Table of Contents
Cover
Title Page
List of contributors
Preface
SECTION 1: An introduction to the human tissue microbiome
CHAPTER 1: The human microbiota
1.1 Introduction: the discovery of the human microbiota: why do we care?
1.2 The importance of the indigenous microbiota in health and disease
1.3 The development of technologies for characterising the indigenous microbiota
1.4 Culture-independent approaches to microbial community analysis
1.5 Determination of microbial community functions
1.6 Closing remarks
References
CHAPTER 2: An introduction to microbial dysbiosis
2.1 Definition of dysbiosis
2.2 The ‘normal’ microbiota
2.3 Main features of dysbiosis
2.4 Conclusions
Acknowledgment
References
CHAPTER 3: The gut microbiota
3.1 Introduction
3.2 Who is there, how is it composed?
3.3 A system in interaction with food
3.4 A system highly impacted by the host
3.5 A system in interaction with human cells
3.6 Conclusion: an intriguing integrated interactive system deserving further study
References
CHAPTER 4: The oral microbiota
4.1 Introduction
4.2 Composition of the oral microbiome
4.3 The oral microbiota in health
4.4 Role of oral microbiome in disease
4.5 Future outlook
References
CHAPTER 5: The skin microbiota
5.1 Normal skin
5.2 Skin diseases
5.3 Experimental studies
5.4 Dynamics of the skin microbiome
5.5 Axillary skin microbiome transplantation
5.6 Mouse skin microbiome studies
5.7 Concluding remarks
References
CHAPTER 6: Metagenomic analysis of the human microbiome
6.1 Introduction
6.2 The human microbiome
6.3 Changes in microbiota composition during host life cycles
6.4 The human microbiome and the environment
6.5 Disease and health implications of microbiome
6.6 Conclusions
References
SECTION 2: Microbiota-microbiota and microbiota-host interactions in health and disease
CHAPTER 7: Systems biology of bacteria-host interactions
7.1 Introduction
7.2 Computational analysis of host-microbe interactions
7.3 Network-based modeling
7.4 Other computational modeling approaches
7.5 Conclusion
Acknowledgments
References
CHAPTER 8: Bacterial biofilm formation and immune evasion mechanisms
8.1 Introduction
8.2 Biofilms in human disease
8.3 Biofilm formation
8.4 Immune responses to biofilms
8.5 Biofilm immune evasion strategies
8.6 Vaccines and biofilm therapeutics
8.7 Conclusions
References
CHAPTER 9: Co-evolution of microbes and immunity and its consequences for modern-day life
9.1 Introduction
9.2 Symbiosis in eukaryotic evolution
9.3 Evolution of the (innate and adaptive) immune system
9.4 Hygiene hypothesis
9.5 What drives the composition of the microbiota?
9.6 The pace of evolution
References
CHAPTER 10: How viruses and bacteria have shaped the human genome
10.1 Genetic symbiosis
10.2 Mitochondria: symbiogenesis in the human
10.3 Viral symbiogenesis
10.4 HERV proteins
References
CHAPTER 11: The microbiota as an epigenetic control mechanism
11.1 Introduction
11.2 Background on epigenetics and epigenomic programming/reprograming
11.3 Epigenomics and link with energy metabolism
11.4 The microbiota as a potential epigenetic modifier
11.5 Epigenetic control of the host genes by pathogenic and opportunistic microorganisms
11.6 Epigenetic control of the host genes by indigenous (probiotic) microorganisms
11.7 Concluding remarks and future directions
References
CHAPTER 12: The emerging role of propionibacteria in human health and disease
12.1 Introduction
12.2 Microbiological features of propionibacteria
12.3 Population structure of
P. acnes
12.4 Propionibacteria as indigenous probiotics of the skin
12.5 Propionibacteria as opportunistic pathogens
12.6 Host interacting traits and factors of propionibacteria
12.7 Host responses to
P. acnes
12.8
Propionibacterium
-specific bacteriophages
12.9 Concluding remarks
References
SECTION 3: Dysbioses and bacterial diseases: Metchnikoff’s legacy
CHAPTER 13: The periodontal diseases
13.1 The tooth: a potential breach in the mucosal barrier
13.2 The periodontium from health to disease
13.3 Periodontitis: one of the most common human diseases
13.4 Periodontal treatment: a non-specific biofilm disruption
13.5 Microbial etiology
13.6 The host response in periodontitis
13.7 Conclusions
References
CHAPTER 14: The polymicrobial synergy and dysbiosis model of periodontal disease pathogenesis
14.1 Introduction
14.2 A (very) polymicrobial etiology of periodontitis
14.3 Synergism among periodontal bacteria
14.4 Interactions between bacterial communities and epithelial cells
14.5 Manipulation of host immunity
14.6 Conclusions
References
CHAPTER 15: New paradigm in the relationship between periodontal disease and systemic diseases
15.1 Introduction
15.2 Association between periodontal and systemic diseases
15.3 Issues in causal mechanisms of periodontal disease for systemic disease
15.4 New insights into the mechanisms linking periodontal disease and systemic disease
15.5 Effect of oral administration of
P. gingivalis
on metabolic change and gut microbiota
15.6 Conclusions
References
CHAPTER 16: The vaginal microbiota in health and disease
16.1 What makes a healthy microbiota
16.2 The vaginal microbiota in disease
16.3 Conclusions
References
SECTION 4: Dysbioses and chronic diseases: is there a connection?
