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The proximity interactome of the peach-potato aphid (Myzus persicae) cathepsin B in Arabidopsis thaliana
<p><strong>Introduction</strong></p> <p>In agriculture, the peach-potato aphid <em>Myzus persicae</em> (Sulzer) has one of the broadest host ranges among insects and cause devastating crop losses worldwide (CABI, 2022). They are highly adaptable, displaying a wide range of plastic responses to environmental cues, including the ability to develop as either winged or wingless forms and to reproduce through either asexual or sexual means (Brisson, 2010; Ogawa and Miura, 2014; Grantham and Brisson, 2018). Remarkably, <em>M. persicae </em>differentially regulate the transcription of certain gene clusters to facilitate colonization of diverse plant species (Mathers et al., 2017; Chen et al., 2020). Among these gene clusters are members of the cysteine protease family, cathepsin B (CathB).</p> <p>Host responsive CathB genes are organized in tandemly repeated clusters in the <em>M. persicae</em> genome and belong to a recently expanded clade in phylogeny (Mathers et al., 2017). They are upregulated when aphids feed on <em>Arabidopsis thaliana </em>and <em>Brassica rapa</em> and knock down of their expression using RNA interference reduces aphid reproduction on <em>A. thaliana </em>(Chen et al., 2020). Intriguingly, peptides corresponding to CathB proteins are detected in <em>M. persicae</em> oral secretion (OS), indicating that at least some CathB proteins are directly delivered into plant cells during aphid feeding (Guo et al., 2020; Liu et al., 2024).</p> <p>Among <em>M. persicae</em> CathB proteins, CathB6 is most highly expressed in aphids on <em>A. thaliana</em> (Chen et al., 2020) and most abundant in <em>M. persicae</em> OS (Liu et al., 2024). To identify the potential plant targets of <em>M. persicae</em> CathB, we optimized the TurboID-based proximity labelling and MS (PL-MS) protocol (Fig. 1).</p> <p>As a first step, we generated stable transgenic <em>A. thaliana</em> lines producing GFP or CathB6 as C-terminal TurboID-3×FLAG fusions (GFP-TurboID or CathB6-TurboID). Seedlings of these plants were treated with biotin followed by affinity capture with streptavidin beads (Fig. 2A). Enrichment of biotinylated proteins was confirmed by western blotting (Fig. 2B), followed by nanoLC-MS/MS analyses.</p> <p>Principal component analysis (PCA) of the MS data showed that the three CathB6-TurboID samples were grouped together, separately from three GFP-TurboID samples (Fig. 2C). Furthermore, MA plot confirmed that the CathB6-TurboID and GFP-TurboID samples are distinct (Fig. 2D). From the complete dataset, 267 <em>A. thaliana</em> proteins exhibited statistically significant enrichment (<em>p</em>-value < 0.05) of more than 2-fold and were consistently identified in at least two replicates of the CathB6-TurboID samples compared to the GFP-TurboID controls (Fig. 2E, Table 1). This compares to 223 proteins in the GFP-TurboID samples versus CathB6-TurboID samples (Fig. 2E, Table 1). Additionally, we identified 20 unique peptides corresponding to CathB6 in the CathB6-TurboID samples and 19 unique peptides corresponding to GFP in the GFP-TurboID samples (Table 1). These data suggest that this PL-MS protocol worked dnd identified genuine interactors of CathB6.</p> <p>Together, this dataset identifies 267 potential plant interactors of aphid CathB6, which may contribute to CathB6 modulation of <em>A. thaliana</em> plant for colonization. Further mechanistic studies should be done to characterize if these potential interactors are involved and how the relevant pathways are affected after CathB delivery through aphid feeding.</p> <p> </p> <p><strong>Materials and Methods</strong></p> <p><em>Plasmid construction</em></p> <p>For the construction of plasmids producing CathB6-TurboID-3×FLAG, the coding sequences corresponding to the catalytic domain (without signal peptide and prodomain regions) of CathB6 (Arg61-Asn338) and TurboID-3×FLAG were separately amplified. Then, the two fragments were connected using overlap PCR (Nelson and Fitch, 2011). After cloning of the sequence corresponding to the CathB6-TurboID-3×FLAG fragment into the pJET vector and sequencing, CathB6-TurboID-3×FLAG was amplified with primers containing <em>attB</em> extensions and cloned into the pDONOR207 vector, followed by the ligation to Gateway destination vector pB7WG2 containing a 35S promoter. Similar cloning methods were used for construction of GFP-TurboID-3×FLAG.</p> <p><em>Plant transformation</em></p> <p>The constructed plasmids were introduced into <em>Agrobacterium tumefaciens</em> strain GV3101, and the cultures were grown on plates at 28 °C for 24–48 hrs. Then, positive colonies were identified via PCR using plasmids extracted from overnight liquid cultures and gene-specific primers. Positive colonies were grown at 28 °C in liquid cultures and transformed into <em>A. thaliana</em> Col-0 plants using the floral dipping method (Bechtold, 1993). Transgenic seeds were harvested and selected on Murashige and Skoog (MS) medium supplemented with 20 μg/mL phosphinothricin (BASTA) and screened for ratio of 3:1 alive/dead segregation. After screening for two or three generations, transgenic plants were deemed to harbor single homozygous transgenes and were used for proximity labeling once germinated seeds achieved a 100% survival rate.