Organisatie

mw. dr. G.J. (Gertien) Smits


  • Faculteit der Natuurwetenschappen, Wiskunde en Informatica
    SILS
  • POSTBUS  94232
    1090 GE  Amsterdam
    Kamernummer: C3.262
  • G.J.Smits@uva.nl
    T:  0205255143

The Group

I am an assistant professor in the department of Molecular Biology and Microbial Food Safety of the Swammerdam Institute for Life Sciences of the University of Amsterdam , headed by  Stanley Brul . Our group is a member of the Netherlands Institute for Systems Biology. In the group we work on the adaptive responses to changing environments, of prokaryotic and eukaryotic microorganisms.

Research interests

Stress response and adaptation in yeasts

I have always been fascinated by the fact that life on earth, based on nearly identical sets of chemical reactions for all organisms, can function in such diverse environments.

We are studying the response to environmental changes in yeasts. There are direct effects of the change on yeast physiology and cellular makeup, and there is an adaptive response which, ideally, helps the organism resume growth under the changed conditions. We ask what the effect of the change is on yeast biology, and how the adaptie response affects cellular functioning. 

Current projects

pH homeostasis and weak acid stress in Saccharomyces cerevisiae

Weak acids in yeast are thought diffuse over the plasma membrane in the undissociated form, and upon intracellular dissociationn affect intracellular pH and intracellular anion concentrations. Both aspects affect energy generating metabolism, and the adaprive response (active extrusion of potons and anions) costs energy. Additionally, lipophilic weak acids can affect memrane structure. In order to quantify the contributions of these effects to cellular growth, we aim to model the response. Therefore we first need to adequately measure intracellular (intra-organellar) pH, anion concentrations, and metabolic fluxes.

We have used the GFP-derivative pHluorin to measure cytosolic and mitochondrial pH in living cells. We are currentlymodeling the response to weak acid stress in S. cerevisiae

Contacts: drs. Rick Orij, Dr. Gertien Smits

Weak acid stress in medical and food spoilage yeasts

Various yeasts have evolved to withstand acidic environments. Zygosaccharomyces bailii is a spoilage yeast that spoils high sugar low pH beverages, and Candida albicans lives as a commensal in the oral cavity but also the acidic vagina. What mechanisms do these yeastspossess that preventthemfrom wasting energy on the constant extrusion of protons and weak acid anions? How does this compare to the adaptive response in S. cerevisiae? How relevant are these mechanisms in the virulence of C. albicans and C. glabrata ?

Contacts: Azmat Ullah MSc., Dr. Gertien Smits

Heat stress and yeast metabolism

Temperature isa constantly changing environmental parameter. We studythe effect of heat stress on yeast central carbon metabolism. Increased temperature causes increased membrane fluidity, increased reaction rates, and decreased protein stability. Adaptation to these effects costs extra ATP. We ask how a yeast central carbon metabolism, crucial for generating the energy requiered for the adaptive response but affected by heat like all other pathways, is regulated in response to heat.

We adapted metabolic regulation analysis to quantify pathway flux regulation by the direct effect of temperature on enzyme kinetics, by the adaptive response through altered enzyme expression, and by thechanges in enzyme metabolic environment. Comparing different steady states, we find that, in these fully adapted conditions, hierarchical and direct temperature play oonly a minor role in the generation of a flux increase in response to a temperature increase. A high glycolytic flux at high temperature is maintained through a chenged metabolic network.

Interestingly, mitochondria and/or the TCA cycle seem to be relatively sensitive to  heat, and this is the current focus of our investigations.

Contacts: Drs. Jarne Postmus and Dr. Gertien Smits

Stress tolerance acquisition

Microbes that areexposedto stress conditionsthat are non-lethal but cause growth inhibition are known to better be able to withstand high doses of the same stress. We find that many non-lethal treatments, with high and low temperature, weak acids, hydrogen peroxide, etc, cause increased resistance to acute level of heat stress, weak acid stress, and oxidative stress.

We systematicallyanalysed the biological processes important for acute stress tolerance, using the yeast haploid barcoded deletion collection.We find that, although specific processes are requiredfor survivalofspecific stresses, growth rate is a general determinant of stress survival. This is true for the mutants, but also forwild-type. Also, growth rate reduction by a non-lethal treatment is a main determinat for stress tolerance acquisition. We hypothesize that growth rate and stress tolerance processes are communicating vessels, and that growth rate reduction is an important aspect of adaptation to environmental change.