CHAPTER 17: Reactive arthritis
17.1 Introduction
17.2 Reactive arthritis
17.3 Pathophysiology of ReA
17.4 Questions remain
17.5 Conclusion
References
CHAPTER 18: Rheumatoid arthritis: the bacterial connection
18.1 Preclinical rheumatoid arthritis
18.2 Predisposition to RA
18.3 MCH-HLA and genetic predisposition to RA
18.4 Molecular mimicry in RA
18.5 Innate immune system and RA
18.6 Bystander activation and pattern recognition receptors
18.7 Antibodies and neoepitopes
18.8 Superantigens
18.9 LPS
18.10 Bacterial DNA and peptidoglycans
18.11 Heat-shock proteins
18.12 Toll-like and bacterial infections
18.13 Proteus mirabilis
18.14 Porphyromonas gingivalis and RA
18.15 Gastrointestinal flora and RA
18.16 Smoking, lung infection and RA
18.17 Where to go from here?
References
CHAPTER 19: Inflammatory bowel disease and the gut microbiota
19.1 The microbiota in inflammatory bowel disease
19.2 Dysbiosis and IBD pathogenesis
19.3 Environmental factors affecting microbiome composition
19.4 Genetics and application to the immune system and dysbiosis in IBD
19.5 An overview of gut microbiota studies in IBD
19.6 Specific bacterial changes in IBD
19.7 Functional composition of microbiota in IBD
19.8 Challenges
19.9 Conclusion
References
CHAPTER 20: Ankylosing spondylitis,
Klebsiella
and the low-starch diet
20.1 Introduction
20.2 Clinical features of AS
20.3 Gut bacteria and total serum IgA
20.4 Molecular mimicry in AS
20.5 Pullulanase system and collagens
20.6 Specific antibodies to
Klebsiella
in AS patients
20.7 The low-starch diet in AS
20.8 Conclusions
References
CHAPTER 21: Microbiome of chronic plaque psoriasis
21.1 Introduction
21.2 Microbiota in psoriasis
21.3 Variation of microbiota with site
21.4 Swabs versus biopsies
21.5 Psoriatic arthritis
21.6 Microbiome and immunity
21.7 Evidence that the skin microbiome may be involved in the pathogenesis of psoriasis
21.8 New hypothesis on the pathogenesis of psoriasis
References
CHAPTER 22: Liver disease
22.1 Introduction
22.2 Non-alcoholic fatty liver disease
22.3 Qualitative and quantitative changes in the intestinal microbiota
22.4 Endotoxin
22.5 Ethanol
22.6 Choline
22.7 Alcoholic liver disease
References
CHAPTER 23: The gut microbiota
23.1 Introduction
23.3 The “obesogenic” microbiota in humans
23.4 A leaky gut contributing to inflammation and adiposity
23.5 Obesity-proneness: mediated by the gut microbiota?
23.6 Bacterial metabolites provide a link between bacteria and host metabolism
23.7 Fecal microbiota transplants: can we change our gut bacterial profiles?
23.8 What happens with the gut microbiota during weight loss?
23.9 The “diabetic” microbiota
23.10 The “atherosclerotic” microbiota
23.11 Conclusions
References
CHAPTER 24: The microbiota and susceptibility to asthma
24.1 Introduction
24.2 The microenvironment of the lower airways
24.3 Development of the airway microbiota in the neonate
24.4 Upper airway microbiota
24.5 What constitutes a healthy airway microbiota
24.6 Microbiota and asthma
24.7 Dietary metabolites and asthma
24.8 Conclusion, future perspectives and clinical implications
References
CHAPTER 25: Microbiome and cancer
25.1 Introduction
25.2 Microbiome and cancer: where is the link?
25.3 Microbiome and barrier disruption
25.4 Microbiome and different types of cancer
25.5 Microbiota and metabolism: the good and the bad sides
25.6 Chemotherapy, the microbiome and the immune system
25.7 Therapeutic avenues
25.8 Unresolved questions and future work
References
CHAPTER 26: Colorectal cancer and the microbiota
26.1 Introduction
26.2 Colon carcinogenesis and epidemiological data
26.3 The microbiota
26.4 Bacteria and CRCs links
26.5 Hypotheses and perspectives
References
CHAPTER 27: The gut microbiota and the CNS
27.1 Introduction
27.2 The microbiota-gut-brain axis: a historical framework
27.3 The microbiota-gut-brain axis: an evolutionary perspective
27.4 The gut microbiota influence on brain and behavior
27.5 Microbes and the hardwired gut brain axis
27.6 Hormonal pathways to the brain
27.7 Microbes and immune pathways to the brain
27.8 Metabolites of the microbiota: short-chain fatty acids
27.9 Clinical implications of the microbiota-gut-brain axis
27.10 Conclusion
References
CHAPTER 28: Genetic dysbiosis
28.1 The holobiont: humans as supra-organisms
28.2 Genetic variants in the host response to microbes
28.3 Genetic dysbiosis
28.4 Summary and conclusions
References
SECTION 5: Mirroring the future: dysbiosis therapy
CHAPTER 29: Diet and dysbiosis
29.1 Introduction
29.2 Coevolution of the host-microbiota super-organism
29.3 Gut microbiota in personalized diets
29.4 The evolution of diet
29.5 Plasticity of the microbiota and diet
29.6 Interaction among gut microbiota, host and food
29.7 Consequences of diet-induced dysbiosis on host health
29.8 The role of gut microbes on the digestion of macronutrients
29.9 Diet induces dysbiosis in the host
29.10 The effect of maternal diet on offspring microbiota
29.11 The effects of post-natal diet on the developing microbiota of neonates
29.12 Conclusion
References
CHAPTER 30: Probiotics and prebiotics
30.1 The gut microbiota, a partnership with the host
30.2 Probiotics
30.3 Prebiotics
30.4 Synbiotics
30.5 Pro-, pre-, and synbiotics in human medicine today
30.6 Concluding remarks
References
CHAPTER 31: The microbiota as target for therapeutic intervention in pediatric intestinal diseases
31.1 Introduction
31.2 Use of probiotics in pediatric intestinal diseases
31.3 Fecal microbiota transplantation for treatment of intestinal diseases
31.4 Conclusion
References
CHAPTER 32: Microbial therapy for cystic fibrosis
32.1 Introduction: pathophysiology of cystic fibrosis
32.2 Intestinal inflammation in CF
32.3 Dysbiosis in CF
32.4 Microbial therapy in CF
32.5 Conclusion
References
Index
End User License Agreement
List of Tables
Chapter 01
Table 1 Early discoveries of the involvement of members of the indigenous microbiota in human infections.
