Name: workflow based on the combination of isotopic tracer experiments to investigate microbial metabolism of multiple nutrient sources
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We thank Jean-Roch Mouret, Sylvie Dequin and Jean-Marie Sabalyrolles for contributing to the conception of the robotic-assisted fermentation system and Martine Pradal, Nicolas Bouvier and Pascale Brial for their technical support. Funding for this project was provided by the Minist&#232;re de l&#39;Education Nationale, de la Recherche et de la Technologie.

1. Fermentation and Sampling
<ol>
	<li>Preparation of media and fermenters 
	NOTE: All the fermentations are carried out in parallel, using the same strain and in the same chemically defined synthetic medium (SM, composition provided in Table 1), which includes a mixture of ammonium and amino acids as nitrogen sources15. For each fermentation, a single nitrogen compound is provided exclusively in a uniformly labeled 13C or 15N form (10...

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Quantifying the partitioning of compounds through metabolic networks using isotopic tracer experiments is a promising approach for understanding the operation of microbial metabolism. This methodology, while successfully applied with one or two labeled substrates, cannot currently be implemented to study metabolism of various sources using multiple labeled elemental isotopes (i.e., more than two substrates). Indeed, the available analytical techniques enable the accurate determination of the labeling patterns of...

Understanding how microbial metabolism operates is a key issue for the design of efficient strategies to improve fermentation processes and modulate the production of fermentative compounds. Advances in genomics and functional genomics in these last two decades largely contributed to extending knowledge of the topology of metabolic networks in many microorganisms. Access to this information led to the development of approaches that aim for a comprehensive overview of cellular function1. These methodologies often rely on a model-based interpretation of measurable parameters. These experimental data include, on one hand, metabolite uptake and production rates and, on the other hand, quantitative intracellular information that is obtained from isotope tracer experiments. These data provide essential information for the deduction of the in vivo activity of different pathways in a defined metabolic network2,3,4. Currently, the available analytical techniques only enable the accurate detection of labeling patterns of molecules when using a single-element isotope and possibly when co-labeling with two isotopic elements. Moreover, under most growth conditions, the carbon source only consists of one or two-compounds. Consequently, approaches based on 13C-isotopic tracers from carbon substrates were widely and successfully applied to develop a complete understanding of carbon metabolic network operations5,6,7,8.
In contrast, in many natural and industrial environments, the available nitrogen resource that supports microbial growth is often composed of a wide range of molecules. For example, during wine or beer fermentation, nitrogen is provided as a mixture of 18 amino acids and ammonium at variable concentrations9. This array of N compounds that are accessible for anabolism makes these complex media conditions greatly different from those commonly used for physiological studies, as the latter are achieved using a unique source of nitrogen, typically ammonium.
Overall, internalized nitrogen compounds may be directly incorporated into proteins or catabolized. The network structure of nitrogen metabolism in many microorganisms, including the yeast Saccharomyces cerevisiae, is very complex in accordance with the diversity of substrates. Schematically, this system is based on the combination of the central core of nitrogen metabolism which catalyzes the interconversion of glutamine, glutamate, and &#945;-ketoglutarate10,11, with transaminases and deaminases. Through this network, amine groups from ammonium or other amino acids are gathered and &#945;-keto acids released. These intermediates are also synthetized through central carbon metabolism (CCM)12,13. This large number of branched reactions and intermediates, involved in both the catabolism of exogenous nitrogen sources and the anabolism of proteinogenic amino acids, fulfills the anabolic requirements of the cells. The activity through these different interconnected routes also results in the excretion of metabolites. In particular, &#945;-keto acids may be redirected through the Ehrlich pathway to produce higher alcohols and their acetate ester derivatives14, which are essential contributors to the sensory profiles of products. Subsequently, how nitrogen metabolism operates plays a key role in biomass production and the formation of volatile molecules (aroma).
The reactions, enzymes, and genes involved in nitrogen metabolism are extensively described in the literature. However, the issue of the distribution of multiple nitrogen sources throughout a metabolic network has not yet been addressed. There are two main reasons that explain this lack of information. First, in view of the important complexity of the nitrogen metabolism network, a large amount of quantitative data is required for a complete understanding of its operation that was unavailable until now. Second, many experimental constraints and limitations of analytical methods prevented the implementation of approaches that were previously used for the elucidation of CCM function.
To overcome these problems, we chose to develop a system-level approach that is based on the reconciliation of data from a series of isotopic tracer experiments. The workflow includes: 
- A set of fermentations carried out under the same environmental conditions, while a different selected nutrient source (substrate) is labeled each time. 
- A combination of analytical procedures (HPLC, GC-MS) for an accurate determination, at different stages of the fermentation, of the residual concentration of labeled substrate and the concentration and the isotopic enrichment of compounds that are derived from the catabolism of the labeled molecule, including derived biomass. 
- A calculation of the mass and isotopic balance for each consumed labeled molecule and a further integrated analysis of the dataset to obtain a global overview of the management of multiple nutrient sources by microorganisms through the determination of flux ratios.
To apply this methodology, attention must be paid to the reproducible behavior of the strain/microorganism between cultures. Furthermore, samples from different cultures must be taken during the same well-defined fermentation progress. In the experimental work reported in this manuscript, a robot-assisted system is used for online monitoring of fermentations to account for these constraints.
Furthermore, it is essential to choose a set of labeled substrates (compound, nature, and position of the labeling) that is appropriate to address the scientific problem of the study. Here, 15N-labeled ammonium, glutamine, and arginine were selected as the three major nitrogen sources found in grape juice. This allowed assessing the pattern of nitrogen redistribution from consumed compounds to the proteinogenic amino acids. We also aimed to investigate the fate of the carbon backbone of the consumed amino acids and their contribution to the production of volatile molecules. To meet this objective, uniformly 13C-labeled leucine, isoleucine, threonine, and valine were included in the study as amino acids that are derived from major intermediates of the Ehrlich pathway.
Overall, we quantitatively explored how yeast manages a complex nitrogen resource by redistributing exogenous nitrogen sources to fulfill its anabolic requirements throughout fermentation while additionally removing the excess of carbon precursors as volatile molecules. The experimental procedure reported in this paper can be applied to investigate other multiple nutrient sources used by any other microorganism. It appears to be an appropriate approach for the analysis of the impact of genetic background or environmental conditions on the metabolic behavior of microorganisms.

