Statistical considerations of optimal study design for human plasma proteomics and biomarker discovery

A mass spectrometry-based plasma biomarker discovery workflow was developed to facilitate biomarker discovery. Plasma from either healthy volunteers or patients with pancreatic cancer was 8-plex iTRAQ labeled, fractionated by 2-dimensional reversed phase chromatography and subjected to MALDI ToF/ToF mass spectrometry. Data were processed using a q-value based statistical approach to maximize protein quantification and identification. Technical (between duplicate samples) and biological variance (between and within individuals) were calculated and power analysis was thereby enabled. An a priori power analysis was carried out using samples from healthy volunteers to define sample sizes required for robust biomarker identification. The result was subsequently validated with a post hoc power analysis using a real clinical setting involving pancreatic cancer patients. This demonstrated that six samples per group (e.g., pre- vs post-treatment) may provide sufficient statistical power for most proteins with changes>2 fold. A reference standard allowed direct comparison of protein expression changes between multiple experiments. Analysis of patient plasma prior to treatment identified 29 proteins with significant changes within individual patient. Changes in Peroxiredoxin II levels were confirmed by Western blot. This q-value based statistical approach in combination with reference standard samples can be applied with confidence in the design and execution of clinical studies for predictive, prognostic, and/or pharmacodynamic biomarker discovery. The power analysis provides information required prior to study initiation.     

天然活性多肽的发掘策略和生产技术

活性多肽可以参与生命机体的多种生理活动,对促进人体健康发挥着重要的作用,如降血压、降血糖、降血脂和抗癌等,其创制技术也逐渐成为重要的研究和应用转化方向。本综述旨在总结天然活性多肽的发掘策略和生产技术的研究进展。目前,天然活性多肽的发掘与生产技术主要包括自上而下和自下而上两种方法,其中自上而下方法在多肽发掘方面主要为直接提取鉴定法,在生产技术方面主要包括直接提取法、酶解法和微生物发酵法;自下而上方法在多肽发掘方面包括天然活性多肽改造和数据库发掘方法,在生产技术方面主要方法包括化学合成法、酶合成法、基因重组表达法和无细胞合成法。自上而下的天然多肽制备与功能验证方法存在步骤烦琐、耗费时间长、功能不确定性大、实验与生产成本高以及质量控制难度大等问题;而自下而上的活性多肽合成与功能验证方法适合多肽药物的开发,而难以用于功能食品。随着测序和质谱技术的发展,人们更容易从分子水平获取物种蛋白组信息。以此蛋白组信息为根据,将自上而下和自下而上两种方法结合,可以克服单独使用这两种方法存在的问题,从而为快速开发和生产天然活性多肽提供新的策略。     

Functional hierarchical models for identifying genes with different time-course expression profiles

Time-course studies of gene expression are essential in biomedical research to understand biological phenomena that evolve in a temporal fashion. We introduce a functional hierarchical model for detecting temporally differentially expressed (TDE) genes between two experimental conditions for cross-sectional designs, where the gene expression profiles are treated as functional data and modeled by basis function expansions. A Monte Carlo EM algorithm was developed for estimating both the gene-specific parameters and the hyperparameters in the second level of modeling. We use a direct posterior probability approach to bound the rate of false discovery at a pre-specified level and evaluate the methods by simulations and application to microarray time-course gene expression data on Caenorhabditis elegans developmental processes. Simulation results suggested that the procedure performs better than the two-way ANOVA in identifying TDE genes, resulting in both higher sensitivity and specificity. Genes identified from the C. elegans developmental data set show clear patterns of changes between the two experimental conditions.     

A data-driven clustering method for time course gene expression data

Gene expression over time is, biologically, a continuous process and can thus be represented by a continuous function, i.e. a curve. Individual genes often share similar expression patterns (functional forms). However, the shape of each function, the number of such functions, and the genes that share similar functional forms are typically unknown. Here we introduce an approach that allows direct discovery of related patterns of gene expression and their underlying functions (curves) from data without a priori specification of either cluster number or functional form. Smoothing spline clustering (SSC) models natural properties of gene expression over time, taking into account natural differences in gene expression within a cluster of similarly expressed genes, the effects of experimental measurement error, and missing data. Furthermore, SSC provides a visual summary of each cluster's gene expression function and goodness-of-fit by way of a 'mean curve' construct and its associated confidence bands. We apply this method to gene expression data over the life-cycle of Drosophila melanogaster and Caenorhabditis elegans to discover 17 and 16 unique patterns of gene expression in each species, respectively. New and previously described expression patterns in both species are discovered, the majority of which are biologically meaningful and exhibit statistically significant gene function enrichment. Software and source code implementing the algorithm, SSClust, is freely available (http://genemerge.bioteam.net/SSClust.html).     

