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Identification of autophagy-related signatures in nonalcoholic fatty liver disease and correlation with non-parenchymal cells of the liver† 鉴定非酒精性脂肪肝中的自噬相关特征以及与非肝实质细胞的相关性。
IF 3 4区 生物学 Q3 BIOCHEMISTRY & MOLECULAR BIOLOGY Pub Date : 2024-07-10 DOI: 10.1039/D4MO00060A
Kaiwei Chen, Ling Wei, Shengnan Yu, Ningning He and Fengjuan Zhang

Non-alcoholic fatty liver disease (NAFLD) is a chronic hepatic disease. The incidence and prevalence of NAFLD have increased greatly in recent years, and there is still a lack of effective drugs. Autophagy plays an important role in promoting liver metabolism and maintaining liver homeostasis, and defects in autophagy levels are considered to be related to the development of NAFLD. However, the molecular mechanisms of autophagy in NAFLD still remain unknown. In this study, we identified 6 autophagy-associated hub genes using gene expression profiles obtained from the GSE48452 and GSE89632 datasets. Biomarkers were screened according to gene significance (GS) and module membership (MM) using weighted gene co-expression network analysis (WGCNA), and the immune infiltration landscape of the liver in NAFLD patients was explored using the CIBERSORT algorithm. Subsequently, we analyzed the relationship between liver non-parenchymal cells and autophagy-related hub genes using scRNA-seq data (GSE129516). Finally, we separated the NAFLD patients into two groups based on 6 hub genes by consensus clustering and screened 10 potential autophagy-related small molecules based on the cMAP database.

非酒精性脂肪肝(NAFLD)是一种慢性肝病。近年来,非酒精性脂肪肝的发病率和患病率大幅上升,但目前仍缺乏有效的治疗药物。自噬在促进肝脏代谢和维持肝脏稳态方面发挥着重要作用,自噬水平的缺陷被认为与非酒精性脂肪肝的发生有关。然而,自噬在非酒精性脂肪肝中的分子机制仍然未知。在本研究中,我们利用从 GSE48452 和 GSE89632 数据集中获得的基因表达谱鉴定了 6 个与自噬相关的枢纽基因。利用加权基因共表达网络分析(WGCNA)根据基因的显著性(GS)和模块成员性(MM)筛选生物标志物,并利用 CIBERSORT 算法探索非酒精性脂肪肝患者肝脏的免疫浸润情况。随后,我们利用 scRNA-seq 数据(GSE129516)分析了肝脏非实质性细胞与自噬相关枢纽基因之间的关系。最后,我们通过共识聚类将非酒精性脂肪肝患者根据 6 个中心基因分为两组,并根据 cMAP 数据库筛选出 10 个潜在的自噬相关小分子。
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引用次数: 0
Integrating host and microbiome biology using holo-omics 利用整体组学整合宿主和微生物组生物学。
IF 3 4区 生物学 Q3 BIOCHEMISTRY & MOLECULAR BIOLOGY Pub Date : 2024-07-04 DOI: 10.1039/D4MO00017J
Carl M. Kobel, Jenny Merkesvik, Idun Maria Tokvam Burgos, Wanxin Lai, Ove Øyås, Phillip B. Pope, Torgeir R. Hvidsten and Velma T. E. Aho

Holo-omics is the use of omics data to study a host and its inherent microbiomes – a biological system known as a “holobiont”. A microbiome that exists in such a space often encounters habitat stability and in return provides metabolic capacities that can benefit their host. Here we present an overview of beneficial host–microbiome systems and propose and discuss several methodological frameworks that can be used to investigate the intricacies of the many as yet undefined host–microbiome interactions that influence holobiont homeostasis. While this is an emerging field, we anticipate that ongoing methodological advancements will enhance the biological resolution that is necessary to improve our understanding of host–microbiome interplay to make meaningful interpretations and biotechnological applications.