</p> <p><em>Proximity labelling</em></p> <p>Seeds of <em>A. thaliana</em> plants stably expressing GFP-TurboID-3×FLAG or CathB6-TurboID-3×FLAG were sowed on ½ MS plates containing 1.0% sucrose and 0.3% phytagel and placed under long-day condition (16 h light/8 h dark) at 22 °C. After 10 days, 2.5 g seedlings were collected and submerged in 50 µM biotin solution for 4 hrs at RT. Afterwards, seedlings were rinsed with ice-cold MilliQ water for 5 times. After removing excess liquid with paper towel, seedings were ground with pestle, mortar and nitrogen to a fine powder. Protein extraction was performed in 5 mL of extraction buffer [150 mM Tris-HCl (pH 7.5), 150 mM NaCl, 1 mM EDTA, 10% Glycerol, 10 mM DTT, 0.4% Nonidet-40, 0.1% (w/v) Deoxycholic acid, 2% (w/v) PVPP, 1 tablet of cOmplete protease Inhibitor cocktail (Roche, Catalog number 10697498001)] and incubation on a rotor wheel at 4 °C for 30 min, followed by centrifugation of the tubes at 5000 g for 15 min to remove the cell debris. The upper soluble fraction was then run through the Zeba Spin Desalting Column (Thermo Fisher Scientific, Catalog number 89893) to remove excess biotin from the lysates. Fifty (50) µL of desalted lystate was used as input for western blot analysis, while the rest of the desalted lysate was incubated with High Capacity Streptavidin Agarose Resin (Thermo Fisher Scientific, Catalog number 20361) on a rotor wheel at 4 °C overnight. The next day, Streptavidin beads were sequentially washed once in 1 mL Buffer 1 (2% SDS in water), once in 1 mL Buffer 2 [150 mM Tris-HCl (pH 7.5), 150 mM NaCl, 1 mM EDTA, 10% Glycerol, 0.1% (w/v) Deoxycholic acid (w/v), 1% Triton X-100], once in buffer 3 [10 mM Tris-HCl (pH 7.4), 250 mM LiCl, 1 mM EDTA, 0.1% (w/v) Deoxycholic acid, 1% (v/v) NP40], twice in Buffer 4 [50mM Tris-HCl (pH 7.5)], and six times in Buffer 5 (50mM ammonium bicarbonate, pH 8.0). Finally, the streptavidin beads were resuspended in 200 µL of 50 mM ammonium bicarbonate. For quality control of the TurboID immunoprecipitation, 10% (20 µL) of the suspension was taken out for Western blot analysis, and the remaining bead suspension was flash-frozen in liquid nitrogen and stored at -80 °C and submitted to nano LC-MS/MS analysis.</p> <p>For western blot analysis, 20 µL of suspended streptavidin beads in washing buffer 5 were added to 10 µL of 4× LDS Sample Loading Buffer, 10 mM DTT and 2 mM biotin, and boiled for 10 min. Samples were loaded onto 12% SDS-PAGE gels (Invitrogen) and transferred to 0.22 μm PVDF membranes using the Bio-Rad mini-PROTEAN Electrophoresis system. Membranes were hybridized with Streptavidin-HRP.</p> <p><em>NanoLC-MS/MS</em></p> <p><em> </em>Biotinylated proteins enriched with streptavidin beads were processed with trypsin via on bead digestion. The beads were washed in water and resuspended in of 1.5% sodium deoxycholate (SDC; Merck) in 0.2 M EPPS-buffer (Merck) to 50% bead slurry vol/vol, pH 8.5 and vortexed under heating. Cysteine residues were reduced with dithiothreitol, alkylated with iodoacetamide, and the proteins digested with trypsin in the SDC buffer according to standard procedures for 8 hrs. The beads were then pelleted by centrifugation and the supernatant was collected for SDC precipitation by adding trifluoroacetic acid (TFA) to a final concentration of 0.2%. The clear supernatant was subjected to C18 SPE using home-made stage tips with C18 Reprosil_pur 120, 5 µm (Dr. Maisch GmbH, Germany). Aliquots were analyzed by nano LC-MS/MS on an Orbitrap Eclipse™ Tribrid™ mass spectrometer equipped with a FAIMS Pro Duo interface coupled to an UltiMate® 3000 RSLC nano LC system (Thermo Fisher Scientific, Hemel Hempstead, UK). The samples were loaded onto a trap cartridge (PepMap™ Neo Trap Cartridge, C18, 5um, 0.3x5mm, Thermo) with 0.1% TFA at 15 µl min-1 for 3 min. The trap column was then switched in-line with the analytical column (Aurora Frontier TS, 60 cm nanoflow UHPLC column, ID 75 µm, reversed phase C18, 1.7 µm, 120 Å; IonOpticks, Fitzroy, Australia) for separation at 55°C using the following gradient of solvents: A (water, 0.1% formic acid) and B (80% acetonitrile, 0.1% formic acid) at a flow rate of 0.26 µl min-1 : 0-3 min 1% B (parallel to trapping); 3-10 min increase B (curve 4) to 8%; 10-102 min linear increase B to 48; followed by a ramp to 99% B and re-equilibration to 0% B. Total runtime was 140 min.</p> <p>Mass spectrometry data were acquired between 10 and 110 min with the FAIMS device set to three compensation voltages (-35V, -50V, -65V) at standard resolution for 1.0 s each with the following MS settings in positive ion mode: OT resolution 120K, profile mode, mass range m/z 300-1600, normalized AGC target 100%, max inject time 50 ms; MS2 in IT Turbo mode: quadrupole isolation window 1 Da, charge states 2-5, threshold 1e4, HCD CE = 30, AGC target standard, max. injection time dynamic, dynamic exclusion 1 count for 15 s with mass tolerance of ±10 ppm, one charge state per precursor only.</p> <p>The mass spectrometry raw data were processed and quantified in Proteome Discoverer 3.1 (PD3.1) (Thermo) using the search engine CHIMERYS (MSAID, Munich, Germany); all mentioned tools of the following workflow are nodes of the proprietary Proteome Discoverer (PD) software. The <em>A. thaliana</em> protein sequence database (TAIR10, 35,386 entries, from 14/12/2010), the two sequences of the used TurboID constructs, and the MaxQuant contaminants database (240812, 246 entries) were imported into PD adding a reversed sequence database for decoy searches.</p> <p>The database search was performed using the search engine CHIMERYS (MSAID, Munich, Germany). The processing workflow started with spectrum recalibration, Minora Feature Detection with min. trace length 5, S/N 2.5, PSM confidence high, and Top N Peak Filter with 20 peaks per 100 Da. For CHIMERYS, the inferys_3.0.0_fragmentation prediction model with FDR targets 0.01 (strict) and 0.05 (relaxed), a fragment tolerance of 0.3 Da, enzyme trypsin with 2 missed cleavages, variable modification oxidation (M), fixed modification carbamidomethyl (C) were used.</p> <p>The consensus workflow in the PD3.1 software was used to evaluate the peptide identifications and to measure the abundances of the peptides based on the LC-peak intensities. For chromatographic alignment and feature mapping, a retention time tolerance of 2 min, a mass tolerance of 1 ppm, and an S/N threshold of 5 were used. For quantification, three replicates per condition were measured. In PD3.1, the following parameters were used for ratio calculation: normalization on total peptide abundances, protein abundance-based ratio calculation using the Top3 most abundant peptides, missing values imputation by low abundance resampling, hypothesis testing by t-test (background based), adjusted <em>p</em>-value calculation by BH-method. The results were exported into a Microsoft Excel table including data for protein abundances, ratios, <em>p</em>-values, number of peptides, protein coverage, the CHIMERYS identification score and other important values.</p> <p> </p> <p><strong>Data availability statement</strong></p> <p>The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier with the dataset identifier PXD057789 and 10.6019/PXD057789.