Explore the vast range of titles available.

Exposure to ambient air particulate matter (PM 2.5 ) is well established as a risk factor for cardiovascular and pulmonary disease. Both epidemiologic and controlled exposure studies in humans and animals have demonstrated an association between air pollution exposure and metabolic disorders such as diabetes. Given the central role of the liver in peripheral glucose homeostasis, we exposed mice to filtered air or PM 2.5 for 16 weeks and examined its effect on hepatic metabolic pathways using stable isotope resolved metabolomics (SIRM) following a bolus of 13 C 6 -glucose. Livers were analyzed for the incorporation of 13 C into different metabolic pools by IC-FTMS or GC-MS. The relative abundance of 13 C-glycolytic intermediates was reduced, suggesting attenuated glycolysis, a feature found in diabetes. Decreased 13 C-Krebs cycle intermediates suggested that PM 2.5 exposure led to a reduction in the Krebs cycle capacity. In contrast to decreased glycolysis, we observed an increase in the oxidative branch of the pentose phosphate pathway and 13 C incorporations suggestive of enhanced capacity for the de novo synthesis of fatty acids. To our knowledge, this is one of the first studies to examine 13 C 6 -glucose utilization in the liver following PM 2.5 exposure, prior to the onset of insulin resistance (IR).

The roles of concealed microproteins encoded by long noncoding RNAs (lncRNAs) are gradually being exposed, but their functions in tumorigenesis are still largely unclear. Here, we identify and characterize a conserved 99-amino acid microprotein named KRASIM that is encoded by the putative lncRNA NCBP2-AS2. KRASIM is differentially expressed in normal hepatocytes and hepatocellular carcinoma (HCC) cells and can suppress HCC cell growth and proliferation. Mechanistically, KRASIM interacts and colocalizes with the KRAS protein in the cytoplasm of human HuH-7 hepatoma cells. More importantly, the overexpression of KRASIM decreases the KRAS protein level, leading to the inhibition of ERK signaling activity in HCC cells. These results demonstrate a novel microprotein repressor of the KRAS pathway for the first time and provide new insights into the regulatory mechanisms of oncogenic signaling and HCC therapy.

Deltaic sediments provide a window for investigating delta development processes and the effects of human activities. Despite the fact that numerous studies have been conducted in the Pearl River Delta (PRD), the chronological data are still very limited, which hinder the detailed interpretation of the sedimentary records. The current study aims to establish high-resolution chronology on two cores from Zhuhai using quartz optically stimulated luminescence (OSL) and radiocarbon ( 14 C) dating and, further, to reconstruct the Holocene sedimentary history of the PRD. Core P1-1 has a depth of 79 m and core P3-2 a depth of 60 m. Thirteen quartz OSL samples from P1-1 produced ages between 10.4 and 0.16 ka. Eight OSL and eight 14 C ages from P3-2 span from 10.7 to 0.3 ka. The OSL and 14 C dates show a good agreement above the depth of 26 m (1.4–0.3 ka), but with discrepancies at depths of 26–54 m. 14 C ages (10.7–8.1 ka) are generally older (up to c. 2 ka) than quartz OSL ages, and the discrepancy decreases with depth. The age model shows three phases of the sedimentation process: (1) rapid accumulation rates of 7.48 (P1-1) and 7.52 (P3-2) m/ka between c. 10.7 and 7.5 ka in response to high sea level, (2) followed by a significantly reduced rate of 2.24 m/ka (P1-1) and a depositional hiatus (P3-2) from 7.5 to 2.5 ka as a result of reduced sediment supply and strong scouring by tidal processes, and (3) high sedimentation rates of 8.86 (P1-1) and 9.07 (P3-2) m/ka since 2.5 ka associated with intensive human activities and weakening tidal hydrodynamics. This sedimentary pattern is also evident in many other Asian deltas.