Table 2 Diseases caused by members of the indigenous microbiota (in addition to those listed in Table 1).
Table 3 Diseases resulting from dysbiosis.
Table 4 Beneficial effects of the human microbiota.
Table 5 Attributes of germ-free animals compared to their counterparts with an indigenous microbiota
22
.
Table 6 List of microbes that had been cultivated from various body sites as reviewed by Sternberg in 1892
74
.
Chapter 04
Table 1 Predominant bacterial phyla and genera found in the human mouth.
Chapter 07
Table 1 Publicly available resources for computational analyses of the gut microbiota. GENRE = genome-scale reconstruction; FBA = flux balance analysis.
Table 2 Applications of constraint-based modeling to the simulation of multi-species interactions.
Chapter 08
Table 1 Biofilm-mediated clinical disease and associated organisms.
Chapter 12
Table 1 Selected studies on the pro-inflammatory activity of
P. acnes.
Chapter 16
Table 1 Comparison of diagnostic criteria for bacterial vaginosis.
Chapter 20
Table 1 Comparison of amino acid sequence homologies between Klebsiella pullulanase (pulA) and collagens I, III and IV (G = glycine, X = any other amino acid, P = proline, A = alanine, D = aspartic acid, E = glutamic acid).
Table 2 Geographical distribution of anti-
Klebsiella
antibodies in patients with ankylosing spondylitis (AS) compared to those with other rheumatic diseases (ORD) or to healthy controls (HC), as reviewed by Rashid and Ebringer in
Curr Rheum Reviews
, 2012; 8: 109–119. (With permission)
Chapter 25
Table 1 Microorganisms associated with cancer development: well established associations between a microbe and the development of a particular type of cancer.
Chapter 26
Table 1 Risk of CRCs in gene mutation carriers.
Chapter 31
Table 1 Use of probiotics in pediatric intestinal diseases.
Table 2 Preparation of donor stools and recipient.
Table 3 Advantages and barriers in different routes of FMT administration.
List of Illustrations
Chapter 01
Figure 1 Relative proportions of the various organisms comprising the cultivable microbiota of a number of skin sites
Figure 2 Frequency of detection of various microbes on the conjunctivae of healthy adults. The data shown are mean values (and ranges) derived from the results of 17 culture-based studies involving 4623 individuals from a number of countries
Figure 3 Organisms most frequently detected in the nasopharynx of adults.
Figure 4 Organisms most frequently detected in the oropharynx.
Figure 5 Frequency of detection (mean value and range) of various microbes in the nasal vestibule
Figure 6 Frequency of detection (mean value and range) of various microbes in the nasal cavity.
Figure 7 Frequency of isolation of bacteria from the urethra of healthy females. The data shown are the means (and ranges) based on the results of six studies involving 219 pre-menopausal females
Figure 8 Relative proportions of the predominant organisms constituting the vaginal microbiota of 21 postmenarcheal/pre-menopausal, healthy, non-pregnant females
Figure 9 Relative proportions of species comprising the cultivable cervical microbiota of 21 healthy, pre-menopausal females
Figure 10 Relative proportions of the various microbes that comprise the cultivable microbiota of the labia majora of post-menarcheal/pre-menopausal females. Data represent the mean values obtained in a study involving 102 individuals
Figure 11 Relative proportions of the various organisms comprising the cultivable urethral microbiota of adult males. Data are derived from an analysis of 60 adult males
Figure 12 The predominant cultivable microbiota of the three main types of supragingival plaque: (a) fissure, (b) approximal, and (c) smooth surface. Data are derived from three studies involving a total of 40 healthy adults
Figure 13 Relative proportions of organisms comprising the cultivable microbiota of the gingival crevice. Data are derived from a study involving seven healthy adults
Figure 14 Relative proportions of the various organisms comprising the cultivable microbiota of the tongue. Data are derived from a study involving 17 healthy adults.
