Loading [MathJax]/jax/output/SVG/jax.js
Research article Special Issues

Proteomic and transcriptomic analyses of Cutibacterium acnes biofilms and planktonic cultures in presence of epinephrine

  • Received: 04 March 2024 Revised: 15 May 2024 Accepted: 21 May 2024 Published: 27 May 2024
  • Transcriptomic and proteomic analysis were performed on 72 h biofilms of the acneic strain Cutibacterium acnes and planktonic cultures in the presence of epinephrine. Epinephrine predominantly downregulated genes associated with various transporter proteins. No correlation was found between proteomic and transcriptomic profiles. In control samples, the expression of 51 proteins differed between planktonic cultures and biofilms. Addition of 5 nM epinephrine reduced this number, and in the presence of 5 µM epinephrine, the difference in proteomic profiles between planktonic cultures and biofilms disappeared. According to the proteomic profiling, epinephrine itself was more effective in the case of C. acnes biofilms and potentially affected the tricarboxylic acid cycle (as well as alpha-ketoglutarate decarboxylase Kgd), biotin synthesis, cell division, and transport of different compounds in C. acnes cells. These findings are consistent with recent research on Micrococcus luteus, suggesting that the effects of epinephrine on actinobacteria may be universal.

    Citation: AV Gannesen, MI Schelkunov, RH Ziganshin, MA Ovcharova, MV Sukhacheva, NE Makarova, SV Mart'yanov, NA Loginova, AM Mosolova, EV Diuvenji, ED Nevolina, VK Plakunov. Proteomic and transcriptomic analyses of Cutibacterium acnes biofilms and planktonic cultures in presence of epinephrine[J]. AIMS Microbiology, 2024, 10(2): 363-390. doi: 10.3934/microbiol.2024019

    Related Papers:

    [1] Jian Gao, Hao Liu, Yang Zhang . Intelligent traffic safety cloud supervision system based on Internet of vehicles technology. Electronic Research Archive, 2023, 31(11): 6564-6584. doi: 10.3934/era.2023332
    [2] Mohd. Rehan Ghazi, N. S. Raghava . Securing cloud-enabled smart cities by detecting intrusion using spark-based stacking ensemble of machine learning algorithms. Electronic Research Archive, 2024, 32(2): 1268-1307. doi: 10.3934/era.2024060
    [3] Mohammed Alshehri . Blockchain-assisted cyber security in medical things using artificial intelligence. Electronic Research Archive, 2023, 31(2): 708-728. doi: 10.3934/era.2023035
    [4] Majed Alowaidi, Sunil Kumar Sharma, Abdullah AlEnizi, Shivam Bhardwaj . Integrating artificial intelligence in cyber security for cyber-physical systems. Electronic Research Archive, 2023, 31(4): 1876-1896. doi: 10.3934/era.2023097
    [5] Gang Cheng, Yijie He . Enhancing passenger comfort and operator efficiency through multi-objective bus timetable optimization. Electronic Research Archive, 2024, 32(1): 565-583. doi: 10.3934/era.2024028
    [6] Liling Huang, Yong Tan, Jinzhu Ye, Xu Guan . Coordinated location-allocation of cruise ship emergency supplies under public health emergencies. Electronic Research Archive, 2023, 31(4): 1804-1821. doi: 10.3934/era.2023093
    [7] Linsen Song, Yichen Du . A real-time pricing dynamic algorithm for a smart grid with multi-pricing and multiple energy generation. Electronic Research Archive, 2025, 33(5): 2989-3006. doi: 10.3934/era.2025131
    [8] Kun Han, Feng Jiang, Haiqi Zhu, Mengxuan Shao, Ruyu Yan . Learning cooperative strategies in StarCraft through role-based monotonic value function factorization. Electronic Research Archive, 2024, 32(2): 779-798. doi: 10.3934/era.2024037
    [9] Ming Wei, Congxin Yang, Bo Sun, Binbin Jing . A multi-objective optimization model for green demand responsive airport shuttle scheduling with a stop location problem. Electronic Research Archive, 2023, 31(10): 6363-6383. doi: 10.3934/era.2023322
    [10] Peiqun Lin, Chenxing He, Lingshu Zhong, Mingyang Pei, Chuhao Zhou, Yang Liu . Bus timetable optimization model in response to the diverse and uncertain requirements of passengers for travel comfort. Electronic Research Archive, 2023, 31(4): 2315-2336. doi: 10.3934/era.2023118
  • Transcriptomic and proteomic analysis were performed on 72 h biofilms of the acneic strain Cutibacterium acnes and planktonic cultures in the presence of epinephrine. Epinephrine predominantly downregulated genes associated with various transporter proteins. No correlation was found between proteomic and transcriptomic profiles. In control samples, the expression of 51 proteins differed between planktonic cultures and biofilms. Addition of 5 nM epinephrine reduced this number, and in the presence of 5 µM epinephrine, the difference in proteomic profiles between planktonic cultures and biofilms disappeared. According to the proteomic profiling, epinephrine itself was more effective in the case of C. acnes biofilms and potentially affected the tricarboxylic acid cycle (as well as alpha-ketoglutarate decarboxylase Kgd), biotin synthesis, cell division, and transport of different compounds in C. acnes cells. These findings are consistent with recent research on Micrococcus luteus, suggesting that the effects of epinephrine on actinobacteria may be universal.



    In the existing application domain of intelligent systems for multi-cloud resource scheduling in cyberspace, academia and industry are mainly concerned with efficiently realizing information processing and knowledge reasoning. However, the number of published system vulnerabilities is increasing [1], and cyberspace system failures and disruptions caused by exploited vulnerabilities may have catastrophic consequences for those affected. Once information processing and knowledge reasoning systems are exploited by attackers due to known or unknown vulnerabilities, the effectiveness of information processing and knowledge reasoning is greatly reduced. Therefore, we need an information processing and knowledge reasoning methodology that improves the efficiency of information processing and knowledge reasoning while guarding against a variety of known and unknown risks and vulnerabilities. Nazir et al. [2] have investigated a number of tools and techniques for mining system vulnerabilities. However, there is no method to mine and discover all unknown system risks and vulnerabilities in information processing and knowledge reasoning systems.

    The theory of endogenous security in cyberspace [3] may be able to solve the above problems. The root of the cyberspace security problem lies in the insufficient consideration of security requirements in architecture design, which needs to be solved by designing architectures with their own security attributes and security capabilities.

    This paper is the first to apply endogenous security theory to a multi-cloud resource scheduling intelligent system. Due to the polymorphic nature of the information processed by the system, its information can be resolved into multiple knowledge representations, such as meta-knowledge, rule-knowledge, instance-knowledge, artificial intelligence (AI) model feature-knowledge, etc., which leads to the natural polymorphism of the information knowledge representations, and different forms of knowledge representations require compatible knowledge reasoning subsystems. Therefore, our first-of-its-kind multi-cloud resource intelligent scheduling system architecture should be endogenously secure due to the mechanism we call Dynamic, Heterogeneous, and Redundant (DHR). We propose an endogenously secure, multi-channel redundant approach and system for intelligent scheduling of multi-cloud resources. The system has three distinctive features (DHR): First, redundancy (R), the four parallel intelligent reasoning channels of the system have redundancy to ensure that while one reasoning channel is processing in a working state, the other is in a backup standby state; second, heterogeneity (H), each of the reasoning systems and inference machines of these four channels also have different knowledge representations, and the heterogeneity increases the difficulty of the attacker; third, dynamism (D), the system has a dynamic immune ability—when the intelligent reasoning channel in the work process encountered external threats using known or unknown system vulnerabilities to attack the failure, one of the standby reasoning channels will be timely transformed into a normal working state, while loading security components to repair the failed channel, to ensure that the system always has the ability to self-heal.

    The main contributions of this work are as follows:

    First, a system model of endogenous security knowledge reasoning has been designed. It provides a polymorphic knowledge representation of the information to be processed and multiple knowledge reasoning methods, which are separately compatible.

    Second, we have developed a dual-channel redundant endogenous security knowledge reasoning system application plan utilizing rule-based and algorithmic reasoning techniques based on AI models to meet the needs of intelligent scheduling for multi-cloud resources.