全息组学是利用全息数据研究宿主及其固有微生物组--一种被称为 "全息体 "的生物系统。存在于这样一个空间中的微生物组往往会遇到栖息地稳定性的问题,并提供有益于宿主的代谢能力。在此,我们概述了有益的宿主-微生物组系统,并提出和讨论了几个方法框架,这些框架可用于研究影响整体生物体平衡的许多尚未明确的宿主-微生物组相互作用的复杂性。虽然这是一个新兴领域,但我们预计,方法学的不断进步将提高生物分辨率,这是我们更好地理解宿主-微生物组相互作用以进行有意义的解释和生物技术应用所必需的。
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引用次数: 0
Serum metabolomics reveals the metabolic profile and potential biomarkers of ankylosing spondylitis† 血清代谢组学揭示强直性脊柱炎的代谢特征和潜在生物标记物
IF 3 4区 生物学 Q3 BIOCHEMISTRY & MOLECULAR BIOLOGY Pub Date : 2024-06-25 DOI: 10.1039/D4MO00076E
Liuyan Li, Shuqin Ding, Weibiao Wang, Lingling Yang, Gidion Wilson, Yuping Sa, Yue Zhang, Jianyu Chen and Xueqin Ma

Ankylosing spondylitis (AS) is a chronic systemic inflammatory disease that significantly impairs physical function in young individuals. However, the identification of radiographic changes in AS is frequently delayed, and the diagnostic efficacy of biomarkers like HLA-B27 remains moderately effective, with unsatisfactory sensitivity and specificity. In contrast to existing literature, our current experiment utilized a larger sample size and employed both untargeted and targeted UHPLC-QTOF-MS/MS based metabolomics to identify the metabolite profile and potential biomarkers of AS. The results indicated a notable divergence between the two groups, and a total of 170 different metabolites were identified, which were associated with the 6 primary metabolic pathways exhibiting a correlation with AS. Among these, 26 metabolites exhibited high sensitivity and specificity with area under curve (AUC) values greater than 0.8. Subsequent targeted quantitative analysis discovered 3 metabolites, namely 3-amino-2-piperidone, hypoxanthine and octadecylamine, exhibiting excellent distinguishing ability based on the results of the ROC curve and the Random Forest model, thus qualifying as potential biomarkers for AS. Summarily, our untargeted and targeted metabolomics investigation offers novel and precise insights into potential biomarkers for AS, potentially enhancing diagnostic capabilities and furthering the comprehension of the condition's pathophysiology.

强直性脊柱炎(AS)是一种慢性全身性炎症,严重损害年轻人的身体功能。然而,强直性脊柱炎的影像学变化常常被延迟发现,HLA-B27 等生物标志物的诊断效果也一般,灵敏度和特异性都不尽如人意。与现有文献相比,本实验采用了更大的样本量,并同时使用了基于超高效液相色谱-质谱-质谱(UHPLC-QTOF-MS/MS)的非靶向和靶向代谢组学方法来鉴定强直性脊柱炎的代谢物谱和潜在生物标志物。结果表明,两组之间存在明显差异,共有170种不同的代谢物与强直性脊柱炎的6种主要代谢途径相关。其中,26种代谢物具有较高的灵敏度和特异性,曲线下面积(AUC)值均大于0.8。随后的靶向定量分析发现了3个代谢物,即3-氨基-2-哌啶酮、次黄嘌呤和十八胺,根据ROC曲线和随机森林模型的结果,这3个代谢物表现出了极佳的鉴别能力,因此可作为强直性脊柱炎的潜在生物标记物。总之,我们的非靶向和靶向代谢组学研究为了解强直性脊柱炎的潜在生物标志物提供了新颖而精确的见解,从而有可能提高诊断能力并加深对该疾病病理生理学的理解。
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引用次数: 0
KIF2C as a potential therapeutic target: insights from lung adenocarcinoma subtype classification and functional experiments† 作为潜在治疗靶点的 KIF2C:肺腺癌亚型分类和功能实验的启示。
IF 3 4区 生物学 Q3 BIOCHEMISTRY & MOLECULAR BIOLOGY Pub Date : 2024-06-18 DOI: 10.1039/D4MO00044G
Zhi Xu, Rui Miao, Tao Han, Yafeng Liu, Jiawei Zhou, Jianqiang Guo, Yingru Xing, Ying Bai, Jing Wu and Dong Hu