Figure 15 Culture-dependent study of the oesophageal microbiota. Relative proportions of the organisms present. Data are mean values derived from the results of two studies involving 17 healthy adults
Figure 16 Frequency of isolation of microbes from the gastric mucosa. The data shown are the means (and ranges) based on the results of two studies involving 58 adults
Figure 17 Relative proportions of organisms comprising the cultivable microbiota of the duodenal mucosa of 26 healthy adults
Figure 18 Relative proportions of organisms comprising the cultivable microbiota of the jejunal mucosa of 20 healthy adults
Figure 19 Relative proportions of organisms comprising the cultivable microbiota of the caecal mucosa in 19 healthy adults
Figure 20 Relative proportions of organisms comprising the cultivable microbiota of the contents of the cecum in 21 healthy adults
Figure 21 Culture-based analysis of the composition of the faecal microbiota. The figures represent mean values for the relative proportions of the various genera —— these have been derived from the results of ten studies involving 212 healthy adults from several countries
Figure 22 Cultivable microbiota of the colonic mucosa. Figures denote mean values for the relative proportions of the various organisms isolated, and are derived from the results of three studies involving 61 healthy adults
Chapter 02
Figure 1 16S rRNA gene surveys reveal hierarchical partitioning of human-associated bacterial diversity. (a to d) Communities clustered using principal component analysis of the unweighted UniFrac distance matrix. Each point corresponds to a sample colored by (a) body habitat, (b) host sex, (c) host individual, or (d) collection date. The same plot is shown in each panel. F, female; M, male. (e and f) Mean (± SEM) unweighted UniFrac distance between communities. In (e) habitats are weighted equally and in (f) skin comparisons are within sites. (g) UPGMA (Unweighted Pair Group Method with Arithmetic Mean) clustering of composite communities from the indicated locales. Leaves are colored according to body habitat as in (a). R, right; L, left.
Figure 2 Gut and salivary microbiota dynamics in two subjects over one year. (a) Stream plots showing OTU (Operational Taxonomic Unit) fractional abundances over time. Each stream represents an OTU and streams are grouped by phylum: Firmicutes (purple), Bacteroidetes (blue), Proteobacteria (green), Actinobacteria (yellow), and Tenericutes (red). Stream widths reflect relative OTU abundances at a given time point. Sampled time points are indicated with gray dots over each stream plot. (b) Horizon graphs of most common OTUs abundance over time. Warmer regions indicate date ranges where a taxon exceeds its median abundance and cooler regions denote ranges where a taxon falls below its median abundance. Colored squares on the vertical axis correspond to stream colors in (A). Lower black bars span Subject A’s travel abroad (days 71 to 122) and Subject B’s Salmonella infection (days 151 to 159).
Figure 3 16S rRNA gene surveys reveal a clear separation of two children populations in Burkina Faso (a) and Europe (b). Pie charts of median values of bacterial genera present in fecal samples of Burkina Faso and European children (>3%). Rings represent corresponding phylum (Bacteroidetes in green and Firmicutes in red) for each of the most frequently represented genera.
Figure 4 Phylum-level microbial composition of ancient dental calculus deposits. The distribution is similar to that of modern oral samples and distinct from those of non-template controls, ancient human teeth and environmental samples. The phylum frequencies for the V3 region of 16S rRNA are presented for the ancient calculus samples (BB, Bell Beaker), modern oral samples, which included pyro-sequenced (calculus, plaque and saliva) and cloned (plaque) data, non-template controls (or extraction blanks), ancient human teeth and environmental samples (freshwater, sediments and soils). Phylum frequencies from the Human Oral Microbiome Database (HOMD) were generated from partial and full-length sequences of the 16S rRNA gene.
Figure 5 Features of intestinal dysbiosis during bacterial infections. (a) A healthy microbiota is typically diverse in structure and performs a wide range of functions (e.g. xenobiotic metabolism, production of SCFAs), thereby maintaining a mutualistic metabolic relationship with the host. Colonization resistance relies in part on the ability of the resident microbiota to outcompete pathogens for niches and nutrients. (b) During dysbiosis induced by pathogen-mediated inflammation or antibiotic perturbation, the microbiota is reduced in both taxonomic diversity and function, and intestinal colonization resistance is impaired. Diverse Gram-negative and Gram-positive pathogens can maintain dysbiosis by acting as keystone species to modulate community-wide shifts in the microbiota, possibly by orchestrating the host inflammatory response. As a result, the microbial community becomes more pathogenic, wherein pathogens and resident pathobionts may overgrow and even invade to cause systemic infection.
Figure 6 Oral inoculation of mice with
P. gingivalis
but not with an isogenic gingipain-deficient mutant (KDP128) causes elevation of the oral bacterial load and an altered microbiota composition. Mice were orally inoculated with
P. gingivalis
ATCC3327 (Pg), its isogenic gingipain-deficient mutant (KDP128), or vehicle only (Sham). At the indicated timepoints (d = day), maxillary periodontal tissue was harvested to determine total bacterial numbers using real-time PCR of the 16S rRNA gene (a). Changes to the qualitative composition of the cultivatable microbiota detected by aerobic (b) or anaerobic culture (c) were determined following sub-culture of the predominant organisms, based on colonial morphology, followed by identification by 16S rRNA sequence analysis. Five mice were used per group at each timepoint. *
p
< 0.01 vs. both Sham and KDP128. Mice inoculated with Pg but not KDP128 developed accelerated periodontal bone loss compared to sham animals.
Figure 7 The red complex bacterium
P. gingivalis
causes periodontal inflammation and bone loss by remodeling the oral commensal microbiota.
P. gingivalis
modulates innate host defense functions that can have global effects on the oral commensal community. Immune subversion of IL-8 secretion, complement activity, or TLR4 activation can result in an impaired host defense. The inability of the host to control the oral commensal microbial community in turn results in an altered oral microbial composition and an increased microbial load. This alteration from a symbiotic to a dysbiotic microbiota is responsible for pathologic inflammation and bone loss.
Chapter 03
Figure 1 (a) Metataxonomic DNA analysis of an infant fecal microbiota in the first year and a half of life showing a chaotic evolution impacted by weaning and antibiotic consumption, from Koenig
et al
13
. (b) Fecal microbial differences between children and adults from Amazonia (Amerindians: green dots), Malawi (red dots) and USA (blue dots), showing reduction of distance to adult microbiota during the first three years of life and then stability until adulthood, from Yatsunenko
et al
14
.
