A multi-tissue metabolome atlas of primate pregnancy.

Pregnancy induces dramatic metabolic changes in females; yet, the intricacies of this metabolic reprogramming remain poorly understood, especially in primates. Using cynomolgus monkeys, we constructed a comprehensive multi-tissue metabolome atlas, analyzing 273 samples from 23 maternal tissues during pregnancy. We discovered a decline in metabolic coupling between tissues as pregnancy progressed. Core metabolic pathways that were rewired during primate pregnancy included steroidogenesis, fatty acid metabolism, and arachidonic acid metabolism. Our atlas revealed 91 pregnancy-adaptive metabolites changing consistently across 23 tissues, whose roles we verified in human cell models and patient samples. Corticosterone and palmitoyl-carnitine regulated placental maturation and maternal tissue progenitors, respectively, with implications for maternal preeclampsia, diabetes, cardiac hypertrophy, and muscle and liver regeneration. Moreover, we found that corticosterone deficiency induced preeclampsia-like inflammation, indicating the atlas's potential clinical value. Overall, our multi-tissue metabolome atlas serves as a framework for elucidating the role of metabolic regulation in female health during pregnancy.

RNA m6A modification in ferroptosis: implications for advancing tumor immunotherapy.

The pursuit of innovative therapeutic strategies in oncology remains imperative, given the persistent global impact of cancer as a leading cause of mortality. Immunotherapy is regarded as one of the most promising techniques for systemic cancer therapies among the several therapeutic options available. Nevertheless, limited immune response rates and immune resistance urge us on an augmentation for therapeutic efficacy rather than sticking to conventional approaches. Ferroptosis, a novel reprogrammed cell death, is tightly correlated with the tumor immune environment and interferes with cancer progression. Highly mutant or metastasis-prone tumor cells are more susceptible to iron-dependent nonapoptotic cell death. Consequently, ferroptosis-induction therapies hold the promise of overcoming resistance to conventional treatments. The most prevalent post-transcriptional modification, RNA m6A modification, regulates the metabolic processes of targeted RNAs and is involved in numerous physiological and pathological processes. Aberrant m6A modification influences cell susceptibility to ferroptosis, as well as the expression of immune checkpoints. Clarifying the regulation of m6A modification on ferroptosis and its significance in tumor cell response will provide a distinct method for finding potential targets to enhance the effectiveness of immunotherapy. In this review, we comprehensively summarized regulatory characteristics of RNA m6A modification on ferroptosis and discussed the role of RNA m6A-mediated ferroptosis on immunotherapy, aiming to enhance the effectiveness of ferroptosis-sensitive immunotherapy as a treatment for immune-resistant malignancies.

The next "sweet" spot for pancreatic ductal adenocarcinoma: Glycoprotein for early detection.

Pancreatic ductal adenocarcinoma (PDAC) is the most common neoplastic disease of the pancreas, accounting for more than 90% of all pancreatic malignancies. As a highly lethal malignancy, PDAC is the fourth leading cause of cancer-related deaths worldwide with a 5-year overall survival of less than 8%. The efficacy and outcome of PDAC treatment largely depend on the stage of disease at the time of diagnosis. Surgical resection followed by adjuvant chemotherapy remains the only possibly curative therapy, yet 80%-90% of PDAC patients present with nonresectable PDAC stages at the time of clinical presentation. Despite our advancing knowledge of PDAC, the prognosis remains strikingly poor, which is primarily due to the difficulty of diagnosing PDAC at the early stages. Recent advances in glycoproteomics and glycomics based on mass spectrometry have shown that aberrations in protein glycosylation plays a critical role in carcinogenesis, tumor progression, metastasis, chemoresistance, and immuno-response of PDAC and other types of cancers. A growing interest has thus been placed upon protein glycosylation as a potential early detection biomarker for PDAC. We herein take stock of the advancements in the early detection of PDAC that were carried out with mass spectrometry, with special focus on protein glycosylation.

Frozen tissue coring and layered histological analysis improves cell type-specific proteogenomic characterization of pancreatic adenocarcinoma.

