Disease Ionomics: Understanding the Role of Ions in Complex Disease

Ionomics is a novel multidisciplinary field that uses advanced techniques to investigate the composition and distribution of all minerals and trace elements in a living organism and their variations under diverse physiological and pathological conditions. It involves both high-throughput elemental profiling technologies and bioinformatic methods, providing opportunities to study the molecular mechanism underlying the metabolism, homeostasis, and cross-talk of these elements. While much effort has been made in exploring the ionomic traits relating to plant physiology and nutrition, the use of ionomics in the research of serious diseases is still in progress. In recent years, a number of ionomic studies have been carried out for a variety of complex diseases, which offer theoretical and practical insights into the etiology, early diagnosis, prognosis, and therapy of them. This review aims to give an overview of recent applications of ionomics in the study of complex diseases and discuss the latest advances and future trends in this area. Overall, disease ionomics may provide substantial information for systematic understanding of the properties of the elements and the dynamic network of elements involved in the onset and development of diseases.     

Target Score—A Proteomics Data Selection Tool Applied to Esophageal Cancer Identifies GLUT1-Sialyl Tn Glycoforms as Biomarkers of Cancer Aggressiveness

Esophageal cancer (EC) is a life-threatening disease, demanding the discovery of new biomarkers and molecular targets for precision oncology. Aberrantly glycosylated proteins hold tremendous potential towards this objective. In the current study, a series of esophageal squamous cell carcinomas (ESCC) and EC-derived circulating tumor cells (CTCs) were screened by immunoassays for the sialyl-Tn (STn) antigen, a glycan rarely expressed in healthy tissues and widely observed in aggressive gastrointestinal cancers. An ESCC cell model was glycoengineered to express STn and characterized in relation to cell proliferation and invasion in vitro. STn was found to be widely present in ESCC (70% of tumors) and in CTCs in 20% of patients, being associated with general recurrence and reduced survival. Furthermore, STn expression in ESCC cells increased invasion in vitro, while reducing cancer cells proliferation. In parallel, an ESCC mass spectrometry-based proteomics dataset, obtained from the PRIDE database, was comprehensively interrogated for abnormally glycosylated proteins. Data integration with the Target Score, an algorithm developed in-house, pinpointed the glucose transporter type 1 (GLUT1) as a biomarker of poor prognosis. GLUT1-STn glycoproteoforms were latter identified in tumor tissues in patients facing worst prognosis. Furthermore, healthy human tissues analysis suggested that STn glycosylation provided cancer specificity to GLUT1. In conclusion, STn is a biomarker of worst prognosis in EC and GLUT1-STn glycoforms may be used to increase its specificity on the stratification and targeting of aggressive ESCC forms.     

Gouda Cheese with Modified Content of β-Casein as a Source of Peptides with ACE- and DPP-IV-Inhibiting Bioactivity: A Study Based on In Silico and In Vitro Protocol

In silico and in vitro methods were used to analyze ACE- and DPP-IV-inhibiting potential of Gouda cheese with a modified content of β-casein. Firstly, the BIOPEP-UWM database was used to predict the presence of ACE and DPP-IV inhibitors in casein sequences. Then, the following Gouda cheeses were produced: with decreased, increased, and normative content of β-casein after 1 and 60 days of ripening each (six variants in total). Finally, determination of the ACE/DPP-IV-inhibitory activity and the identification of peptides in respective Gouda-derived water-soluble extracts were carried out. The identification analyses were supported with in silico calculations, i.e., heatmaps and quantitative parameters. All Gouda variants exhibited comparable ACE inhibition, whereas DPP-IV inhibition was more diversified among the samples. The samples derived from Gouda with the increased content of β-casein (both stages of ripening) had the highest DPP-IV-inhibiting potency compared to the same samples measured for ACE inhibition. Regardless of the results concerning ACE and DPP-IV inhibition among the cheese samples, the heatmap showed that the latter bioactivity was predominant in all Gouda variants, presumably because it was based on the qualitative approach (i.e., peptide presence in the sample). Our heatmap did not include the bioactivity of a single peptide as well as its quantity in the sample. In turn, the quantitative parameters showed that the best sources of ACE/DPP-IV inhibitors were all Gouda-derived extracts obtained after 60 days of the ripening. Although our protocol was efficient in showing some regularities among Gouda cheese variants, in vivo studies are recommended for more extensive investigations of this subject.     

