野生型产气荚膜梭菌β毒素基因的克隆与分析

用多联PCR方法对从猪舍污水中分离的细菌进行鉴定，获得1株B型和1株C型产气荚膜梭菌，用PCR方法扩增了β毒素基因并进行了测序，获得了包括RBS、信号肽基因和全长成熟肽基因以及部分3′末端序列的β毒素基因；测序后与GenBank中B、C型菌β毒素基因进行比较。野生B、C型菌与二者的同源性分别达到99％以上，氨基酸同源性也在99％以上。     

产气荚膜梭菌α毒素基因的克隆表达和生物学活性的鉴定

目的利用原核表达系统表达具有生物学活性的产气荚膜梭菌α毒素。方法设计针对α毒素基因的一对引物，用聚合酶链式反应(PCR)技术扩增出α毒素的全基因。经测序确认正确后，回收的α毒素目的条带与pGEX6p-1表达载体经限制性内切酶Xho Ⅰ和EcoR Ⅰ双酶切后，进行连接，转化大肠杆菌BL21。诱导表达后，做SDS-PAGE电泳，出现特异的表达产物条带。表达产物做溶血活性、卵磷脂水解活性和腹腔接种小鼠实验。结果构建的重组质粒经内切酶Xho Ⅰ和EcoR Ⅰ双酶切后，发现特异的目的基因条带。SDS-PAGE电泳后，发现重组菌株有特异的表达条带。实验证明表达产物具有溶血活性、卵磷脂水解活性和腹腔接种后致死小鼠特性。结论重组菌株BL21(pGEX6p-1-epα)表达了具有活性的α毒素蛋白。     

产气荚膜梭菌血流感染三例

产气荚膜梭菌所致血流感染在临床上罕见。本研究3例产气荚膜梭菌血流感染患者均以发热起病,血培养提示产气荚膜梭菌阳性,予经验性抗感染治疗后,2例好转,1例死亡。产气荚膜梭菌血流感染临床表现不具有特异性但病死率高,对于具有危险因素的患者出现发热,需警惕产气荚膜梭菌血流感染的可能。     

产气荚膜梭菌VirS/VirR双组分系统调控机制研究进展

产气荚膜梭菌是一种重要的人兽共患病病原体,该菌引起各种动物坏死性肠炎、肠毒血症、人畜创伤性气性坏疽及人类食物中毒。该菌产生的毒素和酶协同作用导致病变发生。产气荚膜梭菌毒素和酶的产生受到复杂网络调控。VirS/VirR系统是产气荚膜梭菌中最重要也是研究最深入的一个双组分调节系统。本文对这一双组分系统的研究进展进行综述,为进一步阐明该菌的致病机制奠定基础。     

动物源产气荚膜梭菌耐药性的研究进展

产气荚膜梭菌是可引起多种动物和人类疾病的重要人兽共患病原菌。其抗生素耐药性在动物生产中较为普遍,多数耐药基因常位于接合型质粒或流动遗传因子上,加速了产气荚膜梭菌耐药性的扩散。本文概述了动物源产气荚膜梭菌对常用抗生素（四环素类、大环内酯类、林可胺类、链阳霉素类以及氯霉素和杆菌肽锌等）的耐药机制。在此基础上,阐述了接合型质粒和转座子等在产气荚膜梭菌耐药基因水平扩散中的作用。对动物源产气荚膜梭菌耐药机制及扩散机理的深入认识有助于该菌的控制,为食品安全和人类健康提供重要保障。     

AgrB信号分子对B型产气荚膜梭菌生物被膜形成的影响北大核心CSCD

目的探讨QS系统AgrB分子对B型产气荚膜梭菌生物被膜(biofilm, BF)形成过程中htrA、rpoA基因表达水平的影响。方法体外培养B型产气荚膜梭菌12、24、36、48、56及64 h时期的BF,提取其总RNA,通过实时Real-Time PCR检测不同时间段B型产气荚膜梭菌BF agrB基因对BF形成相关基因htrA、rpoA表达的调节作用。结果 B型产气荚膜梭菌在24、36、48、56及64 h 5个时间段中,QS系统agrB基因和BF rpoA、htrA基因相对表达量均呈现出先增高后降低的趋势,且在36 h达到峰值。在峰值前期,agrB、rpoA、htrA基因的表达量较高,48 h后表达量迅速下降,提示B型产气荚膜梭菌BF形成相关基因rpoA、htrA的表达受到QS系统agrB基因表达的影响。结论 AgrB信号分子对rpoA、htrA基因调控表达趋势在产气荚膜梭菌BF形成过程呈现关联性。     

