Análisis de componentes principales

microbiota. It was hypothesized that sponges are an important resistance reservoir because they often contain diverse and complex microbial communities that can produce bioactive compounds, including those with antimicrobial activity. The main aim was to identify antibiotic resistant bacteria and their resistance genes and associated mobile elements, in order to evaluate to what extent sponges may contribute to emerging resistance. In addition, knowledge about identified resistance genes could contribute to the development of compounds that inhibit resistance mechanisms [423, 424], and could lead to the discovery of novel antibiotic biosynthesis clusters that are frequently co-localized with resistance genes in bacterial genomes [425]. In Chapter 3, we described the isolation of 31 different

bacterial strains from the sponges A. aerophoba, P. ficiformis, and C. candelabrum on agar media supplemented with antibiotics. Subsequently, application of functional 147

7

General Discussion

suggest that a considerable number of resistance genes is present while not being expressed.

However, caution should be taken before concluding that absence of resistance genes in metatranscriptome data also equals absence of expression. In fact, Haas and co-authors found that as much as 5-10 million non-rRNA reads are required to detect all but the rarest transcripts of a single bacterium [414]. Considering that the human gut has been estimated to contain 100-1,000 bacterial species [415-417], it is expected that our datasets of ~6 million non-rRNA reads do not even approach the required sequencing depth to capture most transcripts. A case in point is the forest soil sample in our study from which both metagenome and metatranscriptome datasets of ~1 million reads were available. In the soil metatranscriptome dataset macrolide resistance genes were detected whereas in the metagenome dataset chloramphenicol, aminoglycoside, tetracycline, and β-lactam resistance genes were detected (data not shown). A suitable sequencing depth can be selected based on knowledge about relative abundances of resident bacteria [418]. Clearly, routinely achieved current metatranscriptome sequencing depths, and to a lesser degree metagenome sequencing depths, are not sufficient to fully discern the functionality and activity of low-abundance bacteria in microbial communities. Therefore, while these technical considerations do not undermine the validity of our conclusions based on distinct inter-individual and inter-niche differences in resistance transcripts, it should be considered that observations are limited by an inherent detection threshold.

To validate the hypothesis that resistance genes are often present in microbial communities without being expressed, it is necessary to at the same time analyse the metatranscriptome and metagenome of a sample. The study can include communities that have been subjected to antibiotics to study induction of expression by antibiotics (sample pre- and post-administration of antibiotics). A similar approach can be used to study to what extent other factors such as heat, dietary fibers, solvents or non-relevant antibiotics affect resistance gene expression levels. The inadequate read depth of traditional metatranscriptome sequencing can be circumvented by using a more targeted approach. For example, quantitative PCR can be used to detect the presence or expression of specific resistance genes [78, 419]. Furthermore, RNA CaptureSeq may be applied to achieve unprecedented depth in mining of resistance transcripts [420]. Using this approach, RNA is first transcribed

into cDNA, after which the cDNA pool is enriched by hybridizing it to arrays that contain probes targeting sequences of interest (i.e. resistance gene sequences). Finally, the cDNA pool is deep-sequenced. It should be noted that a targeted approach can only work if there is prior knowledge on the resistance genes that can be present or expressed in a given biological niche. However, at this time, comprehensive information about resistance gene presence is only available for human gut microbiota.

It has been proposed that eventually antibiotic therapy will be based on resistance potentials of gut microbiota of individuals, as determined by metagenome sequencing [104]. Therefore, clinicians need to first screen patient-associated bacteria for resistance, after which an antibiotic therapy can be tailored to fit the patient’s resistome [421]. In our metatranscriptome study, we showed that there are substantial inter-individual differences in the profiles of expressed antibiotic resistance genes. Information about resistance gene expression could provide deeper insights into the immediate resistance potential of bacteria associated with patients suffering from bacterial infections, and as such may be relevant in clinical practice as well. Such individualized treatments could be used to treat non-urgent infections if meta-level resistome sequencing and downstream data analysis can be accomplished in a short time frame [422].