<table>
	<tbody>
		<tr>
			<td>D-Glucose</td>
			<td>PanReac</td>
			<td>141341.0416</td>
			<td></td>
		</tr>
		<tr>
			<td>D-Fructose</td>
			<td>PanReac</td>
			<td>142728.0416</td>
			<td></td>
		</tr>
		<tr>
			<td>DL-Malic acid</td>
			<td>Sigma Aldrich</td>
			<td>M0875</td>
			<td></td>
		</tr>
		<tr>
			<td>Citric acid monohydrate</td>
			<td>Sigma Aldrich</td>
			<td>C7129</td>
			<td></td>
		</tr>
		<tr>
			<td>Potassium phosphate monobasic</td>
			<td>Sigma Aldrich</td>
			<td>P5379</td>
			<td></td>
		</tr>
		<tr>
			<td>Potassium sulfate</td>
			<td>Sigma Aldrich</td>
			<td>P0772</td>
			<td></td>
		</tr>
		<tr>
			<td>Magnesium sulfate heptahydrate</td>
			<td>Sigma Aldrich</td>
			<td>230391</td>
			<td></td>
		</tr>
		<tr>
			<td>Calcium chloride dihydrate</td>
			<td>Sigma Aldrich</td>
			<td>C7902</td>
			<td></td>
		</tr>
		<tr>
			<td>Sodium chloride</td>
			<td>Sigma Aldrich</td>
			<td>S9625</td>
			<td></td>
		</tr>
		<tr>
			<td>Ammonium chloride</td>
			<td>Sigma Aldrich</td>
			<td>A4514</td>
			<td></td>
		</tr>
		<tr>
			<td>Sodium hydroxide</td>
			<td>Sigma Aldrich</td>
			<td>71690</td>
			<td></td>
		</tr>
		<tr>
			<td>Manganese sulfate monohydrate</td>
			<td>Sigma Aldrich</td>
			<td>M7634</td>
			<td></td>
		</tr>
		<tr>
			<td>Zinc sulfate heptahydrate</td>
			<td>Sigma Aldrich</td>
			<td>Z4750</td>
			<td></td>
		</tr>
		<tr>
			<td>Copper (II) sulfate pentahydrate</td>
			<td>Sigma Aldrich</td>
			<td>C7631</td>
			<td></td>
		</tr>
		<tr>
			<td>Potassium iodine</td>
			<td>Sigma Aldrich</td>
			<td>P4286</td>
			<td></td>
		</tr>
		<tr>
			<td>Cobalt (II) chloride hexahydrate</td>
			<td>Sigma Aldrich</td>
			<td>C3169</td>
			<td></td>
		</tr>
		<tr>
			<td>Boric acid</td>
			<td>Sigma Aldrich</td>
			<td>B7660</td>
			<td></td>
		</tr>
		<tr>
			<td>Ammonium heptamolybdate</td>
			<td>Sigma Aldrich</td>
			<td>A7302</td>
			<td></td>
		</tr>
		<tr>
			<td>Myo-inositol</td>
			<td>Sigma Aldrich</td>
			<td>I5125</td>
			<td></td>
		</tr>
		<tr>
			<td>D-Pantothenic acid hemicalcium salt</td>
			<td>Sigma Aldrich</td>
			<td>21210</td>
			<td></td>
		</tr>
		<tr>
			<td>Thiamine, hydrochloride</td>
			<td>Sigma Aldrich</td>
			<td>T4625</td>
			<td></td>
		</tr>
		<tr>
			<td>Nicotinic acid</td>
			<td>Sigma Aldrich</td>
			<td>N4126</td>
			<td></td>
		</tr>
		<tr>
			<td>Pyridoxine</td>
			<td>Sigma Aldrich</td>
			<td>P5669</td>
			<td></td>
		</tr>
		<tr>
			<td>Biotine</td>
			<td>Sigma Aldrich</td>
			<td>B4501</td>
			<td></td>
		</tr>
		<tr>
			<td>Ergost&#233;rol</td>
			<td>Sigma Aldrich</td>
			<td>E6510</td>
			<td></td>
		</tr>
		<tr>
			<td>Tween 80</td>
			<td>Sigma Aldrich</td>
			<td>P1754</td>
			<td></td>
		</tr>
		<tr>
			<td>Ethanol absolute</td>
			<td>VWR Chemicals</td>
			<td>101074F</td>
			<td></td>
		</tr>
		<tr>
			<td>Iron (III) chloride hexahydrate</td>
			<td>Sigma Aldrich</td>
			<td>236489</td>
			<td></td>
		</tr>
		<tr>
			<td>L-Aspartic acid</td>
			<td>Sigma Aldrich</td>
			<td>A9256</td>
			<td></td>
		</tr>
		<tr>
			<td>L-Glutamic acid</td>
			<td>Sigma Aldrich</td>
			<td>G1251</td>
			<td></td>
		</tr>
		<tr>
			<td>L-Alanine</td>
			<td>Sigma Aldrich</td>
			<td>A7627</td>
			<td></td>
		</tr>
		<tr>
			<td>L-Arginine</td>
			<td>Sigma Aldrich</td>
			<td>A5006</td>
			<td></td>
		</tr>
		<tr>
			<td>L-Cysteine</td>
			<td>Sigma Aldrich</td>
			<td>C7352</td>
			<td></td>
		</tr>
		<tr>
			<td>L-Glutamine</td>
			<td>Sigma Aldrich</td>
			<td>G3126</td>
			<td></td>
		</tr>
		<tr>
			<td>Glycine</td>
			<td>Sigma Aldrich</td>
			<td>G7126</td>
			<td></td>
		</tr>
		<tr>
			<td>L-Histidine</td>
			<td>Sigma Aldrich</td>
			<td>H8000</td>
			<td></td>
		</tr>
		<tr>
			<td>L-Isoleucine</td>
			<td>Sigma Aldrich</td>
			<td>I2752</td>
			<td></td>
		</tr>
		<tr>
			<td>L-Leucine</td>
			<td>Sigma Aldrich</td>
			<td>L8000</td>
			<td></td>
		</tr>
		<tr>
			<td>L-Lysine</td>
			<td>Sigma Aldrich</td>
			<td>L5501</td>
			<td></td>
		</tr>
		<tr>
			<td>L-Methionine</td>
			<td>Sigma Aldrich</td>
			<td>M9625</td>
			<td></td>
		</tr>
		<tr>
			<td>L-Phenylalanine</td>
			<td>Sigma Aldrich</td>
			<td>P2126</td>
			<td></td>
		</tr>
		<tr>
			<td>L-Proline</td>
			<td>Sigma Aldrich</td>
			<td>P0380</td>
			<td></td>
		</tr>
		<tr>
			<td>L-Serine</td>
			<td>Sigma Aldrich</td>
			<td>S4500</td>
			<td></td>
		</tr>
		<tr>
			<td>L-Threonine</td>
			<td>Sigma Aldrich</td>
			<td>T8625</td>
			<td></td>
		</tr>
		<tr>
			<td>L-Tryptophane</td>
			<td>Sigma Aldrich</td>
			<td>T0254</td>
			<td></td>
		</tr>
		<tr>
			<td>L-Tyrosine</td>
			<td>Sigma Aldrich</td>
			<td>T3754</td>
			<td></td>
		</tr>
		<tr>
			<td>L-Valine</td>
			<td>Sigma Aldrich</td>
			<td>V0500</td>
			<td></td>
		</tr>
		<tr>
			<td>13C5-L-Valine</td>
			<td>Eurisotop</td>
			<td>CLM-2249-H-0.25</td>
			<td></td>
		</tr>
		<tr>
			<td>13C6-L-Leucine</td>
			<td>Eurisotop</td>
			<td>CLM-2262-H-0.25</td>
			<td></td>
		</tr>
		<tr>
			<td>15N-Ammonium chloride</td>
			<td>Eurisotop</td>
			<td>NLM-467-1</td>
			<td></td>
		</tr>
		<tr>
			<td>ALPHA-15N-L-Glutamine</td>
			<td>Eurisotop</td>
			<td>NLM-1016-1</td>
			<td></td>
		</tr>
		<tr>
			<td>U-15N4-L-Arginine</td>
			<td>Eurisotop</td>
			<td>NLM-396-PK</td>
			<td></td>
		</tr>
		<tr>
			<td>Ethyl acetate</td>
			<td>Sigma Aldrich</td>
			<td>270989</td>
			<td></td>
		</tr>
		<tr>
			<td>Ethyl propanoate</td>
			<td>Sigma Aldrich</td>
			<td>112305</td>
			<td></td>
		</tr>
		<tr>
			<td>Ethyl 2-methylpropanoate</td>
			<td>Sigma Aldrich</td>
			<td>246085</td>
			<td></td>
		</tr>
		<tr>
			<td>Ethyl butanoate</td>
			<td>Sigma Aldrich</td>
			<td>E15701</td>
			<td></td>
		</tr>
		<tr>
			<td>Ethyl 2-methylbutanoate</td>
			<td>Sigma Aldrich</td>
			<td>306886</td>
			<td></td>
		</tr>
		<tr>
			<td>Ethyl 3-methylbutanoate</td>
			<td>Sigma Aldrich</td>
			<td>8.08541.0250</td>
			<td></td>
		</tr>
		<tr>
			<td>Ethyl hexanoate</td>
			<td>Sigma Aldrich</td>
			<td>148962</td>
			<td></td>
		</tr>
		<tr>
			<td>Ethyl octanoate</td>
			<td>Sigma Aldrich</td>
			<td>W244910</td>
			<td></td>
		</tr>
		<tr>
			<td>Ethyl decanoate</td>
			<td>Sigma Aldrich</td>
			<td>W243205</td>
			<td></td>
		</tr>
		<tr>
			<td>Ethyl dodecanoate</td>
			<td>Sigma Aldrich</td>
			<td>W244112</td>
			<td></td>
		</tr>
		<tr>
			<td>Ethyl lactate</td>
			<td>Sigma Aldrich</td>
			<td>W244015</td>
			<td></td>
		</tr>
		<tr>
			<td>Diethyl succinate</td>
			<td>Sigma Aldrich</td>
			<td>W237701</td>
			<td></td>
		</tr>
		<tr>
			<td>2-methylpropyl acetate</td>
			<td>Sigma Aldrich</td>
			<td>W217514</td>
			<td></td>
		</tr>
		<tr>
			<td>2-methylbutyl acetate</td>
			<td>Sigma Aldrich</td>
			<td>W364401</td>
			<td></td>
		</tr>
		<tr>
			<td>3-methyl butyl acetate</td>
			<td>Sigma Aldrich</td>
			<td>287725</td>
			<td></td>
		</tr>
		<tr>
			<td>2-phenylethyl acetate</td>
			<td>Sigma Aldrich</td>
			<td>290580</td>
			<td></td>
		</tr>
		<tr>
			<td>2-methylpropanol</td>
			<td>Sigma Aldrich</td>
			<td>294829</td>
			<td></td>
		</tr>
		<tr>
			<td>2-methylbutanol</td>
			<td>Sigma Aldrich</td>
			<td>133051</td>
			<td></td>
		</tr>
		<tr>
			<td>3-methylbutanol</td>
			<td>Sigma Aldrich</td>
			<td>309435</td>
			<td></td>
		</tr>
		<tr>
			<td>Hexanol</td>
			<td>Sigma Aldrich</td>
			<td>128570</td>
			<td></td>
		</tr>
		<tr>
			<td>2-phenylethanol</td>
			<td>Sigma Aldrich</td>
			<td>77861</td>
			<td></td>
		</tr>
		<tr>
			<td>Propanoic acid</td>
			<td>Sigma Aldrich</td>
			<td>94425</td>
			<td></td>
		</tr>
		<tr>
			<td>Butanoic acid</td>
			<td>Sigma Aldrich</td>
			<td>19215</td>
			<td></td>
		</tr>
		<tr>
