This work was supported by the CNRS, the Universit&#233; de Lille CNRS, and the ANR grants &#34;MathTest&#34; (ANR-18-CE13-0027).

1. Incubation with debranching enzymes
<ol>
	<li>Mix 200 &#181;L of purified glycogen at 0.5-2 mg/mL with 200 &#181;L of 100 mM of acetate buffer (pH 4.8). Add 2 &#181;L of isoamylase (180 U/mg of protein) and 1.5 &#181;L of pullulanase (30 U/mg of protein) (see Table of Materials), mix gently by pipetting up and down, and incubate at 42 &#176;C for 16 h in a 1.5 mL tube. 
	NOTE: Degradation or amylase contamination might occur during the glycogen purification process. To appreciate the presence of free malto-oligosaccharides, samples can be incubated without the debranching enzyme cocktail in parallel. A standard (e.g., maltoheptaose) is included in the analysis to determine the relationship between the elution time and the degree of polymerization.</li>
	<li>Stop the reactions by incubating at 95 &#176;C for 5 min.</li>
	<li>Centrifuge at 16,100 x g for 5 min at room temperature to pellet and remove any insoluble material.</li>
	<li>Remove the supernatants using a pipette and transfer them to new annotated tubes. Desalt the supernatants by adding the equivalent of 100 &#181;L of anion/cation exchange resin beads (AG-501-X8, see Table of Materials) and agitate.
	<ol>
		<li>Regularly agitate the beads for 5 min. Collect the samples by pipetting and place them in new annotated tubes.</li>
	</ol>
	</li>
	<li>Freeze-dry or use a vacuum evaporator set up (see Table of Materials) at 30 &#176;C to dry the samples.</li>
	<li>Store dried samples at room temperature or at -20 &#176;C. 
	&#8203;NOTE: The samples can be stored for 1 month.</li>
</ol>
2. Reductive amination
<ol>
	<li>Mix the dried samples with 2 &#181;L of 1 M sodium cyanoborohydride in tetrahydrofuran (THF) and 2 &#181;L of APTS (5 mg of APTS resuspended in 48 &#181;L of 15% acetic acid) (see Table of Materials).</li>
	<li>Incubate at 42 &#176;C for 16 h in the dark. 
	&#8203;CAUTION: Sodium cyanoborohydride is handled with adapted personal protection equipment and under a chemical hood. Inhalation and contact with skin are highly toxic and can be fatal, causing severe skin burns and eye damage. Highly toxic gases can be generated when mixing sodium cyanoborohydride with acetic acid. In contact with water, sodium cyanoborohydride releases flammable gases, which may ignite spontaneously. Concentrated samples are handled under a chemical hood and using adapted personal protection equipment starting from this step.</li>
</ol>
3. FACE analysis
<ol>
	<li>Add 46 &#181;L of ultrapure water to each sample.</li>
	<li>Dilute the samples directly to 1/50 in micro vials of 100 &#181;L by adding 1 &#181;L of the sample to 49 &#181;L of ultrapure water. Keep the samples in the dark while setting the FACE (5-10 min).</li>
	<li>Perform reverse polarity electrophoresis with a capillary electrophoresis instrument with a laser-induced fluorescence (LIF) detector (see Table of Materials). Set the polarity to &#34;reverse mode&#34; for separation, set the LIF at 488 nm emission wavelength and the detector at 512 nm.</li>
	<li>Set the Injection time for 10 s and the injection pressure at 0.5 psi.</li>
	<li>Carry out the APTS-labelled-glucans separation at 30 kV in a bare fused silica capillary of 60.2 cm in length with an inner diameter of 50 &#181;m (375 &#181;m outer diameter) in the N-linked carbohydrate separation buffer diluted to 1/3 in ultrapure water (see Table of Materials). 
	&#8203;NOTE: The N-linked carbohydrate separation buffer is replaced every 20 runs.</li>
</ol>
4. DATA processing
<ol>
	<li>Export the &#34;.ASC &#34;and &#34;.CDF &#34;files containing the electropherogram profile and integration data, respectively.</li>
	<li>Open the .ASC file and draw the relative fluorescence unit according to the time chart.</li>
	<li>Open the .CDF file, proceed with a first automatic integration and adjust the following parameters: width; valley to valley integration; minimum area.</li>
	<li>Check and correct any improper integration event manually.</li>
</ol>

