Identification of protein associations in organelles, using mass spectrometry-based proteomics

Recent literature that highlights the power of using mass spectrometry (MS) for protein identification from preparations of highly purified organelles and other large subcellular structures is covered in this review with an emphasis on techniques that preserve the integrity of the functional protein complexes. Recent advances in distinguishing contaminant proteins from "bonafide" organelle-localized proteins and the affinity capture of protein complexes are reviewed, as well as bioinformatic strategies to predict protein organellar localization and to integrate protein-protein interaction maps obtained from MS-affinity capture methods with data obtained from other techniques. Those developments demonstrate that a revolution in cellular biology, fueled by technical advances in MS-based proteomic techniques, is well underway.     

A Reconfigurable Index FLASH Memory tailored to Seed-Based Genomic Sequence Comparison Algorithms

Genomic sequence comparison algorithms represent the basic toolbox for processing large volume of DNA or protein sequences. They are involved both in the systematic scan of databases, mostly for detecting similarities with an unknown sequence, and in preliminary processing before advanced bioinformatics analysis. Due to the exponential growth of genomic data, new solutions are required to keep the computation time reasonable. This paper presents a specific hardware architecture to speed-up seed-based algorithms which are currently the most popular heuristics for detecting alignments. The architecture regroups FLASH and FPGA technologies on a common support, allowing a large amount of data to be rapidly accessed and quickly processed. Experiments on database search and intensive sequence comparison demonstrate a good cost/performance ratio compared to standard approaches.

Bioinformatic Tools for the Analysis and Prediction of ncRNA Interactions

Noncoding RNAs (ncRNAs) play prominent roles in the regulation of gene expression via their interactions with other biological molecules such as proteins and nucleic acids. Although much of our knowledge about how these ncRNAs operate in different biological processes has been obtained from experimental findings, computational biology can also clearly substantially boost this knowledge by suggesting possible novel interactions of these ncRNAs with other molecules. Computational predictions are thus used as an alternative source of new insights through a process of mutual enrichment because the information obtained through experiments continuously feeds through into computational methods. The results of these predictions in turn shed light on possible interactions that are subsequently validated experimentally. This review describes the latest advances in databases, bioinformatic tools, and new in silico strategies that allow the establishment or prediction of biological interactions of ncRNAs, particularly miRNAs and lncRNAs. The ncRNA species described in this work have a special emphasis on those found in humans, but information on ncRNA of other species is also included.     

腰果过敏蛋白Anao2与Anao3结构分析及致敏性预测

为了解腰果主要过敏蛋白的分子结构及过敏性特征,通过有机溶剂脱脂、盐析、透析、葡聚糖凝胶Sephadex G-150分离纯化得到腰果主要过敏蛋白,采用紫外分光光度计、BCA定量试剂盒、十二烷基硫酸钠聚丙烯酰胺凝胶电泳(SDS-PAGE)、蛋白质免疫印迹(Western-Blotting)等手段检测腰果过敏蛋白及评价其免疫...     

Effective Identification and Annotation of Fungal Genomes

In the past few decades,the dangers of mycosis have caused widespread concern.With the development of the sequencing technology,the effective analysis of fungal sequencing data has become a hotspot.With the gradual increase of fungal sequencing data,there is now a lack of sufficient approaches for the identification and functional annotation of fungal chromosomal genomes.To overcome this challenge,this paper firstly deals with the approaches of the identification and annotation of fungal genomes based on short and long reads sequenced by using multiple platforms such as Illumina and Pacbio.Then this paper develops an automated bioinformatics pipeline called PFGI for the identification and annotation task.The experimental evaluation on a real-world dataset ENA (European Nucleotide Archive) shows that PFGI provides a user-friendly way to perform fungal identification and annotation based on the sequencing data analysis,and could provide accurate analyzing results,accurate to the species level (97％ sequence identity).     

Mass Spectrometry-Based Proteomics Reveal Alcohol Dehydrogenase 1B as a Blood Biomarker Candidate to Monitor Acetaminophen-Induced Liver Injury

Acute liver injury (ALI) is a severe disorder resulting from excessive hepatocyte cell death, and frequently caused by acetaminophen intoxication. Clinical management of ALI progression is hampered by the dearth of blood biomarkers available. In this study, a bioinformatics workflow was developed to screen omics databases and identify potential biomarkers for hepatocyte cell death. Then, discovery proteomics was harnessed to select from among these candidates those that were specifically detected in the blood of acetaminophen-induced ALI patients. Among these candidates, the isoenzyme alcohol dehydrogenase 1B (ADH1B) was massively leaked into the blood. To evaluate ADH1B, we developed a targeted proteomics assay and quantified ADH1B in serum samples collected at different times from 17 patients admitted for acetaminophen-induced ALI. Serum ADH1B concentrations increased markedly during the acute phase of the disease, and dropped to undetectable levels during recovery. In contrast to alanine aminotransferase activity, the rapid drop in circulating ADH1B concentrations was followed by an improvement in the international normalized ratio (INR) within 10–48 h, and was associated with favorable outcomes. In conclusion, the combination of omics data exploration and proteomics revealed ADH1B as a new blood biomarker candidate that could be useful for the monitoring of acetaminophen-induced ALI.     

1 株水开菲尔粒中植物乳杆菌的鉴定及产细菌素基因分析

目的：对从水开菲尔粒中分离得到1 株能产生抑菌物质的菌株QF01进行菌种鉴定,并对其产细菌素进行实验、鉴定和基因序列分析。方法：通过牛津杯法测定抑菌能力,采用聚合酶链式反应（polymerase chain reaction,PCR）扩增16S rDNA和细菌素相关基因并测序,利用ProParam tool、TMHMM 2.0、InterProScan、SOPM和SWISS-MODEL在线软件对基因编码产物进行分析。结果：该菌株产的细菌素对大肠杆菌（Escherichia coli JM109）有明显的抑制作用,通过16S rDNA序列分析初步确定该菌株属于植物乳杆菌（Lactobacillus plantarum）。该菌株含有plnD、plnEF、plnV、plnR四种基因,其中plnV基因编码产物有跨膜螺旋结构,其结构存在信号肽特征。此外,从三级结构的模型预测得到4?种基因的编码产物均存在α-螺旋、β-转角、伸展链和无规则卷曲。结论：从水开菲尔粒分离得到的L. plantarum QF01菌株,含有plnD、plnEF、plnV、plnR细菌素基因,对大肠杆菌有很好的抑制作用。