BACKGROUND: Omics characterization of pancreatic adenocarcinoma tissue is complicated by the highly heterogeneous and mixed populations of cells. We evaluate the feasibility and potential benefit of using a coring method to enrich specific regions from bulk tissue and then perform proteogenomic analyses. METHODS: We used the Biopsy Trifecta Extraction (BioTExt) technique to isolate cores of epithelial-enriched and stroma-enriched tissue from pancreatic tumor and adjacent tissue blocks. Histology was assessed at multiple depths throughout each core. DNA sequencing, RNA sequencing, and proteomics were performed on the cored and bulk tissue samples. Supervised and unsupervised analyses were performed based on integrated molecular and histology data. RESULTS: Tissue cores had mixed cell composition at varying depths throughout. Average cell type percentages assessed by histology throughout the core were better associated with KRAS variant allele frequencies than standard histology assessment of the cut surface. Clustering based on serial histology data separated the cores into three groups with enrichment of neoplastic epithelium, stroma, and acinar cells, respectively. Using this classification, tumor overexpressed proteins identified in bulk tissue analysis were assigned into epithelial- or stroma-specific categories, which revealed novel epithelial-specific tumor overexpressed proteins. CONCLUSIONS: Our study demonstrates the feasibility of multi-omics data generation from tissue cores, the necessity of interval H&E stains in serial histology sections, and the utility of coring to improve analysis over bulk tissue data.

Ultra-sensitive electrochemical sensor based on in situ grown ultrafine HKUST-1 nanoparticles @ graphite nanosheets and core-shell structured MoO3-polypyrrole nanowires for the detection of rutin in orange juice.

Rutin extracted from natural plants has important medical value, so developing accurate and sensitive quantitative detection methods is one of the most important tasks. In this work, HKUST-1@GN/MoO3-Ppy NWs were utilized to develop a high-performance rutin electrochemical sensor in virtue of its high conductivity and electrocatalytic activity. The morphology, crystal structure, and chemical element composition of the fabricated sensor composites were characterized by SEM, TEM, XPS, and XRD. Electrochemical techniques including EIS, CV, and DPV were used to investigate the electrocatalytic properties of the prepared materials. The electrochemical test conditions were optimized to achieve efficient detection of rutin. The 2-electron 2-proton mechanism, consisting of several rapid and sequential phases, is postulated to occur during rutin oxidation. The results show that HKUST-1@GN/MoO3-Ppy NWs have the characteristics of large specific surface area, excellent conductivity, and outstanding electrocatalytic ability. There is a significant linear relationship between rutin concentration and the oxidation peak current of DPV. The linear range is 0.50-2000 nM, and the limit of detection is 0.27 nM (S/N = 3). In addition, the prepared electrode has been confirmed to be useful for rutin analysis in orange juice.

Differentially expressed glycoproteins in pre- and post-digital rectal examination urine samples for detecting aggressive prostate cancer.

Urinary glycoproteins associated with aggressive prostate cancer (AG-PCa) were previously reported using post-digital rectal examination (DRE) urine specimens. To explore the potential of using pre-DRE urine specimens for detecting AG-PCa, we compared glycoproteins between pre- and post-DRE urine specimens, verified the previously identified post-DRE AG-PCa-associated urinary glycoproteins in pre-DRE urine specimens, and explored potential new glycoproteins for AG-PCa detection in pre-DRE urine specimens. Quantitative glycoproteomic data were acquired for 154 pre-DRE urine specimens from 41 patients with no cancer at biopsy, 48 patients with non-AG-PCa (Gleason score = 6), and 65 patients with AG-PCa (Gleason score 7 or above). Compared to glycopeptides from the post-DRE urine data, humoral immunity-related proteins were enriched in pre-DRE urine samples, whereas cell mediated immune response proteins were enriched in post-DRE urine samples. Analyses of AG-PCa-associated glycoproteins from pre-DRE urine revealed that the three urinary glycoproteins, prostate-specific antigen (PSA), prostatic acid phosphatase (ACPP), and CD97 antigen (CD97) that were previously identified in post-DRE urine samples, were also observed as AG-PCa associated glycoproteins in pre-DRE urine. In addition, we identified three new glycoproteins, fibrillin 1 (FBN1), vitronectin (VTN), and hemicentin 2 (HMCN2), to be potentially associated with AG-PCa in pre-DRE urine specimens. In summary, glycoprotein profiles differ between pre- and post-DRE urine specimens. The identified AG-PCa-associated glycoproteins may be further evaluated in large cohort of pre-DRE urine specimens for detecting clinically significant PCa.

