Sage Journals: Discover world-class research

Abstract

Femoral head necrosis (FHN) is a skeletal problem in broiler chickens, where the proximal femoral head cartilage shows susceptibility to separation from its growth plate. The selected birds with FHN showed higher body weights and reduced plasma cholesterol. The proteomic differences in the plasma of healthy and FHN-affected chickens were explored using matrix-assisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF-MS) and liquid chromatography/electrospray ionization-tandem mass spectrometry (LC-MS/MS) to prospect for protein biomarkers. We isolated two differentially expressed low molecular weight proteins and identified them by MALDI peptide mass fingerprinting as fibrinogen- and fetuinderived peptides, respectively. These peptides were reduced in birds susceptible to femoral head problems. Quantitation of LC-MS/MS spectra showed elevated levels of gallinacin-9, apolipoprotein A1, and hemoglobin and reduced levels of alpha-1-acid glycoprotein, albumin, and SPINK7 proteins in FHN. These results suggest that the bodyweight and the lipid profiles along with the above proteins can be useful as noninvasive biomarkers of FHN.

Keywords

femoral head necrosis biomarkers proteomics mass spectrometry

Introduction

Femoral head separation (FHS) is a skeletal problem that affects the proximal femur in broiler chickens and renders them lame, compromising their walking ability and wellbeing.^1,2 The separation of growth plate (GP) from its articular cartilage (AC) can lead to femoral head necrosis (FHN).^3–6 A weak adhesion between GP and AC, likely, causes their separation in certain population of rapidly growing poultry.⁴ Bacterial infection has also been implicated in the pathogenesis of FHN.^7,8 The diagnosis of FHN is not possible until the late stages of the disease when the external symptoms such as poor posture and walking abnormality are evident. The current methods of diagnosis such as gait assessment are inadequate and subjective, whereas the assessment at necropsy is invasive and terminal.^9,10 Hence, for the genetic selection of breeding population, there is a need for minimally invasive identification of birds with subclinical problems of FHN. Biomarkers such as blood metabolites, proteins, and enzymes have been used for the diagnosis of health problems, disease progression, and genetic selections. Although proteomic methods are widely used to explore for biomarkers associated with femoral head problems in human beings,^11–13 they are seldom utilized to determine FHN-affected chickens.^3,14,15 Hence, the objective of this study is to identify potential biomarkers of the disease by comparing the plasma protein profiles of healthy and FHN-affected chickens. Here, we employed both direct matrix-assisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF-MS; top-down proteomics) and liquid chromatography and tandem mass spectrometry (LC-MS/MS; bottom-up proteomics) to elucidate protein differences to identify possible biomarkers of FHN in chickens. The MALDI analysis was used to compare the low molecular weight (<10 kDa) proteins that could be masked in the total proteome comparison using LC-MS/MS. The results of the studies are presented in this report.

Methods

Animals and blood collection

The animal procedures were approved and carried out in accordance with the University of Arkansas, Institutional Animal Care and Use Committee (IACUC) guidelines. Cobb broiler chickens intended for genetic selection were grown in farms using Standard Industry Practice. A random group of five-week-old chickens, were bled through wing vein and the blood was collected in EDTA-coated (BD Vacutainer^®) tubes. The birds were killed subsequently to verify the separation of GP from AC in their proximal femurs.¹⁵ The necropsy information was used to classify the chickens into two groups based on the propensity of the femur to separate or remain intact. When the GP showed severe damage and/or separation, the samples were classified as FHN. The samples from chickens with intact femur were classified as healthy (HLTH). Plasma samples obtained by centrifugation of blood were pooled in each group mixing in equal volumes from three birds/sample and stored at −20 °C until further analyses. The workflow of experiments is given in Figure 1.

Figure 1.

A workflow chart.

Clinical chemistry

Pooled samples from HLTH and FHN were analyzed for their cholesterol (CH), high-density lipoprotein (HDL), and triglyceride (TG) concentration using an express plus automated clinical chemistry analyzer (Ciba-Corning Diagnostics Corp.). Low-density lipoprotein (LDL) concentrations were calculated using the following formula: LDL = TC –HDL – TG/5.0 (mg/dL).¹⁶

Plasma protein analysis

The pooled plasma samples from HLTH and FHN were depleted of their high-abundance proteins by acetonitrile (ACN) precipitation. ACN containing 0.1% formic acid (FA) was added to the individual tubes (3 HLTH and 3 FHN) to a final concentration of 60% ACN, sonicated for 10 minutes (Branson 3200), and kept at −20 °C for 24 hours.^17,18 Supernatants containing the high-abundance protein-depleted (HAPD) fractions were dried using a CentriVap vacuum concentrator (Labconco) and suspended in 50 mM ammonium bicarbonate (AMBIC) solution with pH 7.8 for subsequent analyses.

