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6.12.1: Considerations in Microbial Control - Biology

6.12.1: Considerations in Microbial Control - Biology


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Controlling microbial growth is important in many fields but the degree of acceptable microbial levels can be quite different.

LEARNING OBJECTIVES

Discover considerations in microbial control

Key Points

  • Controlling microbial growth is important in the medical field, pharmaceutical and biotechnology industries, academic research, and food industry.
  • The degree of acceptable microbial presence can differ based on the circumstances. Sterilization as a definition means that all life was terminated, whereas sanitization and disinfection terminates selectively and partially.
  • Chemical agents that can eliminate or suppress microbial life are separated in different groups based on their use. The major groups are disinfectants, antiseptics, and antibiotics.
  • Antibacterials are divided into two broad groups according to their biological effect on microorganisms: bactericidal agents kill bacteria, and bacteriostatic agents slow down or stall bacterial growth.

Key Terms

  • sterilization: Any process that eliminates or kills all forms of microbial life present on a surface, solution, or solid compound.
  • microbicides: Compounds or substances whose purpose is to reduce the infectivity of microbes, such as viruses or bacteria.
  • parenteral: Administered by some means other than oral intake, particularly intravenously or by injection.

Considerations in Microbial Control

Ever since microbes were shown to cause diseases, people have invented different techniques to control their spread. Controlling microbial growth is important in the medical field, pharmaceutical and biotechnology industries, academic research, and food industry. Each antimicrobial substance or agent achieves a different level of microbial elimination by a certain mechanism.

TYPES OF MICROBIAL CONTROL

Sterilization (or sterilisation ) is a term referring to any process that eliminates (removes) or kills all forms of microbial life, including transmissible agents (such as fungi, bacteria, viruses, and spore forms) present on a surface, contained in a fluid, in medication, or in a compound. Sterilization can be achieved by applying the proper combinations of heat, chemicals, irradiation, high pressure, and filtration.

Chemical agents that can eliminate or suppress microbial life are separated in different groups based on their use.

Disinfectants are substances that are applied to non-living objects to destroy microorganisms that are living on them. Disinfection does not necessarily kill all microorganisms, especially resistant bacterial spores, so it is less effective than sterilisation. Disinfectants are different from other antimicrobial agents such as antibiotics, which destroy microorganisms within the body. Disinfectants are also different from biocides, as these are intended to destroy all forms of life, not just microorganisms. Disinfectants work by destroying the cell wall of microbes or interfering with their metabolism.

Antiseptics are antimicrobial substances that are applied to living tissue or skin to reduce the possibility of infection, sepsis, or putrefaction. Antiseptics are generally distinguished from antibiotics by the latter’s ability to be transported through the lymphatic system to destroy bacteria within the body, and from disinfectants, which destroy microorganisms found on non-living objects.

The term antibiotic was first used in 1942 by Selman Waksman and his collaborators in journal articles to describe any substance produced by a microorganism that is antagonistic to the growth of other microorganisms in high dilution. This definition excluded substances that kill bacteria, but are not produced by microorganisms (such as gastric juices and hydrogen peroxide). It also excluded synthetic antibacterial compounds such as the sulfonamides. With advances in medicinal chemistry, most of today’s antibacterials chemically are semisynthetic modifications of various natural compounds.

Many antibacterial compounds are classified on the basis of chemical or biosynthetic origin into natural, semisynthetic, and synthetic. Another classification system is based on biological activity. In this classification, antibacterials are divided into two broad groups according to their biological effect on microorganisms: bactericidal agents kill bacteria, andbacteriostatic agents slow down or stall bacterial growth.

Microbicides which destroy virus particles are called viricides or antivirals.

LEVEL OF MICROBIAL PRESENCE

The degree of acceptable microbial presence can differ based on the circumstances. Sterilization as a definition means that all life was terminated, whereas sanitization and disinfection terminates selectively and partially. Both sanitization and disinfection reduce the number of targeted pathogenic organisms to what are considered “acceptable” levels – levels that a reasonably healthy, intact body can deal with.

In general, surgical instruments and medications that enter an already aseptic part of the body (such as the bloodstream, or penetrate the skin) must be sterilized to a high sterility assurance level (SAL). Examples of such instruments include scalpels, hypodermic needles, and artificial pacemakers. For example, medical device manufacturers design their sterilization processes for an extremely low SAL. Their “one in a million” devices should be nonsterile.