<ol><li>Konkolewicz, D., Thorn-Seshold, O., Gray-Weale, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Models+for+randomly+hyperbranched+polymers%3A+Theory+and+simulation%5BTitle%5D)+AND+%22The+Journal+of+Chemical+Physics%22%5BJournal%5D)">Models for randomly hyperbranched polymers: Theory and simulation.</a> The Journal of Chemical Physics. 129 (5), 054901(2008).</li>
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<li>Wang, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Recent+progress+in+the+structure+of+glycogen+serving+as+a+durable+energy+reserve+in+bacteria%5BTitle%5D)+AND+%22World+Journal+of+Microbiology+and+Biotechnology%22%5BJournal%5D)">Recent progress in the structure of glycogen serving as a durable energy reserve in bacteria.</a> World Journal of Microbiology and Biotechnology. 36 (1), 14(2020).</li>
<li>Wang, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Influence+of+in+situ+progressive+N-terminal+is+still+controversial+truncation+of+glycogen+branching+enzyme+in+Escherichia+coli+DH5%CE%B1+on+glycogen+structure%2C+accumulation%2C+and+bacterial+viability%5BTitle%5D)+AND+%22BMC+Microbiology%22%5BJournal%5D)">Influence of in situ progressive N-terminal is still controversial truncation of glycogen branching enzyme in Escherichia coli DH5α on glycogen structure, accumulation, and bacterial viability.</a> BMC Microbiology. 15 (1), 1-14 (2015).</li>
<li>Bernard, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((On+the+physiological+mechanism+of+sugar+formation+in+the+liver%5BTitle%5D)+AND+%22Proceedings+of+the+Academy+of+Sciences%22%5BJournal%5D)">On the physiological mechanism of sugar formation in the liver.</a> Proceedings of the Academy of Sciences. 44 (12), 578-586 (1857).</li>
<li>Liu, Q. -H., Tang, J. -W., Wen, P. -B., Wang, M. -M., Zhang, X., Wang, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((From+prokaryotes+to+eukaryotes%3A+Insights+into+the+molecular+structure+of+glycogen+particles%5BTitle%5D)+AND+%22Frontiers+in+Molecular+Biosciences%22%5BJournal%5D)">From prokaryotes to eukaryotes: Insights into the molecular structure of glycogen particles.</a> Frontiers in Molecular Biosciences. 8, 673315(2021).</li>
<li>Zang, L. H., Rothman, D. L., Shulman, R. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((1+H+NMR+visibility+of+mammalian+glycogen+in+solution%5BTitle%5D)+AND+%22Proceedings+of+the+National+Academy+of+Sciences%22%5BJournal%5D)">1 H NMR visibility of mammalian glycogen in solution.</a> Proceedings of the National Academy of Sciences. 87 (5), 1678(1990).</li>
<li>O'Shea, M. G., Samuel, M. S., Konik, C. M., Morell, M. K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Fluorophore-assisted+carbohydrate+electrophoresis+%28FACE%29+of+oligosaccharides%3A+Efficiency+of+labelling+and+high-resolution+separation%5BTitle%5D)+AND+%22Carbohydrate+Research%22%5BJournal%5D)">Fluorophore-assisted carbohydrate electrophoresis (FACE) of oligosaccharides: Efficiency of labelling and high-resolution separation.</a> Carbohydrate Research. 307 (1-2), 1-12 (1998).</li>
<li>Koizumi, K., Fukuda, M., Hizukuri, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Estimation+of+the+distributions+of+chain+length+of+amylopectins+by+high-performance+liquid+chromatography+with+pulsed+amperometric+detection%5BTitle%5D)+AND+%22Journal+of+Chromatography+A%22%5BJournal%5D)">Estimation of the distributions of chain length of amylopectins by high-performance liquid chromatography with pulsed amperometric detection.</a> Journal of Chromatography A. 585 (2), 233-238 (1991).</li>
<li>Morell, M. K., Samuel, M. S., Shea, M. G. O. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Analysis+of+starch+structure%5BTitle%5D)+AND+%22Electrophoresis%22%5BJournal%5D)">Analysis of starch structure.</a> Electrophoresis. 19, 2603-2611 (1998).</li>
<li>Guttman, A., Chen, F. -T. A., Evangelista, R. A., Cooke, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((High-resolution+capillary+gel+electrophoresis+of+reducing+oligosaccharides+labeled+with+1-Aminopyrene-3%2C6%2C8-trisulfonate%5BTitle%5D)+AND+%22Analytical+Biochemistry%22%5BJournal%5D)">High-resolution capillary gel electrophoresis of reducing oligosaccharides labeled with 1-Aminopyrene-3,6,8-trisulfonate.</a> Analytical Biochemistry. 233 (2), 234-242 (1996).</li>
<li>Kadouche, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Characterization+of+function+of+the+GlgA2+glycogen%2Fstarch+synthase+in+cyanobacterium+sp.+clg1+highlights+convergent+evolution+of+glycogen+metabolism+into+starch+granule+aggregation%5BTitle%5D)+AND+%22Plant+Physiology%22%5BJournal%5D)">Characterization of function of the GlgA2 glycogen/starch synthase in cyanobacterium sp. clg1 highlights convergent evolution of glycogen metabolism into starch granule aggregation.</a> Plant Physiology. 171 (3), 1879-1892 (2016).</li>
<li>Hayashi, M., Suzuki, R., Colleoni, C., Ball, S. G., Fujita, N., Suzuki, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Bound+substrate+in+the+structure+of+cyanobacterial+branching+enzyme+supports+a+new+mechanistic+model%5BTitle%5D)+AND+%22The+Journal+of+biological+chemistry%22%5BJournal%5D)">Bound substrate in the structure of cyanobacterial branching enzyme supports a new mechanistic model.</a> The Journal of biological chemistry. 292 (13), 5465-5475 (2017).</li>
<li>Sawada, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Diversity+of+reaction+characteristics+of+glucan+branching+enzymes+and+the+fine+structure+of+%CE%B1-glucan+from+various+sources%5BTitle%5D)+AND+%22Archives+of+Biochemistry+and+Biophysics%22%5BJournal%5D)">Diversity of reaction characteristics of glucan branching enzymes and the fine structure of α-glucan from various sources.</a> Archives of Biochemistry and Biophysics. 562, 9-21 (2014).</li>
<li>Wong, K. S., Jane, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Quantitative+analysis+of+debranched+amylopectin+by+HPAEC-PAD+with+a+post-column+enzyme+reactor%5BTitle%5D)+AND+%22Journal+of+Liquid+Chromatography+%26+Related+Technologies%22%5BJournal%5D)">Quantitative analysis of debranched amylopectin by HPAEC-PAD with a post-column enzyme reactor.</a> Journal of Liquid Chromatography & Related Technologies. 20 (2), 297-310 (1997).</li>
<li>Colleoni, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Biochemical+Characterization+of+the+chlamydomonas+reinhardtii+a-1%2C4+glucanotransferase+supports+a+direct+function+in+amylopectin+biosynthesis%5BTitle%5D)+AND+%22Plant+Physiology%22%5BJournal%5D)">Biochemical Characterization of the chlamydomonas reinhardtii a-1,4 glucanotransferase supports a direct function in amylopectin biosynthesis.</a> Plant Physiology. 120, 9(1999).</li>
<li>Broberg, S., Koch, K., Andersson, R., Kenne, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((A+comparison+between+MALDI-TOF+mass+spectrometry+and+HPAEC-PAD+analysis+of+debranched+starch%5BTitle%5D)+AND+%22Carbohydrate+Polymers%22%5BJournal%5D)">A comparison between MALDI-TOF mass spectrometry and HPAEC-PAD analysis of debranched starch.</a> Carbohydrate Polymers. 43 (3), 285-289 (2000).</li>
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</ol>

The authors have no conflicts of interest associated with this work.