Objective: this study evaluates the prognostic relevance of gene subtypes and the role of kinesin family member 2C (KIF2C) in lung cancer progression. Methods: high-expression genes linked to overall survival (OS) and progression-free interval (PFI) were selected from the TCGA-LUAD dataset. Consensus clustering analysis categorized lung adenocarcinoma (LUAD) patients into two subtypes, C1 and C2, which were compared using clinical, drug sensitivity, and immunotherapy analyses. A random forest algorithm pinpointed KIF2C as a prognostic hub gene, and its functional impact was assessed through various assays and in vivo experiments. Results: The study identified 163 key genes and distinguished two LUAD subtypes with differing OS, PFI, pathological stages, drug sensitivity, and immunotherapy response. KIF2C, highly expressed in the C2 subtype, was associated with poor prognosis, promoting cancer cell proliferation, migration, invasion, and epithelial–mesenchymal transition (EMT), with knockdown reducing tumor growth in mice. Conclusion: The research delineates distinct LUAD subtypes with significant clinical implications and highlights KIF2C as a potential therapeutic target for personalized treatment in LUAD.

目的:本研究评估了基因亚型的预后相关性以及驱动蛋白家族成员 2C(KIF2C)在肺癌进展中的作用。方法:从 TCGA-LUAD 数据集中筛选出与总生存期(OS)和无进展间期(PFI)相关的高表达基因。共识聚类分析将肺腺癌(LUAD)患者分为C1和C2两种亚型,并通过临床、药物敏感性和免疫疗法分析对这两种亚型进行了比较。随机森林算法将KIF2C定位为预后枢纽基因,并通过各种测定和体内实验评估其功能影响。研究结果研究发现了163个关键基因,并区分出了两种LUAD亚型,它们的OS、PFI、病理分期、药物敏感性和免疫治疗反应各不相同。在C2亚型中高表达的KIF2C与预后不良有关,它能促进癌细胞增殖、迁移、侵袭和上皮-间质转化(EMT),敲除KIF2C能减少小鼠的肿瘤生长。结论该研究划分了具有重要临床意义的不同LUAD亚型,并强调KIF2C是LUAD个性化治疗的潜在治疗靶点。
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引用次数: 0
An adaptable in silico ensemble model of the arachidonic acid cascade† 花生四烯酸级联的可调整硅学集合模型
IF 3 4区 生物学 Q3 BIOCHEMISTRY & MOLECULAR BIOLOGY Pub Date : 2024-06-03 DOI: 10.1039/D3MO00187C
Megan Uttley, Grace Horne, Areti Tsigkinopoulou, Francesco Del Carratore, Aliah Hawari, Magdalena Kiezel-Tsugunova, Alexandra C. Kendall, Janette Jones, David Messenger, Ranjit Kaur Bhogal, Rainer Breitling and Anna Nicolaou

Eicosanoids are a family of bioactive lipids, including derivatives of the ubiquitous fatty acid arachidonic acid (AA). The intimate involvement of eicosanoids in inflammation motivates the development of predictive in silico models for a systems-level exploration of disease mechanisms, drug development and replacement of animal models. Using an ensemble modelling strategy, we developed a computational model of the AA cascade. This approach allows the visualisation of plausible and thermodynamically feasible predictions, overcoming the limitations of fixed-parameter modelling. A quality scoring method was developed to quantify the accuracy of ensemble predictions relative to experimental data, measuring the overall uncertainty of the process. Monte Carlo ensemble modelling was used to quantify the prediction confidence levels. Model applicability was demonstrated using mass spectrometry mediator lipidomics to measure eicosanoids produced by HaCaT epidermal keratinocytes and 46BR.1N dermal fibroblasts, treated with stimuli (calcium ionophore A23187), (ultraviolet radiation, adenosine triphosphate) and a cyclooxygenase inhibitor (indomethacin). Experimentation and predictions were in good qualitative agreement, demonstrating the ability of the model to be adapted to cell types exhibiting differences in AA release and enzyme concentration profiles. The quantitative agreement between experimental and predicted outputs could be improved by expanding network topology to include additional reactions. Overall, our approach generated an adaptable, tuneable ensemble model of the AA cascade that can be tailored to represent different cell types and demonstrated that the integration of in silico and in vitro methods can facilitate a greater understanding of complex biological networks such as the AA cascade.