This is also essential in the manufacture of parenteral pharmaceuticals. Preparation of injectable medications and intravenous solutions for fluid replacement therapy requires not only a high sterility assurance level, but also well-designed containers to prevent entry of adventitious agents after the initial product sterilization.

Food preservation is another field where the presence of microorganisms is taken under consideration. The process usually involves preventing the growth of bacteria, fungi (such as yeasts), and other microorganisms (although some methods work by introducing benign bacteria or fungi to the food).


6.12.1: Considerations in Microbial Control - Biology

DUBLIN--(BUSINESS WIRE)--Jun 14, 2021--

This course will educate you about various key elements of sterility assurance and contamination control such as Cleanroom Regulations, Classification, Sources and types of particles, Design Requirements, Validation/Qualification, Operations, Environmental Monitoring Program requirements, Excursion investigations, DataTrending, Microbiological processes/methodology, Cleanroom cleaning/disinfection.

The types of micro-organisms, typical mitigation steps in ensuring an effective contamination control through Personnel Training (Aseptic Practices, Cleanroom Behavior and Contamination Control Procedures),Gowning Controls, Personnel Training, Cleanroom Trafficking (Cleanroom Personnel Material, Product and Equipment Transfer Practices and Training (Entry and Exit Policy), Cleanroom Gowning, Contamination Control, Cleaning and Disinfection Program and the Basics of Sterilization Processes- Physical and Chemical Processes will also be discussed.

The various regulatory bodies’ requirements such as 21 CFR Part 211 (mostly relevant 211.113 “Control of microbiological contamination”, ISO 14644 (Various Parts), FDA Guidance for Industry: Sterile Drug Products Produced By Aseptic Processing - Current Good Manufacturing Practice”) amongst others and the criticality of aseptic processing and other key contamination control evaluators during the manufacture and testing of products are important to the quality determination and release of the finished manufactured products.


Tools for Analysis of the Microbiome

Over the past decade, it has become exceedingly clear that the microbiome is a critical factor in human health and disease and thus should be investigated to develop innovative treatment strategies. The field of metagenomics has come a long way in leveraging the advances of next-generation sequencing technologies resulting in the capability to identify and quantify all microorganisms present in human specimens. However, the field of metagenomics is still in its infancy, specifically in regard to the limitations in computational analysis, statistical assessments, standardization, and validation due to vast variability in the cohorts themselves, experimental design, and bioinformatic workflows. This review summarizes the methods, technologies, computational tools, and model systems for characterizing and studying the microbiome. We also discuss important considerations investigators must make when interrogating the involvement of the microbiome in health and disease in order to establish robust results and mechanistic insights before moving into therapeutic design and intervention.

Keywords: Data analysis Metagenomics Microbiome Visualization.

Figures

Technologies for studying the microbiome

Technologies for studying the microbiome

A “bedside, to bench, and back to bedside” approach to microbiome investigation


Synthetic biology for microbial heavy metal biosensors

Using recombinant DNA technology, various whole-cell biosensors have been developed for detection of environmental pollutants, including heavy metal ions. Whole-cell biosensors have several advantages: easy and inexpensive cultivation, multiple assays, and no requirement of any special techniques for analysis. In the era of synthetic biology, cutting-edge DNA sequencing and gene synthesis technologies have accelerated the development of cell-based biosensors. Here, we summarize current technological advances in whole-cell heavy metal biosensors, including the synthetic biological components (bioparts), sensing and reporter modules, genetic circuits, and chassis cells. We discuss several opportunities for improvement of synthetic cell-based biosensors. First, new functional modules must be discovered in genome databases, and this knowledge must be used to upgrade specific bioparts through molecular engineering. Second, modules must be assembled into functional biosystems in chassis cells. Third, heterogeneity of individual cells in the microbial population must be eliminated. In the perspectives, the development of whole-cell biosensors is also discussed in the aspects of cultivation methods and synthetic cells.