			<td>2-methylpropanoic acid</td>
			<td>Sigma Aldrich</td>
			<td>58360</td>
			<td></td>
		</tr>
		<tr>
			<td>2-methylbutanoic acid</td>
			<td>Sigma Aldrich</td>
			<td>193070</td>
			<td></td>
		</tr>
		<tr>
			<td>3-methylbutanoic acid</td>
			<td>Sigma Aldrich</td>
			<td>W310212</td>
			<td></td>
		</tr>
		<tr>
			<td>Hexanoic acid</td>
			<td>Sigma Aldrich</td>
			<td>153745</td>
			<td></td>
		</tr>
		<tr>
			<td>Octanoic acid</td>
			<td>Sigma Aldrich</td>
			<td>W279900</td>
			<td></td>
		</tr>
		<tr>
			<td>Decanoic acid</td>
			<td>Sigma Aldrich</td>
			<td>W236403</td>
			<td></td>
		</tr>
		<tr>
			<td>Dodecanoic acid</td>
			<td>Sigma Aldrich</td>
			<td>L556</td>
			<td></td>
		</tr>
		<tr>
			<td>Fermentor 1L</td>
			<td>Legallais</td>
			<td>AT1357</td>
			<td>Fermenter handmade for fermentation</td>
		</tr>
		<tr>
			<td>Disposable vacuum filtration system</td>
			<td>Dominique Deutscher</td>
			<td>029311</td>
			<td></td>
		</tr>
		<tr>
			<td>Fermenters (250 ml)</td>
			<td>Legallais</td>
			<td>AT1352</td>
			<td>Fermenter handmade for fermentation</td>
		</tr>
		<tr>
			<td>Sterile tubes</td>
			<td>Sarstedt</td>
			<td>62.554.502</td>
			<td></td>
		</tr>
		<tr>
			<td>Fermentation locks</td>
			<td>Legallais</td>
			<td>AT1356</td>
			<td>Fermetation locks handmade for fermentation</td>
		</tr>
		<tr>
			<td>BactoYeast Extract</td>
			<td>Becton, Dickinson and Company</td>
			<td>212750</td>
			<td></td>
		</tr>
		<tr>
			<td>BactoPeptone</td>
			<td>Becton, Dickinson and Company</td>
			<td>211677</td>
			<td></td>
		</tr>
		<tr>
			<td>Incubator shaker</td>
			<td>Infors HT</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Particle Counter</td>
			<td>Beckman Coulter</td>
			<td>6605697</td>
			<td>Multisizer 3 Coulter Counter</td>
		</tr>
		<tr>
			<td>Centrifuge</td>
			<td>Jouan</td>
			<td></td>
			<td>GR412</td>
		</tr>
		<tr>
			<td>Plate Butler Robotic system</td>
			<td>Lab Services BV</td>
			<td>PF0X-MA</td>
			<td>Automatic instrument</td>
		</tr>
		<tr>
			<td>Plate Butler Software</td>
			<td>Lab Services BV</td>
			<td></td>
			<td>Robot monitor software</td>
		</tr>
		<tr>
			<td>RobView</td>
			<td></td>
			<td></td>
			<td>In-house developed calculation software</td>
		</tr>
		<tr>
			<td>My SQL</td>
			<td></td>
			<td></td>
			<td>International source database</td>
		</tr>
		<tr>
			<td>Cimarec i Telesystem Multipoint Stirrers</td>
			<td>Thermo Fisher Scientific</td>
			<td>50088009</td>
			<td>String Drive 60</td>
		</tr>
		<tr>
			<td>BenchBlotter platform rocker</td>
			<td>Dutscher</td>
			<td>60903</td>
			<td></td>
		</tr>
		<tr>
			<td>Ammonia enzymatic kit</td>
			<td>R-Biopharm AG</td>
			<td>5390</td>
			<td></td>
		</tr>
		<tr>
			<td>Spectrophotometer cuvettes</td>
			<td>VWR</td>
			<td>634-0678</td>
			<td></td>
		</tr>
		<tr>
			<td>Spectrophotometer UviLine 9400</td>
			<td>Secomam</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Amino acids standards physiological - acidics and neutrals</td>
			<td>Sigma Aldrich</td>
			<td>A6407</td>
			<td></td>
		</tr>
		<tr>
			<td>Amino acids standards physiological - basics</td>
			<td>Sigma Aldrich</td>
			<td>A6282</td>
			<td></td>
		</tr>
		<tr>
			<td>Citrate lithium buffers - Ultra ninhydrin reagent</td>
			<td>Biochrom</td>
			<td>BC80-6000-06</td>
			<td></td>
		</tr>
		<tr>
			<td>Sulfosalycilic acid</td>
			<td>Sigma Aldrich</td>
			<td>S2130</td>
			<td></td>
		</tr>
		<tr>
			<td>Norleucine</td>
			<td>Sigma Aldrich</td>
			<td>N1398</td>
			<td></td>
		</tr>
		<tr>
			<td>Biochrom 30 AAA</td>
			<td>Biochrom</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>EZChrom Elite</td>
			<td>Biochrom</td>
			<td></td>
			<td>Instrument control and Data analysis software</td>
		</tr>
		<tr>
			<td>Ultropac 8 resin Lithium</td>
			<td>Biochrom</td>
			<td>BC80-6002-47</td>
			<td>Lithium High Resolution Physiological Column</td>
		</tr>
		<tr>
			<td>Filter Millex GV</td>
			<td>Merck Millipore</td>
			<td>SLGVX13NL</td>
			<td>Millex GV 13mm (pore size 0.22 &#181;m)</td>
		</tr>
		<tr>
			<td>Membrane filter PALL</td>
			<td>VWR</td>
			<td>514-4157</td>
			<td>Supor-450 47mm 0.45&#181;m</td>
		</tr>
		<tr>
			<td>Vacuum pump Millivac Mini</td>
			<td>Millipore</td>
			<td>XF5423050</td>
			<td></td>
		</tr>
		<tr>
			<td>Aluminium smooth weigh dish 70mm</td>
			<td>VWR</td>
			<td>611-1380</td>
			<td></td>
		</tr>
		<tr>
			<td>Precision balance</td>
			<td>Mettler</td>
			<td></td>
			<td>Specifications AE163</td>
		</tr>
		<tr>
			<td>Dimethyl sulfoxid dried</td>
			<td>Merck</td>
			<td>1029310161</td>
			<td>(max. 0.025% H2O) SeccoSolv</td>
		</tr>
		<tr>
			<td>Combustion oven</td>
			<td>Legallais</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Pierce BCA protein assay kit</td>
			<td>Interchim</td>
			<td>UP40840A</td>
			<td></td>
		</tr>
		<tr>
			<td>Formic acid</td>
			<td>Fluka</td>
			<td>94318</td>
			<td></td>
		</tr>
		<tr>
			<td>Hydrogen peroxide</td>
			<td>Sigma Aldrich</td>
			<td>H1009</td>
			<td></td>
		</tr>
		<tr>
			<td>Hydrochloric Acid Fuming 37% Emsure</td>
			<td>Merck</td>
			<td>1003171000</td>
			<td>Grade ACS,ISO,Reag. Ph Eur</td>
		</tr>
		<tr>
			<td>Lithium acetate buffer</td>
			<td>Biochrom</td>
			<td>80-2038-10</td>
			<td></td>
		</tr>
		<tr>
			<td>Commercial solution of hydrolyzed amino acids</td>
			<td>Sigma Aldrich</td>
			<td>AAS18</td>
			<td></td>
		</tr>
		<tr>
			<td>L-Methionine sulfone</td>
			<td>Sigma Aldrich</td>
			<td>M0876</td>
			<td></td>
		</tr>
		<tr>
			<td>L-Cysteic acid monohydrate</td>
			<td>Sigma Aldrich</td>
			<td>30170</td>
			<td></td>
		</tr>
		<tr>
			<td>Pyrex glass culture tubes</td>
			<td>Sigma Aldrich</td>
			<td>Z653586</td>
			<td></td>
		</tr>
		<tr>
			<td>Pyridine</td>
			<td>Acros Organics</td>
			<td>131780500</td>
			<td>99% Extrapure</td>
		</tr>
		<tr>
			<td>Ethyl chloroformate</td>
			<td>Sigma Aldrich</td>
			<td>23131</td>
			<td></td>
		</tr>
		<tr>
			<td>Dichloromethane</td>
			<td>Sigma Aldrich</td>
			<td>32222</td>
			<td></td>
		</tr>
		<tr>
			<td>Vials</td>
			<td>Sigma Aldrich</td>
			<td>854165</td>
			<td></td>
		</tr>
		<tr>
			<td>Microinserts for 1.5ml vials</td>
			<td>Sigma Aldrich</td>
			<td>SU860066</td>
			<td></td>
		</tr>
		<tr>
			<td>GC/MS</td>
			<td>Agilent Technologies</td>
			<td>5890 GC/5973 MS</td>
			<td></td>
		</tr>
		<tr>
			<td>Chemstation</td>
			<td>Agilent Technologies</td>
			<td></td>
			<td>Instrument control and data analysis software</td>
		</tr>
		<tr>
			<td>Methanol</td>
			<td>Sigma Aldrich</td>
			<td>34860</td>
			<td>Chromasolv, for HPLC</td>
		</tr>
		<tr>
			<td>Acetonitrile</td>
			<td>Sigma Aldrich</td>
			<td>34998</td>
			<td>ChromasolvPlus, for HPLC</td>
		</tr>
		<tr>
			<td>N,N-Dimethylformamide dimethyl acetal</td>
			<td>Sigma Aldrich</td>
			<td>394963</td>
			<td></td>
		</tr>
		<tr>
			<td>BSTFA</td>
			<td>Sigma Aldrich</td>
			<td>33024</td>
			<td></td>
		</tr>
		<tr>
			<td>DB-17MS column</td>
			<td>Agilent Technologies</td>
			<td>122-4731</td>
			<td>30m*0.25mm*0.15&#181;m</td>
		</tr>
		<tr>
			<td>Sodium sulfate, anhydrous</td>
			<td>Sigma Aldrich</td>
			<td>238597</td>
			<td></td>
		</tr>
		<tr>
			<td>Technical nitrogen</td>
			<td>Air products</td>
			<td>14629</td>
			<td></td>
		</tr>
		<tr>
			<td>Zebron ZB-WAX column</td>
			<td>Phenomenex</td>
			<td>7HG-G007-11</td>
			<td>30m*0.25mm*0.25&#181;m</td>
		</tr>
		<tr>
			<td>Helium BIP</td>
			<td>Air products</td>
			<td>26699</td>
			<td></td>
		</tr>
		<tr>
			<td>Glass Pasteur pipettes</td>
			<td>VWR</td>
			<td>612-1702</td>
			<td></td>
		</tr>
	</tbody>
</table>