二十酸是一系列生物活性脂质,包括无处不在的脂肪酸花生四烯酸(AA)的衍生物。二十酸类在炎症中的密切参与促使人们开发出预测性的硅学模型,用于系统级的疾病机制探索、药物开发和动物模型替代。利用集合建模策略,我们开发了 AA 级联的计算模型。这种方法克服了固定参数建模的局限性,使可信的、热力学上可行的预测可视化。我们开发了一种质量评分方法,用于量化相对于实验数据的集合预测的准确性,测量过程的整体不确定性。蒙特卡洛集合建模用于量化预测置信度。使用质谱介质脂质组学测量 HaCaT 表皮角质细胞和 46BR.1N 真皮成纤维细胞在刺激(钙离子诱导剂(A23187)、紫外线辐射、三磷酸腺苷)和环氧化酶抑制剂(吲哚美辛)作用下产生的二十烷酸,证明了模型的适用性。实验结果和预测结果在质量上非常吻合,这表明该模型能够适应在 AA 释放和酶浓度分布方面存在差异的细胞类型。通过扩大网络拓扑结构以包括更多的反应,实验结果和预测结果之间的定量一致性可以得到改善。总之,我们的方法生成了一个适应性强、可调整的 AA 级联集合模型,该模型可量身定制以代表不同的细胞类型,并证明了硅学和体外方法的整合有助于加深对 AA 级联等复杂生物网络的理解。
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引用次数: 0
Understanding pulmonary hypertension: the need for an integrative metabolomics and transcriptomics approach 了解肺动脉高压:需要综合代谢组学和转录组学方法
IF 3 4区 生物学 Q3 BIOCHEMISTRY & MOLECULAR BIOLOGY Pub Date : 2024-05-21 DOI: 10.1039/D3MO00266G
Priyanka Choudhury, Sanjukta Dasgupta, Parthasarathi Bhattacharyya, Sushmita Roychowdhury and Koel Chaudhury

Pulmonary hypertension (PH), characterised by mean pulmonary arterial pressure (mPAP) >20 mm Hg at rest, is a complex pathophysiological disorder associated with multiple clinical conditions. The high prevalence of the disease along with increased mortality and morbidity makes it a global health burden. Despite major advances in understanding the disease pathophysiology, much of the underlying complex molecular mechanism remains to be elucidated. Lack of a robust diagnostic test and specific therapeutic targets also poses major challenges. This review provides a comprehensive update on the dysregulated pathways and promising candidate markers identified in PH patients using the transcriptomics and metabolomics approach. The review also highlights the need of using an integrative multi-omics approach for obtaining insight into the disease at a molecular level. The integrative multi-omics/pan-omics approach envisaged to help in bridging the gap from genotype to phenotype is outlined. Finally, the challenges commonly encountered while conducting omics-driven studies are also discussed.

肺动脉高压(PH)的特点是静息时平均肺动脉压(mPAP)为 20 mmHg,是一种与多种临床症状相关的复杂病理生理紊乱。该病发病率高,死亡率和发病率增加,成为全球健康负担。尽管在了解该疾病的病理生理学方面取得了重大进展,但许多潜在的复杂分子机制仍有待阐明。缺乏可靠的诊断测试和特定的治疗靶点也构成了重大挑战。本综述利用转录组学和代谢组学方法全面更新了 PH 患者体内失调的通路和有希望的候选标记物。该综述还强调了使用综合多组学方法从分子水平深入了解疾病的必要性。此外,还概述了有助于弥合从基因型到表型之间差距的综合性多组学/泛组学方法。最后,还讨论了在进行组学驱动的研究时通常会遇到的挑战。
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引用次数: 0
Species-level identification of enterotype-specific microbial markers for colorectal cancer and adenoma† 从物种水平鉴定大肠癌和腺瘤的肠型特异性微生物标记物
IF 3 4区 生物学 Q3 BIOCHEMISTRY & MOLECULAR BIOLOGY Pub Date : 2024-05-09 DOI: 10.1039/D4MO00016A
Ünzile Güven Gülhan, Emrah Nikerel, Tunahan Çakır, Fatih Erdoğan Sevilgen and Saliha Durmuş

