Background Genetic resources are important natural assets. Discovery of new enzyme gene sequences has been an ongoing effort in biotechnology industry. In the genomic age, genomes of microorganisms from various environments have been deciphered. Increasingly, it has become more and more difficult to find novel enzyme genes. In this work, we attempted to use the easily accessible banknotes to search for novel microbial gene sequences. Results We used high-throughput genomic sequencing technology to comprehensively characterize the diversity of microorganisms on the US dollars and Chinese Renminbis (RMBs). In addition to finding a vast diversity of microbes, we found a significant number of novel gene sequences, including an unreported superoxide dismutase (SOD) gene, whose catalytic activity was further verified by experiments. Conclusions We demonstrated that banknotes could be a good and convenient genetic resource for finding economically valuable biologicals.

Banknotes have long been suspected to be biologically “dirty” due to their frequent human contact, which may transmit human microbial pathogens. Still, it is an unsettled issue whether the microbes on banknotes pose a real threat to human health. In several previous studies, metagenomic sequencing was used to reveal the diversities of microbes on banknotes but live microorganism culture and functional verification were lacking. In this study, we collected banknotes of RMB in China as well as dollar bills in the United States and analyzed the microbial biodiversity and drug resistance genes carried by the identified microbes by metagenomic sequencing and in vitro culture methods. We identified eight major genera of drug-resistant bacteria through screening of 30 antibiotics, and the blood agar plate culture uncovered six pathogenic fungal species. Numerous phage and six dangerous viral sequences were also found. These results should substantiate our concern about the potential risk of banknotes to human health.

目的:探讨环指蛋白144B(ring finger protein 144B,RNF144B)在胃癌组织中的表达水平及其与胃癌患者临床预后的关系.方法:回顾性分析2013年1月-2014年1月在兰州大学第二医院确诊并行根治性手术治疗的胃癌患者的临床资料;我们的研究一共纳入了105名患者,记录患者的TNM分期、肿瘤大小、分化水平、癌胚抗原(carcino-embryonic antigen,CEA)等指标并记录患者的总体生存时间(overall survival,OS);利用免疫组织化学染色(immunohistochemistry,IHC)检测RNF144B在胃癌及癌旁组织中的表达,并分析其与患者一般临床病理特征的联系;应用Cox回归模型评估患者术后总体生存时间的影响因素;使用Kaplan-Meier Plotter数据库对RNF144B进行生存相关性分析,随后利用临床数据进行验证.结果:RNF144B在胃癌组织中显著高表达(P=0.0039),RNF144B高表达组的患者CEA水平、Ki67阳性率、临床病理分期、T分期均显著高于低表达组,而分化程度差于低表达组(P<0.05);多因素分析显示RNF144B高表达、肿瘤分化程度低、临床病理分期高均是影响胃癌患者术后总体生存时间的独立因素(P<0.05);Kaplan-Meier法分析得RNF144B高表达组术后5年生存率(6.06％)低于低表达组(29.69％)(P<0.01).结论:RNF144B在胃癌中高表达并标志着更差的临床预后.

Diatom test is the most commonly used method to diagnose drowning in forensic laboratories. However, microscopic examination and identification of diatom frustules is time-consuming and requires taxonomic expertise. At present, the identification of drowning is still a challenge in forensic casework. In this study, we developed a novel diatom microarray based on the detection of specific 18S rRNA gene fragments of diatom species. The array covers 169 diatom species which were documented as commonly found in a wide range of fresh waters in China. Diatom arrays were prepared from species specific oligonucleotide probes targeting to variable regions of the 18S rRNA gene. We also developed an auxiliary sample preparation method for isolation of diatom DNA from tissues, which enabled detection of diatom species in real forensic samples as well as environmental waters. We applied the diatom arrays to analyze six drowned cases and eight environmental samples. The diatom arrays showed much better sensitivity and more consistent results than those of the conventional SEM methods. We discovered major discrepancies between results generated by the diatom arrays and the routinely used SEM based diatom tests. We verified the results of our diatom arrays by species specific PCR and Sanger sequencing and found that the currently used SEM diatom test method has a serious deficiency in sensitivity due to high loss rate of frustules in the sample preparation procedure. We anticipate that the application of diatom arrays will transform current forensic practice of diagnosing drowning deaths.

