2021PROJECT CATALOG SciLifeLab Stockholm Summer Intern program

Ilaria Testa (ilaria.testa@scilifelab.se) KTH Supervisor: Dirk Ollech, dirk.ollech@scilifelab.se Project: New modes of optical bio-imaging using reversible switchable fluorescent proteins

One focus of the Advanced Optical Bio-Imaging Lab is to employ reversible switchable fluorescent proteins (rsFPs) for Reversible Saturable Optical Fluorescence Transitions (RESOLFT) super resolution microscopy. RESOLFT su-per resolution microscopy, also referred to as nanoscopy, allows us to lower the spatial resolution by switching the fluorophores of rsFPs between molecular ON and OFF states, which inhibit or permit their ability to fluorescence if exposed to visible light. Recently, we developed rsFPs with modified photo-physical properties, that enable new modes of multicolor nanoscopy in living mammalian cells. Furthermore, we set out to exploit the “long-lived” (µs-ms) molecular states to measure molecular dynamics that are not accessible with present optical techniques.

The summer fellow will be involved in an ongoing project to develop new modes of RESOLFT imaging. He/she will create fusion constructs of rsFPs with different target proteins or functional peptides via molecular cloning. The fusion constructs will be expressed in mammalian cells after gene transfer or purified from bacterial cultures for biophysical characterizations and measurements of molecular dynamics in vitro. These will involve e.g. protein immobilization on specifically modified surfaces for calibration measurements. The imaging modes that have been tested successfully in vitro will be applied for measuring the dynamics of biomolecules inside living cells.

Techniques to use/learn:• Molecular cloning• Expression, purification and characterization of recombinant proteins from bacterial cultures• Surface functionalization for protein immobilization• Gene transfer and expression of recombinant proteins in mammalian cells• Live cell super resolution imaging with custom made, state of the art RESOLFT microscopes• Testing new modes for RESOLFT imaging

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Samuel Flores (samuel.flores@scilifelab.se) Stockholm University Project: Information Theory and Machine Learning for fitting large nucleic acid structures to Cryo-Electron Microscopy density maps

DescriptionCryo-Electron Microscopy (CryoEM) has greatly improved as a result of new algorithms for aligning images and refining by averaging. The size of the complexes taxes modeling ap-proaches based on Molecular Dynamics, which model all atoms independently, and even many multiscale methods which do not properly apply modern alphabets of backbone and base-pairing structure. The Flores Lab at Stockholm University has a long track record of applying multiscale methods using our MMB software, which focuses computer power on limited flexible regions thus enabling large scale conformational changes – including those of the ribosome and entire viruses -- to be modeled with ease. We achieved a 2000x speed improvement in low-resolution density map fitting, compared with existing methods. In a collaboration with Jiří Černý of the Czech Academy of Sciences, we implemented Nucleotide Conformers (NtC’s), a novel alphabet of nucleic acid conformations, to improve the accuracy of our predicted RNA structures. Prior Summer Fellows have been involved with refining and applying the method to systems such as quadruplexes and phage P68. The recruited student will continue this work, with a focus on discriminating the best-fitted structure from mulitple alternatives using information-theoretic and/or machine learning methods.

TechniquesThe student will learn 3D modeling techniques using MMB and complementary structural analysis and viewing tools, as well as machine learning and information-theoretic methods such as mutual information. They will also gain fluency in terminal commands, scripting, and Linux/Unix operating systems in general. Prior computational coursework, in programming and/or bioinformatics, is useful but not strictly required. The student will also learn the theo-ry of nucleic acid structure, Molecular Dynamics, and other biophysical topics.

Supervision planThe student will immediately begin to learn from Samuel Flores at SciLifeLab, to the extent permitted by their coursework. This is a collaboration with Prof. Jiří Černý and Postdoc Mi-chal Maly (Czech Academy of Sciences) will be engaged virtually and possibly visit. The stu-dent has the option to continue along related lines for the MS thesis. Several PhD students in the Elofsson group are working on nucleic acid structure as well, and will form the part of the Fellow’s social environment in the lab, and possibly collaborate. Funding has been applied for in collaboration with Thomas Caulfield (Mayo Clinic, Florida, USA) and Andrei Korostelev (University of Massachusetts, Boston, USA) for a PhD studentship in ribosomal quality control mechanisms, a closely related topic.

