Comparisons of protein profiles of beech bark disease resistant and susceptible American beech (Fagus grandifolia)

  • Mary E Mason1,

    Affiliated with

    • Jennifer L Koch2Email author,

      Affiliated with

      • Marek Krasowski3 and

        Affiliated with

        • Judy Loo4, 5

          Affiliated with

          Proteome Science201311:2

          DOI: 10.1186/1477-5956-11-2

          Received: 9 May 2012

          Accepted: 23 December 2012

          Published: 14 January 2013

          Abstract

          Background

          Beech bark disease is an insect-fungus complex that damages and often kills American beech trees and has major ecological and economic impacts on forests of the northeastern United States and southeastern Canadian forests. The disease begins when exotic beech scale insects feed on the bark of trees, and is followed by infection of damaged bark tissues by one of the Neonectria species of fungi. Proteomic analysis was conducted of beech bark proteins from diseased trees and healthy trees in areas heavily infested with beech bark disease. All of the diseased trees had signs of Neonectria infection such as cankers or fruiting bodies. In previous tests reported elsewhere, all of the diseased trees were demonstrated to be susceptible to the scale insect and all of the healthy trees were demonstrated to be resistant to the scale insect. Sixteen trees were sampled from eight geographically isolated stands, the sample consisting of 10 healthy (scale-resistant) and 6 diseased/infested (scale-susceptible) trees.

          Results

          Proteins were extracted from each tree and analysed in triplicate by isoelectric focusing followed by denaturing gel electrophoresis. Gels were stained and protein spots identified and intensity quantified, then a statistical model was fit to identify significant differences between trees. A subset of BBD differential proteins were analysed by mass spectrometry and matched to known protein sequences for identification. Identified proteins had homology to stress, insect, and pathogen related proteins in other plant systems. Protein spots significantly different in diseased and healthy trees having no stand or disease-by-stand interaction effects were identified.

          Conclusions

          Further study of these proteins should help to understand processes critical to resistance to beech bark disease and to develop biomarkers for use in tree breeding programs and for the selection of resistant trees prior to or in early stages of BBD development in stands. Early identification of resistant trees (prior to the full disease development in an area) will allow forest management through the removal of susceptible trees and their root-sprouts prior to the onset of disease, allowing management and mitigation of costs, economic impact, and impacts on ecological systems and services.

          Keywords

          Beech bark disease Beech scale Disease resistance Insect resistance Fagus Cryptococcus Neonectria

          Background

          Beech bark disease (BBD) is an insect-fungus complex that has been killing American beech (Fagus grandifolia Ehrh.) trees since the accidental introduction of the beech scale insect (Cryptococcus fagisuga Lind.) to Canada around 1890 [1, 2]. The first phase of BBD is beech scale insect infestation resulting in the production of small fissures in the bark [1]. The fungal component, either Neonectria ditissima Samuels & Rossman or Neonectria faginata Castlebury then infects these fissures causing extensive tissue damage. Mortality in the first wave of the disease can be as high as 50 % [3], with consequent loss to stand health, merchantable timber, and many wildlife and ecosystem services.

          An estimated 1% of American beech trees remain disease free in forests long-affected by beech bark disease [4]. Insect challenge experiments have demonstrated that resistance is to the beech scale portion of the disease complex [4, 5]. Although it has been reported that scale infestation without Neonectria infection may play a role in mortality events [6], there has been no documentation of Neonectria infection leading to widespread stand or landscape level mortality in the absence of prior scale infestation. In aftermath forests, where scale populations have declined, presumably due to loss or reduced quality of habitat [7] and environmental factors [8], the population dynamics of scale and Neonectria are no longer directly correlated [7, 9]. However, in these cases it is suggested that the lower density of scale is still sufficiently high that infection sites (scale-feeding wounds) are not limiting and once the fungus has established in the tree it is no longer influenced by fluctuations in scale density [7]. Therefore, the focus of recent breeding and tree improvement efforts has been on resistance to the scale insect [5, 10]. Current management approaches are based on the objective of increasing the proportion of disease resistant beech by removing susceptible trees along with any resulting root and stump sprouts, retaining the disease-free trees prior to the height of BBD development [11, 12] and supplementing with genetically scale-resistant seedling plantings once such materials are available (management plans of US Forest Service Allegheny National Forest [13] and Michigan Dept. of Natural Resources [14]). However, it is impossible to identify the most resistant beech trees until the scale infestation is heavy, at which point economic losses have already occurred and management operations are more complicated and expensive. Identification of a biomarker for Cryptococcus resistance would provide land managers the opportunity to begin management operations before the economic and ecological losses have occurred, and to spread BBD management activities over several budget cycles. A biomarker for resistance may also be utilized to expedite the breeding and selection process.

          Bark protein differences are likely to be a good source for biomarker candidates. The scale insect feeds in the tissue layers vernacularly known as bark (cork, cork cambium, phloem, and cambium) [1]. Wargo et al. [15] found significant differences in bark amino acid concentrations and total amino nitrogen in different seasons and scale infestation levels. These results suggest bark protein content may be important in the insect-tree interaction, and that bark protein profiles may differ between scale-resistant and susceptible beech trees. Therefore differentially expressed bark proteins may be reliable biomarkers of resistance to beech scale in American beech. One method to identify differentially expressed bark proteins is to examine the proteome of a number of trees using two dimensional electrophoresis (2-DE) gels. A proteomics approach allows the examination and quantification of large numbers of proteins anonymously and simultaneously. Typically, 2-DE analysis is limited to two-sample comparison with simple experimental structure (e.g. one treatment). Utilization of analysis of variance (ANOVA) for statistical analysis allows the testing of three or more treatment levels for several technical and biological factors in one model, and supports unbalanced experimental and sampling designs. This more sophisticated analysis allows the identification of more complex protein quantity patterns and the interactions of factors in protein quantity. In this study we employ the use of 2-DE gel based proteomics and ANOVA to identify proteins in the bark of American beech that are different between healthy and BBD diseased trees, while also considering if the BBD effect is present alone or with a stand effect or interaction between stand and BBD effects. Although the healthy trees in this study are known to be resistant to the scale insect, the diseased trees are susceptible to both scale infestation (Table 1), and had symptoms of an active fungal infection at the time of tissue collection. Proteins that are expressed in response to the scale insect cannot be distinguished from proteins expressed in response to the fungal pathogen in diseased trees so in our analysis we refer to the more general BBD response which includes responses to both. This approach allows selection of proteins for further study that are most likely to be broadly linked to BBD response rather than different in protein quantity due to the relatedness of trees within stands.
          Table 1

          Tree information including stand, field disease score, controlled test insect challenge result, and spot quantities

          Tree name

          Stand code a

          Disease Condition b

          Scale Interaction c

          Total protein spots

          Number of matched spots

          Number of unique spots

          1504

          LM

          H

          R (8)

          382

          295

          87

          1506

          LM

          D

          S (3)

          197

          184

          13

          CL01d

          CL

          H

          R (10)

          258

          241

          17

          CM01

          CM

          H

          R (1)

          280

          270

          10

          CM02Dd

          CM

          D

          S(11)

          329

          329

          0

          DT02d

          DT

          H

          R (2)

          228

          215

          13

          DT02D

          DT

          D

          not tested

          286

          266

          20

          DTOAd

          DT

          H

          R (2)

          522

          421

          101

          JC01d

          JC

          H

          R (14)

          367

          338

          29

          JC02D

          JC

          D

          S (4)

          487

          408

          79

          OD02d

          OD

          H

          R (10)

          284

          272

          12

          OD08Dd

          OD

          D

          S (12)

          239

          225

          14

          RS01d

          RS

          H

          R (9)

          288

          265

          23

          RS02D

          RS

          D

          S (7)

          229

          223

          6

          SL01d

          SL

          H

          R (4)

          249

          240

          15

          SL03d

          SL

          H

          R (8)

          260

          243

          17

          aLM=Ludington, MI, USA; CL= Charlie Lake, NB, Can; CM= Curry Mountain, NB, Can; DT= Roachville, NB, Can; JC=Highfield, NB, Can; OD= Odell Park, Fredericton, NB, Can; RS= Regent St., Fredericton, NB, Can; SL=Spednick Lake, NB, Can.

          bH=Healthy (lacking scale infestation and signs of fungal infection), D= Diseased (beech bark disease present both scale infestation and fungal infection observed (on ortet) in the field).

          cTest result of artificial inoculation of grafted ramets with scale insect eggs. Result (number of ramets tested, i.e. replicates). R= Resistant, 0–5 avg. live scale, S= Susceptible, 30–100 avg. live scale and expected to sustain scale population and subsequent fungal infection.

          dCorrespondence to Ramirez et al. [16] CL01=E1, CM02d=H1, DT02=B3, DT0A=B1, JC01=C1, OD02=A2, OD08d=A3, RS01=I1, SL01=D1, and SL03=D3.

          Results

          Individual tree analysis and spot matching

          The location, field disease score, and the artificial infestation (scale resistant/scale susceptible) results for the ten healthy trees and six diseased trees studied are summarized in Table 1. Artificial infestation of grafted ramets of the healthy trees demonstrated that all of these genotypes are resistant to the scale insect, the details of these experiments are reported elsewhere [16]. Protein was extracted and 2-DE was conducted for three technical replicates per tree. Figure 1 shows a randomly selected experimental gel to illustrate the typical resolution and spot density we achieved in the experiment. PDQuest was used to create a master gel for each tree and the number of protein spots per tree ranged from 197 to 522 with an average of 305.3 and standard error of 23.
          http://static-content.springer.com/image/art%3A10.1186%2F1477-5956-11-2/MediaObjects/12953_2012_Article_405_Fig1_HTML.jpg
          Figure 1

          Raw gel image of one randomly selected gel. Gel image from tree DT02, shown to illustrate the number of spots and quality of separation typical of the experimental gels. The gel has been cropped to remove gel edge and markers, but include both pI and electrophoresis fronts.

