Towards a comprehensive proteome of normal and malignant human colon tissue by 2-D-LC-ESI-MS and 2-DE proteomics and identification of S100A12 as potential cancer biomarker

The aim of this study was to characterize the proteome of normal and malignant colonic tissue. We previously studied the colon proteome using 2‐DE and MALDI‐MS and identified 734 proteins (Roeßler, M., Rollinger, W., Palme S., Hagmann, M.‐L., et al.., Clin. Cancer Res. 2005, 11, 6550–6557). Here we report the identification of additional colon proteins from the same set of tissue samples using a complementary nano‐flow 2‐D‐LC‐ESI‐MS. In total, 484 proteins were identified in colon. Of these, 252 had also been identified by the 2‐DE/MALDI‐MS approach, whereas 232 proteins were unique to the 2‐D‐LC‐ESI‐MS analysis. Comparing protein expression in neoplastic and normal colon tissue indicated elevated expression of several proteins in colorectal cancer, among them the well established tumor marker carcinoembryonic antigen, as well as calnexin, 40S ribosomal protein S15a, serpin H1, and S100A12. Overexpression of these proteins was confirmed by immunoblotting. Serum levels of S100A12 were determined by ELISA and were found to be strongly elevated in colorectal cancer patients compared to healthy individuals. We conclude, that 2‐D‐LC‐ESI‐MS is a powerful approach to identify and compare protein profiles of tissue samples, that it is complementary to 2‐DE/MALDI‐MS approaches and has the potential to identify novel biomarkers.     

基于多头注意力机制和残差神经网络的肽谱匹配打分算法

肽谱匹配打分算法在肽序列鉴定的过程中起着关键性作用，而传统的打分算法无法充分有效地利用肽碎裂规律进行打分。针对这一问题提出了一种结合肽序列信息表征的多分类概率和式打分算法deepScore-α，该算法不需要考虑全局信息进行二次打分，不存在理论质谱与实验质谱相似度计算方法的限制。deepScore-α使用一维残差网络对序列底层信息进行抽取，再通过多头注意力机制融合序列不同肽键位点对当前肽键位点断裂产生的影响从而生成最终的碎片离子相对强度分布概率矩阵，结合肽序列碎片离子的实际相对强度计算出最终的肽谱匹配得分。该算法与常用开源鉴定工具Comet以及MSGF+进行了比较：在人类蛋白组数据集上错误发现率（FDR）为0.01时,deepScore-α保留的肽序列数量提升了约14%，Top1命中率(正确肽序列在得分最高的谱图所占比例)最大提升约5个百分点。使用人类蛋白组数据集训练的模型在ProteomeTools2数据集上进行泛化性能测试，结果表明，在FDR为0.01的条件下deepScore-α保留的肽序列数量提升了约7%，Top1命中率提升了约5个百分点，Top1中来自Decoy库的鉴定结果减少约60%。实验结果证明，deepScore-α在较低FDR值情况下保留更多的肽序列并提升Top1的命中率，且具有较好的泛化性能。     

Proteome of human endometrium: Identification of differentially expressed proteins in proliferative and secretory phase endometrium

Purpose: To exploit the potential of proteomics to identify and study additional yet‐unidentified important proteins present in human endometrium. Experimental design: The proteome of human endometrium would be established using 2‐DE and MALDI and the data analyzed to identify differential protein expression in the proliferative and secretory phase of the menstrual cycle using PDQuest software and MALDI. Results: In the present work, 2‐DE of human endometrium protein led to the resolution of over 200 spots. Subsequent MALDI analysis of 215 spots allowed the identification of 194 proteins. A total of 57 out of the 215 spots were found to be differentially expressed, out of which 49 could be identified using MALDI. These differentially expressed proteins included structural proteins, molecular chaperones, signaling proteins, metabolic proteins, proteins related to immunity, RNA biogenesis, protein biosynthesis and others. The differential expressions of seven representative proteins in secretory and proliferative phase endometrium tissue were confirmed by immunoblot analysis. Conclusion and clinical relevance: This study establishes the 2‐D proteome of human endometrium represented by 194 identified protein spots. The present data provides an important clue towards determining the function of these proteins with respect to endometrium related diseases.     