SupervisorSamuel Coulbourn FloresSciLifeLab A6220 (Alpha Tr. 6)Department of Biochemistry and BiophysicsStockholm Universitysamuel.flores@scilifelab.seMobil: +46.706000464

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Arne Elofsson (arne@bioinfo.se) Stockholm University Project: Structure prediction of RNA

Title: Structure prediction of RNA Name of PI: Arne Elofsson Name of supervisor of intern (if different): Aditi Shenoy and Arne Elofsson Short description: The Elofsson lab is well known for Machine Learning techniques including Deep Learning, and their application to the prediction of signal peptides, transmembrane domains, and 3D structure. The group has repeatedly participated in the Critical Assessment of Structure Prediction (CASP). The recent success of AlphaFold2 in mostly solving the protein folding problem has inspired us to replicate the feat in a comparatively neglected area -- 3D structure prediction of RNA. Rfam is a collection of Multiple Sequence Alignments (MSAs) -- the main input to AlphaFold2 -- many with associated experimental nuclear coordinate sets which can be used as a gold standard. The Fellow will learn to parse the Rfam MSAs, using techniques such as Mutual Information and Direct Contact Analysis to infer residue-residue contacts. S/he will then compare the results to Gold Standard annotation, extracted using e.g. RNAView. This will be the first stage of a project which can later be completed either by the Fellow (as a thesis) or by another student or postdoc. The results will be highly visible in an Elofsson lab publication, and/or as part of the CASP-like RNAPuzzles community competition. List of techniques to learn and use:

1. Structural features of RNA molecules, including different type of interactions. 2. RNA databases, including Rfam. 3. Mutual Information and DCA. The student will learn these methods to predict different

types of contacts in RNA molecules; the student will not be expected to be able to use and understand existing implementations of DCA, including gaussDCA.

4. Multiple sequence alignments methods. Most MSAs will be obtained from Rfam - but the student is also expected to use alternative methods such as Jackhmmer and Rsearch.

5. Python scripting. The student will already have begun learning Python, but will be guided to use it to parse RNA structures, MSAs and analyze using DCA.

6. Time permitting, the student may begin to learn Deep Learning (DL) as an alternative to MI and DCA, to analyze the MSAs and predict not only existence but also type (e.g. Watson-Crick vs. various noncanonical) of residue-residue contacts.

Supervision: Arne Elofsson will hold weekly individual meetings with the student. Aditi and other members of the Elofsson group will help the student daily. The student will also attend weekly Elofsson group meetings. The student will have a desk in Alpha-6, and socialize with MS and PhD students (given that the pandemia allows on campus work) and postdocs who are also working on deep learning, DCA, and structural applications thereof. The group has long experience in protein structure predictions and deep learning and we will collaborate with Samuel Flores for RNA expertise.

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Rozbeh Jafari (rozbeh.jafari@ki.se) Karolinska Institutet Project: Understanding the biology of acute lymphoblastic leukemia using omics methods

