Comprehensive profiling of metastasis-related proteins in paired hepatocellular carcinoma cells with different metastasis potentials

Precise and comprehensive identifications of the proteins associated with metastasis are critical for early diagnosis and therapeutic intervention of hepatocellular carcinoma (HCC). Therefore, we investigated the proteomic differences between a pair of HCC cell lines, originating from the same progenitor, with different metastasis potential using amino acid-coded mass tagging-based LC-MS/MS quantitative proteomic approach. Totally the relative abundance of 336 proteins in these cell lines were quantified, in which 121 proteins were upregulated by >30%, and 64 proteins were downregulated by >23% in the cells with high metastasis potential. Further validation studies by Western blotting in a series of HCC cell types with progressively increasing trend of metastasis showed that peroxiredoxin 4, HSP90β and HSP27 were positively correlated with increasing metastasis while prohibitin was negatively correlated with metastasis potential. These validation results were also consistent with that obtained from comparative analysis of clinic tissues samples. Function annotations of differentially expressed HCC proteome suggested that the emergence and development of high metastasis involved the dysregulation of cell migration, cell cycle and membrane traffics. Together our results revealed a much more comprehensive profile than that from 2-DE-based method and provided more global insights into the mechanisms of HCC metastasis and potential markers for clinical diagnosis.     

Considerations for powering a clinical proteomics study: Normal variability in the human plasma proteome

Proteomics is increasingly being applied to the human plasma proteome to identify biomarkers of disease for use in non‐invasive assays. 2‐D DIGE, simultaneously analysing thousands of protein spots quantitatively and maintaining protein isoform information, is one technique adopted. Sufficient numbers of samples must be analysed to achieve statistical power; however, few reported studies have analysed inherent variability in the plasma proteome by 2‐D DIGE to allow power calculations. This study analysed plasma from 60 healthy volunteers by 2‐D DIGE. Two samples were taken, 7 days apart, allowing estimation of sensitivity of detection of differences in spot intensity between two groups using either a longitudinal (paired) or non‐paired design. Parameters for differences were: two‐fold normalised volume change, α of 0.05 and power of 0.8. Using groups of 20 samples, alterations in 1742 spots could be detected with longitudinal sampling, and in 1206 between non‐paired groups. Interbatch gel variability was small relative to the detection parameters, indicating robustness and reproducibility of 2‐D DIGE for analysing large sample sets. In summary, 20 samples can allow detection of a large number of proteomic alterations by 2‐D DIGE in human plasma, the sensitivity of detecting differences was greatly improved by longitudinal sampling and the technology was robust across batches.     

Analysis of the human testis proteome by mass spectrometry and bioinformatics

The testis is a unique organ responsible for sperm production and androgen secretion in men. To analyze the human testis proteome on a large scale, 1-D SDS-PAGE and RP-LC-MS/MS were applied and 1430 proteins in the human testis were identified. Both the false-positive rate of peptides and protein identification confidence scores were calculated in the present study. Subsequent bioinformatics analysis of the human testis proteome revealed 39 testis-specific proteins which may be important for testis functions. And a large family of proteins were identified possibly involved in alternative splicing, which may also be involved in testis-specific splicing events and explain why splicing is so prevalent in the testis. Compared with the studies on brain proteome, researches on the testis proteome is still very limited. Studies of these proteins will give a better understanding on the function of the testis. Moreover, this large-scale identification of testis proteins in humans could serve as a reference for future studies on the mechanisms underlying male infertility, searching for potential contraceptive targets, and developing new treatments for testis cancer.     

Liver Tumors Segmentation Using 3D SegNet Deep Learning Approach

An ultrasonic filter detects signs of malignant tumors by analysing the image’s pixel quality fluctuations caused by a liver ailment. Signs of malignant growth proximity are identified in an ultrasound filter through image pixel quality variations from a liver’s condition. Those changes are more common in alcoholic liver conditions than in other etiologies of cirrhosis, suggesting that the cause may be alcohol instead of liver disease. Existing Two-Dimensional (2D) ultrasound data sets contain an accuracy rate of 85.9% and a 2D Computed Tomography (CT) data set of 91.02%. The most recent work on designing a Three-Dimensional (3D) ultrasound imaging system in or close to real-time is examined. In this article, a Deep Learning (DL) model is implemented and modified to fit liver CT segmentation, and a semantic pixel classification of road scenes is recommended. The architecture is called semantic pixel-wise segmentation and comprises a hierarchical link of encoder-decoder layers. A standard data set was used to test the proposed model for liver CT scans and the tumor accuracy in the training phase. For the normal class, we obtained 100% precision for chronic cirrhosis hepatitis (73%), offset cirrhosis (59.26%), and offensive cirrhosis (91.67%) for chronic hepatitis or cirrhosis (73,0%). The aim is to develop a Computer-Aided Detection (CAD) screening tool to detect steatosis. The results proved 98.33% exactness, 94.59% sensitivity, and 92.11% case with Convolutional Neural Networks (CNN) classification. Although the classifier’s performance did not differentiate so clearly at this level, it was recommended that CNN generally perform better due to the good relationship between Area under the Receiver Operating Characteristics Curve (AUC) and accuracy.     