Low molecular weight (1–10 kDa) protein analysis by direct MALDI

One hundred microliters of HLTH and FHN samples (n = 9 each) from previous step were dried, reconstituted in the same volume of 0.1% FA, and desalted using reverse-phase (RP) C18 tips (NT1C18; Glygen) as per the manufacturer's protocol with some minor modifications. The binding and washing steps were repeated five times before final elution. One microliter of each sample was spotted on a MALDI 384 target plate and dried, and the spots were overlaid with an equal volume of sinapinic acid (10 mg/mL in 0.1% FA in 50% of ACN) and analyzed using Ultraflex II MALDI-TOF/TOF instrument (Bruker Daltonics) in positive ion linear mode. The instrument was calibrated using a 5–17.5 kDa protein standard (Bruker Daltonics), and the MALDI spectra were collected in a range between 1 and 10 kDa in a fully automated mode using Bruker Flex control software with a constant laser power and 800 laser shots per spot.

ClinProTools analysis

The MALDI-TOF mass spectra from HLTH and FHN samples were compared using ClinProTools software™ (CPT, version 2.2, Bruker Daltonics).¹⁹ A quick classifier algorithm was used to automatically pick and integrate peaks with a signal to noise ratio ≥10, and a threshold intensity of at least 5%, relative to the largest peak.²⁰ Anderson-Darling test was used to establish the data distribution, and the statistical differences and significance were calculated using t- and Wilcoxon tests. Low molecular weight protein peaks with P ≤ 0.05 were considered significant.

HPLC fractionation of differentially expressed low molecular weight proteins

To purify the low molecular weight proteins, shown as differentially expressed by CPT in the previous step, equal volumes of HAPD supernatant from HLTH and FHN samples were pooled, dried, and dissolved in 0.1% FA. HPLC purification was performed using a Supelco C₁₈ column (15 cm × 4.6 mm, 5 μm particle size, 300 å pore size; Sigma-Aldrich) attached to a Hewlett 1100 HPLC system. The HPLC was coupled online to a quadrupole ion trap Electrospray ionization (ESI) mass spectrometer (ESI-MS; Bruker Esquire 2000, Bruker) operated in a positive ion mode with a dry gas temperature of 300 °C, a flow rate of 12 mL/minute, and a nebulizing N2 pressure of 2.1 × 10⁵ Pa (30 psi). HPLC was operated at a solvent flow rate of 0.7 mL/minute using 0%-100% gradient of 0.1% FA (solvent A) and ACN (solvent B) over a 150-minute period similar to a method described earlier.³ Multiple charged ion ESI mass spectra were used to identify the appropriate fractions of interest prior to their collection. The mass spectrometer was optimized at m/z 1000 with low skimmer voltage to avoid ion fragmentation and charge stripping. The HPLC fractions were collected and verified by MALDI-TOF-MS. Relevant fractions collected from several runs were pooled, dried, and reconstituted in 50 mM AMBIC before proceeding to the identification step.

MALDI peptide mass fingerprinting

The isolated low molecular weight proteins in 50 mM AMBIC were reduced with 10 mM dithiothreitol (DTT) for one hour at 60 °C, alkylated with 40 mM iodoacetamide (IAA) (MP Biomedicals) for one hour in the dark at room temperature. Excess IAA in the samples were neutralized with DTT, the samples were digested with trypsin (Promega) at 37 °C for 24 hours and desalted using C18 tips (NT1C18; Glygen). One microliter of the eluted peptide was mixed with an equal volume of α-cyano-4-hydroxycinnamic acid matrix (10 mg/mL in 0.1% FA in 50% of ACN), and spotted on a MALDI 384 target plate for peptide mass fingerprinting (PMF) analysis. The instrument was calibrated using Bruker peptide calibration standard spotted adjacently. Bruker Biotools 3.1 was used to combine PMF and LIFT-TOF/TOF (MS/MS) data and searched with MASCOT 2.1 search engine (Matrix Science). The peptides were identified using the following parameters: single miscleavage, fixed carbamidomethylation of cysteine, variable methionine oxidation, parent ion mass tolerance, and fragment ion mass tolerance of 0.6 Da in the NCBI Gallus gallus protein database. Peptides with fragmentation ion scores of 10 or higher were considered for protein identification.