The physicochemical properties of glycogen particles (e.g., size, morphology, solubility) are directly associated with the length of glucans composing the particles. Any imbalance between biosynthetic and catabolic enzymes results in the alteration of the chain length distribution and, per se, the accumulation of abnormal glycogen that can be hazardous for the cell11. The FACE analysis is a method of choice to determine glycogen&#39;s chain length distribution (CLD). As depicted in Figure 2, the determination of CLD allows the inference of the average chain length value of glycogen (ACL), which mirrors the structure of glycogen particles. Animal glycogens with high ACL values are associated with the appearance of abnormal particles. The subtractive analysis is a helpful method for comparing two glycogen samples from different genetic backgrounds (mutant versus wildtype). By plotting on a logarithmical scale CLDs normalized to the maximum peak, on the other hand, has the advantage of comparing CLD independently of a reference and gave us information on the growing glycogen mechanism.
In addition, FACE analysis is a powerful technique for characterizing the catalytic properties of glycogen metabolizing enzymes. For instance, all glycogen branching enzymes cleave (1 &#8594; 4)-&#945; linkages and transfer oligomaltosyl groups onto a (1 &#8594; 6)-&#945; position or branching points. Branching enzymes are distinguishable due to their affinity for polysaccharides (e.g., amylopectin, glycogen) and the length of transferred glucans despite the similar catalytic mechanism22. Thus, various sources of branching enzymes (human, plant, bacteria) can be characterized and classified through a series of incubation experiments and CLD comparisons using FACE analysis23.
As mentioned in the introduction, HPAEC-PAD is also an alternative method for determining the chain length distribution18. Both techniques require the complete hydrolysis of (1 &#8594; 6)-&#945; linkages or branching points by isoamylase-type debranching enzymes before separating the pool of linear glucans according to their degree of polymerization (DP). However, the HPAEC-PAD method harbors two disadvantages compared to FACE: (1) the amperometric pulse response decreases as the glucan chains increase, which does not provide quantitative information. This mass bias issue can be circumvented using HPAEC-ENZ-PAD that involves a post-column enzyme reactor between the anion exchange column and PAD24. The column enzyme reactor hydrolyzes malto-oligosaccharides into glucose residues allowing a constant pulse amperometric response. (2) The HPAEC-PAD allows the separation of glucan chains with a degree of polymerization up to 70. Although the resolution is enough for determining the CLD of glycogen samples, FACE separates chains with DPs up to 150, suitable for starch samples17. It is essential to keep in mind that both HPAEC-PAD and FACE analysis have pros and cons. For instance, the amination reaction requires a free hemiacetal group to react with the primary amine function of the APTS. This implies that APTS labeling cannot be used for glucan chains lacking reducing ends (e.g., inulin). The HPAEC-PAD method does not require the presence of reducing ends. A second interesting aspect of the HPAEC-PAD method is that the anion exchange column can be loaded with a few milligrams of linear glucans, allowing the purification of malto-oligosaccharide with a specific DP or 14C-radiolabeled malto-oligosaccharide for enzymatic assay18,25. Finally, although mass spectrometry (e.g., MALDI-TOF) is a fast and sensitive technic to determine chain length distribution, this technic appears to be less reproducible. It overestimates the amount of long glucan chains26. Nevertheless, the latter can be used for a specific application such as MALDI imaging to map the presence of glycogen across cellular tissue27.

Glycogen, used as carbon and energy storage, is a homopolymer of glucose consisting of linear chains of glucosyl units linked by (1 &#8594; 4)-&#945; bonds and attached through (1 &#8594; 6)-&#945; glycosidic bonds or branching points. They appear as &#946;- and &#945;-particles in the cytosol of a wide range of organisms. &#946;-particles are tiny water-soluble particles mainly observed in prokaryotes. Their diameter ranges from 20-40 nm, likely dictated by the glycogen metabolizing enzymes and steric hindrance1,2.
First described in animal cells, the larger &#945;-particles display up to 300 nm in diameter with a cauliflower-like shape. This particular organization may originate from the aggregation of several &#946;-particles or may arise by budding out of a single &#946;-particle3. Interestingly, a recent study has reported the presence of &#945;-particles in Escherichia coli4. However, unlike &#945;-particles from animal cells, the latter falls apart quickly during the extraction process, which may explain the lack of data in the literature4. The appearance of &#945;-particles in eukaryotes and prokaryotes involves phylogenetically unrelated glycogen metabolizing enzymes5. This raises questions regarding the function of such particles and the nature of potential cross-linker agents between &#946;-particles5.
Although two opposing mathematical models were proposed for glycogen molecule formation6,7,8,9, it is generally accepted that &#946;-particles have evolved in response to their metabolic function as a highly efficient fuel reserve for the rapid release of large amounts of glucose. A large body of evidence indicates that glycogen properties such as digestibility and solubility in water are correlated with the average chain length (ACL), which will then dictate the percentage of branching points and the particle size2,6,7,8,10,11. ACL is defined by the ratio between the total number of glucose residues and the number of branching points. Typically, the ACL values vary from 11-14 and 7-23 glucose residues in eukaryotes and prokaryotes, respectively10. In humans, several glycogen disorder diseases are due to abnormal glycogen accumulation. For instance, Andersen&#39;s disease is associated with the deficient activity of a glycogen branching enzyme, resulting in the accumulation of abnormal glycogen11. In prokaryotes, cumulative studies suggest that ACL is a critical factor impacting the degradation rate of glycogen and bacterial survival ability12,13. It has been reported that bacteria synthesizing &#946;-particles with a low ACL value degrade more slowly and therefore withstand starvation conditions longer. Thus, knowledge of the architecture of &#946;-particles is essential for understanding the formation of abnormal glycogen particles in human glycogen storage diseases and prokaryotes survival in a nutrient-deficient environment.
Since the first isolation of glycogen from dog liver by the French physiologist Claude Bernard in the late nineteenth century14, many techniques were developed to characterize glycogen particles in detail. For instance, transmission electron microscopy for glycogen morphology (&#945;- or &#946;-particles)15, proton-NMR spectrometry for determining the percentage of &#945;-1,6 linkages16, size exclusion chromatography with multi-detectors for inferring the molecular weight, Fluorophore-Assisted Carbohydrate Electrophoresis (FACE)17 or High-Performance Anion exchange chromatography with Pulsed Amperometric Detection (HPAEC-PAD) for both chain length distribution (CLD) and ACL determination18.
This work focuses on the fluorophore-assisted carbohydrate electrophoresis method, which relies on the reductive amination of the hemiacetal group by primary amine function. Historically, 8-amino-1,3,6-naphthalene trisulfonic acid (ANTS) was first used for labeling. Later, it was replaced by the more sensitive fluorophore, 8-amino 1,3,6 pyrene trisulfonic acid (APTS)19.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/63392/63392fig01.jpg" /> 
Figure 1: The reductive amination reaction with 8-amino 1,3,6 pyrene trisulfonic acid (APTS). Reductive amination reaction of the hemiacetal group by primary amine function of 8-amino 1,3,6 pyrene trisulfonic acid (APTS) under reductive conditions <a href="https://www.jove.com/files/ftp_upload/63392/63392fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
As depicted in Figure 1, the hemiacetal function of the reducing end of a glucan chain interacts with the primary amine of APTS under reducing conditions. The sulfonic groups of APTS carry negative charges that enable the separation of glucan chains according to their degree of polymerization (DP). The reductive amine reaction is highly reproducible and efficient. An average efficiency labeling of 80% is obtained for DP3 to DP135 and up to 88% and 97% for maltose (DP2) and glucose, respectively17,20. Because one molecule of APTS reacts with the reducing end of each glucan chain, individual chains could be quantified and compared to each other on a molar basis.