</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p><strong> </strong>This research was funded by UK Research and Innovation (UKRI) Biotechnology and Biological Sciences Research Council (BBSRC) grants to SAH (BB/V008544/1 and BB/R009481/1). Additional Support is provided by the BBSRC Institute Strategy Programmes (BBS/E/J/000PR9797 and BBS/E/JI/230001B) awarded to the JIC. The JIC is grant-aided by the John Innes Foundation.</p> <p> </p> <p><strong>Conflicts of Interest</strong></p> <p><strong> </strong>The authors declare that no conflicts of interest exist.</p> <p> </p> <p><strong>Legends of figures and tables</strong></p> <p><strong>Figure 1. Principle of CathB6-TurboID based proximity labelling with MS (PL-MS). </strong>The TurboID biotin ligase (TurboID) is fused to C-terminus of CathB6. Exogenous addition of biotin (yellow stars) biotinylates proteins in the proximity of CathB6-TurboID fusion protein, whereas distal proteins are not biotinylated. The biotinylated proteins are captured by incubating total proteins extracts with streptavidin beads. Peptides derived from biotinylated proteins, most of which are in the proximity of CathB6-TurboID, are detected by nanoLC-MS.</p> <p><strong>Fig. 2. Sample preparation and quantification for CathB6-TurboID interactome in <em>A. thaliana</em>. </strong>(<strong>A</strong>) Sample preparation working flow for TurboID-based proximity labeling. GFP-TurboID and CathB6-TurboID seedlings were treated with 50 µM biotin for 4 hrs at room temperature. (<strong>B</strong>) Visualization on western blots of biotinylated proteins detected after desalting step (input) and 12 wash steps of Streptavidin beads (Streptavidin IP) as per workflow shown in (A). (<strong>C</strong>) Principal component analysis (PCA) of three replicates of GFP-TurboID and CathB6-TurboID samples. (<strong>D</strong>) MA plot of three replicates of GFP-TurboID and CathB6-TurboID samples. (<strong>E</strong>) Venn diagrams showing the overlap of proteins identified in three biological replicates of GFP-TurboID (left) and CathB6-TurboID (right) upon a fold-change of CathB6-TurboID/GFP-TurboID > 2, n = 267. </p> <p><strong>Table 1. Full list of proteins detected from CathB6-TurboID PL-MS.</strong></p> <p> </p> <p> </p> <p><strong>References</strong></p> <p><strong>Bechtold, N.</strong> (1993). In planta Agrobacterium-mediated gene transfer by infiltration of adult Arabidopsis thaliana plants. CR Acad. Sci. Paris, Life Sci. <strong>316, </strong>1194-1199.</p> <p><strong>Brisson, J.A.</strong> (2010). Aphid wing dimorphisms: linking environmental and genetic control of trait variation. Philos Trans R Soc Lond B Biol Sci <strong>365, </strong>605-616.</p> <p><strong>CABI, C.f.A.a.B.I.</strong> (2022). Myzus persicae (green peach aphid). CABI Compendium.</p> <p><strong>Chen, Y., Singh, A., Kaithakottil, G.G., Mathers, T.C., Gravino, M., Mugford, S.T., van Oosterhout, C., Swarbreck, D., and Hogenhout, S.A.</strong> (2020). An aphid RNA transcript migrates systemically within plants and is a virulence factor. Proc Natl Acad Sci U S A <strong>117, </strong>12763-12771.</p> <p><strong>Grantham, M.E., and Brisson, J.A.</strong> (2018). Extensive Differential Splicing Underlies Phenotypically Plastic Aphid Morphs. Mol Biol Evol <strong>35, </strong>1934-1946.</p> <p><strong>Guo, H., Zhang, Y., Tong, J., Ge, P., Wang, Q., Zhao, Z., Zhu-Salzman, K., Hogenhout, S.A., Ge, F., and Sun, Y.</strong> (2020). An Aphid-Secreted Salivary Protease Activates Plant Defense in Phloem. Curr Biol <strong>30, </strong>4826-4836.e4827.</p> <p><strong>Liu, Q., Goldberg, J.K., Mugford, S.T., Saalbach, G., Martins, C., Singh, A., Kaithakotti, G.G., Swarbreck, D., and Hogenhout, S.A.</strong> (2024). The salivary proteome of the green peach aphid/peach-potato aphid (Myzus persicae) (Sulzer, 1776) (Hemiptera, Aphididae) (Zenodo).</p> <p><strong>Mathers, T.C., Chen, Y., Kaithakottil, G., Legeai, F., Mugford, S.T., Baa-Puyoulet, P., Bretaudeau, A., Clavijo, B., Colella, S., Collin, O., Dalmay, T., Derrien, T., Feng, H., Gabaldón, T., Jordan, A., Julca, I., Kettles, G.J., Kowitwanich, K., Lavenier, D., Lenzi, P., Lopez-Gomollon, S., Loska, D., Mapleson, D., Maumus, F., Moxon, S., Price, D.R., Sugio, A., van Munster, M., Uzest, M., Waite, D., Jander, G., Tagu, D., Wilson, A.C., van Oosterhout, C., Swarbreck, D., and Hogenhout, S.A.</strong>(2017). Rapid transcriptional plasticity of duplicated gene clusters enables a clonally reproducing aphid to colonise diverse plant species. Genome Biol <strong>18, </strong>27.</p> <p><strong>Nelson, M.D., and Fitch, D.H.</strong> (2011). Overlap extension PCR: an efficient method for transgene construction. Methods Mol Biol <strong>772, </strong>459-470.</p> <p><strong>Ogawa, K., and Miura, T.</strong> (2014). Aphid polyphenisms: trans-generational developmental regulation through viviparity. Front Physiol <strong>5, </strong>1.</p>
Demonstrative simulations of L-PEACH: a computer-based model to understand how peach trees grow
<p>L-PEACH is a computer-based model that simulates source-sink interactions, architecture and physiology of peach trees (Allen et al., 2005, 2006, 2007). The model integrates important concepts related to water transport and carbon assimilation, distribution, and use within the tree (DeJong et al., 2011). L-PEACH is able to simulate crop yield responses to commercial practices such as fruit thinning (Lopez et al., 2008) and pruning (Smith et al., 2008) and could be useful for making fruit growers understand how to optimize these operations. In this work we present several demonstrative simulations of L-PEACH to complement the existing references about L-PEACH and demonstrate its value to study, understand and teach how trees grow (DeJong et al., 2008).