Yeast Recovery after a Heat Shock

On the 13th of april, our uncle, a baker, realized that he didn’t have a lot of yeast left. Although, he still had many breads to bake as they had already been ordered by his customers… So he couldn’t wait until his next delivery. He decided to cultivate his bakery yeast and make them divide in order to have more of them. But his yeast had been left next to the oven, and exposed to very high temperatures. Therefore he asked us, scientific students, if this heat shock would have an impact on the growth rate of his yeast.

    We went to our lab, and wondered: how long do yeast take to recover from a heat shock ? A heat shock can denature proteins, and therefore affect the ability of yeast to function normally. But in which proportions can they survive and progressively recover from that heat shock ? How much and how long does it affect their metabolism ? We knew that when yeast are in good conditions, they divide faster; but damaged yeast don’t divide, so we focused on the impact of a heat shock on their growth rate. The optimal temperature for yeast is between 25 and 37°C, and at this temperature yeast usually divide every 1h30/2h. To study the growth rate, we also took in consideration the fact that a heat shock is a short exposition to a high temperature, 45°C or above for yeast.

               

We prepared the medium with 150ml of YPD, 150ml of water, 6g of glucose. we prepared an erlenmeyer with 100ml of medium with 5g of yeast, incubated this erlenmeyer during 2 hours. We put 1ml of the erlenmeyer in 9 tube ependorff. 3 sample during 5minutes at 50°C, 3 at 55°C and 3 with no change of heat. Put 200ul of each ependorff in 9 erlenmeyer, incubate. Every 20-30minutes take 200μL of each erlenmeyer and make a spectrophometry. Take 8μL of the first erlenmeyer ad 0,8μL of phloxine B and count the dead cells at the microscope.

Our uncle came to join us to know our results !

 - Look, Uncle, We used Sagemath, to make a graph to see our results. But we had a big problem,we observed that the green curve decreases, but, normally it should increase ! The red curve corresponds to the sample of yeast exposed to 50°C. The blue curve show the sample of yeast exposed to 55°C, and the green curve corresponds to the sample of yeast without heat shock. Normally, the sample unexposed to heat shock, should grow... But, we have a curve which decreases.

- Ho.. and what is the reason of this result? ask the baker.

- We don’t know… Maybe we made a mistake of manipulation in the dilution… 

Absorbance measured in function of time

    No heat shock        50°C heat shock       55°C heat shock 

                                                          Counting Chamber

 - Here we tried to count all the dead cells, we used phloxine B that colored the dead cells. We can see that our results are weird, particularly for 50°C

 - Why ?

 - Because we can see only one cell, we must have made a mistake with the dilution. But we can see that for no heat shock there are more white cells, living cells than for 55°C. For 55°C there are more cells but more colored cells so it means that a lot died.

We can conclude that our experience has not really worked because we probably made mistakes and we ran out of time. We had a problem with our protocol: we found it the day we did the experiment so we lost a lot of time. But it was interesting; if we had had more time and a better comprehension of our protocol we could have observed better results. We can say that we had a good idea but we had a lot of problems. Although, we can say that after a heat shock at 50°C, 55°C  the yeast can recover.

Maya Saidi, Anais Martin, Camille Bonne

If you want to know more :

•  Yeast ? But what is it more precisely ? : Click here

• Heat shock on Yeast: McAlister L., Finkelstein D. B. (1980)  Heat shock proteins and thermal resistance in yeast. Biochemical and Biophysical Research Communications.

• To know more about spectrophotometry, Click here

• If you don’t understand what is a growth rate, Click here (there is a video too, for a better comprehension) Click here

• Our presentation for general audience Click here

• If you want to know how to grow yeast: Click here
  

Do pigments have a chance to win thermal wars?