Modifications of human total serum N-glycome during liver fibrosis-cirrhosis, is it all about immunoglobulins?

The study of the total serum N-glycome during liver cirrhosis has demonstrated numerous alterations. The identification of the glycoproteins carrying these modifications and their relative contribution to the modification of the total serum N-glycome has shown the important role of IgA and IgG. The possible mechanisms of glycosylation alteration of the Igs and of liver secreted glycoproteins, the consequences in the pathophysiology of cirrhosis and their relation to the biomarkers of liver diseases are also discussed in the present review.     

Oian基于神经网络的蛋白质序列分类研究

提出了基于Levenberg-Marquardt(LM)算法的BP神经网络对蛋白质序列进行家族分类的新方法．该方法采用二肽含量对蛋白质序列进行特征提取，根据影响因子评价特征的相对重要性，用改进的BP神经网络LM优化算法构造一个三层人工神经网络，通过对PIR数据库中三类家族的学习，该网络对未知蛋白质序列分类的准确率分别达到了98．9％．98．1％，97．8％。     

ANALYSIS OF DIFFUSE PARENCHYMAL LIVER DISEASE BY LIVER SCINTIGRAMS: DIFFERENTIAL DIAGNOSIS USING FUZZY INFERENCE AND GENETIC ALGORITHM IN NUCLEAR MEDICINE

In nuclear medicine, diagnosis of diffuse parenchymal liver disease such as chronic hepatitis (CH) or liver cirrhosis (LC) is evaluated by size and distortion of the liver, distribution of radioactive tracer in the liver, size and activities of the tracer in the spleen, the degree of visualization of the bone marrow, etc. using colloid liver scintigraphy, It is not difficult to read a scintigram for a typical pattern; however in some cases it is difficult to distinguish between normal and CH or CH and LC visually. Therefore, we tried to use fuzzy inferences to perform differential diagnosis. Using fuzzy inference, differential diagnosis of LC could be performed up to 100%, but those of CH and Severe fibrosis (SF) could not be performed sufficiently. Therefore genetic algorithm was tried to determine the fuzzy rules. By combining genetic algorithm and fuzzy inference, CH, SF, and LC could be differentiated to the degree 70%, 60%, and 100%, respectively.     

Proteomic expression analysis and comparison of protein and mRNA expression profiles in human malignant gliomas

Gliomas are highly heterogeneous and therapy resistant tumors with a poor prognosis. Novel experimental therapeutic approaches have shown some promising results, but often target specific molecular mechanisms or antigens, and careful characterization of the molecular subgroup of the tumors will therefore likely be important. Thorough investigations of gene and protein alterations are also important to better understand the tumorigenic mechanisms. We have undertaken a proteomic approach, using 2-D DIGE and LC-MS/MS protein identification, to investigate 38 human gliomas and normal brains. We show that the proteome profile can discriminate between normal brain and tumors, and between tumors of varying grade by a supervised classifier. Furthermore, an analysis of the identified proteins shows an enrichment of proteins associated to pathways known to be central in gliomas, such as MEK/Erk signaling and actin cytoskeleton. It also shows a shift between different glial fibrillary acidic protein (GFAP) representatives in different grades. In a previous study the gene expression profile was characterized in an almost identical set of tumors, which enabled a paired analysis of the gene and protein expression profiles. We show that there is often a weak correlation between the mRNA and protein level. This, together with the ability of proteomics to identify PTMs, emphasizes the benefit of characterization on a protein level.     

Proteome alterations induced in human white blood cells by consumption of Brussels sprouts: Results of a pilot intervention study

Epidemiological studies indicate a correlation of cruciferous vegetables consumption with reduced incidence of cancer. This study was designed to investigate molecular mechanisms, which may help to understand the beneficial effects of Brussels sprout consumption. In order to avoid the limitations of in vitro model systems, we performed a dietary intervention study with five participants. We investigated, whether sprout consumption affects the proteome profile of primary white blood cells. In order to achieve maximal sensitivity in detecting specific adaptive proteome alterations, we metabolically labelled freshly isolated cells in the presence of ³⁵S‐methionine/cysteine and performed autoradiographic quantification of protein synthesis. Proteins were separated by 2‐DE and spots of interest were cut out, digested and identified by MS. After the intervention, we found a significant up‐regulation of the synthesis of manganese superoxide dismutase (1.56‐fold) and significant down‐regulation of the synthesis of heat shock 70 kDa protein (hsp70; 2.27‐fold). Both proteins play a role in malignant transformation of cells. Hsp‐70 is involved in the regulation of apoptosis, which leads to elimination of cancer cells, while SOD plays a key role in protection against reactive oxygen species mediated effects. Our findings indicate that the alteration of the synthesis of these proteins may be involved in the anticarcinogenic effects of cruciferous vegetables, which was observed in earlier laboratory studies with animals.     