</p> <p>The FIRST SIMULATION corresponds with the version of L-PEACH that runs on a daily time-step (L-PEACH-d) (Lopez et al., 2008, 2010). The simulation shows the growth of a peach tree over three years. The color of the stem indicates the direction of the movement of carbon within the tree (white indicates no flux of carbon, increasing apical flux of carbon from light yellow to red, and increasing basal flux of carbon from light blue to deep purple) (see details of colors in Allen et al., 2005). During this simulation the tree was stopped during the dormant season between years and the trees were pruned by the model operator in a manner that is similar to how trees would be pruned when growing in an orchard. Also during the first year of tree growth, grafting is simulated by cutting the tree back in early spring and allowing the tree to grow again as it would in a tree nursery. After this first year the tree is cut back to a single trunk in the same manner as is commonly done when a tree is transplanted from a tree nursery to a commercial fruit orchard.</p> <p>In the SECOND SIMULATION a detailed section of the tree was selected to better appreciate the realism of leaf and fruit growth and in the THIRD SIMULATION we show how to prune a peach tree to a V-system. Responses to pruning were modelled based on the concept of apical dominance as described in Smith et al. (2008) and Lopez et al. (2008).</p> <p>Subsequent simulations correspond to the last version of the L-PEACH model that includes a xylem circuit so that the diurnal water potential of each organ could be simulated along with its physiological functioning and growth. Sub-models for leaf transpiration, soil water potential and the soil-plant interface were also incorporated to provide the driving force and pathway for water flow. In the FOURTH SIMULATION we presented the effect of different irrigation treatments (control irrigation and drought irrigation) on tree development, growth and fruit yield (Da Silva et al., 2011; 2014). L-PEACH-h was also use to illustrate the effect of severity of pruning in tree growth (FIFTH SIMULATION). We tested three levels of pruning: soft, control, and hard. The simulation indicates how trees that received hard pruning are able to recover a similar tree size than control and soft pruned trees due to the generation of vigorous shoots in response to hard pruning.</p> <p>The SIXTH SIMULATION was generated to demonstrate that L-PEACH can be also used to simulate the effect of size-controlling rootstock in tree growth (Da Silva et al., 2015). In this simulation we compared tree growth with a standard rootstock (Control) and a size-controlling rootstock (Rootstock) by reducing the hydraulic conductance of the ‘rootstock” piece (base of the trunk) by 50% in the size-controlling rootstock to simulate a reduction in vessel diameters and consequently reduced hydraulic conductance in that part of the tree. After four years of simulated growth, the virtual tree on the dwarfing rootstock was substantially smaller than the virtual tree on the control rootstock.</p> <p>What you can’t see in the movies is that the L-PEACH model calculates the distribution of light in the tree canopy as the tree grows and the rate of photosynthesis in each leaf during a simulated day or hour (depending on whether the daily or hourly models are used for the simulation). Then the distribution and use of photo-assimilates are calculated by the methods described in the papers cited below. The simulations are based on real environmental input data (light, temperature, day length, etc. collected from a real weather station located near a peach orchard) and development of tree architecture is based on developmental principles governing tree growth and detailed measurements of shoots of peach trees (see references).</p> <p><em><strong>Description of files</strong></em></p> <p>Simulation 1: L-PEACH-d over three years of growth.</p> <p>Simulation 2: Detailed growth of leaves and fruit using L-PEACH.</p> <p>Simulation 3: Pruning L-PEACH-d to a v-system.</p> <p>Simulation 4: Control irrigation vs. Drought irrigation using L-PEACH-h.</p> <p>Simulation 5: Reactions to soft, control and hard pruning using L-PEACH-h.</p> <p>Simulation 6: Simulating the effect of size-controlling rootstock using L-PEACH-h.</p>
Dataset for "Computational prediction of structure, function and interaction of Myzus persicae (green peach aphid) salivary effector proteins "
Open the record for dataset details and reuse information.
Figure. 2 in First Report of the Peach Root-Knot Nematode, Meloidogyne floridensis Infecting Almond on Root-Knot Nematode Resistant´Hansen 536µ and´Brightµs Hybrid 5µ Rootstocks in California, USA
Figure. 2: Meloidogyne floridensis. (A–C) Anterior region of J2s; (D and E) Head region of males; (F–H) Posterior region of J2s; (I and J) Posterior region of males; (K) Lateral field of male; (L–N) Perineal patterns of females. Scale = 10 μm for A–J and 20 μm for L–N.
Figure. 1 in First Report of the Peach Root-Knot Nematode, Meloidogyne floridensis Infecting Almond on Root-Knot Nematode Resistant´Hansen 536µ and´Brightµs Hybrid 5µ Rootstocks in California, USA
Figure. 1: (A) Root galls on 'Hansen 536' rootstock of almond trees (Prunus dulcis) as scion; (B) Almond tree infected tree with Meloidogyne floridensis; (C) Almond tree, healthy.
Figure 1 in Release and Establishment of Encarsia diaspidicola (Hymenoptera: Aphelididae) Against White Peach Scale (Hemiptera: Diaspididae) in Papaya
Figure 1. Number of E. diaspidicola caught in individual yellow sticky traps in the Sibucao papaya field (Kapoho, Hawaii). The first releases were made in February 2013.
Figure 1 in Redetection and description of the European dagger nematode Xiphinema diversicaudatum on peach (Prunus persica L.) in Canada
Figure 1: Micrographs of Xiphinema diversicaudatum: A-C: female anterior region; D: female posterior region; E: male posterior region, F: male anterior region.
Figure 5 in Optimizing in vitro factors for improvement of shoot proliferation of 'Flordaguard' peach rootstock
Figure 5. Shoots proliferation from node explant of 'Flordaguard' peach rootstock cultured in semi-solid MS-½N (without PGR). A. Mean number of shoots per explant; B. number of elongated shoots; C. and mean length of shoots obtained in half strength MS (½N) – double-phase under different concentrations of BAP (0.5–3.0 mg L-1) for 'Flordaguard' peach rootstock.
Figure 4 in Optimizing in vitro factors for improvement of shoot proliferation of 'Flordaguard' peach rootstock
Figure 4. Shoots proliferation from node explant of 'Flordaguard' peach rootstock cultured on full strength MS medium. A. Growth and development of isolated shoot with symptoms of hyperhydricity; B. Growth and development of multiple shoot with shoot-tip necrosis (STN).
