Although viruses may be the most abundant biological entities on the planet, very little is known about phage-host interaction in nature due to the absence of proper experimental tools. VBR with the radius of the bacterium for a single phage-host system 1-8 Figure 1.2 Schematic depiction of bacterial and viral species and strains 1-13 Figure 1.3 Positive feedback evolution model for emerging bacterial and viral species 1-16 Figure 1.4 Total concentration " absorbed" by the evolving bacterial strain and its
List of Tables
Introduction
- Preface
- Some facts phages in nature
- Abundance and activity
- Lytic or lysogenic?
- Phage-host interaction
- Predator-prey dynamics
- Population control versus species control
- Kill the winner hypothesis
- The bacterial-viral “arms race”
- A coarse-grained view of phage-host interaction
- The biophysics of a single phage-host system Two perspectives: biophysical versus dynamical
- The biophysics of many phage-host systems How many is many?
- The evolutionary perspective
- A model for co-speciation of viruses and bacteria
- Is positive feedback driving co-speciation?
- The experimental frontier
- Phage-host co-localization methodology
- The case of the termite hindgut
- The measured end-point fluorescence from the rRNA channel (right half of each chamber) and the terminase channel (left half of each chamber) in a microfluidic array panel. B
- Stress fibers in single fibroblast cells
- References
We find that the most critical parameter that determines the fixed-point concentration of a phage-host pair in the environment is the radius of the bacterium (Fig. 1.1). In the process of the arms race, viruses cause selective sweeps in the bacterial population.
Probing Individual Environmental Bacteria for Viruses Using Microfluidic Digital PCR
Abstract
Introduction
Proposed method for phage-host co-localization
Hunting for phages in the termite hindgut
Further analysis revealed that the viral spirochete genes were part of a larger prophage-like element, with the majority of the recognizable genes most closely related to the phage genes of Siphoviridae ( 19 ). We chose the large terminase subunit gene as a viral marker gene for this prophage-like element.
Identification of novel uncultured bacterial hosts
Treponema phylotypes in the survey comprising ~30, 10 and 9% of the free-swimming spirochetal cells (REPs 1, 2 and 3 in Fig. 2.3; see also Fig. 2.7 and Table 2.6) were never recovered together with the viral marker. gene, to the extent that this target was spanned by our degenerate primers. Given that the degenerate core region (17) of each primer targets residues strictly conserved in gut microbes from highly diverse termite samples (Fig. 2.5), and that these primers successfully amplified this gene from the guts of many different termite species (see above), it appears that these strains are most likely either insensitive to this virus or that only a small percentage are infected (19).
Phage-host cophylogeny
Overall, phage terminase alleles associated with different bacterial hosts were significantly divergent with only three exceptions (Table 2.8). Indeed, using the P test (34) implemented in Fast UniFrac (35) to terminate alleles grouped by bacterial host revealed significant differences between alleles associated with most host pairs (Table 2.9).
Conclusions
The fact that there was little mixing between terminase alleles associated with Host I (V1) and the more closely related Hosts II (V3 and V4) and III (V4), while alleles from the more closely related Hosts II and III (Table 2.5 ) showed a certain degree of admixture (Q4), supporting the idea that the probability of cross-species transfer or lateral gene transfer decreases with the phylogenetic distance of the hosts (37). The rRNA gene of Hosts I to IV also exhibited patterns of microdiversity that may have physiological relevance (38-39) but only reflected by the terminase alleles of Host III.
Appendix
- Contents
- Materials and methods 2.9.2 Supporting text
- Materials and methods
- Supporting text
- Statistical analysis of co-localization in digital PCR microfluidic arrays
- The viral marker gene and its genetic context
- Supporting figures
In Table 2.7, selection pressure was estimated for individual bacterial hosts using several additional methods and resulted in the same conclusion. These types of non-colocalized FAM hits should not contribute to false co-localization or contribute minimally because samples with mixed/chimeric 16S rRNA footprints are discarded from the analysis and the probability of repeatedly amplifying the same 16S rRNA error is extremely small (see discussion below). 3) 16S rRNA qPCR efficiency was measured at ∼60% for ZAS-9 genomic DNA (see Materials and methods). The number of actual co-localizations in a panel of each 16S rRNA target with each terminal target (ie, the total set from which we derive successful hits) would be averaged.