<table><tbody><tr><td>AG-501-X8 Resin</td><td>BioRad</td><td>#1436424</td><td>Storage Room temperature</td></tr><tr><td>100 mM sodium acetate buffer, pH 4.8</td><td></td><td></td><td>Dissolve 0.82 g of sodium acetate in 80 mL of water. Adjust pH to 4.8 with acetic acid and complete the volume to 100 mL with water&amp;mdash;storage at room temperature.</td></tr><tr><td>APTS stock solution</td><td>Merck</td><td>09341-5MG</td><td>Dissolve 5 mg of APT in 48 mL acetic acid 0.2 M. Storage at -20 &amp;deg;C.</td></tr><tr><td>Capillary</td><td>Sciex Separations, Les Ulis, France</td><td></td><td></td></tr><tr><td>Chromeleon 6.80 SR8 Build 2623</td><td>Thermofisher</td><td></td><td>select :File&gt;import/restore&gt;ANDI/chromatography in the open window: select &quot;add&quot; select cdf&amp;nbsp; file &gt; import &gt; next&lt;br/&gt; Choose the folder where your file will downloaded in Chromeleon software&gt; finish click on QNT-Editor&gt; parameter &quot;Min Area&quot; select &quot;Range&quot; 0.05 [Signal]*min.</td></tr><tr><td>Excel</td><td>Microsoft</td><td></td><td>Open Excel&gt; New file&gt; save the file &gt; File menu click on Import &gt; In the open window choose &quot;csv.&quot; as type file &gt; select your asc file &gt; a new window appears Step 1: choose Macintosh or&amp;nbsp; Window and then used default setting for the steps 2 and 3. &gt; Y values appear in column A&gt; Manually add a Time column by incrementing 0.25 second to each cell that corresponds to the frequency (4Hz) for acquisition data . Then plot the graph.</td></tr><tr><td>Free-Dry apparatus</td><td>Christ</td><td>alpha 2-4 LO plus</td><td>before the freezing-drying process, samples are stored at -80 &amp;deg;C for 1 h.</td></tr><tr><td>Isoamylase</td><td>Megazyme</td><td>E-ISAMY</td><td>180 U/mg of protein</td></tr><tr><td>Maltoheptaose</td><td>Merck</td><td>M7753</td><td></td></tr><tr><td>N-Linked carbohydrate separation buffer</td><td>Sciex Separations, Les Ulis, France</td><td>477623</td><td>Storage at 4 &amp;deg;C</td></tr><tr><td>Pullulanase</td><td>Megazyme</td><td>E-PULKP</td><td>30 U/mg of protein</td></tr><tr><td>Sodium cyanoborohydride</td><td>Sigma-Aldrich</td><td>296813-100ML</td><td>1 M Sodium cyanoborohydride in THF</td></tr><tr><td>Vaccum-evaporator</td><td>Eppendorf</td><td>Concentrator 5301</td><td>Set the temperature at 30 &amp;deg;C. Centrifuge until the samples are dried</td></tr></tbody></table>

determination of glucan chain length distribution of glycogen using the fluorophore-assisted carbohydrate electrophoresis (face) method

Glycogen particles are branched polysaccharides composed of linear chains of glucosyl units linked by &#945;-1,4 glucoside bonds. The latter are attached to each other by &#945;-1,6 glucoside linkages, referred to as branch points. Among the different forms of carbon storage (i.e., starch, &#946;-glucan), glycogen is probably one of the oldest and most successful storage polysaccharides found across the living world. Glucan chains are organized so that a large amount of glucose can quickly be stored or fueled in a cell when needed. Numerous complementary techniques have been developed over the last decades to solve the fine structure of glycogen particles. This article describes Fluorophore-Assisted Carbohydrate Electrophoresis (FACE). This method quantifies the population of glucan chains that compose a glycogen particle. Also known as chain length distribution (CLD), this parameter mirrors the particle size and the percentage of branching. It is also an essential requirement for the mathematical modeling of glycogen biosynthesis.

