汉中市中心医院细菌性痢疾志贺菌16S rRNA序列分析、毒力基因与耐药检测

目的对汉中市中心医院细菌性痢疾患者分离的70株志贺菌进行16S rRNA序列分析、毒力基因与耐药检测。方法对2018年6月至2018年12月我院收治的细菌性痢疾患者进行病原菌分离鉴定,并进行16S rRNA遗传进化分析;PCR法检测其6种毒力基因;K-B法检测其对8种抗生素的敏感性。结果 16S rRNA测序结果显示,福氏志贺菌感染率为57.14%(40/70),宋内志贺菌感染率为42.86%(30/70)。16S rRNA系统进化分析结果显示,40株福氏志贺菌与参考菌株(NR_026331.1)同源性为95.2%～99.0%,30株宋内志贺菌与参考菌株(NR_104826.1)同源性为96.3%～99.9%。毒力基因检测结果表明,70株志贺菌均可以检测出6个相关毒力基因set1A、set1B、sen、Ial、ipaH、virA,毒力基因检出率最高的为ipaH,达到100.00%,其次为Ial、virA,均达到90.00%;其中福氏志贺菌set1A、set1B、Ial基因的检出率高于宋内志贺菌,sen和virA基因的检出率低于宋内志贺菌,二者ipaH基因的检出率都为100.00%。药敏试验结果表明,70株志贺菌耐药率最高的抗生素为氨苄西林,耐药率达到80.00%,其次为强力霉素,耐药率达到75.71%,耐药率最低的为头孢吡肟,耐药率为20.00%;其中40株福氏志贺菌耐药率最高的抗生素为强力霉素,其次为氨苄西林,最低的为头孢吡肟;30株宋内志贺菌耐药率最高的抗生素为氨苄西林,其次为哌拉西林,最低的为氧氟沙星。结论 2018年下半年汉中市中心医院从细菌性痢疾患者身上分离得到的志贺菌主要为福氏志贺菌与宋内志贺菌,毒力基因检测和耐药性试验结果显示分离菌株有较强致病性,提示对细菌性痢疾的监测、防控与治疗应进一步加强。     

CRISPR/Cas系统与志贺菌毒力和耐药的关系及插入序列IS600对cse2表达水平的影响北大核心CSCD

【目的】了解志贺菌中成簇的规律间隔短回文重复序列(Clustered regularly interspaced short palindromic repeats,CRISPR)的分布及其与毒力和耐药的关系,并分析志贺菌中插入序列IS600对CRISPR相关蛋白基因cse2 m RNA表达水平的影响。【方法】利用课题组前期设计的引物PCR扩增志贺菌的3个CRISPR位点、CRISPR相关蛋白基因cse2、耐药基因和毒力基因;改良Kirby-Bauer(K-B)纸片法进行药敏试验;台盼蓝计数试验检测细菌毒力;Real-time PCR检测志贺菌中cse2基因m RNA表达水平。分别分析志贺菌中CRISPR/Cas系统与耐药基因、耐药表型、毒力基因、毒力表型的关系;了解IS600对CRISPR相关蛋白基因cse2 m RNA表达水平的影响。【结果】志贺菌中CRISPR1位点阴性细菌的毒力强;插入序列IS600使cse2 m RNA表达水平降低。【结论】志贺菌中存在CRISPR1、2、3位点;CRISPR1位点与毒力有关;插入序列IS600对cse2 m RNA表达水平有影响。     

福氏志贺菌CTX-M型产超广谱β-内酰胺酶的检测及耐药性

目的 产超广谱β-内酰胺酶( extended-spectrum β-lactamases,ESBLs)福氏志贺菌的耐药性及其基因型.方法 K-B法检测福氏志贺菌对抗菌药物的敏感性,改良三维试验检测菌株是否产生ESBLs,ESBLs的基因型通过PCR扩增、DNA测序的方法进行.结果 148株福氏志贺菌检测出6株产ESBLs,其基因型为CTX-M-3和CTX-M-14;产ESBLs福氏志贺菌对氨苄西林、头孢噻肟、复方新诺明、四环素均耐药,对头孢西丁、亚胺培南、环丙沙星敏感.结论 应加强对福氏志贺菌的耐药性监测,合理使用抗生素,防止产ESBLs福氏志贺菌的流行和传播.     