原核表达、纯化T4多聚核苷酸激酶,并尝试将纯化的T4 PNK用于短探针序列的连接.本研究以合成的pseT基因为模板,PCR扩增出带有NdeⅠ和BamH Ⅰ位点的目的片段,构建pseT-pET-15b原核表达载体,并转入E.coli ER2566中诱导表达.Ni-Agarose亲和层析柱纯化重组蛋白后,再进行Western blot鉴定.用纯化后再浓缩的T4PNK参与探针连接反应,并设置商品T4 PNK和阴性对照.PCR扩增成功获得大于900bp的目的基因片段,原核表达载体pseT-pET-15b构建成功,经诱导表达的重组蛋白分子量大小约为35kD,Western blotting确认蛋白表达正确,浓缩后的蛋白浓度达到826 μg/mL.电泳结果显示,重组T4 PNK在探针连接中效果较好.本研究成功表达并纯化了可溶性的T4多聚核苷酸激酶,且具有较好的活性,该蛋白可进一步用于后续大批量探针连接反应或其他相关研究,具有一定实际应用价值.

单核苷酸多态性(SNP)作为第三代分子遗传标记,因其在遗传学、医学等领域的研究重要性而得到广泛的关注,建立高通量自动化的SNP检测技术是十分必要的.该文简要概述了有代表性的传统SNP检测方法:单链构象多态性和限制性片段长度多态性的原理、应用及特点.详尽概述了当今几种有代表性的高通量自动化SNP检测方法:Sanger测序法、焦磷酸测序法、MassARRAY(测序)技术、基因芯片法的原理、应用以及特点,并对未来高通量自动化SNP检测技术做出了展望.

甲状腺癌(TC)是最常见的内分泌系统肿瘤,近几十年来,TC的发病率在全球范围内快速增长.根据国内外最新研究结果,TC的发病与多种因素相关,如肥胖、放射因素、碘摄入量、基因突变、应激等.TC给人类健康,特别是女性身心健康带来极大威胁.因此,了解TC危险因素,重视、加强对TC的预防,对减少TC的危害有重大意义.本文对TC的预防及危险因素进行综述,旨在提高对TC的认识水平,为TC的预防提供策略.

HeLa cells are a commonly used cell line in many biological research areas. They are not picky for culture medium and proliferate rapidly. HeLa cells are a notorious source of cell cross-contamination and have been found to be able to contaminate a wide range of cell lines in cell culture. In this study, we reported a simple and efficient method for detecting the presence of HeLa cell contamination in cell culture. HPV-18 was used as a biomarker. The cell culture supernatant was used directly as the template for nested PCR without extracting nucleic acid. By PCR amplification of the cell culture supernatant with the designed primers, we were able to detect the presence of HeLa cells in the culture. The sensitivity of this method can reach 1%, which is 10-fold higher than Short tandem repeat sequence (STR) profiling. This simple, rapid, and "noninvasive" quality checking method should find applications in routine cell culture practice.

Nuclear receptors are transcriptional regulators involved in almost all biological processes such as cell growth, differentiation, apoptosis, substance metabolism and tumor formation, and they can be regulated by small molecules that bind to them. Autophagy is a special way of programmed cell death and it is a highly conserved metabolic process. Once autophagy defects or excessive autophagy occur, the disease will develop. In recent years, numerous studies have shown that nuclear receptors are related to autophagy. Therefore, this paper mainly reviews the research progress on nuclear receptors involved in the regulation of autophagy, and focuses on the mechanism of several nuclear receptors involved in the regulation of autophagy, aiming at understanding the molecular basis of how nuclear receptors participate in regulating autophagy, as well as providing possible ideas and strategies for the treatment of corresponding diseases.