Contact: Dr. Gertien Smits

Links

Teaching

I teach about the intricate molecular  collaborations that make a cell tick in the following courses
BSc Biology
Cellular Signaling             (B231 6EC)
Cellular Physiology           (B232 6EC)
Molecular Cell Biology      (B206 12EC)
BSc Biomedical Sciences
Cell Biology                         (BW202 6EC)
Molecular Cell Biology      (BW206 12EC)
BSc Bio-exact
Systems Biology 2            (6EC)
BSc Chemistry
Cellular Biochemistry        (3EC)
In both bachelor and master phase I supervise research internships and literaturetheses (see student projects)
Furthermore I am chairman of the Board of Education for Biology, Biological Sciences and Life Sciences

Other activities

IMC weekendschool
Course in Microbiology for children in the final grade of primary school in Amsterdam Zuidoost, 3 sundays of microbiology knowledge, formulating and testing of hypotheses based on knowledge, and experimentation
NEMO wakker worden lezingen
Sunday morning lecture in the Amsterdam Science museum NEMO for children from 8-12 years old

pH homeostasis.......or not?

We have set up a tool to register in vivo the organelle specific pH of yeast. The tool is based on a GFP derivative, ratiometric pHluorin, which can be targeted to different organelles. The pH measurements are based on fluorescence readings in microtiterplates or under the microscope.

Recently, we have expressed pHluorin in knockout strains for all the individual non-essential genes in yeast, and we are working on the essential genes. We have screened this functional genomics library for cytosolic pH and mitochondrial pH, as well as for the pH response to the weak acid preservative sorbic acid, and recovery of this stress. We are looking for an enthusiastic student who will help in the last phases of the screening, and with validation experiments, both of individual knock-outs and of processes we now think are relevant for pH homeostasis. One experiment will be to look at cell cycle dependence of pH in yeast. 

Techniques:yeast cultivation in batch fermentors and microtiterplates, microtiterplate based fluorescence assays, cell cycle synchronization, yeast transformation, functional assays. 

Duration: >3 months

 

Let there be light!

Intracellular pH (pHi) affects the properties of almost everything in cells, and turns out to be highly variable. We recently showed that pHi, or the proton, functions as a second messenger and controls cell division rate. We wish to study this further, but it is extremely difficult to separate the effects of pHi from the rest of metabolism. In this project we aim to generate a method to disentangle the two.

pHi regulation in yeast depends to a large extent on the activity of the proton pumping plasma membrane ATPase. This pump consumes one ATP per proton transported, and therefore pHi homeostasis is very costly to the cell. We aim to use a fungal rhodopsin expressed in yeast, to pump out protons at the expense of light rather than ATP. Thus, we can control pH with lowered use of metabolic energy. This tool should be useful for basic understanding of pH homeostasis, but may also prove useful in fermentation industry, where the product competes with the cell for energy.

In the project, you will clone a Fusarium rhodopsin in baker’s yeast, Saccharomyces cerevisiae. You will determine to what extent we can use light to control pHi, and how this affects metabolic fluxes.

Techniques: PCR, cloning and subcloning, pHi determination, fermentation, metabolic flux analysis.

Period: >6 months

Start: from september 2013

 

pHorcing matters: the role of pHi in metabolic decision making

The intracellular pH of yeast is highly dynamic, but genetically very tightly controlled. Since all enzymes have pH dependent properties, a dynamic pH should alter the properties of metabolic pathways. We aim to study this in a systematic approach, focusing on the decision between fermentation and respiration in yeast. We will study the pH dependence of the rates of the glycolytic enzymes and of some regulatory branches of glycolysis, using assay conditions that approximate the cytosol as closely as possible. The data generated will be used in a mathematical model of glycolysis, to analyze if pHi has what it takes to force decisions in a cell.

Techniques: cultivation of yeast in batch fermenters and chemostats, analysis of yeast physiology, enzyme extraction, microtiterplate based enzyme activity assays, (mathematical modeling).