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2 CHALLENGES IN CHARACTERIZING MICROBIAL FUNCTIONAL DIVERSITY

2.1 General concept of functional diversity

Functional approaches for estimating biodiversity are based on the general premise that to understand the linkage between biodiversity and ecosystems functioning, the functions realized by organisms in natural systems are of greater interest than their identity. The term “functional diversity” has been widely used but most studies simply relied on presumed intuitive understanding of the term's meaning and thus there is no uniform definition, particularly in microbial ecology (Table S1, Petchey & Gaston, 2006 ). Carmona, de Bello, Mason, and Lepš ( 2016 ) provided a simple and operational definition of functional diversity as the “variation of traits between organisms,” which is “estimated as the variation of traits in the functional space occupied by an ecological unit.” Here, rather ambiguous notions appear of crucial importance for defining microbial functional diversity, ecological unit, and functional trait.

An ecological unit corresponds to any scale at which it is meaningful to estimate functional diversity, such as individual organisms, populations, species (or OTUs), communities, meta-communities, geographical regions, and continents (Carmona et al., 2016 ). For macroorganisms, the ecological unit of choice is often the community and its functional diversity can be estimated considering the range, distribution, and variation of the traits carried by the species it contains, or the average trait values across species (i.e., community-aggregated traits). Whatever the chosen ecological unit, it is now generally agreed that conceptualizing, defining, measuring, and ultimately understanding functional diversity depend on the measurement of functional traits (Mlambo, 2014 Petchey & Gaston, 2006 ), and the term “functional ecology” tends to be replaced by the more precise term “trait-based ecology” (Shipley et al., 2016 ). The commonly used definition of functional traits describes those that “impact fitness of an organism via its effect on growth, reproduction, or survival” (Violle et al., 2007 ). This definition and its more recent variations (Carmona et al., 2016 Violle et al., 2017 ) state that functional traits should be measurable at the individual level, which is rarely an option for microorganisms. While quantitative traits (e.g., leaf area, morphological characteristics) are measured at the individual level and then averaged to estimate the trait value for the species, qualitative traits (e.g., phenological or behavioral) are more often estimated at the species level. This approach produces taxa-traits matrices, that depict the functional attributes of taxa, which are then combined with taxa-site matrices representing communities composition in order to estimate the functional diversity of communities using ad hoc indices (Mouchet, Villéger, Mason, & Mouillot, 2010 Villéger, Mason, & Mouillot, 2008 ).

In summary, functional approaches use traits to describe the role of ecological units in the functioning of natural systems. In the following sections, we will see why the definitions of functional diversity and functional traits currently used in macrobial ecology do not fit with the particularities of the microbial world and which aspects should be taken into consideration in order to improve our ability to characterize microbial functional diversity.

2.2 Toward a trait-based approach of microbial functional diversity

2.3 Differences between micro- and macroorganisms in a functional context and limitations of current theoretical frameworks

There are some concepts that pertain to biodiversity and functional relationships in both macro- and microbial ecology. For example, the positive effect of biodiversity on ecosystem functioning is usually attributed to two nonexclusive mechanisms, the selection (or sampling) and the complementarity effects (Cardinale et al., 2006 Loreau, Mouquet, & Gonzalez, 2003 Loreau et al., 2001 ). To put it simply, selection effect reflects the influence of a single hyper competitive species on the overall community function, while complementarity effect depends on the presence of species with complementary traits and results from resource partitioning or facilitation among them. Both concepts relate directly to the fact that organisms' traits determine their impact on ecological process under study.

But, there are prominent differences between micro- and macroorganisms that prevent direct transfer of ecological theories and concepts. These include the small size of individual microbes that contribute to their greater sensitivity to environmental change, their faster metabolic, and growth rates, but also the colonial growth of microbes which is opposed to the unitary construction of most macroorganisms (Plante, 2017 ). Beside these general considerations, microbial functional ecology faces several major challenges that prevent the direct application of concepts and methods developed for macroorganisms.