不同地区宋内志贺菌耐药性及脉冲场凝胶电泳分型分析

摘要:【目的】通过对不同地区的宋内志贺菌株进行药物敏感性检测、耐药基因的扩增以及基因分型,了解不同地区宋内志贺菌的耐药情况与流行趋势。【方法】使用微量肉汤稀释法测定了54 株宋内志贺菌对21种药物的敏感性,用PCR方法扩增相关耐药基因,利用脉冲场凝胶电泳(Pulsed Field Gel Electrophoresis,PFGE)技术进行宋内志贺菌的基因分型,最后采用BioNumerics分析软件对所有菌株进行聚类,分析其相似度。【结果】实验菌株对于甲氧苄氨嘧啶/磺胺甲噁唑、四环素、替卡西林/棒酸、氨苄西林、庆大霉素5种抗生素普遍耐药,对亚胺培南、头孢吡肟、左氟沙星、诺氟沙星、阿米卡星5种抗生素全部表现为敏感。共检测出包括blaTEM,blaCTX以及整合子在内的7 种不同的耐药基因。全部实验菌株可分为26个不同的PFGE带型,分型后表现出较高的基因同源性。宋内志贺菌的耐药性、携带基因与带型具有一定地域相关性。【结 论】目前各地区流行的宋内志贺菌株对甲氧苄氨嘧啶/磺胺甲噁唑与四环素已经普遍耐药,不同地区出现相同聚类分型的菌株表明存在跨区域流行的宋内志贺菌群。因此,加强对不同地区宋内志贺菌的监控对减少多重耐药菌株的产生具有重要意义。     

分离自腹泻成人志贺菌的血清分群及耐药性分析

目的 调查志贺菌在成人腹泻患者中的主要血清群和耐药性,为临床治疗和预防提供依据.方法 对2008年1月至2012年10月浙江萧山医院临床分离自腹泻成人志贺菌菌株,进行生化鉴定、血清学分群和抗生素敏感性试验,所有数据用WHONET 5.6软件进行回顾性分析.结果 共检出125株志贺菌,其中66(52.8％)株为宋内志贺菌,58(46.4％)株为福氏志贺菌;氨苄西林耐药率最高达95.9％,复方新诺明耐药率达79.8％,头孢噻肟为48.6％,未检出头孢哌酮/舒巴坦、哌拉西林/他唑巴坦、亚胺培南和美罗培南耐药株,福氏志贺菌对环丙沙星和左旋氧氟沙星耐药率为87.0％和57.1％,而宋内志贺菌未检出氟喹诺酮类耐药株;福氏志贺菌和宋内志贺菌耐药率在哌拉西林、头孢他啶、头孢噻肟、头孢吡肟、复方新诺明和氟喹诺酮间差异有统计学意义(P＜0.01).结论 萧山地区成人志贺菌感染以宋内和福氏两种血清群为主,青霉素类和复方新诺明耐药率太高已不适合临床使用,含酶抑制剂抗生素是治疗志贺菌感染的首选药物.     

志贺菌GyrA基因突变与耐药相关性研究

收集 1 1 4株志贺菌流行株 ,分别作血清分型和检查对喹诺酮类药物的敏感性 ;PCR扩增耐药相关的GyrA基因片段 ,作单链构象多态性 (single strandconformationpolymorphism ,SSCP)分析和序列测定 ;最终研究突变与喹诺酮类耐药的相关性。结果发现我国志贺菌流行株仍以福氏志贺菌为主 ,约占 98 2 % ;大多数福氏志贺菌株对环丙沙星、诺氟沙星高度敏感 ,敏感率分别为81 2 5 %和 74 1 % ;流行株GyrA基因耐药相关片段与野生菌株相比 ,部分菌株出现C( 2 4 8) T单位点突变或C( 2 4 8) T和A( 2 60 ) G双位点突变 ;卡方检验显示 ,C( 2 4 8) T单位点突变基础上增加A( 2 60 ) G点突变与环丙沙星、诺氟沙星耐药相关 ,故A( 2 60 ) G可作为耐药监测位点 ;另外 ,可用SSCP分析PCR扩增的GyrA基因耐药相关片断 ,作为GyrA基因耐药突变的粗筛方法。     