These days, more and more artists implement biological living systems in their work, such as recently colored yeasts. Indeed, researchers achieved to create from the Saccharomyces cerevisiae wild-type strain, 10 mutated strains which express 10 different colors. Main properties and behaviors of this unicellular-fungus microorganism remain the same. The pigment in charge of the color expression is embedded in the cell via a plasmid, a circular DNA molecule which is separated from chromosomal DNA so that it can replicate independently. Unexpectedly, seekers only resort to 3 types of pigments. The most incredible are Carotene pigments whom rate expression modulates the visible color of yeasts from light yellow to pink. On the contrary, Violacein could be toxic to yeast cells at high concentration.

Picture representing cultures of 9th yeast strain from Yeast Art

As pigment could put in danger future art production, we would like to analyze how far these mutated strain could resist to environment shiftings. Indeed, creation intends to touch people with aesthetic representations implying this works to be easily and sustainably sharable. Thus, they need to resist to Museum’s environment: light overexposure, a continuous flow of bystanders, and temperature changes between night and day. So how could we manage to preserve pigment intensity knowing that optimal temperature for yeast growth rate is 30°C and Violacein is synthesized by an enzymatic reaction?

 We made an overnight culture in a 96-wells plate incubated at 30°C in a plate spectrophotometer. The 11 colored strains were replicated in 8 wells each. So that we could save time determining the most efficient strain -rapid and uniform growth - and making a first comparison between strains’ growth rates.

During day 1 we compared the growth rate of wild-type and purple strain at different temperature (28°C and 35°C) using 3 replicates of each strain culture in YPD medium and Kova slide to count at a precise time number of divided cells in samples. We could study the influence of temperature on cells expansion.

13 hours

We can see from this graph that the red strain didn’t grow up. The strain which grew rapidly is the white one (~3.7x10⁻4 cells/min) (non-mutant). The slowest is the pink one (~1.5x10⁻4 cells/min). We think that the red didn’t grow up because of a lack of adenine in the medium. Yeast needs sugar, oxygen and some amino acids to grow. We used a simple medium without specific nutrients so maybe yeast consumes in a quick way all the nutrient from the medium.

4869.1 secondes

We can see on the graph that the white strain has a growth rate of 7.5x10-5 and the purple strain has a growth rate of 4.8x10-5. So at 35 degrees the white strain grow almost twice as fast than the purple.

4500 secondes

Then, at 30 degrees, the white strain (W) grows also twice as fast as the purple one (P). The white strain has a growth rate of ~6.0x10-5 and the purple strain has a growth rate of ~2.7x10-5.

Our teacher said these different strains of yeast are originally the same. But with our experiment, we discovered that the mutant strains have different behaviors. The induced mutations to create pigment expression drive changes in yeast's metabolisms: colored strains grow slowly and not as the same way.

So if you want to do a picture with yeast to expose in a museum take the white strain.

We notice that each strain has different characteristics regarding the growth rate: each color strains had a specific trend (comparing the 8 wells together) that differs from others and mainly the wild-type one. We must stay vigilant as we studied small samples and plate spectrophotometer doesn't allow us to measure growth rate precisely mostly if we don’t wait enough time or don’t use blank samples to compare with.

Thus as a future project, we would like to analyze the viability of colonies over time at extreme temperatures to determine which is the optimal growth range for each. 

We were also curious to mate two mutated strains together and analyze pigment’s prevalences resorting to a sporulated medium and tetrad dissection to analyze produced colonies. 

Yeast Art opens impressive perspectives and represents more over another way to promote collaboration between artists and scientific.

 Here are additional resources for incorrigible curious.

• YeastArt applications from biopointillism to genetical engineering: This website presents all the steps of these artistic developments.

http://www.yeastart.org/process

• Boeke Lab from NYU school of Medecine where colored yeast were produced  

https://med.nyu.edu/research/boeke-lab/

• Painting with yeast, 9 Mar. 2017, Posted by Jeffrey Perkel

http://blogs.nature.com/naturejobs/2017/03/09/painting-with-yeast/

• Article presenting pigment insertion in yeasts and characteristics of different pigments

Mitchell, L. A., Chuang, J., Agmon, N., Khunsriraksakul, C., Phillips, N. A., Cai, Y., ... & Blomquist, P. (2015). Versatile genetic assembly system (VEGAS) to assemble pathways for expression in S. cerevisiae. Nucleic acids research, gkv466.

http://nar.oxfordjournals.org/content/early/2015/05/07/nar.gkv466.short

• To learn more about new genetic engineering on yeast :

Sc2.0 project hits new milestone: 5 additional chromosomes completed!