目的:比较几种不同的肠道微生物宏基因组DNA的提取方法.方法:分别使用两家不同公司的粪便基因组提取试剂盒,以及CTAB-SDS法提取肠道微生物宏基因组DNA,琼脂糖凝胶电泳、超微量核酸蛋白测定仪Nanodrop 2000、16S rDNA PCR以及二代测序等检测.结果:均能够提取出肠道微生物宏基因组DNA,PCR均能扩增出16S rDNA基因.结论:三种方法都能较好满足提取宏基因组DNA、下游分子生物学实验的要求,可根据具体实际进行选择.

Membrane proteins are central to carrying out impressive biological functions. In general, accurate knowledge of transmembrane (TM) regions facilitates ab initio folding and functional annotations of membrane proteins. Therefore, large-scale locating of TM regions in membrane proteins by wet experiments is needed; however, it is hampered by practical difficulties. In this context, in silico methods for TM prediction are highly desired. Here, we present a TM region prediction method using machine learning algorithms and sequence evolutionary profiles. Hydrophobic properties were also assessed. Furthermore, a combined method using sequence evolutionary profiles and hydrophobicity measures was tested. The model was intensively trained on large datasets by means of neural network and random forest learning algorithms for TM region prediction. The proposed method can be directly applied to identify membrane proteins from proteome-wide sequences. Benchmark results suggest that our method is an attractive alternative to membrane protein prediction for real-world applications. The web server and stand-alone program of the proposed method are publicly available at http://genomics.fzu.edu.cn/nnme/index.html.

目的 检测研究所使用的人肾上腺皮质癌细胞SW-13、人胚肾细胞293T及人胃癌细胞AGS 3种细胞系污染情况,并探索短串联重复序列(short tandem repeat,STR)分型方法检测人类细胞系污染的可靠性.方法 人类基因组上的微卫星位点携带着大量STR,每个STR核心序列的重复次数随个体不同存在差异,作为一种DNA标记使得细胞系在DNA水平上具有个性化.通过荧光聚合酶链式反应体系扩增STR位点,检测的数据与DSMZ细胞库进行匹配以对细胞系进行鉴别.结果 比对得出SW-13基本匹配,其中20个STR基因位点中只有TH01位点的两个等位基因重复数为7和8,与DSMZ细胞库中此等位基因重复数7和7有差异,其余均一致在可接受范围内.细胞系293T、AGS等位基因的重复数与DSMZ细胞库完全匹配.结论 STR分型检测方法被ATCC、DSMZ等权威机构推崇,且此研究采用20位点检测法涵盖ATCC-9位点及中检院-16位点检测法,该法权威可靠.本所3种细胞系遗传稳定,均无种内或种间污染.

Filamentous fungi have been of great interest because of their excellent ability as cell factories to manufacture useful products for human beings. The development of genetic transformation techniques is a precondition that enables scientists to target and modify genes efficiently and may reveal the function of target genes. The method to deliver foreign nucleic acid into cells is the sticking point for fungal genome modification. Up to date, there are some general methods of genetic transformation for fungi, including protoplast-mediated transformation, Agrobacterium-mediated transformation, electroporation, biolistic method and shock-wave-mediated transformation. This article reviews basic protocols and principles of these transformation methods, as well as their advantages and disadvantages.

Residue depth is a solvent exposure measure that quantitatively describes the depth of a residue from the protein surface.

BstDNA聚合酶大片段作为一种常用的DNA聚合酶,因其独特的特点:能引发链置换反应、高保真、耐高温等,而成为一种重要的DNA多重置换扩增酶.目的:为减少成本,设计一种高产,方便且扩增活性高的凰tDNA聚合酶大片段表达体系;探究该酶应用于胃癌石蜡包埋组织基因组DNA的扩增条件.方法:采用pTWIN1质粒作为栽体克隆表达BstDNA聚合酶大片段,应用几丁质亲和层析柱纯化该酶,使用该酶对人类基因组DNA进行不同温度下扩增,探究其最适反应温度,并据此对胃癌石蜡包埋组织基因组DNA进行扩增.结果:由此得到的BstDNA聚合酶大片段能运用于胃癌石蜡包埋组织基因组DNA的扩增,扩增效率可达200倍,并能应用于aCGH芯片.结论:扩增得到保真性高,覆盖基因组范围大的DNA扩增产物.该应用与aCGH结合,使得对少量的癌症石蜡包埋组织DNA样本进行全基因组扩增,并进行其基因拷贝数变异研究成为可能.