In-solution digestion for LC-MS/MS

The protein concentration of HAPD samples were measured by BCA protein assay method (Pierce) and adjusted to a concentration of 1 μg/μL with 50 mM AMBIC, pH 7.8 (n = 3 samples/group). The samples were reduced, alkylated as described earlier, and digested with 2 μg of trypsin (Promega) for 48 hours at 37 °C.

LC-MS/MS

The trypsin-digested samples were subjected to LC-MS/MS using an Agilent 1200 series microflow HPLC coupled to a Bruker Amazon-SL quadrupole ion trap ESI mass spectrometer, capable of performing data-dependent acquisition. ESI mass spectrometer was operated in a positive ion mode with a dry gas temperature of 150 °C, with a flow rate of 7 mL/minute, and a nebulizing N2 pressure of ~10⁵ Pa (12 psi). LC-MS/MS data were acquired in the Auto MS(n) mode with optimized trapping condition for the ions at m/z 1000. MS scans were performed in the enhanced scanning mode (8100 m/z/second), while the collision-induced dissociation or the MS/MS fragmentation scans were performed automatically for top ten precursor ions for one minute in the UltraScan mode (32,500 m/z/second). Tryptic peptides were separated by reverse-phase high-performance liquid chromatography (RP-HPLC) using a Zorbax SB C₁₈ column, (150 × 0.3 mm, 3.5 μm particle size, 300 å pore size, Agilent Technologies), with a solvent flow rate of 6 μL/minute, and a gradient of 0%-40% consisting of 0.1% FA (solvent A) and ACN (solvent B) over a time period of 2000 minutes.

Data analysis

The mzXML files exported from Data analysis 4.0 (Bruker) were submitted to global proteome machine (GPM; http://www.thegpm.org), with the following parameters for X!Tandem²¹: Gallus gallus organism, fragment mass error of 0.6 Da, fixed carbamidomethylation, variable methionine oxidation, trypsin as enzyme, semistyle cleavage, and ion trap as the predefined method. Proteins with at least one unique peptide and upto an acceptable e-value and a false positive rate <5%, as displayed in the corresponding results page, were considered true for protein identifications. The results were downloaded as *.xml files for Skyline software and excel files for qualitative comparison.

Qualitative analysis

The common proteins present in all three biological replicates of the HLTH and FHN samples were selected using an online excel comparison program (www.xlcomparator.net). The list of proteins were mapped to their ensemble gene ID using Biomart²² and analyzed for relative enrichment, clustering, and GO annotations using DAVID (www.david.abcc.ncifcrf.gov)²³ with a default EASE score of 0.1. The proteins unique to FHN or HLTH were considered relevant to identify biomarkers for the disease-affected group.

Label-free quantitation

The spectral files (*.mzXML) and the corresponding protein results from GPM, *.xml files, were imported into the Skyline software (http://proteome.gs.washington.edu/software/skyline), and label-free quantitation was performed using an external tool “MSstats”.²⁴ The software uses the signal intensities (peak areas) corresponding to the precursor ions of the peptides identified by MS/MS (MS1 filtering).²⁵ Group comparison function was used for the label-free quantitation and to generate the volcano plot that shows the differentially expressed proteins. The groups HLTH and FHN were analyzed in MSstats as healthy (H) and diseased (D) with three biological replicates per group, respectively. As indicated in the volcano plot, the candidate proteins are distinguished by their significant differences in the intensities (x-axis, log scale) and P-values < 0.05 (y-axis, log scale).

Statistics

Body weight (BW) and serum chemistry data were analyzed by GLM procedure using SAS software with pooled standard error of mean and significance differentiated by Duncan's multiple range tests.²⁶ Means were considered significant at P ≤ 0.05. The sample size for the methods is given in Table 1. The direct MALDI-TOF-MS profiles were analyzed using ClinProTools software™, whereas the LC-MS/MS data were analyzed with the Skyline software as described earlier.

Table 1.

Sample size (n) and statistical methods of analysis.