Laboratory PI: Rozbeh Jafari, PhD, Assistant Professor, Chemical Proteomics (email: rozbeh.jafari@ki.se) Janne Lehtiö, PhD, Professor, Oncology-Pathology (email: janne.lehtio@ki.se) Supervisor: Rozbeh Jafari, PhD, Assistant Professor, Chemical Proteomics (email: rozbeh.jafari@ki.se) Co-supervisor: Isabelle Leo, PhD Student (email: isabelle.leo@ki.se) Project description: This internship will focus on integration of multiple omics datasets to examine a cohort of clinical acute lymphoblastic leukemia samples and a biobank of acute lymphoblastic leukemia cell lines. The candidate will have the opportunity to work with proteomics, transcriptomics, and genomics data to develop and validate hypotheses about the biology of these samples. This project would provide the opportunity to contribute to novel biological insights by identification of meaningful shared pathways and markers that characterize subtypes of leukemia, and the goal of this internship project is for the candidate to propose effective cell line models that could be used to model biological processes of interest that are also seen in subtypes of clinical samples. This work will involve both bioinformatics and wet lab validation studies (e.g. western blot, flow cytometry), and the results of the project will inform experimental design for future follow up studies. Patients with acute lymphoblastic leukemia experience poor clinical outcomes if they are resistant to chemotherapy, and the results of this project will enable drug screening and identification of promising therapeutics that could be used to improve outcomes for these patients. In addition to the primary project, this internship will provide guidance and support if the candidate is motivated to develop their own hypotheses and independent inquiries, and development of these projects will be supported by our many omics datasets and analytical resources. Our group is integrated within the cancer proteomics research group at SciLifeLab led by Professor Janne Leht iö, and our shared focus is developing and applying proteomics methods to enable proteome quantification for applications in clinical research. This extends to many interdisciplinary projects, and we are proud to be part of a collaborative, innovative group with diverse expertise spanning methods development, bioinformatics, molecular and cellular biology, statistics, machine learning algorithms, and clinical implementation. As an intern within our group, the candidate would have the opportunity to learn from the work being conducted in other projects, and they will be included in group meetings, journal clubs, and other activities. An ideal fit for this position would be a candidate with an interest in molecular biology, immunology, oncology, proteomics, bioinformatics, or clinical medicine, and the candidate would have opportunities to work at the intersection of wetlab studies and computational analysis. Relevant Techniques: Statistical analysis R-programming language, Python programming language, Unix or linux computing Western blot Cell culture Mass spectrometry Omics data analysis: proteomics, transcriptomics, genetics Drug screening Flow cytometry Supervision plan: The primary supervisor (Rozbeh Jafari) will assume responsibility for the scientific direction, project guidance, and obtaining necessary resources for the project. A PhD student will act as the co-supervisor (Isabelle Leo), and will provide training and day-to-day support as needed.

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Erdinc Sezgin (erdinc.sezgin@ki.se) KI Project: Imaging plasma membrane with smart environment sensitive probes

Project PI: Erdinc Sezgin erdinc.sezgin@ki.se

Project Co-PI: Taras Sych taras.sych@ki.se

Project Description

Cellular plasma membranes are laterally heterogeneous, featuring a variety of distinct sub-compartments that differ in their biophysical properties and composition. A large number of studies have focused on understanding the basis for this heterogeneity and its physiological relevance. Artificial model membranes have been developed and used to study the liquid–liquid phase separation that is believed to underlie the physical principle behind certain membrane heterogeneities in cells. Coexisting lipid domains inherently have different physicochemical properties. A defining property of membrane heterogeneity is differences in lipid packing, which is due to the condensing interactions between relatively saturated lipids and cholesterol. This lipid packing differences in the membranes can be quantified using smart environment sensitive probes in combination with advanced microscopy.

However, there are multiple questions standing to be addressed about the smart environment probes. In this project, we will address on of these questions: how do these probes are distributed in the plasma membrane? To address this, we will study the partitioning of environment sensitive probes between co-existing domains. We will employ cell derived phase separated giant plasma membrane vesicles.

Techniques to be used:

Confocal Microscopy

Scanning Fluorescence Correlation Spectroscopy

Super-resolution STED microscopy

Synthetic membrane systems

Supervision:

Main PI and Co-PI will supervise the student in daily basis. We will train the student for the first two weeks on the technologies needed for the project. Next, student will be encouraged to independently use these technologies. We will have weekly meetings with the student to discuss the progress. Whenever needed, we will arrange additional training. Throughout the project, we will help the student with compiling, analyzing and presenting the data.

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Vicent Pelechano (vicent.pelechano@scilifelab.se) Karolinska Institute (MTC) / SciLifeLab Supervisor: Marcel Tarbier, marcel.tarbier@scilifelab.se Project: Predicting nuclear 3D organization from single-cell gene expression

Project description:

Chromatin is organized into highly complex structures in the nucleus. These structures, in-cluding chromatin loops, topologically associating domains, and transcription factories, regu-late gene expression and play important roles in development and disease.

