The first step of our project is the optimization of a cell-free expression system as a manufacturing platform for bacteriophages. For our purpose, it was necessary to produce a high-quality cell extrac, in a reproducible and easy manner. Considering a possible commercial use of our product we deemed it necessary to test the potential of upscaling of our preparation protocol.
We decided on the following optimization goals:
increase protein content
find reproducible methods of quality control
test cell cultivation in a bioreactor to enable upscaling
find optimal lysis conditions, that produce high-quality extract
test upscaling of cell lysis
produce cell-extract that allows phage assembly
Cultivation
Cultivation is the first step in cell extract preparation. The original cell extract preparation protocol uses shaking flask cultivation for biomass production and states that cell harvest at OD 1,8-2,0 is strictly required to produce high-quality extract. To be able to transfer this to the bioreactor we first obtained growth data for both shaking flask and bioreactor cultivation.
growh fermenter vs. shaking flask side by side with FI mtq2
The growth curve from shaking flask cultivation showed us that harvest at OD 1,8-2 correlates to the mid-to late logarithmic growth phase. In bioreactor fermentation this correlates to OD between 4 and 6. We decided to test which of these ODs is best to harvest cells for cell extract preparation.
FI shaking flask vs fermenter
This experiment gave 2 important results:
the optimal OD to harvest culture from the bioreactor is around 5. This gives the highest protein content and also the best expression.
preparing cell extract from bioreactor cultivated cells under comparable conditions gives equal quality extract than cell extract from shaking flask cultivated cells.
In our small lab-scale bioreactor with 2 L cultivation volume we were able to obtain 20 g cell pellet. 2 L cultivation in shaking flasks only yields 4,5 g pellet.
Lysis
Of the three commonly used methods of cell lysis, each has teir advantages and drawbacks. For cell extract preparation bead beating is most established, but the caveat there, is that it doesn’t leave room for upscaling, besides being tedious and time-consuming. Therefore, we decided early on to focus our optimization approach on Sonication as method for cell lysis.
Discussion goes here
Discussion goes here
Processing
The suggested steps in cell extract processing are:
run-off reaction
dialysis
storage at -80°C
We found that the run-off reaction as suggested in the Sun et.al paper is indeed beneficial and that dialysis is superior to diafiltration via centrifugal filters.
Quality Control
Storage
For long term storage of our cell-extract we decided to try lyophilization. After initial tests we found that:
cell extract quality is only preserved when cell-extract is mixed with the txtl- reaction buffer prior to lyophilization.
the retention of quality does not depend on the size of lyophilized extract aliquots.
We then tested the expression quality of several of our home-made cell extracts from fresh vs. lyophilized aliquots.
plot: lyophilisation
growh fermenter vs. shaking flask side by side with FI mtq2
Our two tested samples of cell extract retained 70 and 90 % of expression quality respectively after lyophilization.
Bacteriophage Assembly
Home-Made Systems Can Be Compared To Commercial Systems
Cell-Free Systems Allow Host Independent Bacteriophage Assembly
Quality Control Of Bacteriophages
Assembly Of Clinically Relevant Bacteriophages
Sequ-Into Complements Continuous Engineering Cycles in Phactory
Wetlab3 sequenced several phage genomes after preparation using ….
After receiving the phage genomes, one of the first steps to do is analyse the received sequences.
This has been done using an inhouse software poreSTAT [1].
For each sequencing sample, it is analysed how many bases are sequenced, what bp yield has been achieved and how many pore were used.
Considering the sequence in which the reads have been acquired, it can nicely be seen how the used chip gets worn out with each sequencing experiment.
While for the T7 sequencing almost all pores are usable, the used pores decreases with every sequencing experiment …
_*_*
4 missing figures
_*_*
While the T7 experiment produced most reads and most base-pairs sequenced, the remaining three experiments produced less data.
The read and base-pair yield can be seen in Table 1.