Dissecting the spatial heterogeneity of cancer-associated fibroblasts (CAFs) is vital for understanding tumor biology and therapeutic design. By combining pathological image analysis with spatial proteomics, we revealed two stromal archetypes in hepatocellular carcinoma (HCC) with different biological functions and extracellular matrix compositions. Using paired single-cell RNA and epigenomic sequencing with Stereo-seq, we revealed two fibroblast subsets CAF-FAP and CAF-C7, whose spatial enrichment strongly correlated with the two stromal archetypes and opposing patient prognosis. We discovered two functional units, one is the intratumor inflammatory hub featured by CAF-FAP plus CD8_PDCD1 proximity and the other is the marginal wound-healing hub with CAF-C7 plus Macrophage_SPP1 co-localization. Inhibiting CAF-FAP combined with anti-PD-1 in orthotopic HCC models led to improved tumor regression than either monotherapy. Collectively, our findings suggest stroma-targeted strategies for HCC based on defined stromal archetypes, raising the concept that CAFs change their transcriptional program and intercellular crosstalk according to the spatial context.

Pancreatic ductal adenocarcinoma (PDAC) is highly resistant to chemotherapy. However, PDAC with germline BRCA  mutations, which lead to homologous recombination (HR) deficiency (HRD), demonstrated an increased sensitivity to platinum-based chemotherapy regimens. This increased chemosensitivity was also seen in PDACs with germline or somatic mutations in the DNA double-strand damage response (DDR) genes beyond canonical HR genes such as BRCA1, BRCA2, and PALB2 . However, there are no consensus methods to determine HRD status; and neither is there a well-defined list of HR-DDR genes. In addition, how HRD and/or HR-DDR gene mutation status impacts the tumor immune microenvironment including tumor mutation burden (TMB), neoantigen load, T cell receptor (TCR) repertoire, and effector T cell infiltration is unknown. Thus, in this study, we developed a new method to categorize PDACs into HRD-positive and HRD-negative subgroups by using results from whole exome sequencing, whole genome sequencing, or both into consideration. We classified a cohort of 89 PDACs into HRD-positive (n = 18) and HRD-negative (n = 69) tumors. HR-DDR gene variants were identified more frequently in HRD-positive PDACs than HRD-negative PDACs, with RAD51B , BRCA2 and ATM alterations most frequently identified in HRD-positive PDACs. Notably, TMB and neoantigen load was significantly higher in HRD-positive PDACs compared to HRD-negative tumors. Interestingly, HRD-positive PDACs, PDACs with high tumor mutational burden, and PDAC with high neoantigen load were all associated with lower CD8 + T lymphocyte infiltration and T cell clonal diversity, suggesting a mechanism of resistance to immune checkpoint inhibitors (ICIs). Therefore, this study suggests that treatments to enhance effector T cell infiltration and T cell clonal diversity may overcome resistance to ICI-based immunotherapy in HRD-positive PDACs.

Metabolic differences among and within tumors can be an important determinant in cancer treatment outcome. However, methods for determining these differences non-invasively in vivo is lacking. Using pancreatic ductal adenocarcinoma as a model, we demonstrate that tumor xenografts with a similar genetic background can be distinguished by their differing rates of the metabolism of 13C labeled glucose tracers, which can be imaged without hyperpolarization by using newly developed techniques for noise suppression. Using this method, cancer subtypes that appeared to have similar metabolic profiles based on steady state metabolic measurement can be distinguished from each other. The metabolic maps from 13C-glucose imaging localized lactate production and overall glucose metabolism to different regions of some tumors. Such tumor heterogeneity would not be not detectable in FDG-PET.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Lipids comprise diverse classes of compounds that are important for the structure and properties of membranes, as high-energy fuel sources and as signaling molecules. Therefore, the turnover rates of these varied classes of lipids are fundamental to cellular function. However, their enormous chemical diversity and dynamic range in cells makes detailed analysis very complex. Furthermore, although stable isotope tracers enable the determination of synthesis and degradation of complex lipids, the numbers of distinguishable molecules increase enormously, which exacerbates the problem. Although LC-MS-MS (Liquid Chromatography-Tandem Mass Spectrometry) is the standard for lipidomics, NMR can add value in global lipid analysis and isotopomer distributions of intact lipids. Here, we describe new developments in NMR analysis for assessing global lipid content and isotopic enrichment of mixtures of complex lipids for two cell lines (PC3 and UMUC3) using both 13C6 glucose and 13C5 glutamine tracers.