食蟹猴肠道志贺氏菌感染情况的调查

本文对 337只食蟹猴肠道志贺氏菌进行调查 ,其感染率为 1 1 %。对所分离到的 2 7株志贺氏菌进行生化、血清学鉴定 ,分属三个群 ,八种血清型 ,痢疾志贺氏菌、福氏志贺氏菌、宋内氏志贺氏菌所占比例分别为 1 0 8%、86 5%、2 7% ,以福氏志贺氏 2a型居多 ,占 59 5%。对八种志贺氏菌血清型菌株进行药敏试验 ,结果表明 ,不同血清型菌株对同一种抗菌药物的敏感性、耐药性均有差异。提示不同血清型的菌株均可自然感染食蟹猴 ,在治疗时 ,对感染不同血清型志贺氏菌的食蟹猴区别用药 ,以提高治愈率 ,避免因用药不当所造成的经济损失。     

志贺菌的质粒图谱分析

目的：分析志贺菌的质粒图谱及其与细菌药物敏感性的相关性。方法：从菌痢患者粪便标本中分离6株福氏志贺菌和4株宋内志贺菌,分别对其质粒图谱及药物敏感性进行分析。结果：不同菌株的质粒图谱具有明显的差异,但福氏志贺菌的5株以及宋内志贺菌的3株具有分子量23Kb的质粒带。各菌株的质粒图谱与其对头孢三嗪,头孢唑啉,环丙沙星,诺氟沙星,氯霉素的耐药特性无明显相关性。结论：获自患者的福氏志贺菌和宋内志贺菌具有不同的质粒图谱以及抗菌药物敏感性,提示在我市引起菌痢的志贺菌具有不同的来源。     

志贺菌CRISPR的检测及其与耐药的关系

【目的】检测志贺菌成簇规律间隔的短回文重复序列(Clustered regularly interspaced short palindromic repeats,CRISPR),并分析其与志贺菌耐药的关系。【方法】根据CRISPR DB数据库公布的志贺菌确定的CRISPR结构序列CRISPR-S2、CRISPR-S4和可能的CRISPR结构序列CRISPR-S1、CRISPR-S3设计四对引物,对60株志贺菌进行PCR扩增。采用CRISPR Finder分析CRISPR,采用改良K-B药敏纸片法检测志贺菌耐药情况,并分析CRISPR-S4与耐药的关系。【结果】确定的CRISPR结构的总阳性率为95%,四个CRISPR位点组成12种CRISPR谱型(A-L),除K型外均含确定的CRISPR结构,新发现1种重复序列和12种间隔序列。60株志贺菌的多重耐药率为53.33%。CRISPR-S4阳性菌株与阴性菌株之间,耐药的分布差异无统计学意义,但多重耐药菌株和耐TE菌株CRISPR-S4的重复序列多为R4.1,其3’末端缺失碱基AC;多重耐药菌株CRISPR-S4的间隔序列多为Sp5.1、Sp6.1和Sp7。【结论】CRISPR在志贺菌中广泛分布。CRISPR重复序列的变异和间隔序列的多样性可能与志贺菌耐药有关。     

210株肠道致病菌的分类及耐药性分析

目的：探讨本地区肠道致病菌的分类及主要致病菌的耐药性,给临床治疗腹泻疾病提供指导。方法：药敏试验采用K-B法,用WHONET4软件进行数据分析。结果：210株肠道致病菌中,志贺菌属占70％,沙门菌属占3％,致病性大肠埃希菌占4．8％,副溶血弧菌占3．8％,阴沟肠杆菌占3．3％,鼠伤寒沙门菌占2．4％,霍乱弧菌占1．9％,其他占10．8％等;在志贺菌属中,福氏占87％,宋内占13％。福氏和宋内志贺菌对三代头孢和IMP较敏感,对复方新诺明耐药率较高。结论：福氏志贺菌是肠道的主要致病菌;临床怀疑菌痢时,应首选三代头孢、IMP。     

痢疾杆菌酸抗性系统相关基因缺失突变体的构建

依赖于谷氨酸脱羧酶的酸抗性系统对痢疾杆菌在宿主细胞内的生存和繁殖至关重要,hdeA、hdeB、yhiE和yhiF是其中几个重要的酸抗性基因。借助Red系统的重组功能,PCR扩增两翼与目的基因上下游同源的抗性基因片段,电击转化痢疾杆菌2457T,对筛选到的阳性转化子再导入编码FLP位点特异性重组酶的质粒pCP20以去除抗性基因。结果成功的敲除了hdeA、hdeB、yhiE和yhiF等4个酸抗性系统相关基因,为深入研究痢疾杆菌酸抗性基因的调控网络奠定了基础.     

