Refine
Has Fulltext
- yes (2)
Is part of the Bibliography
- yes (2)
Document Type
- Doctoral Thesis (2)
Language
- English (2)
Keywords
- metagenomics (2) (remove)
Institute
- Graduate School of Life Sciences (2) (remove)
Sonstige beteiligte Institutionen
Sponges (phylum Porifera) are evolutionary ancient, sessile filter-feeders that harbor a largely diverse microbial community within their internal mesohyl matrix. Throughout this thesis project, I aimed at exploring the adaptations of these symbionts to life within their sponge host by sequencing and analyzing the genomes of a variety of bacteria from the microbiome of the Mediterranean sponge Aplysina aerophoba. Employed methods were fluorescence-activated cell sorting with subsequent multiple displacement amplification and single-cell / ‘mini-metagenome’ sequencing, and metagenomic sequencing followed by differential coverage binning. These two main approaches both aimed at obtaining genome sequences of bacterial symbionts of A. aerophoba, that were then compared to each other and to references from other environments, to gain information on adaptations to the host sponge environment and on possible interactions with the host and within the microbial community.
Cyanobacteria are frequent members of the sponge microbial community. My ‘mini-metagenome’ sequencing project delivered three draft genomes of “Candidatus Synechococcus spongiarum,” the cyanobacterial symbiont of A. aerophoba and many more sponges inhabiting the photic zone. The most complete of these genomes was compared to other clades of this symbiont and to closely related free-living cyanobacterial references in a collaborative project published in Burgsdorf I*, Slaby BM* et al. (2015; *shared first authorship). Although the four clades of “Ca. Synechococcus spongiarum” from the four sponge species A. aerophoba, Ircinia variabilis, Theonella swinhoei, and Carteriospongia foliascens were approximately 99% identical on the level of 16S rRNA gene sequences, they greatly differed on the genomic level. Not only the genome sizes were different from clade to clade, but also the gene content and a number of features including proteins containing the eukaryotic-type domains leucine-rich repeats or tetratricopeptide repeats. On the other hand, the four clades shared a number of features such as ankyrin repeat domain-containing proteins that seemed to be conserved also among other microbial phyla in different sponge hosts and from different geographic locations. A possible novel mechanism for host phagocytosis evasion and phage resistance by means of an altered O antigen of the lipopolysaccharide was identified.
To test previous hypotheses on adaptations of sponge-associated bacteria on a broader spectrum of the microbiome of A. aerophoba while also taking a step forward in methodology, I developed a bioinformatic pipeline to combine metagenomic Illumina short-read sequencing data with PacBio long-read data. At the beginning of this project, no pipelines to combine short-read and long-read data for metagenomics were published, and at time of writing, there are still no projects published with a comparable aim of un-targeted assembly, binning and analysis of a metagenome. I tried a variety of assembly programs and settings on a simulated test dataset reflecting the properties of the real metagenomic data. The developed assembly pipeline improved not only the overall assembly statistics, but also the quality of the binned genomes, which was evaluated by comparison to the originally published genome assemblies.
The microbiome of A. aerophoba was studied from various angles in the recent years, but only genomes of the candidate phylum Poribacteria and the cyanobacterial sequences from my above-described project have been published to date. By applying my newly developed assembly pipeline to a metagenomic dataset of A. aerophoba consisting of a PacBio long-read dataset and six Illumina short-read datasets optimized for subsequent differential coverage binning, I aimed at sequencing a larger number and greater diversity of symbionts. The results of this project are currently in review by The ISME Journal. The complementation of Illumina short-read with PacBio long-read sequencing data for binning of this highly complex metagenome greatly improved the overall assembly statistics and improved the quality of the binned genomes. Thirty-seven genomes from 13 bacterial phyla and candidate phyla were binned representing the most prominent members of the microbiome of A. aerophoba. A statistical comparison revealed an enrichment of genes involved in restriction modification and toxin-antitoxin systems in most symbiont genomes over selected reference genomes. Both are defense features against incoming foreign DNA, which may be important for sponge symbionts due to the sponge’s filtration and phagocytosis activity that exposes the symbionts to high levels of free DNA. Also host colonization and matrix utilization features were significantly enriched. Due to the diversity of the binned symbiont genomes, a within-symbionts genome comparison was possible, that revealed three guilds of symbionts characterized by i) nutritional specialization on the metabolization of carnitine, ii) specialization on sulfated polysaccharides, and iii) apparent nutritional generalism. Both carnitine and sulfated polysaccharides are abundant in the sponge extracellular matrix and therefore available to the sponge symbionts as substrates. In summary, the genomes of the diverse community of symbionts in A. aerophoba were united in their defense features, but specialized regarding their nutritional preferences.
The microbial communities that live inside the human gastrointestinal tract -the human gut
microbiome- are important for host health and wellbeing. Characterizing this new “organ”,
made up of as many cells as the human body itself, has recently become possible through
technological advances. Metagenomics, the high-throughput sequencing of DNA directly from
microbial communities, enables us to take genomic snapshots of thousands of microbes living
together in this complex ecosystem, without the need for isolating and growing them.
Quantifying the composition of the human gut microbiome allows us to investigate its
properties and connect it to host physiology and disease. The wealth of such connections was
unexpected and is probably still underestimated. Due to the fact that most of our dietary as well
as medicinal intake affects the microbiome and that the microbiome itself interacts with our
immune system through a multitude of pathways, many mechanisms have been proposed to
explain the observed correlations, though most have yet to be understood in depth.