Figure 2 Bimodal distribution of microbial genes showing individual with low and high gene counts in a Danish cohort of lean and obese (a)
33
and in a French cohort of overweight and obese subjects (b)
34
. Gene richness evolution (c) of French subjects who underwent a six-week hyper-low-calorie diet depending on gene count at the beginning of the study.
Chapter 05
Figure 1 Topographical distribution of bacteria on specific skin sites. The microbial composition is shown at phylum and family level. Moist sites are labelled in green, sebaceous sites are labelled in blue, and dry surfaces in red. Data from Zeeuwen
et al
.
38
except for volar forearm, back of the knee, and the back (from Human Microbiome Project).
Chapter 07
Figure 1 Overview of context-based methods in comparative genomics to identify missing genes in a pathway. This scheme describes the situation when a full pathway (1 to 4) was described in a model organism but in another organism the gene required for the last reaction (4) of the pathway was not found. In all techniques, the gap-filling approach is based on the comparison of the studied genomes with closely related genomes. In the “
Chromosomal clustering
” approach, an alternative form of the gene (5) for the final reaction is predicted by co-localization of genes in multiple genomes. In the “
Phyletic patterns
” approach, two possible scenarios are shown: prediction of an alternative form of the gene (6) for the final reaction by co-occurrence in multiple genomes, and prediction of a transporter (7) for the final product. The transporter was predicted by reciprocal presence of gene (7) with the pathway encoding genes. In the “
Transcriptional regulation
” approach, promoter regions of the genes are analyzed for the presence of conserved sequence motifs. Being identified, these motifs are used for prediction of candidate regulatory sites in multiple genomes. An alternative form of the gene (8) for the final reaction is predicted by the presence of candidate sites in promoter regions of the gene (8) in multiple genomes. Genomes are represented by double lines and individual genes are illustrated as pentagons according to the gene direction. Slashes separated distinct gene loci on the genome. Reactions are shown by arrows, metabolic reactions are marked by circles, while transport reactions are marked by rectangles. Previously known and novel predicted genes are shown by different colors.
Figure 2 Overview of the constraint-based reconstruction and analysis (COBRA) approach. A target organism’s metabolic reconstruction is generated from a draft reconstruction based on the genome sequence and is then manually validated and curated against the organism’s bibliome and available experimental data. The curated reconstruction is converted into a mathematical model in the form of a stoichiometric matrix, with rows representing metabolites and columns representing reactions. The mathematical model is then converted into a predictive simulation platform by implementing condition-specific constraints, e.g., high-throughput data or growth conditions, such as nutrient availability. A variety of methods for biological predictions exist. For instance, flux balance analysis predicts the flux through an objective function Z, e.g., biomass production. The simulation yields the feasible solution space under the given constraints (implemented as lower and upper bounds on fluxes through reactions). Flux variability analysis predicts alternate flux distributions by computing the allowed flux spans for all reactions in the model.
In silico
gene deletion studies simulate gene knock-out phenotypes. If the deleted gene encodes an enzyme for which no isoenzymes exist, the gene deletion results in a simulated enzyme defect. LB = lower bounds, UB = upper bounds, S = representation of the stoichiometric matrix, Z = objective function (e.g., biomass), b0116/b0726/b0727 = example for the gene annotation of an enzyme (in this case, 2-oxoglutarate dehydrogenase, encoded in
Escherichia coli
str K-12 substr. MG1655), lpd/sucA/sucB = example for multiple complexes that compose an enzyme (in this case, 2-oxoglutarate dehydrogenase, EC number 1.2.4.2), AKGDH = 2-oxoglutarate dehydrogenase, akg = 2-oxoglutarate, co2 = carbon dioxide, coa = Coenzyme A, nad = nicotinamide adenine dinucleotide, nadh = reduced nicotinamide adenine dinucleotide, succoa = succinyl-CoA.
Chapter 08
Figure 1 Biofilm formation and development. Initially, planktonic cells adhere to a solid surface (1), and production of extracellular polymeric substances (EPS) stabilizes the adhered colony (2). Some of the cells undergo autolysis, releasing nutrients and eDNA that promote growth and maturation of the biofilm (3). Cells are dispersed from the biofilm and can colonize other sites through three mechanisms: erosion, sloughing, and seeding dispersal (4). Seeding dispersal implicates an active process of autolysis resulting in release of single bacterial cells and cavity formation. Used under Creative Commons License from “Die for the community: an overview of programmed cell death in bacteria” by N Allocati, M Masulli, C Di Ilio and V De Laurenzi in
Cell Death and Disease,
January 2015 (Figure 3 original text).
Chapter 09
Figure 1 Time scale of immune-microbial co-evolution. The evolution of life is shown and important evolutionary (red), immunological (green), and modernization events (black) are indicated. The different eons, eras, epochs, and ages are indicated in the colored boxes.
Chapter 10
Figure 1
Amoeba proteus
.
Figure 2 Close-up of vesicle containing x-bacteria.
Figure 3 Schematic of the genome of a retrovirus.
Figure 4 Schematic of the ERVWE1 locus.
54
Chapter 12
Figure 1 Electron microscope pictures of three prominent cutaneous propionibacteria. (a)
P. acnes
(type IA); (b)
P. granulosum
; (c)
P. avidum
.
