食品中沙门氏菌预测模型的研究进展

沙门氏菌作为引起细菌性食物中毒的主要食源性致病菌之一,引发了国内外一系列食品安全事件,对人类健康造成严重危害。应用预测微生物学理论知识,通过建立能够描述和预测特定微生物在一定条件下的生长和存活模型,可快速对食品的微生物安全性进行评估。笔者通过综述近年来国内外开展的沙门氏菌预测模型研究,列举了肉类、蛋类及蛋制品、牛奶及奶制品等食物中建立的生长模型与失活模型,概述了在动态变化环境下的生长模型研究以及界面模型研究的最新进展,最后对沙门氏菌预测模型的研究提出了展望。     

食品微生物生长预测模型研究新进展

为了更好的了解食品微生物学预测模型的基本内容, 探讨数学模型在预测微生物学中的作用, 达到提高食品卫生检测效率, 保证食品质量安全的目的, 本文以文献综述形式, 简要概述了预测微生物学一级、二级和三级模型的含义与内容。并在此基础上, 着重介绍了全球范围内已经成功推广使用的多种三级模型, 阐述了它们的研究背景、研究进展、使用方法, 分析了各种模型存在的优缺点, 可为实际应用中选择合适的模型提供参考。在比较使用不同种类模型后, 发现Baranyi & Roberts、响应面和ComBase模型在各级模型中具有更好的使用价值。     

食品微生物生长预测模型研究新进展

为了更好的了解食品微生物学预测模型的基本内容,探讨数学模型在预测微生物学中的作用,达到提高食品卫生检测效率,保证食品质量安全的目的,本文以文献综述形式,简要概述了预测微生物学一级、二级和三级模型的含义与内容。并在此基础上,着重介绍了全球范围内已经成功推广使用的多种三级模型,阐述了它们的研究背景、研究进展、使用方法,分析了各种模型存在的优缺点,可为实际应用中选择合适的模型提供参考。在比较使用不同种类模型后,发现Baranyi＆Roberts、响应面和ComBase模型在各级模型中具有更好的使用价值。     

预报微生物学在食品安全风险评估中的作用

随着中国食品工业的发展,食品安全问题日益凸显, 建立一种准确及时的食品安全风险评估是产品市场对食品安全体系提出的挑战。预报微生物学是食品安全风险评估的核心预警技术, 依据建立的预报微生物学模型, 可快速地对食品中的致病菌和腐败菌生长情况进行判断, 对食品中病原微生物和腐败微生物的控制有重要的意义。本文概述了预报微生物学模型的建立和研究现状, 探讨预报微生物学在食品安全风险评估中的应用现状, 概述了预报微生物学模型在食品安全风险评估应用中的发展前景。     

2020年地质微生物学专刊序言

中国微生物学会地质微生物学专业委员会第八届地质微生物学研讨会于2019年6月13-15日在青岛举行,来自国内外127所高校和科研院所的600余位专家学者齐聚此次会议,围绕地质微生物学领域相关的最新研究进展进行了深入交流和研讨。为展示与会学者的部分最新研究成果,特组织这期地质微生物专刊。
本期《微生物学报》专刊以地质微生物学为主题,选取了第八届地质微生物学学术会议报导的地质微生物学研究新技术、极端环境微生物、微生物与元素生物地球化学循环、微生物-矿物相互作用、地质微生物应用、以及环境微生物生态与进化等领域的19篇文章,与读者共享。在“地质微生物学研究新技术”专栏,提出了单细胞拉曼光谱在研究代谢过程、分选活细胞以及揭示细胞对物质利用等方面的应用前景;在“极端环境微生物”专栏,报导了贵州兴义喀斯特洞穴中的微生物多样性及抗菌活性,综述了嗜盐菌中甘氨酸甜菜碱的合成途径及其生物学功能;在“微生物与元素生物地球化学循环”专栏,报导了海洋异养细菌、好氧甲烷氧化耦合反硝化细菌、与农田土壤中丛枝菌根真菌-根际细菌及其在碳氮磷循环过程中作用,综述了海洋氮循环过程及基于基因组代谢网络模型预测研究进展、以及湖泊微生物参与硝化、反硝化与硫循环过程的研究进展;在“微生物-矿物相互作用”专栏,报导了铁还原菌与砷还原菌与矿物相互作用及其对元素循环的影响;在“地质微生物应用”专栏,报导了典型油气藏上方土壤甲烷氧化菌群落特征及其勘探意义与煤灰分和无机矿物对生物产气的影响;在“环境微生物生态与进化”专栏,报导了我国南海、东海与黄河三角洲沉积物微生物群落结构特征,综述了候选门级辐射类群（CPR）细菌与古菌ESCRT系统研究进展。
希望通过本专刊,进一步扩大地质微生物学在国内的影响、并促进地质微生物学相关学科的发展和融合。     