Shotgun immunoproteomics to identify disease‐associated bacterial antigens: Application to human colon cancer

Circulating antibodies reflect a mirror view of invading antigens that are related to infection and cancer. This was recently exemplified by using serum antibodies to capture Streptococcus bovis antigens followed by MS to generate antigen profiles that were diagnostic for colon cancer. These bacterial antigen profiles have a high potential to aid in the immuno‐diagnosis of this disease, as the magnitude of the immune response to bacterial antigens is, in general, superior to the immune response against tumor (self) antigens. In this study, the identity of individual colon cancer‐associated streptococcal antigens was revealed by enrichment of these “diagnostic” antigens by selected patient antibodies followed by high‐accuracy nanoLC‐MS/MS peptide identification. This showed that both the histone‐like protein HlpA and the ribosomal protein Rp L7/L12 are members of the colon cancer‐associated S. bovis immunome. Both antigens also seem to belong to the group of anchorless surface proteins, like 14 additional proteins that were co‐identified in S. bovis cell wall extracts. Among these were the known streptococcal anchorless surface proteins GAPDH and Enolase. Taken together, these data show that shotgun immunoproteomics, combining immunocapture in‐line with LC MS/MS, is a convenient approach for the rapid identification of disease‐associated bacterial antigens.     

Discovery of putative oocyte quality markers by comparative ExacTag proteomics

Purpose: Identification of the biomarkers of oocyte quality, and developmental and reprogramming potential is of importance to assisted reproductive technology in humans and animals. Experimental design: PerkinElmer ExacTag? Kit was used to label differentially proteins in pig oocyte extracts (oocyte proteome) and pig oocyte‐conditioned in vitro maturation media (oocyte secretome) obtained with high‐ and low‐quality oocytes. Results: We identified 16 major proteins in the oocyte proteome that were expressed differentially in high‐ versus low‐quality oocytes. More abundant proteins in the high‐quality oocyte proteome included kelch‐like ECH‐associated protein 1 (an adaptor for ubiquitin‐ligase CUL3), nuclear export factor CRM1 and ataxia‐telangiectasia mutated protein kinase. Dystrophin (DMD) was more abundant in low‐quality oocytes. In the secretome, we identified 110 proteins, including DMD and cystic fibrosis transmembrane conductance regulator, two proteins implicated in muscular dystrophy and cystic fibrosis, respectively. Monoubiquitin was identified in the low‐quality‐oocyte secretome. Conclusions and clinical implications: A direct, quantitative proteomic analysis of small oocyte protein samples can identify potential markers of oocyte quality without the need for a large amount of total protein. This approach will be applied to discovery of non‐invasive biomarkers of oocyte quality in assisted human reproduction and in large animal embryo transfer programs.     

Exploration of the normal human bronchoalveolar lavage fluid proteome

We obtained insight into normal lung function by proteome analysis of bronchoalveolar lavage fluid (BALF) from six normal human subjects using a "Lyse-N-Go' shotgun proteomic protocol. Intra-sample variation was calculated using three different label-free methods, (i) protein sequence coverage; (ii) peptide spectral counts and (iii) peptide single-ion current areas (PICA), which generates protein expression data by summation of the area under the curve for a given peptide single-ion current trace and then adding values for all peptides from that same parent protein. PICA gave the least intra-subject variability and was used to calculate differences in protein expression between the six subjects. We observed an average threefold inter-sample variability, which affects analysis of changes in protein expression that occur in different diseases. We detected 167 unique proteins with >100 proteins detected in each of the six individual BAL samples, 42 of which were common to all six subjects. Gene ontology analysis demonstrated enrichment of several biological processes in the lung, reflecting its expected role in gas exchange and host defense as an immune organ. The same biological processes were enriched compared to either plasma or total genome proteome, suggesting an active enrichment of plasma proteins in the lung rather than passive capillary leak.     