The species or OTU unit is also problematic because it requires a library matching traits to genes or OTUs and it neglects intraspecific variability. Most environmentally important microbes have yet to be cultivated, and most functional traits can only be validated using culturable taxa. Consequently, limited physiological, physical, and metabolic information is available for assessing functional diversity of individual taxa (Schnoes, Brown, Dodevski, & Babbitt, 2009 Turaev & Rattei, 2016 ) and inference of function from taxonomy/phylogeny may only apply to specialized and well conserved functions, such as methanogenesis (Goberna & Verdú, 2016 ). The existence of HGT (Polz et al., 2006 ) and the poorly defined concept of prokaryotic species (Gevers et al., 2005 ) make such a linkage even more difficult. Based on the metabolic or physiological traits measured on culturable taxa, many of these traits differ from one taxon to another and for most functions there exists little-to-no taxonomic resolution (Louca et al., 2018 Martiny et al., 2015 ). The functional approach, especially when applied to microbes, addresses the problem of taxa-traits associations by assessing the community as a multivariate and continuous distribution of traits. Doing so, one could characterize communities using the frequency of different trait values and forget about taxonomic diversity.

Another challenge is presented from the fundamental differences in the nature of the traits measured. Indeed, macroorganisms traits are often constitutive, that is, continuously expressed, and exist in the ecosystem as long as the organism is alive (e.g., the shape of a plant's leaf or the size of a fish's mouth). While this can also be the case in microbes, for instance in bacterial cells that possess pili or phytoplankton organisms with hard shells, the expression of microbial traits tends to be more directly related to their environment. Indeed, the link between genotype and phenotype is narrower in microbes than macroorganisms (Dutilh et al., 2013 Tamura & D'haeseleer, 2008 ). Hence, many microbial traits are genetically regulated (e.g., metabolic pathways, biofilm formation, and virulence) and their induction dependent on population size, cell activity, and environmental conditions.

Despite the above-mentioned constraints, microbes likely represent the best system to apply functional approaches. On one hand, defining species is controversial if not impossible because of gene transfers and asexual reproduction, the diversity is astonishing and sampling constraints make it difficult to measure traits and functions. On the other hand, functional redundancy is widespread, the relative simplicity of microbial physiology facilitates the mapping of genes to functions and novel sequencing methods allow the documentation of many genes simultaneously. The functional approach may thus appear as a solution to reduce the complexity of microbial systems and better understand their functioning. It is worth noting that the field of microbial functional ecology is pretty new and it is not common to apply function diversity measures to characterize microbial communities.


Microbial Pesticides

Microbial Pesticides: Biological Resources, Production and Application provides a concise and accessible introduction on the history of microbial pesticides, their impact on global ecology, human society and economies, as well as a thorough and tangible description of the state-of-the-art technologies available for the production, application, limitations and long–term viability of these bio-products. Information is listed per biological group (i.e., virus, bacteria, fungi, protozoa, microsporidia and microbial metabolites), and is supported by sound scientific data. The book is copiously illustrated, with original pictures clarifying the most common techniques and protocols utilized in microbiological biocontrol technology.

Finally, images of all biological active ingredients currently used in commercially produced formulations, as well as laboratory developed formulations, are illustrated and listed in detailed tables for prompt access.

Microbial Pesticides: Biological Resources, Production and Application provides a concise and accessible introduction on the history of microbial pesticides, their impact on global ecology, human society and economies, as well as a thorough and tangible description of the state-of-the-art technologies available for the production, application, limitations and long–term viability of these bio-products. Information is listed per biological group (i.e., virus, bacteria, fungi, protozoa, microsporidia and microbial metabolites), and is supported by sound scientific data. The book is copiously illustrated, with original pictures clarifying the most common techniques and protocols utilized in microbiological biocontrol technology.

Finally, images of all biological active ingredients currently used in commercially produced formulations, as well as laboratory developed formulations, are illustrated and listed in detailed tables for prompt access.


The role of the microbiome in host evolution

In the last decade, we have witnessed a major paradigm shift in the life sciences: the recognition that the microbiome, i.e. the set of microorganisms associated with healthy animals (including humans) and plants, plays a crucial role in the sustained health and fitness of its host. Enabled by rapid advances in sequencing technologies and analytical methods, substantial advances have been achieved in both identifying the microbial taxa and understanding the relationship between microbiome composition and host phenotype. These breakthroughs are leading to novel strategies for improved human and animal health, enhanced crop yield and nutritional quality, and the control of various pests and disease agents.

This article is part of the theme issue ‘The role of the microbiome in host evolution'.