基于基因差异表达的微弱信号识别疾病相关功能

当两组样本间基因表达的差异程度较低或样本量较少时,采用通常的错误发现率(falsediscovery rate,FDR)控制水平(如5%或10%),可能无法识别足够多的差异表达基因以进行后续的功能富集分析。然而,功能富集分析对差异表达基因中的错误发现具有一定的稳健性。所以,采用较低的FDR控制水平(即允许较高的FDR)识别差异表达基因,可能可以可靠地发现疾病相关功能。本文分析了5套研究乳腺癌转移的基因表达谱,通过其中差异表达信号较强的3套数据,论证了即使差异表达基因的FDR达到25%,功能富集分析的结果仍具有较高的稳健性。然后,在另外2套差异表达信号微弱的数据中,采用25%的FDR控制水平筛选差异表达基因来进行功能富集分析,并与前述3套数据的功能富集结果做比较。结果显示,采用较低的FDR控制水平筛选差异表达基因,仍然可以可靠地识别乳腺癌转移相关功能。分析结果也提示,在乳腺癌转移过程中,一些功能较为宽泛的生物学过程(如细胞分裂、细胞周期和DNA复制等)整体受到了扰动,反映出乳腺癌转移是一种涉及广泛基因表达改变的系统性疾病。     

Discovery of regulatory molecular events and biomarkers using 2D capillary chromatography and mass spectrometry

An important component of proteomic research is the high-throughput discovery of novel proteins and protein-protein interactions that control molecular events that contribute to critical cellular functions and human disease. The interactions of proteins are essential for cellular functions. Identifying perturbation of normal cellular protein interactions is vital for understanding the disease process and intervening to control the disease. A second area of proteomics research is the discovery of proteins that will serve as biomarkers for the early detection, diagnosis and drug treatment response for specific diseases. These studies have been referred to as clinical proteomics. To discover biomarkers, proteomics research employs the quantitative comparison of peptide and protein expression in body fluids and tissues from diseased individuals (case) versus normal individuals (control). Methods that couple 2D capillary liquid chromatography (LC) and tandem mass spectrometry (MS/MS) analysis have greatly facilitated this discovery science. Coupling 2D-LC/MS/MS analysis with automated genome-assisted spectra interpretation allows a direct, high-throughput and high-sensitivity identification of thousands of individual proteins from complex biological samples. The systematic comparison of experimental conditions and controls allows protein function or disease states to be modeled. This review discusses the different purification and quantification strategies that have been developed and used in combination with 2D-LC/MS/MS and computational analysis to examine regulatory protein networks and clinical samples.     

Discovery of regulatory molecular events and biomarkers using 2D capillary chromatography and mass spectrometry

An important component of proteomic research is the high-throughput discovery of novel proteins and protein–protein interactions that control molecular events that contribute to critical cellular functions and human disease. The interactions of proteins are essential for cellular functions. Identifying perturbation of normal cellular protein interactions is vital for understanding the disease process and intervening to control the disease. A second area of proteomics research is the discovery of proteins that will serve as biomarkers for the early detection, diagnosis and drug treatment response for specific diseases. These studies have been referred to as clinical proteomics. To discover biomarkers, proteomics research employs the quantitative comparison of peptide and protein expression in body fluids and tissues from diseased individuals (case) versus normal individuals (control). Methods that couple 2D capillary liquid chromatography (LC) and tandem mass spectrometry (MS/MS) analysis have greatly facilitated this discovery science. Coupling 2D-LC/MS/MS analysis with automated genome-assisted spectra interpretation allows a direct, high-throughput and high-sensitivity identification of thousands of individual proteins from complex biological samples. The systematic comparison of experimental conditions and controls allows protein function or disease states to be modeled. This review discusses the different purification and quantification strategies that have been developed and used in combination with 2D-LC/MS/MS and computational analysis to examine regulatory protein networks and clinical samples.     