Enterotypes have been shown to be an important factor for population stratification based on gut microbiota composition, leading to a better understanding of human health and disease states. Classifications based on compositional patterns will have implications for personalized microbiota-based solutions. There have been limited enterotype based studies on colorectal adenoma and cancer. Here, an enterotype-based meta-analysis of fecal shotgun metagenomic studies was performed, including 1579 samples of healthy controls (CTR), colorectal adenoma (ADN) and colorectal cancer (CRC) in total. Gut microbiota of healthy people were clustered into three enterotypes (Ruminococcus-, Bacteroides- and Prevotella-dominated enterotypes). Reference-based enterotype assignments were performed for CRC and ADN samples, using the supervised machine learning algorithm, K-nearest neighbors. Differential abundance analyses and random forest classification were conducted on each enterotype between healthy controls and CRC–ADN groups, revealing novel enterotype-specific microbial markers for non-invasive CRC screening strategies. Furthermore, we identified microbial species unique to each enterotype that play a role in the production of secondary bile acids and short-chain fatty acids, unveiling the correlation between cancer-associated gut microbes and dietary patterns. The enterotype-based approach in this study is promising in elucidating the mechanisms of differential gut microbiome profiles, thereby improving the efficacy of personalized microbiota-based solutions.

肠型已被证明是根据肠道微生物群组成进行人群分层的一个重要因素,有助于更好地了解人类健康和疾病状态。基于组成模式的分类将对基于微生物群的个性化解决方案产生影响。基于肠型的结直肠腺瘤和癌症研究还很有限。在此,我们对粪便猎枪元基因组研究进行了基于肠型的荟萃分析,共包括 1579 份健康对照(CTR)、结直肠腺瘤(ADN)和结直肠癌(CRC)样本。健康人群的肠道微生物群被分为三种肠型(以反刍球菌、乳酸杆菌和普雷沃特氏菌为主的肠型)。使用监督机器学习算法 K-Nearest Neighbors 对 CRC 和 ADN 样品进行了基于参考的肠型分配。我们对健康对照组和 CRC-ADN 组之间的每种肠型进行了丰度差异分析和随机森林分类,为非侵入性 CRC 筛查策略揭示了新型肠型特异性微生物标记物。此外,我们还发现了每种肠型特有的微生物物种,它们在次级胆汁酸和短链脂肪酸的产生中发挥作用,揭示了癌症相关肠道微生物与饮食模式之间的相关性。本研究中基于肠型的方法有望阐明肠道微生物组特征差异的机制,从而提高基于微生物群的个性化解决方案的功效。
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引用次数: 0
Metabolomic approaches to dissect dysregulated metabolism in the progression of pre-diabetes to T2DM† 用代谢组学方法剖析糖尿病前期向 T2DM 进展过程中的代谢失调问题
IF 2.9 4区 生物学 Q3 BIOCHEMISTRY & MOLECULAR BIOLOGY Pub Date : 2024-04-30 DOI: 10.1039/D3MO00130J
Wenrui Ji, Xiaomin Xie, Guirong Bai, Yanting He, Ling Li, Li Zhang and Dan Qiang

Many individuals with pre-diabetes eventually develop diabetes. Therefore, profiling of prediabetic metabolic disorders may be an effective targeted preventive measure. We aimed to elucidate the metabolic mechanism of progression of pre-diabetes to type 2 diabetes mellitus (T2DM) from a metabolic perspective. Four sets of plasma samples (20 subjects per group) collected according to fasting blood glucose (FBG) concentration were subjected to metabolomic analysis. An integrative approach of metabolome and WGCNA was employed to explore candidate metabolites. Compared with the healthy group (FBG < 5.6 mmol L−1), 113 metabolites were differentially expressed in the early stage of pre-diabetes (5.6 mmol L−1 ⩽ FBG < 6.1 mmol L−1), 237 in the late stage of pre-diabetes (6.1 mmol L−1 ⩽ FBG < 7.0 mmol L−1), and 245 in the T2DM group (FBG 7.0 mmol L−1). A total of 27 differentially expressed metabolites (DEMs) were shared in all comparisons. Among them, L-norleucine was downregulated, whereas ethionamide, oxidized glutathione, 5-methylcytosine, and alpha-D-glucopyranoside beta-D-fructofuranosyl were increased with the rising levels of FBG. Surprisingly, 15 (11 lyso-phosphatidylcholines, L-norleucine, oxidized glutathione, arachidonic acid, and 5-oxoproline) of the 27 DEMs were ferroptosis-associated metabolites. WGCNA clustered all metabolites into 8 modules and the pathway enrichment analysis of DEMs showed a significant annotation to the insulin resistance-related pathway. Integrated analysis of DEMs, ROC and WGCNA modules determined 12 potential biomarkers for pre-diabetes and T2DM, including L-norleucine, 8 of which were L-arginine or its metabolites. L-Norleucine and L-arginine could serve as biomarkers for pre-diabetes. The inventory of metabolites provided by our plasma metabolome offers insights into T2DM physiology metabolism.