TABLE
The actual task here was to assemble the phage genomes from the Nanopore reads.
Particularly Nanopore sequencing is well suitable for genome assembly, since its long reads allow to reduce ambiguity of highly similar and repetitive sequences.
Figure 2 shows the reads distributions of the sequencing experiments.
_*_*
4 missing figures
_*_*
However, during the initial screening it could be seen, that the read lengths do not approach the thought length of the phage genomes in the 50-70kbp range for T7 and 100 kbp range for the remaining phages.
Smaller reads generally lead to more ambiguity and thus are to be avoided from a bioinformatics point of view.
However, since no bioinformatician can change the sequenced data afterwards, we went on with the given data.
There are several tools available for de-novo genome assembly in general, however most approaches are used for short-read genome assembly (2nd generation sequencing, Illumina) and employ a de bruijn graph approach.
Here we have 3rd generation sequencing data which must be handled totally different from old short-read sequencing data: the reads are less perfect in terms of sequencing errors. While short-reads nowadays have error-rates of about 1% (e.g. 1 base out of 100 is reported incorrectly), this error is up to 15% for nanopore sequencing data using newest sequencing chemistry (R9.4 at the time of wiki-freeze).
Thus the number of available assemblers drops dramatically, where canu [2] and miniasm [3] are the most prevalent ones. Both rely on a overlap-layout-consensus approach, which, historically, can be seen as the father of all assemblers (see celera assembler [5]).
We first used canu to assemble our genomes. Unfortunately we have been confronted with a major problem: contamination. We thus developed sequ-into to first detect the contamination and also get rid of contamination-originated reads.
More on the performance and finding while using sequ-into can be found at …[link to /Software].
Particularly for assembly, contamination is bad because it can lead the assembler into wrong directions – depending on the phylogenetic distance of the original sample and the contamination.
After getting rid of the contamination we noticed some strange patterns in the phage genome assemblies after re-aligning the reads to the assembly, which can be seen in Figure 3.
*_*_
missing figure
*_*_
In theory we can expect a uniform coverage over the full genome, since there is no bias for read template generation during sample preparation (thanks to random primers).
However, what we saw here is that the first part has lower coverage than the remaining part and there is a high-coverage region in the middle of the assembled genome.
Since we know phage genomes may have repitions, we thought to splite the genome in the middle and reorganise the structure to no avail.
We thus tried to use the other assembler, miniasm, which is known for very fast assemblies, with little error correction.
However, this error correction can be achieved by combining miniasm with minimap for read mapping and racon for polishing the sequences.
Thus, the assembly pipeline changed to the following calls:
MAY BE COLLAPSIBLE
#!/usr/bin/env sh
INREADS=$1
ASMFOLDER=$2
ASMPREFIX=$3
THREADS=$4
if [ -z "$4" ]
then
THREADS=4
fi
# path to used executables
MINIMAP2=minimap2
MINIASM=miniasm
GRAPHMAP=graphmap
RACON=racon
# first we must overlap all reads with each other
$MINIMAP2 -x ava-ont -t$THREADS $INREADS $INREADS > $ASMFOLDER/$ASMPREFIX.paf
# then miniasm can create alignment
$MINIASM -f $INREADS $ASMFOLDER/$ASMPREFIX.paf > $ASMFOLDER/$ASMPREFIX.gfa
# extract unitigs from miniasm
awk '$1 ~/S/ {print ">"$2"\n"$3}' $ASMFOLDER/$ASMPREFIX.gfa > $ASMFOLDER/$ASMPREFIX.unitigs.fasta
# align reads with unitigs
$MINIMAP2 $ASMFOLDER/$ASMPREFIX.unitigs.fasta $INREADS > $ASMFOLDER/$ASMPREFIX.unitigs.paf
# find contigs from unitigs
$RACON $INREADS $ASMFOLDER/$ASMPREFIX.unitigs.paf $ASMFOLDER/$ASMPREFIX.unitigs.fasta > $ASMFOLDER/$ASMPREFIX.contigs.fasta
~/progs/minimap2/minimap2 -x map-ont -a -t$THREADS $ASMFOLDER/$ASMPREFIX.contigs.fasta $INREADS > $ASMFOLDER/$ASMPREFIX.reads.mm2.sam
$GRAPHMAP align -r $ASMFOLDER/$ASMPREFIX.contigs.fasta -d $INREADS -o $ASMFOLDER/$ASMPREFIX.reads.gm.sam
And can be started simply from the command-line using: ./assemble.sh
This finally led to a good assembly after rearranging the middle part which initially was “over-expressed”.