Current approaches to studying 3D organization are based on DNA crosslinking or imaging of individual sequences. Although useful, some of these approaches fail to capture inter-chro-mosomal interactions while others cannot reveal interactions globally. We therefore propose a novel, complementary approach to studying nuclear organization. Recently, we showed that single-cell co-expression is partially driven by the nuclear organi-zation of chromatin and that such co-expression patterns show promise for regulatory pre-dictions (Tarbier et al. Nat. Commun. 2020). Here we aim to explore the degree to which sing-le-cell co-expression data can be used to predict nuclear organization.

The proposed project will be computational and will focus on single-cell data analysis and method development. The student will have the opportunity to work on cutting-edge app-roaches and to contribute with their own ideas. For this, they must have a solid understan-ding of statistics and need to be confident in using R. They will learn to apply advanced statis-tics to analyze single-cell gene expression data, explore network approaches to model the 3D organization of chromatin, and perform computational validations by integrating their own predictions with public data (e.g. HiC).

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Sarahi Garcia (sarahi.garcia@scilifelab.se) Stockholm University Supervisor: Rui Miao, rui.miao@scilifelab.se Project: Exploring “division of labor” among photosynthetic bacteria for potential biosustain-ability applications

Project description:

In this project, we will explore, for the first-time, a possible division of labor among cyano-bacteria, photosynthetic microbes that grow on carbon dioxide and sunlight. In a first step, we will use cyanobacteria gene knockdown libraries to screen for fitness benefits when ami-no acids are present. The idea is that strains that are auxotrophic for an amino acid may have a fitness benefit over wild-type when the amino acid is available. In consequent steps, we will pair auxotrophs into 2-member symbiotic communities, co-evolve them, and test whether the division of labor can improve overall culture growth. The combination of improved cul-ture fitness and, potentially, chemical productivity would be an important advance for the in-dustrial application of photoautotrophs. MethodsWe will use a CRISPRi gene knockdown library of the model cyanobacterium Synechocys-tis PCC 6803. In these libraries, each mutant is identified by a unique sgRNA integrated into the genome, so that the repressed gene is known, and all mutants in the population can be tracked via NGS-based counting of sgRNAs.

We will culture the libraries (30,000 mutants) for in multiplex, continuous photobioreactors (“turbidostat”) or in shake flasks.  We will screen growth with several different amino acid treatments. Throughout the cultivations, we will monitor population composition via deep sequencing of sgRNA regions. Based on which strains have a fitness advantage with supple-mented amino acid, we can reconstruct auxotrophic mutants and pair them and co-evolve them to exchange amino acids.

Bullet-point list of techniques student will use/learnCRISPR-based gene repressionCultivation of cyanobacteria, either in batch culture or continuous culture.Library preparation and running next generation sequencing Analysis of NGS data, mapping sgRNA reads to genome.

State what type of supervision intern will have during the 8 weeks. For example, are the tasks linked to a PhD-student or postdoc, or is the project a “stand alone”?

The project is a stand-alone project with supervision from a postdoc.

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Marta Carroni (marta.carroni@scilifelab.se) Stockholm University, DBB, cryo-EM Facility Project: Fighting resistance: AAA+ proteases in complex with new antibacterials

Project: “AAA+ proteases in complex with new antibacterials”

PI: Marta Carroni, Cryo-EM Facility, marta.carroni@scilifelab.se

Bacterial resistance to antibiotics is one of the main threats to human and animal health as reported by the World Health Organisation. The WHO report states that “without urgent action, we are heading for a post-antibiotic era, in which common infections and minor injuries can once again kill” and in fact, especially modern surgeries such as organ transplants and chemotherapies become more dangerous as antibiotic-resistant “superbugs” thrive in hospitals. Despite the incredible development in recent years of the so-called biological drugs, very effective in the treatment of cancer and autoimmune diseases, antibiotics for the treatments of bacterial infections are still very “traditional” and only target few cellular regions/activities. Antibiotics target either the bacteria cell-wall and membrane or act as inhibitors in a couple of cellular activities such as protein synthesis and DNA replication and transcription. They work as inhibitors of enzymatic activities and even those of new generation mainly target the protein synthesis machinery. New bacterial targets for novel antibiotic development are required.