1. Introduction

Increasingly, the research community is starting to ask different—and more difficult—questions about the fundamentals of host–microbiome interactions and their evolutionary consequences. This has brought into focus a major gap in our understanding: the role of the microbiome in host evolution. There is genuine confusion in the research community, including concerns that traditional evolutionary theory may be inadequate to explain evolutionary processes involving microbiomes, and uncertainty about the best choice of system and approach to investigate pattern and process from an evolutionary perspective. Opinions are diverse and often polarized.

Our goal for this theme issue is twofold: to provide an overview of the current status of the field for the researcher, teacher and student alike and to spark new ideas and research, including much-needed interdisciplinary collaborations to solve the many outstanding problems. Our rationale is that an evolutionary perspective promotes understanding of biological mechanisms of host–microbial interactions, providing explanations for patterns discovered via different disciplinary approaches, such as genetic, physiological, behavioural or ecological. Such a perspective may explain, for example, why some types of interactions occur and recur, while others are rare or apparently absent globally, in specific host taxa or under particular ecological conditions. For example, microbiome-mediated protection against pathogens is very widespread in both animals and plants, while Archaea other than methanogens are apparently rarely associated with eukaryotes, and beneficial intracellular bacteria are largely unknown in vertebrates but widespread in many invertebrate animal groups. Of equal importance, evolutionary understanding can help make microbiome science more predictive: meticulous studies that demonstrate how change or elimination of the microbiome influences host traits, for example, call for eco-evolutionary frameworks that will explain these findings in a broader context and will aid in inference of mechanisms and processes. Considering, for instance, how hosts have adapted to accommodate or to rely on the microbiome and the consequences for the phenotype and evolutionary trajectory of the host may explain otherwise-puzzling phenomena, from coral bleaching to the effects of antibiotics. As our science becomes more predictive, its application to solve real-world problems will become more reliable. Some important discoveries have already been made, including the resolution of Clostridium difficile infections in human patients by certain gut bacteria, the suppression of Aedes mosquito-transmitted dengue virus by Wolbachia bacteria, and plant tolerance of high temperatures conferred by fungal endophytes. These can best be understood in an eco-evolutionary context: the systematic application of evolutionary principles to microbiome science has the potential to enable transformative advances in medicine, agriculture and public health.

This volume containing 15 reviews and opinion pieces brings together the insights and expertise of 36 authors from six countries. Conceptually, the articles can be assigned to two broad themes. The first theme concerns how the microbiome influences host traits and fitness, revealing both the pervasive role of the microbiome as a selective force on their hosts, and as a modality of host adaptation to environmental challenges. The second theme explores the evolutionary process at scales from micro-evolution in host populations to macro-evolutionary phylogenies. Cutting across these two themes, some articles focus on specific systems, e.g. humans, corals, fish, while others draw on the literature for many animal and plant systems or explore general evolutionary principles without reference to specific taxa.

The issue starts with two articles that highlight fundamental evolutionary processes. First, Kolodny & Schulenberg [1] treat the microbiome as a source of adaptive phenotypic plasticity. They suggest that hosts, faced with a novel environmental challenge, may adapt to the challenge via changes in the composition of their microbiome. Such adaptation is analogous to the well-known Baldwin effect, but in addition involves feedback loops and eco-evolutionary dynamics that play out on a range of time scales, portraying a rich picture of adaptive processes. The following review by Moeller & Sanders [2] develops this theme, focusing particularly on how microbiome effects have shaped, and arguably continue to shape, mammalian evolution.

With this conceptual framework, the volume explores the adaptive response of hosts to the microbiome from a variety of perspectives. Three articles consider host physiological systems that interact directly with the microbiome. Centre-stage in any consideration of host–microbiome interactions is the host immune system, which both controls and is influenced by the microbial partners. Gerardo, Hoang & Stoy [3] review the ways in which the immune system interacts with microbial symbionts, and how immunological processes can constrain the evolution of the participating organisms. The complementary opinion article of McLaren & Callahan [4] argues that hosts are adapted to promote microbial taxa that confer pathogen resistance, generating what the authors aptly term ‘cooperative immunity'. The interaction between host metabolism and the microbiome is considered by Fontaine & Kohl [5], who explore the value of optimality thinking, specifically symmorphosis which hypothesizes that host metabolism is structured by natural selection to match the functional demand of the association. Founded on current understanding of nutritional interactions between various animals and their microbiome, these authors predict specific testable patterns in selection pressures for microbiome-dependent regulation of host metabolic function. These considerations segue directly to the article of Grieneisen, Muehlbauer & Blekhman [6], which reviews the patterns of microbial control over gene expression in primates and its implications for primate and human evolution.