Posted on March 9, 2017 by kyang

Posted on March 9, 2017 by kyang

http://syntheticyeast.org/sc2-0-project-hits-new-milestone-5-additional-chromosomes-completed/

The Algae Purification Project

The Algae Purification Project

By: Louis, Rosalie & Yasmeen

Licence Frontières du Vivant, Centre de Recherche Interdisciplinaire

Our group was really interested by environnemental issues and wanted to study the abilities of plants to purify water.

We had at our disposal a super eco friendly algae named Egeria densa, it doesn’t have the best reputation because it is responsible for crowding rivers in agricultural areas. Not only E. densas have a rhythm to their name, they are also known to be among the best chemical absorbents. This algae also have a really fast growth rate ! If our idea worked it could have been easy to reproduce for anyone who wanted to get their diy water purifier!

The main chemicals responsible for water pollution are nitrates (NO3-) and ammonium (NH4+). Both those chemicals are residues of fertilizer use in Bretagne, which is known as one of the most water polluted area. Plants absorb the nitrates and ammonium in aqueous form  in order to fill their nitrogen supplies because they cannot fix it when in gaseous form. This supply will be used to produce the amino-acids, helping the development of the plant.

We chose to determine the influence of temperature on nitrate absorption by measuring the a nitrate concentrations of samples at different temperatures. We determined that the normal thermal conditions of Egeria densa are 16 to 28°C, but they can survive up to 33°C. We used two color-changing reagents to determine the nitrate concentration by spectrophotometry. We decided to study samples at 18, 25 and 35°C. We found that there were more variables involved than expected, like the mass of the algae samples or the light exposition. We also found that it was difficult to stay on time for the different measurements.

Here are the graphs of our result. The time is on the x axis, in minutes, and on the y axis, the absorbance (the ability of a medium to capture light beams). The green curve represent the erlenmeyer that contains only the medium and the pink curve represent the erlenmeyer that contain both algae and medium. We will only comment the pink curve, because it is the one of interest.  

Let’s have a look at the 25°C graph. We can see that the absorbance increases between the first and the second dot and then it decreases. We were not suppose to find this result, we imagined that the absorbance would decrease slowly but it’s not that we observed at all.

The second graph represents the second condition of our experiment, the temperature of the medium was at 35°C. We can see that the curve decreases quickly and that was not what we were supposed to observe.

The third graph fit a 18°C temperature and seems to be the best graph we have because the absorbance for only the medium was almost stable and the absorbance of the medium and the algae decreased very rapidly, which is the result we expected.

We concluded from our results that despite some imprecisions, temperature did have an influence on nitrate absorption efficiency in Egeria densa. If we were to repeat this experiment we would paint the glass bottles black, in order to control light exposition better. We would try to more precisely measure the mass of algae per sample. What remains unclear is how long algae can absorb nitrate at 35°C, as it should only live up to 32°C. The next question could be to repeat the experiment for ammonium absorption, because Egeria densa is used to purify water of both nitrate and ammonium.

How temperature influences visually the pH of a solution

pH color indication range of red cabbage 

Hello dear followers ! Thank you for always being so faithful to the research projects of the team F5 ! Today let us introduce you to the design of our exciting new proje ct. Indeed we received funding to open a chemical cocktail bar !!! Therefore our task was to design and experiment about the drinks that could be served.

Our first challenge was to know if it was possible to have solutions that would change color in function of temperature. As we know, the color of a solution can vary depending of the pH through a color indicator. Thus we wanted to see if it was possible to make the pH of a solution change in function of the temperature.