Duration: 3 months or more

Start: From june 2013

Oxygen, friend and foe

We live in an oxygen rich environment, without which no multicellular life forms would exist on our planet. However, even though we depend on oxygen, the fact that it is a potent electron acceptor also causes it to be intrinsically dangerous. Respiratory activity leads to the production of oxygen radicals and other reactive oxygen species (ROS), such as hydrogen peroxide. These ROS cause intracellular damage to membranes and proteins, and excessiveROS production leads to early cellular ageing, cancer, apoptosis, etc. Cells have ROS defense mechanisms, including enzymes such as catalase and peroxidase. Our understanding of when ROS are produced and how they are connected to i.e. damage and to the induction of ROS defense mechanisms is currently purely qualitative, because the methods to detect the presence of ROS are not quantitative, or in vivo, or only quantify accumulated damage. Also, we can detect only the average of cellular ROS, and cannot distinguish, for example, between ROS produced in the respiratory chain or during fatty acid oxidation. We aim toset up quantitative, in vivo, and organelle specific method of H2O2 determination using a H2O2 senstitive fluorescent protein, HyPer.

The student project entails the cloning of HyPer into expression vectors targeting the protein to the cytosol, the mitochondria, the vacuole and the peroxisomes. We will express it in baker's yeast , Saccharomyces cerevisiae, and generate in situ calibration curves. Next, we will determine ROS production in response to oxidative stress and heat, to give proof of principle.

Techniques: PCR, subcloning, sequence analysis, DNA isolation, yeast transformation, cultivation in batch fermentors and microtiterplate, microtiterplate based fluorescence analysis,fluorescence microscopy.

Duration: 3 months or more

It only takes one rotten apple......

Although yeasts do wonders in the production of alcohol, they also can spoil foods. In fact, in low pH high osmolarity foods yeasts are a major problem. Interestingly, both low pH and high osmolarity are known to inhibit the growth of yeasts, when studied in the laboratory. We are starting to understand, however, that while we measure very accurate population averages in the laboratory, in real life we are dealingwith a heterogeneous population of microbes, even when they are genetically identical. We have found that the growth rate of yeast is a main determinant for its stress tolerance. In this project we will try to understand the molecular basis for phenotypic heterogeneity, and its relevance for stress tolerance and food spoilage. There are two competing hypotheses: Either an altered growth rate results in a stress tolerance shift of the entire population, oran altered growth rate alters the variance of tolerance for the population. Both can occur at the same time. However, is the growth rate distribution homogeneous? We will generate growth rate reporter genes, and study the distribution of expression in populations of genetically identical yeast grown at different conditions using flow cytometry. We will evaluate the use of these reporters using time-lapse microscopy. Next, we will correlate the growth behavior of individual cells to their capacity to survive commonly used food preservation techniques, such as high salt, heat, or acid stress.   

Techniques: PCR, cloning,expression, fluorescence spectrometry, Flow cytometry,fluorescence microscopy, yeast transformation, stress tolerance assays.  

Duration: 3 months or longer 

Additional topics:

  • Determination of the role of pHi in cellular decision making
  • Determination of intracellular pH in Candida glabrata and Candida albicans
  • Calorie restriction and ageing in yeast: is it calories or mitochondrialfunctioning?
  • Therole of a pole-marking cell wall protein in stress recovery

Contact: Dr. Gertien Smits

2013

  •  Walther T, Mtimet N, Alkim C, Vax, Loret M-O, Ullah A, Gancedo C, Smits GJ, and François JM. 2013. Metabolic phenotypes of Saccharomyces cerevisiae mutants with altered trehalose-6-phosphate dynamics. Biochem J in press.

2012

2011

2010

  • Young BP, Shin JJ, Orij R, Chao JT, Li SC, Guan XL, Khong A, Jan E, WenkMR, PrinzWA, Smits GJ , and Loewen CJR. 2010. Phosphatidic acid is a pH biosensor that links membrane biogenesis to metabolism. Science 329 :1085-1088
  • Zakrzewska A,Boorsma A, Ter Beek AS, Hageman JA, Westerhuis JA, Hellingwerf KJ, Brul S, Klis FM, Smits GJ . 2010 . Comparative analysis of transcriptome and fitness profiles reveals general and condition specific cellular functions involved in adaptation to environmental changein Saccharomyces cerevisiae . OMICS 14 :603-614
  • van Eunen K, Bouwman J, Daran-Lapujade P, Postmus J, Canelas A, Mensonides F, Orij R,Tuzun I, van den Brink J, Smits GJ , van Gulik W, Brul S, Heijnen J, de WindeJ, Teixeira de MattosMJ, Kettner C, Nielsen J, Westerhoff H, Bakker BM. 2010 . Measuring enzyme activities under standardized in vivo-like conditions for Systems Biology. FEBS J . 277 :749-760
  • Christoffels VM, Smits GJ ,Kispert A, Moorman AF. 2010 . Development of the pacemaker tissues of the heart. Circ. Res. 106 :240-254