    Finally, we carried out experiments on the DHR characteristics of the multi-cloud resource intelligent scheduling system that we have designed. The findings indicate that the endogenous security DHR characterization implemented in our application scheme is effective and significantly enhances system reliability.

    The paper is structured as follows: Section 2 presents an overview of related research and Section 3 introduces a knowledge reasoning system model with endogenous secure DHR features featuring multiple channels and a methodology for assessing system reliability. Then, Section 4 systematically outlines the practical design of application scenarios for an intelligent scheduling system in multi-cloud resource management with endogenous security. Section 5 details the research methodology and analysis while interpreting the results. Finally, Section 6 summarizes the findings of the paper.

    In the area of multi-cloud resource scheduling, a range of scheduling approaches and techniques has emerged. Yang et al. presented a multi-population competition-cooperation-based scheduling of field service resources in cloud manufacturing [4]. Meanwhile, Sun et al. put forth an efficient, cost-effective and energy-saving multiple workflow scheduling in hybrid clouds [5]. Zhou et al. introduced a scalable genetic algorithm based on heuristic local search for multidimensional resource scheduling in cloud computing [6]. Agarwal et al. analyzed the scheduling of multiprocessor tasks in fog cloud computing using a multi-objective hybrid genetic algorithm [7]. Zhang et al. utilized an extended multi-factor evolutionary algorithm to propose personalized demand-driven multi-task scheduling in cloud manufacturing [8]. Xiong et al. presented a successful adaptive adjustment model for scheduling tasks and allocating resources in cloud manufacturing that is based on the interests of various stakeholders [9]. Zhang et al. examined scheduling of multiple tasks in cloud remanufacturing systems that incorporate reuse, reprocessing, and replacement while factoring in quality uncertainty [10]. Wang et al. proposed decomposition-based multi-objective evolutionary algorithms for the joint scheduling of virtual machines and tasks in cloud computing within the data space [11].

    A key technology for intelligent scheduling of multi-cloud resources is the knowledge reasoning system, and its associated techniques and methods are continually undergoing innovation. Wu et al. proposed a neural symbol inference method employing dynamic knowledge partitioning technology [12], while Wang et al. suggested a set method for the diagnosis of mechanical faults under unbalanced conditions using DenseNet and evidence reasoning rules [13]. Gao et al. conducted an analysis of a method for managing the health of mechanical equipment. The method is based on improved intuitive fuzzy entropy and case-based reasoning technology [14]. Fard et al. introduced a hybrid method of geographic information systems and evidence reasoning for selecting sustainable waste power plant sites [15]. Xu et al. proposed a new online optimization method for boiler combustion systems based on data-driven technology and case-based reasoning principles [16]. Kalhori et al. introduced a novel fuzzy inference method of interval 2 type for classification systems. This method is grounded on the normal form of possibility-based fuzzy metrics [17]. Wang et al. proposed a task recommendation technique that integrates multi-perspective social relationship learning and reasoning in mobile crowd perception systems [18]. Xu et al. evaluated a new approach to online combustion optimization for boilers. The technique combines dynamic modeling, multi-objective optimization, and improved case-based reasoning [19]. Yadav et al. presented a hybrid method using behavioral reasoning theory [20]. Zhang et al. proposed a warning method based on fuzzy evidence reasoning, which takes into account heterogeneous information [21]. Zhao et al. have introduced a method of spatial case-based reasoning for the assessment of regional landslide risk. In a similar vein, Long et al. have proposed a parameterized extended case-based reasoning approach based on functional foundations to enable automatic experiential reasoning in mechanical product design [22,23]. Finally, Chen et al. have analyzed a decision-making method using logical reasoning to handle qualitative knowledge [24]. Wang and Gao proposed a method for evidence localization in digital forensics, utilizing a case-based variable scale reasoning approach [25]. Cercone et al. highlighted the advantages of a hybrid architecture incorporating rule induction and case-based reasoning [26]. Sottara et al. introduced a configurable Rete-OO engine able to infer various types of incomplete information [27]. Cao et al. conducted an analysis of the interpretability of expert systems employing belief rules [28]. Guo et al. formulated a multi-layer case-based reasoning approach for intricate product systems [29].

    In brief, the prevailing method for knowledge reasoning is the expert system. It utilizes a set of rules to quantify the information obtained from expert interviews. The logical reasoning mechanism then processes this rule set, generating prompts, predictions or advice as appropriate. Several approaches exist to logical reasoning mechanisms, including fuzzy logic. The technique providing the most apt response to data is termed case-based reasoning. It is a learning and reasoning strategy that stores events and compares them.

    An alternative knowledge reasoning methodology lies in machine learning, deep learning, and reinforcement learning applied to various types of neural networks. Ouache et al. have introduced a framework founded on evidence reasoning and machine learning for assessing and forecasting human-caused fire accidents [30]. Kierner et al. presented a classification system for hybrid architectures, which integrate rule-based reasoning and machine learning in clinical decision-making systems [31]. Bride et al. examined the fundamentals of high-performance machine learning for logical reasoning and verification [32]. Chen et al. introduced recursive inference based on training time for machine learning [33]. Bellomarini et al. proposed a knowledge graph with machine learning and reasoning capabilities [34]. Namvar et al. introduced a method for integrating intelligent reasoning into machine learning development [35]. Krüger et al. put forth an interpretable machine learning method for predicting student dropout rates [36], while Gao et al. presented a deep learning method based on evidence reasoning rules [37]. Liu and Qian have conducted an investigation of knowledge graph inference through reinforcement learning in the context of aluminum alloy applications [38]. Meanwhile, Muslim et al. have introduced a reinforcement learning-based framework for offloading computing services in both edge and core cloud environments [39].

    Endogeneity means that one or more explanatory variables in the system model are correlated with the random perturbation term. If all the explanatory variables are Xi and the random perturbation ui, endogeneity can be expressed as: cov(ui,Xi)0.

    Endogenous problems prevail in the field of cyberspace security [3]. Endogenous security (ES) issues of multi-cloud resource scheduling and reasoning systems include functional security issues and information security issues. Functional security concerns pertain to any impairment of a system's expected function (EF), while information security concerns include incidents of information leakage, tampering, and similar occurrences. In addition to knowledge reasoning EFs, knowledge reasoning systems have associated visible side effect functions (VSEFs) or invisible dark functions (IDFs). When a VSEF is detected, a security patch is used. However, security patches themselves can have new security problems. In summary, whenever a knowledge-based reasoning EF is performed, there must be a VSEF or an IDF.

    Ahmad et al. conducted an overview of the security challenges and solutions in 5G [40], while Hu et al. presented a meticulously designed framework for network security defense [41]. Nevertheless, the inherent security issue of intelligently scheduling multi-cloud resources remains unresolved.

    The proposed theoretical model by Wu for endogenous security assurance in cyberspace not only provides protection from identified security risks, but also shields against unknown threats and attacks [3]. The essence of endogenous security lies in the DHR mechanism, which selects groups of actuators dynamically from heterogeneous and redundant ones. This not only fulfils the expected system functions, but also adds uncertainty for adversaries. The system's structure and operation mechanism guarantee its reliability and make it more difficult for attackers to carry out effective attacks.

    The knowledge reasoning system program's evaluation method can draw reference from both the system reliability theory and engineering applications' practical experience [3]. The varied forms of information knowledge representation, including meta-knowledge, rule knowledge, case knowledge, and artificial intelligence model knowledge, combined with the diverse knowledge reasoning methods and redundancy of parallel reasoning architecture design, constitute the fundamental characteristics of multi-channel knowledge reasoning systems with endogenous safety. These features form the basis for calculating system reliability.

    To address the endogenous security issue of the decision-making system used in scheduling multi-cloud resources, the intelligent reasoning system architecture for multi-cloud resource scheduling has been designed with the application of endogenous security theory.