As mentioned at the beginning of this section, the calculated P values for hosts I to IV were very small (P < 10-4) allowing us to reject the null hypothesis, i.e., repeated ribotypes I–IV do not can be explained by the random coefficient. -localization of these ribotypes with free-floating terminal targets. We would like to estimate the mean number of returns where one of the observed hosts co-localized by chance with a terminus (resulting in two terminals - the host and the free-floating terminal, or, in the case where the host terminus was not amplified or was not present, a wrong conclusion). If retrieval failed (i.e., the rRNA co-localized with a false FAM target) a new retrieval trial was attempted until successful (these silent trials were not counted as successful replications).
- Supporting tables
ZA2-2i was selected for alignment because this gene was found to be present in the largest (43.5 kb) prophage-like element of the ZAS genome (see Supporting Text). Similarity analysis of a termite-associated terminase gene and a portal protein gene with similar homologues. The following table describes the BLAST analysis results of the large terminase subunit gene (411 aa in length) and the portal protein gene (396 aa in length) found in T.
The distribution of the test statistic (D) is estimated to be normal, since the number of nucleotides contributing to dS and dN was sufficiently large (>10), which allowed testing the null hypothesis using a one-tailed (Z > 0) Z-test (S80).
Sequence 2 % p-distance (705 bp )
- References
MetaCAT — Metagenome Cluster Analysis Tool
Introduction
This spectrum can be used to quickly identify the "big players" in terms of genes present/genes expressed in the given environment. In this scheme, each known reference gene is blasted against the metagenome (instead of vice versa) and the number of significant hits in the metagenome is counted. So in principle each gene in the reference library is given a score which is the number of significant hits that gene received in the metagenome.
Furthermore, many genes in the reference library yield only thin homologies and can be discarded by placing an E value threshold for the best alignment of a given reference gene.
The MetaCAT algorithm
- Overview
- The MetaCAT algorithm in detail
This list is called the "coverage" of the particular known gene in the metagenome. The number of gene objects in this list is interpreted as the abundance of the particular known gene in the metagenome. The result is a fairly long table describing for each gene in the reference database its abundance in the metagenome.
Similar genes in the reference database (eg, closely related homologous genes) may have an overlapping coverage in the metagenome.
Extracting best E value scores and abundances of known reference genes in metagenome. MetaCAT reads the resulting BLAST table and for each known reference
- Future directions
- Software operation
- First-time run on a metagenome
- Output files generated
- Subsequent runs of MetaCAT
- Installation instructions
- System requirements
- Installation
- Troubleshooting
- Downloading and combining RefSeq files
- MetaCAT folders
- Known bugs
- Description of additional output files
Therefore, there may still be redundancy present in the final list of declared MGOs to be removed. The true abundance of a gene object in nature is proportional to the number of reads found in the database and not the number of times this gene object occurs in the metagenome. 1 If you have already installed blast 2.2.22+ in the default MetaCAT directory, this step is automatically skipped.
This utility combines all files in the given directory, except for the Matlab source, into one file called "combined_all".
RefSeq gene
RefSeq gene definition
Metagenome gene object ID with lowest E value
E value
Alignment length (amino acids) Number of amino acids in the alignment
RefSeq gene length (amino acids)
Index
ID of the RefSeq gene as shown in the definition line of the RefSeq FASTA file (extracted by BLAST). The "Definition" field of the RefSeq gene as shown in the GenPept file (or, if a RefSeq FASTA file was submitted, the definition of the RefSeq gene as shown in the FASTA definition line).
Min % of shared metagenome gene objects
Alignment length (amino acids)
GenPept Features
Metagenome gene object ID with lowest E value
The number of RefSeq genes associated with the given RefSeq gene, including the given RefSeq gene, i.e., the number of cluster members including the cluster representative. Number of identical amino acids in the MGO alignment with the lowest E value and the given RefSeq gene.