环境微生物群落结构与功能多样性研究方法

微生物群落的结构及群落内种间相互作用是影响其生态功能的决定性因素。尽管微生物群落是地球生物化学循环的主要驱动者,但是由于传统的微生物培养方法只能分离约1%10%的环境微生物,对复杂的环境微生物群落结构和功能多样性了解甚少。元基因组学、单细胞分析和群落遗传学等方法的出现,及其与微生物学的交叉融合,使得人们能够从微生物群落组成、物种功能、种间相互作用和预测模型等方面分析微生物群落。重点综述了元基因组学、单细胞分析和群落遗传学等方法及其在环境微生物群落结构和功能多样性中的应用进展。     

单细胞尺度下的微生物学研究:意义与方法

最近研究表明,即便是处于同一种群中的微生物细胞,在基因转录和翻译、蛋白活性、以及代谢物丰度等多个水平都可能存在显著差异,说明微生物细胞间存在着多个层次上的异质性;同时,传统微生物学研究方法需要将所研究的微生物对象在实验室实现再次培养,然后对纯培养的微生物种群进行研究,这样往往造成实验室的研究结果无法真实地反映微生物细胞在自然界中的原始状态,急需发展新的原位研究手段;此外,自然界中的微生物目前只有极少部分可以在实验室中进行培养,仍有大量微生物无法通过传统方法进行发掘和研究。单细胞尺度微生物学为解决这些微生物学研究中的重要挑战提供了一种新的策略和技术思路,有望帮助我们更为直观、深入地了解每个细胞内部的状态,以及其在自然界的生理生态功能。本文对单细胞尺度微生物学研究的意义以及当前单细胞尺度微生物学的研究方法,特别是新兴的微生物单细胞组学方法进行了介绍。     

欢迎订阅《微生物学通报》

《微生物学通报》创刊于1974年,是中国微生物学会和中国科学院微生物研究所主办,国内外公开发行,以微生物学应用基础研究及技术创新与应用为主的综合性学术期刊。刊登内容包括:基础微生物学研究,农业微生物学研究,工业微生物学研究,医学微生物学研究,食品微生物学研究,环境微生物学研究,微生物功能基因组研究,微生物蛋白组学研究,微生物模式菌株研究,微生物工程与药物研究,微生物技术成果产业化及微生物教学研究改革等。     

欢迎订阅《微生物学通报》

《微生物学通报》创刊于1974年,是中国科学院微生物研究所和中国微生物学会主办,国内外公开发行,以微生物学应用基础研究及技术创新与应用为主的综合性学术期刊。刊登内容包括:基础微生物学研究,农业微生物学研究,工业微生物学研究,医学微生物学研究,食品微生物学研究,环境微生物学研究,微生物功能基因组研究,微生物蛋白组学研究,微生物模式菌株研究,微生物工程与药物研究,微生物技术成果产业化及微生物教学研究改革等。     