METHODS	SOFTWARE	TYPE	HLTH	FHN
Body weight	SAS (Statistical Analysis Software)	Individual chicken	289	89
Clinical chemistry	SAS (Statistical Analysis Software)	Pooled plasma from three individual birds	12	12
MALDI-TOF	ClinProTools	Pooled as above and high-abundance proteins depleted	9	9
LC-MS/MS	Skyline-Msstats	Pooled as above and high-abundance proteins depleted	3	3

Note: HLTH and FHN indicate healthy and FHN-affected birds, respectively.

Results

BW and clinical chemistry

The broiler chickens in the FHN group were significantly heavier than HLTH birds (Fig. 2). The plasma CH and the LDL concentrations of FHN birds were lower than those of HLTH chickens, but the HDL and TG concentrations were similar (Table 2).

Figure 2.

BW of HLTH and FHN-affected chickens. HLTH and FHN indicate healthy and FHN-affected birds, respectively.

Table 2.

Plasma lipid clinical chemistry of HLTH and FHN samples (n = 12).

VARIABLES	HLTH	FHN
Cholesterol (mg/dL)	132.2 ± 6.6^a	110.5 ± 7.8^b
High Density lipoprotein (mg/dL)	38.6 ±2.2^a	38.9 ± 2.5^a
Triglycerides (mg/dL)	24.8 ± 2.0^a	25.3 ± 5.1^a
Low density lipoprotein (mg/dL)	88.6 ±5.3^a	66.6 ± 6.1^b

Notes: Values are presented as mean ±SEM. Dissimilar superscripts indicate significant differences between the groups. HLTH and FHN indicate healthy and FHN-affected birds, respectively.

Direct MALDI analysis

Of >70 peaks expressed differentially (Supplementary Table 1), a peak corresponding to m/z 3671 was isolated and identified as a fragment of α2-HS-glycoprotein (AHSG) or fetuin (Fig. 3). Similarly, another low molecular weight protein corresponding to m/z 4707 (Fig. 4) was previously identified as fibrinogen beta chain (FIBB) peptide.²⁷

Figure 3.

(A) ClinProTools comparison of m/z 3671 spectral peaks from HLTH (red), FHN (green) with the average intensities shown in gray. (B) Peptide mass fingerprint analysis of the 3671 Da peptide by Flex analysis. (C) Tandem mass spectrometry analysis (MS/MS) of peptide m/z 1794 Da showing the sequence “VPHPVGFVPPPPLCPGK” corresponding to fetuin protein. HLTH and FHN indicate healthy and FHN-affected birds, respectively.

Figure 4.

ClinProTools comparison of m/z 4707 spectral peaks from HLTH (red) and FHN (green), with the average intensities shown in gray. HLTH and FHN indicate healthy and FHN-affected birds, respectively.

LC-MS/MS analysis

LC-MS/MS data and the results were deposited in the ProteomeXchange Consortium via PRIDE partner repository with the dataset identifiers PXD003292 and 10.6019/PXD003292. The number of common and unique proteins, identified in the HLTH and FHN samples, are represented in Figure 5. Briefly, the number of proteins identified in the three samples of HLTH groups were 322, 319, and 374, respectively. In FHN samples, the number of proteins identified were 380, 329, and 356, respectively. The number of common proteins in the three samples of HTLH and FHN groups were 79 and 76, respectively. Qualitative comparison of these common proteins showed only 23 proteins present in the HTLH group and 20 proteins present in the FHN group, of which only three were unique to the HLTH group and five to the FHN group (Table 3).

Figure 5.

Venn diagram showing the number of proteins identified in each group. HLTH and FHN indicate healthy and FHN-affected birds, respectively.

Table 3.

Proteins present only in the HLTH and FHN samples based on the qualitative analysis of proteins present either in HLTH or in FHN samples.

PROTEIN	NAME	GROUP	FUNCTION
ENSGALP00000031021	MyosinVI	HLTH	Cytoskeleton
ENSGALP00000026627	Additional sex combs like 2 (Drosophila)	HLTH	Regulation of transcription
ENSGALP00000000545	RNA binding motif protein 15	HLTH	Regulation of transcription
ENSGALP00000026921	Meprin A, alpha (PABA peptide hydrolase)	FHN	Proteolysis
ENSGALP00000007080	Ubiquitin specific peptidase 34	FHN	Ubiquitin mediated proteolysis
ENSGALP00000001182	Myosin, heavy chain 13, skeletal muscle	FHN	Cytoskeleton
ENSGALP00000035723	Tudor domain containing 3	FHN	RNA binding
ENSGALP00000012641	Not characterized (golgin)	FHN	Golgi complex associated

Notes: HLTH and FHN indicate healthy and FHN-affected birds, respectively. ENSGALP indicates ENSEMBL Gallus protein ID.