There is growing evidence for developmental orchestration of the composition and function of the microbiome, reflecting the variation in selection pressures and evolutionary constraints at different host life stages. Linking to the emerging interest in the interface between microbiomes and life-history theory, two articles in this issue focus on the microbiome associated with two key life stages. Nyholm [7] reviews the incidence and consequences of egg–microbiome interactions in animals. Until recently, most egg-associated microorganisms were described as a biologically ‘silent' transmission stage ensuring the colonization of newly hatched offspring with maternal microbes. Nyholm calls for a radical reassessment of the adaptive evolution of eggs in the light of growing evidence that egg-associated microorganisms can protect the egg against abiotic stresses and natural enemies (both predators and pathogens). At the other end of the animal lifespan, Popkes & Valenzano [8] summarize findings about the influence of the microbiome on survival as organisms age, using in particular insights from recent studies of vertebrates and their microbiomes, and discuss the mechanisms involved and the ways in which this may have affected host evolution and host–microbiome coevolutionary dynamics. Developmental orchestration of the host–microbiome relationship also involves the localization of the microbiome, including host adaptations that restrict microorganisms to specific organs or sites in the body. Chomicki, Werner, West & Kiers [9] review various modes of microbiome compartmentalization in plants, insects and vertebrates, and they discuss how this allows the host to control resource flow, to discriminate cooperative from defecting microbial partners, and to manipulate the microbiome composition. Weighing costs and benefits of compartmentalization, they explore the different selection pressures that may have determined which hosts evolved compartmentalization and which have not. One form of compartmentalization involves symbiotic organs, i.e. organs whose sole function is to house and maintain the microbial partners. Douglas [10] adopts an evolutionary developmental (evo–devo) approach to explore how the developmental biology of symbiotic organs can provide insights into their evolutionary origins, and advocates for the greater use of genetic technologies to test whether conserved genetic circuitry might underlie the apparently convergent evolution of symbiotic organs in different host lineages.

The possible effects of the microbiome on behaviour has attracted much attention and, until recently, more speculation than data. As the evidence for behavioural correlates of the presence and composition of the microbiome accumulates, an evolutionary perspective becomes imperative. Fortunately, a robust conceptual framework is provided by the well-established discipline of behavioural ecology. This issue includes two stimulating and provocative opinion articles that illustrate the opportunities of interdisciplinary synthesis between microbiome research and behavioural ecology. Gurevich, Lewin-Epstein & Hadany [11] lay out a theoretical model of microbiome effects on paternal care, including the consequences of microbiome-mediated manipulation of host behaviour on the mating and parenting strategies of male hosts. Natan, Fitak, Werber & Vortman [12] suggest that information that hosts derive from magnetotactic bacteria in their microbiomes may be the answer to a long-standing puzzle: how do animals, from protists to birds, sense magnetic fields? They lay out supporting evidence for this suggestion, and they discuss various specific mechanisms by which hosts might incorporate information from resident magnetotactic microbes.

The closing articles of the issue return to the theme of how the microbiome affects pattern and process in host evolution. Two articles focus on specific systems. Hawkes, Bull & Lau [13] reinforce and build on the several articles that address the evolutionary consequences of microbiomes with defensive function. Focusing on plants, they explore the micro-evolutionary consequences of microbiomes that confer protection against both pathogens and abiotic stress, including an analysis of the impact of host–microbial partner fidelity on the evolutionary trajectory of these relationships. The article by van Oppen & Medina [14] on scleractinian corals illustrates how genome sequence data can shed light on the genetic basis of interactions with bacterial and algal partners and the ecological success of these associations. Finally, Koskella & Bergelson [15] address a pressing question facing the study of microbiome effects on host evolution: can these complex and dynamic associations be accommodated within current understanding of evolutionary and coevolutionary processes? This article reviews current understanding of (co)evolution between hosts and microbiomes, including the patterns of selection on the partners, as individuals and a group, and provides a fresh and informed perspective on this hotly debated issue.

