乙酰辅酶A羧化酶抑制剂的研究进展

非酒精性脂肪肝病是以肝细胞内脂肪过度沉积为主要特征的代谢性疾病，脂肪主要以甘油三酯的形式存在，由甘油和脂肪酸通过酯化作用形成；而且肿瘤细胞中脂肪酸的合成异常活跃，明显高于正常细胞，为肿瘤细胞旺盛的增殖、发育过程中生物膜的形成、信号分子和能量的产生提供必要的脂质底物。乙酰辅酶A羧化酶(acetyl-CoA carboxylase，ACC)是脂肪酸从头合成过程的限速酶，同时也是催化该脂肪酸合成通路中第一步反应的酶；其催化生成的产物丙二酰辅酶A亦能抑制脂肪酸的氧化。因此，ACC抑制能降低脂肪酸合成和促进脂肪酸氧化，降低体内脂肪酸的含量，进而减弱肝细胞内脂肪的堆积来达到改善非酒精性脂肪肝病；同时体内脂肪酸含量的降低使肿瘤细胞发育所必须的脂质底物得不到满足，从而能够抑制肿瘤组织的发育，所以乙酰辅酶A羧化酶抑制剂有望成为新型治疗非酒精性脂肪肝病和肿瘤的药物。本文对ACC的结构特点、作用机制及其抑制剂的研究进展进行了综述。     

Screening for the Key lncRNA Targets Associated With Metastasis of Renal Clear Cell Carcinoma

A large number of long noncoding RNAs (lncRNAs) have been found to be implicated in varieties of tumors. However, the contributions of lncRNAs to tumorigenesis in renal clear cell carcinoma (RCCC) remain largely unknown.We performed a genome-wide analysis of lncRNA expression in RCCC and matched nontumor (NT) tissues to identify new targets for further study of renal carcinoma.The genome-wide analysis of lncRNA expression in 3 RCCC and matched NT tissues were conducted using 4 × 180K Agilent lncRNA Chips and 6 lncRNAs were selected and validated by qRT-PCR in 90 RCCC patients. The differentially expressed lncRNAs and mRNAs were recognized through P value and fold-change (FC) filtering. Potential targets associated with RCCC were identified by gene ontology and pathway analyses. Construction of the co-expression network was accomplished using Cytoscape.A total of 3862 lncRNAs and 2935 mRNAs were deregulated in RCCC tissues, compared with paired NT tissues. PCR results showed the expressions of these 6 lncRNAs were consistent with the chips. Moreover, the co-expression network analysis portended 641 nodes and 571 connections between 109 lncRNAs and 532 coding genes. Lastly, NONHSAT123350 could be involved in the pathogenesis of RCCC and its expression level was closely related to disease-free survival (DFS) and overall survival (OS) in patients without distant metastasis.Our results indicated that these abnormal lncRNAs could respond to renal carcinoma progression and NONHSAT123350 may act as a potential target for future treatment of RCCC.     

From the Cover: Searching molecular structure databases with tandem mass spectra using CSI:FingerID