Gene enrichment analysis

The GO enrichment analyses of the HLTH and FHN groups are provided in Supplementary Tables 2 and 3. A cluster enriched with fibronectin was present in the HLTH samples, while a cluster with negative regulation of apoptosis was present in the FHN samples.

Skyline quantitative proteomic analysis

A volcano plot generated using Skyline and the list of proteins differentially expressed is given in Figure 6 and Table 4, respectively. Gallinacin-9 (GAL9), two fragments of hemoglobin (HGB), and apolipoprotein A-I (APO-A1) were elevated, while the α1-acid glycoprotein (AGP), a serine peptidase inhibitor, Kazal type (SPINK7), and albumin (ALB) levels were reduced in the FHN group.

Figure 6.

Volcano plot of differentially expressed proteins in FHN group. Red and blue indicate the increase and decrease of respective proteins. HLTH and FHN indicate healthy and FHN-affected birds, respectively. ENSGALP indicates ENSEMBL Gallus protein ID.

Table 4.

List of differentially expressed proteins.

PROTEIN	NAME	↑/↓	POSSIBLE CAUSE
ENSGALP00000035593	Hemoglobin beta chain (HGBB)	↑	Erythrolysis
ENSGALP00000038912	Hemoglobin subunit alpha-D (HGBA)	↑	Erythrolysis
ENSGALP00000035930	Gallinacin-9 (GAL9)	↑	Immune activation
ENSGALP00000011510	Apolipoprotein A-I Precursor (APOA1)	↑	Dyslipidemia
ENSGALP00000008163	Alpha 1-acid glycoprotein (AGP)	↓	Dyslipidemia
ENSGALP00000019031	Albumin (ALB)	↓	Inflammation/deficiency
ENSGALP00000005535	Serine peptidase inhibitor, Kazal type (SPINK7)	↓	Proteolysis

Notes: Symbols ↑ and ↓ indicate increase and decrease in their levels in FHN samples, respectively. FHN indicates FHN-affected birds. ENSGALP indicates ENSEMBL Gallus protein ID.

Discussion

The objective of this study is to find the differences in plasma proteins of FHN-prone chickens to identify molecular markers that would distinguish the affected birds from the healthy ones. Previously, we explored biomarkers in an avian model of FHN induced by glucocorticoid,³ whereas the current study involved spontaneously affected chickens. In the previous study, we used MALDI-TOF-MS quantitation, and a qualitative comparison of LC-MS/MS identified proteins between healthy and induced FHN. In the current study, both MALDI and LC-MS/MS methods were employed to quantify proteins. In the top–down approach, we used direct MALDI profiles of HLTH and FHN samples to find the differences in the levels of low molecular weight proteins. Based on these data, the differentially expressed proteins were purified by RP-HPLC and identified by MALDI-PMF. For the bottom-up method, the plasma proteome was subjected to trypsin digestion (LC-MS/MS), and the proteins were identified for qualitative analyses, DAVID enrichment, and label-free quantitation.

FHN is often attributed to interruption of blood supply in the developing femoral head.²⁸ Higher BW in chickens can be a contributing factor for the pathogenesis of FHN and the prevalence of leg problems in heavy broiler chickens.^29–32 In the present study, we found elevation of both FIBB and HGB in the FHN group, which may be associated with vascular problems. FIBB is involved in blood clotting and its changes can relate to coagulation and hemostasis.^33,34 Similarly, the plasma-free HGB fragments can be released due to erythrolysis and vascular damage.³⁵ Fetuin or AHSG has been implicated in ectopic mineralization and the change in the level of this protein may affect the skeletal strength.^36,37 GO enrichment analyses showed the absence of a fibronectin type III domain, which is related to extracellular matrix integrity and adhesion.^38,39 The absence of fibronectin type III domain in FHN can compromise adhesion and lead to apoptosis.⁴⁰ The presence of proteins associated with apoptosis was also evident from GO analysis. Since cell death has been implicated in both spontaneous and induced femoral head problems,¹⁵ the changes in these proteins appear consistent with a weaker skeleton and vascular problems.