XAI-enabled neural network analysis of metabolite spatial distributions.

We used deep neural networks to process the mass spectrometry imaging (MSI) data of mouse muscle (young vs aged) and human cancer (tumor vs normal adjacent) tissues, with the aim of using explainable artificial intelligence (XAI) methods to rapidly identify biomarkers that can distinguish different classes of tissues, from several thousands of metabolite features. We also modified classic neural network architectures to construct a deep convolutional neural network that is more suitable for processing high-dimensional MSI data directly, instead of using dimension reduction techniques, and compared it to seven other machine learning analysis methods' performance in classification accuracy. After ascertaining the superiority of Channel-ResNet10, we used a novel channel selection-based XAI method to identify the key metabolite features that were responsible for its learning accuracy. These key metabolite biomarkers were then processed using MetaboAnalyst for pathway enrichment mapping. We found that Channel-ResNet10 was superior to seven other machine learning methods for MSI analysis, reaching > 98% accuracy in muscle aging and colorectal cancer datasets. We also used a novel channel selection-based XAI method to find that in young and aged muscle tissues, the differentially distributed metabolite biomarkers were especially enriched in the propanoate metabolism pathway, suggesting it as a novel target pathway for anti-aging therapy.

Characterization of In Vivo Protein Complexes via Chemical Cross-Linking and Mass Spectrometry.

Cells perform various functions by proteins via protein complexes. Characterization of protein complexes is critical to understanding their biological and clinical significance and has been one of the major efforts of functional proteomics. To date, most protein complexes are characterized by the in vitro system from protein extracts after the cells or tissues are lysed, and it has been challenging to determine which of these protein complexes are formed in intact cells. Herein, we report an approach to preserve protein complexes using in vivo cross-linking, followed by size exclusion chromatography and data-independent acquisition mass spectrometry. This approach enables the characterization of in vivo protein complexes from cells or tissues, which allows the determination of protein complexes in clinical research. More importantly, the described approach can identify protein complexes that are not detected by the in vitro system, which provide unique protein function information.

Optimized data-independent acquisition approach for proteomic analysis at single-cell level.

BACKGROUND: Single-cell proteomic analysis provides valuable insights into cellular heterogeneity allowing the characterization of the cellular microenvironment which is difficult to accomplish in bulk proteomic analysis. Currently, single-cell proteomic studies utilize data-dependent acquisition (DDA) mass spectrometry (MS) coupled with a TMT labelled carrier channel. Due to the extremely imbalanced MS signals among the carrier channel and other TMT reporter ions, the quantification is compromised. Thus, data-independent acquisition (DIA)-MS should be considered as an alternative approach towards single-cell proteomic study since it generates reproducible quantitative data. However, there are limited reports on the optimal workflow for DIA-MS-based single-cell analysis. METHODS: We report an optimized DIA workflow for single-cell proteomics using Orbitrap Lumos Tribrid instrument. We utilized a breast cancer cell line (MDA-MB-231) and induced drug resistant polyaneuploid cancer cells (PACCs) to evaluate our established workflow. RESULTS: We found that a short LC gradient was preferable for peptides extracted from single cell level with less than 2 ng sample amount. The total number of co-searching peptide precursors was also critical for protein and peptide identifications at nano- and sub-nano-gram levels. Post-translationally modified peptides could be identified from a nano-gram level of peptides. Using the optimized workflow, up to 1500 protein groups were identified from a single PACC corresponding to 0.2 ng of peptides. Furthermore, about 200 peptides with phosphorylation, acetylation, and ubiquitination were identified from global DIA analysis of 100 cisplatin resistant PACCs (20 ng). Finally, we used this optimized DIA approach to compare the whole proteome of MDA-MB-231 parental cells and induced PACCs at a single-cell level. We found the single-cell level comparison could reflect real protein expression changes and identify the protein copy number. CONCLUSIONS: Our results demonstrate that the optimized DIA pipeline can serve as a reliable quantitative tool for single-cell as well as sub-nano-gram proteomic analysis.