2009-2008

  • Orij R, Postmus J, Ter Beek A, Brul S, and Smits GJ . 2009 . In vivo measurement of cytosolic and mitochondrial pH using a pH-sensitive GFP derivative in Saccharomyces cerevisiae reveals a relation between intracellular pHand growth. Microbiology 155 :268-278.
  • Postmus J, Canelas AB,Bouwman J, Bakker BM, van GulikW, Teixeira de Mattos MJ, Brul S,and SmitsGJ . 2008 . Quantitative analysis of the high temperature-inducedglycolytic flux increase in Saccharomyces cerevisiae reveals dominant metabolic regulation. J. Biol. Chem . 283 , 23524-23532.
  • Ter Beek A, Keijzer BJ, Boorsma A, Zakrzewska A, Orij R, Smits GJ , and Brul S. 2008 . Transcriptome analysis ofsorbic acid-stressed Bacillus subtilis reveals a nutrient limitation response andindicates plasma membrane remodeling. J. Bact. 190 :1751-1761.
  • Brul S, Kallemeijn W, and Smits GJ . 2008 . Functional genomics for food microbiology: molecular mechanisms of weak organic acid preservative adaptation in yeast. CAB reviews 3 :005.

Book Chapters & Conference proceedings

  • Orij PJ, Urbanus ML, Vizeacoumar FJ, van Dyk N, Boone C, Giaever G, Nislow C, Brul S, and Smits GJ . 2009 .Genome-wide analysis of pH homeostasis using a pH sensitive GFP. Antonie van Leeuwenhoek 95 Suppl.1: 71.
  • Bouwman J, van Eunen K, Tuzun I, Postmus J, Canelas A, van der Brink J, Lindenbergh PA, Teixeira de Mattos MJ, Smits GJ , Daran-Lapujade PAL, van Gulik, WM, van Spanning RJM, Heijnen JJ, de Winde JH, Brul S, Westerhoff HV, and Bakker BM. 2007 . Standardizationand 'In-Vivo'-like enzyme activity measurements in yeast. In M.G. Hicks & C. Kettner(Eds.), Experimental standard conditions of enzyme characterization (vol 2) (pp. 11-20).Frankfurt Germany: Beilstein-Institut.


2007 and before

  • Smits GJ , Schenkman LR, Brul S, Pringle JR, and Klis FM. 2006 . Role of cell cycle-regulated expression in the localized incorporation of cell wall proteins in yeast. Mol. Biol. Cell. 17 :3267-3280.
  • Smits GJ , Brul S. 2005 . Stress tolerance in fungi - to kill a spoilage yeast. Curr. Opin. Biotech . 16 :225-230.
  • Smits GJ , van den Ende H,Klis FM. 2001 . Differential regulation of cell wall biogenesis during growth and development in yeast. Microbiology 147 :781-794.
  • Smits GJ , Kapteyn JC, van den Ende H, Klis FM. 1999 . Cell wall dynamics in yeast. Curr Opin Microbiol . 2 :348-352
  • Caro LH, Smits GJ , van Egmond P, Chapman JW,Klis FM. 1998 . Transcription of multiple cell wall protein-encoding genes in Saccharomyces cerevisiae is differentially regulated during thecell cycle. FEMS Microbiol Lett . 161 :345-349.
  • Smits HP, Smits GJ , PostmaPW, Walsh MC, van Dam K. 1996 . High-affinity glucose uptake in Saccharomyces cerevisiae is not dependent onthe presence of glucose-phosphorylating enzymes. Yeast 12 :439-447.
  • Walsh MC, Smits HP, Scholte M, Smits G , Van DamK. 1994 . Rapid kinetics of glucose-uptake in Saccharomyces cerevisiae. Folia Microbiol . 39 : 557-559

Additional data for Orij et al., Genome Biology 2012

Manuscript
Orij R, Urbanus M, Vizeacoumar F, Giaever G, Boone C, Nislow C, Brul S, and Smits GJ . 2012 . Genome-wide analysis of intracellular pH reveals quantitative control of cell division rate by pHc in Saccharomyces cerevisiae . Genome Biol. 13 :R80