Qualitative comparison showed the presence of two peptidases, meprin-A-alpha and ubiquitin-specific peptidase 34, uniquely present in FHN. At the same time, SPINK7 was downregulated in the FHN group as shown by quantitative analyses. In human literature, SPINK prevents cancer cell migration.⁴¹ Thus, dysregulation of proteolysis could alter skeletal homeostasis and can lead to FHN. The tudor domain containing 3 (TDRD3) present only in FHN has quantitative trait loci, associated with BW, CH abnormality, and bone density in mammals.^42–44 However, its relevance to avian skeletal physiology is not known. The increased levels of antimicrobial peptide, GAL9 along with dyslipidemia,⁴⁵ and lower levels of ALB⁴⁶ can suggest microbial infection of femur.^28,47 The elevated Apo-AI peptides observed in the current as well as in the previous study³ may suggest the breakdown of lipoproteins in the FHN-affected birds.

Although several differences were observed in the plasma protein profiles, their association with FHN pathology is not understood due to limitations. First, the significance of many of these proteins is not known in relation to skeletal physiology. Second, although the changes in plasma proteins can occur due to health problems, these changes may not be sufficient to explain the underlying pathology of the proximal femur, necessitating further explorations. Another limitation of our study is that the proteomic results are based on pooled samples of nine birds selected randomly from each group.

Conclusion

The overall results of this study such as BW, clinical chemistry changes, and the proteomic changes suggest the problems associated with tissue adhesion, proteolysis, and infection as some of the underlying causes of FHN.

Author Contributions

Designed the experiment, selection of birds and analyzed the data BP, NCR, RO. Mass spectrometry analyses RL, BP, JOL. Overall interpretation of the results and wrote the manuscript BP, RL, NCR. All authors reviewed and approved of the final manuscript.

Disclaimer

Mention of a trade name, proprietary product, or specific equipment does not constitute a guarantee or warranty by the USDA and does not imply its approval to the exclusion of other products that may be suitable. USDA is an equal opportunity provider and employer.

Footnotes

Acknowledgments

We thank Scott Zornes, Sonia Tsai, and Wally Mc-Donner for assistance. Part of the study was carried out in the Statewide Mass Spectrometry Facility supported by NIH grant P30 GM103450 to the University of Arkansas.

Supplementary Materials

References

Julian

R.J.

Rapid growth problems: ascites and skeletal deformities in broilers. Poult Sci. 1998; 77(12): 1773–80.

Cook

M.E.

Skeletal deformities and their causes: introduction. Poult Sci. 2000; 79(7): 982–4.

Packialakshmi

, Liyanage

, Lay

J.O.

Jr , Okimoto

, Rath

N.C.

Prednisoloneinduced predisposition to femoral head separation and the accompanying plasma protein changes in chickens. Biomark Insights. 2015; 10: 1–8.

Packialakshmi

, Rath

N.C.

, Huff

Poultry femoral head separation and necrosis: a review. Avian Dis. 2015; 59(3): 349–54.

McNamee

P.T.

, Smyth

J.A.

Bacterial chondronecrosis with osteomyelitis (‘femoral head necrosisλ) of broiler chickens: a review. Avian Pathol. 2000; 29(5): 477–95.

Wideman

R.F.

, Prisby

R.D.

Bone circulatory disturbances in the development of spontaneous bacterial chondronecrosis with osteomyelitis: a translational model for the pathogenesis of femoral head necrosis. Front Endocrinol. 2011; 3: 183–3.

Al-Rubaye

A.A.

, Couger

M.B.

, Ojha

Genome analysis of Staphylococcus agnetis, an agent of lameness in broiler chickens. PLoS One. 2015; 10(11): e0143336.

Wideman

R.F.

, Al-Rubaye

, Kwon

Y.M.

Prophylactic administration of a combined prebiotic and probiotic, or therapeutic administration of enrofloxacin, to reduce the incidence of bacterial chondronecrosis with osteomyelitis in broilers. Poult Sci. 2015; 94(1): 25–36.

Berg

, Sanotra

Can a modified latency-to-lie test be used to validate gait-scoring results in commercial broiler flocks?

Anim Welf. 2003; 12(4): 655–9.

10.

Weeks

C.A.

, Danbury

T.D.

, Davies

H.C.

, Hunt

, Kestin

S.C.

The behaviour of broiler chickens and its modification by lameness. Appl Anim Behav Sci. 2000; 67(1-2): 111–25.

11.

Chen

, Zeng

Comparative serum proteome expression of the steroid-induced femoral head osteonecrosis in adults. Exp Ther Med. 2015; 9(1): 77–83.