Glycogen is a readily deployed intracellular energy storage macromolecule composed of branched chains of glucose anchored to the protein glycogenin. Although glycogen primarily occurs in the liver and muscle, it is found in most tissues, and its metabolism has been shown to be important in cancers and immune cells. Robust analysis of glycogen turnover requires stable isotope tracing plus a reliable means of quantifying total and labeled glycogen derived from precursors such as 13C6-glucose. Current methods for analyzing glycogen are time- and sample-consuming, at best semi-quantitative, and unable to measure stable isotope enrichment. Here we describe a microscale method for quantifying both intact and acid-hydrolyzed glycogen by ultra-high-resolution Fourier transform mass spectrometric (UHR-FTMS) and/or NMR analysis in stable isotope resolved metabolomics (SIRM) studies. Polar metabolites, including intact glycogen and their 13C positional isotopomer distributions, are first measured in crude biological extracts by high resolution NMR, followed by rapid and efficient acid hydrolysis to glucose under N2 in a focused beam microwave reactor, with subsequent analysis by UHR-FTMS and/or NMR. We optimized the microwave digestion time, temperature, and oxygen purging in terms of recovery versus degradation and found 10 min at 110–115 °C to give >90% recovery. The method was applied to track the fate of 13C6-glucose in primary human lung BEAS-2B cells, human macrophages, murine liver and patient-derived tumor xenograft (PDTX) in vivo, and the fate of 2H7-glucose in ex vivo lung organotypic tissue cultures of a lung cancer patient. We measured the incorporation of 13C6-glucose into glycogen and its metabolic intermediates, UDP-Glucose and glucose-1-phosphate, to demonstrate the utility of the method in tracing glycogen turnover in cells and tissues. The method offers a quantitative, sensitive, and convenient means to analyze glycogen turnover in mg amounts of complex biological materials.

Due to many substances in the human exposome, there is a dearth of exposure and toxicity information available to assess potential health risks. Quantification of all trace organics in the biological fluids seems impossible and costly, regardless of the high individual exposure variability. We hypothesized that the blood concentration ( ) of organic pollutants could be predicted via their exposure and chemical properties. Developing a prediction model on the annotation of chemicals in human blood can provide new insight into the distribution and extent of exposures to a wide range of chemicals in humans. Our objective was to develop a machine learning (ML) model to predict blood concentrations ( ) of chemicals and prioritize chemicals of health concern. We curated the of compounds mostly measured at population levels and developed an ML model for chemical predictions by considering chemical daily exposure (DE) and exposure pathway indicators ( ), half-lives ( ), and volume of distribution ( ). Three ML models, including random forest (RF), artificial neural network (ANN) and support vector regression (SVR) were compared. The toxicity potential or prioritization of each chemical was represented as a bioanalytical equivalency (BEQ) and its percentage (BEQ%) estimated based on the predicted and ToxCast bioactivity data. We also retrieved the top 25 most active chemicals in each assay to further observe changes in the BEQ% after the exclusion of the drugs and endogenous substances. We curated the of 216 compounds primarily measured at population levels. RF outperformed the ANN and SVF models with the root mean square error (RMSE) of 1.66 and , the mean absolute error (MAE) values of 1.28 and , the mean absolute percentage error (MAPE) of 0.29 and 0.23, and of 0.80 and 0.72 across test and testing sets. Subsequently, the human of 7,858 ToxCast chemicals were successfully predicted, ranging from to . The predicted were then combined with ToxCast bioassays to prioritize the ToxCast chemicals across 12 assays with important toxicological end points. It is interesting that we found the most active compounds to be food additives and pesticides rather than widely monitored environmental pollutants. We have shown that the accurate prediction of \"internal exposure\" from \"external exposure\" is possible, and this result can be quite useful in the risk prioritization. https://doi.org/10.1289/EHP11305.