Identification of distinctive protein expression patterns in colorectal adenoma

Purpose: As a pre‐malignant precursor, adenoma provides an ideal tissue for proteome profiling to investigate early colorectal cancer development and provide possible targets for preventive interventions. The aim of this study was to identify patterns of differential protein expression that distinguish colorectal adenoma from normal tissue. Experimental design: Twenty paired samples of adenoma and normal mucosa were analysed by 2‐DE and MALDI‐TOF/TOF MS to detect proteins with ≥2‐fold differential expression. Results: Four proteins were up‐regulated in adenoma (Annexin A3, S100A11, S100P and eIF5A‐1) and three were down‐regulated (Galectin‐1, S100A9 and FABPL). S100P, galectin‐1, S100A9 and FABPL expression was localised by immunohistochemistry. Conclusions and clinical relevance: Distinctive patterns of in vivo protein expression in colorectal adenoma were identified for the first time. These proteins have important functions in cell differentiation, proliferation and metabolism, and may play a crucial role in early colorectal carcinogenesis. The ability to recognise premalignant lesions may have important applications in cancer prevention.     

Investigation of an albumin‐enriched fraction of human serum and its albuminome

The removal of albumin and other high abundance proteins is a routine first step in the analysis of serum and plasma proteomes. However, as albumin can bind proteins and peptides, there is a universal concern as to how the serum proteome is changed by the removal of albumin. To address this concern, the current study was designed to identify proteins and peptides removed from the serum during albumin depletion; to determine which of these are bound to albumin (rather than copurified) and whether the bound proteins are intact proteins or peptide fragments. Sequential, independent analyses including both anti‐albumin antibody (anti‐HSA) affinity chromatography and SEC were used to isolate albumin‐bound proteins. RP‐HPLC and 1‐D SDS‐PAGE were then used to further separate the proteins prior to identification by MS/MS. Finally, whole protein molecular weight (MW) MS measurements coupled with protein coverage obtained by MS were combined to assess whether the bound proteins were intact or peptide fragments. Combining the results from multiple approaches, 35 proteins, of which 24 are intact, were found to be associated with albumin, and they include both known high and low abundance proteins.     

结肠癌患者诊断的基因标志物识别算法*

为了得到具有强分类信息的极少结肠癌特征基因,实现对结肠癌患者的准确识别,文中提出结肠癌患者诊断的基因标志物识别算法.首先提出基因密度和基因距离的概念,构造以基因密度和基因距离分别为横纵坐标的基因2D空间散列图,选择处于密度峰值点的基因构成优选基因子集,然后采用密度峰值K中心点(DP_K-medoids)算法对降维后的结肠数据集样本进行聚类分析.基因距离和样本距离分别采用欧氏距离、曼哈顿距离、切比雪夫距离和夹角余弦距离度量.实验表明,在夹角余弦距离下,文中算法可以选择到具有高准确率、高灵敏度、高特异度和高马修斯相关系数的规模较小的结肠癌基因子集.     

Proteomics cataloging analysis of human expressed prostatic secretions reveals rich source of biomarker candidates

Expressed prostatic secretions (EPS) contain proteins of prostate origin that may reflect the health status of the prostate and be used as diagnostic markers for prostate diseases including prostatitis, benign prostatic hyperplasia, and prostate cancer. Despite their importance and potential applications, a complete catalog of EPS proteins is not yet available. We, therefore, undertook a comprehensive analysis of the EPS proteome using 2‐D micro‐LC combined with MS/MS. Using stringent filtering criteria, we identified a list of 114 proteins with at least two unique‐peptide hits and an additional 75 proteins with only a single unique‐peptide hit. The proteins identified include kallikrein 2 (KLK2), KLK3 (prostate‐specific antigen), KLK11, and nine cluster of differentiation (CD) molecules including CD10, CD13, CD14, CD26, CD66a, CD66c, CD 143, CD177, and CD224. To our knowledge, this list represents the first comprehensive characterization of the EPS proteome, and it provides a candidate biomarker list for targeted quantitative proteomics analysis using a multiple reaction monitoring (MRM) approach. To help prioritize candidate biomarkers, we constructed a protein–protein interaction network of the EPS proteins using Cytoscape (www.cytoscape.org), and overlaid the expression level changes from the Oncomine database onto the network.     