Competition among Hfq-binding small RNAs in Escherichia coli

A major class of small bacterial RNAs (sRNAs) regulate translation and mRNA stability by pairing with target mRNAs, dependent upon the RNA chaperone Hfq. Hfq, related to the Lsm/Sm families of splicing proteins, binds the sRNAs and stabilizes them in vivo and stimulates pairing with mRNAs in vitro. Although Hfq is abundant, the sRNAs, when induced, are similarly abundant. Therefore, Hfq may be limiting for sRNA function. We find that, when overexpressed, a number of sRNAs competed with endogenous sRNAs for binding to Hfq. This correlated with lower accumulation of the sRNAs (presumably a reflection of the loss of Hfq binding), and lower activity of the sRNAs in regulating gene expression. Hfq was limiting for both positive and negative regulation by the sRNAs. In addition, deletion of the gene for an expressed and particularly effective competitor sRNA improved the regulation of genes by other sRNAs, suggesting that Hfq is limiting during normal growth conditions. These results support the existence of a hierarchy of sRNA competition for Hfq, modulating the function of some sRNAs.     

Small RNAs and Gene Network in a Durable Disease Resistance Gene—Mediated Defense Responses in Rice

Accumulating data have suggested that small RNAs (sRNAs) have important functions in plant responses to pathogen invasion. However, it is largely unknown whether and how sRNAs are involved in the regulation of rice responses to the invasion of Xanthomonas oryzae pv. oryzae (Xoo), which causes bacterial blight, the most devastating bacterial disease of rice worldwide. We performed simultaneous genome-wide analyses of the expression of sRNAs and genes during early defense responses of rice to Xoo mediated by a major disease resistance gene, Xa3/Xa26, which confers durable and race-specific qualitative resistance. A large number of sRNAs and genes showed differential expression in Xa3/Xa26-mediated resistance. These differentially expressed sRNAs include known microRNAs (miRNAs), unreported miRNAs, and small interfering RNAs. The candidate genes, with expression that was negatively correlated with the expression of sRNAs, were identified, indicating that these genes may be regulated by sRNAs in disease resistance in rice. These results provide a new perspective regarding the putative roles of sRNA candidates and their putative target genes in durable disease resistance in rice.     

Identification of Novel Regulatory Small RNAs in Acinetobacter baumannii

Small RNA (sRNA) molecules are non-coding RNAs that have been implicated in regulation of various cellular processes in living systems, allowing them to adapt to changing environmental conditions. Till date, sRNAs have not been reported in Acinetobacter baumannii (A. baumannii), which has emerged as a significant multiple drug resistant nosocomial pathogen. In the present study, a combination of bioinformatic and experimental approach was used for identification of novel sRNAs. A total of 31 putative sRNAs were predicted by a combination of two algorithms, sRNAPredict and QRNA. Initially 10 sRNAs were chosen on the basis of lower E- value and three sRNAs (designated as AbsR11, 25 and 28) showed positive signal on Northern blot. These sRNAs are novel in nature as they do not have homologous sequences in other bacterial species. Expression of the three sRNAs was examined in various phases of bacterial growth. Further, the effect of various stress conditions on sRNA gene expression was determined. A detailed investigation revealed differential expression profile of AbsR25 in presence of varying amounts of ethidium bromide (EtBr), suggesting that its expression is influenced by environmental or internal signals such as stress response. A decrease in expression of AbsR25 and concomitant increase in the expression of bioinformatically predicted targets in presence of high EtBr was reverberated by the decrease in target gene expression when AbsR25 was overexpressed. This hints at the negative regulation of target genes by AbsR25. Interestingly, the putative targets include transporter genes and the degree of variation in expression of one of them (A1S_1331) suggests that AbsR25 is involved in regulation of a transporter. This study provides a perspective for future studies of sRNAs and their possible involvement in regulation of antibiotic resistance in bacteria specifically in cryptic A. baumannii.     

An integrated approach for finding overlooked genes in Shigella

Background

The completion of numerous genome sequences introduced an era of whole-genome study. However, many genes are missed during genome annotation, including small RNAs (sRNAs) and small open reading frames (sORFs). In order to improve genome annotation, we aimed to identify novel sRNAs and sORFs in Shigella, the principal etiologic agents of bacillary dysentery.

Methodology/Principal Findings

We identified 64 sRNAs in Shigella, which were experimentally validated in other bacteria based on sequence conservation. We employed computer-based and tiling array-based methods to search for sRNAs, followed by RT-PCR and northern blots, to identify nine sRNAs in Shigella flexneri strain 301 (Sf301) and 256 regions containing possible sRNA genes. We found 29 candidate sORFs using bioinformatic prediction, array hybridization and RT-PCR verification. We experimentally validated 557 (57.9%) DOOR operon predictions in the chromosomes of Sf301 and 46 (76.7%) in virulence plasmid.We found 40 additional co-expressed gene pairs that were not predicted by DOOR.