许多糖尿病前期患者最终会发展成糖尿病。因此,对糖尿病前期代谢紊乱进行分析可能是一种有效的针对性预防措施。我们旨在从代谢角度阐明糖尿病前期发展为 2 型糖尿病(T2DM)的代谢机制。根据空腹血糖(FBG)浓度采集的四组血浆样本(每组 20 人)进行了代谢组学分析。采用代谢组和 WGCNA 的综合方法来探索候选代谢物。与健康组(FBG 5.6 mmol L-1)相比,113个代谢物在糖尿病前期(5.6 mmol L-1 ⩽ FBG 6.1 mmol L-1)、237个在糖尿病后期(6.1 mmol L-1 ⩽ FBG 7.0 mmol L-1)和245个在T2DM组(FBG 7.0 mmol L-1)有差异表达。在所有比较中,共有 27 种差异表达代谢物(DEMs)。其中,L-正亮氨酸被下调,而乙硫酰胺、氧化谷胱甘肽、5-甲基胞嘧啶和 alpha-D-glucopyranoside beta-D-fructofuranosyl 则随着 FBG 水平的升高而增加。令人惊讶的是,在 27 个 DEMs 中,有 15 个(11 个溶血磷脂酰胆碱、L-正亮氨酸、氧化谷胱甘肽、花生四烯酸和 5-氧代脯氨酸)是铁变态反应相关代谢物。WGCNA 将所有代谢物聚类为 8 个模块,对 DEMs 的通路富集分析表明,这些代谢物与胰岛素抵抗相关的通路有显著的注释关系。对DEMs、ROC和WGCNA模块的综合分析确定了12种糖尿病前期和T2DM的潜在生物标志物,包括L-亮氨酸,其中8种是L-精氨酸或其代谢物。L-亮氨酸和 L-精氨酸可作为糖尿病前期的生物标记物。我们的血浆代谢组提供的代谢物清单为了解 T2DM 的生理代谢提供了线索。
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引用次数: 0
A blood-based multi-omic landscape for the molecular characterization of kidney stone disease† 用于肾结石病分子特征描述的基于血液的多组学图谱
IF 2.9 4区 生物学 Q3 BIOCHEMISTRY & MOLECULAR BIOLOGY Pub Date : 2024-04-16 DOI: 10.1039/D3MO00261F
Weibing Pan‡, Tianwei Yun, Xin Ouyang, Zhijun Ruan, Tuanjie Zhang, Yuhao An, Rui Wang and Peng Zhu

Kidney stone disease (KSD, also named renal calculi, nephrolithiasis, or urolithiasis) is a common urological disease entailing the formation of minerals and salts that form inside the urinary tract, frequently caused by diabetes, high blood pressure, hypertension, and monogenetic components in most patients. 10% of adults worldwide are affected by KSD, which continues to be highly prevalent and with increasing incidence. For the identification of novel therapeutic targets in KSD, we adopted high-throughput sequencing and mass spectrometry (MS) techniques in this study and carried out an integrative analysis of exosome proteomic data and DNA methylation data from blood samples of normal and KSD individuals. Our research delineated the profiling of exosomal proteins and DNA methylation in both healthy individuals and those afflicted with KSD, finding that the overexpressed proteins and the demethylated genes in KSD samples are associated with immune responses. The consistency of the results in proteomics and epigenetics supports the feasibility of the comprehensive strategy. Our insights into the molecular landscape of KSD pave the way for a deeper understanding of its pathogenic mechanism, providing an opportunity for more precise diagnosis and targeted treatment strategies for KSD.