Prediction of human breast and colon cancers from imbalanced data using nearest neighbor and support vector machines

This study proposes a novel prediction approach for human breast and colon cancers using different feature spaces. The proposed scheme consists of two stages: the preprocessor and the predictor. In the preprocessor stage, the mega-trend diffusion (MTD) technique is employed to increase the samples of the minority class, thereby balancing the dataset. In the predictor stage, machine-learning approaches of K-nearest neighbor (KNN) and support vector machines (SVM) are used to develop hybrid MTD-SVM and MTD-KNN prediction models. MTD-SVM model has provided the best values of accuracy, G-mean and Matthew's correlation coefficient of 96.71%, 96.70% and 71.98% for cancer/non-cancer dataset, breast/non-breast cancer dataset and colon/non-colon cancer dataset, respectively. We found that hybrid MTD-SVM is the best with respect to prediction performance and computational cost. MTD-KNN model has achieved moderately better prediction as compared to hybrid MTD-NB (Naïve Bayes) but at the expense of higher computing cost. MTD-KNN model is faster than MTD-RF (random forest) but its prediction is not better than MTD-RF. To the best of our knowledge, the reported results are the best results, so far, for these datasets. The proposed scheme indicates that the developed models can be used as a tool for the prediction of cancer. This scheme may be useful for study of any sequential information such as protein sequence or any nucleic acid sequence.     

Salivary biomarkers associated with perceived satiety and body mass in humans

Regulation of food intake and energy homeostasis is controlled by a delicate balancing of numerous central and peripheral factors, including circulating peptide hormones. This study investigated the proteome of saliva using SELDI‐TOF‐MS in relation to satiety and body mass index (BMI) in humans. Within a longitudinal test session, 18 subjects were exposed to a lunch‐induced hunger‐satiety shift, while every 15 min collecting their whole saliva and rating their hunger and satiety. Saliva was analysed by SELDI‐TOF‐MS using IMAC arrays with a chromatographic copper surface (IMAC‐Cu). From all subjects and time points measured, peptide and protein profiles showed 190 common peaks. Their interrelationships show that 37% of the variation was accounted for in one dimension. About 30 means had a strong association (0.70<|r|<0.95) with all subjective satiety ratings across time during the test session, and seven peaks were significantly correlated to BMI. Database MS searches indicated characterisation of some relevant metabolic peptide hormones. In conclusion, SELDI‐TOF‐MS on human saliva provides a valuable and noninvasive way of profiling that enables characterisation of novel and differently expressed peptides and proteins which can be used as biomarkers of satiety and overweight.     

Proteins that underlie neoplastic progression of ulcerative colitis

Patients with ulcerative colitis (UC) have an increased risk for developing colorectal cancer. Because UC tumorigenesis is associated with genomic field defects that can extend throughout the entire colon, including the non‐dysplastic mucosa, we hypothesized that the same field defects will include abnormally expressed proteins. Here, we applied proteomics to study the protein expression of UC neoplastic progression. The protein profiles of colonic epithelium were compared with (i) UC patients without dysplasia (non‐progressors), (ii) non‐dysplastic colonic tissue from UC patient with high‐grade dysplasia or cancer (progressors), (iii) high‐grade dysplastic tissue from UC progressors, and (iv) normal colon. We identified differential protein expression associated with UC neoplastic progression. Proteins relating to mitochondria, oxidative activity, and calcium‐binding proteins were some of the interesting classes of these proteins. Network analysis discovered that Sp1 and c‐myc proteins may play roles in UC early and late stages of neoplastic progression, respectively. Two over‐expressed proteins in the non‐dysplastic tissue of UC progressors, carbamoyl‐phosphate synthase 1 and S100P, were further confirmed by immunohistochemistry analysis. Our study provides insight into the molecular events associated with UC neoplastic progression, which could be exploited for the development of protein biomarkers in fields of non‐dysplastic mucosa that identify a patient's risk for UC dysplasia.     

Floating prioritized subset analysis: A powerful method to detect differentially expressed genes

Controlling the false discovery rate (FDR) is a powerful approach to deal with a large number of hypothesis tests, such as in gene expression data analyses and genome-wide association studies. To further boost power, here we propose a floating prioritized subset analysis (floating PSA) that can more effectively use prior knowledge and detect more genes that are differentially expressed. Genes are first allocated into two subsets: a prioritized subset and a non-prioritized subset, according to investigators’ prior biological knowledge. We allow the FDRs of the two subsets to vary freely (to float) but aim to control the overall FDR at a desired level. An algorithm for the floating PSA is developed to detect the largest number of true positives. Theoretical justifications of the algorithm are given, and computer simulation studies show that the method has good statistical properties. We apply this method to detect genes that are differentially expressed between acute lymphoblastic leukemia and acute myeloid leukemia patients. The result shows that our floating PSA identifies 32 more genes (permutation-based FDR=0.0427) than the conventional (fixed) FDR control. Another example is a colon cancer study, and our floating PSA identifies 43 more genes (permutation-based FDR=0.0502). The floating PSA method is to be recommended for the detection of differentially expressed genes, in light of its power, robustness, and ease of implementation.     