Chapter 13
Figure 1 A tooth affected by periodontitis. While the left side of the tooth presents alveolar bone and periodontal ligament fibres in their correct location, the right side presents deposits attached to the root surface, associated with apical migration of the periodontal attachment apparatus and bone loss and gingival inflammation and swelling.
Chapter 14
Figure 1 The polymicrobial synergy and dysbiosis (PSD) model of periodontal disease etiology. (a) Model overview. In health, communities assembled through co-adhesion and physiological compatibility participate in balanced interactions with the host in a controlled immuno-inflammatory state. In disease, there is colonization by keystone pathogens such as
P. gingivalis
that enhance community virulence through interactive communication with accessory pathogens such as
S. gordonii,
and disruption of immune surveillance. The dysbiotic community proliferates, pathobionts (green) overgrow and become more active, and tissue destruction ensues. The participation of individual species is less important than the presence of the appropriate suite of genes. (b) Summary of synergistic interbacterial interactions that have been documented among oral bacteria. Signaling can occur through direct contact, primarily adhesin-receptor binding, and sensing of compounds in solution. Interspecies communication can modulate the phenotypic properties of partner organisms, and ultimately affect virulence potential. From Lamont and Hajishengallis, 2014 (Ref. 77) with permission.
Figure 2 Interactions between
S. gordonii
and
P. gingivalis
. Metabolites produced by
S. gordonii
induce autophosphorylation of the Ptk1 tyrosine kinase of
P. gingivalis
. Ptk1 initiates a signaling cascade that results in inactivation of the transcriptional repressor CdhR. Consequently, expression of the minor fimbrial adhesin subunit Mfa1 and the AI-2 biosynthetic enzyme LuxS is elevated, and
P. gingivalis
is “primed” for attachment to
S. gordonii
. Ptk1 is also a participant in the machinery of secretion of extracellular polysaccharide, and active Ptk1 increases the level of polysaccharide material on the surface of
P. gingivalis
. Over time, direct contact mediated by Mfa1-SspA/B binding elevates expression of the tyrosine phosphatase Ltp1, which dephosphorylates Ptk1, ultimately relieving repression of ChdR. Expression of Mfa1 and LuxS is reduced, and community development is constrained. From Lamont and Hajishengallis, 2014 (Ref. 77) with permission.
Figure 3 Cytokine disruption by
P. gingivalis
. Oral bacteria such as
F. nucleatum
engage TLRs on epithelial cell surfaces and activate pro-inflammatory signaling pathways.
P. gingivalis,
however, suppresses production of the neutrophil chemokine IL-8 (CXCL8) and the T-cell chemokine IP-10 (CXCL10) from epithelial cells. Mechanistically, invasive
P. gingivalis
inactivates Stat1 which in turn reduces expression of IP10 promoted by the IRF1 transcription factor. Intracellular
P. gingivalis
also secrete the serine phosphatase SerB, which dephosphorylates the serine 536 residue of the p65 NF-κB subunit and prevents the formation of p65 homodimers and consequently translocation of NF-κB into the nucleus. Transcription of the
IL8
gene, which is under the control of NF-κB, is reduced. The gingipain proteases Rgp A/B and Kgp, which are secreted by
P. gingivalis
, can degrade a number of cytokines and chemokines including IL-8. From Lamont and Hajishengallis, 2014 (Ref. 77) with permission.
Figure 4 Synergistic inhibition of complement-dependent antimicrobial activities.
P. gingivalis
,
P. intermedia,
and
T. forsythia
can inhibit the classical, lectin, and alternative pathways of complement activation by degrading C3, C4, mannose-binding lectin (MBL), or ficolins (FCN) through the action of proteases, as indicated. Such activities are synergistic and prevent the deposition of C3b opsonin or the membrane attack complex (MAC) on the surface of these pathogens as well as bystander bacterial species. Moreover,
P. gingivalis
and
P. intermedia
also protect themselves against complement also by using surface molecules (HRgpA gingipain for
P. gingivalis
, an undefined molecule for
P. intermedia
) to capture the circulating C4b-binding protein (C4BP), a physiological negative regulator of the classical and lectin pathways. Furthermore,
P. gingivalis
(via its Arg-specific gingipains HRgpA and RgpB) and
T. forsythia
(via its karilysin) can release biologically active C5a from C5, thereby stimulating inflammation through the activation of the C5a receptor (C5aR). This receptor is also exploited by the bacteria for immune evasion of leukocyte killing. InpA, interpain A; Kgp, Lys-specific gingipain. From Lamont and Hajishengallis, 2014 (Ref. 77) with permission.
Figure 5 Subversion of neutrophil function and dysbiosis.
P. gingivalis
co-activates TLR2 and C5aR in neutrophils. and the resulting crosstalk leads to E3 ubiquitin ligase Smurf1-dependent ubiquitination and proteasomal degradation of MyD88, thereby inhibiting a host-protective antimicrobial response. Moreover, the C5aR-TLR2 crosstalk activates PI3K, which prevents phagocytosis through inhibition of RhoA activation and actin polymerization, while stimulating an inflammatory response. In contrast to MyD88, another TLR2 adaptor, Mal, is involved in the subversive pathway and acts upstream of PI3K. The integrated mechanism provides “bystander” protection to otherwise susceptible bacterial species and promotes polymicrobial dysbiotic inflammation
in vivo
. From Maekawa
et al
., 2014 (Ref. 59) with permission.
Chapter 15
Figure 1 Link between periodontal disease and systemic diseases. Clinical and epidemiological studies strongly suggest that periodontal disease increases the risk of the various diseases in the figure.