          An experiment-wide master gel was constructed using the 'compare experiments' function of PDQuest where each individual tree master was considered an 'experiment'. The experiment wide master gel (Figure 2) included all spots on individual tree masters that were added to the experiment wide master because they were present in two or more trees. Most, but not all, of these spots were present in more than two trees, and some spots were present in all trees. The number of matched and unique spots for each tree is listed in Table 1. The total number of spots added to the experiment wide master (matched in two or more trees) was 531. Matched spot per tree ranged from 184 to 421 and average 277.2 +/− 16.7. Unique spots per tree (spots in the tree master not matched and added to the experiment wide master) range from 0 (base tree CM02d has all spots on the master gel by definition) to 101 with an average of 28.5 +/− 7.75. The grand total of spots identified is 987 which is the sum of the 531 matched spots plus 456 spots unique to only one tree. Spots unique to a single tree were excluded from further analysis. While unique spots in aggregate are 46% of total spots, a maximum of 22.7% of the spots in any one tree are unique indicating that the spot matching of trees to the experiment wide master was efficient.
          http://static-content.springer.com/image/art%3A10.1186%2F1477-5956-11-2/MediaObjects/12953_2012_Article_405_Fig2_HTML.jpg
          Figure 2

          Global master gel showing the Gaussian spots from all experimental gels. Spots identified as having a significant BBD effect are marked and numbered, with a blue circle for spots that are higher in healthy trees (compared to BBD diseased trees), and a green box for spots that are lower in healthy trees . The image is a composite of the full 48 gels so weaker spots may not be visible in the image (but are present and can be seen at higher magnification and contrast).

          To consider whether these unique spots could be artifacts due to low spot intensity, four trees were selected at random and the spot intensity distribution examined. Graphing showed that the distribution of unique spots was slightly biased toward lighter spots. However, comparison of summary statistics (mean, min, Q1, mean, Q3, max) illustrates that spots of similar intensity are both matched and unmatched, and the faintest spot is matched in three of the four trees (data not shown). This indicates that the spots unique to each tree are not artifacts of poor matching related to the intensity of the spots or variances in protein quantification. Spot quantities for all matched spots in all trees were exported for analysis (missing spots were estimated) in order to use more robust statistical procedures than PDQuest allows.

          Constitutive proteins and technical effects

          General linear models were fitted to assess whether technical effects (gel and batch) were significant or could be ignored, and to identify constitutive proteins (spot quantity equal in all individual trees, tested by fitting a tree effect). Technical effects were significant for only six spots and these spots were dropped from further analysis, allowing technical effects to be dropped from later models. The biological effects of interest are stand and disease state, and constitutive spots will obviously not vary for these effects. Therefore the 103 constitutive spots (tree effect not significant) were removed from the dataset to reduce its size for more efficient analysis (leaving 422 spots that had a significantly different mean spot quantity in at least one tree and no significant technical effects).

          Stand and disease condition effects

          General linear models were fitted to test for disease condition effect (BBD), stand (STAND) and the disease condition by stand interaction (INT) for the retained spots. The count of spots significant for different combinations of effects is shown in Table 2. The largest class of proteins contains those that are quantitatively different in several trees (demonstrated in the first model, but not significant for any tested effects (209 spots)). Protein spots with a significant STAND effect but no significance for BBD (61 spots of which 28 have a significant interaction and 33 do not) are interesting as potential markers for geographic variation in beech. These spots and an additional 32 spots with only the interaction effect found significant were not studied further in this experiment.
          Table 2

          Significant protein counts

           

          Stand x BBD Interaction

           

          Significant

          Not Significant

          STAND effect

          Significant

          Not Sig.

          Significant

          Not Sig.

          BBD Effect Significant

          47

          9

          14

          50

          BBD Effect Not Significant

          28

          32

          33

          209

          Count of proteins with significant effects for stand, beech bark disease state, and stand x disease state interaction. The most likely biomarker candidates are the protein spots with a significant BBD effect in the absence of stand effects and interactions. To see the correspondence to Figure 3 may require summing across cells, for example the 56 spots where the interaction and BBD effect are significant is the sum of the two stand levels, 47 plus nine.

          Table 3 shows the protein spots with significant BBD effect, including the p-value and q-value for the BBD effect, the mean spot quantity and standard error for the diseased and healthy trees, and the ratio and direction of differences of spot quantity of healthy to diseased to trees (for all p-values underlying Table 3, see Additional file 1). One-hundred and twenty spots have a significant BBD effect, and of these 50 have no other significant effects and are the most logical candidates for biomarker development (marked with an asterisk in Table 3).
          Table 3

          Significant effects, means, and expression ratio for protein spots with significant beech bark disease (BBD) effect

          Spot

           

          BBD q-value a

          STAND b

          INT c

          mean_H d

          sd_H d

          mean_D d

          sd_D d

          Ratio of means e

          Direction in healthy

          11

           

          0.0470

          NS

          SIG

          26,174

          65,030

          72,775

          87,910

          2.7804

          down

          13

           

          0.0328

          SIG

          SIG

          94,894

          219,401

          59,736

          143,489

          1.5886

          up

          19

           

          0.0000f

          SIG

          SIG

          8,004

          5,915

          62,232

          97,523

          7.7749

          down

          103

          *

          0.0199

          NS

          NS

          55,653

          177,669

          296,935

          581,023

          5.3354

          down

          108

           

          0.0004

          SIG

          SIG

          258,261

          376,114

          111,723

          168,407

          2.3116

          up

          109

          *

          0.0023

          NS

          NS

          617,595

          986,852

          153,958

          248,739

          4.0114

          up

          110

           

          0.0001

          SIG

          SIG

          39,356

          97,166

          220,462

          387,308

          5.6017

          down

          210

           

          0.0004

          NS

          SIG

          757,282

          605,616

          285,096

          446,196

          2.6562

          up

          303

           

          0.0002

          SIG

          SIG

          173,237

          616,608

          633,878

          976,099

          3.6590

          down

          306

           

          0.0022

          NS

          SIG

          8,004

          5,915

          149,834

          274,377

          18.7195

          down

          307

           

          0.0275

          SIG

          NS

          759,264

          688,274

          302,438

          516,028

          2.5105

          up

          408

           

          0.0000f

          SIG

          SIG

          258,525

          539,218

          856,239

          1,833,144

          3.3120

          down

          410

           

          0.0041

          SIG

          NS

          26,580

          63,389

          118,326

          141,769

          4.4517

          down

          411

           

          0.0295

          SIG

          NS

          165,226

          431,575

          708,030

          1,274,849

          4.2852

          down

          412

           

          0.0054

          SIG

          SIG

          447,495

          1,004,238

          310,856

          722,114

          1.4396

          up

          505

          *

          0.0470

          NS

          NS

          925,741

          886,816

          477,691

          544,289

          1.9379

          up

          601

          *

          0.0318

          NS

          NS

          333,862

          424,610

          725,308

          843,336

          2.1725

          down

          602

           

          0.0000f

          SIG

          SIG

          365,242

          873,890

          1,394,639

          1,721,661

          3.8184

          down

          603

           

          0.0076

          SIG

          NS

          1,533,077

          1,657,207

          1,053,773

          1,360,574

          1.4548

          up

          606

           

          0.0216

          SIG

          SIG

          43,523

          126,690

          8,417

          8,283

          5.1708

          up

          610

           

          0.0001

          SIG

          SIG

          146,957

          447,440

          8,417

          8,283

          17.4593

          up

          704

           

          0.0000f

          SIG

          SIG

          68,064

          212,603

          899,841

          2,069,461

          13.2205

          down

          804

          *

          0.0013

          NS

          NS

          8,004

          5,915

          148,985

          250,618

          18.6134

          down

          1004

          *

          0.0023

          NS

          NS

          8,004

          5,915

          175,554

          294,631

          21.9328

          down

          1005

           

          0.0012

          SIG

          SIG

          83,946

          197,401

          354,800

          440,759

          4.2265

          down

          1109

           

          0.0000f

          SIG

          SIG

          83,319

          265,430

          503,759

          1,143,792

          6.0462

          up

          1303

           

          0.0012

          SIG

          SIG

          364,487

          1,135,686

          155,251

          282,766

          2.3477

          up

          1309

          *

          0.0043

          NS

          NS

          297,936

          428,232

          70,605

          95,878

          4.2197

          up

          1401

          *

          0.0075

          NS

          NS

          1,381,518

          1,428,751

          573,957

          882,187

          2.4070

          up

          1402

          *

          0.0168

          NS

          NS

          1,221,781

          1,194,937

          2,454,219

          2,386,797

          2.0087

          down

          1405

           

          0.0219

          SIG

          NS

          536,821

          589,544

          164,445

          364,867

          3.2644

          up

          1704

           

          0.0076

          NS

          SIG

          107,476

          344,505

          651,980

          1,109,723

          6.0663

          down

          2101

          *

          0.0238

          NS

          NS

          1,068,400

          1,220,098

          824,152

          883,156

          1.2964

          up

          2107

           