基于空间通道挤压激励模块的肝硬化识别

针对卷积神经网络对特征信息学习不全面、识别准确率和分类精度不高的问题,提出一种采用空间通道挤压激励模块的scSE_MVGG网络,将其应用于肝硬化识别。对肝硬化图像进行数据增强,以避免深度学习训练出现过拟合现象,改进VGG网络使其适应不同实验样本尺寸,同时将scSE模块与改进的MVGG网络相融合,通过提高网络提取特征的指向性增强肝硬化识别效果。实验结果表明,该网络对肝硬化图像的识别率达到98.78%,较scSE_VGG、scSE_AlexNet等网络识别效果更优。     

Application of proteomic technologies to discover and identify biomarkers for periodontal diseases in gingival crevicular fluid: A review

Periodontal disease is a bacterial infection that destroys the gingiva and surrounding tissues of the oral cavity. In recent years, studies have shown a definite association between periodontal disease and other inflammatory conditions of the body. High-throughput analysis of proteins has become possible with the development of MS technology. This breakthrough in proteome technology enables comparative studies of comprehensive protein expression and identification of protein. In case of periodontal disease, proteome analysis using 2DE, as well as gel-free methods, has been reported. As a fluid lying in close proximity to periodontal tissue, the gingival crevicular fluid (GCF) is the principal target in the search for biomarkers of periodontal disease, because its protein composition may reflect the disease pathophysiology. Biochemical marker analysis of GCF is effective for objective diagnosis in the early and advanced stages of periodontal disease. Increasing numbers of recent reports have provided evidence that the proteomic approach is a promising tool for the discovery and identification of biochemical markers of periodontal disease. This search is of continuing interest in the field of experimental and clinical periodontal disease research. In this article, we summarize recent comprehensive proteomic studies aimed at discovering and identifying biomarkers of periodontal disease in GCF.     

Differential proteomics of human seminal plasma: A potential target for searching male infertility marker proteins

The clinical fertility tests, available in the market, fail to define the exact cause of male infertility in almost half of the cases and point toward a crucial need of developing better ways of infertility investigations. The protein biomarkers may help us toward better understanding of unknown cases of male infertility that, in turn, can guide us to find better therapeutic solutions. Many clinical attempts have been made to identify biomarkers of male infertility in sperm proteome but only few studies have targeted seminal plasma. Human seminal plasma is a rich source of proteins that are essentially required for development of sperm and successful fertilization. This viewpoint article highlights the importance of human seminal plasma proteome in reproductive physiology and suggests that differential proteomics integrated with functional analysis may help us in searching potential biomarkers of male infertility.     

Non-invasive biomarker sampling and analysis of the exhaled breath proteome

Exhaled breath condensate (EBC) is a biological fluid that contains trace amounts of secreted pulmonary proteins, and is emerging as a potentially valuable and non-invasively obtained source of disease biomarkers. Proteome analysis of these samples could lead to the identification of prognostic indicators of airway diseases. The objective of this study was to develop a protocol for proteome analysis of EBC samples. In this report, an improved procedure for EBC sample preparation and concentration is presented, together with a method for comparison of the protein profiles between two groups. The presented approach enabled to study the condensed exhaled breath proteome for biomarker analysis, and revealed proteins not previously identified in an EBC proteomics approach. In a comparative pilot study, EBC protein profiles obtained from smokers and non-smokers showed distinct differences and are illustrative for its potential in clinical studies. EBC from smokers contained higher concentrations of the more abundant proteins, such as cytokeratins, compared to non-smokers, and calgranulin B was identified uniquely in EBC samples from smokers.     