Discovery of multi-dimensional modules by integrative analysis of cancer genomic data

Recent technology has made it possible to simultaneously perform multi-platform genomic profiling (e.g. DNA methylation (DM) and gene expression (GE)) of biological samples, resulting in so-called ‘multi-dimensional genomic data’. Such data provide unique opportunities to study the coordination between regulatory mechanisms on multiple levels. However, integrative analysis of multi-dimensional genomics data for the discovery of combinatorial patterns is currently lacking. Here, we adopt a joint matrix factorization technique to address this challenge. This method projects multiple types of genomic data onto a common coordinate system, in which heterogeneous variables weighted highly in the same projected direction form a multi-dimensional module (md-module). Genomic variables in such modules are characterized by significant correlations and likely functional associations. We applied this method to the DM, GE, and microRNA expression data of 385 ovarian cancer samples from the The Cancer Genome Atlas project. These md-modules revealed perturbed pathways that would have been overlooked with only a single type of data, uncovered associations between different layers of cellular activities and allowed the identification of clinically distinct patient subgroups. Our study provides an useful protocol for uncovering hidden patterns and their biological implications in multi-dimensional ‘omic’ data.     

A streamlined platform for high-content functional proteomics of primary human specimens

Achieving information content of satisfactory breadth and depth remains a formidable challenge for proteomics. This problem is particularly relevant to the study of primary human specimens, such as tumor biopsies, which are heterogeneous and of finite quantity. Here we present a functional proteomics strategy that unites the activity-based protein profiling and multidimensional protein identification technologies (ABPP-MudPIT) for the streamlined analysis of human samples. This convergent platform involves a rapid initial phase, in which enzyme activity signatures are generated for functional classification of samples, followed by in-depth analysis of representative members from each class. Using this two-tiered approach, we identified more than 50 enzyme activities in human breast tumors, nearly a third of which represent previously uncharacterized proteins. Comparison with cDNA microarrays revealed enzymes whose activity, but not mRNA expression, depicted tumor class, underscoring the power of ABPP-MudPIT for the discovery of new markers of human disease that may evade detection by other molecular profiling methods.     

Gene‐Centric Metagenome Analysis Reveals Gene Clusters for Carbon Monoxide Conversion and Validates Isolation of a Clostridial Acetogen for C2 Chemical Production

Syngas fermentation is largely dependent on acetogens that occur in various anaerobic environmental samples including soil, sediment, and feces. Here the authors report the metagenomic isolation of acetogens for C2 chemical production from syngas. Screening acetogens for C2 chemical production typically involves detecting the presence of the Wood‐Ljungdahl Pathway for carbon monoxide conversion. The authors collect samples from river‐bed sediments potentially having conditions suitable for carbon monoxide‐converting anaerobes, and enrich the samples under carbon monoxide selection pressure. Changes in the microbial community during the experimental procedure are investigated using both amplicon and shotgun metagenome sequencing. Combined next‐generation sequencing techniques enabl in situ tracking of the major acetogenic bacterial group and lead to the discovery of a 16 kb of gene cluster for WLP. The authors isolat an acetogenic clostridial strain from the enrichment culture (strain H21‐9). The functional activity of H21‐9 is confirmed by its high level of production of C2 chemicals from carbon monoxide (77.4 mM acetate and 2.5 mM of ethanol). This approach of incorporating experimental enrichment with metagenomic analysis can facilitate the discovery of novel strains from environmental habitats by tracking target strains during the screening process, combined with validation of their functional activity.     