An obvious prerequisite to characterizing the microbiome and its interactions with the host is
the accurate quantification of its composition, i.e. determining which microbes are present and
in what numbers they occur. Historically, standard practices have existed for sample handling,
DNA extraction and data analysis for many years. However, these were generally developed for
single microbe cultures and it is not always feasible to implement them in large scale
metagenomic studies. Partly because of this and partly because of the excitement that new
technology brings about, the first metagenomic studies each took the liberty to define their own
approach and protocols. From early meta-analysis of these studies it became clear that the
differences in sample handling, as well as differences in computational approaches, made
comparisons across studies very difficult. This restricts our ability to cross-validate findings of
individual studies and to pool samples from larger cohorts. To address the pressing need for
standardization, we undertook an extensive comparison of 21 different DNA extraction methods
as well as a series of other sample manipulations that affect quantification. We developed a
number of criteria for determining the measurement quality in the absence of a mock
community and used these to propose best practices for sampling, DNA extraction and library
preparation. If these were to be accepted as standards in the field, it would greatly improve
comparability across studies, which would dramatically increase the power of our inferences
and our ability to draw general conclusions about the microbiome.
Most metagenomics studies involve comparisons between microbial communities, for example
between fecal samples from cases and controls. A multitude of approaches have been proposed
to calculate community dissimilarities (beta diversity) and they are often combined with
various preprocessing techniques. Direct metagenomics quantification usually counts
sequencing reads mapped to specific taxonomic units, which can be species, genera, etc. Due to
technology-inherent differences in sampling depth, normalizing counts is necessary, for
instance by dividing each count by the sum of all counts in a sample (i.e. total sum scaling), or by
subsampling. To derive a single value for community (dis-)similarity, multiple distance
measures have been proposed. Although it is theoretically difficult to benchmark these
approaches, we developed a biologically motivated framework in which distance measures can
be evaluated. This highlights the importance of data transformations and their impact on the
measured distances.
Building on our experience with accurate abundance estimation and data preprocessing
techniques, we can now try and understand some of the basic properties of microbial
communities. In 2011, it was proposed that the space of genus level variation of the human gut
microbial community is structured into three basic types, termed enterotypes. These were
described in a multi-country cohort, so as to be independent of geography, age and other host
properties. Operationally defined through a clustering approach, they are “densely populated
areas in a multidimensional space of community composition”(source) and were proposed as a
general stratifier for the human population. Later studies that applied this concept to other
datasets raised concerns about the optimum number of clusters and robustness of the
clustering approach. This heralded a long standing debate about the existence of structure and
the best ways to determine and capture it. Here, we reconsider the concept of enterotypes, in
the context of the vastly increased amounts of available data. We propose a refined framework
in which the different types should be thought of as weak attractors in compositional space and
we try to implement an approach to determining which attractor a sample is closest to. To this
end, we train a classifier on a reference dataset to assign membership to new samples. This way,
enterotypes assignment is no longer dataset dependent and effects due to biased sampling are
minimized. Using a model in which we assume the existence of three enterotypes characterized
by the same driver genera, as originally postulated, we show the relevance of this stratification
and propose it to be used in a clinical setting as a potential marker for disease development.
Moreover, we believe that these attractors underline different rules of community assembly and
we recommend they be accounted for when analyzing gut microbiome samples.
While enterotypes describe structure in the community at genus level, metagenomic sequencing
can in principle achieve single-nucleotide resolution, allowing us to identify single nucleotide
polymorphisms (SNPs) and other genomic variants in the gut microbiome. Analysis
methodology for this level of resolution has only recently been developed and little exploration
has been done to date. Assessing SNPs in a large, multinational cohort, we discovered that the
landscape of genomic variation seems highly structured even beyond species resolution,
indicating that clearly distinguishable subspecies are prevalent among gut microbes. In several
cases, these subspecies exhibit geo-stratification, with some subspecies only found in the
Chinese population. Generally however, they present only minor dispersion limitations and are
seen across most of our study populations. Within one individual, one subspecies is commonly
found to dominate and only rarely are several subspecies observed to co-occur in the same
ecosystem. Analysis of longitudinal data indicates that the dominant subspecies remains stable
over periods of more than three years. When interrogating their functional properties we find
many differences, with specific ones appearing relevant to the host. For example, we identify a
subspecies of E. rectale that is lacking the flagellum operon and find its presence to be
significantly associated with lower body mass index and lower insulin resistance of their hosts;
it also correlates with higher microbial community diversity. These associations could not be
seen at the species level (where multiple subspecies are convoluted), which illustrates the
importance of this increased resolution for a more comprehensive understanding of microbial
interactions within the microbiome and with the host.
Taken together, our results provide a rigorous basis for performing comparative metagenomics
of the human gut, encompassing recommendations for both experimental sample processing
and computational analysis. We furthermore refine the concept of community stratification into
enterotypes, develop a reference-based approach for enterotype assignment and provide
compelling evidence for their relevance. Lastly, by harnessing the full resolution of
metagenomics, we discover a highly structured genomic variation landscape below the
microbial species level and identify common subspecies of the human gut microbiome. By
developing these high-precision metagenomics analysis tools, we thus hope to contribute to a
greatly improved understanding of the properties and dynamics of the human gut microbiome.