Conclusions/Significance

We provide an updated and comprehensive annotation of the Shigella genome. Our study increased the expected numbers of sORFs and sRNAs, which will impact on future functional genomics and proteomics studies. Our method can be used for large scale reannotation of sRNAs and sORFs in any microbe with a known genome sequence.     

Small RNAs encoded within genetic islands of Salmonella typhimurium show host-induced expression and role in virulence

The emergence of pathogenic strains of enteric bacteria and their adaptation to unique niches are associated with the acquisition of foreign DNA segments termed ‘genetic islands’. We explored these islands for the occurrence of small RNA (sRNA) encoding genes. Previous systematic screens for enteric bacteria sRNAs were mainly carried out using the laboratory strain Escherichia coli K12, leading to the discovery of ~80 new sRNA genes. These searches were based on conservation within closely related members of enteric bacteria and thus, sRNAs, unique to pathogenic strains were excluded. Here we describe the identification and characterization of 19 novel unique sRNA genes encoded within the ‘genetic islands’ of the virulent strain Salmonella typhimurium. We show that the expression of many of the island-encoded genes is associated with stress conditions and stationary phase. Several of these sRNA genes are induced when Salmonella resides within macrophages. One sRNA, IsrJ, was further examined and found to affect the translocation efficiency of virulence-associated effector proteins into nonphagocytic cells. In addition, we report that unlike the majority of the E. coli sRNAs that are trans regulators, many of the island-encoded sRNAs affect the expression of cis-encoded genes. Our study suggests that the island encoded sRNA genes play an important role within the network that regulates bacterial adaptation to environmental changes and stress conditions and thus controls virulence.     

SPORTS1.0: A Tool for Annotating and Profiling Non-coding RNAs Optimized for rRNA- and tRNA-derived Small RNAs

High-throughput RNA-seq has revolutionized the process of small RNA (sRNA) discovery, leading to a rapid expansion of sRNA categories. In addition to the previously well-characterized sRNAs such as microRNAs (miRNAs), piwi-interacting RNAs (piRNAs), and small nucleolar RNA (snoRNAs), recent emerging studies have spotlighted on tRNA-derived sRNAs (tsRNAs) and rRNA-derived sRNAs (rsRNAs) as new categories of sRNAs that bear versatile functions. Since existing software and pipelines for sRNA annotation are mostly focused on analyzing miRNAs or piRNAs, here we developed the sRNA annotation pipelineoptimized for rRNA- and tRNA-derived sRNAs (SPORTS1.0). SPORTS1.0 is optimized for analyzing tsRNAs and rsRNAs from sRNA-seq data, in addition to its capacity to annotate canonical sRNAs such as miRNAs and piRNAs. Moreover, SPORTS1.0 can predict potential RNA modification sites based on nucleotide mismatches within sRNAs. SPORTS1.0 is precompiled to annotate sRNAs for a wide range of 68 species across bacteria, yeast, plant, and animal kingdoms, while additional species for analyses could be readily expanded upon end users’ input. For demonstration, by analyzing sRNA datasets using SPORTS1.0, we reveal that distinct signatures are present in tsRNAs and rsRNAs from different mouse cell types. We also find that compared to other sRNA species, tsRNAs bear the highest mismatch rate, which is consistent with their highly modified nature. SPORTS1.0 is an open-source software and can be publically accessed at https://github.com/junchaoshi/sports1.0.     

细菌sRNA基因及其靶标预测研究进展

摘要：细菌sRNA是一类长度在40～500 nt之间的非编码RNA,主要以不完全碱基配对方式与靶标mRNA5′端相互作用进而发挥其生物学功能。鉴于预测方法可以为细菌sRNA及其靶标的实验发现提供指导,因此,细菌sRNA与靶标预测研究受到了广泛重视。文章首先将sRNA预测方法分为3类,分别是基于比较基因组学的预测方法、基于转录单元的预测方法和基于机器学习的预测方法;其次,将sRNA靶标预测方法分为2类,分别是序列比较方法与基于RNA二级结构的预测方法;最后对各类方法的原理、核心思想、优点和局限性进行了分析,并探讨了进一步的发展方向。