    The EF of a multi-cloud resource scheduling intelligent system is to safely and reliably process and transmit cloud network resource scheduling information and cloud network data streams to realize the knowledge reasoning function. However, as shown in Figure 1, there are different VSEFs in addition to the existence of IDFs. Due to the existence of meta-knowledge, rule-knowledge, instance-knowledge, AI model-knowledge, etc., natural polymorphic knowledge reasoning represented by information knowledge is characterized by time-varying, heterogeneity, and redundancy in cloud network resource scheduling intelligence. At the same time, the knowledge polymorphism of reasoning system intelligent resource scheduling in cloud network data stream security and network transmission of cloud network resource scheduling information also leads to predictable VSEFs such as difficulty in knowledge representation of complex information data, inappropriate matching of knowledge representation with inference engine type, and abnormal results when inference engine handles unknown boundary conditions.

    Figure 1.  Causes of reasoning system ES issues.

    There are two reasons for the endogenous security problem of reasoning systems in data flow security, as shown in Figure 2. On one hand, it refers to the polymorphism, naturalness and human interference in the knowledge representation of the information data to be processed. Since the multi-path effect of intelligent scheduling of cloud network resources starts from the knowledge representation of scheduling information, it will reach different knowledge inference systems through many different paths such as meta-knowledge, rule-based knowledge, case-based knowledge, and AI model-based knowledge. We expect to build a multi-level inference system where the raw information data to be processed will converge in a more accurate and reasonable way in the multi-level inference system through a number of accessible paths. However, the information processing paths with different accessibility are different, and the knowledge representation of the information data to be processed may be uncertain. Therefore, this VSEF can also be called the predictable side effect function of path selection. On the other hand, an attacker who receives a signal from an unknown path eavesdrops on it or launches an attack. This is an artificial security attack on the IDF in the integrated resource scheduling path of the cloud network. The occurrence of the above VSEFs and IDFs will surely bring endogenous security problems to the multi-cloud resource scheduling inference system.

    Figure 2.  The ES issues of a reasoning system in multi-cloud resource scheduling.

    The endogenous security model of the inference system, demonstrated in Figure 3, comprises four layers of distinct inference engines.

    Figure 3.  Endogenous security model for reasoning systems in multi-cloud resource scheduling.

    ● Heterogeneous 1. Principle-based reasoning system based on meta knowledge includes meta knowledge data representation: raw data, data elements, element parameters; and principle reasoning system: axiom of business, business formula, business rules.

    ● Heterogeneous 2. Rule-based reasoning system based on expert knowledge includes expert knowledge data representation: business rule characteristic, rule characteristic parameters; and rule reasoning system: business rules, rule-based reasoning.

    ● Heterogeneous 3. Case-based reasoning system includes business case data representation: case structure characteristic, structural characteristic parameters; and case-based reasoning system: successful instances, case-based reasoning.

    ● Heterogeneous 4. AI algorithm reasoning system based on machine learning includes data representation of machine learning model: AI model characteristic, model characteristic parameters; and AI algorithm reasoning: AI algorithm, AI algorithm reasoning.

    The principle-based reasoning system based on meta knowledge is the most basic reasoning method and can be used in both rule-based reasoning and case-based reasoning, while rule-based reasoning can be used in case-based reasoning. The feature characteristic parameters representation of business knowledge in all the above reasoning systems is uniform.

    The elements of the heterogeneous redundancy reasoning systems set L with 4 reconfigurable equivalent multi-clod resource scheduling function F are equal to 15 and are the sum of the combinations in Cn4, where n(1,2,3,4).

    For reasoning scenario elements j (j = 1, 2, 3, 4), i.e., meta-knowledge-based reasoning, rule-knowledge-based reasoning, instance-knowledge-based reasoning, and AI algorithm-based reasoning, design flaws or loopholes are allowed to exist that are different in nature (patterns of differences) from the other elements in the set L.

    The workflow of the intelligent system for multi-cloud resource scheduling is as follows: the requirement for multi-cloud resource scheduling is entered from the input sequence of the system. The input agent then uniformly analyzes and consolidates the data to form representations of meta knowledge data, expert knowledge data, business instance data, and machine learning model data. According to the endogenous security mechanism, the default selection strategy of the dynamic heterogeneous reasoning system is utilized to invoke the relevant dynamic heterogeneous reasoning system. The output agent of the system generates the reasoning results of the output sequence and completes the self-learning case of the knowledge system.

    When faced with a sudden security threat, the immune response system is triggered automatically. This, alongside the activation of other heterogeneous reasoning systems, is designed to maintain the normal functioning of the system and achieve a redundant backup immune effect.

    The following is a quantitative analysis and comparison of the reliability of single channel modules and multi-redundant channel modules.

    Due to uncertain internal resource depletion or external threat attacks, we assume that the failure events of the channel units included in the system follow an exponential distribution.

    Fault density function:

    f(t)=λeλt (1)

    Unreliability:

    F(t)=1eλt (2)

    Reliability:

    R(t)=1F(t)=eλt (3)

    Failure rate:

    λ(t)=f(t)R(t)=λ (4)

    Mean time between failures:

    MTBF=1λ (5)

    According to the definition and reliability logic diagram of a parallel system, its unreliability mathematical model is:

    Fs(t)=ni=1Fi(t) (6)

    where Fs(t) represents the system's unreliability, and Fi(t) represents the ith unit's unreliability.

    Set the failure rates of the two channel modules as λ1 and λ2. The unreliability can be obtained by using the following formulas:

    F1(t)=1eλ1t
    F2(t)=1eλ2t

    According to Eq (6), the unreliability of the dual redundant module composed of two channel modules is:

    Fs(t)=2i=1Fi(t)=F1(t)F2(t)=(1eλ1t)(1eλ2t)=1eλ1teλ2t+e(λ1t+λ2t) (7)

    According to Eq (3), the reliability of dual redundancy module is:

    Rs(t)=1Fs(t)=eλ1t+eλ2te(λ1t+λ2t) (8)

    In particular, when the control module and task management module adopt the same redundancy in the dual redundancy design, namely λ1=λ2=λ. According to Eq (8), the reliability of dual redundancy module is:

    Rs(t)=2eλte2λt (9)

    Assuming that the mean time between failures (MTBF) of the single channel module is 20000 h, it can be seen from Eq (5) that the failure rate of the single channel module is

    λp1=1MTBF=0.00005.

    According to Eq (3), the reliability of the single channel module for 2000 hours is

    Rp1(t)=eλp1t=0.9048.

    According to Eq (9), the reliability of the dual redundant channel module for 2000 hours is

    Rp2(t)=2eλp1te2λp1t=0.9909.

    Table 1 lists the reliability of endogenous security reasoning systems in different redundant channels. It can be seen that the reliability of the system can be greatly improved after the multi-redundant channel design is adopted, that is, the task reliability of the system has been greatly improved.

    Table 1.  List of the reliability of endogenous security reasoning systems in different redundant channels.
    Working hours Single channel Dual channels Three channels Four channels
    20,000 0.3678 0.6004 0.7474 0.8403
    16,000 0.4493 0.6968 0.8330 0.9080
    12,000 0.5488 0.7964 0.9082 0.9586
    8000 0.6703 0.8913 0.9642 0.9882
    4000 0.8187 0.9671 0.9940 0.9989
    2000 0.9848 0.9909 0.9991 0.9999

     | Show Table
    DownLoad: CSV

    Based on the security industry experience and with reference to the information security technology related standards, the security components in the cloud network system can be categorized and graded. The system security protection level, security layering and security capability components correspond to the following Table 2.