致人腹泻产气荚膜梭菌的分离鉴定与基因分型

目的　研究产气荚膜梭菌与我国人群中引起食物中毒的关系。方法　从江苏扬州三家医院采集腹泻病人临床粪便样品进行细菌分离培养与生化鉴定 ,然后根据ＧｅｎｅＢａｎｋ中已发表的产气荚膜梭菌毒素基因序列 ,设计出针对ｃｐａ、ｃｐｂ、ｅｔｘ、ｉＡ、ｃｐｅ和ｃｐｂ2的 6对引物 ,应用多重ＰＣＲ方法对鉴定出的产气荚膜梭菌进行基因分型。 结果　从 40份样品中分离到 8株 (占 2 0 %)产气荚膜梭菌 ,分型结果均为Ａ型 ,其中 4株还扩增出ｃｐｂ2基因。 结论　产气荚膜梭菌是致人类腹泻的重要病原菌 , β2毒素可能是一重要的致病因子     

粪样中产气荚膜梭菌的分离及cpe＋菌株的鉴定

目的分离粪样中的产气荚膜梭菌基因分型。方法按照国标方法从粪样中分离产气荚膜梭菌,应用PCR进行毒素基因型分型和鉴定。结果从1 411份粪样中分离到557株产气荚膜梭菌,其中553个为A型,其余为C型。1株人源分离株携带cpe基因,该cpe的ORF与标准菌株在核苷酸和氨基酸水平上的同源性分别为99.9%和100%,而且该基因位于染色体上。结论初步摸清了我国粪样中产气荚膜梭菌的动态分布,并在人粪样品中发现了cpe＋产气荚膜梭菌,为分子流行病学研究提供了重要理论依据。     

应用多重PCR方法鉴定粪样和食品中分离的产气荚膜梭菌

目的用国标方法和多重PCR方法检测粪样和食品中产气荚膜梭菌分离株。方法采集粪样和食品样品按照国标方法进行分离鉴定，再应用多重PCR方法进行基因型鉴定。结果346份粪便样品分离与鉴定出了69株产气荚膜梭菌，275份食品样品分离到97株产气荚膜梭菌。经过生化试验鉴定和多重PCR方法鉴定，证实均为A型产气荚膜梭菌，携带胆毒素的产气荚膜梭菌共为53株，另检出了2株肠毒素阳性A型产气荚膜梭菌。结论初步摸清了我国的粪便中和食品中的产气荚膜梭菌的动态分布。并在食品中发现了导致人食物中毒的肠毒素阳性A型产气荚膜梭菌，为分子流行病学研究提供了重要理论依据。     

四川地区鸡场产气荚膜梭菌的扩增片段长度多态性分析

目的了解四川部分地区10个规模化鸡场产气荚膜梭菌的基因型及遗传学关系。方法采用EcoRI、MseI酶对34株不同地区鸡场的产气荚膜梭菌分离株进行扩增片段长度多态性(amplified fragment length polymorphism,AFLP)分析。结果AFLP能全部区别34株产气荚膜梭菌,辨别力指数为100%,其不同来源菌株呈现多态性分布。按Dice系数≥0.8,可分为19个AFLP基因亚型,AFLP-14为优势基因型,占总菌株数的20.59%。分析亚型分布情况表明,产气荚膜梭菌的流行特点与地区差异相关:不同鸡场产气荚膜梭菌的亚型差异明显;而同一鸡场的亚型较单一,以一种亚型为主,交叉有少数其他亚型。结论四川地区鸡场产气荚膜梭菌的基因型十分丰富,AFLP技术是其分子流行病学研究的有效手段。     

Host cell-induced signaling causes Clostridium perfringens to upregulate production of toxins important for intestinal infections

Clostridium perfringens causes enteritis and enterotoxemia in humans and livestock due to prolific toxin production. In broth culture, C. perfringens uses the Agr-like quorum sensing (QS) system to regulate production of toxins important for enteritis/enterotoxemia, including beta toxin (CPB), enterotoxin, and epsilon toxin (ETX). The VirS/VirR two-component regulatory system (TCRS) also controls CPB production in broth cultures. Both the Agr-like QS and VirS/VirR systems are important when C. perfringens senses enterocyte-like Caco-2 cells and responds by upregulating CPB production; however, only the Agr-like QS system is needed for host cell-induced ETX production. These in vitro observations have pathophysiologic relevance since both the VirS/VirR and Agr-like QS signaling systems are required for C. perfringens strain CN3685 to produce CPB in vivo and to cause enteritis or enterotoxemia. Thus, apparently upon sensing its presence in the intestines, C. perfringens utilizes QS and TCRS signaling to produce toxins necessary for intestinal virulence.     

