1. Balance of the 2021#

1.1. Main acivities/tasks/things done in 2020#

  • Experimentally related

    • Population growth experiments to check the viability and growth of pGal-Cdc42 strains on \(\Delta\) bem1 , \(\Delta\) bem1 \(\Delta\) bem3 , and WT background.

      • The experiments were carried out in the Biotek .

      • They took a week per experiment, since they first grow at 30C in the different Gal concentrations during 48 hours , then they are diluted 100X to the same Gal concentrations and measure the growth for another 48h in 36C. (a mistake that is been dragged from previous Ilse experiments)

      • It was proven that the sfGFP in those conditions do not affect the growth behaviour of the strains.

      Failing experiments

      • Strain construction without the fluorophuore of \(\Delta\) bem1 , \(\Delta\) bem1 \(\Delta\) bem3, due they were contaminated with mutations or other strains in their original stocks from Werner.

      • The strategy followed was in rich galactose media , manually delete BEM1 since in that media they can survive. The “mistake” was not to check in all the transformed colonies for the abscence of BEM1 and the presence of the KanMX cassete. This lead to take aneuplidy colonies that have BEM1 and the KanMx at the same time. This is a common problem of difficult transformations.

    • Strain construction for SATAY

      • Succesfully made \(\Delta\) bem1 \(\Delta\) bem3 from \(\Delta\) bem3 mutant. (done by my Master student Thomas)

      Failing experiments

      • \(\Delta\) bem1 \(\Delta\) nrp1

      • \(\Delta\) bem1 \(\Delta\) nrp1 \(\Delta\) bem3

    • SATAY

      • On \(\Delta\) bem1 \(\Delta\) bem3

      • \(\Delta\) bem1-AID (degradable system upon AUXIN addition)

      • \(\Delta\) bem1-AID \(\Delta\) bem3

      • \(\Delta\) bem3

  • Theoretical/software related

    • SATAY data analysis pipeline completed with the support of the Digital Competence Center in TU Delft , specifically the team of the Research Software Engineers . I worked with Maurits Kok.
      Outputs:

      • Python software package (Transposonmapper) to make downstream analysis of the processed transposon data. Or to make the complete analysis from the raw data for Linux users.

      • A docker image for the complete analysis in LINUX. This docker image encapsulates the bash script that Gregory made. The great advantadge of this is that the analysis can be also used by Windows and Mac users.

    • Bash script for converting the raw data from sequencing to the format the GUI understands for the analysis.

      • In a nutshell , is taking the reads that contain the sequencing primer and stack them. These are the so called forward reads. This function is using the program grep which will search in the string of characteres for the target one (in this case the sequencing primer ). Possibly in real life we should allow for mutations of this target sequence, so more reads are taken into account. Here are the stats of the analysis:

Strain

Total Forward reads

Reads after trimming

Reads after alignment

Reads on genes

Reads on Ade2

ylic137_a

57385778

99%

95%

10083210 ~17.5%

1003795 ~ 10%

ylic137_b

62180340

99%

93%

7122010 ~11%

1060793 ~15%

yTW001_a

68221798

99%

96%

3989352 ~6%

1260353 ~31%

yTW001_b

66415830

99%

96%

7774150 ~12%

1298680 ~16%

yWT003_b

68027344

99%

73%

2444615 ~4%

745860 ~30%

  • Daily practices

    • Keep using and improving my local electronic labnotebook in Visual Studio Code, using the same experimental template as before.

    • For every coding/ analysis project , open a new repository with the basic structure of :

    ├── data

    ├── docs

    ├── figures

    ├── postprocessed-data

    └── src

    • Use of the bash for handling and analysis of data .

  • Capacities acquired

    • Research software development (beginner level)

    • working with LINUX

    • Became a Carpentry Instructor . I taught my first lesson on a Software Carpentry about GIT.

  • PhD practicalities

    • Successful 2nd Yearly meeting.

    • Discipline related skills ECTS:

    • Transferables related skills ECTS:

    • Research related skills ECTS:

1.2. Reflection from the outlook to the 2021(in the Balance from 2020)#

1.3. Reflections#

  • This year has continued with corona, though some vaccines has been developed.

  • I have had a good combination with software and experimental projects in this year.

1.4. Retrospective from the tasks proposed for 2021#

1.4.1. Plan for 2021#

  • Finish the Fridtjof paper, about the evolution of self organization related functions,and integrate this results with my original PhD.

    • Sent to Elife and rejected because of lack of match between the paper title and aims and the experimental back up.

    • Working on more suplementary experiments to back up some important controls associated with demonstrating that the sfGFP does not influence the behaviour of the strains with/without the Gal promoter.

