High Identification Rates of Endogenous Neuropeptides from Mouse Brain

Mass spectrometry-based neuropeptidomics is one of the most powerful approaches for identification of endogenous neuropeptides in the brain. Until now, however, the identification rate of neuropeptides in neuropeptidomics is relatively low and this severely restricts insights into their biological function. In the present study, we developed a high accuracy mass spectrometry-based approach to enhance the identification rates of neuropeptides from brain tissue. Our integrated approach used mixing on column for loading aqueous and organic extracts to reduce the loss of peptides during sample treatment and used charge state-directed tandem mass spectrometry to increase the number of peptides subjected to high mass accuracy fragmentation. This approach allowed 206 peptides on average to be identified from a single mouse brain sample that was prepared using 15 μL of solutions per 1 mg of tissue. In total, we identified more than 500 endogenous peptides from mouse hypothalamus and whole brain samples. Our identification rate is about two to four times higher compared to previously reported studies conducted on mice or other species. The hydrophobic peptides, such as neuropeptide Y and galanin, could be presented and detected with hydrophilic peptides in the same LC–MS run, allowing a high coverage of peptide characterization over an organism. This will advance our understanding of the roles of diverse peptides and their links in the brain functions

High Identification Rates of Endogenous Neuropeptides from Mouse Brain

Mass spectrometry-based neuropeptidomics is one of the most powerful approaches for identification of endogenous neuropeptides in the brain. Until now, however, the identification rate of neuropeptides in neuropeptidomics is relatively low and this severely restricts insights into their biological function. In the present study, we developed a high accuracy mass spectrometry-based approach to enhance the identification rates of neuropeptides from brain tissue. Our integrated approach used mixing on column for loading aqueous and organic extracts to reduce the loss of peptides during sample treatment and used charge state-directed tandem mass spectrometry to increase the number of peptides subjected to high mass accuracy fragmentation. This approach allowed 206 peptides on average to be identified from a single mouse brain sample that was prepared using 15 μL of solutions per 1 mg of tissue. In total, we identified more than 500 endogenous peptides from mouse hypothalamus and whole brain samples. Our identification rate is about two to four times higher compared to previously reported studies conducted on mice or other species. The hydrophobic peptides, such as neuropeptide Y and galanin, could be presented and detected with hydrophilic peptides in the same LC–MS run, allowing a high coverage of peptide characterization over an organism. This will advance our understanding of the roles of diverse peptides and their links in the brain functions

High Identification Rates of Endogenous Neuropeptides from Mouse Brain

Mass spectrometry-based neuropeptidomics is one of the most powerful approaches for identification of endogenous neuropeptides in the brain. Until now, however, the identification rate of neuropeptides in neuropeptidomics is relatively low and this severely restricts insights into their biological function. In the present study, we developed a high accuracy mass spectrometry-based approach to enhance the identification rates of neuropeptides from brain tissue. Our integrated approach used mixing on column for loading aqueous and organic extracts to reduce the loss of peptides during sample treatment and used charge state-directed tandem mass spectrometry to increase the number of peptides subjected to high mass accuracy fragmentation. This approach allowed 206 peptides on average to be identified from a single mouse brain sample that was prepared using 15 μL of solutions per 1 mg of tissue. In total, we identified more than 500 endogenous peptides from mouse hypothalamus and whole brain samples. Our identification rate is about two to four times higher compared to previously reported studies conducted on mice or other species. The hydrophobic peptides, such as neuropeptide Y and galanin, could be presented and detected with hydrophilic peptides in the same LC–MS run, allowing a high coverage of peptide characterization over an organism. This will advance our understanding of the roles of diverse peptides and their links in the brain functions

TAG species analysis and uptake of labeled oleate.

<p>(A) Dominant TAG species in procyclic <i>T. brucei</i> cells identified by ESI/MS/MS after oleate feeding for three days (black columns) or in the control (white columns). For a complete list of TAG species detected see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0114628#pone.0114628.s001" target="_blank">S1 Figure</a>. The nomenclature 54:X indicates the total carbon number of all three acyl chains and the sum of all unsaturated double bonds within the acyl chains. (B) Uptake kinetics upon growth in the presence of radiolabeled oleate for up to 8 h. The incorporation of ¹⁴C oleate into lipid species was quantified by HPTLC and a Storm 860 phosphorimager. PPL, phospholipids; TAG, triacylglycerol; SE, Steryl-esters; DAG, diacylglycerol.</p

NADPH-dependent 3-hydroxyacyl-CoA dehydrogenase activity in WT and Δ<i>tfeα1</i>/Δ<i>tfeα1</i> cells.

1<p>WCE, whole cell exctract.</p>2<p>glyco, partially purified glycosome fraction.</p>3<p>Mean ± SEM of n experiments (mU/mg of protein).</p>4<p>+gluc: cells cultured in SDM79 containing 10 mM glucose.</p>5<p>−gluc: cells cultured in glucose-depleted SDM79GluFree.</p><p>NADPH-dependent 3-hydroxyacyl-CoA dehydrogenase activity in WT and Δ<i>tfeα1</i>/Δ<i>tfeα1</i> cells.</p

Phenotypic analysis of Δ<i>tfeα1</i>/Δ<i>tfeα1</i> cell.