Ultrasensitive catechin electrochemical sensor based on uniform ordered mesoporous carbon hollow spheres (MCHSs) advanced carbon-based conductive materials.

Catechin is one of the flavonoids with antioxidant activity and has attracted great interest. A rapid and accurate detection of catechin is of great significance. Herein, an ultrasensitive catechin electrochemical sensor based on uniform ordered mesoporous carbon hollow spheres (MCHSs) advanced carbon-based conductive material modified glass carbon electrode was constructed. The MCHSs were synthesized by pyrolysis using nitrogen protection and template removal methods, and they exhibited excellent electrochemical detection for catechin owing to their high conductivity and uniform and small spheres with a large specific surface area and hollow structure. Under optimal conditions for the detection of catechin, the MCHSs/GCE showed a wider linear range (10 -1400 nM) and lower detection limit (LOD, 2.82 nM, (S/N = 3)). Furthermore, the electrochemical reaction sites and redox mechanisms of catechin were revealed by electrochemical behavior and density flooding theory. Moreover, the sensor we constructed exhibited good accuracy and stability for the detection of catechin in actual sample detections.

Evaluation of the Sensitivity and Reproducibility of Targeted Proteomic Analysis Using Data-Independent Acquisition for Serum and Cerebrospinal Fluid Proteins.

There is a need for targeted analysis of biological fluids for diagnosis, prognosis, or monitoring the progression of diseases. Cerebrospinal fluid (CSF) and serum have been widely used for the development of protein analysis for neurodegenerative diseases and other diseases, respectively. Recently, data-independent acquisition (DIA) mass spectrometry (MS) has been developed to increase the throughput over data-dependent acquisition (DDA) on screening of a large number of samples and discovery of candidate targets. When it comes to target validation, the analytical performance of targeted analysis is critical. However, the inter- and intralaboratory analytical performances of the DIA-MS for targeted proteomic analysis of CSF and serum samples have not yet been investigated. In this study, we showed that the DIA-MS approach allowed us to identify and quantify 1732 CSF and 424 serum proteins, with 90% of proteins identified and quantified in at least 50% of DIA-MS runs. To evaluate the sensitivity, linearity, and dynamic range of the DIA approach, we included the stable isotope-labeled (SI) peptides into CSF and serum samples with serial dilutions. The lower limit of quantification (LLOQ) of peptides was 0.1-0.5 fmol, and the dynamic range was over 3.53 orders of magnitude, with excellent linearity (r2 < 0.978) in CSF and serum samples. Finally, the reproducibility of the DIA-MS approach was evaluated using entire proteins identified in CSF and serum samples. The intralaboratory three replicate results showed reliable reproducibility with 12.5 and 17.3% of the median coefficient of variation (CV) in both CSF and serum matrices, whereas the median CVs of interlaboratory three replicates were 23.8 and 32.0% in CSF and serum samples, respectively. The comparison of the quantitative result between replicates showed close similarity at intra- and interlaboratories with a median Pearson correlation value of >0.98 in CSF and serum, respectively. In conclusion, we demonstrate the capability of the DIA approach as a targeted proteomic analysis for candidate proteins from CSF and serum samples.

Dysregulated N6-methyladenosine (m6A) processing in hepatocellular carcinoma.

N6-methyladenosine (m6A) is the most thoroughly studied type of internal RNA modification, as this epigenetic modification is the most abundant in eukaryotic RNAs to date. This modification occurs in various types of RNAs and plays significant roles in dominant RNA-related processes, such as translation, splicing, export and degradation. These processes are catalyzed by three types of prominent enzymes: writers, erasers and readers. Increasing evidence has shown that m6A modification is vital for the regulation of gene expression, carcinogenesis, tumor progression and other abnormal changes, and recent studies have shown that m6A is important in the development of hepatocellular carcinoma (HCC). Herein, we summarize the nature and regulatory mechanisms of m6A modification, including its role in the pathogenesis of HCC and related chronic liver diseases. We also highlight the clinical significance and future strategies involving RNA m6A modifications in HCC.