          0.0158

          SIG

          SIG

          65,274

          230,953

          293,355

          519,761

          4.4942

          down

          2312

          *

          0.0284

          NS

          NS

          350,710

          704,649

          8,417

          8,283

          41.6664

          up

          2506

          *

          0.0469

          NS

          NS

          1,897,806

          1,096,881

          2,497,681

          1,221,943

          1.3161

          down

          2507

          *

          0.0012

          NS

          NS

          417,660

          599,615

          1,048,242

          1,050,906

          2.5098

          down

          2611

           

          0.0498

          SIG

          SIG

          1,535,001

          1,783,491

          2,205,570

          2,796,718

          1.4369

          down

          2614

           

          0.0000f

          SIG

          SIG

          8,004

          5,915

          285,838

          432,421

          35.7112

          down

          2615

           

          0.0008

          SIG

          SIG

          62,288

          208,601

          431,929

          1,018,078

          6.9344

          down

          2801

           

          0.0000f

          SIG

          SIG

          8,004

          5,915

          869,550

          1,285,546

          108.6371

          down

          2803

           

          0.0010

          SIG

          SIG

          712,668

          1,115,376

          580,029

          825,345

          1.2287

          up

          2806

           

          0.0001

          SIG

          SIG

          443,822

          1,084,784

          129,571

          442,770

          3.4253

          up

          3201

          *

          0.0016

          NS

          NS

          1,336,983

          1,205,431

          794,583

          908,778

          1.6826

          up

          3305

           

          0.0000f

          SIG

          SIG

          89,683

          248,936

          303,148

          448,565

          3.3802

          down

          3306

           

          0.0078

          SIG

          SIG

          116,998

          272,340

          59,537

          124,166

          1.9651

          up

          3404

           

          0.0082

          SIG

          NS

          4,068,995

          2,823,334

          3,452,364

          2,764,490

          1.1786

          up

          3406

           

          0.0000f

          SIG

          SIG

          44,870

          125,570

          462,745

          547,706

          10.3130

          down

          3412

           

          0.0177

          NS

          SIG

          322,035

          683,636

          130,942

          334,190

          2.4594

          up

          3502

           

          0.0188

          SIG

          NS

          442,850

          650,679

          786,812

          1,011,948

          1.7767

          down

          3607

          *

          0.0105

          NS

          NS

          2,247,225

          2,722,559

          1,430,733

          2,093,163

          1.5707

          up

          3702

          *

          0.0001

          NS

          NS

          633,076

          1,243,273

          3,792,473

          3,730,678

          5.9905

          down

          3708

          *

          0.0096

          NS

          NS

          633,632

          796,621

          407,271

          620,398

          1.5558

          up

          3802

           

          0.0015

          SIG

          SIG

          8,004

          5,915

          111,820

          262,030

          13.9703

          down

          3905

          *

          0.0039

          NS

          NS

          8,004

          5,915

          137,656

          242,371

          17.1980

          down

          4106

           

          0.0066

          SIG

          SIG

          105,844

          212,452

          397,149

          845,289

          3.7522

          down

          4201

           

          0.0010

          NS

          SIG

          8,004

          5,915

          254,207

          454,577

          31.7593

          down

          4305

           

          0.0000f

          SIG

          SIG

          46,506

          128,075

          470,932

          768,247

          10.1263

          down

          4706

          *

          0.0481

          NS

          NS

          2,739,148

          3,047,552

          917,692

          1,062,566

          2.9848

          up

          4802

           

          0.0000f

          SIG

          SIG

          48,748

          143,131

          408,963

          769,834

          8.3894

          down

          4906

          *

          0.0302

          NS

          NS

          873,770

          1,402,616

          628,704

          1,038,074

          1.3898

          up

          5301

           

          0.0202

          SIG

          SIG

          450,470

          363,776

          399,574

          418,863

          1.1274

          up

          5303

          *

          0.0062

          NS

          NS

          3,456,984

          1,373,851

          5,360,468

          2,202,726

          1.5506

          down

          5505

          *

          0.0009

          NS

          NS

          604,226

          742,627

          2,522,891

          2,358,561

          4.1754

          down

          5601

          *

          0.0063

          NS

          NS

          456,028

          681,368

          1,497,166

          1,370,793

          3.2831

          down

          5605

           

          0.0000f

          SIG

          SIG

          124,350

          177,741

          8,004

          5,915

          15.5356

          up

          5606

           

          0.0000f

          SIG

          SIG

          110,019

          153,695

          8,004

          5,915

          13.7452

          up

          5703

          *

          0.0297

          NS

          NS

          3,051,574

          5,434,263

          478,287

          1,621,642

          6.3802

          up

          5802

          *

          0.0011

          NS

          NS

          1,190,828

          915,352

          508,682

          528,700

          2.3410

          up

          5908

           

          0.0010

          SIG

          SIG

          161,910

          387,868

          8,004

          5,915

          20.2282

          up

          5909

           

          0.0012

          SIG

          SIG

          8,417

          8,283

          143,259

          352,729

          17.0200

          down

          6208

           

          0.0297

          SIG

          SIG

          186,317

          276,277

          80,227

          222,514

          2.3224

          up

          6303

           

          0.0328

          NS

          SIG

          253,951

          331,973

          397,930

          349,663

          1.5670

          down

          6304

           

          0.0018

          NS

          SIG

          2,888,828

          3,401,904

          820,317

          1,445,597

          3.5216

          up

          6306

          *

          0.0099

          NS

          NS

          1,800,296

          1,365,419

          2,730,178

          2,859,788

          1.5165

          down

          6501

          *

          0.0318

          NS

          NS

          1,468,936

          1,183,570

          604,825

          1,255,327

          2.4287

          up

          6707

          *

          0.0193

          NS

          NS

          11,970,218

          12,515,695

          6,247,307

          6,508,633

          1.9161

          up

          6709

          *

          0.0238

          NS

          NS

          8,974,280

          5,564,143

          5,289,975

          6,857,853

          1.6965

          up

          6710

          *

          0.0238

          NS

          NS

          1,400,824

          2,287,877

          404,306

          569,323

          3.4648

          up

          6807

           

          0.0070

          SIG

          SIG

          118,758

          291,241

          8,004

          5,915

          14.8370

          up

          6808

           

          0.0009

          SIG

          SIG

          1,174,714

          1,224,320

          239,503

          580,829

          4.9048

          up

          6809

           

          0.0002

          SIG

          SIG

          376,863

          587,605

          95,365

          213,349

          3.9518

          up

          7009

           

          0.0004

          SIG

          SIG

          175,933

          391,166

          265,335

          410,728

          1.5082

          down

          7106

           

          0.0000f

          SIG

          SIG

          962,980

          1,282,010

          39,820

          101,833

          24.1835

          up

          7107

          *

          0.0009

          NS

          NS

          387,387

          501,608

          50,505

          130,300

          7.6703

          up

          7110

          *

          0.0245

          NS

          NS

          84,648

          139,984

          256,705

          307,196

          3.0326

          down

          7111

           

          0.0000f

          SIG

          SIG

          658,929

          1,249,222

          148,370

          341,871

          4.4411

          up

          7112

           

          0.0022

          NS

          SIG

          73,598

          133,128

          8,004

          5,915

          9.1949

          up

          7203

           

          0.0261

          SIG

          NS

          1,311,783

          732,419

          927,380

          799,253

          1.4145

          up

          7207

           

          0.0186

          SIG

          NS

          60,957

          126,879

          21,092

          40,907

          2.8901

          up

          7303

           

          0.0052

          SIG

          SIG

          246,921

          602,207

          615,797

          1,105,070

          2.4939

          down

          7408

          *

          0.0126

          NS

          NS

          3,049,802

          2,337,843

          1,336,825

          1,182,165

          2.2814

          up

          7510

           

          0.0098

          SIG

          SIG

          305,493

          558,423

          397,578

          1,073,627

          1.3014

          down

          7602

          *

          0.0412

          NS

          NS

          7,296,723

          7,488,294

          3,609,984

          5,505,222

          2.0213

          up

          7609

           

          0.0016

          SIG

          SIG

          381,570

          946,188

          8,004

          5,915

          47.6713

          up

          7803

          *

          0.0238

          NS

          NS

          716,697

          474,399

          341,082

          391,607

          2.1012

          up

          7808

          *

          0.0075

          NS

          NS

          704,023

          626,350

          305,591

          487,526

          2.3038

          up

          7809

          *

          0.0267

          NS

          NS

          582,174

          1,475,329

          8,004

          5,915

          72.7338

          up

          7810

          *

          0.0297

          NS

          NS

          442,246

          1,119,936

          8,004

          5,915

          55.2519

          up

          8002

          *

          0.0082

          NS

          NS

          175,231

          300,741

          22,986

          74,010

          7.6235

          up

          8104

          *

          0.0188

          NS

          NS

          1,096,503

          874,012

          394,837

          541,448

          2.7771

          up

          8202

           

          0.0230

          SIG

          NS

          29,407

          58,674

          56,069

          97,123

          1.9066

          down

          8303

          *

          0.0311

          NS

          NS

          1,850,424

          1,058,481

          1,293,017

          1,164,851

          1.4311

          up

          8309

          *

          0.0008

          NS

          NS

          340,383

          449,968

          902,563

          716,532

          2.6516

          down

          8311

           

          0.0318

          SIG

          NS

          85,387

          133,171

          26,149

          58,170

          3.2654

          up

          8312

          *

          0.0498

          NS

          NS

          306,017

          504,921

          549,086

          736,070

          1.7943

          down

          8503

          *

          0.0193

          NS

          NS

          587,330

          907,526

          123,323

          558,974

          4.7626

          up

          8506

          *

          0.0010

          NS

          NS

          1,709,206

          2,026,022

          334,292

          701,018

          5.1129

          up

          8508

          *

          0.0158

          NS

          NS

          1,395,005

          1,198,636

          633,833

          820,628

          2.2009

          up

          8512

          *

          0.0012

          SIG

          NS

          8,417

          8,283

          1,173,776

          1,657,190

          139.4515

          down

          8604

           