G protein coupled receptors (GPCR), a general designation of a large class of membrane proteins, contain seven transmembrane helices in its three?dimensional structure, which currently are the drug targets more than 30%in the market. In contrast to the importance of GPCR, the knowledge of scientific community to understand its structure and function is very limited. The main reason is the difficulty to obtain the structure and function of GPCR information by wet experiment. Now, it is feasible to use bioinformatics methods to identify and predict the 3D structure of GPCR. Research on GPCR based on bioinformatics is beneficial to novel drug targets screening and new drugs developing. This paper discusses some typical bioinformatics methods. In addition, several possible new research strategies are presented to address the identification of GPCR proteins from a genome scale database, position its transmembrane region and predict the three?dimensional structure of GPCR and drug ligand binding mode.

Protein three-dimensional (3D) structures provide insightful information in many fields of biology. One-dimensional properties derived from 3D structures such as secondary structure, residue solvent accessibility, residue depth and backbone torsion angles are helpful to protein function prediction, fold recognition and ab initio folding. Here, we predict various structural features with the assistance of neural network learning. Based on an independent test dataset, protein secondary structure prediction generates an overall Q3 accuracy of ~80%. Meanwhile, the prediction of relative solvent accessibility obtains the highest mean absolute error of 0.164 and prediction of residue depth achieves the lowest mean absolute error of 0.062. We further improve the outer membrane protein identification by including the predicted structural features in a scoring function using a simple profile-to-profile alignment. The results demonstrate that the accuracy of outer membrane protein identification can be improved by ~3% at a 1% false positive level when structural features are incorporated. Finally, our methods are available as two convenient and easy-to-use programs. One is PSSM-2-Features for predicting secondary structure, relative solvent accessibility, residue depth and backbone torsion angles, the other is PPA-OMP for identifying outer membrane proteins from proteomes.

Protein fold recognition method is one of template?based protein three?dimensional structure modeling methods and was elegantly used in many fields of biological sciences. We witnessed the development of a series of novel fold recognition algorithms using different computational techniques in the past ten years.Machine learning and profile?profile alignment are widely used and effective methods.In addition the enlarging Protein Data Bank is one of important factors that substantially enhance the accuracy of prediction.In this paper,we briefly reviewed the state?of?the?art algorithms used in the protein fold recognition and some potential aspects that can be used to improve performance were also discussed.

Sequence alignment provides insightful information to functional annotation and comparable modeling. Among the alignment algorithms, profile-to-profile alignment is the most sensitive method to identify functional homologs from databases. To broaden the applications of fold recognition, developing new and easy alignment tools are necessary. In this work, we developed a novel fold recognition program, namely EasyAlign. EasyAlign consists of three main modules, i.e. sequence-to-sequence, profile-to-sequence and profile-to-profile alignments. The parameters of various alignments were trained based on structural alignments. In our benchmark experiments, profile-to-profile alignment is the best method and, meanwhile, profile-to-sequence alignment is better than sequence-to-sequence alignment. However, profile-based method is not better in all cases. The multiple sequence alignment, which is used to calculate profile, may contain some false homologous sequences. Based on this observation, we developed three types of alignments in EasyAlign for advanced users to select specific alignment algorithm in their cases.In summary, EasyAlign is a highly optimized program for protein sequence alignment, and it can be utilized to search sequence databases without knowing experimental structures. The program is publicly available at: http://genomics.fzu.edu.cn/EasyAlign/.