Determination of the average chain length of glycogen 
Figure 2 represents the workflow required to infer the chain length distribution and the average chain length (ACL) of glycogen.
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/63392/63392fig02.jpg" /> 
Figure 2: Workflow to determine the chain length distribution (CLD) and average chain length. <a href="https://www.jove.com/files/ftp_upload/63392/63392fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Figure 3 displays the electropherograms of commercial maltohexaose and debranched bovine liver glycogen. The fluorescence signals observed between 4.13-4.67 min in all experiments originated from the unreacted APTS. The elution time of labeled maltohexaose (DP6) was estimated at 8.49 min (Figure 3A). The APTS-labelled glucans of bovine glycogen were identified based on the elution time of DP6 (Figure 3B). No trace of free malto-oligosaccharide was detected in the control sample (glycogen not incubated with debranching enzymes) (Figure 3C).
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/63392/63392fig03.jpg" /> 
Figure 3: Electropherograms of a standard and bovine liver glycogen. (A)&#160;The time elution (8.49 min) of a glucan standard, maltohexaose (DP6), was used as a reference to determine the degree of polymerization (DP) of APTS-labelled glucans released from bovine liver glycogen after the action of debranching enzyme activities (B). The inset panel shows a separation of glucan chains up to 44 DP. In parallel, untreated bovine glycogen was labeled with APTS to detect possible traces of free malto-oligosaccharides in sample (C). <a href="https://www.jove.com/files/ftp_upload/63392/63392fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
It can be concluded that the release of APTS-labelled glucan is due to the cleavage of branching points by both isoamylase and pullulanase activities. It should be noticed that the capillary electrophoresis profile can be redrawn in a more appropriate format to create a mosaic figure containing several profiles. To do this, DATA files containing fluorescence values are generated with &#34;asc&#34; extension and opened in the spreadsheet program by choosing CSV (comma-separated value) format. Unfortunately, the exported fluorescence values are not associated with the corresponding elution time. Consequently, they must be manually added according to the acquisition frequency setup on the FACE apparatus (4 Hz means one value acquisition every 0.25 s).
Peak areas were then inferred using the FACE instrument&#39;s native application or exported as a DATA file with a &#34;cdf&#34; extension to use another application. The area values are exported in a spreadsheet program and normalized by expressing the DP as a percentage of the total surface area (Figure 4).
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/63392/63392fig04.jpg" /> 
Figure 4: Data normalization, chain length distribution, and average chain length value. Fluorescence peak areas were imported and normalized in a spreadsheet. The chain length distribution is shown as the percentage of DP for each DP. The average chain length (ACL) is calculated by summing each percentage chain times the corresponding degree of polymerization. <a href="https://www.jove.com/files/ftp_upload/63392/63392fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Finally, the average chain length (ACL) is inferred by calculating the sum of each percentage chain times the corresponding degree of polymerization. Similar experiments were carried out in triplicate on rabbit liver glycogen (Figure 5A), on bovine liver glycogen (Figure 5B), and oyster glycogen (Figure 5C).
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/63392/63392fig05.jpg" /> 
Figure 5: Chain length distribution of commercial glycogen. The rabbit liver (A), bovine liver (B), and oyster glycogen (C) were incubated in the presence of debranching enzymes (isoamylase and pullulanase). The APTS-labelled glucans were then separated according to their degree of polymerization (DP) using FACE analysis. Maltose (DP2), maltohexaose (DP6) maltoheptaose (DP7) represent the most abundant glucans in rabbit liver, oyster, and bovine liver glycogen, respectively. The Standard Error of Mean (SEM) was inferred from three independent experiments. <a href="https://www.jove.com/files/ftp_upload/63392/63392fig05large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
The chain length distribution of rabbit liver glycogen clearly showed a higher content of short malto-oligosaccharide (DP2) than bovine liver glycogen (DP 7) or oyster glycogen (DP6). As a result, the rabbit liver glycogen possesses the lowest average chain length (ACL = 9.8) compared to bovine liver glycogen (ACL = 11.9) and oyster glycogen (ACL = 12.6). It is to be noted that those commercial glycogens are usually used for assaying the glycogen phosphorylase or glycogen synthase activity. This suggests that the determination of kinetic parameters (Vmax and Km) of glycogen metabolizing enzyme activities will vary according to the source of glycogen.
Subtractive analyses 
The subtractive analysis is a simple method to compare the glucan chains distribution of two samples. For example, CLDs of glycogen produced by the wildtype (WT) Synechocystis PCC6803 strain and single isogenic glgA1 and glgA2 mutant strains were determined (Figure 6A).
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/63392/63392fig06.jpg" /> 
Figure 6: Comparison of chain length distributions using subtractive analysis. (A)&#160;The chain length distributions of glycogen purified from cyanobacterial strains: wildtype (WT) Synechocystis PCC6803 and single isogenic glgA1 and glgA2 mutant strains were determined using FACE analysis. The Standard Error of Mean (SEM) was inferred from three independent experiments. (B) Subtractive analyses were performed by subtracting the % of each DP of WT to the % of each DP of &#916;glgA1 and subtracting the % of each DP of WT to the % of each DP of DP &#916;glgA2. This straightforward mathematical manipulation displays the alteration of glucan chains in mutant strains (black lines). (C) The average chain length distributions of glycogen from wildtype and mutants of Synechocystis were normalized according to the maximum peak observed for each CLD (DP6 for all samples). Two components are evidenced by plotting the normalized CLD on a logarithmic scale (Nde (DP)). Each component indicates a different mechanism of growth stoppage (for more information, see Reference2). <a href="https://www.jove.com/files/ftp_upload/63392/63392fig06large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
To recall, most cyanobacteria strains possess two genes encoding glycogen synthase activities: GlgA1 and GlgA221. Both enzymes transfer the residue glucose of ADP-glucose onto the non-reducing ends of linear glucan chains. As depicted in Figure 6A, it is challenging to compare samples by only looking at the chain length distribution profiles. The subtractive analysis consists of subtracting the percentage of each DP between samples (Figure 6B). The subtractive analysis of %DP of WT minus % of DP &#916;glgA1 mutant reveals an excess of DP3, 4, and 5 (negative values) and a decreased content of DP 10-20. In contrast, the subtractive analysis between %DP of WT and % of DP &#916;glgA2 mutant indicates an opposite effect. Because the subtractive analysis profiles are different between GlgA1 and GlgA2, this suggests a specific function of each glycogen synthase isoform in glycogen biosynthesis21.
It is important to note that subtractive analysis is only applicable to experiments that involve a reference sample performed in parallel with the samples. Otherwise, subtractive analysis can be empirical since it lies on the normalized CLD of the reference. In 2015, Deng and collaborators, who investigated stopping mechanisms of glycogen growth in mice and humans, proposed an alternate plotting and interpreting glycogen CLDs to address this issue. This plot uses the maximum peak area to normalize each CLD. The data are then plotted on a logarithmic scale highlighting two components. The latter illustrate two different mechanisms for chain elongation stopping2. By drawing lines fitting to the higher DP component, absolute parameters (i.e., slopes and intercepts of the lines) can be used for CLD comparison without normalization to a reference profile. CLDs of the wildtype (WT) Synechocystis PCC6803 strain and the single isogenic glgA1 and glgA2 mutants were plotted on a log scale, and fitting lines were determined for each profile (Figure 6C). The first component was highly similar between samples, peaking with a maximum at DP 6. This illustrates that the branching enzyme explicitly produces a maximum of such chains. The second component appeared as a broad shoulder, which was already described for mice and human glycogens2. The inclination of the second component arose at a higher DP in &#916;glgA1 and the slope of the corresponding fitting line (red line) was lower than the wildtype profile. Thus, the lack of GlgA1 slows down the arrest of chain elongation during biosynthesis that was proposed to occur by steric hindrance for mice and human glycogen2. These data suggest that the remaining elongating enzyme (i.e., GlgA2) produces longer chains before chain crowding. In &#916;glgA2, the opposite effect was observed with a more dramatic drop of the fitting line, corroborating that the chains produced by the remaining GlgA1 are overall shorter than those synthesized by GlgA2 alone before steric hindrance. This analysis suggests that both isoforms possess distinct kinetics and/or that their respective concertation with branching enzyme activity differ.