          0.0267

          SIG

          SIG

          2,262,933

          3,169,913

          1,147,023

          2,200,353

          1.9729

          up

          8607

           

          0.0020

          SIG

          NS

          1,865,790

          3,095,124

          369,996

          861,448

          5.0427

          up

          8708

          *

          0.0076

          NS

          NS

          177,743

          461,377

          632,706

          1,403,084

          3.5597

          down

          8801

          *

          0.0316

          NS

          NS

          751,522

          573,471

          517,615

          499,829

          1.4519

          up

          8802

          *

          0.0034

          NS

          NS

          511,552

          398,150

          269,962

          349,834

          1.8949

          up

          9302

          *

          0.0238

          NS

          NS

          710,525

          848,447

          1,268,492

          1,442,080

          1.7853

          down

          9305

           

          0.0085

          SIG

          SIG

          301,789

          462,883

          368,200

          614,918

          1.2201

          down

          9401

           

          0.0192

          SIG

          NS

          717,956

          641,915

          419,853

          410,107

          1.7100

          up

          9402

           

          0.0096

          SIG

          SIG

          63,499

          152,855

          120,990

          362,645

          1.9054

          down

          9802

           

          0.0021

          SIG

          SIG

          112,023

          263,531

          8,004

          5,915

          13.9956

          up

          An asterisk to the right of the spot number indicates a spot with significant BBD effect and non-significant STAND and STANDxBBD interaction effect, and are the primary targets for biomarker development.

          aBBD effect with q-value <0.05. q-value controls the False Discovery rate, and q<0.05 allows for no more than 26 false positives in the 425 spots tested.

          bSTAND effect with p<0.0001 (Bonferroni adjusted critical value).

          cSTANDxBBD interaction effect with p<0.0001 (Bonferroni adjusted critical value).

          dGaussian image spot intensity after normalization expressed in counts. Mean is the mean over the three technical replicates per tree for all trees in that disease condition. SD is standard deviation.

          eRatio is ratio of larger mean over smaller mean.

          Mean = Gaussian image spot intensity after normalization expressed in counts. Mean is the mean over the three technical replicates for all trees in that disease condition.

          SD = standard deviation.

          fQ-values displayed as 0.0000 are positive values <0.0001. See additional table 1 for actual q-values.

          Spot selection and LC-MS/MS analysis

          Spots were selected for coring and sequencing based upon the BBD effect being significant (including some with other significant effects as well) and the location of the spot in the gel being conducive to excising a clean gel core (only one protein in the core, some areas were too densely packed to allow coring). The trees 1504 and CM02d were chosen for use in preparative gels because these trees contained the most proteins of interest (33 and 26 respectively). Attempts were made to sample all BBD significant spots in these trees, and images were carefully evaluated after spot cutting to verify the intended spot was recovered for analysis. In addition to the spots of interest, several well isolated spots were cored for quality control purposes. A total of 28 gel spots (20 BBD significant, 4 control, and 4 cut from both gels) were successfully recovered and analysed by LC-MS/MS. Of the 50 highest interest spots, 15 were successfully cored and sequenced. Resulting peptide spectra were identified by matching to NCBI datasets, or in a 2-phase matching strategy matched to beech EST's that were then matched to NCBI sequences. Of the 28 spots sequenced, 20 were identified (highest scoring matches or ties only, Table 4) based upon homology to known plant sequences, or homology of the matched EST to plant sequences (including all four control spots). Of the 15 sequenced from the 50 highest interest spots, 11 were identified by sequence homology. There are a few cases where spots were matched to more than one significant identification (spots 307, 2101, and 7408), but in two of them identical peptides returned multiple database entries with different annotations (spots 307 and 2101). The use of the EST database in spot identification greatly improved the success rate at identifying proteins, as over half of the identifications were made using the EST database and would have been unidentified had only Genbank been used. The majority of the spots that were identified based on sequence homology have been shown to be stress-related in other plant systems (Table 5).
          Table 4

          Protein spots analyzed by MS/MS

          Spot

          number

          Tree & core

          number

          Protein ID or EST homology

          [PRIDE accession: 17706] a

          NCBI or EST Accession

          Number b

          Mascot

          Score c

          Percent

          Coverage

          Matching

          peptides

          Peptide sequence d

          Peptide score e

          m/z

          Mr(expt)

          109*f

          1504_B6

          polyphenol oxidase

          ABWP2_088861_1753_2628

          180

          33

          2

          TEFAGSFVNVPSK

          72.2

          692.1743

          1382.334

                

          4

          GVATIGGIK

          47.2

          408.4638

          814.913

                

          2

          GVATIGGIK IQFVS

          233.8

          695.8282

          1389.6418

                

          2

          IQFVS

          26.9

          593.343

          592.3357

          307

          1504_H5

          unnamed protein product

          gi|157327346

          735

          44

          2

          NILFVISK PDVFK

          71.3

          760.6617

          1519.3088

                

          4

          SPTSDTYVIFGEAK

          73.9

          758.1431

          1514.2716

                

          6

          IEDLSSQLQTQAAEQFK

          107.3

          969.241

          1936.4674

                

          2

          DIELVM TQAGVSR

          75.7

          718.1302

          1434.2458

                

          4

          DIELVMTQAGVSR

          98.1

          709.7012

          1417.3878

          307

          1504_H5

          alpha nascent polypeptide

          CCWP2_066819_3631_1428

          255

          52

          2

          NILFVISK PDVFK

          71.3

          760.6617

          1519.3088

            

          associated complex

             

          4

          SPTSDTYVIFGEAK

          74.8

          758.1431

          1514.2716

                

          5

          IEDLSSQLQTQAAEQFK

          108.5

          969.241

          1936.4674

          505

          1504_A6

          none

          none

                 

          1004*

          CM02d_C1

          thioredoxin

          WOA_067025_0312_1563

          120

          25

          2

          VNTDESSSIATR

          76.5

          640.5275

          1279.0404

                

          2

          STLTTSIEK

          46.2

          490.4947

          978.9748

          2101*

          1504_G7

          ribonuclease activity regulator

          CCNHS_110619_3244_2001

          214

          24

          2

          VFEDNVLVR

          57.1

          546.048

          1090.0814

                

          2

          VLVVDGGGSLR

          67.6

          537.0695

          1072.1244

          2101*

          1504_G7

          dimethylmenaquinone

          gi|15232963

          199

          21

          4

          ALQPVFQIYGR

          46.8

          647.1753

          1292.336

            

          methyltransferase family protein

             

          2

          VLVVDGGGSLR

          78.9

          537.0695

          1072.1244

                

          2

          DVDEINGCDIGVR

          66.5

          731.6575

          1461.3004

          2101*

          CM02d_C3

          Eukaryotic translation initiation

          gi|3024017

          75

          19

          1

          ELVFK EDGQEYAQVLR

          45.4

          642.4005

          1924.1797

            

          factor 1A

             

          2

          DYQDDK ADVILK

          52.4

          712.098

          1422.1814

          3101

          CM02d_E5

          heat shock protein

          ROA_067935_0424_3607

          120

          22

          2

          ADLPGLK

          39.2

          357.4087

          712.8028

            

          [homologous match]

             

          1

          K EEVK VEVEEGR

          32.3

          477.7563

          1430.2471

                

          2

          EEVK VEVEEGR

          53.2

          652.0981

          1302.1816

          4103

          1504_F5

          17.7 kDa heat shock protein

          gi|1235898

          238

          26

          2

          ADLPGLK

          39.7

          357.3622

          712.7098

                

          7

          K EEVK VEVEEGR

          79.9

          715.8541

          1429.6936

                

          5

          EEVK VEVEEGR

          58.7

          652.0201

          1302.0256

                

          5

          VLQISGER

          30.3

          451.5257

          901.0368

                

          5

          AAMENGVLTVTVPK

          32.4

          723.6349

          1445.2552

          5001

          CM02d_F5

          stress and pathogenesis-related

          gi|3901018

          858

          65

          3

          GVFTYESENTSVIPPAR

          84.8

          934.2761

          1866.5376

            

          protein

             

          13

          AFVLDADNLIPK

          30.5

          659.2544

          1316.4942

                

          2

          AFVLDADNLIPK VAPQSIK

          42.0

          680.6581

          2038.9525

                

          7

          STETLEGDGGPGTIK

          44.6

          731.3245

          1460.6344

                

          5

          STETLEGDGGPGTIK K

          66.3

          795.6741

          1589.3336

                

          3

          K ITFGEGSQFK

          60.5

          414.6845

          1241.0317

                

          4

          ITFGEGSQFK

          49.7

          1113.4546

          1112.4473

                

          1

          ISYEIK IVASPDGGSLLK

          25.6

          630.7996

          1889.377

                

          6

          IVASPDGGSLLK

          63.3

          578.6285

          1155.2424

                

          2

          GNHEIK EEEVK

          55.8

          656.6091

          1311.2036

                

          10

          AVEAYLLAHPDAYN

          47.7

          774.1621

          1546.3096

          5002

          1504_G5

          superoxide dismutase

          gi|38228697

          263

          40

          1

          HAGDLGNVNVGDDGTVSFTIIDK

          72.8

          782.4584

          2344.3534

                

          4

          QIPLCGPNSIIGR

          53.1

          713.6349

          1425.2552

                