Clostridium perfringens bacteremia caused by choledocholithiasis in the absence of gallbladder stones

A 67-years-old male presented with periumbilical abdominal pain, fever and jaundice. His anaerobic blood culture was positive for clostridium perfringens. Computed tomogram scan of the abdomen and abdominal ultrasound showed normal gallbladder and common bile duct (CBD). Subsequently magnetic resonance cholangiopancreaticogram showed choledocholithiasis. Endoscopic retrograde cholangiopancreaticogramwith sphincterotomy and CBD stone extraction was performed. The patient progressively improved with antibiotic therapy Choledocholithiasis should be considered as a source of clostridium perfringens bacteremia especially in the setting of elevated liver enzymes with cholestatic pattern.     

畜舍环境中魏氏梭菌分离及基因型鉴定

目的从山东省泰安、青岛等地6个市的2个牛舍、4个猪舍、5个兔舍空气及畜舍动物粪便中分离魏氏梭菌,从空气中分离到66株,从畜舍粪便中分离到70株,通过多重聚合酶链反应(multi-PCR)对分离到的魏氏梭菌进行型别的鉴定,结果发现:分离到的魏氏梭菌均为A型。     

Features of hemolysis due to Clostridium perfringens infection

Infection by Clostridium perfringens can be an unsuspected cause of hemolysis in emergency room patients. Historically, this condition has been associated with wound contamination and other tissue infections. We report the case of an autistic patient who presented to our emergency department with a distended abdomen and hemolysis of unknown etiology. The patient had no history of recent surgery. Exploration of the abdomen revealed a hepatic abscess. Blood cultures tested culture positive for C. perfringens. We present images demonstrating the salient features of the peripheral blood smear in cases of this uncommon but deadly cause of hemolysis.     

澳门地区鸡源产气荚膜梭菌的污染情况及基因分型

目的 调查澳门地区鸡源性产气荚膜梭菌的污染情况及基因分型。方法 利用双管法分离产气荚膜梭菌,再作生化测试以鉴定;利用多重PCR测定分离菌株的基因分型。结果 从120个样本中检出率为37.5%,所分离到的产气荚膜梭菌均为 A 型产气荚膜梭菌。结论 初步研究指出本澳供应的鸡肠、新鲜鸡和冰鲜鸡中存在A 型产气荚膜梭菌污染,为产气荚膜梭菌所致的公共卫生问题提供了参考数据。     

Cloning of α-β fusion gene from Clostridium perfringens and its expression

AIM: To study the cloning of α-β fusion gene from Clostridium perfringens and the immunogenicity of α-β fusion expression.METHODS: Cloning was accomplished after PCR amplification from strains NCTC64609 and C58-1 of the protective antigen genes of α-toxin and β-toxin. The fragment of the gene was cloned using plasmid pZCPAB. This fragment coded for the gene with the stable expression of α-β fusion gene binding. In order to verify the exact location of the α-β fusion gene, domain plasmids were constructed. The two genes were fused into expression vector pBV221. The expressed α-β fusion protein was identified by ELISA, SDS-PAGE, Western blotting and neutralization assay.RESULTS: The protective α-toxin gene (cpa906) and the β-toxin gene (cpb930) were obtained. The recombinant plasmid pZCPAB carrying α-β fusion gene was constructed and transformed into BL21(DE3). The recombinant strain BL21(DE3)(pZCPAB) was obtained. After the recombinant strain BL21(DE3)(pZCPAB) was induced by 42℃, its expressed product was about 22.14% of total cellular protein at SDS-PAGE and thin-layer gel scanning analysis. Neutralization assay indicated that the antibody induced by immunization with α-β fusion protein could neutralize the toxicity of α-toxin and β-toxin.CONCLUSION: The obtained α-toxin and β-toxin genes are correct. The recombinant strain BL21(DE3)(pZCPAB)could produce α-β fusion protein. This protein can be used for immunization and is immunogenic. The antibody induced by immunization with α-β fusion protein could neutralize the toxicity of α-toxin and β-toxin.     