Exploiting aberrant mRNA expression in autism for gene discovery and diagnosis

Autism spectrum disorder (ASD) is characterized by substantial phenotypic and genetic heterogeneity, which greatly complicates the identification of genetic factors that contribute to the disease. Study designs have mainly focused on group differences between cases and controls. The problem is that, by their nature, group difference-based methods (e.g., differential expression analysis) blur or collapse the heterogeneity within groups. By ignoring genes with variable within-group expression, an important axis of genetic heterogeneity contributing to expression variability among affected individuals has been overlooked. To this end, we develop a new gene expression analysis method—aberrant gene expression analysis, based on the multivariate distance commonly used for outlier detection. Our method detects the discrepancies in gene expression dispersion between groups and identifies genes with significantly different expression variability. Using this new method, we re-visited RNA sequencing data generated from post-mortem brain tissues of 47 ASD and 57 control samples. We identified 54 functional gene sets whose expression dispersion in ASD samples is more pronounced than that in controls, as well as 76 co-expression modules present in controls but absent in ASD samples due to ASD-specific aberrant gene expression. We also exploited aberrantly expressed genes as biomarkers for ASD diagnosis. With a whole blood expression data set, we identified three aberrantly expressed gene sets whose expression levels serve as discriminating variables achieving >70 % classification accuracy. In summary, our method represents a novel discovery and diagnostic strategy for ASD. Our findings may help open an expression variability-centered research avenue for other genetically heterogeneous disorders.     

Expression of Functional Plasmodium falciparum Enzymes Using a Wheat Germ Cell-Free System

One decade after the sequencing of the Plasmodium falciparum genome, 95% of malaria proteins in the genome cannot be expressed in traditional cell-based expression systems, and the targets of the best new leads for antimalarial drug discovery are either not known or not available in functional form. For a disease that kills up to 1 million people per year, routine expression of recombinant malaria proteins in functional form is needed both for the discovery of new therapeutics and for identification of targets of new drugs. We tested the general utility of cell-free systems for expressing malaria enzymes. Thirteen test enzyme sequences were reverse amplified from total RNA, cloned into a plant-like expression vector, and subjected to cell-free expression in a wheat germ system. Protein electrophoresis and autoradiography confirmed the synthesis of products of expected molecular masses. In rare problematic cases, truncated products were avoided by using synthetic genes carrying wheat codons. Scaled-up production generated 39 to 354 μg of soluble protein per 10 mg of translation lysate. Compared to rare proteins where cell-based systems do produce functional proteins, the cell-free yields are comparable or better. All 13 test products were enzymatically active, without failure. This general path to produce functional malaria proteins should now allow the community to access new tools, such as biologically active protein arrays, and lead to the discovery of new chemical functions, structures, and inhibitors of previously inaccessible malaria gene products.     

The functional pathway analysis and clinical significance of miR-20a and its related lncRNAs in breast cancer

BackgroundmiR-20a is a critical molecule in various biological processes and cancer progression procedures. However, its relationships with lncRNAs and their functional pathway analysis in breast tumorigenesis are less intensively studied.MethodsThe expression data from TCGA database and multiple bioinformatics resources were used to check the expression levels, survival curves, interactions and functional illustrations of miR-20a and its related lncRNAs (XIST, H19 and MALAT1) in breast cancer patients. The luciferase reporter assays and Pearson's correlation analyses were utilized to verify the direct regulatory relationship between miR-20a and three lncRNAs (XIST, H19 and MALAT1). In vitro cell proliferation, migration and invasion assays, were performed to check the biological effects of miR-20a and XIST in different breast cancer cell lines. The receiver operating characteristic curve (ROC) analyses were done for evaluating diagnostic values of serum miR-20a and XIST in breast cancer patients.ResultsThe miR-20a expression was significantly up-regulated in both breast cancer samples and serum samples, and correlated with poor survival rate in breast cancer patients. LncRNAs (XIST, H19 and MALAT1) directly bound to hsa-miR-20a and were negatively correlated with hsa-miR-20a expression in breast cancer patient samples. For functional illustrations and downstream signaling pathways analysis, XIST, H19 and MALAT1 mainly shared their regulatory functions in cell motility and interleukin signaling in breast cancer progression. Additionally, over-expression of miR-20a and inhibition of XIST promoted breast cancer cell growth, migration and invasion in vitro, and serum miR-20a and XIST served as potential diagnostic biomarkers for breast cancer with the area under ROC curve (AUC) of 0.87 (95% CI = 0.78 to 0.97), and 0.78 (95% CI = 0.67 to 0.89) respectively.ConclusionsTaken together, these findings provide us novel insights and avenues for utilizing miR-20a and its related lncRNAs as potential diagnostic biomarkers and promising therapeutic targets for breast cancer treatment.