          4

          AVVVHGDPDDLGK

          55.5

          661.611

          1321.2074

                

          2

          STGNAGGRIACGIIGLQG

          45.4

          851.7599

          1701.5052

                

          4

          IACGIIGLQG

          45.6

          1001.4746

          1000.4673

          5303*

          1504_F7

          triosephosphate isomerase

          ABWP2_034459_1554_1107

          250

          52

          3

          VASPAQAQEVHFGLR

          66.9

          805.7179

          1609.4212

                

          3

          K WLQVNTSPEVAATTR

          48.9

          601.354

          1801.0402

                

          1

          IIYGGSVNGANCK

          63.6

          677.621

          1353.2274

          7107*

          1504_B7

          unknown, CBS domain protein

          CCWP1_110480_2642_0865

          274

          53

          2

          VGDIM TEENK

          49.3

          576.5307

          1151.0468

                

          2

          VGDIM TEENK LITVTLDTK

          59.3

          713.168

          2136.4822

                

          1

          LITVTLDTK

          45.3

          502.5542

          1003.0938

                

          2

          GM IGM VSIGDVVR

          84.6

          683.4301

          1364.8456

                

          2

          LNAFIQGGY

          38.5

          982.3546

          981.3473

          7110*

          1504_C7

          unknown, CBS domain protein

          CCWP1_110480_2642_0865

          230

          53

          2

          VGDIM TEENK

          56.2

          576.5154

          1151.0162

                

          2

          VGDIM TEENK LITVTLDTK

          61.3

          713.1682

          2136.4828

                

          2

          GM IGM VSIGDVVR

          80.2

          683.6461

          1365.2776

                

          2

          LNAFIQGGY

          40.0

          982.4321

          981.4248

          7203

          CM02d_H3

          none

                  

          7408*

          CM02d_A4

          annexin

          ACHS1n_159819_2640_1758

          209

          66

          2

          WTSSNQVLMEIACTR

          84.0

          906.6581

          1811.3016

                

          2

          SSHDLLLAR

          37.8

          506.6234

          1011.2322

                

          1

          SLEEDVAYHTTGDFR

          28.8

          580.6663

          1738.9771

                

          2

          YEGDEVNMTLAK

          60.4

          693.4765

          1384.9384

          7408*

          CM02d_A4

          isoflavone reductase like

          ABWP1_026351_0296_1851

          203

          37

          1

          SKILIIGGTGYIGK

          57.7

          710.562

          1419.1094

                

          2

          ILIIGGTGYIGK

          70.4

          602.8907

          1203.7668

                

          2

          SGHPTFALVR

          36.0

          542.7687

          1083.5228

                

          2

          ESTVSDPVK

          38.7

          481.347

          960.6794

          7602*

          CM02d_B4

          dehydrin

          ABWP1_045441_0451_3051

          225

          53

          1

          VFEKIPGAGNK

          46.8

          580.3038

          1158.593

            

          [homologous match]

             

          1

          IPGAGNKDR

          40.9

          465.1174

          928.2202

                

          2

          DR VQGEQYR

          41.0

          576.0114

          1150.0082

                

          1

          GAM DKVFEK

          30.4

          521.1149

          1040.2152

                

          2

          IPGAGNKDK

          43.2

          450.2458

          898.477

                

          2

          DKVQGDQYR

          34.7

          555.0321

          1108.0496

          8002

          CM02d_E4

          none

          none

                 

          8104*

          1504_C9

          glycine-rich RNA binding protein

          ABWP1_015278_0178_1196

          100

          24

          2

          AFSPYGEILESK

          49.6

          671.1248

          1340.235

            

          [homologous match]

             

          2

          NITVNEAQSR

          50.5

          566.5034

          1130.9922

          8104*

          CM02d_F4

          glycine-rich RNA-binding protein

          ABWP1_015278_0178_1196

          97

          25

          2

          AFSPYGEILESK

          50.4

          671.1497

          1340.2848

            

          [homologous match]

             

          2

          GFGFVTFSNEK

          46.6

          617.2156

          1232.4166

          8202

          1504_D9

          none

          none

                 

          8303

          1504_H8

          none

          none

                 

          8309

          1504_A9

          none

          none

                 

          8309

          CM02d_A2

          none

          none

                 

          8506*

          1504_F8

          glyceraldehyde 3-phosphate

          ROB_045015_1217_3381

          176

          57

          2

          AASFNIIPSSTGAAK

          39.8

          718.1123

          1434.21

            

          dehydrogenase, cytosolic

             

          2

          VPTVDVSVVDLTVR

          82.6

          750.6729

          1499.3312

                

          2

          FGIVEGLM TTVHSITATQK

          54.0

          684.0022

          2048.9848

          8607

          1504_E8

          glyceraldehyde-3-phosphate

          gi|120666

          237

          21

          2

          FGIVEGLMTTVHSITATQK

          38.7

          684.0699

          2049.1879

            

          dehydrogenase, cytosolic

             

          2

          AASFNIIPSSTGAAK

          47.5

          718.1649

          1434.3152

                

          2

          VPTVDVSVVDLTVR

          88.8

          750.368

          1498.7214

                

          4

          AGIALNDNFVK

          37.9

          581.4758

          1160.937

                

          2

          LVSWYDNEWGYSTR

          72.0

          888.5875

          1775.1604

          8607

          CM02d_C2

          Ran binding protein

          ABWP1_041027_0417_3079

          146

          33

          4

          LEEVAVTTGEEDETSILDLK

          117.4

          1096.213

          2190.4114

                

          2

          FDKDGNQWK

          28.9

          569.5167

          1137.0188

          9302*

          1504_G8

          germin-like/unknown/predicted

          CCNHS_162322_3610_0519

          282

          48

          3

          IPGLNTLGVSLSR

          85.5

          663.9008

          1325.787

                

          3

          IDYAPGGLNPPHTHPR

          77.0

          872.1409

          1742.2672

                

          2

          SIKKGEIFVFPK

          66.0

          697.1452

          1392.2758

                

          4

          KGEIFVFPK

          53.6

          532.7322

          1063.4498

          9401

          CM02d_B5

          none

                  

          aIdentification of the NCBI protein match, or for EST peptide matches, the BLASTP identification of the EST. Mass spectrometry data and identifications have been submitted to the PRIDE database. See the field ‘gel spot identifier’ in the Identification Detail View to review individual spot spectra and identifications.

          bNCBI number of the matching protein, or the Fagaceae genomics EST ID for peptided matching EST's.

          cMASCOT score is a probability based score. Reported scores are sums of peptide identifications that are significant (except homologous where noted), and higher scores are equivalent to lower probability of an equal or better match occurring at random.

          dUnderline indicates an oxidized residue. Bold indicates a missed trypsin cleavage.

          ePeptide mascot score for each peptide (or the highest match for multiple peptides).

          f * indicates spot with significant BBD effect and non-significant STAND and STANDxBBD interaction effect, and are the primary targets for biomarker development.

          Table 5

          Previously published stress response of proteins identified by MS/MS

          Category

          Protein (or protein family)

          Spot(s)

          Quantification in healthy trees relative to diseased trees

          Previously published stress response

          References

          reactive oxygen species

          polyphenol oxidase

          109

          up

          wound, pathogen, insect

          [1719]

          glyceraldehyde-3-phosphate dehydrogenase

          8506 8607, 1st ID

          up

          hypersensitive response mimic, hydrogen peroxide, wounding

          [2023]

          annexin

          7408, 1st ID

          up

          oxidative stress

          [2426]

          thioredoxin

          1004

          down

          oxidative stress

          [2730]

          stress and pathogenesis related proteins

          dehydrins

          7602

          up

          drought, low temperature, freezing salinity, abscisic acid treatment

          [3133]

          isoflavone reductase-like protein

          7408, 2nd ID

          up

          lignification

          [34]

          germin-like proteins

          9302

          down

          pathogen, aluminum, wounding

          [3539]

          transcription and translation

          eukayrotic translation initiation factor (eif1)

          2101

          up

          salt

          [40, 41]

          glycine-rich RNA binding protein

          8104

          up

          hydrogen peroxide, flooding, cold, drought, salinity, abscisic acid treatment

          [42, 43]

          alpha nascent polypeptide associated complex 1 (αNAC1)

          307

          up

          salt, systemic acquired resistance inducer

          [4446]

          Ran binding protein(RanBP)

          8607, 2nd ID

           

          auxin treatment

          [47]

          unknown, or previously metabolic only

          triose-phosphate isomerase (TPI)

          5303

          down

          systemic acquired resistance inducer, abscisic acid treatment, pathogen

          [4649]

           

          CBS domain containing protein

          7107, 7110

          up, down

          stress responsive

          [50]

          Many of the proteins with a BBD effect that were identified by MS/MS are previously published as stress responsive in other plant systems.

          Utility of the analysis to narrow the biomarker candidate pool

          In order to illustrate the discriminatory power of our approach we have illustrated the spot set reduction strategy in Figure 3. Beginning with the 987 total protein spots identified, we show how at each step some spots are discarded from further consideration as a biomarker. The final set for continued biomarker considerations is eleven spots that have a BBD effect only (not confounded by stand effects or interactions between BBD and STAND) and are identified by their sequence homology.
          http://static-content.springer.com/image/art%3A10.1186%2F1477-5956-11-2/MediaObjects/12953_2012_Article_405_Fig3_HTML.jpg
          Figure 3

          Flow chart illustrating the reduction in the number of protein spots of interest using our experimental and statistical approach. The flow chart shows the starting number of protein spots and how many of the spots were eliminated from further consideration at each step or decision point. The dramatic reduction in the number of spots of interest to only 11 spots demonstrates the discriminatory power of the experimental and statistical approach.