Figure 3 in Optimizing in vitro factors for improvement of shoot proliferation of 'Flordaguard' peach rootstock
Figure 3. Shoot proliferation in 'Flordaguard' peach rootstock. A. Induction of multiple shoot in explants cultured in half strength MS (½N) containing 1.0 mg L-1 of GA and 1.0 g L-1 of activated charcoal (double-phase medium); B. Growth and development of isolated shoots after 20 3 days in double-phase medium.
Fig. 1 in Phloeotribus pruni (Coleoptera: Scolytidae) associated with peach (Prunus persica) collapse in Coatepec de Harinas, State of Mexico, Mexico
Fig. 1. Peach (Prunus persica) collapse associated with the attack of Phloeotribus pruni. (A) Initial peach collapse symptom. (B) Adult lateral view. (C) Head front view and antennae detail. (D) Phloeotribus pruni adult dorsal view.
Fig 4 in Spatiotemporal distribution of stink bugs (Hemiptera: Pentatomidae) in peach orchards and surrounding habitat
Fig 4. Interpolated maps of Euschistus tristigmus adult distributions in peach orchards and surrounding habitat early season (wk 1), mid-season (wk 8), and late season (wk 12) in 2002 and 2003.
Fig. 2 in Spatiotemporal distribution of stink bugs (Hemiptera: Pentatomidae) in peach orchards and surrounding habitat
Fig. 2. Seasonal capture of Euschistus servus and Euschistus tristigmus in pheromone-baited traps; (A) mean number of E. servus and E. tristigmus per pheromonebaited trap in peach and non-crop habitat over time in 2002; (B) mean number of E. servus and E. tristigmus per pheromone-baited trap in peach and surrounding habitat over time in 2003.
Fig 3 in Spatiotemporal distribution of stink bugs (Hemiptera: Pentatomidae) in peach orchards and surrounding habitat
Fig 3. Interpolated maps of Euschistus servus adult distributions in peach orchards and surrounding non-crop habitat early season (wk 1), mid-season (wk 8), and late season (wk 12) in 2002 and 2003.
Fig. 1 in Spatiotemporal distribution of stink bugs (Hemiptera: Pentatomidae) in peach orchards and surrounding habitat
Fig. 1. Spatiotemporal distribution patterns of Euschistus servus and Euschistus tristigmus: (A) number of traps in which E. servus and E. tristigmus were present in peach and surrounding habitat; (B) number of traps in which stink bugs were present over the season in peach and surrounding habitat.
Fig. 2 in Nocturnal vs. diurnal pollination of self-fertile peaches and muscadine grapes
Fig. 2. Moth visitors to Redhaven peaches: (A) Mythimna unipunctata; (B) Peridroma saucia – note the blurred wings as an indication of vibration; (C) P. saucia (3×); (D) P. saucia (4×).
Fig. 2 in A physical barrier reduces capture of Euschistus servus (Hemiptera: Pentatomidae) in pheromone-baited traps near peach trees
Fig. 2. Capture of adult Euschistus servus in pheromone-baited traps placed near peach trees when the trap occurred within the enclosure formed by a 3.7-m-tall physical barrier around the peach tree, or when there was no barrier around the tree: (A) 2014 and (B) 2015.
Fig. 1. A 3.7 in A physical barrier reduces capture of Euschistus servus (Hemiptera: Pentatomidae) in pheromone-baited traps near peach trees
Fig. 1. A 3.7 × 3.7 × 3.7 m fence was erected around a peach tree to determine if this physical barrier would reduce adult Euschistus servus from reaching a pheromone-baited trap placed inside the enclosure near the tree.
Figures 1–6 in Parasitoids (Hymenoptera: Chalcidoidea) of the white peach scale, Pseudaulacaspis pentagona (Targioni-Tozzetti) (Hemiptera: Diaspididae) on Prunus salicina Lindl. (Rosales: Rosaceae) in South Korea
Figures 1–6. Five species of parasitoids associated with Pseudaulacaspis pentagona (Targioni-Tozzetti) on Japanese plum trees in South Korea. 1) Parasitoid emergence holes in scale covers of P. pentagona (white peach scale). 2) Aphytis proclia (Walker), female. 3) Encarsia berlesei (Howard), female. 4) Marietta carnesi (Howard), female. 5) Pteroptrix orientalis (Silvestri), female. 6) Arrhenophagus chionaspidis Aurivillius, female.
The salivary proteome of the green peach aphid/peach-potato aphid (Myzus persicae) (Sulzer, 1776) (Hemiptera, Aphididae).