12.

R.W.

, Wang

F.S.

, Ko

J.Y.

, Wang

C.J.

, Wu

S.L.

Comparative serum proteome expression of osteonecrosis of the femoral head in adults. Bone. 2008; 43(3): 561–6.

13.

Tan

, Cai

, Wu

Comparative analysis of serum proteomes: discovery of proteins associated with osteonecrosis of the femoral head. Transl Res. 2006; 148(3): 114–9.

14.

O'Reilly

, Burchmore

, Sandilands

, Sparks

, Walls

, Eckersall

The plasma proteome and acute phase proteins of broiler chickens with gait abnormalities. In: Rodrigues

, Eckersall

, de Almeida

, eds. Farm Animal Proteomics. Berlin: Springer; 2012: 177–80.

15.

Durairaj

, Okimoto

, Rasaputra

, Clark

F.D.

, Rath

N.C.

Histopathology and serum clinical chemistry evaluation of broilers with femoral head separation disorder. Avian Dis. 2009; 53(1): 21–5.

16.

Friedewald

W.T.

, Levy

R.I.

, Fredrickson

D.S.

Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin Chem. 1972; 18(6): 499–502.

17.

Kay

, Barton

, Ratcliffe

Enrichment of low molecular weight serum proteins using acetonitrile precipitation for mass spectrometry based proteomic analysis. Rapid Commun Mass Spectrom. 2008; 22(20): 3255–60.

18.

Fernández

, Santos

H.M.

, Ruíz-Romero

, Blanco

F.J.

, Capelo-Martínez

J.L.

A comparison of depletion versus equalization for reducing high-abundance proteins in human serum. Electrophoresis. 2011; 32(21): 2966–74.

19.

Ketterlinus

, Hsieh

S.Y.

, Teng

S.H.

, Lee

, Pusch

Fishing for biomarkers: analyzing mass spectrometry data with the new ClinProTools software. Biotechniques. 2005; Suppl: 37–40.

20.

Bruker . Clinprotools 2.1 User Manual. Billerica, MA: Bruker; 2006.

21.

Bjornson

R.D.

, Carriero

N.J.

, Colangelo

X!!Tandem, an improved method for running X!tandem in parallel on collections of commodity computers. J Proteome Res. 2008; 7(1): 293–9.

22.

Kasprzyk

BioMart: driving a paradigm change in biological data management. Database (Oxford). 2011; 2011: bar049.

23.

Huang

D.W.

, Sherman

B.T.

, Lempicki

R.A.

Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 2009; 37(1): 1–13.

24.

Choi

, Chang

C.Y.

, Clough

MSstats: an R package for statistical analysis of quantitative mass spectrometry-based proteomic experiments. Bioinformatics. 2014; 30(17): 2524–6.

25.

Schilling

, Rardin

M.J.

, MacLean

B.X.

Platform-independent and label-free quantitation of proteomic data using MS1 extracted ion chromatograms in skyline: application to protein acetylation and phosphorylation. Mol Cell Proteomics. 2012; 11(5): 202–14.

26.

SAS I. SAS/Stat User's Guide, Version 8. Cary, NC: SAS Institute Inc.; 2009.

27.

Packialakshmi

, Liyanage

, Lay

J.O.

Jr , Makkar

S.K.

, Rath

N.C.

Proteomic Changes in Chicken Plasma Induced by Salmonella typhimurium Lipopolysaccharides. Proteomics Insights. 2016; 7: 1–9. http://dx.doi.org/10.4137/PRI.S31609.

28.

Wideman

R.F.

Bacterial chondronecrosis with osteomyelitis and lameness in broilers: a review. Poult Sci. 2016 Feb; 95(2): 325–44.

29.

Havenstein

G.B.

, Ferket

P.R.

, Qureshi

M.A.

Growth, livability, and feed conversion of 1957 versus 2001 broilers when fed representative 1957 and 2001 broiler diets. Poult Sci. 2003; 82(10): 1500–8.

30.

Kestin

S.C.

, Gordon

, Su

, Sørensen

Relationships in broiler chickens between lameness, liveweight, growth rate and age. Vet Rec. 2001; 148(7): 195–7.

31.

Knowles

T.G.

, Kestin

S.C.

, Haslam

S.M.

Leg disorders in broiler chickens: prevalence, risk factors and prevention. PLoS One. 2008; 3(2): e1545.