          Discussion

          Challenges of proteomic investigation of forest trees

          In general, protein extraction from plant tissue is technically challenging due to the high proportion of contaminants relative to the low concentration of protein. Proteomics in forest trees is further complicated by the complexity of working with trees as an experimental system due to factors such as their large size, long life cycle, and large genome [51]. In contrast to most proteomics studies conducted on model organisms, our subjects are wild, unrelated, mature trees selected from multiple stands. Like many forest trees, American beech is wind-pollinated and has a low self-pollination rate [52], resulting in high heterozygosity among trees within stands [53]. We selected trees from eight non-contiguous stands, further decreasing any chance of relatedness between trees across the study and likely increasing the number of alleles per locus sampled [54]. These factors lead to our study having a much higher degree of genetic complexity in the sampling units than is generally encountered in proteomics work where the use of inbred lines, clones, or pooling across genotypes is common. In addition, the multi-component nature of beech bark disease also adds to the complexity of protein patterns. Due to BBD having both an insect and a fungal component, both wound/insect and pathogen responsive genes are likely to be detected in diseased trees. In addition, BBD develops over a time scale of months or years, rather than the time course of days often studied in wound (insect), gene-for-gene, or viral pathosystems. BBD develops as a chronic disease, with significant associated bark damage including cracking, callous formation [1], and likely secondary local stress effects such as dehydration or nutrient and photosynthate transport disruptions. These bark stress factors may induce other, poorly understood sets of stress responses. The signalling pathways triggered by these multiple sources of stress are also likely interconnected through their regulatory pathways, which in other plant systems have been shown to be antagonistic, further adding to the complexity and variation of the response of the tree to the causal components of beech bark disease [55].

          A high level of genetic diversity across geographical regions was previously reported in maritime pine [54]. In our study, genetic diversity across geographical regions is combined with the complexity of host responses to a dual pathogen disease system making the high number of spots identified in samples (up to 522 per tree, total of 987) not unexpected. Resolution of similar ranges of spot numbers (from 451 up to 753) have been previously reported in other plant species using similar supplies, equipment, and technical approaches [5660]. Technical effects were significant for only six spots indicating that the observed differences between gels and samples were accounted for by the experimental design and analysis. Bark samples were anecdotally different for some visible features (such as chlorophyll content) that may vary by season, micro-site, or sample. Several spots that were not identified by tandem mass spectrometry had some (non-significant) homology to Fagaceae EST's, suggesting matching of unidentified peptides may be possible as additional woody plant, bark, and particularly American beech sequences become available. The low number of identified proteins in forest tree proteomic studies in general is a direct reflection of the lack of genomic DNA, EST and protein sequence data entries in public databases [51] for forest trees and for woody tissues.

          The genetic complexity of the sample unit, the sampling across a wide geographic area, and the complexity of the BBD phenotype all contribute to possible protein differences between trees in the study. This complexity, especially combined with typical technical sources of variation, required careful study design and more elaborate statistical considerations than many proteomics studies. Identification of up to 101 protein spots unique to an individual tree emphasizes the genetic diversity captured in our study. One-hundred-twenty protein spots (22.5% of the matched spots) were identified as BBD significant despite the experimental complexity, so the experiment was effective at finding proteins of interest. We controlled the false discovery rate to 5% (using the q-value), so we would expect only six of the 120 proteins identified as differentially expressed to be erroneous.

          Sequenced spots have homology to known stress, insect, and pathogen related proteins in other plants

          Most of the proteins identified by MS/MS in this study have homology to proteins known to be involved in stress responses in other plants (Table 5). The variety of biological responses to which these proteins are linked are consistent with the complexity of BBD. Since BBD has both an insect and a fungal component, it is not unexpected that both insect and pathogen related proteins would be found. BBD is also a long-term disease with both bark damage and significant healing as part of the physiology of the disease. Wounding, reactive oxygen species and drought responsive proteins are also expected. The variety of stress responses apparently influenced by BBD along with the identification of proteins involved both in transcription and translation control and basic metabolic responses, supports a model that beech trees have an active physiological response to BBD. These proteins are good targets for further research to understand the pathways involved in response to BBD.

          The proteins identified in this study also expand on recent studies of the proteome of European Beech (Fagus sylvatica L.) [6163]. While our study is the first publication of the proteome of bark tissue or any tissue from mature trees from Fagus, a few proteins identified in our study were also identified in other studies in Fagus. Of particular interest, Valcu et al. [63] identified triose-phosphate isomerase (TPI) as having a lower protein level in wounded leaves, while we found the protein to have higher expression in the bark of BBD diseased trees. Beech trees afflicted with beech bark disease would be responding to both scale feeding and pathogen infection (Neonectria spp.). The higher expression of TPI in diseased trees is consistent with the reports of higher expression of TPI in response to a fungal pathogen in Brassica carinata [48]. Further study of TPI to understand its role in defense in different tissues and different stages of beech bark disease infestation is certainly warranted.

          Biomarker candidates for further analysis

          The long term goal of our research program is to identify broadly useful markers for BBD resistance that forest managers can use to plan for and mitigate BBD damage as it spreads to new regions and stands. A resistance biomarker could also be used to expedite the selection and breeding of scale-resistant trees in on-going tree improvement programs [10].

          Twelve of the sequence identified spots (marked with an asterisk in both Table 3 and Table 4) showed a significant BBD effect and no effect of stand or BBDxStand interaction effect. Proteins different in healthy versus diseased trees regardless of stand (i.e. across stands) are the best biomarker candidates. These proteins are the most likely to be linked to resistance or susceptibility across broad geographic and genetic ranges, and so are the highest priority for follow-up study and biomarker development. Quantitation of these proteins in additional trees, especially in trees from other stands and regions, will be important in determining if these proteins can be used as biomarkers. Additional 2-DE gel studies, or development of antibody based (western blot, or ELISA) methods will facilitate this. RNA expression studies may also be helpful in understanding which proteins (genes) are the best markers for BBD resistant trees. An additional five spots that weren't identified by sequence homology fit into the same class and may be identified in the future as additional sequence becomes available. Often spots unidentified by MS/MS are dropped from further study. But given both the low amount of Fagus sequence available for comparison and the low amount of sequences in the database from bark tissues and forest trees in general, these spots are still good candidates for further study for biomarker development.

          Proteins for which BBD effect is significant along with stand, interaction, or both require more careful interpretation, but may still become useful biomarkers. In combination with significant stand and BBD effects, a significant interaction effect means that the direction of association of a BBD effect (e.g. increased protein in healthy trees) is not consistent in the different stands. When STAND and BBD effects are both significant (but with a non-significant interaction effect), the protein may not be consistently distributed in American beech and may be present in only some of the stands managers wish to screen. Both of these groups of proteins are less attractive as biomarker candidates, but interpretation of them along with other higher priority candidate proteins may be insightful.

          Most of the proteins identified by sequence have been found to be responsive to stress, insect, or pathogens in other plant systems, and are differentially expressed between the healthy and diseased trees. A small number of these proteins will be selected for further study, with priority given to those that are higher in the healthy trees and are predicted to be involved in insect resistance because of the known scale-insect resistance of the healthy trees and the requirement of scale feeding induced bark wounding to provide subsequent entryway for Neonectria infection [1, 2, 7]. Polyphenol oxidase and glyceraldehyde dehydrogenase both fall into this class and are considered high priority for further analysis. It will be important to continue validation of expression results using additional trees from the same stands, and additional stands from new geographic regions. In addition, the best biomarker protein may not be the one identified in this study, but rather a protein acting upstream in a response pathway, or regulating a response pathway(s). Further characterization of the biochemical pathways, and their induction through time, season, and spatially through the tree will be important. It is possible the proteins identified in this study will coincide with quantitative trait loci for scale resistance.

          Conclusions

          American beech is an ecologically important species in many North American forests, only a portion of which are currently impacted by BBD [64]. Development of management options to reduce the economic losses and ecological costs of BBD are critically needed. This study has identified protein spots differentially expressed in the bark of healthy, scale-resistant trees and BBD-susceptible trees. This identification suggests that American beech has an active physiological response to BBD. Confirming this response is an important first step in understanding how BBD may progress physiologically and mechanistically in BBD susceptible trees, and how BBD resistance may be manifested. Additionally the results of this study should support and complement on-going strategies to find biomarkers for BBD resistance.

          Methods

          Selection of beech trees and collection of bark samples

          Ten healthy trees were identified in seven stands in Fredericton, New Brunswick, Canada (Table 1). Healthy trees comprised only 5% of the beech trees in this area and were included in this study only if they were greater than 15 cm DBH [16]. All of these stands have been under attack by both Cryptotoccus fagisuga and Neonectria spp. since the early 1930’s [1] and would be considered an aftermath forest. After initial mortality waves, the remaining trees in an aftermath forest are primarily heavily cankered and a lower density persistent scale infestation is present in the stand. Diseased trees were selected along with healthy trees in five of the stands. A healthy tree (greater than 10 cm DBH) and a susceptible tree from Ludington, MI., USA were also included in this study. Beech scale is estimated to have been established in Ludington as early as 1990 and the presence of Neonectria was confirmed in 2001 [11]. At the time of tissue collection, 2004, this was considered a killing front. All diseased trees selected for this study showed visible signs of Neonectria infection such as cankers or the presence of perithecia and scale infestation.