Sponges as a reservoir for antibiotic resistance genes

Chapter 3 and Chapter 4 covered the investigation of antibiotic resistance in sponge

microbiota. It was hypothesized that sponges are an important resistance reservoir because they often contain diverse and complex microbial communities that can produce bioactive compounds, including those with antimicrobial activity. The main aim was to identify antibiotic resistant bacteria and their resistance genes and associated mobile elements, in order to evaluate to what extent sponges may contribute to emerging resistance. In addition, knowledge about identified resistance genes could contribute to the development of compounds that inhibit resistance mechanisms [423, 424], and could lead to the discovery of novel antibiotic biosynthesis clusters that are frequently co-localized with resistance genes in bacterial genomes [425]. In Chapter 3, we described the isolation of 31 different

bacterial strains from the sponges A. aerophoba, P. ficiformis, and C. candelabrum on agar media supplemented with antibiotics. Subsequently, application of functional 147

7

General Discussion

146 Chapter 7

suggest that a considerable number of resistance genes is present while not being expressed.

However, caution should be taken before concluding that absence of resistance genes in metatranscriptome data also equals absence of expression. In fact, Haas and co-authors found that as much as 5-10 million non-rRNA reads are required to detect all but the rarest transcripts of a single bacterium [414]. Considering that the human gut has been estimated to contain 100-1,000 bacterial species [415-417], it is expected that our datasets of ~6 million non-rRNA reads do not even approach the required sequencing depth to capture most transcripts. A case in point is the forest soil sample in our study from which both metagenome and metatranscriptome datasets of ~1 million reads were available. In the soil metatranscriptome dataset macrolide resistance genes were detected whereas in the metagenome dataset chloramphenicol, aminoglycoside, tetracycline, and β-lactam resistance genes were detected (data not shown). A suitable sequencing depth can be selected based on knowledge about relative abundances of resident bacteria [418]. Clearly, routinely achieved current metatranscriptome sequencing depths, and to a lesser degree metagenome sequencing depths, are not sufficient to fully discern the functionality and activity of low-abundance bacteria in microbial communities. Therefore, while these technical considerations do not undermine the validity of our conclusions based on distinct inter-individual and inter-niche differences in resistance transcripts, it should be considered that observations are limited by an inherent detection threshold.

To validate the hypothesis that resistance genes are often present in microbial communities without being expressed, it is necessary to at the same time analyse the metatranscriptome and metagenome of a sample. The study can include communities that have been subjected to antibiotics to study induction of expression by antibiotics (sample pre- and post-administration of antibiotics). A similar approach can be used to study to what extent other factors such as heat, dietary fibers, solvents or non-relevant antibiotics affect resistance gene expression levels. The inadequate read depth of traditional metatranscriptome sequencing can be circumvented by using a more targeted approach. For example, quantitative PCR can be used to detect the presence or expression of specific resistance genes [78, 419]. Furthermore, RNA CaptureSeq may be applied to achieve unprecedented depth in mining of resistance transcripts [420]. Using this approach, RNA is first transcribed

into cDNA, after which the cDNA pool is enriched by hybridizing it to arrays that contain probes targeting sequences of interest (i.e. resistance gene sequences). Finally, the cDNA pool is deep-sequenced. It should be noted that a targeted approach can only work if there is prior knowledge on the resistance genes that can be present or expressed in a given biological niche. However, at this time, comprehensive information about resistance gene presence is only available for human gut microbiota.

It has been proposed that eventually antibiotic therapy will be based on resistance potentials of gut microbiota of individuals, as determined by metagenome sequencing [104]. Therefore, clinicians need to first screen patient-associated bacteria for resistance, after which an antibiotic therapy can be tailored to fit the patient’s resistome [421]. In our metatranscriptome study, we showed that there are substantial inter-individual differences in the profiles of expressed antibiotic resistance genes. Information about resistance gene expression could provide deeper insights into the immediate resistance potential of bacteria associated with patients suffering from bacterial infections, and as such may be relevant in clinical practice as well. Such individualized treatments could be used to treat non-urgent infections if meta-level resistome sequencing and downstream data analysis can be accomplished in a short time frame [422].

Sponges as a reservoir for antibiotic resistance genes

Chapter 3 and Chapter 4 covered the investigation of antibiotic resistance in sponge

microbiota. It was hypothesized that sponges are an important resistance reservoir because they often contain diverse and complex microbial communities that can produce bioactive compounds, including those with antimicrobial activity. The main aim was to identify antibiotic resistant bacteria and their resistance genes and associated mobile elements, in order to evaluate to what extent sponges may contribute to emerging resistance. In addition, knowledge about identified resistance genes could contribute to the development of compounds that inhibit resistance mechanisms [423, 424], and could lead to the discovery of novel antibiotic biosynthesis clusters that are frequently co-localized with resistance genes in bacterial genomes [425]. In Chapter 3, we described the isolation of 31 different

bacterial strains from the sponges A. aerophoba, P. ficiformis, and C. candelabrum on agar media supplemented with antibiotics. Subsequently, application of functional 147

7

General Discussion

suggest that a considerable number of resistance genes is present while not being expressed.