Together, the articles in this issue demonstrate the key opportunities and challenges that an evolutionary perspective can offer to researchers in the discipline of microbiome science. Evolutionary thinking provides the basis for rational explanation and prediction in biology, and it is most powerful when combined with explicit formulation of testable hypotheses. Our discipline is most fortunate to have access to a broad range of genetic, phylogenetic, physiological, behavioural and ecological methodologies. These tools and an evolutionary mindset offer the strongest route for scientific advance in our understanding and application of host–microbiome interactions.


Principles of Vaccination

While many of the currently available vaccines have been developed empirically, with limited understanding on how they activate the immune system and elicit protective immunity, the recent progress in basic sciences like immunology, microbiology, genetics, and molecular biology has fostered our understanding on the interaction of microorganisms with the human immune system. In consequence, modern vaccine development strongly builds on the precise knowledge of the biology of microbial pathogens, their interaction with the human immune system, as well as their capacity to counteract and evade innate and adaptive immune mechanisms. Strategies engaged by pathogens strongly determine how a vaccine should be formulated to evoke potent and efficient protective immune responses. The improved knowledge of immune response mechanisms has facilitated the development of new vaccines with the capacity to defend against challenging pathogens and can help to protect individuals particular at risk like immunocompromised and elderly populations. Modern vaccine development technologies include the production of highly purified antigens that provide a lower reactogenicity and higher safety profile than the traditional empirically developed vaccines. Attempts to improve vaccine antigen purity, however, may result in impaired vaccine immunogenicity. Some of such disadvantages related to highly purified and/or genetically engineered vaccines yet can be overcome by innovative technologies, such as live vector vaccines, and DNA or RNA vaccines. Moreover, recent years have witnessed the development of novel adjuvant formulations that specifically focus on the augmentation and/or control of the interplay between innate and adaptive immune systems as well as the function of antigen-presenting cells. Finally, vaccine design has become more tailored, and in turn has opened up the potential of extending its application to hitherto not accessible complex microbial pathogens plus providing new immunotherapies to tackle diseases such as cancer, Alzheimer's disease, and autoimmune disease. This chapter gives an overview of the key considerations and processes involved in vaccine development. It also describes the basic principles of normal immune respoinses and its their function in defense of infectious agents by vaccination.

Keywords: B cell Immunology Infectious disease Pathogen T cell Vaccination Vaccine.


Predictive interactome modeling for precision microbiome engineering

Accounting for context-dependent alterations in microbial interaction networks is key for accurate microbiome modeling.

Integration of multi-omics data into predictive interactome modeling enables precision microbiome engineering.

Combining process-based/data-driven modeling with ecological theory is important for future advances.

Microbiome engineering aims to manipulate, control, and design community-level properties through targeted interventions of existing microbial communities or the construction of new synthetic consortia. These efforts often lead to unexpected or undesirable outcomes because of highly complex input-output relationships that are primarily ascribable to adaptive responses of interspecies interactions to perturbation. Therefore, accurate prediction of microbial interaction networks and context-specific organization will aid success in future microbiome engineering efforts. Here, we review state-of-the-art modeling approaches to evaluate their scope of prediction as in silico tools for microbiome design. We highlight the utility of advanced models for predicting context-dependent interactions, multi-omics data integration, and combined use of complementary modeling and computational tools for enhanced prediction and eventual facilitation of in silico microbiome design.


Conclusions

Like existing molecular epidemiology technologies, the translation of population studies of the human microbiome will require complex processes in order to achieve observational discovery, reproducibility across cohorts, and mechanistic validation (typically in models or in vitro). To date, a small number of studies have achieved this goal. For example, combining mouse models with a small cohort of 20 human subjects, Haiser and colleagues [111] built on decades of work linking Eggerthella lenta to inactivation of digoxin [112] to identify an operon that is expressed in a strain-specific manner in a subset of human microbiome carriers. As a further example, it has been shown that early-life exposure to distinct forms of taxon-specific lipopolysaccharide correlate with immune development and type 1 diabetes (T1D) risk, a result that was subsequently confirmed in mouse models (Box 5) [16]. Finally, in Clostridium difficile infection, models linking antibiotic exposure to bacterial species that are responsible for secondary bile acid synthesis in the gut have been successful in reducing recurrence [113]. In each of these cases, a combination of human population surveys with appropriate statistical modeling and mechanistic follow-up was able to identify specific bioactive microbes and, often, molecules. Further examples are emerging, particularly in the area of cancer immunotherapy, which can be dramatically modulated by the microbiome [114].