Microfiltration isolation of human urinary exosomes for characterization by MS

Purpose: The purpose of this study was to address the hypothesis that small vesicular urinary particles known as exosomes could be selectively microfiltered using low protein‐binding size exclusion filters, thereby simplifying their use in clinical biomarker discovery studies. Experimental design: We characterized a microfiltration approach using a low protein binding, hydrophilized polyvinylidene difluoride membrane to easily and efficiently isolate urinary exosomes from fresh, room temperature or 4°C urine, with a simultaneous depletion of abundant urinary proteins. Using LC‐MS, immunoblot analysis, and electron microscopy methods, we demonstrate this method to isolate intact exosomes and thereby enrich for a low abundant urinary proteome. Results: In comparison to other standard methods of exosome isolation including ultracentrifugation and nanofiltration, we demonstrate equivalent enrichment of the exosome proteome with reduced co‐purification of abundant urinary proteins. Conclusion and clinical relevance: In conclusion, we demonstrate a microfiltration isolation method that preserves the exosome structure, reduces contamination from higher abundant urinary proteins, and can be easily implemented into mass spectrometry analysis for biomarker discovery efforts or incorporation into routine clinical laboratory applications to yield higher sample throughput.     

Multidimensional protein identification technology analysis highlights mitoxantrone‐induced expression modulations in the primary prostate cancer cell proteome

Chemotherapeutic agents as they are used today have limited effectiveness against prostate cancer, but may potentially be used in new combinations with more efficacious results. Mitoxantrone, used for palliation of prostate cancer, has recently been found by our group to improve the susceptibility of primary prostate cancer cells to killing through the Fas‐mediated death pathway. Here we used a shotgun proteomics approach to first profile the entire prostate cancer proteome and then identify specific factors involved in this mitoxantrone response. Peptides derived from primary prostate cancer cells treated with or without 100 nM mitoxantrone were analyzed by multidimensional protein identification technology (MudPIT). Strict limits and data filtering hierarchies were applied to identify proteins with high confidence. We identified 1498 proteins belonging to the prostate cancer proteome, 83 of which were significantly upregulated and 27 of which were markedly downregulated following mitoxantrone treatment. These proteins perform diverse functions, including ceramide production, tumour suppression, and oxidative reduction. Detailed proteomic analyses of prostate cancer cells and their response to mitoxantrone will further our understanding of its mechanisms of action. Identification of proteins influenced by treatment with mitoxantrone or other compounds may lead to the development of more effective drug combinations against prostate cancer.     

High-throughput DNA methylation datasets for evaluating false discovery rate methodologies

When analyzing high-throughput genomic data, the multiple comparison problem is most often addressed through estimation of the false discovery rate (FDR), using methods such as the Benjamini & Hochberg, Benjamini & Yekutieli, the q-value method, or in controlling the family-wise error rate (FWER) using Holm's step down method. To date, research studies that have compared various FDR/FWER methodologies have made use of limited simulation studies and/or have applied the methods to one or more microarray gene expression dataset(s). However, for microarray datasets the veracity of each null hypothesis tested is unknown so that an objective evaluation of performance cannot be rendered for application data. Due to the role of methylation in X-chromosome inactivation, we postulate that high-throughput methylation datasets may provide an appropriate forum for assessing the performance of commonly used FDR methodologies. These datasets preserve the complex correlation structure between probes, offering an advantage over simulated datasets. Using several methylation datasets, commonly used FDR methods including the q-value, Benjamini & Hochberg, and Benjamini & Yekutieli procedures as well as Holm's step down method were applied to identify CpG sites that are differentially methylated when comparing healthy males to healthy females. The methods were compared with respect to their ability to identify CpG sites located on sex chromosomes as significant, by reporting the sensitivity, specificity, and observed FDR. These datasets are useful for characterizing the performance of multiple comparison procedures, and may find further utility in other tasks such as comparing variable selection capabilities of classification methods and evaluating the performance of meta-analytic methods for microarray data.