    Table 2.  Security component classification hierarchy list.
    Security level Security layer Security capability component
    2 Terminal Terminal virus defense
    2 Data Data identification
    2 Data Document encryption
    2 Application Website content monitoring
    2 Application Web page tamper proof (extranet)
    2 Cloud WAF (internet outlet)
    2 Cloud VPN
    2 Cloud Firewall (FW)
    2 Cloud Fortress machine
    2 Cloud Vulnerability scanning
    2 Network Mobile malicious program
    2 Network Network abnormal traffic monitoring
    2 Network Flow direction monitoring
    2 Network Online log retention
    2 Network Stiff wood creep detection
    2 Network Unrecorded website detection
    2 Network Domain name information security management
    2 Network Spam message interception
    2 Network IDC/ISP
    3 Terminal Terminal access management
    3 Terminal Terminal data leakage prevention
    3 Data Data desensitization
    3 Data Database audit
    3 Data Network DLP
    3 Data Data encryption
    3 Application Unified access (4A)
    3 Application Mobile app shell
    3 Application Code audit
    3 Cloud IPS (internet outlet)
    3 Cloud WAF (intranet outlet)
    3 Cloud Anti-Virus Gateway
    3 Cloud Honeypot system (extranet)
    3 Cloud Full flow (extranet)
    3 Network DNS
    4 Terminal Enterprise terminal leakage prevention
    4 Data Data destruction
    4 Application Web page tamper proof (intranet)
    4 Application Mimicry defense
    4 Cloud Host protection
    4 Cloud Honeypot system (intranet)
    4 Cloud Full flow (intranet)
    4 Network Attack traceability

     | Show Table
    DownLoad: CSV

    The cloud network data flow environment needs to focus on the security components that need to be changed as the data flows through different levels of protection.

    A function of data flow security protection level adjustment related components can be designed. Referring to Table 2.

    ● Adjustments must be made to security level 3 components when there is data flow between protection level 2 and protection level 3.

    ● Adjustments to security level 4 components are necessary when transferring data between protection level 3 and protection level 4.

    ● When data flows between protection level 2 and protection level 4, it is necessary to adjust security level 3 and 4 components.

    The main strategies are:

    ● Private cloud resources offer better security value compared to public and industry clouds.

    ● When deciding between industry and public cloud options, consider the economic impact of billing costs.

    ● It is important to note that different clouds offer varying degrees of data security. Choose the most appropriate security assurance capabilities according to your needs.

    ● The varying cloud usage methods have different reliability and resource utilization benefits, thus it's crucial to opt for the one that offers optimal reliability and resource utilization benefits.

    These principles are also the basis for reasoning rules and also for selecting the feature space when using AI algorithms as shown in Table 3.

    Table 3.  Attribute characteristics of multi-cloud resource scheduling.
    Symbols Attribute Name Supplementary note
    C1,C2,Cpub, Cpri Name of cloud Indicate the first and second public cloud, public cloud, and private cloud, respectively.
    Cmaxvol Maximum cloud storage capacity
    Cused Cloud storage space assigned
    Crem Remaining cloud storage space
    Coccratio Cloud storage space utilization
    Ccat Classification of clouds Common cloud classifications include public, private and industry clouds.
    Ceco Cloud economics metrics
    Csec Security capability levels for the Cloud For example, information security assurance level protection level 2, level 3 and level 4.
    Cperf Integrated performance of cloud usage Cloud usage performance and reliability
    Cdem Cloud space demanded Cloud space to be allocated
    Cdemsec Cloud space demanded security level Security level of cloud space to be allocated
    Csch Cloud selected for scheduling

     | Show Table
    DownLoad: CSV

    Main reasoning rules are shown in Table 4.

    Table 4.  Main reasoning rules.
    No. Name of rule Rule application process
    Rule 1 Private cloud first In multi-cloud resource scheduling, if Cpub and Cpri are public and private clouds respectively, then private cloud C1 is selected first. Csch= Cpri
    Rule 2 Choose a cloud with lower operating costs first In multi-cloud resource scheduling, if C1, C2 are both public clouds, and C1 is more low-cost than C2, then C1 is selected first. Csch= C1
    Rule 3 Cloud with higher security level first In multi-cloud resource scheduling, if C1, C2 are both public clouds, and C1 is higher security level than C2, then C1 is selected first. Csch= C1cc
    Rule 4 First choose the cloud with higher overall performance In multi-cloud resource scheduling, if C1, C2 are both public clouds, and C1 is higher overall performance than C2, then C1 is selected first. Csch= C1

     | Show Table
    DownLoad: CSV

    Based on the size of data storage space and data flow security protection level division of existing multiple cloud networks, with the help of cloud network security components, the intelligent scheduling of multiple cloud network resources is carried out according to the user's cloud network data space size and security protection level requirements.

    We customize the endogenous security model of the inference system based on the application scenario of cloud network resource scheduling in data flow security. Since the number of heterogeneous redundant knowledge reasoning functions is 2, the endogenous secure dual-channel multi-cloud resource scheduling intelligent reasoning system model, as shown in Figure 4, has a set of three different reasoning system scenarios with three different reasoning elements: rule-based reasoning scenarios, AI algorithmic reasoning scenarios, and scenarios with both rule-based reasoning and AI algorithmic reasoning.

    Figure 4.  Dual channel intelligent system model with endogenous security in multi-cloud resource scheduling.

    Supporting polymorphic knowledge representation.

    Input sequence:

    ● Multi-cloud resource scheduling requirements.

    ● Demanded cloud capacity, protection level requirements, etc.

    Polymorphic knowledge representation:

    ● AI algorithm feature knowledge extraction: the attribute features are shown in Table 3.

    ● Rule knowledge representation: as Table 4 parses the premise of the rule used for reasoning.

    Two heterogeneous functions:

    Rule reasoning system based on expert knowledge (Heterogeneous 1)

    The rule-based reasoning algorithm, as shown in Algorithm 1, is given below.

    Algorithm 1 Rule-Based Reasoning Algorithm (Heterogeneous 1)
    1. Input Data: multi-cloud resource scheduling operations
    2. Initialize Security Policy Parameters: SP0(ST0, Zone0 = {Clouds, Networks, Security-levels})
    3. Configure Cloud Network Data Flow Security Policy: SP (ST, Zone)
    4. Call rules in order of priority:
    a. Rule 1: Choose a private cloud first
    b. Rule 2: Choose a cloud with lower operating costs first
    c. Rule 3: Choose a cloud with a high level of security first
    d. Rule 4: Choose a cloud that has high overall performance first
    5. Allocate multi-cloud resources: SP = SP + SP0 = {MaxST, MinZone0} = {Max (ST, ST0), Min (Zone, Zone0)}
    6. Call function of data flow security protection level adjustment related components
    7. Output operation scheme of resource scheduling

    AI algorithm inference reasoning system based on machine learning (Heterogeneous 2)

    Feature selection:

    In each multi-cloud resource scheduling operation, feature combinations with high relevance are selected to form a vector representation and stored as a training set in the database.

    In the multi-cloud resource scheduling operation, the Crem, Csec, Ceco, Cperf feature parameters are the key parameters that reflect the business operation strategy. Among them, Csec, Ceco feature parameters are relatively fixed in the scheduling operation process, so we should focus on Crem,Cperf feature parameters. For example, in the scenario with two clouds, if we focus only on the remaining storage space of the clouds C1.Crem350GB, C2.Crem200GB and the allocated space Cdem100GB each time, the feature space vector can be represented as follows:

    (Cdem,C1.Crem,C2.Crem)=(100,350,200).

    Selection of KNN algorithm:

    The KNN algorithm handles the classification problem with high immunity and reliability. The above features ensure that the KNN algorithm works well in intelligent processing of running logs in multi-cloud resource scheduling scenarios. When using the KNN algorithm, the model parameter k is set to 3, and we can take the three nearest neighbor samples each time.

    Design of endogenous security immunization mechanisms:

    The selection of a particular heterogeneous redundant reasoning system to participate in the reasoning task should be uncertainty specific.

    ● Running state monitoring of each inference system in case of failure.

    ● Formulation of selection strategy, such as selection triggered by failure of any of the reasoning systems, random selection, specific selection, and priority selection.

    Self-learning of AI algorithms involves saving the output sequences of each resource scheduling operation policy execution into the training sample library, while using the updated training sample library for learning and training of AI algorithms to obtain the latest model parameters.

    Databases and AI models:

    Databases: Datasets of scheduling operation logs for multi-cloud resources, cloud feature database, multi-cloud resource Security Policy Library (SPL), list of layered and graded security capability components and resource scheduling operation database.

    AI models: Business rule and AI algorithm bases.