Analysis of the differential proteome of human erythroblasts during in vitro erythropoiesis by 2-D DIGE

In the present study we have used an in vitro culture system that induces differentiation of human CD34(+) cells down the erythroid lineage along with 2-D DIGE to determine the differential proteome of erythroblasts at specific developmental stages during erythropoiesis. We initially distinguished 154 proteins differentially expressed between pro-normoblasts and polychromatic/orthochromatic erythroblasts, of which 24 protein spots, representing 21 different proteins, were identified following MS/MS and verification in replicate experiments with cells from different individuals. These data were confirmed by analysis of the differential proteome of erythroblasts at more discrete stages of erythropoiesis using 2-D DIGE and by mapping the expression of three identified proteins (Annexin I, Annexin II, Carbonic Anhydrase I) throughout erythropoiesis by Western blot with specific antisera. In addition, the differential expression of proteins due to biological variation, such as polymorphism, was determined by comparing erythroblasts at the same developmental stage from different individuals; none of the proteins thus identified were represented in the above data set. Finally, we discuss the problems associated with 2-D DIGE gel-based proteomic approaches such as ours and suggest a modified approach for decreased inter-gel variation, improved protein resolution and increased protein concentration, which should significantly facilitate protein identification.     

Proteomic analysis of sera of asymptomatic,early‐stage patients with Wilson's disease

Wilson's disease (WD) is characterized by excessive accumulation of intracellular copper in liver and extrahepatic tissues, leading to significant oxidative stress and tissue damage. To date, several diagnostic biomarkers for WD such as serum ceruloplasmin, serum or urine copper levels and copper content in liver have been identified. However, these biomarkers may not be convincing for the diagnosis in some WD patients. To identify additional novel diagnostic biomarkers, we compared the serum protein profiles of asymptomatic childhood WD patients (n=20), without neurologic manifestation or liver cirrhosis, with normal controls (n=13). Fourteen spots, five up‐regulated and nine down‐regulated (>2‐fold), were differentially expressed in WD patients in comparison to normal control on 2‐DE. Among them, three spots were down‐regulated in both male and female WD. MS/MS analysis revealed that the three spots were complement component C3, complement factor B and alpha‐2 macroglobulin. By comparative proteome analysis, complement component C3, complement factor B and alpha‐2 macroglobulin, which are related to oxidative stress and inflammation, turned out to be good candidates for novel diagnostic biomarkers for early stages of WD.     

A fast and memory-efficient N-gram language model lookup method for large vocabulary continuous speech recognition

Recently, minimum perfect hashing (MPH)-based language model (LM) lookup methods have been proposed for fast access of N-gram LM scores in lexical-tree based LVCSR (large vocabulary continuous speech recognition) decoding. Methods of node-based LM cache and LM context pre-computing (LMCP) have also been proposed to combine with MPH for further reduction of LM lookup time. Although these methods are effective, LM lookup still takes a large share of overall decoding time when trigram LM lookahead (LMLA) is used for lower word error rate than unigram or bigram LMLAs. Besides computation time, memory cost is also an important performance aspect of decoding systems. Most speedup methods for LM lookup obtain higher speed at the cost of increased memory demand, which makes system performance unpredictable when running on computers with smaller memory capacities. In this paper, an order-preserving LM context pre-computing (OPCP) method is proposed to achieve both fast speed and small memory cost in LM lookup. By reducing hashing operations through order-preserving access of LM scores, OPCP cuts down LM lookup time effectively. In the meantime, OPCP significantly reduces memory cost because of reduced size of hashing keys and the need for only last word index of each N-gram in LM storage. Experimental results are reported on two LVCSR tasks (Wall Street Journal 20K and Switchboard 33K) with three sizes of trigram LMs (small, medium, large). In comparison with above-mentioned existing methods, OPCP reduced LM lookup time from about 30–80% of total decoding time to about 8–14%, without any increase of word error rate. Except for the small LM, the total memory cost of OPCP for LM lookup and storage was about the same or less than the original N-gram LM storage, much less than the compared methods. The time and memory savings in LM lookup by using OPCP became more pronounced with the increase of LM size.     

The comparative analysis of postural and biomechanical parameters of preschool teachers pre- and post-intervention within the ErgoKiTa study

Abstract

The ErgoKiTa study aimed to determine the musculoskeletal strain of preschool teachers and to identify and evaluate suitable prevention measures to reduce this strain. A comprehensive work analysis using objective and subjective methods was performed to determine the present work situation in preschools in Germany, and the results were used to derive suitable intervention measures. The musculoskeletal strain was determined by means of a comprehensive analysis of postures, forces and movements using the CUELA system and calculated as cumulative shift workloads. The intervention measures were evaluated in a pre- and post-intervention assessment for 12 participants. Significant alterations in the duration of postures were determined, specifically for the daily duration of knee-straining postures as well as the degree of trunk flexion between 60° and 90°, which were reduced from 8.4 to 3.1% and from 3.7 to 2.4%, respectively, following the intervention.

Practitioner Summary: Research has shown that preschool teachers are at risk of developing musculoskeletal disorders. The effects of a situation-orientated and behaviour-orientated intervention approach were assessed with regard to awkward working postures. Significant alterations in the duration of postures following the intervention were found, specifically for knee-straining postures.