<p>(A) growth curve of WT and Δ<i>tfeα1</i>/Δ<i>tfeα1</i> cell knock cells in glucose-rich (SDM79 with 10 mM glucose) or glucose-free (SDM79GluFree) conditions. (B) Global protein abundance in the partially purified glycosome fraction of WT (x-axis) and Δ<i>tfeα1</i>/Δ<i>tfeα1</i> cell knock cells (y-axis). Each protein identification is presented by a point at log₁₀ of normalized peptide count values taken from the proteome data in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0114628#pone.0114628.s004" target="_blank">S4 Figure</a>. Proteins on the dashed grey line have identical normalized peptide counts in both samples; the grey lines represent a 2-fold abundance in one condition.</p

LD and TAG turnover in WT and Δ<i>tfeα1</i>/Δ<i>tfeα1</i> cells.

<p>Cells were fed with oleate in glucose-rich SDM79 medium for three days, and after oleate withdrawal samples were taken at the time points indicated. (A) WT cells stained with BODIPY and analyzed by flow cytometry (left y-axis). Error bars represent the SEM of independent replicates (n = 3). The growth curve is given as dashed line (right y-axis). (B) Growth curve and sampling time points (arrows) for the experiments in panels (C) and (D). Total TAG content was determined in triplicate by HPTLC and densitometry in WT (C) and Δ<i>tfeα1</i>/Δ<i>tfeα1</i> (D) cells. Error bars represent the SEM of independent replicates (n = 3). The calculated values (filled symbols) account for dilution of LDs or TAG content by cell division, based on the matched growth data.</p

Quantification of the oleate-induced lipid droplet formation.

<p>(A) BODIPY 493/503 stained LDs were counted in stacks of confocal laser scanning microscopy (CLSM) images; the average number of LDs per cell is given after oleate feeding (black column) or in the control (white column). (B) Distribution of LD numbers per cells in the population after oleate feeding (black columns) or in the control (white columns). (C) Quantification of BODIPY-stained LDs by flow cytometry after oleate feeding (black column) or in the control (white column). BODIPY 493/503 preferentially stains nonpolar lipids. Error bars give the SEM (n = 3) of values normalized to the control. (D) Quantification of TAG content by HPTLC and densitometry after oleate feeding (black columns) or in the control (white columns). Values are normalized to the control.</p

Oleate feeding stimulates lipid droplet formation in procyclic <i>T. brucei</i> cells.

<p>Staining of lipid droplets with nile red (A) or BODIPY 493/503 (B) was as detailed in experimental procedures. Myriocin treatment (0.5 µM for 24 h) was included for comparison to a previous report <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0114628#pone.0114628-Bird1" target="_blank">[36]</a>. An example of several experiments is shown.</p

Metabolic flux distributions in parental and mutant cell lines.

<p>Schemes in panel A compare metabolic flux distribution between the different branches of fatty acid and sterol biosynthesis of the <i>T</i>. <i>brucei</i> procyclic parental and mutant cell lines grown in the carbon source-rich SDM79 medium (<i>in vitro</i>). The carbon sources included in the model are leucine, acetate, glucose and threonine, but not fatty acids, since their incorporation into lipids through <i>de novo</i> biosynthetic pathways has not been demonstrated in rich medium yet. The arrow thickness reflects the strength of metabolic flux redistributions, such as upregulation of leucine metabolism and fatty acid preference, observed in the Δ<i>ivdh</i>, Δ<i>ach</i>/^<i>RNAi</i>ASCT, ^<i>RNAi</i>AceCS, ^<i>RNAi</i>SCP2 and/or ^<i>RNAi</i>TDH/^<i>RNAi</i>PDH mutants compared to the parental PCF cell line. The estimated flux distribution in PCF trypanosomes developing in the tsetse fly midgut is presented in the right box chart. The question mark indicates that the <i>in vivo</i> ketogenic carbon source(s) supplementing threonine, as well as the flux through the acetyl-CoA/HMG-CoA bridge are unknown; this diagram assumes a limited availability of ketogenic carbon sources. Panel B describes metabolic adaptations using as reference the parental PCF grown in rich <i>in vitro</i> conditions. The question mark means that the possible metabolic adaptation <i>in vivo</i> is still unknown, since the carbon source contents in the tsetse's organs, including the gut and salivary glands, remain unknown. In Panel C, these metabolic adaptations are re-interpreted considering the probable physiological conditions that PCF have to face <i>in vivo</i> as reference, with the assumption that ketogenic carbon sources are limited in the tsetse midgut and/or in the salivary glands. Abbreviations: A, acetate; AcCoA, acetyl-CoA; FA, fatty acids; G, glucose; HMGCoA, 3-hydroxy-3-methylglutaryl-CoA; L, leucine; T, threonine; Ste, sterols.</p