<p><strong>*for correspondence: </strong>saskia.hogenhout@jic.ac.uk</p> <p> </p> <p><strong>Introduction</strong><strong> </strong></p> <p>The green peach aphid/peach-potato aphid <em>Myzus persicae</em> colonizes hundreds of plant species, an ability that is in part due to the delivery of saliva proteins – often referred to as effectors – into the host plant that suppress plant defence. As a generalist herbivore with a remarkable ability to colonize new host plants (Dedryver et al., 2010), <em>M. persicae</em> represents an outstanding model system for studying the molecular mechanisms underlying plant-insect interactions.</p> <p>Recent advancements in mass spectrometry instrumentation (Yu et al. 2020) and database search software (Frejno et al., 2024), along with a new high-quality reference genome assembly for <em>M. persicae</em> (Mathers et al., 2021) and a simplified method for improved aphid saliva recovery that we describe here, collectively enhance the detection of saliva proteins with unprecedented sensitivity and specificity.</p> <p>Here, we present a high-quality salivary proteome of <em>M. persicae </em>generated using a nanoLC-MS/MS analysis in combination with an updated annotation of the <em>Myzus persicae </em>clone O genome.</p> <p>Saliva from over 10,000 <em>M. persicae</em> aphids was collected and analysed using nanoLC-MS/MS (Figure 1). We identified 1557 peptide sequences mapped to the <em>M. persicae</em> clone O genome v2.1 annotation (Table 1); 210 of those peptides additionally appeared as modified by oxidation (M) and/or carbamidomethylation (C) so that a total of 1767 peptide forms mapping to <em>M. persicae</em> were detected with high confidence (combined from both search engines). Of those peptides, 120 were only identified by the CHIMERYS search engine, and 52 were only identified by the Mascot search engine (all with high confidence). Most peptides were detected in the concentrated sample; of the 1767 peptide forms, only 411 were detected in the unconcentrated sample and only 10 of those were exclusively identified in the unconcentrated sample.</p> <p>Those peptides were assigned to a total of 423 <em>M. persicae</em> proteins with high confidence and at least 1 unique peptide including all proteins with shared peptides. The software generated 169 protein groups each represented by one master protein. The master protein is the largest protein with the most peptide matches in the group. Of the 169 protein groups, 126 groups were identified with at least 2 unique peptides and 43 groups were identified with only 1 unique peptide. Protein groups that included products of a single gene model were identified from the peptide output of Proteome Discoverer. The 169 protein groups included proteins encoded by 219 gene models which are listed in Table 3, of which 155 had peptides that did not match to any other gene model.</p> <p>Of note, 8 Cathepsin B cysteine peptidases are listed (highlighted in Table 3) which have previously been shown to play an important role in colonisation of host plants (Mathers et al. 2017). This is a recently diversified gene family including 28 members in the <em>Myzus persicae </em>genome. The detected proteins belong to several protein groups including highly similar or identical proteins with shared peptides detected, and also include CathB17 (MYZPE13164_O_EIv2.1_0124700) which was excluded from the database because it is identical to CathB18 (MYZPE13164_O_EIv2.1_0124690)</p> <p>Together, these findings suggest that aphid saliva contains enzymes that likely alter plant physiology by interacting with both plant proteins and small molecules. Future mechanistic studies will be able to precisely characterize the role of these salivary proteins to understand the pathways, hormones, and chemical defences that may be suppressed by aphids.</p> <p> </p> <p><strong>Materials and Methods</strong></p> <p><strong><em>Aphid rearing</em></strong></p> <p><em>M. pers</em>icae Clone O colonies were reared on <em>Arabidopsis thaliana</em> Col-0 plants in a growth chamber maintained at 20°C with a 14-hour light/10-hour dark cycle and 75% relative humidity. The <em>A. thaliana</em> plants were grown at short-day conditions (10 hours light/14 hours dark) at 22°C and 70% relative humidity. For aphid rearing, 4-week-old <em>A. thaliana</em> plants were used, and plants were replaced every two weeks to ensure optimal conditions for aphid growth. </p> <p><strong><em>Saliva collection</em></strong></p> <p>Figure 1 illustrates the schematic overview of our sample collection and preparation workflow. <em>M. persicae</em> Clone O aphids were transferred to 4-week-old <em>A. thaliana</em> plants and maintained for 2-3 weeks to enable reproduction. Approximately 100 aphids were then placed into a 50 mm Petri dish, which was sealed with parafilm and had a hole in the base for introducing the aphids. The hole was subsequently covered with mesh to allow ventilation while preventing aphid escape. A 300 μL aliquot of artificial diet (15% w/v sucrose in Milli-Q water, sterilized via 0.22 μm filtration) was added to the inverted lid of the Petri dish. The dish, with the aphid chamber positioned over the lid, was set up so that the parafilm made contact with the artificial diet. This setup was kept under short-day conditions (14 hours light/10 hours dark) at 20°C and 75% relative humidity for 24 hours.</p> <p>After 24 hours, the artificial diet, now containing aphid saliva, was collected and pooled to produce an unconcentrated saliva sample. A portion of this sample was then concentrated using a Vivaspin concentrator with a 3 kDa molecular weight cut-off (MWCO) at 4°C. The concentrated saliva was snap-frozen in liquid nitrogen and stored at -80°C until further analysis.</p> <p>This procedure was repeated until saliva was collected from approximately 10,000 aphids.</p> <p> </p> <p><strong><em>Saliva preparation and nanoLC-MS/MS analysis</em></strong></p> <p>Saliva samples were precipitated by adding 4 volumes of methanol and 1 volume of chloroform, followed by centrifugation at maximum speed for 10 minutes (Wessel and Flügge, 1984). After removing the supernatant, the pellet was washed once with acetone before proceeding to trypsin digestion. The protein pellet was resuspended in 50 µL of 1.5% sodium deoxycholate (SDC; Merck) in 0.2 M EPPS buffer (Merck), pH 8.5, and vortexed under heating. Cysteine residues were reduced with dithiothreitol, alkylated with iodoacetamide, and proteins were digested with trypsin in the SDC buffer following standard protocols.</p> <p>After digestion, SDC was precipitated by adjusting the solution to 0.2% trifluoroacetic acid (TFA). The clear supernatant was then subjected to C18 solid-phase extraction (SPE) using OMIX 10-100 μL C18 pipette tips (Agilent).