欢迎订阅《微生物学通报》

正《微生物学通报》创刊于1974年,月刊,是中国科学院微生物研究所和中国微生物学会主办,国内外公开发行,以微生物学应用基础研究及技术创新与应用为主的综合性学术期刊。刊登内容包括:基础微生物学研究,农业微生物学研究,工业微生物学研究,医学微生物学研究,食品微生物学研究,环境微生物学研究,微生物功能基因组研究,微生物蛋白组学研究,微生物模式菌株研究,微生物工程与药物研究,微生物技术成果产业化及微生物教学研究改革等。     

Evaluation of a mathematical model structure describing the effect of (gel) structure on the growth of Listeria innocua, Lactococcus lactis and Salmonella Typhimurium

Aims:  To evaluate a novel secondary model structure ( Int J Food Microbiol 2008; 128: 67) that describes the effect of medium structure on the maximum specific growth rate ( μ _max) of Salmonella Typhimurium on the growth of S. Typhimurium, Listeria innocua , Lactococcus lactis and Listeria monocytogenes .
Methods and Results:  In the present study, the novel secondary model is validated for S . Typhimurium in more realistic media, namely, pasteurized milk and a cheese mimicking medium. The predictions were accurate. Next, the secondary model structure was evaluated in a two step and a global regression procedure on literature data. On the one hand, the growth of two other micro-organisms, namely L. innocua and L. lactis , in monoculture for varying gelatine concentrations was tested and on the other hand the growth rate of L. monocytogenes was fitted in a broth of which the viscosity was altered with polyvinylpyrrolidone. The model was able to describe the effect of increasing gelatine concentration or viscosity accurately.
Conclusions:  The proposed secondary model structure is able to describe the effect of gelatine concentration on the μ _max of the micro-organisms tested in this study.
Significance and Impact of the Study:  In predictive microbiology, much attention has been paid to the effect of food structure on the μ _max of bacteria. However, to the authors' knowledge, a lack of secondary models still exists to describe this effect. Although the proposed model is empirical, the model parameters have clear biological meaning. The predictive power of the model to describe the effect of food structure is clearly illustrated.     

Forecasting and control of lactate bifurcation in Chinese hamster ovary cell culture processes

Biomanufacturing exhibits inherent variability that can lead to variation in performance attributes and batch failure. To help ensure process consistency and product quality the development of predictive models and integrated control strategies is a promising approach. In this study, a feedback controller was developed to limit excessive lactate production, a widespread metabolic phenomenon that is negatively associated with culture performance and product quality. The controller was developed by applying machine learning strategies to historical process development data, resulting in a forecast model that could identify whether a run would result in lactate consumption or accumulation. In addition, this exercise identified a correlation between increased amino acid consumption and low observed lactate production leading to the mechanistic hypothesis that there is a deficiency in the link between glycolysis and the tricarboxylic acid cycle. Using the correlative process parameters to build mechanistic insight and applying this to predictive models of lactate concentration, a dynamic model predictive controller (MPC) for lactate was designed. This MPC was implemented experimentally on a process known to exhibit high lactate accumulation and successfully drove the cell cultures towards a lactate consuming state. In addition, an increase in specific titer productivity was observed when compared with non-MPC controlled reactors.     

Structure and Dynamics of a Benthic Invertebrate Community in an Intertidal Area of the Tagus Estuary, Western Portugal: A Six Year Data Series