workflow based on the combination of isotopic tracer experiments to investigate microbial metabolism of multiple nutrient sources

Studies in the field of microbiology rely on the implementation of a wide range of methodologies. In particular, the development of appropriate methods substantially contributes to providing extensive knowledge of the metabolism of microorganisms growing in chemically defined media containing unique nitrogen and carbon sources. In contrast, the management through metabolism of multiple nutrient sources, despite their broad presence in natural or industrial environments, remains virtually unexplored. This situation is mainly due to the lack of suitable methodologies, which hinders investigations.
We report an experimental strategy to quantitatively and comprehensively explore how metabolism operates when a nutrient is provided as a mixture of different molecules, i.e., a complex resource. Here, we describe its application for assessing the partitioning of multiple nitrogen sources through the yeast metabolic network. The workflow combines information obtained during stable isotope tracer experiments using selected 13C- or 15N-labeled substrates. It first consists of parallel and reproducible fermentations in the same medium, which includes a mixture of N-containing molecules; however,a selected nitrogen source is labeled each time. A combination of analytical procedures (HPLC, GC-MS) is implemented to assess the labeling patterns of targeted compounds and to quantify the consumption and recovery of substrates in other metabolites. An integrated analysis of the complete dataset provides an overview of the fate of consumed substrates within cells. This approach requires an accurate protocol for the collection of samples&#8211;facilitated by a robot-assisted system for online monitoring of fermentations&#8211;and the achievement of numerous time-consuming analyses. Despite these constraints, it allowed understanding, for the first time, the partitioning of multiple nitrogen sources throughout the yeast metabolic network. We elucidated the redistribution of nitrogen from more abundant sources toward other N-compounds and determined the metabolic origins of volatile molecules and proteinogenic amino acids.