But first, let’s all be clear about what pH and color indicators are and their interaction with each other.

The pH is a scale that goes from 1 to 14 indicating the acidity or alkalinity of a solution, 1 being the most acid, 7 neutral and 14 the most alkaline. pH is defined as the concentration of H30+ (hydronium ions) in the solution (pH= -log[H3O⁺]) and so, each unit change represents a tenfold change in the concentration of the hydrogen-ion concentration.

Color indicators are weak acids that indicates the pH of a solution by color change. They are changing their shape through molecular interaction with H3O+ directly impacting on the color of the solution.

There are lots of natural different color indicators, as red cabbage or hortensias.

To answer our challenge, we first determined a set of cocktail that we would test (including vinegar, water, ethanol, grapefruit juice and unicorn blood, just kidding~) and then we measured the evolution of pH in function of temperatures with a pH meter. We proceed to the smartest protocol that we could think off : we immerged each solution in different water-baths at different temperatures and then we measured their pH. Therefore we selected the solutions with the biggest variation of pH to test a color indication with red cabbage juice.

What do you mean you’re “not entertained” ? This graph is awesome ! On this graph we can actually compare the pH changes due to temperature, for all our solutions !!! Indeed with the data we collected with the pH meter we were able to calculate the change in pH between 5°C and 25°C and between 25°C and 40°C for each of our solutions. Then we calculated the average of the two previous results and applied absolute value to it. So we had calculated the multiplying factor of pH changes given temperature changes, for each of our solution. Therefore we could visualize our data with a linear relationship between pH value and temperature. On our graph we can see that sodium bicarbonate has the most steepest line, experiencing the highest variations in pH due to temperature changes, and vinegar has the most flat line, experiencing the lowest variations in pH due to temperature changes.

Change in color, and so pH, in our solution (cold left and hot right)

Thanks to our wonderful experiment, we can now present you the composition our marvelous cocktails: the sodium bicarbonate “parfait”, the washing powder “café” and finally, the salted water “juice”. All of our beverage are served with their tasteful red-cabbage-juice ice cube and at any temperature you want ! If you want to reproduce this at home, try to have a well calibrated pH-meter and a large range of water bathing temperature.  

If you want to know more ...

• Or promotional video will be online soon !
• About pH : https://en.wikipedia.org/wiki/PH
• About color indicators : https://en.wikipedia.org/wiki/PH_indicator
• About how to use a pH meter correctly: https://www.youtube.com/watch?v=vwY-xWMam7o
• About maths behind the measurment of pH with a pHmeter: https://chem.libretexts.org/Core/Physical_and_Theoretical_Chemistry/Acids_and_Bases/Acids_and_Bases_in_Aqueous_Solutions/The_pH_Scale/Temperature_Dependence_of_the_pH_of_pure_Water
• A scientific article about the influence of temperature on pH: https://reagecon.com/pdf/technicalpapers/Effects_of_Temperature_on_pH_v4-_TSP-01-2.pdf
• An interesing blog post about the influence of temperature on pH: https://www.quora.com/How-does-temperature-affect-the-pH-of-liquids
• The detailed protocol we used in our experiments: https://docs.google.com/document/d/1LSw2fPz0HYHn7Rp0svFvTyKRE9EP1skVe0h8ia8pY04/edit
• The presentation of our project for a general public : https://docs.google.com/presentation/d/1luXLQfkcw7lxQRCfYME0ORoh-0-zTgPcSuR1073knps/edit?ts=58f0d05f#slide=id.p
  
  
  
  
  Hortense Carlevan, Benoit Van Der Zyppe, Camille Duquesne
  02/05/17

Simulation of bacterial growth

How could we simulate the bacteria growth under different temperatures?