Additional file 1 : All mutants with deviating pHc at various pHex, and during respiratory growth. Mutants with aberrant pHc under the standard condition (glucose, pH 5.0) were subjected to growth in pH 3.0, 7.5, as well as 2% ethanol/2% glycerol pH 5.0. Mutants were pre-grown overnight under standard conditions except for the 2% ethanol/2% glycerol experiment, in which case mutants were pre-grown in 2% ethanol/2% glycerol because of the long adaptation time to nonfermentable carbon source conditions. Cells were re-inoculated in described conditions and grown for 4 hours prior to measurements. Mutants were measured at least six timesat pH 5.0and at least three times in all other conditions. Mutants with significantly low pHc in any condition are indicated in orange, while mutants with significantly high pHc are indicated in blue.
Additional file 2 : pHc analysis of 432 slow growing mutants. All strains were grown in standard conditions (2% glucose, pHex of 5.0) and fluorescence was registered in three to six biological replicates, and are presented as average and 95% confidence interval. pHc was compared to wild-type (WT) controls in the same replicate, to determine a Z-value. Significance of the pHc difference with WT was determined using a two-tailed t-test assuming equal variance with a P-value < 0.05. ND refers to mutants for which fewer than three replicates were successfully measured. Significantly low pHc values are shown in orange, significantly high pHc values in blue.
Additional file 3 : Classification of mutants. Mutants are classified as having a growth rate-pHc relationship similar to wild type (WT; no significant deviation from the predicted growth rate based on pHc-growth rate relationship of the parent strain, low growth rate/pHc (significant positive deviation from the parent fit), or high growth rate/pHc (significant negative deviation from the parent fit), and are categorized according to functional classification.
Additional file 4 : Figures S1 to S4. See Additional file 5 for further data pertaining to Figure S3.
Additional file 5 : Data belonging to the hierarchical cluster plot in Figure S3 in Additional file 4. Mutants are listed in the order in which they appear in the cluster plot, for all three clusters. Mutant growth profiles were fitted to the parent strain pHc-growth rate relationship, and at each time point the Z-value of the digression from the fit was determined compared to the average and variance of 96 parent strain growth curves at the same time point. Time courses during the growth phase (t = 4 h to t = 9 h) of these Z-values were usedtostatistically categorize the mutants as wild type (WT; 92/173 mutants; 96 parent strain profiles also fall in this category), significantly (corrected P-value <0.01) slow growing (62/173 mutants), or significantly fast growing (19/173 mutants) with respect to pHc.

Additional data for Zakrzewska et al., MBoC 2011

Manuscript
Zakrzewska A, van Eikenhorst G , Burggraaff JEC ,  Vis DJ, Hoefsloot H, Delneri D, Oliver SG, Brul S, Smits GJ. 2011.   Genome-wide analysis of yeast stress survival and tolerance acquisition to analyze the central trade-off between growth rate and cellular robustness. Mol. Biol. Cell,  22 :4435-4446
Log transformations of viability percentages without and with growth rate correction.
All direct log transformed viability values of normal and heat pretreated samples are listed in "log transformed viability". All growth rate corrected log transformed viabilities are listed in "viability after growth rate cor". The non-pretreated viabiliy was corrected for growth rate at 30oC, the pretreated viability for growth rate at 38oC, and the acquired tolerance for the change in growth rate.

Primary data
Each microarray dataset (separated for UPtag sense, UPtag antisense, DOWNtag sense and DOWNtag antisense) was background subtracted. Fractional intensities after 24 hours of growth (FI24) foreach tag werecorrected for the growth rate of the mutant to which it belongs, leading to the FI0. These values were normalized for the used for DNA isolation.
In the table these normalized FI0 values were used to calculate viability based on each tag independently. For instance,for mutant yal004w the strain abundance after severe oxidative stress (oxi 30oC) was determined using duplicate measurement of both sense and antisense UPtags, relative to the abundance of the strain in the growing culture). For each strain outlier values (p <0.0001) were removed. These individual ratios were averaged and multiplied by the population survival (in the case of severe oxidative stress in non-pretreated cultures 13%) to determine the survival of each individual strain (in the case of yal004w 9.9%).

Supplemental data for Zakrzewska et al, 2010

Supplemental data accompanying  Zakrzewska et al., OMICS , 14 :350-360

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