Capillary Electrophoresis Assessment of Plasma Protein Changes in an African Penguin (Spheniscus demersus) With Aspergillosis.

A decrease of avian biodiversity in the African continent has been the result of anthropogenic pressure in the region. This has resulted in the African penguin (Spheniscus demersus) being placed on the endangered species list and requires conservation efforts to maintain its free-ranging population and placement under managed care. In the latter environment, infection by Aspergillus fumigatus can be common. The diagnosis and treatment of this fungal disease in birds has presented with many difficulties, largely due to the diversity and limited knowledge that exists about this species. In this study, we implement a high-resolution capillary electrophoresis system for the fractionation of African penguin plasma, followed by mass spectrometry analysis for the identification of proteins associated with aspergillosis. Several protein differences were revealed, including changes in acute phase proteins and lipid metabolism. In addition, our results demonstrated that fibrinogen β chain is a protein largely present during the inflammatory process in an African penguin infected with A. fumigatus. These findings present a new avenue for the measurement of plasma proteins as a potential method for identifying important biomarkers to aid in monitoring African penguin health.

Introduction

Biodiversity in the African continent has been experiencing tremendous pressure during the last century, which has been largely propagated by an increase in anthropogenic pressure. From the seabird family, the most threatened members are the albatross (Diomedeidae) and the African penguin (Spheniscus demersus), which has experienced a 70% decrease in population from 2001 to 2013.[1−4] Although conservation efforts have been relatively successful in retaining populations under managed care, other factors have emerged to threaten this endangered species. These include reproduction difficulties and infectious diseases among animals under human care in zoological institutions and aquaria of which aspergillosis is one of the most common diseases afflicting African penguins.[5] Previous efforts to monitor and diagnose this disease have met with difficulties, since birds are often present with nonspecific signs and the tests available do not provide certainty.[6] Furthermore, treatment for aspergillosis has also met with many difficulties since there is limited knowledge about the efficacy and pharmacokinetics in different bird species.[6]

Despite these limitations, our group has documented an electrophoretogram skewed shift observed in the plasma of an African penguin with aspergillosis.[7] However, the composition of this shift remains largely unknown. This study aims to bridge this gap in the literature by applying the high-resolution capacity of a capillary electrophoresis (CE) system, coupled with protein mass spectrometry analysis for the identification of the constitutes contributing to this shift.

Results and Discussion

Avian aspergillosis is a fungal disease caused by infection of the respiratory tract primarily mediated by Aspergillus fumigatus. Aspergillosis is among the most common disease afflicting birds in captivity and is one of the leading causes of mortality in this group.[6] A previous report has documented the electrophoretic shift observed in African penguin plasma, but the protein composition of this shift remains under investigation.[7]

Capillary Electrophoresis Analysis

To address this gap in the literature, we implemented CE as our primary mode of separation, due to the high separation efficiency provided by CE when compared to other separation techniques. In addition, the high resolution of CE provides a direct approach for fractionating complex mixtures. CE was able to replicate the shift previously reported, which constituted a left skewed (normal) to right skewed shift (abnormal) (Figure A). The CE results demonstrated that this shift is primarily due to a change in the charge-to-frictional coefficient ratio, which can be interpreted as an increase in charge and a decrease in the mass of analytes. Such changes are driven by the fractionation of proteins, which reduce the molecular size and provide a larger surface area for ionization. However, it is important to recognize the limitations of this interpretation as proteins with a higher molecular mass can still have a low retention time when their charge state significantly overcomes the drag force provided by a higher molecular mass.

Figure 1

Analysis of African penguin plasma demonstrates a shift from left to right skewed electropherogram in disease penguins. (A) Capillary electrophoresis fractionation of African penguin plasma samples (left panel = normal, right panel = abnormal). Normal samples exhibit a left skewed electropherogram, while abnormal samples exhibit a right skewed electropherogram. Brackets indicate regions collected for the mass spectrometry analysis. Abs = Absorbance. (B) Comparison between proteins identified in normal and abnormal penguin plasma via mass spectrometry. UniProt database: Gallus gallus and S. demersus (both downloaded on January 2020) and NCBI database: G. gallus and S. demersus (both downloaded on September 2020).