Figure 3 presents a schematic diagram of the workflow that was implemented to investigate the management by yeast of the multiple nitrogen sources that are found during wine fermentation. 
For different points of sampling, the biological parameters&#8211;growth characteristics, nitrogen consumption patterns, and the profile of proteinogenic amino acids&#8211;show a high reproducibility among fermentations (Figure 4). This co...

Watch this Scientific Journal Video about Workflow Based on the Combination of Isotopic Tracer Experiments to Investigate Microbial Metabolism of Multiple Nutrient Sources at JoVE.com

Workflow Based on the Combination of Isotopic Tracer Experiments to Investigate Microbial Metabolism of Multiple Nutrient Sources

This protocol describes an experimental procedure to quantitatively and comprehensively investigate the metabolism of multiple nutrient sources. This workflow, based on a combination of isotopic tracer experiments and an analytical procedure, allows the fate of consumed nutrients and the metabolic origin of molecules synthetized by microorganisms to be determined.

Studies in the field of microbiology rely on the implementation of a wide range of methodologies. In particular, the development of appropriate methods ...

The overall goal of this procedure is to quantitatively investigate the metabolism of multiple nutrient sources using labeled substrates. This workflow allows the fate of consumed nutrients and the metabolic origin of compounds synthesized by yeasts to be determined. This method can help to answer key questions in the microbial metabolism field such how all the metabolism operate depending on environmental conditions.The main advantage of this technique is that it allow the elucidation and quantification of the protein of nutrient distribution from multiple sources. This method was used to investigate the nitrogen metabolism of yeast, but it can also be applied to other metabolisms and microorganisms. The key to success with this experiment lies in the reproducibility of fermentations.Users must pay particular attention to collect the cells in the same defined physiological stage. Demonstrating the procedure will be Christian Picou from our technical staff, Pauline Seguinot, a PhD student from the laboratory, and Stephanie Rollero, a post-doctorate fellow. In an autoclave, pasteurize each synthetic medium prepared for each nitrogen source in a one-liter flask containing a magnetic stir bar.Dissolve the appropriate amount of labeled molecule to reach the desired final concentration in the medium. Sterilize the medium using a disposable vacuum filtration system. Using a sterile measuring cylinder, divide the medium between two pre-sterilized fermenters that contain a magnetic stir bar and are equipped with fermentation locks to avoid the entry of oxygen but allow the release of carbon dioxide.After growing a cerevisiae strain in YPD medium, collect a pre-culture aliquot under laminar flow and quantify the cell population using an electronic particle counter that is fitted with a 100-micrometer aperture. After centrifuging the pre-culture, suspend the pellet in an appropriate volume of sterile water to obtain a final concentration of 2.5 times 10 to the 9th cells per milliliter. Then, inoculate each fermenter with one milliliter of the cell suspension.Following this, prepare a fermentation platform by installing the fermenters in the support guides that are properly placed on the 21-position stirring plates and set the stirring rate at 270 RPM. To start the online monitoring of each fermentation, launch the robot control application, click the Start Assay button and select the fermentation volume to be carried out. To ensure that the displayed interface permits the indication of the number and the position of fermenters on the platform, right-click on the slot location and choose Disable to inactivate the monitoring of the empty positions.To initialize the calculation software, click on the Initializer button and validate with OK.Then, click on the Start button of the robot control application to start the weight acquisitions. Next, centrifuge two six-milliliter samples taken from the fermenters at 2, 000 times G for five minutes at four degrees Celsius. When finished, transfer each frozen supernatant into two tubes and store at minus 80 degrees Celsius.Wash each pellet twice with five milliliters of distilled water and store at minus 80 degrees Celsius for the measurement of isotopic enrichment. Add 200 microliters of a 25%sulfosalicylic acid solution that contains 2.5 millimolar norleucine as an internal standard to 800 microliters of sample to remove molecules with high molecular weights. After incubation and centrifugation, filter the mixture through a 0.22-micrometer pore-size nitrocellulose membrane and place the sample in an amino acid analysis system.In the program or software of the analysis system, click on the button Run to begin the liquid chromatography analyses with the analyzer equipped with a cation exchange column. Next, prepare an oxidized extract by suspending a previously prepared labeled cell pellet in 200 microliters of performic acid. After incubating for four hours at four degrees Celsius, stop the reaction by adding 33.6 milligrams of sodium sulfate.Add 800 microliters of six normal hydrochloric acid to the oxidized extract, and incubate the sample in a hermatically-sealed glass tube for 16 hours at 110 degrees Celsius in a dry-heat oven. After allowing the sample to cool to room temperature, add 200 microliters of 2.5 millimolar neuroleucine and remove the hydrogen chloride gas with a stream of nitrogen. Wash the dried extract twice with 800 microliters of distilled water, and then 800 microliters of ethanol.Then, add 800 microliters of 200-millimolar lithium acetate buffer to the extract. Determine the relative concentrations of amino acids within proteins in the sample using the chromatographic method previously described. Hydrolyze a labeled cell pellet by adding 1.2 milliliters of six-molar hydrochloric acid and incubating the sample for 16 hours at 105 degrees Celsius in a tightly-closed glass tube in a dry-heat oven.After allowing the sample to cool to room temperature, add 1.2 milliliters of distilled water. Following centrifugation to remove the cellular debris, distribute the supernatant into six 400-microliter fractions in open glass tubes. Dry the fractions in the dry-heat oven at 105 degrees Celsius for four to five hours until they reach the consistency of syrup.During the biomass hydrolysis, dry the fraction until the consistency of a syrup by frequently controlling the acid of operation. For ECF derivatization, dissolve the cooled dried hydrolysate in 200 microliters of 20-millimolar hydrochloric acid and 133 microliters of a one-to-four mixture of pyridine and ethanol. Add 50 microliters of ethyl chloroformate to derivatize the amino acids.After the carbon dioxide has been released, transfer the mixture to centrifuge tubes that contain 500 microliters of dichloromethane to extract the derivatized compounds. Vortex the samples for 10 seconds. After centrifuging for four minutes at 10, 000 times G, collect the lower organic phase of each sample with a Pasteur pipette and transfer to a GC autosampler vial that contains a conical glass insert so that each sample may be directly injected into the GCMS instrument.HPLC derivatization reagents are used for the ECF procedure. Change frequently the ECF reagents. For DMF-DMA derivatization, dissolve the dried hydrolysate in 50 microliters of methanol and 200 microliters of acetonitrile.Add 300 microliters of DMF-DMA and vortex the sample. Then, transfer the sample to a GC autosampler vial that contains a conical glass insert. For BSTFA derivatization, suspend the hydrolysate in 200 microliters of acetonitrile.Add 200 microliters of BSTFA to the sample and hermatically close the glass tube. After incubation for four hours at 135 degrees Celsius, transfer the cooled extract directly to a GC autosampler vial that contains a conical glass insert. Now, analyze the samples with a gas chromatograph equipped with an autosampler injector and coupled to a mass spectrometer.Click on Instrument in the menu of the computer software. Under the Sequence tab, click the Edit Sample Log Table to create a sample list, and click Run and Run Sequence to start the injections. After the analyses are complete, record a cluster of intensities for each amino acid that corresponds to its different mass isotopomers.Add 10 microliters of the appropriate deuterated internal standards to five milliliters of supernatant in a 15-milliliter glass tube. Add one milliliter of dichloromethane, tightly close the tube and shake the sample on a rocking platform for 20 minutes. After centrifugation, collect the lower organic phase in a 15-milliliter glass tube.After repeating the dichloromethane extraction, dry the organic extract over 500 milligrams of anhydrous sodium sulfate and collect the liquid phase with a Pasteur pipette. Concentrate the extract by a factor of four under nitrogen flux. Then, transfer the concentrated sample to a GC autosampler vial.For GCMS quantification, inject two microliters of sample with a split ratio of 10-to-one, and separate the extracted volatile molecules using the appropriate oven temperature profile. Using the spreadsheets in the text protocol, enter the raw data values that correspond to the concentration of extracellular amino acids, cell dry weight, protein content of cells, concentration of volatile molecules and isotopic enrichment of proteinogenic amino acids and volatile molecules. A schematic diagram of the workflow to investigate the management by yeast of the multiple nitrogen sources that are found during wine fermentation is shown here.For different points of sampling, the biological parameters show a high reproducibility among fermenations. An overview of the redistribution of nitrogen from the sources in grape juice to all the proteinogenic amino acids is shown here. Combined with the determination of the mass balances, the measurement of the isotopic enrichment of proteinogenic amino acids provided the first quantification of the contribution of nitrogen-originated arginine, glutamine, ammonium and other sources to the amine groups of each of these compounds.A comparison between the amount of consumed amino acid that is directly recovered into the biomass is depicted here. Partitioning of the consumed aliphatic amino acids, leucine and valine in the metabolic network in yeast is shown here. More than 96%of the consumed valine and leucine are recovered in their conversion products.Surprisingly, a substantial fraction of valine and leucine are catabolized despite a considerable imbalance between the availability of these compounds in the medium and their content in biomass. Once mastered, this technique can be done in five days. While attempting this procedure, it's important to remember to carefully select the labeling part of the substrate according to the scientific issues.To ensure a good biological reproducibility, perform the fermentation and chromatographic analysis in duplicate. After its development, this technique paved the way for researchers in microbiology field to explore how the metabolism and physiology of microorganism change depending on the environmental conditions. Don't forget that working with derivatization reagents and arginine solvent can be extremely hazardous, thus precautions such as wearing gloves and working under a fume hood should always be taken while performing this procedure.