          The experimental sampling is unbalanced with respect to disease resistance because the primary interest is in resistant genotypes for breeding (more resistant trees are selected for grafting, testing, and planting). Modern statistical algorithms and computer power are sufficient to allow significant imbalance in experiments to be modelled, and we take advantage of this in our experimental design. All trees selected were tested for resistance to the beech scale insect in studies reported previously [16, 65] and summarized in Table 1. These tests demonstrated that all the healthy trees were resistant to the beech scale insect and all of the diseased trees were susceptible to the beech scale insect.

          Branches, 1–2 cm in diameter, were removed from the crown of each of the selected trees in October, 2006 in the New Brunswick stands and in September 2004 in Ludington, MI. Bark was peeled from the branches with a potato peeler and bark strips were placed in labelled 50mL falcon tubes, flash frozen and stored in liquid Nitrogen or a dry ice ethanol bath on site. Peeled bark collected from each tree was divided among three tubes and transferred to a −80°C freezer for storage either at the Natural Resources Canada Lab in Fredericton, New Brunswick, Canada or the US Forest Service (USFS) Lab at Delaware, Ohio, USA. In February of 2007, samples from New Brunswick, Canada were shipped overnight on dry ice to Delaware, OH, USA.

          Protein extraction

          Protein was extracted according to Bona et al. [66] with minor modifications to account for the high soluble phenolic content of tree bark and phloem tissues. Bark tissue from each tree was combined with dry ice and ground to a course powder in a standard household coffee grinder and then transferred to a −80°C freezer. Three technical replicates were produced from the tissue from each tree. For each replicate, 2g of powdered tissue (after dry ice sublimated off) were combined with 2g of frozen polyvinyl-polypyrrolidone and 20mL of lysis buffer (as per Bona et al. [66] except that 1% Sigma Plant Proteinase inhibitor cocktail, P-9599, was used in place of 2% phenylmethylsulfonyl floride/dimethyl sulphoxide) and homogenized using a tissue homogenizer (Janke & Kunkel, IKA Labortecknik, Ultra-Turrax T25 with 18N tip). The resulting homogenate was centrifuged at 26,000gn for 10 minutes at 4°C to pellet solids. The supernatant (generally 10 mL) was combined with 10 mL of tris(hydroxymethyl)aminomethane (Tris–HCl, pH 8.8) saturated phenol and mixed for one hour at room temperature. The phenolic phase was separated by centrifugation and rinsed with another 10 mL of lysis buffer, followed by further centrifugation to separate the phenolic phase. The final phenolic phase was recovered and proteins were precipitated by adding five volumes of methanol/0.1M ammonium acetate and incubating overnight at −20°C. Proteins were pelleted by centrifuging at 26,000gn for 20 minutes and the resulting pellet rinsed three times with cold methanol, once with cold acetone, and dried under vacuum. The pellet was resolubilized in 450uL of resolubilization buffer (Biorad ReadyPrep sequential extraction reagent II (8M Urea, 4% 3-[(3-cholamidopropyl)dimethylamonio]-1-propanesulphonate (CHAPS), 40mM Tris, 0.2% Bio-Lyte 3/10 ampholytes) plus 1% tris-butyl phosphate (TBP, Sigma T-7567) and 1% plant proteinase inhibitor cocktail). Proteins were quantified (in triplicate) using the Biorad RC DC protein assay kit (Biorad 500–0118) microfuge tube assay protocol with the optional second wash. Protein quality was checked by running 40μg of protein on a denaturing polyacrylamide gel and staining with coomassie stain as per standard protocols (Biorad mini-protean-3 cell Instruction Manual).

          Two-dimensional electrophoresis (2-DE)

          2-DE was conducted at the Plant Microbe Genomics Facility at The Ohio State University (OSU). Isoelectric focusing (IEF) was performed using 11cm pH 3–10 immobilized pH gradient strips (Biorad, 163–2014) in the Protean IEF Cell (Biorad). For quantitative gels, 100 μg of protein was mixed with rehydration buffer (7.0M Urea, 2.0M Thiourea, 2.0% w/v CHAPS, 2.0% w/v sulfo-betain 10 and focused for 55 kVH at 25°C. After IEF, the strips were treated according to the ReadyPrep Reduction-Alkylation kit (Biorad, 163–2090) which uses TBP for reduction, and iodoacetamide for alkylation. Second dimension separation was carried out on Criterion 8-16% Tris–HCl gels (Biorad 161–1394) using a Criterion Dodeca cell so that all eight gels in the replicate could be run in parallel. Gels were run at 200V for 60 minutes and then fixed for 30 minutes in a solution of 10% methanol and 6% acetic acid. Gels were then stained with 1x SYPRO-Ruby (Biorad, 170–3138) following manufacturer's instructions. Post staining, gels were de-stained for 1 hour in identical solution as that used for fixation. Preparative gels for spot cutting to recover proteins were prepared in the same way, except that 450 μg of protein was used per sample and gels were stained with Coomassie stain (Biorad, 161–0787) following manufacturer's instructions.

          Image analysis and quantification

          Gels were imaged on a VersaDoc imager (Biorad), and the software program PDQuest (version 8.0, Biorad) was used to conduct the image analysis, spot identification and quantification. Gel images were digitally cropped along the outer edge to remove the molecular size marker and gel edges, and to standardize image size, but both pI fronts and the full size resolving area were retained.

          The spot selection and gel matching were conducted in two stages, first a separate master gel was created for each tree by auto-matching the three replicate gels using the 'create experiment' dialog boxes of PDQuest. For these tree master gels, the spot detection and automated spot matching are conducted as part of the same procedure. For spot detection we used the spot detection wizard with vertical streak reduction on, and selecting the user chosen reference spot for small spot, faint spot, and large spot cluster from the same region of the gel for all gels. In addition we selected the local area regression method of normalization, which is proprietary but appears to be based on similar microarray normalization methods (see Quackenbush [67]). For spot matching, we defined no groups and spots were added to the master image only if present in two of three gels. Auto-matched spots were manually checked and corrected by dividing the gel area into 81 quadrants and hand marking landmark spots in each quadrant present in all three gels. All of the matches were hand checked based upon these landmark spots, and manual corrections to the spot detection and auto-matching were made, including removal of spots detected on the unresolved pI fronts and gel edges.

          The second phase of image analysis was to create a 'compare experiments' analysis including all sixteen individual tree 'master gels' (CM02d was software selected as the base master gel). Automated matching was used to create the initial master file, then all matches were manually checked. Additional spots were added to the master manually if they were present in two or more tree masters. We applied the same hand check quality control as for individual tree masters (manually checking and correcting each of 81 quadrants using landmark spots) and applied the same normalization method (local area regression model option). Of note, we did not incorporate an additional scaling factor and the normalization method doesn’t scale the data, so the final spot quantities still have the original unit of counts.

          Once the compare experiment master gel was fully checked, a quantitative dataset was created. The quantitative dataset was output from PDQuest using the function Report| Quantity Table Report, with the settings: all matched spots checked, configuration set to 'individual gels', missing spots set to 'estimate', and saturated spots set to 'estimate'. Spot quantities were estimated so that analysis options that require balanced and nonzero datasets could be used. PDQuest estimates saturated spots by fitting a Gaussian spot to the edges only and extrapolating the peak, then calculating the estimated volume from the extrapolated value. PDQuest estimates missing spots as the value of a minimum detectable spot. The resulting report contained spot quantities for all spots in the master gel across all 48 experimental gels. Graphical analysis of the spot quantities by spot were deemed sufficiently normally distributed to proceed with modelling.

          To be sure the unmatched spots that are unique to one tree were not artifacts related to low spot intensity or variance in protein quantification making it difficult to match them, a random check of the intensity distribution of unmatched spots was conducted. Four trees were selected at random, then the spot intensity output for all spots on the tree master for these four trees was examined both visually by inspecting spot intensities in the gels and by comparing the distribution of intensities of the unique spots to the matched spots. Comparisons were made both graphically and by using summary statistics (mean, minimum, Q1, median, Q3, and maximum values).

          Experimental design and statistical analysis

          The experiment included 16 trees, each of which had a location (stand) code and a disease condition code. Three replicate extractions (for three separate gels) were run per tree. For each replicate there were a total of 16 extractions, one per tree. Each protein extraction was assigned first to one of four extraction sets (three healthy and one diseased tree per set), then extraction sets paired to form gel sets (two gel sets per replicate). Samples in an extraction set were extracted in parallel, and gel sets were run in parallel for both IEF and polyacrylamide gel separation. Hence, each extraction had a full list of factors assigned: tree, stand, disease state, replicate, extraction set, gel set. The full dataset included these factors and spot quantities for each spot on the master gel for each of the 48 gels (see additional files: Additional file 2, full study design; Additional file 3, full set of spot quantities). Calculated spot quantities were from the normalized gel images, and were evaluated and determined to need no additional transformation. An ANOVA approach to statistical analysis was used to so that multiple effects and interactions could be included in the same model to better control error variance, and because the other biological effects (i.e. stand and its interaction with BBD) will be informative in selecting proteins for future study.

          Statistical analysis was generated using SAS® software version 9.2 of the SAS system for Windows, copyright 2002–2008 SAS Institute Inc. SAS and all other SAS Institute Inc. products or service names are registered trademarks or trademarks of SAS Institute, Cary, NC, USA.