32.

Paxton

, Daley

M.A.

, Corr

S.A.

, Hutchinson

J.R.

The gait dynamics of the modern broiler chicken: a cautionary tale of selective breeding. J Exp Biol. 2013; 216(17): 3237–48.

33.

Larsson

, Björk

, Lundberg

Nephelometric determination of rat fibrinogen as a marker of inflammatory response. Vet Immunol Immunopathol. 1997; 59(1): 163–9.

34.

Jennewein

, Tran

, Paulus

, Ellinghaus

, Eble

J.A.

, Zacharowski

Novel aspects of fibrin(ogen) fragments during inflammation. Mol Med. 2011; 17(5-6): 568–73.

35.

Rother

R.P.

, Bell

, Hillmen

, Gladwin

M.T.

The clinical sequelae of intravascular hemolysis and extracellular plasma hemoglobin: a novel mechanism of human disease. JAMA. 2005; 293(13): 1653–62.

36.

Schäfer

, Heiss

, Schwarz

The serum protein a2-Heremans-Schmid glycoprotein/fetuin-A is a systemically acting inhibitor of ectopic calcification. J Clin Invest. 2003; 112(3): 357–66.

37.

Lebreton

J.P.

, Joisel

, Raoult

J.P.

, Lannuzel

, Rogez

J.P.

, Humbert

Serum concentration of human alpha 2 HS glycoprotein during the inflammatory process: evidence that alpha 2 HS glycoprotein is a negative acute-phase reactant. J Clin Invest. 1979; 64(4): 1118–29.

38.

Pankov

, Yamada

K.M.

Fibronectin at a glance. J Cell Sci. 2002; 115(20): 3861–3.

39.

Potts

J.R.

, Campbell

I.D.

Structure and function of fibronectin modules. Matrix Biol. 1996; 15(5): 313–20.

40.

Zhang

, Vuori

, Reed

J.C.

, Ruoslahti

The alpha 5 beta 1 integrin supports survival of cells on fibronectin and up-regulates Bcl-2 expression. Proc Natl Acad Sci USA. 1995; 92(13): 6161–5.

41.

Cheng

, Shen

, Yang

, Lu

S.H.

, Cui

ECRG2 disruption leads to centrosome amplification and spindle checkpoint defects contributing chromosome instability

J Biol Chem. 2008; 283(9): 5888–98.

42.

Seda

, Liska

, Krenova

Dynamic genetic architecture of metabolic syndrome attributes in the rat. Physiol Genomics. 2005; 21(2): 243–52.

43.

Kato

, Tamada

, Nabika

Identification of quantitative trait loci for serum cholesterol levels in stroke-prone spontaneously hypertensive rats. Arterioscler Thromb Vasc Biol. 2000; 20(1): 223–9.

44.

Alam

, Sun

, Liu

Identification of a quantitative trait locus on rat chromosome 4 that is strongly linked to femoral neck structure and strength. Bone. 2006; 39(1): 93–9.

45.

Alvarez

, Ramos

Lipids, lipoproteins, and apoproteins in serum during infection. Clin Chem. 1986; 32(1 pt 1): 142–5.

46.

Don

B.R.

, Kaysen

Serum albumin: relationship to inflammation and nutrition. Semin Dial. 2004; 17(6): 432–7.

47.

Jiang

, Mandal

R.K.

, Wideman

R.F.

, Khatiwara

, Pevzner

, Min Kwon

Molecular survey of bacterial communities associated with bacterial chondronecrosis with osteomyelitis (BCO) in broilers. PLoS One. 2015; 10(4): e0124403.

Proteomic Changes in the Plasma of Broiler Chickens with Femoral Head Necrosis

Abstract

Keywords

Introduction

Methods

Animals and blood collection

Clinical chemistry

Plasma protein analysis

Low molecular weight (1–10 kDa) protein analysis by direct MALDI

ClinProTools analysis

HPLC fractionation of differentially expressed low molecular weight proteins

MALDI peptide mass fingerprinting

In-solution digestion for LC-MS/MS

LC-MS/MS

Data analysis

Qualitative analysis

Label-free quantitation

Statistics

Results

BW and clinical chemistry

Direct MALDI analysis

LC-MS/MS analysis

Gene enrichment analysis

Skyline quantitative proteomic analysis

Discussion

Conclusion

Author Contributions

Disclaimer

Footnotes

Acknowledgments

Supplementary Materials

References