  • Write a paper with the results of the mapping of essential/non essential genes from the bem1\(\Delta\) , bem1\(\Delta\)bem3\(\Delta\) and bem1\(\Delta\)bem3\(\Delta\)nrp1\(\Delta\) backgrounds. (\(\Delta\) - symbolyze knockout)

    • Still working on that. So far I have done SATAY from \(\Delta nrp1\) and WT. Next one can be \(\Delta bem3\) and \(\Delta\) bem3 \(\Delta\) nrp1 .

Output

  • Still on the plan , not yet finished. Though all the data is present, from the whole team.

  • \(\Delta bem1\) is a tricky strain due to its high instability. A master student from Enzo , called Wessel, has been working on that , and managed to do a SATAY experiment in this background using an auxin degradable protein system . With this , bem1 gets degraded in situ just before the reseeding , which is the fitness step (regrowth of the libraries) in SATAY. Still the results has not been sequenced.

  • Write a paper about, how gene expression can influence genetic interactions in bem1\(\Delta\) strains. Specifically, how Cdc42 changes in gene expression can alter the SATAY map of essential and non-essential genes of the Bem1 deleted strain. Or perhaps, engineer a bem1 promoter to control its native expression in the cell.

    • Interesting idea but difficult to tune the gene expression from Cdc42. We have currently a strain made by Els which has Bem1 under a galactose inducible promoter.

Output:

  • Not realisic plan , will involve many more experiments and the tuning of the gene expression with the galactose promoter seems really tricky.

  • Write a paper about how the correlation between type of interactions and shared interactors, can be extended to more than one gene deletion. Basically is asking the question, how in a double deleted background, can we identify possible positive, negative and synthetic lethal interactions? with the results of SATAY with strains with multiple mutations.

    • This is a mindset of constantly looking for correlations among biological properties like gene ontology terms , protein domains, pathways , etc with genetic interactions. So far when looking at all genes to the shared number of interactors the correlation is not very clear , but there is a tendency for SL to share more interactors and also GO terms.

Output:

  • This could be something to dig when I start to make a model from a machine learning algorithm that takes all these features per gene interaction pairs.

  • Test that in order to nrp1 be deleted to improve the overall fitness of the organism, in bem1\(\Delta\)bem3\(\Delta\) background, the hub genes cla4, cdc3 and act1 have to be present. Therefore, I am planning to perform a “short” evolution experiment starting with bem1\(\Delta\)bem3\(\Delta\) background to test the nrp1 deletion in 4 different genetic lines:

  • bem1\(\Delta\)bem3\(\Delta\)cdc3\(\Delta\)

  • bem1\(\Delta\)bem3\(\Delta\)act1\(\Delta\)

  • bem1\(\Delta\)bem3\(\Delta\)cla4\(\Delta\)

  • This could be tested with SATAY on those triplicates and double mutants , by finding in the double mutants a significant higher reads on nrpi1 locus than in the triplicates where the “hub” genes are deleted.

Output:

  • This is not something realistic to do . It will involve more experiments , and the hypothesis which is that if the common interactors of bem1,bem3,nrp1 and bem2 are not present then nrp1 can not become a positive interactor when bem1 and bem3 are removed from the network is not so strong. These common interactors , are also highly connected genes in the yeast genetic interaction network , so their removal may affect many other processes that could cause that nrp1 deletion dont cause any improvement. Yet the explanation is because hidden effects.

1.5. Outlook for 2021#

  • My focus in this year will be experimental , I will try to do the biggest number of experiments to get the data from .

Output:

  • I actually did a combination of both, because I spent a lot of time transforming our SATAY pipeline into a FAIR software product capable of being distributed among other users from the community.

  • I have realized that the data analysis / modelling and the conceptualization tasks are better to perform alone when writing the thesis for example.

Output:

That is not totally true, the most important is to have goals and concrete tasks to achieve , regardless whether they require software or experimental skills to accomplish them.

1.5.1. Main experiments for 2021#

  • done

    1. Strain construction:

    • \(\Delta bem3\)

    • \(\Delta bem3\Delta nrp1\)

    • \(\Delta bem3\Delta bem1\)

    • \(\Delta bem3\Delta bem1\Delta cla4\) for testing linkage model

    • \(\Delta bem3\Delta bem1\Delta cdc3\) for testing linkage model

    • \(\Delta bem3\Delta bem1\Delta act1\) for testing linkage model

  1. SATAY on:

  • all strains above

2021:

  • \(\Delta bem3\)

  • \(\Delta bem3\) \(\Delta bem1\)

  1. Analyse sequencing data

  • done

  1. Make the software pipeline FAIR

  • done

2. Perspective for 2022 and end of my PhD#

  1. Thesis writing , most of it before March due to Maternity leave :D I will have my baby at the end of April. I would like to have finished the chapters 1, 2, 3 and 4…

  2. Global data analysis on how genes change the essentiality among backgrounds. See here

  3. Refine fitness calculation with the contribution of the domains and non domains regions within a gene.