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Research

JoVE Journal

Bioengineering

Design of a Cyclic Pressure Bioreactor for the Ex Vivo Study of Aortic Heart Valves

The aortic valve, located between the left ventricle and the aorta, allows for unidirectional blood flow, preventing backflow into the ventricle. Aortic valve leaflets are composed of interstitial cells suspended within an extracellular matrix (ECM) and are lined with an endothelial cell monolayer. The valve withstands a harsh, dynamic environment and is constantly exposed to shear, flexion, tension, and compression. Research has shown calcific lesions in diseased valves occur in areas of high mechanical stress as a result of endothelial disruption or interstitial matrix damage1-3. Hence, it is not surprising that epidemiological studies have shown high blood pressure to be a leading risk factor in the onset of aortic valve disease4. 
The only treatment option currently available for valve disease is surgical replacement of the diseased valve with a bioprosthetic or mechanical valve5. Improved understanding of valve biology in response to physical stresses would help elucidate the mechanisms of valve pathogenesis. In turn, this could help in the development of non-invasive therapies such as pharmaceutical intervention or prevention. Several bioreactors have been previously developed to study the mechanobiology of native or engineered heart valves6-9. Pulsatile bioreactors have also been developed to study a range of tissues including cartilage10, bone11 and bladder12. The aim of this work was to develop a cyclic pressure system that could be used to elucidate the biological response of aortic valve leaflets to increased pressure loads. 
The system consisted of an acrylic chamber in which to place samples and produce cyclic pressure, viton diaphragm solenoid valves to control the timing of the pressure cycle, and a computer to control electrical devices. The pressure was monitored using a pressure transducer, and the signal was conditioned using a load cell conditioner. A LabVIEW program regulated the pressure using an analog device to pump compressed air into the system at the appropriate rate. The system mimicked the dynamic transvalvular pressure levels associated with the aortic valve; a saw tooth wave produced a gradual increase in pressure, typical of the transvalvular pressure gradient that is present across the valve during diastole, followed by a sharp pressure drop depicting valve opening in systole. The LabVIEW program allowed users to control the magnitude and frequency of cyclic pressure. The system was able to subject tissue samples to physiological and pathological pressure conditions. This device can be used to increase our understanding of how heart valves respond to changes in the local mechanical environment.

Design of a Cyclic Pressure Bioreactor for the Ex Vivo Study of Aortic Heart Valves

A cyclic pressure bioreactor capable of subjecting heart valve tissue to physiological and pathological pressure conditions has been designed. A LabVIEW program allows users to control pressure magnitude, amplitude and frequency. This device can be used to study the mechanobiology of heart valve tissue or isolated cells.

The aortic valve, located between the left ventricle and the aorta, allows for unidirectional blood flow, preventing backflow into the ventricle. Aortic ...

Establishment of a Surgically-induced Model in Mice to Investigate the Protective Role of Progranulin in Osteoarthritis

Destabilization of medial meniscus (DMM) model is an important tool for studying the pathophysiological roles of numerous arthritis associated molecules in the pathogenesis of osteoarthritis (OA) in vivo. However, the detailed, especially the visualized protocol for establishing this complicated model in mice, is not available. Herein we took advantage of wildtype and progranulin (PGRN)-/- mice as examples to introduce a protocol for inducing DMM model in mice, and compared the onset of OA following establishment of this surgically induced model. The operations performed on mice were either sham operation, which just opened joint capsule, or DMM operation, which cut the menisco-tibial ligament and caused destabilization of medial meniscus. Osteoarthritis severity was evaluated using histological assay (e.g. Safranin&#160;O staining), expressions of OA-associated genes, degradation of cartilage extracellular matrix molecules, and osteophyte formation. DMM operation successfully induced OA initiation and progression in both wildtype and PGRN-/- mice, and loss of PGNR growth factor led to a more severe OA phenotype in this surgically induced model.