    Multi-cloud resource size allocation and data security protection level in the resulting sequence of inference system outputs are compared with the input user requirements to fine-tune the security components needed for the data flow security protection level to form the final sequence of outputs that satisfy the user requirements.

    The environment used for the experiment, see Figure 3, consists of two parts: a multi-cloud resource scheduling intelligent system with endogenous security and dual-channel redundancy and an external attacker, in which the multi-cloud resource scheduling intelligent system with endogenous security and dual-channel redundancy consists of a multi-cloud resource scheduling demand input, a multi-cloud resource scheduling intelligent system, and a scheduling operation scheme output; the external attack part is responsible for various attacks on the resource scheduling system and for interfering with the reasoning channel that destroys the current working state. The objectives and contents of the experiment are detailed in Table 5.

    Table 5.  The objectives and contents of the experiment of endogenous security DHR features.
    No. Experiment name Experimental contents
    Experiment 1 Dynamic feature ● Setting the dynamic channel selection policy.
    ● External attack is applied to the current working channel so that the channel cannot work normally.
    ● Verifying whether the system can automatically switch to another channel so that the system can continue to maintain the working state, and loading the security components to repair the reasoning channel damaged by the attack.
    Experiment 2 Heterogeneous feature ● Selecting a rule-based reasoning channel and an AI algorithm KNN-based reasoning channel.
    ● Accomplishing the task of multi-cloud resource scheduling operation
    ● Verify whether the results of the output scheduling operation scheme are consistent.
    Experiment 3 Redundant feature ● Calculating and comparing the reliability of a single rule-based reasoning channel, a single AI algorithm-based reasoning channel, and a dual-channel redundant reasoning system working continuously for a period of time.
    ● Verify the practical effect of reliability enhancement in redundant channel mode.

     | Show Table
    DownLoad: CSV

    The dynamic feature is shown by the uncertainty of heterogeneous reasoning systems selected to participate in reasoning tasks. The following measures will ensure the uncertainty of the reasoning task involved in the selection of heterogeneous redundant reasoning systems:

    ● Formulation of selection strategies, such as random selection, specific selection and priority selection. The Table 6 shows the situation of using random selection strategy to select an inference engine for each task. It can be seen from the table that the uncertainty of inference engine selection is guaranteed. According to the parity of random integers, they correspond to different heterogeneous inference systems.

    Table 6.  The situation of using random selection strategy to select the inference engine.
    Task name Random number Selected inference engine
    Task1 23 Rule Based (Heterogeneous 1)
    Task2 68 AI Algorithm (Heterogeneous 2)
    Task3 54 AI Algorithm (Heterogeneous 2)
    Task4 17 Rule Based (Heterogeneous 1)
    Task5 6 AI Algorithm (Heterogeneous 2)
    Task6 85 Rule Based (Heterogeneous 1)
    Task7 39 Rule Based (Heterogeneous 1)

     | Show Table
    DownLoad: CSV

    ● External attack is applied to the current working channel so that the channel cannot work normally.

    ● Running state monitoring of each inference system, the application program of each inference system can ensure that when an exception occurs, another inference system can turn to the formal working mode by providing whether the running state is normal.

    The heterogeneous feature is shown by the consistency of results achieved by heterogeneous reasoning systems. The same reasoning task should obtain the same reasoning result in two heterogeneous reasoning systems. The experimental requirements are detailed in Table 7 below.

    Table 7.  Experimental requirements for the heterogeneous feature of reasoning systems with endogenous security.
    Experimental procedure Experimental content Detailed description
    1. Initial parameter setting (1) Selecting dual channels a) Rule-based inference channel
    b) AI algorithm KNN based reasoning channel
    (2) Initial state of multi-cloud space and security policy a) Multi-cloud maximum storage, allocated space and remaining space
    b) Cloud network security protection zones
    c) Cloud security levels
    (3) Multi-cloud resource scheduling operations task a) Resource space, security level requirements, multi-cloud residual space
    2. Rule-based reasoning system (Heterogeneous 1) Resource scheduling process a) Resource scheduling operation arithmetic rules
    b) Channel 1 output scheme
    3. KNN-based reasoning system (Heterogeneous 2) Resource scheduling process a) Input parameters and algorithm description of the KNN algorithm
    b) Channel 2 output scheme
    4. Experimental conclusion judgment Comparing resource scheduling output schemes for two-channel reasoning for consistency a) If the output schemes of channel 1 and channel 2 are the same, functional consistency is successfully verified.
    b) Otherwise, the functional consistency is inconsistent and the verification fails.

     | Show Table
    DownLoad: CSV

    1) Selecting dual channels

    Here dual redundant inference channels are chosen, rule-based inference channel and KNN-based inference channel.

    2) Initial state of multi-cloud space and security policy

    First, security policy (SP) includes security tokens (ST) and security area (Zone):

    SP(ST,Zone)

    Then, security policy library (SPL):

    SPL=SP1,SP2,SP3

    Initial SP parameters:

    SP0(ST0,Zone0)

    Initial cloud C1:

    C1.Cused=1650GBC1.Crem=350GB

    Initial cloud C2:

    C2.Cused=800GBC2.Crem=200GB

    Cloud C1 and C2 initial states are shown in Table 8.

    Table 8.  Cloud C1 and C2 initial states.
    ID Name of Cloud Maximum storage space Cloud space used Cloud space remaining Boundary Security level
    ID0 C1 2000 GB 1650 GB 350 GB N1, N2 Level 2
    C2 1000 GB 800 GB 200 GB N3, N4 Level 2

     | Show Table
    DownLoad: CSV

    (ⅰ) Cloud network data flow security policy configuration:

    SP(ST,Zone).

    (ⅱ) Implementation of security policies at all layers of data flow security protection system:

    Calling Algorithm 1: Rule-based reasoning algorithm to MinZone and MaxST.

    SP=SP+SP0=MaxST,MinZon0=Max(ST,ST0),Min(Zone,Zone0) (10)

    3) Multi-cloud resource scheduling task

    The multi-cloud resource scheduling intelligent reasoning system training sample set in Table 9 is a repository of historical operations. Now only the features C1.Crem, C2.Crem in the Table 3 are taken, and they can be serialized as follows:

    (Cdem,C1.Crem,C2.Crem)=(100,350,200).
    Table 9.  Resource scheduling intelligent reasoning system training sample set.
    ID C1.Ceco C1.Crem C1.Ceco C1.Crem Operation scheme
    (C1, C2)
    Class
    1 6 200 5.5 700 (100, 0) 1
    2 6 250 5.5 600 (50, 50) 2
    3 6 750 5.5 100 (100, 0) 1
    4 6 300 5.5 800 (0,100) 3
    5 6 400 5.5 200 (50, 50) 2
    6 6 350 5.5 250 (50, 50) 2
    7 6 450 5.5 100 (100, 0) 1
    8 6 500 5.5 850 (0,100) 3
    …… …… …… …… …… …… ……
    N 6 350 5.5 200 (50, 50) 2
    N + 1 6 300 5.5 150 (50, 50) 2

     | Show Table
    DownLoad: CSV

    The resource scheduling results for the two heterogeneous inference systems should adhere to the same operation-scheme (50, 50), with cloud C1 and C2 assigned to schedule 50 GB of resource space each.

    As you can see from Table 8, the current resource scheduling task is to allocate 100 GB of security level 3 storage and the associated application deployment. It can be serialized as follows:

    (Cdem,C1.Crem,C2.Crem)=(100,350,200).

    Calling Algorithm 1, multi-cloud resource calling rules 1–4 and Eq (10), the operation-scheme assigns 50 GB of resource space to clouds C1 and C2, resulting in corresponding resource scheduling results.

    Now only the features C1.Crem, C2.Crem in the Table 3 are taken, and multi-cloud resource scheduling demand can be serialized as follows:

    (Cdem,C1.Crem,C2.Crem)=(100,350,200).

    When using the KNN algorithm, the model parameter k is set to 3. By querying Table 9, it has been determined that samples with the IDs 5, 6, and 7 are the three nearest neighbor samples. Since IDs 5 and 6 belong to Class 2 and ID 7 belongs to Class 1, it is classified as Class 2, using the operation scheme (50, 50).

