Non-Targeted Metabolomics Approach Reveals Two Distinct Types of Metabolomics Response

Supplemental Information

Non-targeted metabolomics approach reveals two distinct types of metabolomics response to telomerase dysfunction in S. cerevisiae

Florian Buettner,1,‡ Kyle Jay,2 Harry Wischnewski,3 Thomas Stadelmann,4 Shady Saad,3 Konstantins Jefimovs,5 Madina Mansurova,6 Juan Gerez,3 Claus M. Azzalin,3 Reinhard Dechant,3 Alfredo J. Ibáñez.4,6,‡,*

1 European Bioinformatics Institute, European Molecular Biology Laboratory Hinxton, CB10 1SD Cambridge, United Kingdom. 2 Department of Biochemistry and Biophysics, University of California San Francisco, CA 94158 San Francisco, USA. 3 Institute of Biochemistry, ETH Zurich, CH-8093 Zurich, Switzerland. 4 Department of Chemistry and Applied Biosciences, ETH Zurich, CH-8093 Zurich, Switzerland. 5 Laboratory of Macromolecules and Bioimaging (LSB), Paul Scherrer Institut (PSI), CH-5232 Villigen, Switzerland 6 Pontificia Universidad Católica del Perú, Lima 32 Lima, Peru. 7 Instituto de Medicina Molecular, 1649-028 Lisboa, Portugal.

‡ Authors contributed equally to this work. * To whom correspondence should be addressed. E-mail: aibanez(at)pucp.edu.pe.

Cell death counting

Cell death counting was performed using a cytometer device. Hydrogen peroxide (H 2O2) induces oxidative stress to the cells by forming reactive oxygen species (ROS). The strains to be monitored were ALT-negative strains: wild-type (WT), zwf1, tlc1 pre-senescence, and tlc1 rescued; and ALT-positive strains: tlc1, tlc1 zwf1 survivor. The selected yeast strains (1 mL at OD 0.4) were incubated for 30 min with different concentrations of H2O2 (0, 1, 2, and 3 mM) at 30 C, with shaking at 300 rpm. After the incubation period with

H2O2, the cells were centrifuged at -10C and 1000g for 1 minute. The cell pellet was re-suspended in 1mL of phosphate buffered saline (Sigma-Aldrich, #P7059-1L). The cells were vortex for 10 seconds, and centrifuged once more at -10C and 1000g for 1 minute. The cell pellet was then reconstituted in 1mL of phosphate buffered saline containing 0.2% (v/v) propidium iodide (Sigma-Aldrich, P4864-10mL).

The cytometry measurements were performed in a BD Accuri C6 instrument, (488 excitation / 670 emission). The cell suspensions were diluted (1:1) with phosphate buffer, the final solution flowed at 14 µL/min, and we performed dead cell counts in a fixed volume of 20 µL.

Quenching of the metabolism – Sample preparation – for non-targeted metabolomics MS measurements

For quenching the metabolism 1 mL of cell culture was rapidly mixed with 4 mL of -40°C pure methanol:water mixture (600 mL of 100% methanol mixed with 400 mL of 25 mM ammonium acetate buffer, pH 7.4), as described by Ewald et al. 2009. For extraction, one mL of a, preheated (80°C), 75% ethanol solution (750 mL of 100% ethanol mixed with 250 mL of 40 mM ammonium acetate buffer, pH 7.4) was added to the cell pellet and vortexed quickly. Samples were incubated in a water bath at 80°C for 3 min, vortexing every minute. Cell debris were removed by centrifugation (10 min at maximum speed), and the extract (supernatant) was transferred to fresh tubes and stored at -80°C until further use.

Cellular extracts were analyzed on a flow injection tandem (quadrupole time-of-flight) mass spectrometer (Agilent 6520) operating in negative mode scanning from 50 to 1000 m/z at a frequency of 1.4 full spectra per

1 second. Samples were stored in a cooled autosampler tray and injected about every minute using full loop injection (1.6 μL) using as mobile phase 5 mM ammonium carbonate with 60% 2-propanol.

Data normalization for non-targeted metabolomics MS measurements.

Spectral data (i.e., accurate mass, signal intensity, etc.) were calculated from the raw spectra using a MATLAB (MathWorks, Natick, MA, USA) peak recognition software that was kindly made available by Uwe Sauer and Nicola Zamboni (Institute of Molecular Systems Biology, ETH Zürich). Using this specialized peak recognition software allowed us to confidently detect a relatively large number of signals, i.e., ~10,000. From this large number of signals, we removed all signals that were not accurately measured due to a failure in background subtraction by the software (minimum 0.001 counts in all spectra). Thus, after this filtration step, each mass spectrum consisted of 96 signals (i.e., this number includes possible isotopic peaks and adducts for a single ionic species).

Supplemental tables and figures captions:

Supplemental table 1. List of S. cerevisiae strains used in this work. The different strains are auxotrophic, were as the selection markers can be: (i) amino acids (Leucine = Leu), or (ii) nucleotides (Uracil = Ura; Adenine = Ade). In addition, genetic modifications were marked using resistant genes toward the following drugs: hygromycin, nourseothricin, and kanamycine (Hyg, Nat, and Kan, respectively).

Supplemental table 2. We were able to detect ~11333 ion signals. From these signals only 86 ion signals were identified based on their precise mass, isotope pattern and S. cerevisiae database (Biocyc). In this table, we present if these metabolites were more abundant in the WT or ALT II phenotypes.

Supplemental table 3. In this table, we present proteins that were identified by an untargeted proteomic analysis using both Progenesis and MaxQuant softwares for protein identification.