We describe a protocol for the destabilization of the medial meniscus (DMM) model in mice, an effective tool for osteoarthritis (OA) research. In addition, we have demonstrated that deficiency of progranulincan exaggerate OA development and progression by using this model, indicating that progranulin plays a protective role in the pathogenesis of OA.

Destabilization of medial meniscus (DMM) model is an important tool for studying the pathophysiological roles of numerous arthritis associated molecules ...

Combination of Microstereolithography and Electrospinning to Produce Membranes Equipped with Niches for Corneal Regeneration

Corneal problems affect millions of people worldwide reducing their quality of life significantly. Corneal disease can be caused by illnesses such as Aniridia or Steven Johnson Syndrome as well as by external factors such as chemical burns or radiation. Current treatments are (i) the use of corneal grafts and (ii) the use of stem cell expanded in the laboratory and delivered on carriers (e.g., amniotic membrane); these treatments are relatively successful but unfortunately they can fail after 3-5 years. There is a need to design and manufacture new corneal biomaterial devices able to mimic in detail the physiological environment where stem cells reside in the cornea. Limbal stem cells are located in the limbus (circular area between cornea and sclera) in specific niches known as the Palisades of Vogt. In this work we have developed a new platform technology which combines two cutting-edge manufacturing techniques (microstereolithography and electrospinning) for the fabrication of corneal membranes that mimic to a certain extent the limbus. Our membranes contain artificial micropockets which aim to provide cells with protection as the Palisades of Vogt do in the eye.

We report a technique for the fabrication of micropockets within electrospun membranes in which to study cell behavior. Specifically, we describe a combination of microstereolithography and electrospinning for the production of PLGA (Poly(lactide-co-glycolide)) corneal biomaterial devices equipped with microfeatures.

Corneal problems affect millions of people worldwide reducing their quality of life significantly. Corneal disease can be caused by illnesses such as ...

An Experimental and Finite Element Protocol to Investigate the Transport of Neutral and Charged Solutes across Articular Cartilage

Osteoarthritis (OA) is a debilitating disease that is associated with degeneration of articular cartilage and subchondral bone. Degeneration of articular cartilage impairs its load-bearing function substantially as it experiences tremendous chemical degradation, i.e. proteoglycan loss and collagen fibril disruption. One promising way to investigate chemical damage mechanisms during OA is to expose the cartilage specimens to an external solute and monitor the diffusion of the molecules. The degree of cartilage damage (i.e. concentration and configuration of essential macromolecules) is associated with collisional energy loss of external solutes while moving across articular cartilage creates different diffusion characteristics compared to healthy cartilage. In this study, we introduce a protocol, which consists of several steps and is based on previously developed experimental micro-Computed Tomography (micro-CT) and finite element modeling. The transport of charged and uncharged iodinated molecules is first recorded using micro-CT, which is followed by applying biphasic-solute and multiphasic finite element models to obtain diffusion coefficients and fixed charge densities across cartilage zones.

We propose a protocol to investigate the transport of charged and uncharged molecules across articular cartilage with the aid of recently developed experimental and numerical methods.

Osteoarthritis (OA) is a debilitating disease that is associated with degeneration of articular cartilage and subchondral bone. Degeneration of articular ...

An Experimental Protocol for Assessing the Performance of New Ultrasound Probes Based on CMUT Technology in Application to Brain Imaging

The possibility to perform an early and repeatable assessment of imaging performance is fundamental in the design and development process of new ultrasound (US) probes. Particularly, a more realistic analysis with application-specific imaging targets can be extremely valuable to assess the expected performance of US probes in their potential clinical field of application.
The experimental protocol presented in this work was purposely designed to provide an application-specific assessment procedure for newly-developed US probe prototypes based on Capacitive Micromachined Ultrasonic Transducer (CMUT) technology in relation to brain imaging.
The protocol combines the use of a bovine brain fixed in formalin as the imaging target, which ensures both realism and repeatability of the described procedures, and of neuronavigation techniques borrowed from neurosurgery. The US probe is in fact connected to a motion tracking system which acquires position data and enables the superposition of US images to reference Magnetic Resonance (MR) images of the brain. This provides a means for human experts to perform a visual qualitative assessment of the US probe imaging performance and to compare acquisitions made with different probes. Moreover, the protocol relies on the use of a complete and open research and development system for US image acquisition, i.e. the Ultrasound Advanced Open Platform (ULA-OP) scanner.
The manuscript describes in detail the instruments and procedures involved in the protocol, in particular for&#160;the calibration, image acquisition and registration of US and MR images. The obtained results prove the effectiveness of the overall protocol presented, which is entirely open (within the limits of the instrumentation involved), repeatable, and covers the entire set of acquisition and processing activities for US images.

The development of new ultrasound (US) probes based on Capacitive Micromachined Ultrasonic Transducer (CMUT) technology requires an early realistic assessment of imaging capabilities. We describe a repeatable experimental protocol for US image acquisition and comparison with magnetic resonance images, using an ex vivo bovine brain as an imaging target.

The possibility to perform an early and repeatable assessment of imaging performance is fundamental in the design and development process of new ...

Visual Detection of Multiple Nucleic Acids in a Capillary Array

Multi-target, short time, and resource-affordable methodologies for the detection of multiple nucleic acids in a single, easy to operate test are urgently needed in disease diagnosis, microbial monitoring, genetically modified organism (GMO) detection, and forensic analysis. We have previously described the platform called CALM (Capillary Array-based Loop-mediated isothermal amplification for Multiplex visual detection of nucleic acids). Herein, we describe improved fabrication and performance processes for this platform. Here, we apply a small, ready-to-use cassette assembled by capillary array for multiplex visual detection of nucleic acids. The capillary array is pre-treated into a hydrophobic and hydrophilic pattern before fixing loop-mediated isothermal amplification (LAMP) primer sets in capillaries. After assembly of the loading adaptor, LAMP reaction mixture is loaded and isolated into each capillary, due to capillary force by a single pipetting step. The LAMP reactions are performed in parallel in the capillaries. The results are visually read out by illumination with a hand-held UV flashlight. Using this platform, we demonstrate monitoring of 8 frequently appearing elements and genes in GMO samples with high specificity and sensitivity. In summary, the platform described herein is intended to facilitate the detection of multiple nucleic acids. We believe it will be widely applicable in fields where high-throughput nucleic acid analysis is required.

This protocol describes the fabrication of a small, ready-to-use cassette that can be applied for visual detection of multiple nucleic acids in a single, test that is easy to operate. In this approach, a capillary array was used for multiplex and highly efficient detection of GMO targets.

Multi-target, short time, and resource-affordable methodologies for the detection of multiple nucleic acids in a single, easy to operate test are urgently ...