</p> <p>The samples were analysed using nanoLC-MS/MS on an Orbitrap Eclipse™ Tribrid™ mass spectrometer, coupled with an UltiMate® 3000 RSLCnano LC system (Thermo Fisher Scientific, Hemel Hempstead, UK). Samples were loaded onto a trap column (nanoEase M/Z Symmetry C18 Trap Column, Waters) with 0.1% TFA at a flow rate of 15 µL/min for 3 minutes. The trap column was then switched in-line with the analytical column (nanoEase M/Z HSS C18 T3, 1.8 µm, 100 Å, 250 mm x 0.75 µm, Waters) for separation at 40°C. The gradient used for separation was as follows: solvent A (water with 0.1% formic acid) and solvent B (80% acetonitrile with 0.1% formic acid) at a flow rate of 0.2 µL/min: 0-3 minutes at 3% B (parallel to trapping); 3-10 minutes with B increasing to 8%; 10-130 minutes with B linearly increasing to 45%; 130-145 minutes with B linearly increasing to 55%; followed by a ramp to 99% B and re-equilibration to 0% B, for a total runtime of 180 minutes.</p> <p>Mass spectrometry data were acquired in positive ion mode with the following settings: Orbitrap resolution at 120K, profile mode, mass range m/z 300-1800, normalized AGC target at 100%, and a maximum injection time of 50 ms. For MS2 analysis in IT Turbo mode, parameters included quadrupole isolation window of 1.2 Da, charge states 2-5, threshold at 1.9e4, HCD CE and CID CE both set to 33 in parallel, AGC target at 1e4, maximum injection time of 35 ms, and dynamic exclusion set to 1 count for 15 seconds with a mass tolerance of ±7 ppm.</p> <p> </p> <p><em><strong>M. persicae v2.1 annotation</strong></em></p> <p>The chromosome scale genome assembly of <em>M. persicae </em>clone O (Mathers et al. 2021) was re-annotated for accurate gene prediction as follows. Illumina short read RNAseq data from <em>M. persicae</em> used for previous annotation (Mathers et al. 2017; EBI ENA SAMEA4469192) was used in addition to stranded RNAseq reads of <em>M. persicae </em>clone O<em> </em>feeding from 9 different host plant species (Chen et al. 2020 ; NCBI GEO GSE129669); from males, alate asexual females and winged asexual females, and nymphs (Mathers et al. 2019; NCBI SRA PRJNA437622); dissected organs from winged and alate asexual female <em>M. persicae</em> (EBI ENA PRJEB79119<a>)</a><em>, </em>and PacBio Isoseq RNAseq data from asexual female <em>M. persicae</em> (EBI ENA PRJEB79119<a>).</a></p> <p>Candidate transcript sequences were assembled from RNA-seq reads with Scallop (Shao and Kingsford 2017) and StringTie (Pertea et al. 2015) using a genome guided approach. A filtered set of non-redundant transcripts are derived using Mikado (Venturini et al. 2018) for the final transcript set for annotation. Mikado models together with aligned proteins and repeat annotation are provided as hints to Augustus (<a href="http://bioinf.uni-greifswald.de/augustus/">http://bioinf.uni-greifswald.de/augustus/</a>). Multiple Augustus gene builds were created from alternative evidence inputs or weightings. These were supplemented with gene models derived directly from protein alignments and high confidence models from the Mikado transcript selection stage. Metrics were generated to assess how well supported each gene model is by available evidence and an integrated set of models produced by Mikado. </p> <p>Long non-coding (lnc) RNAs were identified from the assembled RNAseq. Transcripts with open reading frames (Transdecoder, <a href="https://transdecoder.github.io">https://transdecoder.github.io</a>) showing similarity to arthropod protein coding genes (BlastP e<1e-5), or with HAMMER hits against the Pfam database were excluded. Remaining transcripts with coding potential >0.5 (CPC2, Kang et al. 2017) or that were shorter than 200bp were also excluded. Transcripts mapping to rRNA, tRNA, miRNA or transposon loci were excluded using Mikado (Venturini et al. 2018). </p> <p>In total we identified 37,720 total genes (with 58,609 total splice variant isoforms), including 22,796 (47,508 total isoforms encoding 39,681 unique proteins) protein coding and 7,990 (11,101 total isoforms) non-coding genes. (Further details of the annotation process and statistics can be found in CloneO_v2.1_annotation_summary_stats.txt and Myzus_persicae_O_annotation_readme.doc).</p> <p> </p> <p><strong><em>Mass spectrometry</em> <em>data processing</em></strong></p> <p>The mass spectrometry raw data were processed and quantified in Proteome Discoverer 3.1 (Thermo), all mentioned tools of the following workflows are nodes of the proprietary Proteome Discoverer (PD) software. The database search was performed using the search engines CHIMERYS (MSAID, Munich, Germany) and Mascot Server 2.8.3 (Matrixscience, London) in parallel on the following databases: MYZPE13164_O_EIv2.1.annotation.gff3.pep.fasta (39,681 entries after removal of duplicate protein sequences) and common contaminants (MaxQuant.org, 20240812, 246 entries). The databases were imported into PD adding a reversed sequence database for decoy searches. The processing workflow started with spectrum recalibration on the <em>Myzus</em> protein database, Minora Feature Detection with min. trace length 7, S/N 2.5, PSM confidence high, and Top N Peak Filter with 20 peaks per 100 Da. For CHIMERYS, the inferys_3.0.0_fragmentation prediction model with FDR targets 0.01 (strict) and 0.05 (relaxed), a fragment tolerance of 0.3 Da, enzyme trypsin with 2 missed cleavages, variable modification oxidation (M), fixed modification carbamidomethyl (C) were used. For Mascot, the same parameters were used including a precursor tolerance of 5 ppm and a fragment tolerance of 0.5 Da; validation was performed using Percolator based on q-values and FDR targets 0.01 (strict) and 0.05 (relaxed).</p> <p>The consensus workflow in the PD software was used to evaluate the peptide identifications and to measure the abundances of the peptides based on the LC-peak intensities. For identification, an FDR of 0.01 was used as strict threshold. Protein abundance was calculated using the Top3 most abundant peptides. The results were exported into Microsoft Excel including data for protein abundances, number of peptides, protein coverage, the search identification score and other important values (Tables 1 and 2). Identification of protein groups with members encoded by a single gene model was performed by first identifying peptides that mapped to a single gene model, then counting the number of peptides that were specific to each gene model.</p> <p> </p> <p><strong>Data availability statement</strong></p> <p>The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE (<a href="https://www.ebi.ac.uk/pride/">https://www.ebi.ac.uk/pride/</a>) partner repository with the dataset identifier PXD055051 and 10.6019/PXD055051.</p> <p><strong>Acknowledgements</strong></p> <p>We would like to thank the Informatics, and Entomology technology platforms at the John Innes Centre for technical support.