Team:Munich/Results

Phactory

Results

First Blick of Phactory

A generic square placeholder image with rounded corners in a figure. — BIG BROTHER IS WATCHING YOU

Optimizing a Cell-Free System

Cultivation

Lysis

Processing

Quality Control

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)