    It can be seen from the above that this reasoning result of operation-scheme (50, 50) is the same as that of rule-based reasoning. Therefore, it verifies the consistency of results achieved by heterogeneous reasoning systems.

    Redundant feature is shown by reliability analysis of multi-channel reasoning in data flow security. The failure rate of the rule-based reasoning system is λ1, and the failure rate of the AI-algorithm-based reasoning system is λ2.

    Due to uncertain internal resource depletion or external threat attacks, assuming that the mean time between failures (MTBF) of the rule-based reasoning channel of the single-channel module is 10000 h, it can be seen from Eq (5) that the failure rate of the single channel module is:

    λ1=1MTBF=0.0001.

    Assuming that the mean time between failures (MTBF) of the reasoning channel of the single channel module based on the AI algorithm is 8000 h, it can be seen from Eq (5) that the failure rate of the single channel module is:

    λ2=1MTBF=0.000125.

    It can be seen from Eq (3) that the reliability of rule-based reasoning channel of the single channel module working for 2000 h is:

    R1(t)=eλ1t=0.8187.

    It can be seen from Eq (3) that the reliability of reasoning channel based on AI algorithm of the single channel module working for 2000 h is:

    R2(t)=eλ2t=0.7788.

    According to Eq (8), the reliability of the dual redundant channel module for 2000 hours is:

    Rs(t)=eλ1t+eλ2te(λ1t+λ2t)=0.9599.

    The reliability of the reasoning system with different channel designs is shown in Table 10.

    Table 10.  List of the reliability of the dual redundant channel reasoning system.
    Working hours AI algorithm-based reasoning Rule-based reasoning Dual redundant channel
    8000 0.3679 0.4493 0.6519
    6000 0.4724 0.5488 0.7619
    4000 0.6065 0.6703 0.8703
    2000 0.7788 0.8187 0.9599

     | Show Table
    DownLoad: CSV

    Table 10 lists the reliability of the reasoning system in multi-cloud resource scheduling with different channel designs. The reliability of the rule-based channel, the AI algorithm-based reasoning channel, and the dual redundant channel for 2000 hours are 0.7788, 0.8187, and 0.9599, respectively.

    So far, the above three experiments validate the endogenous security DHR property of our designed multi-cloud resource scheduling intelligent system.

    The above proves the endogenous DHR property of the dual-channel system, so that when the intelligent reasoning channel in the work process encountered external threats using known or unknown system vulnerabilities to attack the failure, one of the standby reasoning channels will be timely transformed into a normal working state, while loading security components to repair the failed channel, to ensure that the system always has the ability to self-heal. We have used the multi-cloud resource scheduling intelligent run log database to carry out experiments. The security and reliability of the whole reasoning system have improved, as shown in Table 11, and the endogenous security of the system is achieved.

    Table 11.  Some reasoning results of the endogenous dual-channel system.
    Task name Random number Selected inference engine Heterogeneous system 1
    Operation scheme
    (C1, C2)
    Heterogeneous system 2
    Operation scheme
    (C1, C2)
    Whole system state
    Task 1 23 Rule Based (Heterogeneous 1) (50, 50) Failure, restored Running continuously
    Task 2 68 AI Algorithm (Heterogeneous 2) Failure, restored (100, 0) Running continuously
    Task 3 54 AI Algorithm (Heterogeneous 2) Failure, restored (50, 50) Running continuously
    Task 4 17 Rule Based (Heterogeneous 1) (0, 100) Failure, restored Running continuously
    Task 5 6 AI Algorithm (Heterogeneous 2) Failure, restored (50, 50) Running continuously
    Task 6 85 Rule Based (Heterogeneous 1) (100, 0) Failure, restored Running continuously
    Task 7 39 Rule Based (Heterogeneous 1) (50, 50) Failure, restored Running continuously
    Task n …… …… …… …… ……

     | Show Table
    DownLoad: CSV

    Different reasoning systems have different computational efficiencies and time delays, and in order to better support the demand of high-intensity cloud computing for low-latency cloud applications, the more efficient subsystems with rule-based reasoning system should be prioritized as the default setting option when deploying endogenous security reasoning systems for multi-cloud resource scheduling.

    In this paper, we first apply endogenous security theory to a multi-cloud resource scheduling intelligent reasoning system for the first time. Second, we construct a dual-redundant endogenous security inference system in multi-cloud resource scheduling. Finally, we validate the endogenous security mechanism of the dual-redundant inference system by combining the historical operation data in the cloud resource intelligent scheduling of cloud network systems, and analyze the enhancement of system reliability by the dual-redundant inference system. The results show that our scheme outperforms several representative inference system schemes commonly used in practice.

    In conclusion, the endogenous secure inference system incorporated in multi-cloud resource scheduling guarantees system security and significantly enhances system reliability when performing inference tasks. It is suitable for multi-cloud network resource scheduling scenarios with elevated security and reliability demands.

    Going forward, we intend to focus on two areas to expand our research work. On one hand, we will conduct reliability tests on various types of inference systems in an organized and methodical manner to gather relevant data. On the other hand, we will strive to optimize and enhance the design of endogenous security mechanisms in inference systems to ensure they meet the new demands and challenges of enterprise digital transformation and ensure the security and reliability of multi-cloud resource invocation intelligent systems.

    The authors declare they have not used Artificial Intelligence (AI) tools in the creation of this article.

    This research has been sponsored by the National Key R & D Program for Privacy Data Protection (Project number: 2021YFB3101300), Topic 5 "Research on Key Technologies for the Protection of Personal Rights and Interests" (Project number: 2021YFB3101305).

    The authors declare there is no conflict of interest.


    Acknowledgments



    Transcriptome sequencing and analysis were performed in the Genomics Core Facility of the Skolkovo Institute of Science and Technology (https://www.skoltech.ru/research/en/gcf-2/ (accessed on March1, 2024). qRT–PCR was performed using the scientific equipment of the Core Research Facility ‘Bioengineering’ (Research Center of Biotechnology RAS). A.V. Gannesen and coauthors sincerely thank V.V. Sorokin and A.V. Mulyukin (Core Facility “UNIQEM collection” of Research Center of Biotechnology RAS) for their kind assistance and for providing liquid nitrogen for this study. The authors are grateful to the staff of the Laboratory of Molecular Ecology and Phylogenomics of Bacteria of the Research Center of Biotechnology RAS (the head Svetlana N. Dedysh) and, personally to Svetlana E. Belova, Igor. Yu. Oshkin, Kirill K. Miroshnikov, and Anastasiya A. Ivanova for their kind assistance and giving access to the FastPrep disintegrator. Authors cordially thank Maya M. Polovitskaya for her kind assistance in the editing of the manuscript.

    Funding



    The work is supported by the RSCF, the grant nº 19-74-10071. The work of V.K. Plakunov is supported by the Ministry of Science and Higher Education of Russian Federation.

    Conflict of interests



    Authors declare no conflict of interests.

    Data availability statement



    The transcriptomic data are deposited at the Sequence Read Archive (SRA) database under the identifiers SRR27884912 and SRR27884920. The mass spectrometry proteomics data have been deposited at the ProteomeXchange Consortium via the PRIDE partner repository [57][58] with the data set identifier PXD049410.

    Author contributions



    Conceptualization, A.V.G.; investigation, M.A.O., M.I.S., R.H.Z., N.A.L., A.M.M., N.E.M., S.V.M., E.D.N., E.D.V. and A.V.G.; validation, M.A.O., M.I.S., S.V.M., R.H.Z., N.A.L., A.M.M., N.E.M., S.V.M., E.D.N., V.K.P., and A.V.G.; formal analysis, M.A.O., M.I.S., R.H.Z., and A.V.G.; writing—original draft, M.A.O., M.I.S., S.V.M., R.H.Z., E.D.N., N.E.M., N.A.L., A.M.M., E.D.V., and A.V.G.; data curation, M.A.O., M.I.S., R.H.Z., N.E.M., and A.V.G.; writing—review and editing, M.A.O., M.I.S., R.H.Z., S.V.M., V.K.P. and A.V.G.; visualization, M.A.O., M.I.S., R.H.Z., S.V.M., E.D.N., N.A.L., A.M.M., and A.V.G.; supervision, V.K.P. and A.V.G.; resources, A.V.G.; funding acquisition, A.V.G.; project administration, A.V.G. All authors have read and agreed to the published version of the manuscript.