Proposition of new bacteriophage genomes
After having a core genome we want to check how many protein-coding genes we can find on the genome.
For this task, again several programs exist. Two of the more common programs are glimmer [7] and genemark [8].
Because the reputation among the target audience is higher for genemark [9] we used this tool for the genome annotation.
We ran the tool on the assembled genome in FASTA format generating a gene annotation file (gff3) for the genome highlighting all coding sequences.
For easier and more compact usage, we transformed the genome in fasta format with the annotation in gff3 into the embl flat file format.
Finally, we can describe the assembled genomes as follows:
TABLE
Using the embl flat file format we visualized the phage genomes in a circular genome diagram plot (Figure 5).
*-+-+*
figures
*_*_*
Here we can see see several things.
For the 3S genome, we can see that at certain position we see a high decrease in the coverage (at 58kbp, 72kbp and 110kbp).
At these positions no reads align to the reference genome.
For the NES genome we can see a similar behaviour at 330kbp. Additionally we can see two spikes at the ends of the genome. However, we could also see …
Finally the FFP genome again has the same problems as the NES genome ends.
Some final bliblubb …
Modular Bacteriophage Composition
Isolation
Phenol-Chloroform Precipitation Achieves High Titers
Encapsulation
Bacteriophages Encapsulated In Alginate Can Withstand Gastric Acid
Phactory yields phages with toxicity levels that allow oral administration to the patient. However, oral delivery requires protection of the phages from rapid degradation in the acidic gastric juice, while direct intravenous application requires additional purification steps. To overcome these hurdles, we prototyped two 3D-printed fluidic devices that can be assembled for less than $5. For oral application, we built a nozzle to encapsulate the phages in monodisperse calcium-alginate microspheres protecting them in the stomach. The alginate solution, ejected from a dispenser needle with a syringe pump, was sheared off by a parallel stream of air. Our results show that after 1 hour incubation in simulated gastric fluid, active phages are successfully released in simulated intestinal fluid. For intravenous administration, we can purify the bacteriophages from the remaining cell-extract via fractionation in a pressure-driven size-exclusion filter system. Additionally, we built microfluidic hardware for our human practice project OraColi.
- Alginat für lokale pH wert erhöhung im magen und auflösung in chelatoren(darm), da Ca2+ Ionen quervernetzen
- Enkapsulierung durch Co-Flow System. Druckluft schert Tropfen von Spritzennadel ab, Düse == Hardware Quervernetzung 1h in CaCl2 Lsg Waschen Verifikation durch Mikroskopie Fertig für Einsatz (insg. Ca 1.5-2h)
Experimente
1. 3D Modell von SYBR Gold gelabelten Phagen im Droplet durch z-Stack (BF/GFP)
2. Darkfield Bilder von gelabelten Droplets (bild)
3. Droplet Zusammensetzung (Viskosität zweier Alginate)
4. Droplet Größen bei Flussrate und Druck
5. Verhalten im Magen bzw. saurem Millieu (pH: 1) und Pepsin (1h @37°C, 10h @ RT) -> Phagen Degradation (barplot)
6. Verhalten im Darm bzw. pH 7 und Pankreatin (2h @ 37°C) ->Phagen relase über Zeit (plot)