Metabolites provide a direct functional signature of cellular state. Untargeted metabolomics experiments usually rely on tandem MS to identify the thousands of compounds in a biological sample. Today, the vast majority of metabolites remain unknown. We present a method for searching molecular structure databases using tandem MS data of small molecules. Our method computes a fragmentation tree that best explains the fragmentation spectrum of an unknown molecule. We use the fragmentation tree to predict the molecular structure fingerprint of the unknown compound using machine learning. This fingerprint is then used to search a molecular structure database such as PubChem. Our method is shown to improve on the competing methods for computational metabolite identification by a considerable margin.Metabolites, small molecules that are involved in cellular reactions, can provide detailed information about cellular state. Untargeted metabolomic studies may use NMR or MS technologies, but liquid chromatography followed by MS (LC/MS) can detect the highest number of metabolites from minimal amounts of sample (1, 2). Untargeted metabolomics comprehensively compares the mass spectral intensities of metabolite signals (peaks) between two or more samples (3, 4). Advances in MS instrumentation allow us to simultaneously detect thousands of metabolites in a biological sample. Identification of these compounds relies on tandem MS (MS/MS) data, produced by fragmenting the compound and recording the masses of the fragments. Structural elucidation remains a challenging problem, in particular for compounds that cannot be found in any spectral library (1): In total, all available spectral MS/MS libraries of pure chemical standards cover fewer than 20,000 compounds (5). Growth of spectral libraries is limited by the unavailability of pure reference standards for many compounds.In contrast, molecular structure databases such as PubChem (6) and ChemSpider (7) contain millions of compounds, with PubChem alone having surpassed 50 million entries. Searching in molecular structure databases using MS/MS data is therefore considered a powerful tool for assisting an expert in the elucidation of a compound. This problem is considerably harder than the fundamental analysis step in the shotgun proteomics workflow, namely, searching peptide MS/MS data in a peptide sequence database (8): Unlike proteins and peptides, metabolites show a large structural variability and, consequently, also large variations in MS/MS fragmentation. Computational approaches for interpreting and predicting MS/MS data of small molecules date back to the 1960s (9): Due to the unavailability of molecular structure databases at that time, structure libraries were combinatorially generated and then “searched” using the experimental MS/MS data. “Modern” methods for this question have been developed since mid-2000. Particular progress has been made for restricted metabolite classes such as lipids (5), but as with peptides, results cannot be generalized to other metabolite classes. For the general case, several strategies have been proposed during recent years, including simulation of mass spectra from molecular structure (10, 11), combinatorial fragmentation (12–17), and prediction of molecular fingerprints (18, 19).Searching in a molecular structure database is clearly limited to those compounds present in the database. Fragmentation trees have been introduced as a means of analyzing MS/MS data without the need of any (structural or spectral) database (20–22). In this paper, the term “fragmentation tree” is exclusively used to refer to the graph-theoretical concept introduced in ref. 20, not “spectral trees” that describe the dependencies of multiple MS measurements; see Vaniya and Fiehn (23) for a review. In more detail, our fragmentation trees are predicted from MS/MS data by an automated computational method such that peaks in the MS/MS spectrum are annotated with molecular formulas of the corresponding fragments, and fragments are connected via assumed losses. Clearly, there exist other approaches with the broad aim of identifying metabolites, such as network-based methods (24–26) and combined approaches (27); see Hufsky et al. (28) for a review of computational methods in MS-based metabolite identification.It is undisputed that MS/MS data alone are insufficient for full structural elucidation of metabolites. We argue that elucidation of stereochemistry is currently beyond the power of automated search engines, so we try to recover the correct constitution (bond structure) of the query molecule, that is, the identity and connectivity (with bond multiplicities) of the atoms, but no stereochemistry information. Throughout this paper, we refer to the constitution of the molecule as its structure. In practice, orthogonal information is usually available, both analytical (retention time, ion mobility drift time, infrared and UV spectroscopy, and NMR data) and on the experimental setup (extraction procedure and organism) (29, 30). We assume that this information is not presented to the search engines but rather used in a postprocessing step to manually select the best solution from the output list of the engine. This is comparable to the everyday use of search engines for the internet.Here, we present CSI (Compound Structure Identification):FingerID for searching a molecular structure database using MS/MS data. Our method combines computation and comparison of fragmentation trees with machine learning techniques for the prediction of molecular properties of the unknown compound (19). Our method shows significantly increased identification rates compared with all existing state-of-the-art methods for the problem. CSI:FingerID is available at www.csi-fingerid.org/. Our method can expedite the identification of metabolites in an untargeted workflow for the numerous cases where no reference measurements are available in spectral libraries.     