One of the outstanding gaps in translational population-scale microbiome studies is the lack of frameworks integrating host and microbiome functional properties at scale. For example, functional profiling of microbiome metagenomes and metatranscriptomes might be combined with cell-circuit reconstructions of immune cell subsets [115] and with electronic medical records for precision medicine. At the methodological level, few profiles of the microbiome have been carried out with scale and precision appropriate for advanced machine-learning tools such as causal inference and mediation analysis. Indeed, it is not yet clear which covariates should be collected to disambiguate cause from effect in the highly modifiable microbiome, particularly to facilitate risk-prediction models or clinical decision-making tools incorporating microbiome profiles. The microbiome has shown a remarkable combination of long-term persistence (e.g., strain retention for months or years [41, 116, 117]) with modifiability by a wide range of environmental factors (diet, pharmaceuticals, physical activity, age, and so on), making population structure and unobserved confounders a risk in large cohort studies.

Finally, human population studies provide a starting point for the follow-up characterization of microbial biochemical mechanisms, which can integrate characterization techniques such as culture-based physiology, microbial metabolism, co-culture, and interactions. Several of the most successful translational microbiome studies to date have—as in other areas of molecular epidemiology—begun with a population-level observation that was, eventually, traced back to one or more specific molecular mechanisms. In the case of the microbiome, this provides unique opportunities not only for prioritization of novel human drug targets, but also for the modulation of microbial activities by small molecules, diet or prebiotics, targeted probiotics, or engineered microbes or communities. To achieve these goals, studies of the microbiome must continue to refine the multiomic tools in the setting of population-scale epidemiology with rich study designs that can fully realize the therapeutic and diagnostic potential of the microbiome.

Box 5. An integrative analysis of longitudinal microbiome multiomics: the DIABIMMUNE study

The DIABIMMUNE (Pathogenesis of Type 1 Diabetes—Testing the Hygiene Hypothesis) [118] study of the microbiome in the development of infant type 1 diabetes (T1D) is one example that incorporates many of the aspects of microbiome epidemiology discussed here. The DIABIMMUNE cohort includes newborn infants with genetic susceptibility to autoimmune disorders who were followed for 3 years with monthly stool sampling and collection of phenotype data through serum samples and questionnaires. This design was constructed to enable multiple types of microbiome analyses, such as tracking the longitudinal trajectories of the developing microbiomes, studying the implications of common early-life events (e.g., birth mode, weaning, introduction of solid foods, antibiotic courses) and case–control comparison between diseased and healthy children.

One of the study’s first analyses of the gut microbiome focused on early-life colonization and the development of islet autoimmunity and T1D [1]. The sub-cohort included four children with early onset T1D, seven children with T1D-associated autoantibodies, and 22 healthy controls. All subjects provided monthly stool samples, regardless of disease status, yielding a detailed view of microbiome structure and function during early development (including the transition to solid food). Strains in particular were subject-specific and retained for substantial periods of time, even during this active developmental window. In an early example of multiomic data integration, a subset of 214 serum and 104 stool samples were also profiled using untargeted mass spectrometry techniques, allowing covariation between metabolites and microbial taxa to be assessed statistically.

Another analysis within this study followed neonates from Finland, Estonia, and Russia, motivated by the disparate autoimmune prevalence between these three countries [16]. This began with 16S amplicon sequencing of > 1500 stool samples from 222 infants (74 per country), allowing the assessment of broad trends in microbiome development over time. These initial amplicon data were then used to select a representative set of 785 stool samples for metagenomic sequencing, which enabled deeper analyses including taxonomic and functional profiling, and strain tracking. All of these features were then amenable to linear mixed-effect modeling in order to identify aspects of the gut microbiome that covaried with phenotypes such as age, geography, early feeding, and mode of birth.


Watch the video: ΦΑΓΟΚΥΤΤΑΡΩΣΗ (May 2022).