Calorespirometry: A Powerful, Noninvasive Approach to Investigate Cellular Energy Metabolism

Many cell lines used in basic biological and biomedical research maintain energy homeostasis through a combination of both aerobic and anaerobic respiration. However, the extent to which both pathways contribute to the landscape of cellular energy production is consistently overlooked. Transformed cells cultured in saturating levels of glucose often show a decreased dependency on oxidative phosphorylation for ATP production, which is compensated by an increase in substrate-level phosphorylation. This shift in metabolic poise allows cells to proliferate despite the presence of mitochondrial toxins. In neglecting the altered metabolic poise of transformed cells, results from a pharmaceutical screening may be misinterpreted since the potentially mitotoxic effects may not be detected using model cell lines cultured in the presence of high glucose concentrations. This protocol describes the pairing of two powerful techniques, respirometry and calorimetry, which allows for the quantitative and noninvasive assessment of both aerobic and anaerobic contributions to cellular ATP production. Both aerobic and anaerobic respirations generate heat, which can be monitored via calorimetry. Meanwhile, measuring the rate of oxygen consumption can assess the extent of aerobic respiration. When both heat dissipation and oxygen consumption are measured simultaneously, the calorespirometric ratio can be determined. The experimentally obtained value can then be compared to the theoretical oxycaloric equivalent and the extent of the anaerobic respiration can be judged. Thus, calorespirometry provides a unique method to analyze a wide range of biological questions, including drug development, microbial growth, and fundamental bioenergetics under both normoxic and hypoxic conditions.

This protocol describes calorespirometry, the direct and simultaneous measurement of both heat dissipation and respiration, which provides a noninvasive approach to assess energy metabolism. This technique is used to assess the contribution of both aerobic and anaerobic pathways to energy utilization by monitoring the total cellular energy flow.

Many cell lines used in basic biological and biomedical research maintain energy homeostasis through a combination of both aerobic and anaerobic ...

Conducting Multiple Imaging Modes with One Fluorescence Microscope

Fluorescence microscopy is a powerful tool to detect biological molecules in situ and monitor their dynamics and interactions in real-time. In addition to conventional epi-fluorescence microscopy, various imaging techniques have been developed to achieve specific experimental goals. Some of the widely used techniques include single-molecule fluorescence resonance energy transfer (smFRET), which can report conformational changes and molecular interactions with angstrom resolution, and single-molecule detection-based super-resolution (SR) imaging, which can enhance the spatial resolution approximately ten&#160;to twentyfold compared to diffraction-limited microscopy. Here we present a customer-designed integrated system, which merges multiple imaging methods in one microscope, including conventional epi-fluorescent imaging, single-molecule detection-based SR imaging, and multi-color single-molecule detection, including smFRET imaging. Different imaging methods can be achieved easily and reproducibly by switching optical elements. This set-up is easy to adopt by any research laboratory in biological sciences with a need for routine and diverse imaging experiments at a reduced cost and space relative to building separate microscopes for individual purposes.

Here we present a practical guide of building an integrated microscopy system, which merges conventional epi-fluorescent imaging, single-molecule detection-based super-resolution imaging, and multi-color single-molecule detection, including single-molecule fluorescence resonance energy transfer imaging, into one set-up in a cost-efficient way.

Fluorescence microscopy is a powerful tool to detect biological molecules in situ and monitor their dynamics and interactions in real-time. In addition to ...

Gene Expression Analysis of Endothelial Cells Exposed to Shear Stress Using Multiple Parallel-plate Flow Chambers

We describe a workflow for the analysis of gene expression from endothelial cells subject to a steady laminar flow using multiple monitored parallel-plate flow chambers. Endothelial cells form the inner cellular lining of blood vessels and are chronically exposed to the frictional force of blood flow called shear stress. Under physiological conditions, endothelial cells function in the presence of various shear stress conditions. Thus, the application of shear stress conditions in in vitro models can provide greater insight into endothelial responses in vivo. The parallel-plate flow chamber previously published by Lane et al.9 is adapted to study endothelial gene regulation in the presence and absence of steady (non-pulsatile) laminar flow. Key adaptations in the set-up for laminar flow as presented here include a large, dedicated environment to house concurrent flow circuits, the monitoring of flow rates in real-time, and the inclusion of an exogenous reference RNA for the normalization of quantitative real-time PCR data. To assess multiple treatments/conditions with the application of shear stress, multiple flow circuits and pumps are used simultaneously within the same heated and humidified incubator. The flow rate of each flow circuit is measured continuously in real-time to standardize shear stress conditions throughout the experiments. Because these experiments have multiple conditions, we also use an exogenous reference RNA that is spiked-in at the time of RNA extraction for the normalization of RNA extraction and first-strand cDNA synthesis efficiencies. These steps minimize the variability between samples. This strategy is employed in our pipeline for the gene expression analysis with shear stress experiments using the parallel-plate flow chamber, but parts of this strategy, such as the exogenous reference RNA spike-in, can easily and cost-effectively be used for other applications.

Here, a workflow for the culture and gene expression analysis of endothelial cells under fluid shear stress is presented. Included is a physical arrangement for simultaneously housing and monitoring multiple flow chambers in a controlled environment and the use of an exogenous reference RNA for quantitative PCR.

We describe a workflow for the analysis of gene expression from endothelial cells subject to a steady laminar flow using multiple monitored parallel-plate ...

Generating Controlled, Dynamic Chemical Landscapes to Study Microbial Behavior

We demonstrate a method for the generation of controlled, dynamic chemical pulses&#8213;where localized chemoattractant becomes suddenly available at the microscale&#8213;to create micro-environments for microbial chemotaxis experiments. To create chemical pulses, we developed a system to introduce amino acid sources near-instantaneously by photolysis of caged amino acids within a polydimethylsiloxane (PDMS) microfluidic chamber containing a bacterial suspension. We applied this method to the chemotactic bacterium, Vibrio ordalii, which can actively climb these dynamic chemical gradients while being tracked by video microscopy. Amino acids, rendered biologically inert (&#39;caged&#39;) by chemical modification with a photoremovable protecting group, are uniformly present in the suspension but not available for consumption until their sudden release, which occurs at user-defined points in time and space by means of a near-UV-A focused LED beam. The number of molecules released in the pulse can be determined by a calibration relationship between exposure time and uncaging fraction, where the absorption spectrum after photolysis is characterized by using UV-Vis spectroscopy. A nanoporous polycarbonate (PCTE) membrane can be integrated into the microfluidic device to allow the continuous removal by flow of the uncaged compounds and the spent media. A strong, irreversible bond between the PCTE membrane and the PDMS microfluidic structure is achieved by coating the membrane with a solution of 3-aminopropyltriethoxysilane (APTES) followed by plasma activation of the surfaces to be bonded. A computer-controlled system can generate user-defined sequences of pulses at different locations and with different intensities, so as to create resource landscapes with prescribed spatial and temporal variability. In each chemical landscape, the dynamics of bacterial movement at the individual scale and their accumulation at the population level can be obtained, thereby allowing the quantification of chemotactic performance and its effects on bacterial aggregations in ecologically relevant environments.

A protocol for the generation of dynamic chemical landscapes by photolysis within microfluidic and millifluidic setups is presented. This methodology is suitable to study diverse biological processes, including the motile behavior, nutrient uptake, or adaptation to chemicals of microorganisms, both at the single cell and population level.

We demonstrate a method for the generation of controlled, dynamic chemical pulses&#8213;where localized chemoattractant becomes suddenly available at the ...