| Abstract |
Rhizobia are soil and rhizospheric bacteria that form nitrogen fixing symbioses in
leguminous plants allowing their growth in poor nitrogen soils. Several rhizobia traits,
associated with secondary metabolism activities, secretion systems, biofilm formation etc,
have been termed Plant-Growth-Promoting traits, since they assist the plant’s growth. Whole
genome sequencing on rhizobia isolates has been established as a common practice,
nowadays. Several genera and species in the Rhizobiaceae family have been characterized for
their Plant-Growth-Promoting capabilities. Novel symbiovars and genospecies constantly
arise within the Rhizobiaceae family. In this study, we constructed chromosome-level
assemblies from sequencing data from isolates in Greece and attempted to characterize their
properties using. We identified their phylogeny using traditional and novel methods.
Moreover, we attempted to discover genomic differences between those isolates and some
type strains.
Microbial time series analysis, typically, examines the abundances of individual taxa
over time and attempts to assign etiology to observed patterns. This approach assumes
homogeneous groups in terms of profiles and response to external effectors. These
assumptions are not always fulfilled, especially in complex natural systems, like the
microbiome of the human gut. It is actually established that humans with otherwise the same
demographic or dietary backgrounds can have distinct microbial profiles. We suggest an
alternative approach to the analysis of microbial time series, based on the following premises
a) microbial communities are organized in distinct clusters of similar composition at any time
point, b) these intrinsic subsets of communities could have different responses to the same
external effects, c) the fate of the communities is largely deterministic given the same
external conditions. Therefore, tracking the transition of communities, rather than individual
taxa, across these states, can enhance our understanding of the ecological processes and allow
prediction of future states, by incorporating applied effects. We implement these ideas into
Cronos, an analytical pipeline written in R. Cronos’ inputs are a microbial composition table
(e.g., OTU table), their phylogenetic relations as a tree and the associated metadata. Cronos
detects the intrinsic microbial profile clusters on all time points, describes them in terms of
composition and records the transitions between them. Cluster assignment, combined with
the provided metadata, are used to model the transitions and predict samples' fate under
various effects. We applied Cronos on available data from growing infant’s gut microbiomes
and we observe two distinct trajectories corresponding to breastfed and formula fed infants
that eventually converge to profiles resembling those of mature individuals. Cronos is freely
available at: https://github.com/Lagkouvardos/Cronos
|
Composition and population genomics of microbial communities