Watch this Scientific Journal Video about Determination of Glucan Chain Length Distribution of Glycogen Using the Fluorophore-Assisted Carbohydrate Electrophoresis FACE Method at JoVE.com

Determination of Glucan Chain Length Distribution of Glycogen Using the Fluorophore-Assisted Carbohydrate Electrophoresis FACE Method

In the present protocol, the Fluorophore-Assisted Carbohydrate Electrophoresis (FACE) technique is used to determine the chain length distribution (CLD) and the average chain length (ACL) of glycogen.

Determination of Glucan Chain Length Distribution of Glycogen Using the Fluorophore-Assisted Carbohydrate Electrophoresis (FACE) Method

Glycogen particles are branched polysaccharides composed of linear chains of glucosyl units linked by &#945;-1,4 glucoside bonds. The latter are attached ...

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Biochemistry

3.4K Views.  University of Lille. In the present protocol, the Fluorophore-Assisted Carbohydrate Electrophoresis (FACE) technique is used to determine the chain length distribution (CLD) and the average chain length (ACL) of glycogen. 

Video: Determination of Glucan Chain Length Distribution of Glycogen Using the Fluorophore-Assisted Carbohydrate Electrophoresis FACE Method

Determination of the Glycogen Content in Cyanobacteria

Cyanobacteria accumulate glycogen as a major intracellular carbon and energy storage&#160;during photosynthesis. Recent developments in research have highlighted complex mechanisms of glycogen metabolism, including the diel cycle of biosynthesis and catabolism, redox regulation, and the involvement of non-coding RNA. At the same time, efforts are being made to redirect carbon from glycogen to desirable products in genetically engineered cyanobacteria to enhance product yields. Several methods are used to determine the glycogen contents in cyanobacteria, with variable accuracies and technical complexities. Here, we provide a detailed protocol for the reliable determination of the glycogen content in cyanobacteria that can be performed in a standard life science laboratory. The protocol entails the selective precipitation of glycogen from the cell lysate and the enzymatic depolymerization of glycogen to generate glucose monomers, which are detected by a glucose oxidase-peroxidase (GOD-POD) enzyme coupled assay. The method has been applied to Synechocystis sp. PCC 6803 and Synechococcus sp. PCC 7002, two model cyanobacterial species that are widely used in metabolic engineering. Moreover, the method successfully showed differences in the glycogen contents between the wildtype and mutants defective in regulatory elements or glycogen biosynthetic genes.

Here, we present a reliable and easy assay to measure the glycogen content in cyanobacterial cells. The procedure entails precipitation, selectable depolymerization, and the detection of glucose residues. This method is suitable for both wildtype and genetically engineered strains and can facilitate the metabolic engineering of cyanobacteria.

Cyanobacteria accumulate glycogen as a major intracellular carbon and energy storage&#160;during photosynthesis. Recent developments in research have ...

Structural Characterization of Mannan Cell Wall Polysaccharides in Plants Using PACE

Plant cell wall polysaccharides are notoriously difficult to analyze, and most methods require expensive equipment, skilled operators, and large amounts of purified material. Here, we describe a simple method for gaining detailed polysaccharide structural information, including resolution of structural isomers. For polysaccharide analysis by gel electrophoresis (PACE), plant cell wall material is hydrolyzed with glycosyl hydrolases specific to the polysaccharide of interest (e.g., mannanases for mannan). Large format polyacrylamide gels are then used to separate the released oligosaccharides, which have been fluorescently labeled. Gels can be visualized with a modified gel imaging system (see Table of Materials). The resulting oligosaccharide fingerprint can either be compared qualitatively or, with replication, quantitatively. Linkage and branching information can be established using additional glycosyl hydrolases (e.g., mannosidases and galactosidases). Whilst this protocol describes a method for analyzing glucomannan structure, it can be applied to any polysaccharide for which characterized glycosyl hydrolases exist. Alternatively, it can be used to characterize novel glycosyl hydrolases using defined polysaccharide substrates.

A protocol for the structural analysis of polysaccharides by gel electrophoresis (PACE), using mannan as an example, is described.

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N-glycan Profiling of Glycoproteins by Hydrophilic Interaction Liquid Chromatography with Fluorescence and Mass Spectrometric Detection

Glycosylation is a vital modification found in proteins. N-glycan profiling of glycoproteins is required to detect novel biomarker candidates and determine glycan alterations in diseases. Most commercially available biopharmaceutical proteins are glycoproteins. The efficacy of these drugs is affected by glycosylation patterns. Therefore, an in-depth characterization method for the N-glycans is necessary. Here, we present a comprehensive approach for qualitative and quantitative analysis of N-glycans using hydrophilic interaction liquid chromatography equipped with fluorescence detection and tandem mass spectrometry (HILIC-FLD-MS/MS). N-glycans were released from glycoproteins with a facile method and labeled by a procainamide fluorophore tag in the strategy. Subsequently, the procainamide labeled N-glycans were analyzed by a HILIC-FLD-MS/MS technique. In this approach, N-glycan structures were confirmed by the tandem mass spectrometric analysis, whereas fluorescence detection was used for the quantitative analysis. An application for data analysis of the detected N-glycan peaks is described in the study. This protocol can be applied to any glycoprotein extracted from various species.