A predictive model, incorporating macroinvertebrate and environmental data, similar to that developed for Australian rivers (AUSRIVAS) and British rivers (RIVPACS) was constructed using a dataset collected from 23 reference (least altered) wetlands on the Swan Coastal Plain, Western Australia, sampled in summer and spring, 1989 and spring, 1990. Four main groups of reference wetlands were identified by UPGMA classification (using the Bray–Curtis dissimilarity measure). Distinguishing environmental variables identified by Stepwise Multiple Discriminant Function Analysis were: calcium, colour (gilvin), latitude, longitude, sodium and organic carbon. Observed to expected ratios of taxa with a >50% chance of occurrence (OE50) derived from the model for a suite of 23 test wetlands sampled in spring, 1997, were significantly correlated with pH and the depth of the sampling sites. Greater discrimination between the test wetlands was provided by the OE50 ratios than either raw richness (number of families) or a biotic index (SWAMPS). Results obtained for a subset of 11 test wetlands sampled with both a rapid bioassessment protocol (incorporating field picking of 200 invertebrates collected in 2 min sweeps from selected habitats) and a semi-quantitative protocol (incorporating laboratory picking of all invertebrates collected in sweeps along 10 m transects at randomly allocated sites) were not significantly different, indicating that the former could be used to reduce the time and costs associated with macroinvertebrate-based wetland monitoring programs. In addition to providing an objective method of assessing wetland condition, predictive modelling provides a list of taxa expected to occur under reference conditions, which can be used as a target in wetland restoration programs. The probable impediment to widespread adoption of predictive modelling for wetland bioassessment is the need to produce models tailored to specific geographic regions and specific climatic conditions. This may incur significant costs in countries, such as Australia, which span a wide range of climatic zones.     

A system identification approach for developing model predictive controllers of antibody quality attributes in cell culture processes

As the biopharmaceutical industry evolves to include more diverse protein formats and processes, more robust control of Critical Quality Attributes (CQAs) is needed to maintain processing flexibility without compromising quality. Active control of CQAs has been demonstrated using model predictive control techniques, which allow development of processes which are robust against disturbances associated with raw material variability and other potentially flexible operating conditions. Wide adoption of model predictive control in biopharmaceutical cell culture processes has been hampered, however, in part due to the large amount of data and expertise required to make a predictive model of controlled CQAs, a requirement for model predictive control. Here we developed a highly automated, perfusion apparatus to systematically and efficiently generate predictive models using application of system identification approaches. We successfully created a predictive model of %galactosylation using data obtained by manipulating galactose concentration in the perfusion apparatus in serialized step change experiments. We then demonstrated the use of the model in a model predictive controller in a simulated control scenario to successfully achieve a %galactosylation set point in a simulated fed‐batch culture. The automated model identification approach demonstrated here can potentially be generalized to many CQAs, and could be a more efficient, faster, and highly automated alternative to batch experiments for developing predictive models in cell culture processes, and allow the wider adoption of model predictive control in biopharmaceutical processes. © 2017 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers Biotechnol. Prog., 33:1647–1661, 2017     

River condition assessment may depend on the sub-sampling method: field live-sort versus laboratory sub-sampling of invertebrates for bioassessment

Aquatic macroinvertebrates are commonly used biological indicators for assessing the health of freshwater ecosystems. However, counting all the invertebrates in the large samples that are usually collected for rapid site assessment is time-consuming and costly. Therefore, sub-sampling is often done with fixed time or fixed count live-sorting in the field or with preserved material using sample splitters in the laboratory. We investigate the differences between site assessments provided when the two sub-sampling approaches (Live-sort and Lab-sort) were used in conjunction with predictive bioassessment models. The samples showed a method bias. The Live-sort sub-samples tended to have more large, conspicuous invertebrates and often fewer small and, or cryptic animals that were more likely to be found in Lab-sort samples where a microscope was used. The Live-sort method recovered 4–6 more taxa than Lab-sorting in spring, but not in autumn. The magnitude of the significant differences between Live-sort and Lab-sort predictive model outputs, observed to expected (O/E) taxa scores, for the same sites ranged from 0.12 to 0.53. These differences in the methods resulted in different assessments of some sites only and the number of sites that were assessed differently depended on the season, with spring samples showing most disparity. The samples may differ most in spring because many of the invertebrates are larger at that time (and thus are more conspicuous targets for live-sorters). The Live-sort data cannot be run through a predictive model created from Lab-sort data (and vice versa) because of the taxonomic differences in sub-sample composition and the sub-sampling methods must be standardized within and among studies if biological assessment is to provide valid comparisons of site condition. Assessments that rely on the Live-sorting method may indicate that sites are ‘less impaired’ in spring compared to autumn because more taxa are retrieved in spring when they are larger and more visible. Laboratory sub-sampling may return fewer taxa in spring, which may affect assessments relying on taxonomic richness.     