By Claire Baudelet, Gabriel Ragala, Léo Houairi

Life sciences have definitely something to do with computational ones! And that’s what we experimented, during this one-day so exciting project. After spending a whole week growing bacteria in the laboratory, we decided then to continue characterizing them… ...but with the hindsight of modelisation. What is striking is that modelisation is both a way to go deeper in the topic and to take distance from it. As a matter of fact, despite having no contact with any bacteria, we had to ask ourselves about all the little things that are actually the keys for a good understanding of bacterial growth! If creativity does have a place in designing an ingenious program, the main problem is indeed to be as realistic as possible. So, here, as we wanted to design a model for bacterial growth under different temperatures, we expected both to have a pattern coherent with the observable circular shape of any real colony and to get different patterns depending on temperature. It’s useful to know that till a certain point where cells die instead of growing, an increase in temperature corresponds to an increase in the growth rate. A good model would then help predicting the spatial repartition of growing bacteria of a colony under any temperature.

To solve our question, we coded for an entire day! The traditional Petri dish was simulated by a matrix, containing for each position either 0 for  “no bacterium” or 1 for “bacterium”. But during each replication step, newly created bacteria were rather symbolized by a 3 in order to prevent them from being also duplicated. At the end of the step, every 3 becomes a 1. Then, at each timestep, a random number between 0 and 7 is generated to make the new bacterium go into one of the eight positions around the mother cell. And, if the box chosen is already full,we simulated the shift provoked by the creation of the bacteria, by saying that  if there’s a 3 or a 1, you  look at the next case until you find an empty one. And, last but not least,  the influence of temperature on growth is controlled by an input parameter for which the user has to enter the time needed for one cell to divide once under the wanted temperature. The division period of a bacterium at a certain temperature can be deduced from the growth rate at the same temperature: for example if the growth rate at 20 °C is 0,2 division by minute, then you know that a division will take 5 minutes to occur. We eventually made a cloud of points at each timestep to represent the results.

Here are two simulations of a growing colony. As expected, both colonies are quite circular. Also, due to different temperatures, they don’t grow at the same speed. When temperature requires a division time of 20 mn, after 300 mn of growth, the colony is clearly bigger than the one undergoing a division time of 30 mn!

Also, have a look at that picture! Here we put five colonies on the same matrix. And if you look closely, you can see that they don’t have exactly the same shape, which is proof of how our program is random!

From our results, we can conclude that we managed to make a simple but quite coherent simulation of bacterial growth in function of temperature. The follow up of our project would probably be to include many more parameters that have an impact on cell growth such as the quantity of nutrients. Also, natural death of the bacteria has to be taken into account, by adding a limited lifetime to each bacterium. So far we obtained indeed a so ideal but not that realistic number of bacteria of exactly 2 to the power of the number of division…  Finally, we could also do a 3D simulation, which would gave to the bacteria the possibility to grow on top of another bacterium.

Feel free to contact us for any reason about this project, at :

• claire.baudelet@cri-paris.org
• leo.houairi@cri-paris.org
• gabriel.ragala@cri-paris.org

And if you want to have a look at our code, see below!

The explanations are given after each “#”.

#!/usr/bin/python

# -*- coding: utf-8 -*-

import random #We need function random for this program

import numpy as np

import matplotlib.pyplot as plt

x = 1000 #We set the size of the matrix

y = 1000

m = x - 1 #If we use the full matrix, the program will fail at some point when it will try to add a number outside the

         #matrix. Not using the border of the matrix solve this problem.

n = y - 1

Z = [(600,600)] #This variable store the position of the bacteria

X = [] #This two lines will be use to store both coordinate of the bacteria

Y = []

xpos = np.zeros((x,y)) #This is generating the matrix

xpos[600,600] = 1 #This is setting the first bacterium

L = [0,1,2,3,4,5,6,7] #This is list is use to generate a random direction

print('Choose a period of division at the temperature used:')