    [1] Lyte M (2014) Microbial endocrinology and the microbiota-gut-brain axis. Microbial Endocrinology: The MicrobiotaGut-Brain Axis in Health and Disease . New York: Springer 3-24. https://doi.org/10.1007/978-1-4939-0897-4_1
    [2] Luqman A (2023) The orchestra of human bacteriome by hormones. Microb Pathog 106125. https://doi.org/10.1016/j.micpath.2023.106125
    [3] Niu L, Gao M, Wen S, et al. (2023) Effects of catecholamine stress hormones norepinephrine and epinephrine on growth, antimicrobial susceptibility, biofilm formation, and gene expressions of enterotoxigenic Escherichia coli. Int J Mol Sci 24: 15646. https://doi.org/10.3390/ijms242115646
    [4] Reading NC, Rasko D, Torres AG, et al. (2009) A transcriptome study of the QseEF two-component system and the QseG membrane protein in enterohaemorrhagic Escherichia coli O157: H7. Microbiology 156: 1167. https://doi.org.10.1099/mic.0.033027-0
    [5] Moreira CG, Sperandio V (2010) The epinephrine/norepinephrine/autoinducer-3 interkingdom signaling system in Escherichia coli O157: H7. Microbial Endocrinology: Interkingdom Signaling in Infectious Disease and Health . New York: Springer 213-227. https://doi.org/10.1007/978-1-4419-5576-0_12
    [6] Lopes JG, Sourjik V (2018) Chemotaxis of Escherichia coli to major hormones and polyamines present in human gut. ISME J 12: 2736-2747. https://doi.org/10.1038/s41396-018-0227-5
    [7] Qin T, Xie J, Xi B, et al. (2021) Integrative transcriptomic and proteomic analyses of pathogenic Aeromonas hydrophila in response to stress hormone norepinephrine. Aquac Res 53: 1693-1705. https://doi.org/10.1111/are.15700
    [8] Cambronel M, Nilly F, Mesguida O, et al. (2020) Influence of catecholamines (epinephrine/norepinephrine) on biofilm formation and adhesion in pathogenic and probiotic strains of Enterococcus faecalis. Front Microbiol 11: 1501. https://doi.org/10.3389/fmicb.2020.01501
    [9] Cambronel M, Tortuel D, Biaggini K, et al. (2019) Epinephrine affects motility, and increases adhesion, biofilm and virulence of Pseudomonas aeruginosa H103. Sci Rep 9: 20203. https://doi.org/10.1038/s41598-019-56666-7
    [10] Intarak N, Muangsombut V, Vattanaviboon P, et al. (2014) Growth, motility and resistance to oxidative stress of the melioidosis pathogen Burkholderia pseudomallei are enhanced by epinephrine. Pathog Dis 72: 24-31. https://doi.org/10.1111/2049-632X.12181
    [11] Perraud Q, Kuhn L, Fritsch S, et al. (2022) Opportunistic use of catecholamine neurotransmitters as siderophores to access iron by Pseudomonas aeruginosa. Environ Microbiol 24: 878-893. https://doi.org/10.1111/1462-2920.15372
    [12] Beasley FC, Marolda CL, Cheung J, et al. (2011) Staphylococcus aureus transporters Hts, Sir, and Sst capture iron liberated from human transferrin by Staphyloferrin A, Staphyloferrin B, and catecholamine stress hormones, respectively, and contribute to virulence. BMC Microbiol 11: 199. https://doi.org/10.1128/IAI.00117-11
    [13] Boukerb AM, Cambronel M, Rodrigues S, et al. (2021) Inter-kingdom signaling of stress hormones: sensing, transport and modulation of bacterial physiology. Front Microbiol 12: 690942. https://doi.org/10.3389/fmicb.2021.690942
    [14] Axelrod J, Gordon Ayhitby L, Hertting G, et al. (1961) Studies on the metabolism of catecholamines. Circ Res 9: 715-719. https://doi.org/10.1161/01.RES.9.3.715
    [15] Notelovitz M, Funk S, Nanavati N, et al. (2002) Estradiol absorption from vaginal tablets in postmenopausal women. Obstet Gynecol 99: 556-562. https://doi.org/10.1016/S0029-7844(01)01385-0
    [16] Leis S, Drenkhahn S, Schick C, et al. (2004) Catecholamine release in human skin—a microdialysis study. Exp Neurol 188: 86-93. https://doi.org/10.1016/j.expneurol.2004.03.013
    [17] Stradford AF, Zoutman DE, Dick E, et al. (2002) Effect of lidocaine and epinephrine on Staphylococcus aureus in a guinea pig model of surgical wound infection. Plast Reconstr Surg 110: 1275-1279. https://doi.org/10.1097/01.PRS.0000025427.86301.8A
    [18] Lyte M, Freestone PPE, Neal CP, et al. (2011) Stimulation of Staphylococcus epidermidis growth and biofilm formation by catecholamine inotropes. Lancet 361: 130-135. https://doi.org/10.1016/S0140-6736(03)12231-3
    [19] Mart'yanov SV, Botchkova EA, Plakunov VK, et al. (2021) The impact of norepinephrine on mono-species and dual-species staphylococcal biofilms. Microorganisms 9: 820. https://doi.org/10.3390/microorganisms9040820
    [20] Gannesen AV, Schelkunov MI, Geras'kina OV, et al. (2021) Epinephrine affects gene expression levels and has a complex effect on biofilm formation in Micrococcus luteus strain C01 isolated from human skin. Biofilm 3: 100058. https://doi.org/10.1016/j.bioflm.2021.100058
    [21] Gannesen AV, Ziganshin RH, Zdorovenko EL, et al. (2022) Epinephrine extensively changes the biofilm matrix composition in Micrococcus luteus C01 isolated from human skin. Front Microbiol 13: 1003942. https://doi.org/10.3389/fmicb.2022.1003942
    [22] Gannesen AV, Ziganshin RH, Ovcharova MA, et al. (2023) Epinephrine affects ribosomes, cell division, and catabolic processes in Micrococcus luteus skin strain C01: revelation of the conditionally extensive hormone effect using orbitrap mass spectrometry and proteomic analysis. Microorganisms 11: 2181. https://doi.org/10.3389/fmicb.2022.1003942
    [23] Fournière M, Latire T, Souak D, et al. (2020) Staphylococcus epidermidis and Cutibacterium acnes: two major sentinels of skin microbiota and the influence of cosmetics. Microorganisms 8: 1752. https://doi.org/10.3390/microorganisms8111752
    [24] Akaza N, Takasaki K, Nishiyama E, et al. (2022) The microbiome in comedonal contents of inflammatory acne vulgaris is composed of an overgrowth of Cutibacterium Spp. and other cutaneous microorganisms. Clin Cosmet Investig Dermatol 21: 2003-2012. https://doi.org/10.2147/CCID.S379609
    [25] Jahns AC, Alexeyev OA (2014) Three dimensional distribution of Propionibacterium acnes biofilms in human skin. Exp Dermatol 23: 687-689. https://doi.org/10.1111/exd.12482
    [26] Chiu A, Chon SY, Kimball AB (2003) The response of skin disease to stress: changes in the severity of acne vulgaris as affected by examination stress. Arch Dermatol 139: 897-900. https://doi.org/10.1001/archderm.139.7.897
    [27] Yosipovitch G, Tang M, Dawn AG, et al. (2007) Study of psychological stress, sebum production and acne vulgaris in adolescents. Acta Derm Venereol 87: 135-139. https://doi.org/10.2340/00015555-0231
    [28] Jusuf NK, Putra IB, Sutrisno AR (2021) Correlation between stress scale and serum substance P level in acne vulgaris. Int J Gen Med 1: 681-686. https://doi.org/10.2147/IJGM.S294509
    [29] Boyanova L (2017) Stress hormone epinephrine (adrenaline) and norepinephrine (noradrenaline) effects on the anaerobic bacteria. Anaerobe 44: 13-19. https://doi.org/10.1016/j.anaerobe.2017.01.003