Recently ATP-driven bacterial AAA+ proteases have been recognized as possible drug targets (1,2). AAA+ proteases play key roles in bacterial virulence, sporulation and physiology in general. They degrade misfolded or aggregated proteins thus playing as central role in protein quality control of the cell, but also in the regulation of specific pathogenetic paths. They are made of two components: an ATPase unfoldase part, which unfolds protein targets using ATP as fuel and a protease part, which cuts into pieces the unfolded substrate (Figure 1).

Figure 1. Maurer et al., 2019 Schematic representation of the main function and control of the ClpCP AAA+ protease system. Uncontrolled proteolysis leads to bacterial cell-death. The ClpCP complex is one of the major AAA+ protease system in Gram-positive and cyanobacteria and is made of the AAA+ unfoldase ClpC and the protease ClpP. The proteolytic activity is tightly regulated to avoid degradation of functional proteins and to have conversely the timely degradation of stress factors important for virulence (3). The regulation is mediated by co-factor proteins that bind

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the ClpC AAA+ unfoldase. We have contributing to elucidate the mechanism by which the cofactor MecA activates ClpC in S. aureus and we showed that overriding of this mechanism causes bacterial cell toxicity (4). Generally, co-factors bind to the N-terminal regions of ClpC, which is the more variable part of this AAA+ protein and homologue. Thus this region is of particular interest in understanding regulatory effect in different bacteria (e.g. M.tuberculosis, S. aureus) and designing better strategies for efficiently target ClpC for drug development. ClpC has being already identified as the target of the bactericidal cyclic peptides Cyclomarin A (CymA), Lassomycin (Lassmy), and Ecumicin (Ecumy) (5-7) in M. tuberculosis and a co-crystal structure of the N-terminus of ClpC shows clearly where the species-specificity comes from (8) but not how it affects the ClpC activity. To understand it a structural analysis of the full ClpC or even of the full ClpCP complex in complex with the drug is necessary. Very recently, we started elucidating the mechanism of action of CymA on a chimera of S. aureus ClpC which has the N-terminus of M. tuberculosis. We showed that the antibacterial cyclic peptide CymA stimulates ATPase and proteolytic activity thus causing cellular toxicity because CymA overrules the ClpC activity control and persistent activation and less-stringent substrate selection takes place (9). All the woks so far indicate that using drugs to overrule the control of the ClpCP AAA+ protease system can unleash cell activities that cause the bacteria death. It is extremely important to know the atomic mechanism of such action to make it as specific as possible, ideally as specie-specific as possible. Drug characterization and design around the unfoldase ClpC have the high potential for the design of second-line, narrow-spectrum antibiotics.

The intern will work on the structural characterization of ClpC in complex with antibacterials CymA, Lassomycin and/or Ecumicin. The student will be using uniquely cryo-EM techniques both in imaging and diffraction mode, under the supervision of myself Marta Carroni and embedded in the research environment of the cryo-EM Facility where expertise in protein-sample production and handling and structure determination are strong. The student will therefore get inputs from all the staff members of the Facility as well as from other students from different labs getting trained in the cryo-EM premises.

The student will start by learning cryo-EM single particle analysis thus acquiring IT skills required in a number of structural biology approaches. The first dataset of ClpC in complex with the protease ClpP will be provided to the student so that she/he will be immediately immersed in structure determination. In parallel, the intern will be taught how to prepare and optimize specimens for cryo-EM by working on preparation of ClpC in complex with cymA, Lassomycin and/or Ecumicin. The student will be also taught how to operate both the low and high-end electron microscopes at SciLifeLab, a skill that opens many opportunities nowadays among in the structural biology community. Once/if the structure/s is obtained molecular modelling will be performed to obtain an atomic model/description of ClpC in complex with CymA, Lassomycin or Ecumicin. With these activities and in the described order the student will have the possibility to gain technical and analytical skills while helping to fill up a puzzle piece in the the rising problem of antibiotic resistance. Additionally the intern will have the possibility to interact with a large number of PhDs and postdocs coming from very diverse biological subjects but united under the umbrella of structural biology.

1. Malik, I.T. and H. Brotz-Oesterhelt, Conformational control of the bacterial Clp protease by natural product antibiotics. Nat Prod Rep, 2017. 34(7): p. 815-831.