T = input() #This is use to ask a period of division to the user

t = 0 #This line is setting the time counter to 0

while t < 2: #This line control the number of divisions

    for a in range(0,m):

           for b in range(0,n): #This two lines as the computer to look to all the matrix

                   if xpos[a][b] == 1: #If the computer find a 1 (a bacterium)

                   alea = random.choice(L) #Generate an aleatory number between 0 and 7

                   if alea == 0:

                           if xpos[a+1][b] == 0: #Look at the case [a+1][b], if it's empty:

                                   xpos[a+1][b] = 3 #Add a 3 to the case

                           else:  #If not, look to the next case until you find an empty one

                                   i = 1

                                   while xpos[a+i][b] != 0:

                                       i = i + 1

                                   else:

                                   xpos[a+i][b] = 3

                   if alea == 1: #All the other lines starting by if alea ... are the same than the first one I explained but for the other 7 directions   

                          if xpos[a-1][b] == 0:

                                   xpos[a-1][b] = 3

                           else:

                                   j = 1

                                   while xpos[a-j][b] != 0:

                                       j = j + 1

                                   else:

                                       xpos[a-j][b] = 3

                   if alea == 2:

                           if xpos[a][b+1] == 0:

                                   xpos[a][b+1] = 3

                           else:

                                   k = 1

                                   while xpos[a][b+k] != 0:

                                       k = k + 1

                                   else:

                                       xpos[a][b+k] = 3

                   if alea == 3:

                           if xpos[a][b-1] == 0:

                                   xpos[a][b-1] = 3

                           else:

                                   l = 1

                                   while xpos[a][b-1] != 0:

                                       l = l + 1

                                   else:

                                       xpos[a][b-1] = 3

                   if alea == 4:

                           if xpos[a+1][b+1] == 0:

                                   xpos[a+1][b+1] = 3

                           else:

                                   e = 1

                                   f = 1

                                   while xpos[a+e][b+f] !=0 :

                                       e = e + 1

                                       f = f + 1

                                   else:

                                       xpos[a+e][b+f] =3

                   if alea == 5:

                           if xpos[a+1][b-1] == 0:

                                   xpos[a+1][b-1] = 3

                           else:

                                   g = 1

                                   h = 1

                                   while xpos[a+g][b-h] != 0:

                                       g = g + 1

                                       h = h + 1

                                   else:

                                       xpos[a+g][b-h] = 3

                   if  alea == 6:

                           if xpos[a-1][b+1] == 0:

                                   xpos[a-1][b+1] = 3

                           else:

                                   v = 1

                                   u = 1

                                   while xpos[a-v][b+u] != 0:

                                       v = v + 1

                                       u = u +1

                                   else:

                                       xpos[a-v][b+u] = 3

                   if alea == 7:

                           if xpos[a-1][b-1] == 0:

                                   xpos[a-1][b-1] = 3

                           else:

                                   p = 1

                                   q = 1

                                   while xpos[a-p][b-q] != 0:

                                       p = p + 1

                                       q = q + 1

                                   else:

                                       xpos[a-p][b-q] = 3

t = t + 1 #Up the time counter

    for a in range(0,m):

           for b in range(0,n): #Look at all the matrix

                   if xpos[a][b] == 3:

               X    pos[a][b] = 1 #If you find a 3 (a new bacterium); transform into a 1 (a real bacterium)

    for a in range (0,m):

           for b in range (0,n): #Look at all the matrix

                   if xpos[a][b] == 1: #If you find a one

                       if (a,b) not in [Z]: #And if it's not already in the matrix

                               X.append(b) #Add his b coordinate to list X

                               Y.append(a) #Add his a coordinate to list Y

                               Z.append((b,a)) #Add his position to Z

time = t*T #This is a counter of time in minutes, period of division * number of division

plt.scatter(X,Y) #This set the cloud of point

plt.axis([500, 700, 500, 700]) #This set the part of the cloud of point you want to see

plt.title('Colonies of bacteria after ' + str(time) + ' min') #This set the title of the graph

plt.show() #This allows you to see the cloud of points