N-glycan Profiling of Glycoproteins by Hydrophilic Interaction Liquid Chromatography with Fluorescence and Mass Spectrometric Detection

N-glycan profiling of glycoproteins is essential for discovering novel biomarkers and understanding glycan functions in cellular events. Additionally, N-glycan analysis of protein biopharmaceuticals is very important for human use. In this current article, a high-throughput strategy for identifying and quantifying N-glycan structures was presented using the HILIC-FLD-MS/MS technique.

Glycosylation is a vital modification found in proteins. N-glycan profiling of glycoproteins is required to detect novel biomarker candidates and ...

Comprehensive Compositional Analysis of Plant Cell Walls (Lignocellulosic biomass) Part II: Carbohydrates

The need for renewable, carbon neutral, and sustainable raw materials for industry and society has become one of the most pressing issues for the 21st century. This has rekindled interest in the use of plant products as industrial raw materials for the production of liquid fuels for transportation2 and other products such as biocomposite materials6. Plant biomass remains one of the greatest untapped reserves on the planet4. It is mostly comprised of cell walls that are composed of energy rich polymers including cellulose, various hemicelluloses, and the polyphenol lignin5 and thus sometimes termed lignocellulosics. However, plant cell walls have evolved to be recalcitrant to degradation as walls contribute extensively to the strength and structural integrity of the entire plant. Despite its necessary rigidity, the cell wall is a highly dynamic entity that is metabolically active and plays crucial roles in numerous cell activities such as plant growth and differentiation5. Due to the various functions of walls, there is an immense structural diversity within the walls of different plant species and cell types within a single plant4. Hence, depending of what crop species, crop variety, or plant tissue is used for a biorefinery, the processing steps for depolymerisation by chemical/enzymatic processes and subsequent fermentation of the various sugars to liquid biofuels need to be adjusted and optimized. This fact underpins the need for a thorough characterization of plant biomass feedstocks. Here we describe a comprehensive analytical methodology that enables the determination of the composition of lignocellulosics and is amenable to a medium to high-throughput analysis (Figure 1). The method starts of with preparing destarched cell wall material. The resulting lignocellulosics are then split up to determine its monosaccharide composition of the hemicelluloses and other matrix polysaccharides1, and its content of crystalline cellulose7. The protocol for analyzing the lignin components in lignocellulosic biomass is discussed in Part I3.

Comprehensive Compositional Analysis of Plant Cell Walls Lignocellulosic biomass Part II: Carbohydrates

Plant biomass is a major carbon-neutral renewable resource that could be used for the production of biofuels. Plant biomass consists mainly of cell walls, a structurally complex composite material termed lignocellulosics. Here we describe a protocol for a comprehensive analysis of the content and composition of wall derived carbohydrates.

The need for renewable, carbon neutral, and sustainable raw materials for industry and society has become one of the most pressing issues for the 21st ...

Biology

Spectrophotometric Methods for the Study of Eukaryotic Glycogen Metabolism

Glycogen is synthesized as a storage form of glucose by a wide array of organisms, ranging from bacteria to animals. The molecule comprises linear chains of &#945;1,4-linked glucose residues with branches introduced through the addition of &#945;1,6-linkages. Understanding how the synthesis and degradation of glycogen are regulated and how glycogen attains its characteristic branched structure requires the study of the enzymes of glycogen storage. However, the methods most commonly used to study these enzyme activities typically employ reagents or techniques that are not available to all investigators. Here, we discuss a battery of procedures that are technically simple, cost-effective, and yet still capable of providing valuable insight into the control of glycogen storage. The techniques require access to a spectrophotometer, operating in the range of 330 to 800 nm, and are described assuming that the users will employ disposable, plastic cuvettes. However, the procedures are readily scalable and can be modified for use in a microplate reader, allowing highly parallel analysis.

Techniques to measure the activity of key enzymes of glycogen metabolism are presented, using a simple spectrophotometer operating in the visible range.

Glycogen is synthesized as a storage form of glucose by a wide array of organisms, ranging from bacteria to animals. The molecule comprises linear chains ...

Fractionation of Lignocellulosic Biomass using the OrganoCat Process

The shift from a petroleum-based to a more sustainable and bio-based economy requires the development of new refinery concepts to maintain the supply of raw materials and energy. For these novel and sustainable biorefinery concepts, it is important to use catalysts and solvents that are aligned with the principles of Green Chemistry. Therefore, the implementation of biogenic alternatives can be a promising solution. The lignocellulose pretreatment and fractionation process presented herein-OrganoCat-is an integrated fractionation of lignocellulose into its main components using biogenic acids such as 2,5-furandicarboxylic acid as catalyst. Hemicelluloses and other non-cellulosic polysaccharides are selectively depolymerized by the diluted acid and dissolved, while the crystalline cellulose remains in the solid pulp. In the presence of a second organic phase consisting of biogenic 2-methyltetrahydrofuran, disentangled lignin is extracted in situ. The process allows for the efficient fractionation of the three main components-lignin, cellulose, and non-cellulosic sugars. This helps to focus on the quality of the lignin, the improvement of enzymatic hydrolysis of the cellulose-enriched pulp, and the mild non-cellulosic sugar extraction with low degradation.

OrganoCat is a method for the pretreatment and fractionation of lignocellulose under mild conditions into lignin, fermentable sugars, and cellulose pulp. In a biogenic, biphasic solvent system of water and 2-methyltetrahydrofuran with 2,5-furancarboxylic acid as catalyst, the OrganoCat products are separated in situ for straightforward product recovery.

The shift from a petroleum-based to a more sustainable and bio-based economy requires the development of new refinery concepts to maintain the supply of ...