Marta Carroni (marta.carroni@scilifelab.se) Stockholm University, DBB, cryo-EM Facility Project: Fighting resistance: AAA+ proteases in complex with new antibacterials

2. Bhandari, V., et al., The Role of ClpP Protease in Bacterial Pathogenesis and Human Diseases. ACS Chem Biol, 2018. 13(6): p. 1413-1425.

3. Frees, D., U. Gerth, and H. Ingmer, Clp chaperones and proteases are central in stress survival, virulence and antibiotic resistance of Staphylococcus aureus. Int J Med Microbiol, 2014. 304(2): p. 142-9.

4. Carroni, M., et al., Regulatory coiled-coil domains promote head-to-head assemblies of AAA+ chaperones essential for tunable activity control. Elife, 2017. 6.

5. Gao, W., et al., The cyclic peptide ecumicin targeting ClpC1 is active against Mycobacterium tuberculosis in vivo. Antimicrob Agents Chemother, 2015. 59(2): p. 880-9.

6. Gavrish, E., et al., Lassomycin, a ribosomally synthesized cyclic peptide, kills mycobacterium tuberculosis by targeting the ATP-dependent protease ClpC1P1P2. Chem Biol, 2014. 21(4): p. 509-518.

7. Schmitt, E.K., et al., The natural product cyclomarin kills Mycobacterium tuberculosis by targeting the ClpC1 subunit of the caseinolytic protease. Angew Chem Int Ed Engl, 2011. 50(26): p. 5889-91.

8. Vasudevan, D., S.P. Rao, and C.G. Noble, Structural basis of mycobacterial inhibition by cyclomarin A. J Biol Chem, 2013. 288(43): p. 30883-91.

9. Maurer, M., et al., Toxic Activation of an AAA+ Protease by the Antibacterial Drug Cyclomarin A. Cell Chem Biol, 2019.

Marta Carroni (marta.carroni@scilifelab.se) Stockholm University, DBB, cryo-EM Facility Project: Fighting resistance: AAA+ proteases in complex with new antibacterials

David Drew (david.drew@dbb.su.se) Stockholm University Supervisor: Sarah McComas, sarah.mccomas@scilifelab.se Project: Substrate specificity of sugar porters

Principal Investigator: David Drew – david.drew@dbb.su.se Project supervisor: Sarah McComas (Ph.D. student / MTLS alumni) – sarah.mccomas@scilifelab.se University: Stockholm University Project description:

The sugar porters called GLUTs in mammals, are central to metabolism by bringing in sugar to the cell. Sugar porters are needed in all life, so we focus our attention not only on the GLUTs, but also the homologous bacterial and malaria parasite sugar porters. These sugar porters have various affinities for different sugars, sometimes transporting more than one type of sugar. How these transporters achieve such vast specificity for sugars is unknown. In this project, we will further our understanding sugar recognition in transporters, which can be applied to antimalarial drug development as well as biomass fuel production, for example.

We will perform protein mutagenesis to test sugar porter substrate specificity, and analyze the resulting data. The techniques mentioned here are useful for those interested in protein biochemistry. Expression and purification skills are easily transferred to structural or pharmacological studies which require protein preparation. There is flexibility around the project depending on preference for experimental or computational work. Sarah will provide technical supervision and David will be involved with scientific discussion and project direction. This work can be extended to a Master’s thesis or any other future projects in the Drew lab. Recent relevant publication: Qureshi, A.A., Suades, A., Matsuoka, R., Brock, J., McComas, S.E., Nji, E., Orellana, L., Claesson, M., Delemotte, L. and Drew, D., 2020. The molecular basis for sugar import in malaria parasites. Nature, 578(7794), pp.321-325. List of techniques - both computational and experimental anticipated: Molecular biology, protein purification, protein bioinformatics, protein structure analysis/ visualization, molecular dynamics simulations, thermostability assays, preparation for transport assays, data analysis Supervision plan - The student works with Sarah (3rd year PhD student) to do the following:

1. Examine multiple sequence alignments and MD simulation analyses to propose point mutations on relevant sugar porters.

2. Perform relevant molecular biology to express/mutate the protein of interest. 3. Purify the protein and prepare the target for proteoliposome transport assay.