Incorporating Machine Learning into Established Bioinformatics Frameworks

The exponential growth of biomedical data in recent years has urged the application of numerous machine learning techniques to address emerging problems in biology and clinical research. By enabling the automatic feature extraction, selection, and generation of predictive models, these methods can be used to efficiently study complex biological systems. Machine learning techniques are frequently integrated with bioinformatic methods, as well as curated databases and biological networks, to enhance training and validation, identify the best interpretable features, and enable feature and model investigation. Here, we review recently developed methods that incorporate machine learning within the same framework with techniques from molecular evolution, protein structure analysis, systems biology, and disease genomics. We outline the challenges posed for machine learning, and, in particular, deep learning in biomedicine, and suggest unique opportunities for machine learning techniques integrated with established bioinformatics approaches to overcome some of these challenges.     

基于字典的DNA序列压缩算法研究及应用*

在现有DNA序列数据压缩算法的基础上,以DNA序列数据的存储效率及生物学解释综合考虑,设计并实现了基于字典的DNA序列压缩算法DNADCompress.算法核心包括重复子串字典建立、字典项筛选、字串压缩编码三方面.实验数据表明,数据压缩算法压缩效果达到常用DNA序列压缩算法水平,并为序列生物学解释提供了基础.     

Polyphenol Supplementation Reverses Age-Related Changes in Microglial Signaling Cascades

Microglial activity in the aging neuroimmune system is a central player in aging-related dysfunction. Aging alters microglial function via shifts in protein signaling cascades. These shifts can propagate neurodegenerative pathology. Therapeutics require a multifaceted approach to understand and address the stochastic nature of this process. Polyphenols offer one such means of rectifying age-related decline. Our group used mass spectrometry (MS) analysis to explicate the complex nature of these aging microglial pathways. In our first experiment, we compared primary microglia isolated from young and aged rats and identified 197 significantly differentially expressed proteins between these groups. Then, we performed bioinformatic analysis to explore differences in canonical signaling cascades related to microglial homeostasis and function with age. In a second experiment, we investigated changes to these pathways in aged animals after 30-day dietary supplementation with NT-020, which is a blend of polyphenols. We identified 144 differentially expressed proteins between the NT-020 group and the control diet group via MS analysis. Bioinformatic analysis predicted an NT-020 driven reversal in the upregulation of age-related canonical pathways that control inflammation, cellular metabolism, and proteostasis. Our results highlight salient aspects of microglial aging at the level of protein interactions and demonstrate a potential role of polyphenols as therapeutics for age-associated dysfunction.     

CPL: Detecting Protein Complexes by Propagating Labels on Protein-Protein Interaction Network

Proteins usually bind together to form complexes, which play an important role in cellular activities. Many graph clustering methods have been proposed to identify protein complexes by finding dense regions in protein-protein interaction networks. We present a novel framework （CPL） that detects protein complexes by propagating labels through interactions in a network, in which labels denote complex identifiers. With proper propagation in CPL, proteins in the same complex will be assigned with the same labels. CPL does not make any strong assumptions about the topological structures of the complexes, as in previous methods. Tile CPL algorithm is tested on several publicly available yeast protein-protein interaction networks and compared with several state-of-the-art methods. The results suggest that CPL performs better than the existing methods. An analysis of the functional homogeneity based on a gene ontology analysis shows that the detected complexes of CPL are highly biologically relevant.     

针对H1N1病毒的多特征siRNA设计

针对甲型流感病毒H1N1基因,从RNAi的角度出发,采用多特征融合的方法,进行siRNA预测。对2009年的46株病毒序列的PA片段进行分析,从经过序列分析所获得的众多靶序列中,采用结构分析手段对靶序列进行筛选,获得较易干扰的靶序列及设计出相应的siRNA。2009年爆发的H1N1病毒,序列保守性高,靶序列一致性高,结构保守性高。该方法可以有效选择可能的靶序列,并在此基础上进一步筛选,以获得少量较易干扰的靶序列,该方法为复杂序列siRNA的设计提供了新思路,对siRNA的优化设计有指导意义,有助于利用RNAi进行H1N1治疗的后续研究。     

基于动态算法的序列分析

为了获得2009年新型甲型H1N1流感病毒与2008流感病毒的基因序列及氨基酸序列的一致性,以便对一个基因家族的生物学特征有一个简明扼要的了解,针对目前流行的新型甲型H1N1流感病毒的基因序列及其所编码的氨基酸序列,采用动态规划算法对其一级序列进行序列相似度分析,获得了2009年新型甲型H1N1流感病毒的NA和M基因片段以及2008年猪源性甲型H1N1流感病毒的相应基因片段同源性高、在有些位点发生了基因突变增添和突变缺失等重要基因信息。为此次新型甲型H1N1流感病毒的研究提供了依据。