However, caution should be taken before concluding that absence of resistance genes in metatranscriptome data also equals absence of expression. In fact, Haas and co-authors found that as much as 5-10 million non-rRNA reads are required to detect all but the rarest transcripts of a single bacterium [414]. Considering that the human gut has been estimated to contain 100-1,000 bacterial species [415-417], it is expected that our datasets of ~6 million non-rRNA reads do not even approach the required sequencing depth to capture most transcripts. A case in point is the forest soil sample in our study from which both metagenome and metatranscriptome datasets of ~1 million reads were available. In the soil metatranscriptome dataset macrolide resistance genes were detected whereas in the metagenome dataset chloramphenicol, aminoglycoside, tetracycline, and β-lactam resistance genes were detected (data not shown). A suitable sequencing depth can be selected based on knowledge about relative abundances of resident bacteria [418]. Clearly, routinely achieved current metatranscriptome sequencing depths, and to a lesser degree metagenome sequencing depths, are not sufficient to fully discern the functionality and activity of low-abundance bacteria in microbial communities. Therefore, while these technical considerations do not undermine the validity of our conclusions based on distinct inter-individual and inter-niche differences in resistance transcripts, it should be considered that observations are limited by an inherent detection threshold.

To validate the hypothesis that resistance genes are often present in microbial communities without being expressed, it is necessary to at the same time analyse the metatranscriptome and metagenome of a sample. The study can include communities that have been subjected to antibiotics to study induction of expression by antibiotics (sample pre- and post-administration of antibiotics). A similar approach can be used to study to what extent other factors such as heat, dietary fibers, solvents or non-relevant antibiotics affect resistance gene expression levels. The inadequate read depth of traditional metatranscriptome sequencing can be circumvented by using a more targeted approach. For example, quantitative PCR can be used to detect the presence or expression of specific resistance genes [78, 419]. Furthermore, RNA CaptureSeq may be applied to achieve unprecedented depth in mining of resistance transcripts [420]. Using this approach, RNA is first transcribed

into cDNA, after which the cDNA pool is enriched by hybridizing it to arrays that contain probes targeting sequences of interest (i.e. resistance gene sequences). Finally, the cDNA pool is deep-sequenced. It should be noted that a targeted approach can only work if there is prior knowledge on the resistance genes that can be present or expressed in a given biological niche. However, at this time, comprehensive information about resistance gene presence is only available for human gut microbiota.

It has been proposed that eventually antibiotic therapy will be based on resistance potentials of gut microbiota of individuals, as determined by metagenome sequencing [104]. Therefore, clinicians need to first screen patient-associated bacteria for resistance, after which an antibiotic therapy can be tailored to fit the patient’s resistome [421]. In our metatranscriptome study, we showed that there are substantial inter-individual differences in the profiles of expressed antibiotic resistance genes. Information about resistance gene expression could provide deeper insights into the immediate resistance potential of bacteria associated with patients suffering from bacterial infections, and as such may be relevant in clinical practice as well. Such individualized treatments could be used to treat non-urgent infections if meta-level resistome sequencing and downstream data analysis can be accomplished in a short time frame [422].

Sponges as a reservoir for antibiotic resistance genes

Chapter 3 and Chapter 4 covered the investigation of antibiotic resistance in sponge

microbiota. It was hypothesized that sponges are an important resistance reservoir because they often contain diverse and complex microbial communities that can produce bioactive compounds, including those with antimicrobial activity. The main aim was to identify antibiotic resistant bacteria and their resistance genes and associated mobile elements, in order to evaluate to what extent sponges may contribute to emerging resistance. In addition, knowledge about identified resistance genes could contribute to the development of compounds that inhibit resistance mechanisms [423, 424], and could lead to the discovery of novel antibiotic biosynthesis clusters that are frequently co-localized with resistance genes in bacterial genomes [425]. In Chapter 3, we described the isolation of 31 different

bacterial strains from the sponges A. aerophoba, P. ficiformis, and C. candelabrum on agar media supplemented with antibiotics. Subsequently, application of functional

7

146

Chapter 7

suggest that a considerable number of resistance genes is present while not being expressed.