A phenotype-directed chemical screen identifies ponalrestat as an inhibitor of the plant flavin monooxygenase YUCCA in auxin biosynthesis.

Plant development is regulated by both synergistic and antagonistic interactions of different phytohormones, including a complex crosstalk between ethylene and auxin. For instance, auxin and ethylene synergistically control primary root elongation and root hair formation. However, a lack of chemical agents that specifically modulate ethylene or auxin production has precluded precise delineation of the contribution of each hormone to root development. Here, we performed a chemical genetic screen based on the recovery of root growth in ethylene-related Arabidopsis mutants with constitutive "short root" phenotypes (eto1-2 and ctr1-1). We found that ponalrestat exposure recovers root elongation in these mutants in an ethylene signal-independent manner. Genetic and pharmacological investigations revealed that ponalrestat inhibits the enzymatic activity of the flavin-containing monooxygenase YUCCA, which catalyzes the rate-limiting step of the indole-3-pyruvic acid branch of the auxin biosynthesis pathway. In summary, our findings have identified a YUCCA inhibitor that may be useful as a chemical tool to dissect the distinct steps in auxin biosynthesis and in the regulation of root development.

Discovery of a Potent GLUT Inhibitor from a Library of Rapafucins by Using 3D Microarrays.

Glucose transporters play an essential role in cancer cell proliferation and survival and have been pursued as promising cancer drug targets. Using microarrays of a library of new macrocycles known as rapafucins, which were inspired by the natural product rapamycin, we screened for new inhibitors of GLUT1. We identified multiple hits from the rapafucin 3D microarray and confirmed one hit as a bona fide GLUT1 ligand, which we named rapaglutin A (RgA). We demonstrate that RgA is a potent inhibitor of GLUT1 as well as GLUT3 and GLUT4, with an IC50 value of low nanomolar for GLUT1. RgA was found to inhibit glucose uptake, leading to a decrease in cellular ATP synthesis, activation of AMP-dependent kinase, inhibition of mTOR signaling, and induction of cell-cycle arrest and apoptosis in cancer cells. Moreover, RgA was capable of inhibiting tumor xenografts in vivo without obvious side effects. RgA could thus be a new chemical tool to study GLUT function and a promising lead for developing anticancer drugs.

Microbial analysis of MAP pot-stewed duck wings under different conditions during 15 °C storage.

This study was to investigate the changes of microbial community and counts of MAP pot-stewed duck wing (PSDW) under different packaging films and spices ratio during 15 °C storage, using the traditional bacterial cultivation and PCR-DGGE. Results of microbial counting showed that the shelf-life of PDSW during 15 °C storage for recommendation was within six days, and the packaging films and spices ratio didn't affect the change of microbial numbers in PSDW during storage. PCR-DGGE analysis revealed that Staphylococcus equorum, Weissella sp., Leuconostoc mesenteroides became the dominating bacteria of PSDW at the end of storage, and high barrier cover film, general barrier base film and spice ratio 1:1, had a better inhibition effect on bacteria in PSDW products, which could be used as the condition for PSDW storage. This study will help PSDW processing enterprises visualize the biodiversity of PSDW during storage, and choose the best condition for the subsequent processing.

[Prognostic value of bone marrow hematogones in childhood B-lineage acute lymphoblastic leukemia].