    [30] Borrel V, Thomas P, Catovic C, et al. (2019) Acne and stress: Impact of catecholamines on Cutibacterium acnes. Front Med 6: 155. https://doi.org/10.3389/fmed.2019.00155
    [31] Ovcharova MA, Schelkunov MI, Geras'kina OV, et al. (2023) C-Type natriuretic peptide acts as a microorganism-activated regulator of the skin commensals Staphylococcus epidermidis and Cutibacterium acnes in dual-species biofilms. Biology 12: 436. https://doi.org/10.3390/biology12030436
    [32] Illumina: bcl2fastq and bcl2fastq2 Conversion Software (2024). Available from: https://support.illumina.com/sequencing/sequencing_software/bcl2fastq-conversion-software.html
    [33] Bolger AM, Lohse M, Usadel B (2014) Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 30: 2114-2120. https://doi.org/10.1093/bioinformatics/btu170
    [34] Li H, Durbin R (2009) Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25: 1754-1760. https://doi.org/10.1093/bioinformatics/btp324
    [35] Patro R, Duggal G, Love MI, et al. (2017) Salmon provides fast and bias-aware quantification of transcript expression. Nat Methods 14: 417-419. https://doi.org/10.1038/nmeth.4197
    [36] Camacho C, Coulouris G, Avagyan V, et al. (2009) BLAST+: Architecture and applications. BMC Bioinform 10: 421. https://doi.org/10.1186/1471-2105-10-421
    [37] Cold Spring Harbor Protocols: Lysis buffer for protein extraction (2024). Available from: https://cshprotocols.cshlp.org/content/2014/9/pdb.rec081273.full
    [38] Kulak NA, Pichler G, Paron I, et al. (2014) Minimal, encapsulated proteomic-sample processing applied to copy-number estimation in eukaryotic cells. Nat Methods 11: 319-324. https://doi.org/10.1038/nmeth.2834
    [39] Kovalchuk SI, Jensen ON, Rogowska-Wrzesinska A (2019) FlashPack: fast and simple preparation of ultrahigh-performance capillary columns for LC-MS*[S]. Mol Cell Proteom 18: 383-390. https://doi.org/10.1074/mcp.TIR118.000953
    [40] Tyanova S, Temu T, Cox J (2016) The MaxQuant computational platform for mass spectrometry-based shotgun proteomics. Nat Protoc 11: 2301-2319. https://doi.org/10.1038/nprot.2016.136
    [41] Tyanova S, Temu T, Sinitcyn P, et al. (2016) The Perseus computational platform for comprehensive analysis of (prote) omics data. Nat Methods 13: 731-740. https://doi.org/10.1038/nmeth.3901
    [42] The UniProt Consortium.UniProt: the Universal Protein Knowledgebase in 2023. Nucl Acids Res (2022) 51: D523-D531. https://doi.org/10.1093/nar/gkac1052
    [43] STRING: Protein-Protein Interaction Networks, Functional Enrichment Analysis (2024). Available from: https://string-db.org/cgi/input?sessionId=bJepuA5XwxF1&input_page_show_search=on
    [44] NCBI Basic Local Alignment Search Tool: Protein BLAST (2024). Available from: https://blast.ncbi.nlm.nih.gov/Blast.cgi?PAGE=Proteins
    [45] Benjamini Y, Hochberg (1995) Controlling the false discovery rate: A practical and powerful approach to multiple testing. J R Stat Soc B: Stat Methodol 57: 289-300. https://doi.org/10.1111/j.2517-6161.1995.tb02031.x
    [46] Benjamini Y, Krieger AM, Yekutieli D (2006) Adaptive linear step-up procedures that control the false discovery rate. Biometrika 93: 491-507. https://doi.org/10.1093/biomet/93.3.491
    [47] Staudenmaier H, Van Hove B, Yaraghi Z, et al. (1989) Nucleotide sequences of the fecBCDE genes and locations of the proteins suggest a periplasmic-binding-protein-dependent transport mechanism for iron(III) dicitrate in Escherichia coli. J Bacteriol 171: 2626-2633. https://doi.org/10.1128/jb.171.5.2626-2633.1989
    [48] Brugger SD, Bomar L, Lemon KP (2016) Commensal–pathogen interactions along the human nasal passages. PLoS Pathog 12: e1005633. https://doi.org/10.1371/journal.ppat.1005633
    [49] Penberthy WT, Sadri M, Zempleni J (2020) Biotin. Present knowledge in nutrition . Cambridge: Academic Press 289-303. https://doi.org/10.1016/B978-0-323-66162-1.00017-2
    [50] Hu T, Wei Z, Ju Q, et al. (2021) Sex hormones and acne: State of the art. JDDG 19: 509-515. https://doi.org/10.1111/ddg.14426
    [51] Ray RR, Lahiri D, Chatterjee A, et al. (2021) Bacteria and biofilms as natural inhabitants of our body. Biofilm-Mediated Diseases: Causes and Controls . New York: Springer 47-71. https://doi.org/10.1007/978-981-16-0745-5_3
    [52] Ponomarenko EA, Krasnov GS, Kiseleva OI, et al. (2023) Workability of mRNA sequencing for predicting protein abundance. Genes 14: 2065. https://doi.org/10.3390/genes14112065
    [53] Bakker R, Ellers J, Roelofs D, et al. (2023) Combining time-resolved transcriptomics and proteomics data for Adverse Outcome Pathway refinement in ecotoxicology. Sci Total Environ 869: 161740. https://doi.org/10.1016/j.scitotenv.2023.161740
    [54] Sharma VK, Akavaram S, Bayles DO (2022) Genomewide transcriptional response of Escherichia coli O157:H7 to norepinephrine. BMC Genomics 23: 107. https://doi.org/10.1186/s12864-021-08167-z
    [55] Achermann Y, Tran B, Kang M, et al. (2015) Immunoproteomic identification of in vivo-produced Propionibacterium acnes proteins in a rabbit biofilm infection model. Clin Vaccine Immunol 22: 467-476. https://doi.org/10.1128/CVI.00760-14
    [56] Perraud Q, Kuhn L, Fritsch S, et al. (2020) Opportunistic use of catecholamine neurotransmitters as siderophores to access iron by Pseudomonas aeruginosa. Environ Microbiol 24: 878-893. https://doi.org/10.1111/1462-2920.15372
    [57] Perez-Riverol Y, Bai J, Bandla C, et al. (2022) The PRIDE database resources in 2022: A Hub for mass spectrometry-based proteomics evidences. Nucleic Acids Res 50: D543-D552. https://doi.org/10.1093/nar/gkab1038
    [58] Deutsch EW, Bandeira N, Perez-Riverol Y, et al. (2023) The ProteomeXchange Consortium at 10 years: 2023 update. Nucleic Acids Res 51: D1539-D1548. https://doi.org/10.1093/nar/gkac1040
  • microbiol-10-02-019-s001.pdf
  • This article has been cited by:

    1. Yaxi Xu, Yi Liu, Ke Shi, Xin Wang, Yi Li, Jizong Chen, An airport apron ground service surveillance algorithm based on improved YOLO network, 2024, 32, 2688-1594, 3569, 10.3934/era.2024164
    2. S. Adlin Jebakumari, Shriya Mahajan, Harshit Raichura, B. Reddy, Zahid Ahmed, Innovative model for security of multi-cloud platform: data integrity perspective, 2024, 0975-6809, 10.1007/s13198-024-02579-2
    3. Danni Liu, Shengda Wang, Chunhui Shi, Jia Li, Xiuhong Jiang, Application and Optimization of Endogenous Security Mechanisms in Photovoltaic Data Transmission and Storage, 2025, 10, 2444-8656, 10.2478/amns-2025-0223
  • Reader Comments
  • © 2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(1911) PDF downloads(95) Cited by(0)

Article outline

Figures and Tables

Figures(4)  /  Tables(10)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog