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Eubacterium Limosum Bottleneck for Systematic Engineering

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Eubacterium Limosum Bottleneck for Systematic Engineering

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"Eubacterium limosum is one of the important bacteria in C₁ feedstock utilization as well as in human gut microbiota. Although E. limosum has recently garnered much attention and investigation on a genome-wide scale, a bottleneck for systematic engineering in E. limosum is the lack of available genetic tools and an efficient genome editing platform. To overcome this limitation, we here report expanded genetic tools and the CRISPR-Cas9 system."

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Genome Engineering of Eubacterium limosum Using Expanded Genetic Tools and the CRISPR-Cas9 System.

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The phylogenetic tree shows the Kingella Kingae is closest related to Neisseria lactamica due to both being gram negative anaerobes. There closest cousins include Cardiobacterium hominis and Haemophilus Influenzae. The farthest distance related cousines are the Johnsonella ignava and the cardiobacterium hominis and its closest cousins due to how many "generation spreads there are.

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Eubacterium Limosum may positively contribute to the health of the human intestine that may have inflammation, or it is aged. Eubacterium Limosum has anti-inflammatory traits and is able to transform dietary isoflavonoids into other phytoestrogens.

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Abstract

Background

Age-related physiological changes in the gastrointestinal tract, as well as modifications in lifestyle, nutritional behaviour, and functionality of the host immune system, inevitably affect the gut microbiota, resulting in a greater susceptibility to infections.

Methodology/Principal Findings

By using the Human Intestinal Tract Chip (HITChip) and quantitative PCR of 16S rRNA genes of Bacteria and Archaea, we explored the age-related differences in the gut microbiota composition among young adults, elderly, and centenarians, i.e subjects who reached the extreme limits of the human lifespan, living for over 100 years. We observed that the microbial composition and diversity of the gut ecosystem of young adults and seventy-years old people is highly similar but differs significantly from that of the centenarians. After 100 years of symbiotic association with the human host, the microbiota is characterized by a rearrangement in the Firmicutes population and an enrichment in facultative anaerobes, notably pathobionts. The presence of such a compromised microbiota in the centenarians is associated with an increased inflammatory status, also known as inflammageing, as determined by a range of peripheral blood inflammatory markers. This may be explained by a remodelling of the centenarians' microbiota, with a marked decrease in Faecalibacterium prauznitzii and relatives, symbiotic species with reported anti-inflammatory properties. As signature bacteria of the long life we identified specifically Eubacterium limosum and relatives that were more than ten-fold increased in the centenarians.

Conclusions/Significance

We provide evidence for the fact that the ageing process deeply affects the structure of the human gut microbiota, as well as its homeostasis with the host's immune system. Because of its crucial role in the host physiology and health status, age-related differences in the gut microbiota composition may be related to the progression of diseases and frailty in the elderly population.

Introduction

Ageing is defined as "the regression of physiological function accompanied by the development of age" [1]. With a global impact on the physiology of the intestinal tract, the ageing process can seriously affect the composition of the human gut microbiota. The decreased intestinal motility results in a slower intestinal transit that affects defecation and leads to constipation [2]. The subsequent reduced bacterial excretion alters the gut fermentative processes in an unfavourable way [3], [4]. Inevitably, this affects the homeostasis of the bacterial ecosystem in the intestinal tract. Moreover, the age-related decline in the functionality of the immune system (immunosenescence) [5] is characterized by a chronic low-grade inflammatory status (inflammageing) [6]-[8]. In a healthy intestinal tract, the microbiota and the gut-associated immune system are assumed to share a fine and dynamic homeostatic equilibrium [9]. The inflammageing process can undermine this balance, leading to changes in intestinal microbial structure and composition [10]. Finally, considering the impact of the diet on the gut microbiota composition [11], changes in nutritional behaviour and life style of the aged people concur to the age-related unbalances of the intestinal microbial community.

Coevolved with the human host and its ancestors for millions of years, the intestinal microbiota contributes significantly to our physiology, among others by supplying nutrients and protecting against pathogens [9]. This has led to the hypothesis that age-related changes in the composition of the symbiotic microbiota may contribute to the progression of diseases and frailty in the elderly [10], [12]. The reported age-related differences in the intestinal microbiota composition include increase in the total number of facultative anaerobes and shifts in the dominant species within several bacterial groups, whereas no significant changes are reported in the total number of anaerobic bacteria [13]-[18]. The majority of earlier studies has relied on cultivation-based approaches but high-throughput culture-independent molecular techniques are considered essential to provide a global insight in the intestinal microbiota [19]. A 16S rRNA gene-based diversity microarray for the characterization of the human gut microbiota (Human Intestinal Tract Chip, HITChip) has been recently developed and validated [20]. The HITChip was found to be comparable but faster than deep pyrosequencing [21], indicating that it is among the most powerful tools presently available for characterizing the human gut microbiota. A pilot study with five adults and five elderly subjects using HITChip analysis showed the structure and composition of the gut microbiota to be different in different age groups [20], [21].

In this study, we undertook to explore the age-related differences both in the inflammatory status and in the gut ecosystem composition, by using HITChip analysis. In particular, we decided to expand the usual target populations of comparative studies, addressing not only young adults (20-40 years old) and elderly (60-80 years old), but also centenarians, representative of a group of people who reached the extreme limits of the human lifespan [22]. This approach aims at the broadening of our view of the differences in the gut microbiota of adult human beings of increasing age.

Results

Fecal microbiota profiles of centenarians, elderly and young adults

The fecal microbiota of centenarians (group C), elderly (E) and young adults (Y) was characterized using the HITChip [20]. Hybridization was performed with at least 2 replicates for each sample, and reproducibility was tested by calculating Pearson's correlation coefficient (Pearson's r) and Relative Standard Deviation (RSD). For each subject a phylogenetic fingerprint of the intestinal microbiota was obtained. Pearson's r ranged between 0.982 and 0.999, with a mean value of 0.993, showing that the HITChip profiles were highly reproducible, and the RSD ranged between 1.8% and 13.2%, with a mean value of 5.1%. Hierarchical clustering of the genus-like taxa in a heat map showed that centenarians tended to group together, whereas no separation was observed with the subjects belonging to groups E and Y (). The proportion of centenarians in Cluster 3 (67%) was found to be significantly higher than in the other clusters (χ ² test, P value = 0.001). Clusters 1 and 2 contain similar proportions of each age groups (18% C, 44% E, 39% Y, for Cluster 1; 22% C, 38% E, 38% Y, for Cluster 2). The same results were obtained by using analysis of similarity (ANOSIM, [23]), which showed that the microbiota profiles of centenarians are significantly different (P = 0.001, R value = 0.32) from that of all younger people (E and Y). The gut microbiota of subjects in Cluster 3 is characterized by higher amounts of Proteobacteria and Bacilli, and decreased amounts of Clostridium cluster XIVa bacteria. Finally, Bacteroidetes showed lower intensities in cluster 1, which is composed by mixed subjects, highlighting that the decrease in Bacteroidetes amount in the gut microbiota does not seem to be related to the ageing.

In order to highlight the bacterial groups principally responsible for the age-related differences in the gut microbiota structure, a multivariate analysis was carried out using the age groups C, E, and Y as nominal "environmental variables", and the log-transformed average hybridization signal for the 129 genus-like phylogenetic groups as "species variables". The resulting ordination plot shows the redundancy analysis (RDA), which focuses on the major carriers of the difference among the groups of subjects (). The differences shown in RDA are significant (P = 0.002) as assessed by Monte Carlo Permutation Procedure (MCPP). A total of 6.1% of the total variation in the dataset (microbiota composition at genus-like level) could be related to the environmental variables (age-groups). Most of this variation (5.2%) is plotted on the first axis that separates the centroid of group C from the other centroids (E and Y), plotted on a line along the second axis, which shows only 0.9% of variability. This analysis indicated that the differences in the microbiota fingerprint between centenarians and all the other subjects are greater than the differences between elderly and young adults. The samples belonging to groups C and Y form almost separate clusters in RDA, whereas the group E cluster overlaps with both of them. Confirming the heatmap results, RDA showed that the fecal microbiota of centenarians is enriched in many facultative anaerobes, mostly belonging to Proteobacteria ( Escherichia coli et rel., Haemophilus, Klebsiella pneumoniae et rel., Leminorella, Proteus et rel., Pseudomonas, Serratia, Vibrio, and Yersinia et rel.), and Bacilli ( Bacillus, Staphylococcus). On the other side of the plot, higher amounts of many bacterial groups belonging to Clostridium cluster XIVa seemed to characterize the fecal microbiota of E and Y samples. Remarkably, the arrows corresponding to the phylogroups Faecalibacterium prausnitzii et rel. and Clostridium leptum et rel., both belonging to the Clostridium cluster IV, pointed in opposite directions, indicating that F. prausnitzii group decreased in centenarians whereas C. leptum group increased.

Since 95% of subjects in group C were females, the RDA described above was repeated excluding male subjects from groups E and Y. This restriction did not change the overall shape of the plot (data not shown), suggesting that gender differences play no or marginal role in the effect of ageing on the gut microbiota composition.

Subsequently, the diversity of the microbial communities in the different age groups was assessed. The diversity discovered by the HITChip was expressed as Simpson reciprocal index of diversity. Simpson indices obtained for group C (127.0±54.2) were significantly lower than those obtained for E (149.4±40.5, P = 0.02) and Y (162.8±35.1, P = 0.002), indicating that the microbiota of centenarians is significantly less diverse than that of elderly or young adults. On the contrary, the difference between Simpson indices obtained for E and Y was not statistically significant.

Quantitative differences between the gut microbiota of centenarians, elderly and young adults

The relative contributions of the major phyla were assessed by analysing the phylum/order taxa in the fecal microbiota of the subjects in groups C, E and Y (). Together, the Bacteroidetes and Firmicutes contributed to the fecal microbiota for the 93% in the case of centenarians, and 95% in both groups E and Y. Bacteroidetes contributed for 20, 16, and 19% to the total microbiota of subjects belonging to groups C, E and Y, respectively. The Firmicutes/Bacteroidetes ratios obtained for groups C, E and Y were 3.6, 5.1, and 3.9, respectively. To evaluate the significance of this difference, the sum of the hybridization signals of the probes which referred to the Bacteroidetes and the Firmicutes was calculated for each subject. The differences among groups of samples, in both the Bacteroidetes proportions and the Firmicutes/Bacteroidetes ratios, were not statistically significant (P = 0.73, and P = 0.55, respectively).

Clostridium cluster IV contributed for the 22% to the microbiota of all age groups. qPCR for C. leptum group, which is comparable to Clostridium cluster IV, showed no significant difference between centenarians and other age groups (), confirming the HITChip results. Conversely, qPCR showed group E to have significantly more bacteria belonging to C. leptum group when compared to Y (P = 0.05), although no statistical difference in HITChip signals was observed. The relative contribution of Clostridium cluster XIVa is clearly lower in centenarians (34%) than in the other groups (49%, group E; 44%, group Y). By comparing the sum of the hybridization signals of the probes which referred to Clostridium cluster XIVa in the C and E groups, and in the C and Y groups, P values of 0.001 and 0.02 were obtained, respectively. The proportion of Bacilli is significantly higher in centenarians (12%), if compared to groups E (5%, P = 0.05) and Y (5%, P = 0.03). Centenarians also tended to have a higher proportion of Proteobacteria (2.6%) with respect to E (1.2%, P = 0.06) or Y (1.2%, P = 0.07).

Table 1

To provide a more detailed view of the age-related differences in the core gut microbiota composition, quantitative analysis was focused on the genus-like bacterial groups having 100% of prevalence (defined as percentage of positive samples in each study group) in all the age groups. This core microbiota was constituted principally of Bacteroidetes and Firmicutes, mostly Clostridium clusters XIVa and IV. Several genus-like groups showed a significantly different abundance in groups C, E and Y, while others showed a tendency (P = 0.05-0.08) of increased or decreased abundance in the different age groups (). In accordance with the RDA result (), the vast majority of the significant differences in bacterial groups were found when group C was compared to E and Y. Relative contribution of each genus-like bacterial group to the total microbiota was calculated for the three groups of subjects. The ratio of the average hybridization signals obtained for groups C and E, and C and Y, was determined to better highlight the increase or decrease of each genus-like group at the different stages of life. Most of the bacteria that significantly decreased in group C with respect to both E and Y belonged to Clostridium cluster XIVa. In Clostridium cluster IV, Papillibacter cinnamovorans et rel., and F. prausnitzii et rel. showed a significant decrease in centenarians, while C. leptum et rel., Sporobacter termiditis et rel., Anaerotruncus colihominis et rel., Clostridium orbiscindens et rel. showed a significant increase. The decrease of F. prausnitzii in centenarians with respect to the other two age groups was confirmed by qPCR analysis (). Considering that the relative proportion of Clostridium cluster IV in the total microbiota remained unaffected by the age of the subjects (, ), the analysis at genus-like level suggests a rearrangement in the composition of this bacterial cluster, in terms of genera or species, in centenarians. In line with the RDA results (), the average amounts of all the bacteria listed above did not significantly differ between E and Y groups. Differences between E and Y were found only for Acquabacterium (P = 0.05), Collinsella et rel. (P = 0.04), and Dialister et rel. (P = 0.02). These phylogenetic groups, belonging to Proteobacteria, Actinobacteria and Clostridium cluster IX, respectively, are not highly represented in the human gut. Moreover, for all the bacterial groups showing significant differences between centenarians and all the other subjects, the HITChip signal ratios C/E and C/Y were often very similar (), suggesting that the changes in the composition of the gut microbiota are not linearly correlated with the age.

Table 2

In addition to the qPCR analyses of Clostridium cluster IV and F. prausnitzii, performed to confirm the HITChip results, we quantified bifidobacteria and Akkermansia muciniphila, which in previous studies had shown to decrease in the old age (). The qPCR revealed that the amount of total bifidobacteria was significantly lower in C when compared to Y (P = 0.023). However, no statistical difference was detected in the Bifidobacterium signals in the HITChip. This discrepancy is likely due to the lower quantitative sensitivity of the HITChip, with respect to the qPCR, which detected a difference of only 0.3 log unit between groups C and Y. The amounts of Akkermansia spp. were comparable between centenarians and other age groups, both in qPCR and HITChip analysis. However, by qPCR, subjects in group Y were observed to harbour significantly less Akkermansia than group E, both in means of prevalence and amount of 16S rRNA gene copies. We also quantified by qPCR the Archaea, which are not targeted by the HITChip. The amount of archaeal DNA did not show any significant difference between centenarians and the other age groups (). Interestingly, centenarians tended to harbour Archaea more frequently than the elderly or young adults (prevalence 65%, 36% and 45%, respectively), although the difference was not significant.

Microbiota comparison between centenarians and their offspring

In order to assess whether the microbiota of centenarians can be related to that of their offspring, the HITChip analysis was performed on samples from group F, composed of 21 offspring subjects of centenarians. The average age in group F was 67.5 years. The HITChip profile of each centenarian was compared to that of his/her offspring, and to the profile of randomly selected subjects belonging to the group E, and Pearson's r were calculated. A mean Pearson's r of 0.42±0.18 for the C-F pairs and 0.44±0.20 for the random C-E pairs were obtained. These results demonstrate that the gut microbiota composition of parents and offspring shows no significant similarity at the old age (P = 0.79).

The average Simpson indices obtained for group F was 151.7±49.3. The microbiota of elderly belongings to groups E and F did not differ in diversity as assessed by Simpson index (P = 0.87). Also, the proportions of the phylum/order-like phylogenetic groups in E and F subjects were comparable (data not shown). Furthermore, RDA analysis showed that samples of the F and E groups were mixed and the centroids were close to each other, whereas the centroid of group C was plotted distant from both of them (Supporting Fig. S1). No significant differences in the overall microbiota composition were found between groups F and E (MCPP, P = 0.39). The statistically significant differences in genus-like bacterial groups between C and E () were also found between C and F (data not shown). In general, the microbiota of elderly people in group F seemed more similar to the microbiota of subjects in the age-matched group E, than to that of their centenarian parents. This indicates that centenarians have a unique microbial composition.

Phylotypes belonging to the major bacterial phyla of the human gut microbiota, such as Clostridium cluster IV and XIVa and Bacteroidetes, were present in all study subjects. On the other hand, some bacterial groups which are less represented in the human gut ecosystem, such as Fusobacteria and Proteobacteria, showed higher prevalence (the percentage of positive samples in each study group) in centenarians and their offspring than in E or Y groups (). The most noticeable differences in the prevalence concerned Campylobacter, Helicobacter and Fusobacterium. This trend is mainly limited to opportunistic or potentially pathogenic bacterial groups, and it is likely to reflect the microbial exposure in the shared living environment and/or the close contacts between family members, since most of the subjects in group F (13 out of 21, defined as group FC) lived with their centenarian parents. Several of the phylogroups listed in , Asteroplasma et rel., Peptostreptococcus micros et rel., Fusobacterium, Alcaligenes faecalis et rel., Campylobacter, Desulfovibrio et rel., Helicobacter, Leminorella, Moraxellaceae, Proteus et rel., Brachyspira, showed higher prevalence values in group FC than in group FN, a subset of group F including the offspring subjects who did not live with their parents. This supports the explanation that the observed common microbiota between family members is due to the sharing of a common household.

Table 3

Immunophenotyping analysis

In order to assess the immune system status of the subjects, we performed a cytofluorimetric analysis of the major lymphocytes subsets, with particular attention to the T cell compartment. No significant differences between the groups E and F were observed regarding both T and B cell subpopulations (data not shown). Hence we grouped all these elderly subjects (designated group S, for Seventy years which is the average age of these subjects) and compared their immune status with that of the other groups (). A significant, progressive age related decrease of naïve T cells was observed with a concomitant significant increase of memory T cells, as was expected as characteristic features of immunosenescence.

Table 4

Evaluation of the inflammatory status

To explore the inflammation level of the subjects involved in this study, plasma levels of the major pro- and anti-inflammatory cytokines were evaluated. The pro-inflammatory cytokines IL-6 and IL-8 increased in C with respect to S and Y. In the case of IL-6 the increase was significant (C vsS, P = 0.003; C vsY, P = 0.05). On the contrary, IL-1α and TNF-α levels were significantly lower in C in comparison to Y (IL-1α, P = 0.03; TNF-α, P = 0.03). IL-1β and IFN-γ levels did not change significantly in the different age groups. Regarding anti-inflammatory cytokines (TGF-β1 and IL-10), no significant differences were observed among the age groups. Moreover, plasma levels of IL-2 and IL-12 which play an important role in activating the immune response, showed a decrease in C with respect to S and Y, which was significant in the case of IL-2 (C vs Y, P = 0.04).

The calculation of the Inflammation Score (IS) highlighted that most of the centenarians (66.7%) were characterized by highly inflamed status, whereas in groups S and Y the majority of the subjects fell in the "low inflammation" group (S, 69.2%; Y, 75%). The difference in the proportions of highly inflamed subjects was significant when groups C and S (χ ² test, P = 0.02), and C and Y (χ ² test, P = 0.02) were compared. Average IS values of 4.8, 4.0, and 3.3 were obtained for groups C, S, and Y, respectively.

Correlation between cytokine levels and gut microbiota

To find possible correlations between the microbiota composition and the cytokines pattern, log-transformed results of pro-inflammatory cytokines quantification and HITChip profiling of the gut microbiota were used in a multivariate analysis, using cytokines plasma levels and the age groups as "environmental variables". RDA shows that 8.9% of the total variability of the gut microbiota can be related to the pro-inflammatory cytokines pattern (). Relations shown in the plot are statistically significant, as established by MCPP (P = 0.014). In accordance with previous analysis (), the centroid of group C is plotted distant from both the S and Y centroids, highlighting the similarity in the gut microbiota asset and the relation with the inflammatory status between elderly and young adults. Several bacteria belonging to the phylum Proteobacteria seemed to be positively correlated with IL-6 and IL-8. IL-8 was correlated with Alcaligenes faecalis et rel., Leminorella, and Proteus et rel., while IL-6 was correlated with Escherichia coli et rel., Haemophilus, Klebsiella pneumoniae et rel., Pseudomonas, Serratia, Yersinia et rel., and Vibrio. IL-8 and IL-6 were correlated also with Bacillus (Bacilli), Egghertella lenta et rel. (Actinobacteria), and Eubacterium cylindroides et rel. (Clostridium cluster XIVa). On the other side, Eubacterium hallii et rel., Eubacterium ventriosum et rel., Eubacterium rectale et rel., Clostridium nexile et rel., and Outgrouping Clostridium cluster XIVa (all belonging to the Clostridium cluster XIVa) are inversely correlated with IL-6 and IL-8.

Pearson's correlation analysis of the HITChip data and cytokines revealed significant Pearson's r for IL-6 and IL-8 (). All the bacterial groups which exhibited a slight positive correlation with either IL-6 or IL-8 (ranging between 0.41 and 0.55) belonged to the phylum Proteobacteria. Interestingly, only one bacterial group, Ruminococcus lactaris et rel. ( Clostridium cluster XIVa), has been found with a slight negative correlation with IL-8 (−0.44, P = 0.0001).

Table 5

Discussion

In this paper we addressed the age-related differences in both the gut microbiota and the inflammatory status among different stages of the whole adult life, starting from young adults (30 years old in average), through elderly (70 years old), to the extreme limit of the human lifespan, represented by a group of centenarians (100 years old). The enrolled subjects were phenotypically well characterized for their physical and cognitive health status. They represent the general population of a restricted geographic area of Northern Italy and can be considered as a relatively homogeneous cohort regarding lifestyle and dietary habits.

The uniqueness of this comparative study resides in the introduction of this third, extremely aged, group, alongside to the young and elderly adults that have been addressed by earlier studies [14], [15], [17], [20]. The analysis of the microbiota that had been coexisting with their host for over 100 years allowed us to highlight that changes in the gut ecosystem, in terms of composition and diversity, do not follow a linear relation with age. In fact, the difference between the gut microbiota of young adults and elderly, separated by more than 40 years in average, is remarkably small when compared to that observed between centenarians and elderly, separated by less than 30 years of life.

Young and elderly adults showed a very comparable overall structure of the gut microbiota, which confirms the most recent characterizations, with Bacteroidetes and Firmicutes highly dominant (contributing for approximately 95% to the microbiota), and smaller fractions of Actinobacteria, and Proteobacteria [24]-[26]. The diversity values of the gut microbiota of elderly and young adults were found comparable (average Simpson index 149.3 and 162.8, respectively), and fits the range of diversity expected for healthy adults (Simpson index  = 150-200) [27].

In contrast, centenarians stand out as a separate population, whose microbiota shows an organization which significantly differs from the adult-like pattern, and a low diversity in terms of species composition (Simpson index  = 127). Bacteroidetes and Firmicutes still dominate the gut microbiota of extremely old people (representing over 93% of the total bacteria). However, in comparison to the younger adults, specific changes in the relative proportion of Firmicutes subgroups were observed, with a decrease in the contributing Clostridium cluster XIVa, an increase in Bacilli, and a rearrangement of the Clostridium cluster IV composition. Moreover, the gut microbiota of centenarians is enriched in Proteobacteria, a group containing many of those bacteria recently redefined as "pathobionts" [28], [29]. These are considered to be minor and opportunistic components of the human gut ecosystem that, under some circumstances, e.g. inflammation, may escape surveillance, overtake mutualistic symbionts and induce pathology.

The structure of the gut microbiota is well known to be relatively stable through adulthood [30], [31]. Our findings suggest that this stability may last longer than expected, and that the ageing process starts to affect the gut microbiota later than 65 years old, the usual threshold age for being defined as "elderly". Few molecular studies have been published on this topic, reporting compositional differences between groups of young adults (20-50 years old) and elderly with a wide interval of age, ranging from 60 to 95 years old [14], [16], [17]. Thanks to a narrower range of age for the recruited elderly (63-78 years old), and the introduction of an additional time-point by analyzing the centenarians, we now provide a higher resolution on the effects of ageing on the gut microbiota. Our comprehensive approach indicates that the threshold for an "aged" microbiota should be moved to the age of 75-80 years.

In the recent description of the HITChip technology an age-related increase in the number of Bacilli, and a decrease in bacterial groups belonging to Bacteroidetes were reported in elderly adults with an average age of 71 years [20]. Interestingly, the present, more extensive HITChip study with a considerably larger sample set, revealed only centenarians to contain significantly higher proportions of Bacilli, while the Bacteroidetes population remained unchanged among the age groups. A possible explanation of this discrepancy may reside in the demographic and geographic differences between the study population, also defined as country specificity, which has been reported to strongly affect the age-related changes in the gut microbiota composition [16].

Our experimental data show that, after 100 years of symbiosis with its host, the gut ecosystem is characterized by a lower relative contribution of Clostridium cluster XIVa than in younger people. This decrease has already been described as an effect of the ageing process [14], [32], [33]. Furthermore, our results indicate that the proportion of Clostridium cluster IV in the total microbiota is comparable in all the subjects, but the proportions of different phylotypes within the cluster differ significantly between the age groups, suggesting a rearrangement in the composition of this Clostridium cluster in centenarians. In particular, we observed a rearrangement in the population of butyrate-producing bacteria in centenarians. Butyrate is a short chain fatty acids mainly produced in the gut by Firmicutes of the Clostridium clusters IV and XIVa [34]-[36], which is receiving a growing interest in the gut ecology, as it represents a major energy source for the enterocytes and has been implicated in the protection against inflammatory bowel diseases [37]. Several butyrate producers were found in lower amounts in centenarians than in the other age groups. These included Ruminococcus obeum et rel., Roseburia intestinalis et rel., E. ventriosum et rel., E. rectale et rel., E. hallii et rel. (all belonging Clostridium cluster XIVa), and Papillibacter cinnamovorans et rel., and F. prausnitzii et rel. ( Clostridium cluster IV). Conversely, the butyrate producers Anaerotruncus colihominis et rel. ( Clostridium cluster IV), and Eubacterium limosum et rel. (Clostridium cluster XV) increased in centenarians. The increase of E. limosum is high (approximately 15-fold), and could point to a group of bacteria characteristic of the long life. Also the decrease of F. prausnitzii in centenarians is of interest as this species is known to affect the gut inflammation processes, exerting a butyrate-independent anti-inflammatory effect [38], [39].

Another feature of the centenarians' gut ecosystem is the increase in facultative anaerobes, such as bacteria belonging to the groups Fusobacterium, Bacillus, Staphylococcus, Corynebacterium, Micrococcaceae, and many members of the phylum Proteobacteria. Such opportunistic species, especially Enterobacteriaceae, thrive in an inflamed environment [40], [41], and are known to increase in elderly [10].

We detected a significant decrease of bifidobacteria in centenarians when compared to young adults, which is another well reported effect of age [15], [16]. Interestingly, we found increased levels of the mucin degrading A. muciniphila in aged people if compared to the young adults. This contrasts with the earlier results obtained by Collado et al[42], who demonstrated with FISH and PCR that A. muciniphila counts slightly decline with the age. There is evidence for the presence of several A. muciniphila-like phylotypes in the gut and it is possible that these are detected by the HITChip hybridization but not with specific qPCR primers [43]. Alternatively, this discrepancy may be explained by the country-specificity effects, as well as differences in the diet of the aged people, that may favour populations of bacteria able to degrade mucins.

It is evident that the fecal microbiota of centenarians shows all the features of a partially compromised ecosystem, whose balance with the immune system is likely to be upset. The immune system of these very old people deteriorates as a result of the immunosenescence. The immuno-phenotyping allowed us to confirm that the major, expected age-related changes of the T cells compartment are present in the enrolled subjects [44]-[48]. This suggests that the population here considered represents a suitable sample to investigate the possible relationship between gut microbiota composition and immune/inflammatory status. In fact, in our study population, the proportion of centenarians showing a high inflammation score was significantly higher than in the other age groups, confirming the inflammageing hypothesis [6], [7], [49]. The parallel analysis of the gut microbiota composition and the inflammatory parameters shows that the increase in pro-inflammatory cytokines in the peripheral blood correlates with changes in the gut microbiota profile of centenarians. In particular, the increase of IL-6 and IL-8, which is expected for these exceptionally old subjects in spite of their reasonably good health status [7], [50], [51], was linked with an enrichment in Proteobacteria and a decrease in the amount of some butyrate producing bacteria, such as E. rectale, E. hallii, and E. ventriosum. This observation supports the hypothesis that the age-related differences in the gut microbiota composition, particularly evident between centenarians and the other age groups (i.e. proliferation of opportunistic Proteobacteria at the cost of symbionts Firmicutes and Bacteroidetes), may either contribute to inflammageing or be affected by the systemic inflammatory status. This is supported by recent findings demonstrating that inflammation shifts the balance between symbionts and pathobionts, to the side of the latter [29]. However, despite their inflammatory status, centenarians reached the extreme limit of human lifespan, and escaped or delayed the major age-related diseases which share an inflammatory pathogenesis. This suggests that the inflammatory status may have been counterbalanced by other physiological events, to help the survival of these exceptional individuals. As for the gut microbiota, we demonstrated that there is a remodelling in the composition of the Firmicutes population in centenarians. Among the decreasing Firmicutes, we found many species known to have anti-inflammatory properties, but others with unknown anti-inflammatory properties may be among the increasing ones. For instance, the remarkable increase of E. limosum in centenarians could be of particular interest, opening the way to the fascinating hypothesis that E. limosum may positively contribute to the health status of an aged and inflamed intestine due to its anti-inflammatory properties [52], and its ability to convert dietary isoflavonoids into other phytoestrogens [53], [54]. Phytoestrogens are being intensively studied because of their potential protective role against the development of several cancer, and coronary heart diseases [55].

In conclusion, the differences in the gut microbiota that we detected between centenarians and the other adults could support the possibility that, to attain longevity, a complex and pervasive remodelling, including that of gut microbiota, should occur, favouring the balance between inflammatory and anti-inflammatory processes. With the growing life expectancy of the human beings, it is estimated that the elderly population (over-60 years old) will reach 2 billion by 2050 [56]. Consequently, the quality of life of this increasing share of the human population is going to become an imperative concern, also in relation to the health-care cost. Maintenance of the homeostasis in the gut ecosystem could be essential for the gut health during the ageing process, and, in this perspective, dietary manipulation of the gut microbiota of the elderly may represent a mean for preserving a healthy gastrointestinal microbial community.

Materials and Methods

Subjects and study groups

Eighty-four subjects belonging to different age groups were enrolled for this study in Emilia Romagna, which includes Bologna city and surrounding area, Italy. Group C was composed of 21 centenarians (20 women, 1 men) aged 99 to 104 years (average 100.5). Group E was composed of 22 elderly (11 women, 11 men) aged 63 to 76 years (average 72.7) genetically unrelated to the centenarians in group C, and they were offspring of parents who did not reach longevity (average parents death age, 59.3). Group Y was composed of 20 young adults (9 women, 11 men) aged 25 to 40 years (average 31). Group F, offspring of the centenarians belonging to group C, composed of 21 elderly people (10 women, 11 men) aged 59 to 78 (average 67.5), was also included in the analysis. The study protocol was approved by the Ethical Committee of Sant'Orsola-Malpighi University Hospital (Bologna, Italy). After obtaining written informed consent, a standard questionnaire to collect information regarding the health status, drugs use, clinical anamnesis, and life style was administrated. Subjects of groups E, F, and Y were non-institutionalized and living in their own household. They all showed good physical and cognitive health conditions, as evaluated by tests to assess cognitive ability (Mini Mental State Examination, MMSE), self sufficiency and autonomy (ADL, IADL), physical ability (Handgrip Test and Chair Stand Test), which are used by geriatricians to define the physical and cognitive health status (these data are summarized in Supporting Table S1). The physical and cognitive health status of centenarians in group C mirrors that of the majority of Italian centenarians, as previously characterized by Franceschi et al[22]. Subjects affected by malignant neoplasia and/or in therapy with immunosuppressive drugs like cyclosporin, methotrexate, glucorticoids, anticoagulant drugs, and who recently (at one month) used antibiotics were excluded from the study. Subjects in group Y were drug-free, whereas older subjects (groups C, E and F) were assuming a variety of medication as it is common for elderly (Supporting Table S1). Arterial blood pressure measurement, and Body Mass Index (BMI) calculation were also performed. Peripheral blood and feces were collected from each subject. Blood samples were not collected from 3 centenarians and 1 subject in group F because of refusal or impossibility of the subjects. Principal haemato-biochemical parameters evaluation and immunophenotypical analyses were performed on freshly collected blood samples. The resulting plasma samples were stored at −80°C for less than 3 months, and used for the cytokines pattern evaluation. Feces were stored at −80°C and analyses were performed within 3 months.

DNA extraction from feces

Total microbial DNA was extracted using QIAamp DNA Stool Min Kit (Qiagen, Duesseldorf, Germany) with a modified protocol. 250 mg of feces were suspended in 1 ml of lysis buffer (500 mM NaCl, 50 mM Tris-HCl pH 8, 50 mM EDTA, 4% SDS). Four 3 mm glass beads and 0.5 g of 0.1 mm zirconia beads (BioSpec Products, Bartlesville, OK) were added, and samples were treated in FastPrep (MP Biomedicals, Irvine, CA) at 5.5 ms for 3 min. Samples were heated at 95°C for 15 min, then centrifuged for 5 min at full speed to pellet stool particles. 260 µl of 10 M ammonium acetate were added to the supernatant, followed by incubation in ice for 5 min and centrifugation at full speed for 10 min. One volume of isopropanol was added to each sample and incubated in ice for 30 min. Precipitated nucleic acids were collected by centrifugation for 15 min at full speed and washed with ethanol 70%. Pellets were suspended in 100 µl of TE buffer and treated with 2 µl of DNase-free RNase (10 mg/ml) at 37°C for 15 min. Protein removal by Proteinase K treatment and DNA purification with QIAamp Mini Spin columns were performed following the kit protocol. Final DNA concentration was determined by using NanoDrop ND-1000 (NanoDrop® Technologies, Wilmington, DE).

16S rRNA gene amplification

The total bacterial 16S rRNA genes were amplified from fecal DNA using FastStart Taq DNA polymerase, dNTPack PCR Amplification kit (Roche, Mannheim, Germany). T7prom-Bact-27-F and Uni-1492-R primer set was used for amplification [20], [57]. PCR reactions were carried out as previously described [20], using 20 ng of DNA as template. Per DNA sample 2 reactions were performed, pooled, and purified by using High Pure PCR Clean up Micro kit (Roche), following the manufacturer instructions.

RNA preparation

In vitro transcription of T7-modified 16S rRNA amplicons was carried out as previously described [20]. Ammino-allyl modified rUTP (Ambion, Austin, TX) was incorporated during the transcription to allow the following CyDye incorporation. RNA was purified using the RNeasy Mini-Elute Kit (Qiagen) according to the manufacturer instructions. Ammino-allyl-modified nucleotides were coupled with CyDye using Post Labelling Reactive Dye (Amersham Bioscience, Piscataway, NJ).

HITChip analysis

HITChip slides were custom synthesized by Agilent Technologies (Wilmington, DE), having a 8×15K format with 8 arrays per slides. Two different RNA samples, labelled with Cy3 and Cy5 respectively, were hybridized on each array. At least 2 hybridizations on different arrays were carried out for each sample. Fragmentation of the labelled RNA and hybridization were carried out as previously described [20]. Microarrays were scanned by using an Agilent Microarray Scanner at 2 UV lamp intensities (40 and 10% of maximum PMT voltage for red, 60 and 20% of maximum PMT voltage for green). Intensity values for each spot were quantified by using Agilent Feature Extraction software, version 9.5.

Data storage, spatial and quantile normalization, and probe profile extraction were performed using a custom-designed MySQL-based relation database ( www.mysql.com) and the R statistical software ( www.r-project.org) [20]. The reproducibility of the experiments was assessed by calculating Pearson's correlation of the natural logarithm of spatially normalized signals. Hybridizations which resulted in a Pearson's correlation coefficient <0.98 were repeated. For each probe, hybridization signal was calculated as the mean value of the normalized fluorescence intensities obtained in the 2 replicates. Hierarchical clustering of HITChip profiles was carried out using Pearson's correlation to calculate the distance among samples and Ward's minimum variance as agglomeration method.

For quantification purpose, the analysis was focused on the 129 "level 2" (termed here "genus-like") phylogenetic groups, corresponding to subsets of phylotypes with 90% or more 16S rRNA sequence similarity [20]. The average signal intensity of all probes which are specific for one phylogenetic group was used as quantitative measure of that group in the sample. Cut-off values for positive responding probes were calculated as described by Rajilic-Stojanovic et al[20].

Statistical analysis was performed on log-transformed data using the R software and the Canoco package for Windows [58]. To evaluate the significance of the difference between datasets, P value was calculated by Student t-test for normally distributed data, or Wilcoxon rank sum test for not-normally distributed data. Simpson's reciprocal index of diversity [59], was used to express the diversity of microbial communities, calculated using the equation λ = 1/Σ Pi ², where Pi is the proportion of the i th taxon. The proportion of each taxon was calculated as the proportion of each probe signal compared to the total signal. A higher value of Simpson index corresponds to a more diverse community.

Quantitative PCR

Quantitative PCR (qPCR) analysis was carried out in a Applied Biosystems 7300 Fast Real-Time PCR System (software version 1.4.0), using SYBR Green chemistry (Power SYBR Green PCR Master Mix, Applied Biosystems, Foster City, CA) and 0.2 µM primer concentration. The thermocycling conditions and universal primers for bacteria and specific primers for Clostridium leptum group, Bifidobacterium genus, Akkermansia muciniphila, Faecalibacterium prausnitzii and for the genus Methanobrevibacter, representing a dominant archaeon in the human gut bacteria have been described previously [42], [60]-[65]. The qPCR reaction volume was 25 µl, employing 100 ng of DNA sample as template. Samples were assayed in duplicate in 2 independent runs. Melting curve analysis was used to confirm specificity of amplification. The amount of 16S rRNA gene copies of specific bacterial groups in the fecal samples was determined by comparing the Ct (threshold cycle) values of samples to those of the standard curves. The standards were prepared by amplifying the 16S rRNA gene from a representative species from each bacterial group, purifying the amplicons with the QIAquick PCR purification kit (Qiagen), and measuring the concentration spectrophotometrically. Appropriate dilutions with a known number 16S rRNA gene copies were used as standards. Statistical analysis of qPCR data was carried out with log-transformed data. Non-detected values were imputed with the half of the theoretical detection limit. Nonparametric tests were used since data were not normally distributed. Kruskall-Wallis test was used to determine the statistical differences among the age groups. Mann-Whitney U-test was used for pair-wise comparisons.

Immunophenotype characterization

Identification of the major lymphocyte subsets (B lymphocytes, T lymphocytes, virgin T lymphocytes, memory T lymphocytes and NK cells) was performed using the following combination of monoclonal antibodies: CD5/CD19 to identify B and autoreactive B lymphocytes, CD3/CD4/CD8 to identify T lymphocytes subsets, CD28/CD95/CD4 and CD28/CD95/CD8 to identify effector T helper and cytotoxic lymphocytes, CD45RA/CD4/CCR7 and CD45RA/CD4/CCR7 to identify naïve, central memory, effector memory and terminal effector T helper and cytotoxic lymphocytes and CD4/CD25/CD8 to identify activated T lymphocytes. Phenotypical analysis of peripheral blood lymphocytes on whole blood samples was performed as previously described [44], [47]. A cytometric approach with fluorochrome-labelled monoclonal antibodies (mAbs, BD Biosciences Pharmingen, San Diego, CA) directly conjugated to fluorescent molecules (fluorescein isothiocyanate, FITC; phycoerythrin, PE; phycoerythrin-cy-crhome, PE-Cy7) staining was used. Phenotypic analyses of cytotoxic and helper subsets were performed placing an electronic gate on CD8⁺ bright and CD4⁺ bright cells and evaluating the expression of CD45RA versus CCR7. An example of the gating strategy is reported in Supporting Figure S2. Similarly, the expression of CD25 was analyzed on CD8⁺ bright and CD4⁺ bright gate. A minimum of 30,000 cells per sample was acquired using a FACScalibur (BD Biosciences) flow cytometer and data were analyzed with FlowJo (Tree Star Inc., Ashland, OR) software.

Evaluation of plasma cytokines levels

The concentrations of IL-1α, IL-1β, IL-2, IL-6, IL-8, IL-10, IL-12p70, IFN-γ, and TNF-α trimer in plasma samples of all enrolled subjects were measured in duplicate by multiplex sandwich ELISA technology (Human Cytokine Array 1, SearchLight, Aushon Biosystems, Billerica, MA) according to the manufacturer's instructions. Plasma TGF-β1 concentration was determined in duplicate by ELISA using a commercial kit (DRG Instruments GmbH, Marburg, Germany) according to the manufacturer's instructions.

Inflammation score

To evaluate the inflammatory status of each subject, an Inflammation Score (IS) was calculated as inspired by previous studies [66], [67]. The score was composed of 8 parameters: white blood cell count, C reactive protein, number of central memory helper and cytotoxic T lymphocytes (CD45RA −CCR7⁺), number of effector helper and cytotoxic T lymphocytes (CD28 −), IL-6 and IL-8 plasma levels. IS, ranging from 0 to 8, was calculated by adding 1 unit for each value greater than the median of the study samples for each inflammatory parameter. Subjects with IS value between 5 and 8 were considered highly inflamed.

Supporting Information

Figure S1

Score plot of the PCA, centered by species and grouped by samples, of the microbiota composition of centenarians (C, green circles), elderly (E, blue squares), and offspring of the centenarians (F, pink boxes). Explanatory variables are indicated by red triangles. First and second ordination axes are plotted, explaining together the 31% of the variability in the considered dataset. Log transformed data were used for the analysis.

(0.35 MB TIF)

Figure S2

Cytofluorimetric strategy used to identify naïve and memory CD4+ T cells (an example). Lymphocytes were gated according to their physical parameters (FSC and SSC) and then selected for CD4+bright cells, which were subdivided using CD45RA and CCR7. Thus, the following subsets were identified: naïve CD4+ bright T lymphocytes (CD45RA+CCR7+), central memory CD4+ bright T lymphocytes (CD45RA-CCR7+), effector memory CD4+ bright T lymphocytes (CD45RA-CCR7-), terminal effector memory CD4+ bright T lymphocytes (CD45RA+CCR7-).

(0.14 MB PDF)

Table S1

Health status and medication of centenarians, offspring of centenarians, and unrelated elderly subjects included in the study.

(0.05 MB PDF)

Acknowledgments

We would like to acknowledge Prof. Gastone Castellani, University of Bologna, Italy, for the helpful assistance during part of the statistical analysis.

Footnotes

Competing Interests: The authors have declared that no competing interests exist.

Funding: The authors have no support or funding to report.

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# data[].attributes.attachment_link.title
Through Ageing, and Beyond: Gut Microbiota and Inflammatory Status in Seniors and Centenarians

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Age-related physiological changes in the gastrointestinal tract, as well as modifications in lifestyle, nutritional behaviour, and functionality of the host immune system, inevitably affect the gut microbiota, resulting in a greater susceptibility to infections.

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They are normal flora of the intestinal tract and cause infection under opportunistic conditions. Eubacterium lentum is the species that is most often isolated; it has been linked to endocarditis.

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4.1.2.1 Eubacterium lentum

In 1999, Kageyama et al. called for a change in the classification of E. lentum and proposed that it be grouped with Eggerthella lenta , the type species of genus Eggerthella.

E. lentum is a gram-positive, irregular, nonsporulating, strictly anaerobic bacillus (Figure 4.2(A) and (B)). As a strict anaerobe, most strains can grow between 30 °C and 45 °C, while some strains can grow at 25 °C. Arginine can promote bacterial growth. Characteristic colonies are shown in Figure 4.2(C) and (D).

E. lentum does not ferment carbohydrates. The cells do not hydrolyze esculin, hippurate, and gelatin. Ammonia can be produced from arginine, and H ₂O ₂ can be produced from agar medium containing 1% arginine. Under anaerobic conditions, the bacteria can produce H ₂S in the beveled bottom of triple sugar iron agar but cannot produce H ₂ S in SIM culture medium. The GC is 62% in DNA, and the type strain is JCM 9979 (= DSM2243 = ATCC25559 = NCTC11813).

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Eubacterium and Propionibacterium

Eubacterium spp. are of only minor clinical importance. They are normal flora of the intestinal tract and cause infection under opportunistic conditions. Eubacterium lentum is the species that is most often isolated; it has been linked to endocarditis. Biochemical testing can distinguish Eubacterium from the other gram-positive, anaerobic rods. Propionibacterium spp. are common gram-positive anaerobes isolated in the laboratory. Propionibacterium acnes is typically noninvasive and harmless, but it has pathogenic potential and has been linked to endocarditis, wound infections, and abscesses. Despite its name, P. acnes is not the causative agent of acne, although it may infect acne sites. Microscopically, Propionibacterium clumps together and shows a minor branching tendency, with uneven gram-staining patterns. Colonies grow best in anaerobic or microaerophilic environments on blood agar.

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Eggerthella and Related Species

Members of the genus Eggerthella have shown to possess particular pathogenic potential. E. lenta has been isolated from blood, abscesses, wounds, obstetric and genitourinary tract infections, and intra-abdominal infections. 27,49,93,94,115 In a tertiary medical center in Taiwan, all Eubacterium-associated bacteremias in the years 2001 to 2010 were analyzed; of the 16 patients found, 7 harbored Eggerthella lenta and three Paraeggerthella hongkongensis; furthermore, 9 had polymicrobial bacteremia, with Bacteroides spp. the most common concomitant finding. The gastrointestinal and female genital tracts were suggested as the main entry points of these bacteria into blood. In a hospital in Detroit, 25 bacteremia cases with E. lenta, from 1999 to 2010, were retrospectively reviewed; half the bacteremias were monobacterial, and the main sources were abdominal and complicated soft tissue infections. Also, Catabacter hongkongensis and Gordonibacter pamelaeae have been found in blood samples using pure culture assay, and bacteremia had mainly a gastrointestinal source. Of concern are the high mortality rates for bacteremia with C. hongkongensis, E. lenta, E. sinensis, or P. hongkongensis. 27,28,48,94,116 An oral species, Slackia exigua, is frequently found in periodontal disease, infected root canals, and severe odontogenic infections but also in abdominal and pilonidal abscesses.

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Interactions Affecting Absorption and Bioavailability.

Digoxin tablets are absorbed slowly; therefore agents that increase gastrointestinal motility (e.g., metoclopramide) may decrease its absorption, whereas agents that slow gastrointestinal transit (e.g., propantheline, other anticholinergic agents) may increase its absorption. Elixir preparations are usually not subject to these interactions because their absorption is more rapid.

Treatment with high-dose chemotherapeutic agents resulting in intestinal mucosal injury can reduce digoxin absorption from tablets by as much as 50% but does not significantly affect absorption from elixir.

In about 10% of patients, digoxin that is not absorbed in the upper gastrointestinal tract or that is excreted in the bile is reductively metabolized by the anaerobic bacterium Eubacterium lentum, which is part of the normal flora of the colon. Such metabolism can account for about 40% of digoxin elimination. These patients may be recognized by the characteristic of needing higher-than-usual doses of digoxin to achieve therapeutic levels. In this group of patients, treatment with broad-spectrum antibiotics (e.g., erythromycin, tetracycline) can result in significantly increased bioavailability and digoxin toxicity.

Cholesterol-binding resins (e.g., cholestyramine, colestipol) bind digoxin in the gut and may reduce its absorption by 20% to 30%. This effect can be avoided by giving digoxin at least 1 hour before the resins. Sucralfate has been reported to decrease the absorption of digoxin. No significant interaction has been found between digoxin and antacids.

Intestinal P-glycoprotein (P-gp) plays an important role in the bioavailability of digoxin. Rifampin increases P-gp content in the intestine and decreases digoxin bioavailability, resulting in significantly lower digoxin AUC after oral administration. On the other hand, drugs that inhibit intestinal P-gp increase digoxin bioavailability and AUC. Dipyridamole was shown to increase digoxin bioavailability in vitro and in vivo; however, the effect on AUC was slight and clinically insignificant. Carvedilol significantly decreased oral clearance of digoxin in children, resulting in digoxin toxicity in some cases. However, in adults, carvedilol caused only a modest increase in digoxin bioavailability and AUC.

St. John's wort ( Hypericum perforatum) is an herbal medicine frequently used for treatment of depression. H. perforatum induces intestinal P-gp, resulting in a 1.4-fold increased expression of duodenal P-gp in humans. Coadministration of St. John's wort and digoxin caused a 25% decrease in AUC and a 33% reduction in trough and C max concentrations of digoxin.

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Pressures on the Aging Microbiome

More than 75% of people over 65 years of age take one or more prescription drugs (Chrischillers et al., 1992; Tiihonen et al., 2010). Therefore it is important to understand how these can affect the elderly nutritional status (Genser, 2008; Biagi et al., 2012), gut microbiota function, and how the microbiota might interfere with the intended drug effect via altered turnover in the aging body. Various examples have emerged recently in which the human gastrointestinal microbiome affects drug therapeutic efficacy and/or toxicity. Haiser et al. (2013) recently published a study of the inactivation of the cardiac drug digoxin by the gut bacterium Actinobacterium Eggerthella lenta . More studies like this are needed before we fully understand the impact of the microbiota on the compounds that are prescribed to the elderly often in numbers and for prolonged lengths of time.

Elderly subjects are frequently treated with antibiotics. Broad-spectrum antibiotic therapy affects not only the target pathogenic bacterium but also the wider intestinal microbiota as a whole, leading to perturbations of the commensal flora and decreasing its colonization resistance (O'Sullivan et al., 2013). As mentioned before, this can have numerous unappreciated consequences. Even after cessation of an antibiotic course, up to one-third of the bacterial diversity lost might not be recovered (Dethlefsen et al., 2008). Particularly, hospitalization and antibiotic use reduces the abundance of members of the Bacteroides-Prevotella group (Bartosch et al., 2004) while producing shifts in the group's composition and reducing the Bifidobacterium spp., Desulfovibrio spp., Clostridium spp., and Faecalibacterium spp. populations (Jernberg et al., 2010). The dangers of a low diversity gut ecosystem are epitomized by Clostridium difficile-associated diarrhea (CDAD) after antibiotic therapy. Colonization of a susceptible gut by a virulent strain of C. difficile-producing toxins and shedding spores can lead to a wide array of illnesses from mild diarrhea to fulminant relapsing diarrhea and pseudomembranous colitis that may be extremely difficult to eradicate despite repeated courses of antibiotics (Rea et al., 2012). C. difficile is an important nosocomial pathogen among the elderly with carriage rates in hospitals up to 20% (Karlström et al., 1998). Fecal transplantation, a procedure in which an individual with a severe dysbiotic gut receives a fecal microbiota transplant from a healthy donor to restore diversity and a state of health, has emerged as an extremely effective method for fighting CDAD in cases of recurrent C. difficile infection (CDI; Fuentes et al., 2014).

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3.4 Pd-Catalyzed ¹⁸F Labeling of Proteins

A modified Pd-catalyzed Suzuki-Miyaura coupling of ¹⁸F-labeled arylboronic acids with aryl iodides affords the corresponding ¹⁸F-labeled compounds; the use of N,N-dimethylguanidine as the ligand allows the reaction to proceed under mild conditions (Figure 20). This modified method, using the [ ¹⁸F]-4-fluorophenylboronic acid as the prosthetic group, allows the synthesis of ¹⁸F-labeled small molecules, peptides, and proteins, under biologically compatible conditions. ⁵²

The ¹⁸F-prosthetic arylating agent 9 was synthesized in two steps from the di(4-iodophenyl)iodonium triflate 7; the aromatic nucleophilic substitution reaction of the triflate 7 with fluoride anion using K ¹⁸F-Kryptofix-2.2.2 complex gave 4-[ ¹⁸F]fluoroiodobenzene 8, which was transformed into the 4-[ ¹⁸F]fluoroboronic acid 9 in a single step using tetrahydroxydiborane under Pd catalysis. The direct attachment of this ¹⁸F-labeled prosthetic group to the haloarylamino acid residue (Pic156) of the protein subtilisin from Bacillus lentus (SBL) ( 10) was carried out in phosphate buffer (pH 8.0) and (L) ₂Pd(OAc) ₂ at 37 °C. At the end of the reaction (30 min), 3-mercaptopropanoic acid was added to scavenge the palladium catalyst, and the radiolabeled protein 11 was purified by size exclusion chromatography; RCYs of about 2-5% were obtained for this conversion (Figure 20). ⁵²

3.4.1 Pd-Catalyzed Fluorination of Arylboronic Acids

Ritter and coworkers have pioneered Pd-catalyzed transformation of aryl boronic acids or boronates to [ ¹⁸F]-labeled arenes using the ¹⁸F-labeled fluoride source. 53,54 In this process the palladium-based electrophilic fluorinating agent 16 was obtained from the reaction of the radiolabeled [ ¹⁸F]F − with the Pd(IV) complex 15. This synthetic method is conceptually interesting in that an appropriately ligated Pd(IV) species allows conversion of nucleophilic fluoride anion into an electrophilic fluorinating agent. The oxidative transfer of "F⁺" from Pd(IV)F ( 16) to ArPd(II) ( 14) species results in the formation of a high-valency ArPd(IV)F species ( 18), which undergoes reductive elimination to give the corresponding aryl fluorides. Pharmaceutically interesting compounds such as [ ¹⁸F]fluorodeoxyestrone ( 20) and a serotonin receptor agonist 21 could be synthesized starting from the corresponding arylboronic acid in RCYs of about 33% (Figure 21). Although the synthesis of the specific arylboronates involves multistep process, radiofluorination is the last step of the synthesis so that high RCYs could be achieved. ¹⁸F-labeled derivatives of selective serotonin reuptake inhibitors, such as [ ¹⁸F]paroxetine, have been synthesized using this procedure. ⁵⁵

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2 Design of pCRISPomyces for Genome Editing in Streptomyces Species

The Type II CRISPR/Cas system for targeted genome editing contains three components: Cas9 nuclease, tracrRNA, and crRNA. The tracrRNA and crRNA can be either individually expressed or fused together as a chimeric guide RNA (gRNA). The former is more flexible and is most commonly used in multiple-gene targeting, while the latter is more simplified, has higher targeting efficiency, and is applied for single-gene targeting in most cases (Xu, 2014). To harness the CRISPR/Cas system for genome editing in Streptomyces species, we constructed two versions of the pCRISPomyces expression system: pCRISPomyces-1 and pCRISPomyces-2 (Fig. 1). pCRISPomyces-1 contains both tracrRNA and crRNA along with cas9, and pCRISPomyces-2 includes a gRNA expression cassette and cas9. In both cases, previously identified strong promoters-rpsLp(XC), the promoter of 30S ribosomal protein S12 in Xylanimonas cellulosilytica; rpsLp(CF), the promoter of 30S ribosomal protein S12 in Cellulomonas flavigena; and gapdhp(EL), the promoter of the GAPDH operon in Eggerthella lenta -were selected to drive expression of the CRISPR/Cas elements (Shao et al., 2013). The widely used terminators from phages fd, λ, and T7 were used for each expression cassette. Although Streptococcus and Streptomyces are both genera of Gram-positive eubacteria, they have quite different phylogeny (Muto & Osawa, 1987). Streptococcus has low average genomic G + C content of around 41.2%, while Streptomyces has high G + C content of up to 73% (Zhang et al., 2011). Analysis of the S. pyogenes cas9 gene revealed the presence of several rare codons, including many bldA codons, translation-level regulators of secondary metabolism in Streptomyces species. To ensure the proper expression level of Cas9 in Streptomyces species, the cas9 gene has been optimized to favor the Streptomyces codon bias.

To facilitate scarless, one-step Golden Gate assembly of specific spacers into crRNA expression cassettes, a lacZα cassette flanked by BbsI restriction sites, was incorporated between two direct repeat units in pCRISPomyces-1. A BbsI-flanked lacZα cassette was also included in the sgRNA expression cassette for the same purpose. In both systems, a unique XbaI site was added for recombination-driven repair template insertion via Gibson assembly or simple restriction digestion-ligation. In addition, both pCRISPomyces systems contain the ColE1 origin for replication in E. coli, the RP4 origin of transfer oriT for conjugative transfer of pCRISPomyces plasmids from an E. coli donor strain to Streptomyces hosts (Mazodier, Petter, & Thompson, 1989), aac(3)IV for selection in both E. coli and Streptomyces strains, and the temperature-sensitive replication origin from pSG5 for fast clearance of pCRISPomyces plasmids from Streptomyces hosts (Muth, Nußbaumer, Wohlleben, & Pühler, 1989). Since pCRSIPomyces-2 has much higher editing efficiency than pCRISPomyces-1, we will mainly discuss pCRISPomyces-2 in the following sections.

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Human Leukocyte Antigen, Microbiome, and Rheumatoid Arthritis

The mechanism by which HLA genes control intestinal microbial composition is unknown. However, one can speculate that the role of HLA molecules in immune response may determine the colonization of the species in the intestine. Another mechanism by which HLA class II alleles influence microbial colonization could be by the HLA-mediated presentation of bacterial polysaccharides (PSA) by dendritic cells. Indeed the presentation of PSA of Bacteroides fragilis generates immunoregulatory response (Surana and Kasper, 2012) that may influence colonization in the gut. Autoimmune predisposing HLA class II alleles may lead to the colonization of opportunistic bacteria in the gut, which under certain inflammatory conditions can lead to break in tolerance. In addition to class II alleles, a role of HLA class I has also been suggested in bacterial colonization as bacterial adhesion protein called curli has been shown to bind immunoglobulin-like domain of the heavy chain and β2m as well as fibronectin and lead to the production of cytokines (Johansson et al., 2001).

Recent studies have shown perturbed gut and oral microbiome in patients with RA (Zhang et al., 2015; Chen et al., 2016). Patients showed dysbiosis with reduced microbial diversity. RA was characterized by an abundance of rare taxa of phylum Actinobacteria, Collinsella aerofaciens and Eggerthella lenta , with a decrease in the presence of Faecalibacterium prausnitzii (Chen et al., 2016). Fecal microbiota analysis of naive humanized mice expressing RA-susceptible DRB1*0401 showed the difference from DRB1*0402 expressing mice (Gomez et al., 2012). Interestingly, naïve DRB1*0401 mice lost sex-dependent microbial differences while *0402 mice showed significant gut microbial composition between male and female mice. Moreover, naïve *0401 mice showed an increase in gut permeability as they aged. MHC molecules have been suggested to regulate diseases by determining cytokine profile by the selection of T cells (Mangalam et al., 2013). HLA class II provides the strongest risk factor for autoimmune diseases. One can envisage that by controlling the immune system, HLA genes can control the colonization of microbes in the gut. It's possible that autoimmune-associated HLA class II molecules generate an environment in the gut that is favorable for microbes that, if present in abundance, can cause inflammation. Intestinal microbial composition is influenced by other factors associated with autoimmune diseases such as smoking and infections. However, since not all individuals carrying the autoimmune-susceptible HLA alleles develop disease, it would appear that another insult to immune system via gut may lead to dysbiosis and inflammation that breaks tolerance. In patients who do not carry the autoimmune-prone HLA genes, other intestinal bacteria or bacterial products may be involved. Interestingly, E. lenta, observed with abundance in RA, is involved in ornithine metabolic pathway resulting in the production of citrulline (Chen et al., 2016). While citrulline generated may not be directly involved in the citrullination process, it is transported out of the intestine and converted into arginine that is again utilized for protein synthesis. Also, microbes can lead to the differentiation of T cells into T follicular cells resulting in the formation of germination centers in the gut and the production of antibodies (Taneja, 2017). Thus an abundance of a taxa or species can generate an abundance of certain byproducts, and based on the specific species, it can cause a significant increase of the byproducts. As many microbes may be able to perform a similar function, metabolites produced as byproducts of pathways microbes are involved in or bacterial products may provide targets for treatment.

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4.1 Optimizing single-cell methods with isolates and fecal samples

For each dye, we used unstained, stained, heat, and/or ethanol-treated samples to determine the optimal dye concentration. Cells were discriminated from noise or unstained cells using 2-parameter scatter plots of light scatter signals (SSC) and the appropriate emission filter channel (FL1 or FL3) (Fig. 5.2A).

We validated our experimental approach with representative strains from the five major phyla found in the human gut: Eggerthella lenta (Actinobacteria), Bacteroides fragilis (Bacteroidetes), Lactobacillus casei (Firmicutes), Escherichia coli (Proteobacteria), and Akkermansia muciniphila (Verrumicrobia), and fecal communities from three unrelated individuals. The optimal SybrGreen I concentration of 1 × was based on previously reported values and was assessed mainly with the gut isolates. Total cell abundance was obtained using the FSC and SSC signals, and the SybrGreen I-stained cells. Considering all isolates, both methods for determining cell abundance were equivalent (Fig. 5.3A), confirming that SybrGreen I can stain all bacterial cells, irrespective of their phylum. The proportion of HNA cells during exponential growth averaged 92.9 ± 1.6% of the total cells, with the exception of A. muciniphila (6.8 ± 0.7% of the total cells). Although HNA and LNA cells have been associated with distinct levels of activity in microbial communities, more extensive analyses of representative cultured isolates and mock communities are needed.

The optimal concentrations for DiBAC and Pi were determined based on a dose-response curve of the dyes for untreated samples (gut isolates and fecal communities), heat-treated samples (60 °C, 10 min), and ethanol-treated samples (70% final concentration, 10 min) (Fig. 5.2A). This allowed us to confirm dye uptake when cells are exposed to stressful conditions. DiBAC concentrations higher than 1 μg ml − 1 overestimated the damaged cells, both in the normal and heat-treated samples, with more damaged cells than the total cells. On average, the proportions of damaged DiBAC⁺ cells remained low during exponential phase of the gut isolates (4.5 ± 1.2% DiBAC⁺) and were higher in untreated fecal communities (27.4 ± 2.3% across three individuals, N = 21). These values significantly increased in the heat treatments (Fig. 5.3B), reaching 68.5 ± 8.5% and 92.7 ± 13.5% of total cells for the isolates and the fecal communities, respectively (Maurice et al., 2013). We tested Pi concentrations ranging from 0.02 to 0.1 mg ml − 1 with normal and ethanol-treated samples. We determined an optimal Pi concentration of 0.04 mg ml − 1; Pi⁺ cells reached nearly 100% of total cells in the ethanol-treated samples and higher concentrations did not significantly modify the proportions of Pi⁺ cells in the untreated samples. Damaged Pi⁺ cells also remained low during the exponential growth of the gut isolates (3.4 ± 0.7% Pi⁺ cells) and averaged 17.2 ± 2.1% in human fecal samples ( N = 21). These proportions increased with the ethanol treatments to 59.5 ± 8.0% for the gut isolates and 97.7 ± 6.8% for the fecal microbiota (Fig. 5.3C).

The experimental procedure for CTC staining was only optimized with fecal samples, using previously published concentrations for anaerobic bacteria (Bhupathiraju et al., 1999). However, we did test different incubation times, using once again untreated and stained samples, untreated and unstained samples, formaldehyde-killed and stained samples, as well as rPBS incubated with CTC (no cells). Incubation times longer than 3 h increased the proportions of CTC⁺ cells in the killed samples threefold, although the values remained below 1%. On average, 12.0 ± 2.2% of the fecal microbiota was CTC positive.

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# data[].attributes.attachment_link.description
In 1999, Kageyama et al. called for a change in the classification of E. lentum and proposed that it be grouped with Eggerthella lenta , the type species of genus Eggerthella. E. lentum is a gram-positive, irregular, nonsporulating, strictly anaerobic bacillus (Figure 4.2(A) and (B)).

# data[].attributes.body
Kingdom: | Bacteria
Phylum: | Actinobacteria
Class: | Actinobacteria
Order: | Coriobacteriales
Family: | Coriobacteriaceae
Genus: | Eggerthella

Species
E. hongkongensis[1]
E. lenta[1]
E. sinensis[1]

# data[].attributes.body
Eggerthella Lenta is part of the microbiome that is associated with human intestines. The environment of the Eggerthella Lenta leads to gastrointestinal tract infections. Some cases includes "severe, disseminated" disease and the mortality rate is high from 36% to 43%.

# data[].attributes.attachment_link.title
Clinical and Microbiological Characteristics of Eggerthella lenta Bacteremia

# data[].attributes.attachment_link.description
Eggerthella lenta is an emerging pathogen that has been underrecognized due to historical difficulties with phenotypic identification. Until now, its pathogenicity, antimicrobial susceptibility profile, and optimal treatment have been poorly characterized. In this article, we report the largest cohort of patients with E.

# data[].attributes.body
I thought how amazing it was that we have the ability to look up some very minute organism and find it's genotype within seconds. It is also amazing that connections can be made and find similarities and differences in each microbial species and such. It makes me wonder what other things are able to be accomplished in the future.

I liked Kayla Smith's padlet.

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"Here, we characterize a molybdenum-dependent enzyme from the human gut bacterium Eggerthella lenta that dehydroxylates catecholamine neurotransmitters. Our findings suggest that this activity enables E. lenta to use dopamine as an electron acceptor. We also identify candidate dehydroxylases that metabolize additional host- and plant-derived catechols that are relevant to a broad range of species and habitats."

# data[].attributes.attachment_link.title
A widely distributed metalloenzyme class enables gut microbial metabolism of host- and diet-derived catechols.

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Eubacteria mainly colonize the saliva and plaque as a member of the normal oral microflora. Most Eubacteria from the oral cavity are relatively biochemically inactive. In most cases, cells test negative for catalase and do not hydrolyze hippurate.

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Utilization of ERF by Microbiota

Enzyme-treated rice bran fiber is considered to be a prebiotic; therefore, it is very important to evaluate the assimilation rate of ERF by the intestinal microbiota. This evaluation was carried out by representative anaerobic human intestinal bacterial strains, including two Bifidobacterium strains ( B. breve, JCM 1192; and B. longum, JCM 1217) and two lactobacilli strains ( L. acidophilus, JCM 1132; and L. casei, JCM 1134) as probiotics; two Bacteroides strains ( B. distasonis, JCM 5825; and B. ovatus, JCM 5824) as opportunistic microbiota; and the butyrate-producing microbiota Eubacterium limosum ( E. limosum, JCM 6421). A detailed experimental procedure has been published. Briefly, ERF was added to the peptone-yeast (PY) medium (sole carbon source) at a rate of 0.5% (PY-ERF medium) under anaerobic condition at 37°C. After incubation, the pH and concentrations of organic acids (acetate, propionate, butyrate, iso-butyrate, succinate, lactate) were determined.

In addition, six of the above mentioned strains (excluding E. limosum) were used to investigate the mechanism of bacterial butyrate production. Respective strains were inoculated with or without E. limosum. ERF was efficiently utilized by B. longum and L. acidophilus, which showed that microbiota could convert ERF to organic acids. B. breve, L. casei, the two bacteroides strains, and E. limosum could not utilize ERF directly, as evidenced by the fact that no major increases in organic acid production were observed in the PY-ERF medium as compared with the PY medium. The total organic acid contents increased and the organic acid production profiles changed dramatically for all strains incubated together with E. limosum. Among the two probiotic strains ( L. acidophilus and L. casei), the butyrate content was higher than that for E. limosum alone. Furthermore, there was a dramatic decrease in lactate production and a marked increase in acetate production among both of the lactobacillus strains. With regard to the two Bacteroides strains, there was an increase in total organic acid content and a decrease in succinate content, although there was no change in butyrate content, as compared with E. limosum alone (Table 40.3).

In this experiment, ERF was added to peptone yeast medium as the sole carbon source at 0.5%. Data are the means of triplicate samples.

We used seven representative microbiota: two Bifidobacterium strains ( B. breve, JCM 1192 and B. longum, JCM 1217) and two Lactobacillus strains ( L. acidophilus, JCM 1132 and L. casei, JCM 1134) as probiotics, two Bacteroides strains ( B. distasonis, JCM 5825 and B. ovatus, JCM 5824) as opportunitistic microbiota, and the butyrate-producing microbiota, Eubacterium limosum ( E. limosum, JCM 6421).

In regard to L. acidophilus and L. casei, the butyrate content was greater than that for E. limosum alone. For both lactobacillus strains, there was a dramatic decrease in lactate production and a marked increase in acetate and butyrate production. In the case of the two Bacteroides strains, there was an increase in the total organic acid content and a decrease in the succinate content, although there was no increase in butyrate content, as compared with E. limosum alone.

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4.1.2 Eubacterium

Eubacterium is a genus of gram-positive nonsporulating strictly anaerobic bacilli. The name of the genus is still disputed. Bergey's Manual of Systematic Bacteriology vol. 2 (1986) points out that the Greek prefix eu means good, useful, rather than true. Therefore, the author believes that Eubacterium is the more appropriate name. Currently, bacteria species detected in the oral cavity that belong to this genus include Eubacterium alactolyticum, Eubacterium saburreum, Eubacterium lentum, Eubacterium limosum , Eubacterium nodatum, Eubacterium brachy, Eubacterium timidum, Eubacterium saphenus, and Eubacterium minutum.

Cells can be homogeneous or polymorphous rod-shaped. No spores are produced. Cells are gram-positive, but Gram staining old cultures and cultures that have produced acid in the culture medium will yield negative results.

Eubacteria are strictly anaerobic. Culturing cells can be difficult due to this bacteria's strict anaerobic demands, and some strains can only grow in prereduced medium. The optimum growth temperature is 37 °C, while the optimum pH is 7.0.

Eubacteria are chemoheterotrophs and produce energy from mixed organic acids produced by carbohydrates or protein metabolism. These mainly consist of butyric acid, acetic acid, and formic acid.

Most Eubacteria from the oral cavity are relatively biochemically inactive. In most cases, cells test negative for catalase and do not hydrolyze hippurate. Carbohydrate fermentation, indole production, nitrate reduction, esculin hydrolysis, and other biochemical tests can help differentiate the different species in the genus.

Eubacteria mainly colonize the saliva and plaque as a member of the normal oral microflora. Eubacterium lentum and E. limosum can be detected in the oral cavity. Eubacterium nodatum, E. brachy, E. timidum, E. saphenus, and E. minutum are new species isolated from subgingival plaque of patients with periodontitis and are considered as potential periodontal pathogens. The GC content in Eubacterium DNA is 30-55% (analyzed by Tm), and the percentage in the type species is 47% (by Tm).

4.1.2.1 Eubacterium lentum

In 1999, Kageyama et al. called for a change in the classification of E. lentum and proposed that it be grouped with Eggerthella lenta, the type species of genus Eggerthella.

E. lentum is a gram-positive, irregular, nonsporulating, strictly anaerobic bacillus (Figure 4.2(A) and (B)). As a strict anaerobe, most strains can grow between 30 °C and 45 °C, while some strains can grow at 25 °C. Arginine can promote bacterial growth. Characteristic colonies are shown in Figure 4.2(C) and (D).

E. lentum does not ferment carbohydrates. The cells do not hydrolyze esculin, hippurate, and gelatin. Ammonia can be produced from arginine, and H ₂O ₂ can be produced from agar medium containing 1% arginine. Under anaerobic conditions, the bacteria can produce H ₂S in the beveled bottom of triple sugar iron agar but cannot produce H ₂ S in SIM culture medium. The GC is 62% in DNA, and the type strain is JCM 9979 (= DSM2243 = ATCC25559 = NCTC11813).

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7.20.7.5 Formation of the Benzimidazole Moiety

One difference between the aerobic and anaerobic corrinoid pathways in bacteria is in the formation of the benzimidazole moiety, the 'lower' or 'α-' cobalt ligand. It was known that in anaerobes the 5,6-dimethylbenzimidazole is derived from a flavin, but the nature of the transformation and the enzyme involved was only recently discovered. 137,138

Archaea have been found to contain a variety of modified cobamides with alternate lower ligands. Analysis of the cobamides isolated from methanogenic archaea has shown that the coenzyme B ₁₂ analogues Coα-[α-(7-adenyl)]-cobamide (pseudo vitamin B ₁₂) and Coα-[α-(5-hydroxybenzimidazolyl]-cobamide (factor III, Figure 28) are the predominant corrinoids. 5-Methylbenzimidazole was found to be the α-ligand in the cobamide analogue of the acetate metabolizing Methanothrix soehngenii; however, this strain was later found not to be a pure culture, 139,140 so the source of the 5-methylbenzimidazole is not certain.

The archaeal corrinoid factor III contains 5-hydroxybenzimidazole, that has been shown to be a precursor to the common bacterial ligand, 5,6-dimethylbenzimidazole in the anaerobic corrinoid biosynthetic pathway. ¹⁴¹ Extensive labeling studies in the anaerobe Eubacterium limosum has revealed the precursors of 5,6-dimethylbenzimidazole to be erythrose, formate, glutamine, glycine, and methionine. ¹⁴² The typical biosynthetic origin of erythrose is the nonoxidative pentose-phosphate pathway; however, many archaea do not possess gene homologues for known pentose-phosphate pathway enzymes and the operation of this pathway in the archaea has been questioned. ¹⁴³⁻¹⁴⁵ M. thermoautotrophicus has been shown to contain corrinoid factor III, ¹³⁹ but genomic analysis failed to identify a complete set of genes for the nonoxidative pentose-phosphate pathway. ¹⁴³ Erythrose is also an intermediate in canonical pathways for AroAA and ribose biosynthesis; however, archaea have been found to utilize an alternate pathway that does not require erythrose. 41,144 In addition, an analysis of M. jannaschii cell free extract failed to detect the presence of erythrose-4-phosphate. Collectively, these results suggest that erythrose is not an archaeal metabolite and, consequently, an alternate biosynthetic route to the 5-hydroxybenzimidazole must be considered. Evidence for the presence of an alternate archaeal pathway to 5-hydroxybenzimidazole has been obtained through labeling studies in M. barkeri and M. thermoautotrophicus. 136,142 The hydroxybenzimidazole moiety of factor III from M. thermoautotrophicus had the same labeling pattern as the purine imidazole ring. 136,142 This further supports the existence of an alternate pathway for the formation of hydroxybenzimidazole.

It is possible that the existence of pseudo B ₁₂ in many methanogens may be a consequence of their inability to produce erythrose and thus the benzimidazole lower ligand. In these cases, an alternate ligand, such as adenosine, may be utilized. Indeed, studies with M. thermoautotrophicus showed that it was not only able to incorporate a variety of benzimidazole and purine bases into its corrinoids, but also that its growth rate with the alternate ligands was not significantly affected. ¹⁴⁶ This suggests that the absolute identity of the lower (α) ligand is not a strict requirement for activity of cobamide-dependent enzymes in the archaea.

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1.3 Structural Variations in Peptidoglycan Structure

PG is essential in most bacteria, and its features described in the preceding paragraphs are highly conserved across bacterial species. However, structural variations have been found in the glycan strands, the peptide chains, and the nature or type of the interpeptide cross-link (Vollmer et al., 2008).

One example of a structural variation within a glycan strand can be found at its reducing end after cleavage by lytic transglycosylases. In Staphylococcus aureus, the reducing end could be either a MurNAc or a GlcNAc residue, depending on the enzyme involved in the remodelling reaction (Boneca, Huang, Gage, & Tomasz, 2000). In some Gram-negative species, however, neither MurNAc nor GlcNAc is found. Instead, it is a cyclized MurNAc derivative, called 1,6-anhydroMurNAc, which terminates the glycan strands. The existence of 1,6-anhydroMurNAc is a hallmark of glycan strand ends in these species and has been used to determine glycan strand length using HPLC in combination with MS (König, Claus, & Varma, 2010).

The amino acid composition in PG peptide chains may vary between bacterial species. In most species, the first amino acid in peptide chains is l-Ala. However, Gly and l-Ser are encountered in some species, such as Mycobacterium leprae and Butyribacterium rettgeri (Table 1) (Mahapatra, Crick, & Brennan, 2000). This probably results from the low specificity of MurC towards l-Ala, Gly, and l-Ser in these species (see Fig. 2) and the concentration of each in the respective growth environments.

Position

Residue Encountered

Examples

Gly

Mycobacterium leprae, Brevibacterium imperiale

d -lsoglutamine a

Most Gram-positive species, Mycobacteria

threo-3-Hydroxyglutamate a
Microbacterium lacticum

3

meso-A ₂ pm

Most Gram-negative species, Bacilli, Mycobacteria

l -Lys

Most Gram-positive species

l -Lys/l -Orn
Bifidobacterium globosum

l -Lys/d -Lys
Thermotoga maritima

ll -A ₂ pm

Streptomyces albus, Propionibacterium petersonii

meso-Lanthionine
Fusobacterium nucleatum

Amidated meso-A ₂pm a
Bacillus subtilis

2,6-Diamino-3-hydroxypimelate b
Ampuraliella regularis

l -5-Hydroxylysine b

Streptococcus pyogenes c

N γ-Acetyl-l -diaminobutyrate a
Corynebacterium insidiosum

The second amino acid in PG peptide chains, d-γ-Glu, is relatively conserved across all bacterial species. Modifications of the d-γ-Glu residue are known in several bacterial species, but these modifications arise from reactions subsequent to its incorporation into the stem peptide. The greatest variation, on the other hand, is found at the third position. MurE catalyses the ligation of the third amino acid into the growing stem peptide. In most Gram-negative species, and some Gram-positives, this amino acid is meso-Dap. In most Gram-positive organisms, this amino acid is l-Lys. Other amino acid variations at this site have been encountered, including meso-lanthionine, l-Orn, and l-Glu (Schleifer & Kandler, 1972).

The amino acids at the fourth and fifth positions are incorporated into PG as a dipeptide, specifically, d-Ala-d-Ala (Duncan, van Heijenoort, & Walsh, 1990). Synthesis of the d-Ala-d-Ala dipeptide is facilitated by d-Ala-d-Ala ligase (Ddl). The dipeptide is subsequently incorporated into the stem peptide by MurF, as described earlier (Fig. 2). The structure of this dipeptide is highly conserved. d-Ala is predominantly found at the fourth position in all bacteria known to date, while d-Lactate and d-Ser may be found, albeit rarely, in the fifth position in some species (Cetinkaya, Falk, & Mayhall, 2000).

In addition to the stem peptide, the structure of cross-linkages may also vary. In E. coli and Bacillus subtilis, cross-linkages are installed directly without any additional bridging amino acids. In contrast, S. aureus utilizes a bridge link composed of five glycine residues attached to the terminal amino group of the lysine residue. Other organisms may also employ various bridge links (Vollmer et al., 2008).

Scientists have been attempting to develop methods for PG-specific analysis based on the unique structure of PG. In the following sections, we will discuss the labelling strategies that have been commonly employed for PG imaging.

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2.2.1. Variation in the peptide structure

Structural variations in the stem peptide may be introduced at different steps of peptidoglycan biosynthesis, either on the level of the nucleotide activated precursors or on the level of the lipid intermediates (Barreteau Bouhss et al., 2008; et al., 2008) (Figure 2.2).

The amino acid residue in the first position of the peptide moiety is predominantly l-Ala and it is linked with its amino group to the lactyl group of the MurNAc residue. Only in a few species, as with glycine (Gly) in Draper Microbacterium lacticum, Mycobacterium leprae, Corynebacterium or l-serine (l-Ser) in Butyribacterium rettgeri has another amino acid been found to occur instead of l-Ala (Schleifer and Kandler, 1972; et al., 1987).

A d-isoglutamic acid (d-iGlu) residue is linked in all species via its γ-carboxylic group to the amino group of the first amino acid. It often undergoes secondary modifications mainly on the lipid II level. For example, hydroxylation of d-iGlu results in the occurrence of Schleifer Kamio Pittenauer Pfanzagl Hirao Quintela threo-3-hydroxyglutamic acid in M. lacticum. Hydroxylation depends on the availability of oxygen during growth; cells grown in a microaerobic environment contain mainly non-hydroxylated d-iGlu (d-iGlu residue; it can be amidated resulting in d-isoglutamine (d-iGln) that is common for Gram-positive species, or it can be substituted either by Gly (e.g. in et al., 1967). The more abundant modifications affect the free α-carboxyl group of the Thermus thermophilus and Eubacterium nodatum), glycine amide (in Arthrobacter athrocyaneus), d-alanine amide (in Renibacterium salmoninarum), or by polyamines like cadaverine (in Selenomonas ruminantium), putrescine (in Veillonella alcalescens), N-acetylputrescine (in the cyanelles of Cyanophora paradoxa) or spermidine (in Anaerovibrio lipolytica) (Schleifer and Kandler, 1972; Fiedler and Draxl, 1986; T. thermophilus, the Gly residue attached to d-iGlu can be modified itself by the addition of a phenylacetic acid residue ( et al., 1981a,b; et al., 1993; et al., 1996a,b; et al., 2000). Interestingly, in et al., 1995b).

The greatest variation occurs at the third position of the peptide unit (see Figure 2.2). Usually, it is a diamino acid attached with its α-amino group to the γ-carboxyl group of the d-iGlu residue at position-2. The most widespread amino acids at this position are Shockman Mahapatra meso-A ₂pm (in almost all Gram-negative species and species of Bacillus, Lactobacilli and Mycobacterium) or l-Lys (mainly in Gram-positive species). Other diamino acids (l-ornithine, d-Lys, meso-lanthionine, ll-A ₂pm and l-2,4-diaminobutyric acid) or monoamino acids (l-homoserine, l-Ala, l-Glu) have been identified (Schleifer and Kandler, 1972). Like d-iGlu in position-2, meso-A ₂pm and l-Lys can be further modified. Thus, the hydroxylated forms (2,6-diamino-3-hydroxypimelate and l-hydroxylysine) or amidated meso-A ₂pm have been found in a few species (Perkins, 1969; et al., 1965; et al., 2005b).

The amino acids of position 4 and 5 are represented in most bacteria by d-Ala-d-Ala and are added during synthesis of the cytoplasmic precursor as a dipeptide. In contrast to the conserved d-Ala residue in position 4, the d-Ala in position 5 can be replaced in some species by either d-lactate (d-Lac) or d-Ser. The presence of these alternative residues correlates with a natural or acquired resistance to vancomycin of the related species, e.g. in Allen Arthur Ferain Glauner Markiewicz Glauner Enterococcus hirae or Lactobacillus plantarum (d-Ala residues ( Escherichia coli, the percentage of peptides with a Gly residue is relatively low (about 1%), but it can reach up to 19% in Caulobacter crescentus ( et al., 1992; et al., 1992; et al., 1996). Under certain growth conditions, Gly can be incorporated in the peptide moiety instead of one of the terminal et al., 1988). In et al., 1983; et al., 1988).

The peptide moiety functions as an anchor for covalent linkage of surface proteins (Dramsi Magnet Ton-That et al., 2008). In E. coli and related species, Braun's lipoprotein (Lpp) is linked by the ɛ-amino group of its C-terminal lysine residue to the α-amino group of the meso-A ₂pm residue of the tripeptide unit in the peptidoglycan (Braun, 1975; et al., 2007b). In Gram-positive bacteria, many proteins are found to be covalently attached to the peptidoglycan (e.g. protein A and adhesins). The protein precursor carries a C-terminal signal peptide containing the general sequence l-Leu-l-Pro-X-l-Thr-Gly (where l-Leu, l-leucine; l-Pro, l-proline; X, any l-amino acid). The membrane-bound protein sortase A cleaves between the Thr and Gly residues and links the protein with the α-carboxylic group of Thr to the amino group in the side chain of the diamino acid (position 3) of a lipid II molecule ( et al., 2004).

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3 Natural Products of Acetogens

The majority of acetogens produce acetic acid as sole metabolic end product, as the conversion of acetyl-CoA by the enzymes Pta and Ack is an ATP-yielding reaction. Some autotrophic acetogens such as A. longum (Kane & Breznak, 1991), " Butyribacterium methylotrophicum" (Worden, Grethlein, Jain, & Datta, 1991), C. carboxidivorans, Clostridium drakei (Liou, Balkwill, Drake, & Tanner, 2005), C. magnum (Groher & Weuster-Botz, 2016b), C. scatologenes (Küsel, Dorsch, Acker, Stackebrandt, & Drake, 2000), Clostridium methoxybenzovorans (Mechichi et al., 1999), Eubacterium aggregans (Groher & Weuster-Botz, 2016b), Eubacterium limosum (Genthner, Davis, & Bryant, 1981; Lindley, Loubiere, Pacaud, Mariotto, & Goma, 1987), and Oxobacter pfennigii (Krumholz & Bryant, 1985) can also produce butyric acid by the action of thiolase (Thl), 3-hydroxybutyryl-CoA dehydrogenase (Hbd), crotonase (Crt), butyryl-CoA dehydrogenase (Bcd) coupled to a pair of electron-transferring flavoproteins (EtfA, EtfB), phosphotransbuturylase (Ptb), and butyrate kinase (Buk). The proteins EtfA and EtfB are crucial for the activity of the Bcd protein (Inui et al., 2007). In these acetogens, genes encoding the enzymes Thl, Hbd, Crt, Bcd, EtfA, and EtfB are clustered in varying order and form the bcs operon (Fig. 3). In Clostridium acetobutylicum, a highly similar gene cluster is present and was described as bcs (butyryl-CoA synthesis) operon (Boynton, Bennet, & Rudolph, 1996). This bcs operon consists of the genes crt, bcd, etfB, etfA, and hbd in this order and was suggested to be transcribed as a transcriptional unit controlled by a promoter upstream of the crotonase gene (Boynton et al., 1996). bcs genes present in different acetogens might also form an operon, since they are just separated by a few nucleotides that contain the ribosome binding site motif (5′-AGGAGG-3′) and a potential promoter sequence upstream of the first gene in the complex. Interestingly, C. carboxidivorans and C. scatologenes harbor two clusters of the bcs genes. One of each cluster additionally contains a gene with similarity to hydratase genes, possibly encoding a putative R-specific 3-hydroxybutyryl-CoA-dehydratase (marked with ⋆), which is also present in A. longum. It is tempting to speculate that Hbd produces R- and S-enantiomers of 3-hydroxybutyrate and that the crotonase specifically hydrolyzes only the S-enantiomer. Therefore, the remaining R-enantiomer could be converted by reaction of a R-specific 3-hydroxybutyryl-CoA-dehydratase.

" B. methylotrophicum" (Worden et al., 1991), C. carboxidivorans, and C. drakei (Liou et al., 2005) are known for their ability to produce not only acetate and butyrate, but also ethanol and butanol as metabolic products. Moreover, the acetogens C. carboxidivorans (Phillips et al., 2015; Ramió-Pujol, Ganigué, Bañeras, & Colprim, 2015) and E. limosum (Genthner et al., 1981; Lindley et al., 1987) are known for their ability to produce hexanoate, whereas only C. carboxidivorans also produces hexanol (Phillips et al., 2015; Ramió-Pujol et al., 2015). C. carboxidivorans was shown to produce hexanoate and hexanol using carbon monoxide, whereas E. limosum uses methanol as carbon source.

For C. carboxidivorans, the fermentation pattern of a continuous gas-fed batch experiment without pH control shows production of organic acids that are subsequently converted to alcohols, indicating the activity of aldehyde:ferredoxin oxidoreductases (Aor) leading to the conversion of organic acids into alcohols, as shown in Fig. 4 (Fernández-Naveira, Veiga, & Kennes, 2017a; HBE). The pathway of hexanoate formation operates as reverse β-oxidation with coenzyme A (CoA) thioester intermediates and direct use of acetyl-CoA for acyl-chain elongation (Fig. 4). Butyryl-CoA is elongated with acetyl-CoA by the action of a second thiolase (Thl2) and results in 3-ketohexanoyl-CoA, which is subsequently converted in two further steps to hexanoyl-CoA. The enzymes of this pathway are not known yet, but are assumed to be similar compared to the enzymes necessary to form butyryl-CoA (Fernández-Naveira, Veiga, & Kennes, 2017b; Seedorf et al., 2008). The term HBE (hexanol, butanol, ethanol) fermentation was introduced for this type of fermentation (Fernández-Naveira et al., 2017b). HBE fermentation is deduced from ABE (acetone butanol, ethanol) fermentation which is well known for C. acetobutylicum and related bacteria (Bengelsdorf et al., 2017).

Gene expression and enzyme activities were analyzed from C. carboxidivorans P7 T cells cultivated in autotrophic batch mode continuously fed with a synthetic syngas mixture (Ukpong et al., 2012). Enzyme activities of carbon monoxide dehydrogenase (CODH), Fe-only hydrogenases, and two bifunctional acetaldehyde/alcohol dehydrogenases were highest during exponential growth phase, but expression of the respective coding genes was highest in stationary growth stages. Autotrophic growth and product formation (acetate, butyrate, ethanol, and butanol) of the strain C. carboxidivorans P7 T in batch mode were subdivided into three stages: initial growth phase, an acidogenic phase, and a solventogenic phase (Ukpong et al., 2012) and in a similar manner observed by Fernández-Naveira, Abubackar, Veiga, and Kennes (2016). Such a division was already described for other solventogenic clostridial strains cultivated in batch mode (Bengelsdorf et al., 2017). Shen and coworkers did not observe a subdivision in different fermentation stages during cultivation (Shen, Brown, & Wen, 2014a, 2014b; Shen, Brown, & Wen, 2017).

Bacterial strains such as C. ljungdahlii, " C. autoethanogenum," and " Clostridium ragsdalei " are known for their ability to produce ethanol as major end product instead of acetate (Abrini, Naveau, & Nyns, 1994; Maddipati, Atiyeh, Bellmer, & Huhnke, 2011; Phillips, Klasson, Clausen, & Gaddy, 1993). Those strains have a narrow pH range that allows growth (between 5.9 and 4.5). The physiological way to eliminate acetic acid is the conversion to ethanol by the action of an aldehyde:ferredoxin oxidoreductase and an aldehyde dehydrogenase. In general, the central intermediate acetyl-CoA is used to produce biomass that consumes carbon and reducing equivalents. Additionally, acetyl-CoA is also used for acetate production and subsequently ATP generation. The acetate is actively transported out of the cell and a minor amount undissociated acetic acid remains in the cell. According to Richter et al. (2016), the enzymes for ethanol production are always present in the cell and the conversion of undissociated acetic acid via acetaldehyde produces immediately ethanol, while requiring reducing equivalents. A thermodynamic threshold concentration of undissociated acetic acid in the cell (diffusing freely over the cell membrane) causes an increased ethanol production as an overflow mechanism to maintain the intracellular pH stable as long as possible (Richter et al., 2016). Ethanol production from acetyl-CoA by bifunctional aldehyde/alcohol dehydrogenases of the AdhE type is of only marginal importance (Bengelsdorf, Poehlein, Linder, et al., 2016).

2,3-Butanediol is also produced by the acetogens C. ljungdahlii, " C. autoethanogenum," and " C. ragsdalei" as metabolic products (Köpke et al., 2011). The involved reactions (Fig. 1) convert pyruvate to acetolactate (acetolactate synthase), which is further splitted into acetoin and CO ₂ (acetolactate decarboxylase). Acetoin is finally reduced to 2,3-butanediol (NADPH-dependent primary-secondary alcohol dehydrogenase or NADH-dependent 2,3-butanediol dehydrogenase) (Köpke, Gerth, et al., 2014). Lactate is a further product that can be produced from pyruvate (via lactate dehydrogenase) by the mentioned strains (Köpke et al., 2011). A metabolic shift in fructose fermentation of " C. autoethanogenum" toward lactate and 2,3-butanediol was induced by extracellular electron supply in form of electricity (Kracke, Virdis, Bernhardt, Rabaey, & Krömer, 2016). Moreover, bacterial strains of E. limosum, Blautia producta, and " B. methylotrophicum " are also known to be able to produce lactate (Heiskanen, Virkajärvi, & Viikari, 2007; Misoph & Drake, 1996; Wade, 2015).

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Enzyme-treated rice bran fiber is considered to be a prebiotic; therefore, it is very important to evaluate the assimilation rate of ERF by the intestinal microbiota. This evaluation was carried out by representative anaerobic human intestinal bacterial strains, including two Bifidobacterium strains ( B. breve, JCM 1192; and B.