Figure 2 Hypothetical mechanisms linking periodontitis and systemic diseases. Bacteria and/or products invade into systemic circulation via the disrupted epithelial barrier of periodontal pockets. Inflammatory mediators produced in the inflamed periodontal tissues gain access to the systemic circulation. Antibodies against bacterial products cross-react with host molecules, resulting in tissue inflammation.
Figure 3 Association between periodontal disease and gut dysbiosis. The diseases associated with periodontal disease are also reported to be affected by gut dysbiosis.
Figure 4 Effect of oral administration of
P. gingivalis
. Gut microbiota was compared between
P. gingivalis
-administered and sham-administered mice by 16S rRNA sequencing analysis. Relative abundances of each bacterial group in the phylum are shown. The proportion of Bacteroidetes was significantly higher in
P. gingivalis
-administered mice than in sham-administered mice. The proportion of Firmicutes tended to be lower in
P. gingivalis
-administered mice than in sham-administered mice (a). The gene expression of tight junction protein (
tjp1
) was significantly lower in
P. gingivalis
-administered mice than in sham-administered mice (b). Serum endotoxin levels increased after
P. gingivalis
administration (c).
Figure 5 Proposed mechanism for the effect of periodontal infection on various diseases. A large number of swallowed bacteria induce change in gut microbiota as well as gut barrier function. This results in the endotoxemia that is similar to the metabolic endotoxemia seen in obesity; it causes low-grade systemic inflammation in various tissues and organs and increases the risk of various systemic diseases.
Chapter 16
Figure 1 The complex relationship among the transition from healthy to unhealthy vaginal microbiota, sexual behavior and poor clinical outcomes. The transition is associated with severe clinical and global health consequences. SLPI: secretory leukocyte protease inhibitor.
Chapter 20
Figure 1 Chemical structure showing the point of action by
Klebsiella
pullulanase enzyme on the α(1–6) links.
Figure 2 Pathogenesis of ankylosing spondylitis in a patient following exposure to
Klebsiella
bacteria in the gut.
Figure 3 Individual erythrocyte sedimentation rate values before and after 10 months on low-starch diet in 43 ankylosing spondylitis patients, belonging to the group initially having an erythrocyte sedimentation rate level of 15 mm/hour or above at the start of the diet.
Chapter 24
Figure 1 The microbiota in early life. An interplay of several early life factors shape the development of the microbiota in infants and risk of asthma later in life. Early host-microbe interactions commence in utero and may influence the programming of developing immune cells. Vaginal delivery signals the first exposure of neonatal body surfaces to the maternal cervico-vaginal microbiota. Antibiotic use in infancy may be related to increased risk of asthma childhood. Breastfeeding is associated with direct ingestion by the neonate of maternal skin-resident bacteria, and maternal gut bacteria via the entero-mammary pathway. Maternal transfer of skin and oral microbiota occurs by direct contact and pre-mastication of food. Growing up in a rural environment leads to early-life exposure to diverse, environment-derived microbes and is associated with protection against the development of allergy. Fibre-rich diets increase short- chain fatty acid levels, which have a protective effect on airway inflammation.
Chapter 25
Figure 1 Overview of the core concepts: microbiome and cancer progression
.
The microbiome is a complex collection of microorganisms that inhabit the human body. In cancer, the microbiome can influence cancer initiation and promotion in a wide variety of ways. First, pathogenic microbes can induce damage to cells directly through induction of DNA damage, ROS/RNS, and chronic inflammation, which promotes the initiation of transformation. Pathogenic microbes can also alter the resident microbiota in the host, leading to dysbiosis. Commensal microbes can also enhance tumorigenesis by overamplifying inflammation when mislocalized, due to barrier disruption, to compartments of tissue they normally do not reside in. The microbiome can also generate genotoxic products through metabolism, further advancing tumor growth. Question marks (?) signify unanswered questions pertaining to dysbiosis in the field. Is dysbiosis a result of cancer, or is it a two-way street? What are the mechanisms involved with inflammation and dysbiosis?
Figure 2 Barrier disruption in mucosal tissue. Under homeostatic conditions a mucosal tissue, such as the colon, has an intact dual mucus layer, with the upper layer serving as an ecosystem for resident microbes and the bottom layer attached to the epithelium as barrier to microbes. The epithelial layer maintains tight contacts and has basal amounts of immune cells. When the tissue barrier integrity is disrupted, as in cancer, the mucus layers are diminished and tight junctional contacts between epithelial cells deteriorate, increasing permeability. Cells undergo death, releasing inflammatory factors that recruit and activate the immune system. Bacterial translocation then occurs as commensals come into contact with the epithelium and penetrate deep into the tissue, further exacerbating inflammation. This leads to perpetual inflammatory conditions, promoting tumor progression.
Figure 3 Microbial sensing in the progression of cancer. In many cancers, microbial sensing through TLRs and MyD88-dependent signaling generally promotes cancer. The microbial sensing pathway can be activated in two ways, directly through the binding of microbial products (as in barrier disruption), such as LPS, to TLRs or indirectly through the production of danger signals by tumor cells or immune cells in the tumor microenvironment and then subsequent binding of danger signals to TLRs or other pattern-recognition receptors. In both cases, the adaptor protein MyD88 is activated, which in turn activates the master inflammatory transcription factor NFκB, generating tumor promoting inflammation. Tumor cells or immune cells can respond to danger signals or microbial products.