Emergent robustness of bacterial quorum sensing in fluid flow

Bacteria use intercellular signaling, or quorum sensing (QS), to share information and respond collectively to aspects of their surroundings. The autoinducers that carry this information are exposed to the external environment; consequently, they are affected by factors such as removal through fluid flow, a ubiquitous feature of bacterial habitats ranging from the gut and lungs to lakes and oceans. To understand how QS genetic architectures in cells promote appropriate population-level phenotypes throughout the bacterial life cycle requires knowledge of how these architectures determine the QS response in realistic spatiotemporally varying flow conditions. Here we develop and apply a general theory that identifies and quantifies the conditions required for QS activation in fluid flow by systematically linking cell- and population-level genetic and physical processes. We predict that when a subset of the population meets these conditions, cell-level positive feedback promotes a robust collective response by overcoming flow-induced autoinducer concentration gradients. By accounting for a dynamic flow in our theory, we predict that positive feedback in cells acts as a low-pass filter at the population level in oscillatory flow, allowing a population to respond only to changes in flow that occur over slow enough timescales. Our theory is readily extendable and provides a framework for assessing the functional roles of diverse QS network architectures in realistic flow conditions.

Bacteria share and respond collectively to information about their surrounding environment through the production, release, and detection of small diffusible molecules called autoinducers (AIs), in a process termed quorum sensing (QS). In QS systems, the individual bacterial expression of genes relevant to the community is promoted when AIs accumulate to a threshold concentration, typically associated with an increasing cell density (1). Population-level behaviors exhibited in QS-activated states include bioluminescence (2, 3), virulence factor production (4), modified mutation rates (5), biofilm and aggregate formation (6, 7), and biofilm dispersal (8). As AIs diffuse between cells, they are often subject to complex and fluctuating features of their environment, such as extracellular matrix components (9, 10), interference by other bacterial species (or the host organism), and external fluid flow. Recent research has started to show how such environmental factors are closely linked to the QS response, building on foundational knowledge gained from studying well-mixed laboratory cultures (11–13). However, improving our understanding of the functional role of QS systems requires understanding how these systems promote appropriate population-level phenotypes in realistic bacterial environments.Fluid flow is ubiquitous in a diverse range of bacterial habitats from rivers, lakes, and medical devices to the host teeth, gut, lungs, and nasal cavity (14). In addition to its mechanical effects on the structure of cell populations (15–19), external fluid flow has been found to have a strong influence on the transport of relevant chemicals including nutrients (8, 20), antibiotics during host treatment (21, 22), and QS AIs (23–26). Recent experimental (23–27) and numerical (28–34) studies suggest that flow-induced AI transport can affect population-level phenotypes by introducing chemical gradients within populations and, if the flow is strong enough, suppressing QS altogether. These results raise two important questions about QS genetic networks. First, how can QS networks ensure a robust population-level response in order to avoid individual cells committing to a costly multicellular phenotype in isolation, while also avoiding premature population-level QS activation in a spatiotemporally complex environment? Second, how can QS networks enable populations to sense cell density in flow environments that promote high mass transfer (35–38)?Here we answer these questions by combining simulations and a systematic asymptotic analysis of QS in a cell layer subject to an external flow; we focus on the effect of positive feedback in AI production, a common feature of QS genetic circuits (39). We begin by establishing the conditions required for the emergence of population-level QS activation in steady flow. Our results illustrate how the required conditions for activation depend on the ratio of the timescale of the external flow to the timescale of diffusion through the cell layer. If the required conditions are met in a region of the cell layer, positive feedback causes AIs to flood the population, inducing population-wide QS activation. Interestingly, by accounting for a dynamic flow in our model we find that an ability to avoid premature QS activation is built into systems with positive feedback. We predict that positive feedback acts as a low-pass filter to oscillations in the shear rate; if such oscillations occur over a time period shorter than a critical time that we calculate, the QS system is not activated, even if the required conditions for activation are met during the oscillations. Furthermore, we find that by combining multiple QS signals, a population can infer both cell density and external flow conditions. Overall, our findings suggest that positive feedback allows QS systems to act as spatiotemporally nonlocal sensors of fluid flow.