肾结石病(KSD,又称肾结石、肾结石或泌尿系结石)是一种常见的泌尿系统疾病,是一种在尿路内形成的矿物质和盐类结石,常由糖尿病、高血压、高血脂和大多数患者的单基因遗传因素引起。全球有 10% 的成年人受到 KSD 的影响,这种疾病的发病率仍然很高,而且还在不断上升。为了确定 KSD 的新型治疗靶点,我们在这项研究中采用了高通量测序和质谱(MS)技术,并对正常人和 KSD 患者血液样本中的外泌体蛋白质组数据和 DNA 甲基化数据进行了综合分析。我们的研究描绘了健康人和 KSD 患者的外泌体蛋白质和 DNA 甲基化图谱,发现 KSD 样本中过表达的蛋白质和去甲基化的基因与免疫反应有关。蛋白质组学和表观遗传学结果的一致性证明了综合策略的可行性。我们对 KSD 分子图谱的深入研究为更深入地了解其致病机制铺平了道路,为更精确地诊断 KSD 和制定有针对性的治疗策略提供了机会。
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引用次数: 0
PerSEveML: a web-based tool to identify persistent biomarker structure for rare events using an integrative machine learning approach† PerSEveML:使用整合机器学习方法识别罕见事件持久生物标记物结构的网络工具
IF 2.9 4区 生物学 Q3 BIOCHEMISTRY & MOLECULAR BIOLOGY Pub Date : 2024-04-16 DOI: 10.1039/D4MO00008K
Sreejata Dutta, Dinesh Pal Mudaranthakam, Yanming Li and Mihaela E. Sardiu

Omics data sets often pose a computational challenge due to their high dimensionality, large size, and non-linear structures. Analyzing these data sets becomes especially daunting in the presence of rare events. Machine learning (ML) methods have gained traction for analyzing rare events, yet there has been limited exploration of bioinformatics tools that integrate ML techniques to comprehend the underlying biology. Expanding upon our previously developed computational framework of an integrative machine learning approach, we introduce PerSEveML, an interactive web-based tool that uses crowd-sourced intelligence to predict rare events and determine feature selection structures. PerSEveML provides a comprehensive overview of the integrative approach through evaluation metrics that help users understand the contribution of individual ML methods to the prediction process. Additionally, PerSEveML calculates entropy and rank scores, which visually organize input features into a persistent structure of selected, unselected, and fluctuating categories that help researchers uncover meaningful hypotheses regarding the underlying biology. We have evaluated PerSEveML on three diverse biologically complex data sets with extremely rare events from small to large scale and have demonstrated its ability to generate valid hypotheses. PerSEveML is available at https://biostats-shinyr.kumc.edu/PerSEveML/ and https://github.com/sreejatadutta/PerSEveML.

由于 Omics 数据集具有维度高、规模大和非线性结构等特点,通常会给计算带来挑战。在出现罕见事件时,分析这些数据集变得尤为困难。机器学习(ML)方法在分析罕见事件方面已经获得了广泛的关注,但对整合 ML 技术以理解潜在生物学的生物信息学工具的探索仍然有限。在我们之前开发的综合机器学习方法计算框架的基础上,我们推出了基于网络的交互式工具 PerSEveML,该工具利用众包智能预测罕见事件并确定特征选择结构。PerSEveML 通过评估指标全面概述了综合方法,帮助用户了解单个 ML 方法对预测过程的贡献。此外,PerSEveML 还能计算熵和等级分数,直观地将输入特征组织成一个由选定、未选定和波动类别组成的持久结构,帮助研究人员发现有关潜在生物学的有意义的假设。我们已经在三个不同的复杂生物数据集上对 PerSEveML 进行了评估,这些数据集包含从小到大的极其罕见的事件,并证明了它生成有效假设的能力。PerSEveML 可在 https://biostats-shinyr.kumc.edu/PerSEveML/ 和 https://github.com/sreejatadutta/PerSEveML 上查阅。
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引用次数: 0
期刊
Molecular omics
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