王小琴教授桂枝茯苓丸加减治疗肾结石并积水经验

目的探讨肾结石并积水的中医证治。方法根据中医理论,结合王小琴教授的临床经验,从中医病因病机、临床治疗等方面深入探讨肾结石并积水的病因病机及治法、方药。结果肾结石并积水的中医病因病机为湿热内蕴、虚实夹杂和瘀结内停,其病机关键在"瘀"。结论根据仲景血水同治理论,用桂枝茯苓丸合三金汤为祖方加减治疗肾结石并积水往往能取得较好的疗效。     

男性慢阻肺合并肾功能损害患者多索茶碱血药浓度影响因素分析

目的 分析男性慢性阻塞性肺疾病合并肾功能损害患者多索茶碱血药浓度监测结果,探讨多索茶碱血药浓度影响因素。方法 采用回顾性分析,收集2018年7月—2021年7月郑州市第二人民医院使用多索茶碱治疗的慢性阻塞性肺疾病合并肾功能损害的男性患者病例121例,记录血药浓度监测结果和患者人口学数据、检查指标、合并用药情况等资料。进行单因素分析,初步探讨可能的影响因素和血药浓度的关联性,并将因素纳入构建多因素线性回归模型,用以矫正混杂因素的影响。结果 单因素结果显示多索茶碱血药浓度与患者年龄(r=0.32,P<0.001),体质量(r=–0.40,P<0.001),肾小球滤过率(r=–0.24,P=0.008)的关联性存在统计学意义。相对于重度肾功能损伤的患者,轻度损伤的患者多索茶碱血药浓度更低(P=0.017),吸烟患者的多索茶碱血药浓度更低(P=0.018)。在矫正了其他因素后,结果显示年龄越大,血药浓度越高(b=0.08,P=0.032),体质量越大,血药浓度越低(b=–0.14,P<0.001),吸烟患者的血药浓度相较于不吸烟的患者更低(b=–1.30,P=0.048)。结...     

Ophiopogonin D Inhibiting Epithelial NF-κB Signaling Pathway Protects Against Experimental Colitis in Mice

Inflammation - The sustained activation of the nuclear factor κB (NF-κB) signaling pathway has been observed in human inflammatory bowel disease (IBD). Ophiopogonin D (OP-D) is a small...     

小分子AMPK直接激动剂的最新研究进展

2型糖尿病是一种慢性代谢性疾病,其主要表现为高血糖和微血管并发症,是影响人类生活的重大疾病之一,目前尚无理想的治疗药物。腺苷酸活化蛋白激酶(AMP-activated protein kinase,AMPK)是一种高度保守的丝氨酸/苏氨酸蛋白激酶,在细胞和机体能量代谢与平衡等方面发挥重要作用;小分子AMPK直接激动剂因在降低血糖方面的作用而有望成为新一代治疗2型糖尿病的药物。本文按结构分类,对近几年报道的小分子AMPK直接激动剂进行综述。     

管状胃在高龄食管癌和贲门癌根治术中的应用

目的 总结70岁以上高龄食管癌、贲门癌根治手术中应用管状胃代食管的手术操作方法, 分析术后肺部并发症的发生及术后生存的情况。方法 回顾分析30例70岁以上高龄食管癌和贲门癌患者临床资料, 术中应用管状胃代替食管, 统计分析术后发生的肺部并发症和随访资料。结果 术中保留胃右动脉及分支的管状胃制作成功, 23例术后无明显肺部并发症发生, 4例出现明显肺部感染, 积极治疗后康复出院;2例术后气管切开, 呼吸机辅助呼吸, 1周后顺利脱离呼吸机;1例较长时间呼吸机辅助呼吸, 出现吻合口瘘, 最终死亡。术后随访21例, 无明显反流性食管炎发生, 术后1月后生活基本自理。1、3年生存率分别为42.8%(9/21)和19.0%(4/21)。结论 应用管状胃代食管, 对高龄食管癌和贲门癌患者, 可以有效预防吻合口瘘, 减少胸腔胃对心肺功能的影响, 减少反流性食管炎的发生, 提高患者术后生存质量。