Figure 4 Therapeutic opportunities: selective targeting is the best medicine. Inhibition of bacterial enzymes with selective inhibitors can be a minimally disruptive approach to reduce harmful effects of commensal microbes without inducing dysbiosis. Therapeutically, chemotherapy and metabolism could be improved, reducing pro-tumorigenic capacity of commensals. Administration of “good” bacteria, through probiotic intake or fecal microbiota transplant, to patients suffering from dysbiosis could help to restore balance to the host microbiome, limiting pro-tumorigenic inflammation and enhancing antitumor immunity.
Chapter 26
Fusobacterium
P. asaccharolytica
E. ventriosum
Figure 5 Functional changes in the CRC-associated metagenome. A. Significant changes in relative abundance of genes summarized by KEGG module annotations between cancer and non-cancer metagenomes are shown for cases with a >1.5-fold change and FDR-adjusted p-value < 0.1. General trends in functional potential, such as enrichment of secretion and transport systems, two-component regulatory systems (TCRS), iron (Fe) manganese (Mn) transport, and putrefaction in the CRC microbiome are summarized to the right of the heatmap (biosynth., biosynthesis; ascorbate-sp., ascorbate-specific). B. The heatmap displays significant abundance changes of genes summarized by CAZy family annotation with a >1.33 fold change and an FDR-adjusted p-value < 0.1. A metabolic switch from utilization of dietary fibre to degradation of host carbohydrates, e.g. mucins, in CRC metagenomes, as well as an CRC-associated increase in metabolism of potentially pro-inflammatory bacterial cell wall components, such as lipopolysaccharide (LPS) and peptidoglycan (PG) is annotated to the right.
Chapter 27
Figure 1 The multiple pathways that enable bidirectional communication along the microbiota-gut-brain axis. Adapted from Forsythe 2010
175
.
Chapter 29
Figure 1 (a) Interactions among food, microbiota, and the host. A delicate and complex relationship exists among the microbiota, food and the host, and even slight deviations in this tripartite relationship can have deleterious effects on the host. (b) Food-microbiota: Dietary intake can directly influence microbial composition by promoting blooms of certain groups while inhibiting the growth of others. Partially digested food such as complex carbohydrates are broken down in the gut by the bacteria via fermentation and produce short-chain fatty acids (SCFAs). Lipid digestion in the gut requires the secretion of liver-derived bile from the gallbladder. High-lipid diets demand higher bile release, which could induce dysbiosis in part by changing intestinal pH levels. Fermentation of proteins in the gut can result in production of branched-chain fatty acids (BCFA) as well as potentially toxic products such as amines, ammonia (NH3), phenols, and indoles
26
. Gut bacteria are essential in synthesizing various vitamins such as riboflavin, folate, B12, and vitamin K
26
. Microbiota-host: The microbiota acts as a first line of defense against enteropathogens by competition for food and niche. Host immunity is modulated by the intestinal microbiota starting at birth. Antigen-presenting cells (APC) such as dendritic cells (DC) or macrophages phagocytose invading bacteria and subsequently initiate differentiation of naïve T cells (Th0) to subset of T-helper cells (Th): Th1, Th2, Th17, or regulatory T-cells (Treg). B-cells presented by bacterial antigen differentiate into antibody-producing effector B cells or memory cells. Antibodies produced by effector B-cells neutralize and tag the specific pathogen for removal. Bacteria can regulate mucus production by stimulation of goblet cells. Paneth cells of the intestinal epithelium layer sense bacteria and in response produce antimicrobial peptides (AMPs) that protect the host from bacterial invasion. Evidence has emerged showing that gut microbiota interact with central and peripheral neural processes, suggesting their involvement in hormone signaling as well as psychological health (see chapter 27 by Barwani and Forsythe). Host-Food: Food antigens stimulate endocrine secretion, which initiates downstream digestion cascades such as bile and amylase production by the liver and pancreas, respectively. SCFAs produced by bacteria in the colon are used by colonocytes as a major energy source. Ingestion of contaminated food is a major source of pathogen entry into the body. Hormonal regulations by the host control appetite and satiety.
Chapter 31
Figure 1 Antimicrobial mechanisms of probiotics. The known mechanisms whereby probiotics exert their antimicrobial effects include: 1) modification of gut microbiota in a healthy state; 2) secretion of antimicrobial substances such as bacteriocins, hydrogen peroxide and short-chain fatty acids; 3) competition for nutrients; 4) antitoxin effect; 5) inhibition of pathogen adhesion to the intestinal epithelium (barrier function).
Chapter 32
Figure 1 The intestine is a target organ in CF and a major role may be played by the disrupted microflora. This contributes to the intestinal inflammation and may affect the rates and severity of respiratory involvement. Probiotic administration may in part restore intestinal microbiota, reduce intestinal inflammation and, together with other treatment, reduce the risk of pulmunary exacerbations, ultimately improving the long-term outcome of this progressive disease.
Figure 2 Gut-lung axis in CF. A link between intestinal microbiota and bacterial colonization of the respiratory tract has been hypothesized. Manipulation of intestinal microbiota through the administration of probiotics, prebiotics and synbiotics may influence lung function and nutritional status in patients with CF . SCFA: short-chain fatty acids; GALT: gut-associated lymphatic tissue.
Figure 3 Habitually constitutive bacteria intestinal microflora evaluated by FISH (fluorescence in situ hybridization). Top: Total bacteria and Faecalibacterium prausnitzii were evaluted by EUB (green) and Fpra (red) probes in healthy (a) and cystic fibrosis (b) children. Bottom: Total bacteria and Eubacterium rectale were evaluted by EUB (green) and Erec (red) probes in healthy (c) and cystic fibrosis (d) children.
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