in frequency of each subpopulation between two groups, we applied a multiple unpaired t-test with correction by Holm-Sidak method. Results: We discovered that all leukemia samples - regardless of IgH clonal composition - were significantly enriched for phenotypic features reflecting a pro-BII cell state, whereas enrichment of other more or less mature cell stages varied according to IgH composition. Developmental classification demonstrated that cases lacking a clonal IgH rearrangement (N=3) - presumed to have derived from a B cell stage preceding an initial DJ joining event - were enriched at the pro-BII population (P&amp;lt;0.0001), as were cases with extensive V-DJ diversity (N=7; P=0.019), and those lacking in diversity (N=8; P=0.0007) compared to healthy BM. However, cases with V-DJ diversity were uniformly enriched for OXPHOS, glycolytic, and pentose phosphate pathway (PPP)-associated protein expression compared to those without V-DJ diversity. In cells with a pro-BII phenotype, differentially expressed proteins between diverse and non-diverse cases included proteins essential for glycolysis (GLUT1, ENO1, PKM1), PPP (PGD, TKT), and OXPHOS (SDHA, CS, ATPA5, CytC). Conclusions: Cellular metabolism is a known hallmark of cancer proliferation, leukemia stem cell maintenance, and therapeutic escape, just as genetic diversification is integral to tumor evolution. Using primary B-ALL specimens, we show that metabolic pathway activation coincides with extensive RAG-mediated IGHV gene diversification across B cell developmental phenotypes. Contrary to the canonical assumption in B-ALL that V-DJ rearrangement and diversity is tied to developmental state, our analysis suggests that all cases are enriched at the proB-preB transition, but with differences in cellular metabolism that distinguish the diverse from homogeneous rearrangement state. These data suggest that IgH composition is not merely a passive genetic signature exploitable for tracking purposes in B-ALL, but may also inherently reflect underlying biology relevant to clinical outcomes.

Integrative transcriptomics and proteomics profiling of Arabidopsis thaliana elucidates novel mechanisms underlying spaceflight adaptation

Spaceflight presents a unique environment with complex stressors, including microgravity and radiation, that can influence plant physiology at molecular levels. Combining transcriptomics and proteomics approaches, this research gives insights into the coordination of transcriptome and proteome in Arabidopsis’ molecular and physiological responses to Spaceflight environmental stress. Arabidopsis seedlings were germinated and grown in microgravity (µg) aboard the International Space Station (ISS) in NASA Biological Research in Canisters – Light Emitting Diode (BRIC LED) hardware, with the ground control established on Earth. At 10 days old, seedlings were frozen in RNA-later and returned to Earth. RNA-seq transcriptomics and TMT-labeled LC-MS/MS proteomic analysis of cellular fractionates from the plant tissues suggest the alteration of the photosynthetic machinery (PSII and PSI) in spaceflight, with the plant shifting photosystem core-regulatory proteins in an organ-specific manner to adapt to the microgravity environment. An overview of the ribosome, spliceosome, and proteasome activities in spaceflight revealed a significant abundance of transcripts and proteins involved in protease binding, nuclease activities, and mRNA binding in spaceflight, while those involved in tRNA binding, exoribonuclease activity, and RNA helicase activity were less abundant in spaceflight. CELLULOSE SYNTHASES (CESA1, CESA3, CESA5, CESA7) and CELLULOSE-LIKE PROTEINS (CSLE1, CSLG3), involved in cellulose deposition and TUBULIN COFACTOR B (TFCB) had reduced abundance in spaceflight. This contrasts with the increased expression of UDP-ARABINOPYRANOSE MUTASEs, involved in the biosynthesis of cell wall non-cellulosic polysaccharides, in spaceflight. Both transcripts and proteome suggested an altered polar auxin redistribution, lipid, and ionic intracellular transportation in spaceflight. Analyses also suggest an increased metabolic energy requirement for plants in Space than on Earth, hence, the activation of several shunt metabolic pathways. This study provides novel insights, based on integrated RNA and protein data, on how plants adapt to the spaceflight environment and it is a step further at achieving sustainable crop production in Space.

Microglial immunometabolism endophenotypes contribute to sex difference in Alzheimer's disease.

The molecular mechanisms that contribute to sex differences, in particular female predominance, in Alzheimer's disease (AD) prevalence, symptomology, and pathology, are incompletely understood. To address this problem, we investigated cellular metabolism and immune responses ("immunometabolism endophenotype") across AD individuals as a function of sex with diverse clinical diagnosis of cognitive status at death (cogdx), Braak staging, and Consortium to Establish a Registry for AD (CERAD) scores using human cortex metabolomics and transcriptomics data from the Religious Orders Study / Memory and Aging Project (ROSMAP) cohort. We identified sex-specific metabolites, immune and metabolic genes, and pathways associated with the AD diagnosis and progression. We identified female-specific elevation in glycerophosphorylcholine and N-acetylglutamate, which are AD inflammatory metabolites involved in interleukin (IL)-17 signaling, C-type lectin receptor, interferon signaling, and Toll-like receptor pathways. We pinpointed distinct microglia-specific immunometabolism endophenotypes (i.e., lipid- and amino acid-specific IL-10 and IL-17 signaling pathways) between female and male AD subjects. In addition, female AD subjects showed evidence of diminished excitatory neuron and microglia communications via glutamate-mediated immunometabolism. Our results point to new understanding of the molecular basis for female predominance in AD, and warrant future independent validations with ethnically diverse patient cohorts to establish a likely causal relationship of microglial immunometabolism in the sex differences in AD. Sex-specific immune metabolites, gene networks and pathways, are associated with Alzheimer's disease pathogenesis and disease progression. Female AD subjects exhibit microglial immunometabolism endophenotypes characterized by decreased glutamate metabolism and elevated interleukin-10 pathway activity. Female AD subjects showed a shift in glutamate-mediated cell-cell communications between excitatory neurons to microglia and astrocyte.

TMET-13. DNA DAMAGE SIGNALING ACTIVATES GTP SYNTHESIS TO PROMOTE GLIOBLASTOMA TREATMENT RESISTANCE

Abstract Glioblastoma (GBM) is the most common type of invasive brain tumor in adults and is uniformly fatal due to inherent resistance to radiation therapy (RT) and chemotherapy. Our group and others have found that metabolites can regulate DNA repair and therapy resistance in brain tumors, but little is known about how DNA damage regulates metabolic pathway activity in cancer. Here we show that following treatment with RT, GBMs increase rates of de novo guanylate synthesis in vitro and in orthotopic patient-derived xenograft models via signaling through the DNA repair protein DNA-PK. To determine if disrupting this regulation can augment GBM treatment efficacy, we combined an FDA-approved inhibitor of purine synthesis (mycophenolate mofetil, MMF) with chemoradiation in a variety of mouse models of GBM. Critically, targeting GTP synthesis improved the efficacy of both RT alone and chemoradiation in multiple patient-derived and syngeneic intracranial models. Our translational studies have shown that mycophenolic acid, the active metabolite of MMF, penetrates the blood-brain barrier at concentrations sufficient to inhibit GTP synthesis in GBMs of human patients. The phase 1 arms of our study, which are ongoing, are testing the safety and efficacy of combining MMF with standard of care chemoradiation for patients with primary and recurrent GBM.