          A series of tests were used to categorize each protein spot, to arrive at a list of candidate spots for further analysis (summarized in Figure 3). The first phase of the analysis sought to exclude constitutive proteins that did not differ between any trees, and assess the significance of the technical factors. The following model was fit using the General Linear Model procedure of SAS:
          intensity = μ + t j + g k 1 + ε ijk 1 http://static-content.springer.com/image/art%3A10.1186%2F1477-5956-11-2/MediaObjects/12953_2012_Article_405_Equa_HTML.gif

          where μ is an overall average, tj is the effect of the jth tree (tree effect), gk(l) is the effect of the lth extraction set nested within the kth gel set (technical effect), and εijk(l) is a random error term. The model was fit for each spot, and the test of significant effects computed using the type III sums of squares. Model fit was evaluated by verifying that the overall model fit had a significant F value (p-value < 0.05) and by examination of standardized residuals (especially by plotting against the levels of effects). For each model, careful assessment of residual plots confirmed model assumptions about error distribution and equal variances were sufficiently met (i.e. the spot quantities did not deviate from the assumed normal distribution enough to warrant additional transformation). Degrees of freedom (df) were the same for each spot model: tree effect has 15 df, technical effect had 11 df, and error df = 21 (total df=47). For the technical effect, a Bonferroni adjustment was used to determine significance level, but for the tree effect a p<= 0.05 was considered significant. This permissive cut off is appropriate since the goal of the test was to eliminate constitutive proteins (and reduce the physical size of the dataset to ease computations) and because a false acceptance of the null is more problematic than a false rejection at this point in the analysis. Any spots that are not significantly different in at least one tree (tree effect p<0.05) were dropped from the dataset. Technical effects were found to be not significant and were dropped from further analysis for all but six spots that were dropped from the dataset.

          The second phase of the analysis was designed to determine how spots differed between trees. Technical factors were dropped and stand and disease state factors were added (there were insufficient degrees of freedom to analyse technical and biological factors in the same model, partly due to imbalance in the number of trees selected per stand). The following model was fit using the General Linear Model procedure of SAS:
          intensity = μ + s j + d k + sd jk + ε ijk http://static-content.springer.com/image/art%3A10.1186%2F1477-5956-11-2/MediaObjects/12953_2012_Article_405_Equb_HTML.gif

          where μ is an overall average, sj is the effect of the jth stand (stand effect), dk is the effect of the kth disease state (BBD effect), sdjk is the interaction effect of stand and disease state, and εijk is a random error term. Model fit was evaluated by verifying that the overall model fit had a significant F value (p-value < 0.05) and by examination of standardized residuals (especially by plotting against the levels of effects). For each model, careful assessment of residual plots confirmed model assumptions about error distribution and equal variances were sufficiently met (i.e. the spot quantities did not deviate from the assumed normal distribution enough to warrant additional transformation). Degrees of freedom were the same for each spot model: BBD effect has 1df, STAND effect has 7 df, BBDxSTAND interaction effect has 5 df, and error has 34 df (total df=47). The model was fit for each spot, and the test of significant effects computed using the type III sums of squares. Interaction effects and stand effect were tested using the conservative Bonferroni correction. For the BBD effect, p-values from the tests were output to a new dataset and the package 'qvalue' (q-value 1.1) for the statistical program R (R-2.4.0-win32, [68]) was used to compute the associated q-value for each test. Significance was determined using q-values while controlling the false discovery rate at 5% [69]. False discovery rate controls the percentage of null hypothesis rejected in error (false positives) rather than the overall error rate, and is an accepted and typical statistical analysis for large genomic and proteomic datasets [70].

          Spot selection and cutting

          All spots with a significant effect for the disease state factor were considered for spot cutting and sequencing. Spot quantities were evaluated in all of the trees and trees ranked as the best trees were those having the most BBD significant spots at the highest spot densities. The two top trees were used for preparative gels and spot cutting. All BBD significant spots in the two selected trees were evaluated on the gel images to determine if the spot could be excised cleanly and was sufficiently intense to support sequencing. Spots were excised from the preparative gels at the PMGF using the Protean 2-D spot cutter (Biorad Laboratories). Several constitutive spots were also selected as sequencing reference spots. High resolution pre-cut and post-cut images of preparative gels were captured on the VersaDoc imager and evaluated for quality control. Only protein spots that were cleanly excised and had no evidence of contamination from adjacent spots were sent for MS/MS analysis.

          Mass spectrometry

          Mass spectrometry was performed at the OSU Campus Chemical Instrumentation Center. Gel pieces were washed twice in 50% methanol/5% acetic acid for one hour each, followed by dehydration in acetonitrile. Cysteines were reduced by rehydrating and incubating in dithiothreitol (DTT) solution (5mg/mL in 100 mM ammonium bicarbonate) for 30 minutes. Cysteins were alkylated by the addition of 15mg/mL iodoacetamide in 100 mM ammonium bicarbonate solution, and incubation in the dark for 30 min. The gel cores were washed again with cycles of acetonitrile and ammonium bicarbonate (100mM) in 5 min increments, then dried under vacuum. Protein was digested in Multiscreen Solvinert Filter Plates from Millipore (Bedford, MA) with sequencing grade modified trypsin (Promega, Madison WI) overnight. The peptides were extracted from the polyacrylamide by washing several times with 50% acetonitrile and 5% formic acid, pooled, and concentrated under vacuum to ~30 uL.

          Capillary-liquid chromatography-nanospray tandem mass spectrometry (Nano-LC/MS/MS) was performed on a Thermo Finnigan LTQ mass spectrometer equipped with a nanospray source operated in positive ion mode. The LC system was an UltiMate™ 3000 system from Dionex (Sunnyvale, CA). Five microliters of each sample were first injected on to the micro-Precolumn Cartridge (Dionex, Sunnyvale, CA), and washed with 50 mM acetic acid. The injector port was switched to inject and the peptides were eluted off of the trap onto the column. A 5 cm 75 μm ID ProteoPep II C18 column (New Objective, Inc. Woburn, MA) packed directly in the nanospray tip was used for chromatographic separations. Peptides were eluted directly off the column into the LTQ system using a gradient of 2-80% acetonitrile over 45 minutes, with a flow rate of 300 nl/min and total run time was 65 minutes. The MS/MS was acquired using a nanospray source operated with a spray voltage of 3 KV and a capillary temperature of 200°C. The analysis was programmed for a full scan recorded between 350 – 2000 Da, and a MS/MS scan to generate product ion spectra to determine amino acid sequence in consecutive instrument scans of the ten most abundant peaks in the spectrum. The CID fragmentation energy was set to 35%. Dynamic exclusion is enabled with a repeat count of 30s, exclusion duration of 350s and a low mass width of 0.5 Da and high mass width of 1.50 Da.

          Sequence data processing and matching

          Sequence information from the MS/MS data were searched using Mascot Daemon (version 2.2.1 Matrix Scientific, Boston, MA) [71] against several databases (detailed below). The search parameters were: mass accuracy of the precursor ions = 2.0, fragment mass accuracy = 0.5 Da, considered (variable) modifications = methionine oxidation and carbamidomethyl cysteine, missed cleavages = 2–4. Due to the low representation of woody plant and bark tissue sequences in the databases, the search was conducted against several databases. Searching against the full SwissProt database version 54.1 (283454 sequences; 104030551 residues) was unproductive (only procedural peptides identified, data not shown). A second search was conducted restricting the search set to taxon Viridiplantae (version, sequences, residues). The Fagaceae genomics project [72] has constructed EST libraries from American Beech, Red Oak (Quercus rubra L.), White Oak (Quercus alba L.), American chestnut (Castanea dentata (Marsh.) Borkh) and Chinese chestnut (Castanea mollissima Blume) including libraries constructed from both healthy and diseased stem tissues. Twenty-four individual EST libraries (#13696, 8-21-2009, 10691208 sequences; 751178460 residues) were compiled into a custom database and searched. Peptide matches were checked manually and only those identifications with a Mascot score of 50 or higher and having two or more unique peptides of five or more residues were accepted. For EST matches, peptides were matched to EST's (by the same criteria), then EST's searched against GenBank (BLASTP, default settings [73] to obtain protein identifications. Analysis data is available in the PRIDE database [74, 75] under the accession numbers 17706. The data was converted using the PRIDE Converter [76, 77].

          Abbreviations

          2-DE: 

          2 Dimensional Electrophoresis

          ANOVA: 

          Analysis Of Variance

          BBD: 

          Beech Bark Disease, or its effect in a statistical model

          CHAPS: 

          3-[(3-Cholamidopropyl)Dimethylamonio]-1-Propanesulphonate

          DF: 

          Degrees of Freedom in a statistical model or test

          IEF: 

          Iso-Electric Focusing

          INT: 

          The Interaction Effect (Bbdxstand) In Statistical Models

          OSU: 

          The Ohio State University

          STAND: 

          The Effect Of Stand In The Statistical Models

          TBP: 

          Tris-Butyl Phosphate

          Tris–HCl: 

          Tris(Hydroxymethyl)Aminomethane.

          Declarations

          Acknowledgements

          We would like to thank Donnie McPhee and Kathleen Forbes for their assistance with collecting material, Tannis Beardmore for practical advice, and The Fagaceae Genomics Tools project [72] for access to EST data from American Beech. We thank John Carlson, Sasha Popova-Butler, and Sourav Chakraborty for helpful comments on a draft version of this manuscript. And we are grateful to Dave Carey for his excellent technical work and advice on all aspects of the beech bark disease research project.

          Authors’ Affiliations

          (1)
          Department of Entomology, Ohio Agricultural Research and Development Center, The Ohio State University
          (2)
          US Forest Service, Northern Research Station
          (3)
          Faculty of Forestry and Environmental Management, University of New Brunswick
          (4)
          Formerly: Canadian Forest Service, Natural Resources Canada
          (5)
          Bioversity International

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