Mass Spectrometry Proteomic Analysis

To further analyze the composition of the analytes that contributed to this shift, this region was collected for the mass spectrometry analysis (shown in brackets, Figure A). Following mass spectrometry analysis using both the UniProt G. gallus database (Table ) and the S. demersus database (Table ), the comparison revealed a small number of high confidence proteins found in the CE fraction (Figure B). This small number of proteins provided a specific representation of the proteins found in the CE fraction; however, due to the limitations of the S. demersus database, the majority of the proteins founds using this database were not peer-reviewed and curated. To corroborate the results found in the UniProt database, the same analysis was done using the NCBI database. This analysis revealed more potential inflammatory markers associated with the S. demersus database (Figure B, Tables and 4). However, the UniProt database presented us with more reliable results due to its high-quality protein curation (both manually and automatic annotation and review).

Table 1

Capillary Electrophoresis Fraction Comparison between Normal and Abnormal Penguin Plasma Using G. gallus UniProt Protein Database

	UniProt Acc #	protein description	#PSMs	score MS Amanda
unique to normal	A0A3Q2U743	A2M_recep domain-containing protein	79	6757.63
	E1BQC2	Ovotransferrin	35	975.9
	E1C7T1	SERPIN domain-containing protein	29	0
	Q197X2	Apolipoprotein B	32	266.76
	A0A1D5PWR4	uncharacterized protein	24	0
	O93601	Apolipoprotein AIV	9	0
	A0A146F0A0	Keratin, type II cytoskeletal cochleal	13	788.66
	F1NPJ8	uncharacterized protein	7	471.12
	A0A3Q2UKP2	MG2 domain-containing protein	19	418.74
	E1BV78	Fibrinogen C-terminal domain-containing protein	6	206.84
	A0A3Q2U324	A2M domain-containing protein	12	1093.8
	F1NPN5	SERPIN domain-containing protein	2	0
	P51890	Lumican	6	0
	Q90633	Complement C3	19	13.51
	A0A1D5PX29	Uncharacterized protein	13	38.63
	A0A3Q2UGR6	Ig-like domain-containing protein	26	1648.05
	F1NX74	uncharacterized protein	11	0
	Q6BCB8	Vitellogenin (fragment)	49	2596.24
	F1NAK4	TOG domain-containing protein	15	358.21
	A0A2U8UYC6	BG protein	11	0
	A0A146F031	Type II α-keratin IIB	4	23.84
	A0A1L1RNH7	IF rod domain-containing protein	7	0
	A0A1L1RRQ8	SERPIN domain-containing protein	5	0
	A0A1D5PRL3	uncharacterized protein	14	121.24
shared between normal and abnormal	P19121	Serum albumin	217	1751.1
	P08250	Apolipoprotein A-I	208	15973.42
	F1NDN6	Keratin 12	17	1395.89
	A0A1D5PMQ5	IF rod domain-containing protein	39	3118.05
	A0A1L1RIW5	IF rod domain-containing protein	18	2643.58
	A0A1L1RWG9	Keratin, type I cytoskeletal 19	23	1688.5
	A0A1I7Q422	Transthyretin	17	738.56
	F1NUL9	Fibrinogen β chain	6	422.5
	A0A1L1RKR4	IF rod domain-containing protein	20	1743.7
	F1P4V1	Fibrinogen α chain	5	302.83
	E1BWR7	J domain-containing protein	30	1552.64
unique to abnormal	A0A1D5PCD2	A2M_recep domain-containing protein	37	3310.88
	A0A3Q3AU25	TED_complement domain-containing protein	23	2056.6
	A0A146F047	Type II α-keratin IIC	30	1207.63
	F1NPG2	Isocitrate dehydrogenase [NADP]	6	547.32
	Q5ZLC5	ATP synthase subunit β, mitochondrial	9	335.35
	A0A1D5P9F9	uncharacterized protein	13	495.72
	F1NDN5	IF rod domain-containing protein	14	1102.05
	Q01406	Src substrate protein p85	9	388.04
	A0A3Q2UHJ9	RIMS-binding protein 2	11	126.64
	A0A1D5PAG3	FHA domain-containing protein	10	509.57
	F1NKX8	uncharacterized protein	14	702.97
	A0A1L1RZ04	IF rod domain-containing protein	22	608.59

Proteins identified using the G. gallus protein database (Downloaded from UniProt on January 2020) were filtered for high confidence peptides. Comparison was made on Proteome Discoverer 2.0. Black Bold = proteins significantly identified in the volcano plot analysis.