Anhydrobiosis quotient: a novel approach to evaluate stability in desiccated bacterial cells

Aim:  The major objective of this study was the development of a methodology to quantify the anhydrobiotic ability of bacteria and its application to evaluate the stability of desiccated bacterial cells using the biocontrol agent Tsukamurella paurometabola C-924 as a model of anhydrobiote.
Methods and Results:  Tsukamurella paurometabola C-924 was desiccated by spray-drying. Samples of desiccated cells were stored at several temperatures and viability and residual moisture were measured at different intervals of time. The term anhydrobiosis quotient (ε) was defined, and a scale of anhydrobiotic ability for classifying micro-organisms in terms of tolerance to desiccation was established (1 ≤  ε  ≤ 15). The anhydrobiosis quotient was used to evaluate the stability of the anhydrobiotic cells. As a main result, changes in the anhydrobiosis quotient at several temperatures were fitted using a reparameterized Weibull model, which was found to be robust for the prediction of the stability at 4°C.
Conclusions:  A novel methodology was developed to evaluate the desiccated state in bacteria. The anhydrobiosis quotient allows the quantitative estimation of the anhydrobiotic ability, and the mathematical model developed allows the prediction of the desiccated state of bacterial populations.
Significance and Impact of the Study:  The new methodology could be applied in studying the anhydrobiosis state of bacterial populations as a predictive tool for industrial and environmental microbiology.     

Comparison of Arrhenius-type and Bêlehrádek-type models for prediction of bacterial growth in foods

D.A. RATKOWSKY, T. ROSS, T.A. WCMEEKIN AND J. OLLEY. 1991. The development of Arrhenius-type ('Schoolfield') and Bêlehrádek-type (square root) models that describe microbial growth rates is briefly described. Both types of model have been advocated for use in predictive microbiology. On the basis of published data sets for the growth of bacteria, the consequences of mathematical transformation of data and the use of invalid stochastic assumptions upon model predictions are demonstrated. Mean square error is shown to be an inappropriate criterion by which to compare the performance of predictive models. The data show that bacterial growth responses such as generation time and lag time become more variable as their mean magnitude increases. The practical consequences of such variability for predictive microbiology are discussed.     

The genetic basis of population fecundity prediction across multiple field populations of Nilaparvata lugens

Identifying the molecular markers for complex quantitative traits in natural populations promises to provide novel insight into genetic mechanisms of adaptation and to aid in forecasting population dynamics. In this study, we investigated single nucleotide polymorphisms (SNPs) using candidate gene approach from high‐ and low‐fecundity populations of the brown planthopper (BPH) Nilaparvata lugens Stål (Hemiptera: Delphacidae) divergently selected for fecundity. We also tested whether the population fecundity can be predicted by a few SNPs. Seven genes (ACE, fizzy, HMGCR, LpR, Sxl, Vg and VgR) were inspected for SNPs in N. lugens, which is a serious insect pest of rice. By direct sequencing of the complementary DNA and promoter sequences of these candidate genes, 1033 SNPs were discovered within high‐ and low‐fecundity BPH populations. A panel of 121 candidate SNPs were selected and genotyped in 215 individuals from 2 laboratory populations (HFP and LFP) and 3 field populations (GZP, SGP and ZSP). Prior to association tests, population structure and linkage disequilibrium (LD) among the 3 field populations were analysed. The association results showed that 7 SNPs were significantly associated with population fecundity in BPH. These significant SNPs were used for constructing general liner models with stepwise regression. The best predictive model was composed of 2 SNPs (ACE_‐862 and VgR_‐816) with very good fitting degree. We found that 29% of the phenotypic variation in fecundity could be accounted for by only two markers. Using two laboratory populations and a complete independent field population, the predictive accuracy was 84.35–92.39%. The predictive model provides an efficient molecular method to predict BPH fecundity of field populations and provides novel insights for insect population management.