Chemistry

The Extraction of Liver Glycogen Molecules for Glycogen Structure Determination

Liver glycogen is a hyperbranched glucose polymer that is involved in the maintenance of blood sugar levels in animals. The properties of glycogen are influenced by its structure. Hence, a suitable extraction method that isolates representative samples of glycogen is crucial to the study of this macromolecule. Compared to other extraction methods, a method that employs a sucrose density gradient centrifugation step can minimize molecular damage. Based on this method, a recent publication describes how the density of the sucrose solution used during centrifugation was varied (30%, 50%, 72.5%) to find the most suitable concentration to extract glycogen particles of a wide variety of sizes, limiting the loss of smaller particles. A 10 min boiling step was introduced to test its ability to denature glycogen degrading enzymes, thus preserving glycogen. The lowest sucrose concentration (30%) and the addition of the boiling step were shown to extract the most representative samples of glycogen.

An optimal sucrose concentration was determined for the extraction of liver glycogen using sucrose density gradient centrifugation. The addition of a 10 min boiling step to inhibit glycogen-degrading enzymes proved beneficial.

Liver glycogen is a hyperbranched glucose polymer that is involved in the maintenance of blood sugar levels in animals. The properties of glycogen are ...

Concanavalin A-Based Sedimentation Assay to Measure Substrate Binding of Glucan Phosphatases

Glucan phosphatases belong to the larger family of dual specificity phosphatases (DSP) that dephosphorylate glucan substrates, such as glycogen in animals and starch in plants. The crystal structures of glucan phosphatase with model glucan substrates reveal distinct glucan-binding interfaces made of DSP and carbohydrate-binding domains. However, quantitative measurements of glucan-glucan phosphatase interactions with physiologically relevant substrates are fundamental to the biological understanding of the glucan phosphatase family of enzymes and the regulation of energy metabolism. This manuscript reports a Concanavalin A (ConA)-based in vitro sedimentation assay designed to detect the substrate binding affinity of glucan phosphatases against different glucan substrates.&#160;As a proof of concept, the dissociation constant (KD) of glucan phosphatase Arabidopsis thaliana Starch Excess4 (SEX4) and amylopectin was determined. The characterization of SEX4 mutants and other members of the glucan phosphatase family of enzymes further demonstrates the utility of this assay to assess the differential binding of protein- carbohydrate interactions. These data demonstrate the suitability of this assay to characterize a wide range of starch and glycogen interacting proteins.

This method describes a lectin-based in vitro sedimentation assay to quantify the binding affinity of glucan phosphatase and amylopectin. This co-sedimentation assay is reliable for measuring glucan phosphatase substrate binding and can be applied to various solubilized glucan substrates.

Glucan phosphatases belong to the larger family of dual specificity phosphatases (DSP) that dephosphorylate glucan substrates, such as glycogen in animals ...

Automated Modular High Throughput Exopolysaccharide Screening Platform Coupled with Highly Sensitive Carbohydrate Fingerprint Analysis

Many microorganisms are capable of producing and secreting exopolysaccharides (EPS), which have important implications in medical fields, food applications or in the replacement of petro-based chemicals. We describe an analytical platform to be automated on a liquid handling system that allows the fast and reliable analysis of the type and the amount of EPS produced by microorganisms. It enables the user to identify novel natural microbial exopolysaccharide producers and to analyze the carbohydrate fingerprint of the corresponding polymers within one day in high-throughput (HT). Using this platform, strain collections as well as libraries of strain variants that might be obtained in engineering approaches can be screened. The platform has a modular setup, which allows a separation of the protocol into two major parts. First, there is an automated screening system, which combines different polysaccharide detection modules: a semi-quantitative analysis of viscosity formation via a centrifugation step, an analysis of polymer formation via alcohol precipitation and the determination of the total carbohydrate content via a phenol-sulfuric-acid transformation. Here, it is possible to screen up to 384 strains per run. The second part provides a detailed monosaccharide analysis for all the selected EPS producers identified in the first part by combining two essential modules: the analysis of the complete monomer composition via ultra-high performance liquid chromatography coupled with ultra violet and electrospray ionization ion trap detection (UHPLC-UV-ESI-MS) and the determination of pyruvate as a polymer substituent (presence of pyruvate ketal) via enzymatic oxidation that is coupled to a color formation. All the analytical modules of this screening platform can be combined in different ways and adjusted to individual requirements. Additionally, they can all be handled manually or performed with a liquid handling system. Thereby, the screening platform enables a huge flexibility in order to identify various EPS.

We present an automated modular high-throughput-method for the identification and characterization of microbial exopolysaccharides in small scale. This method combines a fast preselection to analyze the total amount of secreted polysaccharides with a detailed carbohydrate fingerprint to enable the fast screening of newly isolated bacterial strains or entire strain collections.

Many microorganisms are capable of producing and secreting exopolysaccharides (EPS), which have important implications in medical fields, food ...

A Non-Degradative Extraction Method for Molecular Structure Characterization of Bacterial Glycogen Particles

Currently, there exist a variety of glycogen extraction methods, which either damage glycogen spatial structure or only partially extract glycogen, leading to the biased characterization of glycogen fine molecular structure. To understand the dynamic changes of glycogen structures and the versatile functions of glycogen particles in bacteria, it is essential to isolate glycogen with minimal degradation. In this study, a mild glycogen isolation method is demonstrated by using cold-water (CW) precipitation via sugar density gradient ultra-centrifugation (SDGU-CW). The traditional trichloroacetic acid (TCA) method and potassium hydroxide (KOH) method were also performed for comparison. A commonly used lab strain, Escherichia coli BL21(DE3), was used as a model organism in this study for demonstration purposes. After extracting glycogen particles using different methods, their structures were analyzed and compared through size exclusion chromatography (SEC) for particle size distribution and fluorophore-assisted capillary electrophoresis (FACE) for linear chain length distributions. The analysis confirmed that glycogen extracted via SDGU-CW had minimal degradation.

Bacterial glycogen structure is greatly impacted by extraction methods which may result in molecular degradation and/or biased sampling. It is essential to develop methods to minimize these problems. Here, four extraction methods have been compared using size distribution and chain length distribution as key criteria for minimizing extraction artifacts.

Currently, there exist a variety of glycogen extraction methods, which either damage glycogen spatial structure or only partially extract glycogen, ...

Determination of Glucan Chain Length Distribution of Glycogen Using the Fluorophore-Assisted Carbohydrate Electrophoresis (FACE) Method

Summary

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