OBJECTIVE: To study the prognostic value of hematogones (HGs) for childhood B-lineage acute lymphoblastic leukemia (B-ALL) during consolidation chemotherapy. METHODS: A retrospective analysis was conducted for 196 children with newly-diagnosed B-ALL. They were divided into high-risk group (n=55), intermediate-risk group (n=69), and low-risk group (n=72) by risk stratification, and into complete remission group (n=165) and relapse group (n=31) by clinical outcome. The European BIOMED-1 standard flow cytometry for minimal residual disease (MRD) was used to determine the number of HGs during consolidation chemotherapy. The Kaplan-Meier survival curve was used to assess event-free survival (EFS). RESULTS: The high-risk group had a significantly lower number of HGs than the intermediate-risk and low-risk groups (P<0.05). The number of HGs in the complete remission group was significantly higher than in the relapse group (P<0.05). The children with HGs ≤1.0% had a significantly lower EFS than those with HGs <1.0% (P<0.05). CONCLUSIONS: HGs can be used to assess the treatment outcome and prognosis in children with B-ALL, and proliferation of HGs reflects the good effect of chemotherapy in such children.

Asymmetric Total Synthesis of Propindilactone G.

A concise total synthesis of (+)-propindilactone G, a nortriterpenoid isolated from the stems of Schisandra propinqua var. propinqua, has been achieved for the first time. The key steps of the synthesis include an asymmetric Diels-Alder reaction, a Pauson-Khand reaction, a Pd-catalyzed reductive hydrogenolysis reaction, and an oxidative heterocoupling reaction. These reactions enabled the synthesis of (+)-propindilactone G in only 20 steps. As a consequence of our synthetic studies, the structure of (+)-propindilactone G has been revised.

Disruption of the peripheral biological clock may play a role in sleep deprivation-induced dysregulation of lipid metabolism in both the daytime and nighttime phases.

STUDY OBJECTIVES: This study aimed to examine the effect of sleep deprivation (SD) on lipid metabolism or lipid metabolism regulation in the liver and white adipose tissue (WAT) during the light and dark phases and explored the possible mechanisms underlying the diurnal effect of SD on lipid metabolism associated with clock genes. METHODS: Male C57BL/6J mice aged 2 months were deprived of sleep daily for 20 h for ten consecutive days with weakly forced locomotion. The body weights and food consumption levels of the SD and control mice were recorded, and the mice were then sacrificed at ZT (zeitgeber time) 2 and ZT 14. The peripheral clock genes, enzymes involved in fat synthesis and catabolism in the WAT, and melatonin signalling pathway-mediated lipid metabolism in the liver were assessed. Untargeted metabolomics and tandem mass tag (TMT) proteomics were used to identify differential lipid metabolism pathways in the liver. RESULTS: Bodyweight gain and daily food consumption were dramatically elevated after SD. Profound disruptions in the diurnal regulation of the hepatic peripheral clock and enzymes involved in fat synthesis and catabolism in the WAT were observed, with a strong emphasis on hepatic lipid metabolic pathways, while melatonin signalling pathway-mediated lipid metabolism exhibited moderate changes. CONCLUSIONS: In mice, ten consecutive days of SD increased body weight gain and daily food consumption. In addition, SD profoundly disrupted lipid metabolism in the WAT and liver during the light and dark periods. These diurnal changes may be related to disorders of the peripheral biological clock.

Mordo2: A Personalization Framework for Silent Command Recognition.

Wearable human-computer interactions in daily life are increasingly encouraged by the prevalence of intelligent wearables. It poses a demanding requirement of micro-interaction and minimizing social awkwardness. Our previous work demonstrated the feasibility of recognizing silent commands through around-ear biosensors with the limitation of user adaptation. In this work, we ease the limitation by a personalization framework that integrates spectral factorization of signals, temporal confidence rejection and commonly used transfer learning algorithms. Specifically, we first empirically formulate the user adaptation issue by presenting the accuracies of applying transfer learning algorithms to our previous method. Second, we improve the signal-to-noise ratio by proposing the supervised spectral factorization method that learns the amplitude and phase mappings between around-ear signals and the signals of articulated facial muscles. Third, we leverage the time continuity of commands and introduce the time decay into confidence rejection. Finally, extensive experiments have been conducted to evaluate the feasibility and improvements. The results indicate an average accuracy of 92.38% which is significantly larger than solely using transfer learning algorithms. And a comparable accuracy can be achieved with significantly reduced data of new users. The overall performance shows the framework can significantly improve the accuracy of user adaptations. The work would aid a further step toward commercial products for silent command recognition and inspire the solution to the user adaptation challenge of wearable human-computer interactions.