</p> <p><strong>Conflicts of Interest</strong></p> <p><strong>T</strong>he authors declare that no conflicts of interest exist.</p> <p><strong>Funding Information</strong></p> <p>This work was funded by UK Research and Innovation (UKRI) Biotechnology and Biological Sciences Research Council (BBSRC) grants to S.A.H. (BB/V008544/1 and BB/R009481/1). Additional Support as provided by the BBSRC Institute Strategy Programmes (BBS/E/J/000PR9797 and BBS/E/JI/230001B) awarded to the John Innes Centre (JIC). The JIC is grant-aided by the John Innes Foundation.</p> <p> </p> <p><strong>Figures and legends</strong></p> <p><strong>Fig. 1.</strong> Experimental procedure for detecting <em>M. persicae</em> secretome. (A) The workflow of <em>M. persicae</em> saliva collection and concentration for nanoLC-MS/MS. (B) The diagrammatic representation of mass spectrometry data analysis of <em>M. persicae </em>saliva.</p> <p><strong>Table 1. Full list of detected peptides.</strong></p> <p>List of peptides detected in <em>M. persicae </em>saliva from Mascot and Chimerys searches of the <em>M. persicae </em>clone O v2.1 database.</p> <p><strong>Table 2. Full list of proteins detected</strong></p> <p>List of proteins detected in <em>M. persicae </em>saliva. Closely related proteins with shared peptides are grouped into protein groups by Proteome Discoverer, the highest confidence of these is the Master protein of the group. Master proteins of the 169 protein groups are indicated in the ‘Master’ column as ‘Master protein’, other proteins belonging to these groups have shared peptides. Proteins belonging to groups containing only different isoforms (i.e. splice variants) from the same gene model are indicating by the number of peptides that specifically match that gene model</p> <p><strong>Table 3. Annotated list of unique gene models representing detected proteins.</strong></p> <p>Two hundred and nineteen (219) gene models that encode at least one protein detected in the <em>M. persicae saliva. </em>For each gene model, the highest confidence detected protein is shown: either a master protein of the protein group, or else the longest isoform of that protein. Annotation is derived from Interproscan, including descriptions from pfam, GO and BlastP hits, and include the identities of candidate effector proteins (e.g. Mp1, Mp2) identified in Bos et al. (2010). Cathepsin B proteins are highlighted in green.</p> <p> <strong> </strong></p> <p><strong>Supplementary files:</strong></p> <p><strong>Genome annotation files:</strong></p> <p>Myzus_persicae_O_v2.0.scaffolds.fa (as described in Mathers et al. 2021)</p> <p>CloneO_v2.1_annotation_summary_stats.txt</p> <p>MYZPE13164_O_EIv2.1.annotation.gff3</p> <p>MYZPE13164_O_EIv2.1.annotation_w.functions.gff3</p> <p>MYZPE13164_O_EIv2.1.annotation.gff3.cdna.fasta</p> <p>MYZPE13164_O_EIv2.1.annotation.gff3.cds.fasta</p> <p>MYZPE13164_O_EIv2.1.annotation.gff3.metrics.txt</p> <p>MYZPE13164_O_EIv2.1.annotation.gff3.pep.fasta</p> <p>MYZPE13164_O_EIv2.1.annotation.gff3.cdna.LTPG.fasta</p> <p>MYZPE13164_O_EIv2.1.annotation.gff3.cds.LTPG.fasta</p> <p>MYZPE13164_O_EIv2.1.annotation.gff3.pep.LTPG.fasta</p> <p>Myzus_persicae_O_annotation_readme.doc</p> <p><strong> </strong></p> <p><strong>Literature Cited</strong></p> <p><strong>Chen Y, Singh A, Kaithakottil GG, Mathers TC, Gravino M, Mugford ST, van Oosterhout C, Swarbreck D, Hogenhout SA.</strong> (2020). An aphid RNA transcript migrates systemically within plants and is a virulence factor. Proc Natl Acad Sci U S A. 117(23):12763-12771. doi: 10.1073/pnas.1918410117.</p> <p><strong>Dedryver, C.A., Le Ralec, A., and Fabre, F.</strong> (2010). The conflicting relationships between aphids and men: a review of aphid damage and control strategies. C R Biol <strong>333, </strong>539-553.</p> <p><strong>Frejno, M., Berger, M.T., Tüshaus, J., … and Wilhelm, M.</strong> (2024). Unifying the analysis of bottom-up proteomics data with CHIMERYS. bioRxiv 2024.05.27.596040; doi: https://doi.org/10.1101/2024.05.27.596040</p> <p><strong>Kang, Y. J., Yang, D. C., Kong, L., Hou, M., Meng, Y. Q., Wei L., and Gao, G.</strong> (2017). CPC2: a fast and accurate coding potential calculator based on sequence intrinsic features. Nucleic Acids Research 45, W12–W16.</p> <p><strong>Mathers, T.C., Chen, Y., Kaithakottil, G</strong>.<em>, …</em> <strong>Hogenhout, S.A. </strong>(2017)<em>.</em> Rapid transcriptional plasticity of duplicated gene clusters enables a clonally reproducing aphid to colonise diverse plant species. <em>Genome Biol</em> <strong>18</strong>, 27. https://doi.org/10.1186/s13059-016-1145-3</p> <p><strong>Mathers TC, Mugford ST, Percival-Alwyn L, Chen Y, Kaithakottil G, Swarbreck D, Hogenhout SA, van Oosterhout C.</strong> (2020) Sex-specific changes in the aphid DNA methylation landscape. Mol Ecol. 28(18):4228-4241. doi: 10.1111/mec.15216.</p> <p><strong>Mathers, T.C., Wouters, R.H.M., Mugford, S.T., Swarbreck, D., van Oosterhout, C., Hogenhout, S.A.</strong> (2021). Chromosome-Scale Genome Assemblies of Aphids Reveal Extensively Rearranged Autosomes and Long-Term Conservation of the X Chromosome, <em>Molecular Biology and Evolution</em> <strong>38</strong>, 856–875.</p> <p><strong>Perez-Riverol Y, Bai J, Bandla C, …Vizcaíno JA</strong> (2022). The PRIDE database resources in 2022: A Hub for mass spectrometry-based proteomics evidences. Nucleic Acids Res 50(D1):D543-D552 (PubMed ID: 34723319).</p> <p><strong>Pertea, M., Pertea, G.M., Antonescu, C.M., Chang, T.C., Mendell, J.T. and Salzberg, S.L.</strong> (2015). StringTie enables improved reconstruction of a transcriptome from RNA-seq reads Nature Biotechnology 33,290-295. doi:10.1038/nbt.3122</p> <p><strong>Shao, M., Kingsford, C</strong>. (2017). Accurate assembly of transcripts through phase-preserving graph decomposition. Nat Biotechnol 35, 1167–1169 (2017). https://doi.org/10.1038/nbt.4020</p> <p><strong>Venturini, L., Caim, S., Kaithakottil, G.G., Mapleson, D.L., and Swarbreck, D.</strong> (2018). Leveraging multiple transcriptome assembly methods for improved gene structure annotation, GigaScience7giy093 https://doi.org/10.1093/gigascience/giy093</p> <p><strong>Wessel, D., and Flügge, U.I.</strong> (1984). A method for the quantitative recovery of protein in dilute solution in the presence of detergents and lipids. Analytical Biochemistry <strong>138, </strong>141-143.</p> <p><strong>Yu, Q., Paulo, J.A., Naverrete-Perea, J., McAlister, G.C., … and Gygi, S.P., and Schweppe, D.K. </strong>(2020). Benchmarking the Orbitrap Tribrid Eclipse for Next Generation Multiplexed Proteomics. <em>Analytical Chemistry</em> <strong>2020</strong> <em>92</em>, 6478-6485. DOI: 10.1021/acs.analchem.9b05685</p> <p> </p> <p> </p> <p> </p>
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