** The assay must be performed by Sarah alone, given radiation safety protocols at SU. The student can observe the assay for educational purposes without problem.

4. Perform thermostability assays as needed. 5. Analyze data from assays.

The student can perform the following independently: 1. Perform literature review and partake in journal clubs/ group meetings. 2. Once comfortable with protein expression and purification techniques, can work

independently as desired. * Sarah herself has partaken in the Summer internship program with the MTLS masters and has good experience with the skills acquired during this program, as well as a reasonable timeframe for the internship. We look forward to meeting you!

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Marc Friedländer (marc.friedlander@scilifelab.se) SU Project: Computational detection of transcript covariances from scRNA-seq data

Computational detection of transcript covariances from scRNA-seq data

Lab principle investigator: Associate professor Marc Friedländer

Student supervisors: Dr. Emilio Mármol Sánchez and Associate professor Marc Friedländer

Project description

The Friedländer lab has recently established a framework to detect transcripts that covary in

expression across single cells (Tarbier et al., Nature Communications, 2020). The student will work

within this established framework to identify transcripts that covary specifically because of post-

transcriptional regulation caused by for instance RNA-binding proteins or miRNA action. This will be

done by comparing counts of scRNA-seq reads mapping to exons versus introns, since post-

transcriptional regulation of mRNA transcripts mostly happen after splicing and will therefore be

evident at the exon but not intron level. The predictions of the students will at the end of the project

be tested by analyzing scRNA-seq data from mutant cells that are devoid of miRNAs. In the longer

perspective, the software and methods developed by the students will be used for identifying novel

types of post-transcriptional regulation, by combining scRNA-seq with methods to detect RNA-RNA-

interactions and also to measure abundances of RNA-binding proteins in single cells (Reimegård et al.,

bioRxiv, 2019).

Student requirement

The project will require a student with expertise in R statistics, scripting and in NGS analysis. Any

substantial contributions will be awarded with co-authorships on future publications.

Skills that will be acquired

The student will learn how to analyze and integrate scRNA-seq (Smart-Seq2) data and will learn how

to apply binomial statistical tests to them.

Supervision

The student will be mainly supervised by Dr. Emilio Mármol Sánchez, who is a post doc in the lab, but

will also receive co-supervision by Marc Friedländer.

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Erik Lindahl (erik.lindahl@scilifelab.se) Stockholm University (DBB) Supervisor: Rebecca (Reba) J Howard, rebecca.howard@scilifelab.se Project: Computational or laboratory studies of drug binding to ligand-gated ion channels

PI: Erik Lindahl, erik.lindahl@scilifelab.se Supervisor: Rebecca J (Reba) Howard, rebecca.howard@scilifelab.se Title: Computational or laboratory studies of drug binding to ligand-gated ion channels Description: Drugs such as anesthetics, anxiolytics, and alcohol have depressant effects on the human nervous system, mediated at least in part by allosteric modulation of ligand-gated ion channels. However, due to a lack of high-resolution structural data, the mechanistic basis for these effects remains poorly understood. In this project, you will characterize drug modulation in a ligand-gated ion channel using computational and/or laboratory methods. Areas of focus may include computational simulations of drug binding and dynamics in ion channels, electrophysiological studies of receptor function, and/or preparation and analysis of data from cryo-electron microscopy. Whatever your focus, all team members will be engaged in regular interdisciplinary interaction with colleagues specializing in complementary methods. Techniques may include: Molecular dynamics simulations, macromolecular visualization and modeling, molecular biology, oocyte electrophysiology, protein purification, cryo-electron microscopy, data analysis Short plan for supervision: Professor Lindahl will advise project direction, periodic status reports, large-scale goals and assessments. Senior Researcher Howard manages the ligand-gated ion channels team, including currently active postdoctoral fellows and students, and will supervise training, data collection and analysis, weekly group meetings and journal clubs, and project reports. Depending on the specific project selected, a postdoc or senior student in the team will mentor and advise the Fellow’s daily research activities. Person responsible for salary payment: Ann Nielsen, Department of Biochemistry & Biophysics, Stockholm University, ann_n@dbb.su.se

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