E value
Alignment length (amino acids)
RefSeq gene length (amino acids)
RefSeq gene definition
Definition field for the given RefSeq gene as it appears in the GenPept file (or if a RefSeq FASTA file is provided, the RefSeq gene definition as it appears in the FASTA definition line). If the sequence has a coding region (CDS), the description may be followed by a completeness qualifier, such as The GenBank partition field for the given RefSeq gene as it appears in the GenPept file.
GenBank division field — "The GenBank division to which a record belongs is indicated by a three-letter abbreviation.
GenPept molecule type
GenPept source
GenPept classification
GenPept comments
Additional COMMENTS are provided for some records to provide information about sequence function, notes on the aspects of curation, or comments describing transcript variants.” [19].
GenPept Features
Protein products: Signal peptide and mature peptide annotation is provided by reproduction from the GenBank submission on which the RefSeq is based, if provided by the contributing group or when determined by the curation process. Domains: “Domains are calculated by matching the NCBI database of conserved domains for human, mouse, rat, zebrafish, nematode, and cow. For some records, administrative staff may annotate additional functionally important regions of the protein.
3. 7 References
The Biophysics of Prokaryotic and Viral Diversity in Aqueous Environments
Abstract
103 prokaryotic species in a maximum of ~102 to ~104 liters of water, consistent with current empirical estimates of species richness. We use this observation to calculate an absolute lower and upper bound for the total number of active bacterial species in the ocean water column (excluding sediment), by considering the case of completely homogeneous oceans and maximally heterogeneous oceans. We find that the number of species in the ocean water column should be between 104 and 1021.
Introduction
We derive basic relationships for the total concentration of bacteria and their viruses in the environment, and a basic relationship for the total prokaryotic mass in the environment. We will further argue that the precise definition of a species is beyond the scope of our biophysical model. Therefore, in Chapter 5 we will consider an evolutionary model for the generation of bacterial and viral species, consistent with the definition of a species used herein.
The evolutionary model is the first step in linking the predictions of the biophysical model described in this chapter with empirical observations of diversity in the environment.
General assumptions
- Decoupling phage-host systems
- Host mortality Causes of mortality
- Virus decay
- The physiological state of the host
- Bacterial and viral abundance distribution
In surface water for example, viruses are thought to be responsible for ~10–50% of total bacterial mortality, while in environments in which protists do not thrive, such as low-oxygen lake water, viruses are thought to be responsible for 50–100% of bacterial mortality [4 ]. Regardless of the mechanism controlling the total concentration of bacteria, in our model we simply assume that the total concentration of bacteria is fixed by some process and refer to this limiting factor as the "carrying capacity" of the environment. Note that we have not defined exactly what a bacterial "species" is or what a viral "species" is.
For example, in the cold waters of the Barents Sea in the Arctic Ocean, growth rates were estimated to range between 0.05 and 0.25 day-1 [22] while in the warmer coastal water near Santa Monica growth rates were measured to be more high, ~1–3 day-1 [18].
A biophysical model of phage-host interaction
- Model development part I: A single phage-host system .1 Viral diffusion and infection rate
- Predator-prey relations
- The virus diffusion constant
- The virus-to-bacterium ratio for a given phage-host system
- Correlation between burst size and host/virus dimensions
- Dependence of host concentration on bacterium size
- Large bacteria are rare
- Application of the model to environmental systems The Synechococcus phage-host system in the Gulf of Mexico
- Model development part II: Non-interacting phage-host systems .1 A stochastic interpretation of bacterial and viral parameters
- A simple evolutionary scenario
- The size spectra of bacteria in aqueous environments
- Possible deviation from a uniform distribution
- Total bacterial concentration
- Species richness
- What is a species?
- Volume of diversity
- Species density
- Observed species diversity in nature .1 Estimates of microbial diversity
- Bounds on global marine diversity
- Factors determining species richness Nutrient availability
7 is the dependence of the concentration of the bacteria on the fourth power of the ratio Rvirus( )i Rbact( )i. Using the diffusion constant of the virus, one can also calculate the fixed point concentration of viruses using Eq. To calculate the concentration of the bacteria, you need to know the viral decay rate and the burst size.
10, we find that the average concentration of bacteria expected to exist in a given environment is given by