However, caution should be taken before concluding that absence of resistance genes in metatranscriptome data also equals absence of expression. In fact, Haas and co-authors found that as much as 5-10 million non-rRNA reads are required to detect all but the rarest transcripts of a single bacterium [414]. Considering that the human gut has been estimated to contain 100-1,000 bacterial species [415-417], it is expected that our datasets of ~6 million non-rRNA reads do not even approach the required sequencing depth to capture most transcripts. A case in point is the forest soil sample in our study from which both metagenome and metatranscriptome datasets of ~1 million reads were available. In the soil metatranscriptome dataset macrolide resistance genes were detected whereas in the metagenome dataset chloramphenicol, aminoglycoside, tetracycline, and β-lactam resistance genes were detected (data not shown). A suitable sequencing depth can be selected based on knowledge about relative abundances of resident bacteria [418]. Clearly, routinely achieved current metatranscriptome sequencing depths, and to a lesser degree metagenome sequencing depths, are not sufficient to fully discern the functionality and activity of low-abundance bacteria in microbial communities. Therefore, while these technical considerations do not undermine the validity of our conclusions based on distinct inter-individual and inter-niche differences in resistance transcripts, it should be considered that observations are limited by an inherent detection threshold.

To validate the hypothesis that resistance genes are often present in microbial communities without being expressed, it is necessary to at the same time analyse the metatranscriptome and metagenome of a sample. The study can include communities that have been subjected to antibiotics to study induction of expression by antibiotics (sample pre- and post-administration of antibiotics). A similar approach can be used to study to what extent other factors such as heat, dietary fibers, solvents or non-relevant antibiotics affect resistance gene expression levels. The inadequate read depth of traditional metatranscriptome sequencing can be circumvented by using a more targeted approach. For example, quantitative PCR can be used to detect the presence or expression of specific resistance genes [78, 419]. Furthermore, RNA CaptureSeq may be applied to achieve unprecedented depth in mining of resistance transcripts [420]. Using this approach, RNA is first transcribed

into cDNA, after which the cDNA pool is enriched by hybridizing it to arrays that contain probes targeting sequences of interest (i.e. resistance gene sequences). Finally, the cDNA pool is deep-sequenced. It should be noted that a targeted approach can only work if there is prior knowledge on the resistance genes that can be present or expressed in a given biological niche. However, at this time, comprehensive information about resistance gene presence is only available for human gut microbiota.

It has been proposed that eventually antibiotic therapy will be based on resistance potentials of gut microbiota of individuals, as determined by metagenome sequencing [104]. Therefore, clinicians need to first screen patient-associated bacteria for resistance, after which an antibiotic therapy can be tailored to fit the patient’s resistome [421]. In our metatranscriptome study, we showed that there are substantial inter-individual differences in the profiles of expressed antibiotic resistance genes. Information about resistance gene expression could provide deeper insights into the immediate resistance potential of bacteria associated with patients suffering from bacterial infections, and as such may be relevant in clinical practice as well. Such individualized treatments could be used to treat non-urgent infections if meta-level resistome sequencing and downstream data analysis can be accomplished in a short time frame [422].

Sponges as a reservoir for antibiotic resistance genes

Chapter 3 and Chapter 4 covered the investigation of antibiotic resistance in sponge

microbiota. It was hypothesized that sponges are an important resistance reservoir because they often contain diverse and complex microbial communities that can produce bioactive compounds, including those with antimicrobial activity. The main aim was to identify antibiotic resistant bacteria and their resistance genes and associated mobile elements, in order to evaluate to what extent sponges may contribute to emerging resistance. In addition, knowledge about identified resistance genes could contribute to the development of compounds that inhibit resistance mechanisms [423, 424], and could lead to the discovery of novel antibiotic biosynthesis clusters that are frequently co-localized with resistance genes in bacterial genomes [425]. In Chapter 3, we described the isolation of 31 different

bacterial strains from the sponges A. aerophoba, P. ficiformis, and C. candelabrum on agar media supplemented with antibiotics. Subsequently, application of functional 147

7

General Discussion

suggest that a considerable number of resistance genes is present while not being expressed.

However, caution should be taken before concluding that absence of resistance genes in metatranscriptome data also equals absence of expression. In fact, Haas and co-authors found that as much as 5-10 million non-rRNA reads are

In document CONTAMINACIÓN AMBIENTAL, BIOACUMULACIÓN Y BIOMAGNIFICACIÓN POR METALES PESADOS EN UN SECTOR DEL RIO ALTO-CHICAMOCHA (página 88-98)