Table 2

Capillary Electrophoresis Fraction Comparison between Normal and Abnormal Penguin Plasma Using S. demersus UniProt Protein Database

	UniProt Acc #	protein description	#PSMs	score MS Amanda
unique to normal	Q2QCG7	Cytochrome c oxidase subunit 1 (fragment)	0	0
	B7ZJW4	Cytochrome c oxidase subunit 1 (fragment)	2	0
	B7ZJZ2	Cytochrome b (fragment)	6	0
	B7ZK06	NADH-ubiquinone oxidoreductase chain 3	2	0
shared between normal and abnormal	C4T8T5	DRB-like molecule (fragment)	105	0
	C4T8T3	DRB-like molecule (fragment)	59	0
	C4T8T6	DRB-like molecule (fragment)	23	0
	Q2QCC6	V(D)J recombination-activating protein 1 (fragment)	162	0
	C4T8T4	DRB-like molecule (fragment)	50	0
	A0A3G9CMG7	MHC class I antigen (fragment)	61	0
	A0A3G9CMI2	MHC class I antigen (fragment)	49	0
	A0A3G9CP11	MHC class I antigen (fragment)	57	0
	A0A3G9CMI4	MHC class I antigen (fragment)	49	0
	U5JGX7	Cytochrome b	22	0
	A0A3G9CMG1	MHC class I antigen (fragment)	43	0
	U5JGW1	NADH-ubiquinone oxidoreductase chain 1	27	0
	B7ZK20	NADH-ubiquinone oxidoreductase chain 4 (fragment)	9	0
	G9M9K7	Mesotocin receptor (fragment)	12	0
	U5JGU2	NADH-ubiquinone oxidoreductase chain 6	2	0
	U5JGW3	NADH-ubiquinone oxidoreductase chain 5	8	0
	B7ZJS2	ATP synthase subunit a	1	0
	U5JGU0	Cytochrome c oxidase subunit 1	2	0
	D1KS77	NADH-ubiquinone oxidoreductase chain 2	1	0
	B7ZJX8	Cytochrome c oxidase subunit 2	6	0
	B7ZJT6	ATP synthase protein 8	1	0
unique to abnormal	B2WW97	Ornithine decarboxylase (fragment)	2	0

Proteins identified using the S. demersus protein database (Downloaded from UniProt on January 2020) were filtered for high confidence peptides. Comparison was made on Proteome Discoverer 2.0.

Table 3

Capillary Electrophoresis Fraction Comparison between Normal and Abnormal Penguin Plasma Using G. gallus NCBI Protein Database

	A/N	NCBI Acc #	protein description	#PSMs	score MS Amanda
unique to normal	(−)	BAE16337.1	ovotransferrin BB type	47	3170.43
	(−)	NP_001264422.1	α-1-antitrypsin precursor	37	1895.87
	(−)	ABF70173.1	apolipoprotein B	43	1726.98
	(−)	XP_418162.6	keratin, type I cytoskeletal 19	29	2282.34
	(−)	CAA76273.1	apolipoprotein AIV	13	1011.79
		1TFP	A Chain A, transthyretin	35	0
	(−)	NP_001156704.1	glutathione peroxidase 3 precursor	10	1244.25
	(−)	NP_001263286.1	lumican precursor	11	698.47
	(−)	XP_015143259.1	α-1-antiproteinase isoform X1	4	399.12
	(−)	NP_990320.2	fibrinogen γ chain precursor	9	452.51
	(−)	BAD32701.1	vitellogenin, partial	5	252.01
	(−)	AAA64694.1	complement C3 precursor	25	786.7
	(−)	XP_004937173.1	dnaJ homolog subfamily B member 5 isoform X1	68	4311.68
	(−)	XP_004935636.2	utrophin isoform X1	20	1278.33
	(−)	AAS92202.1	type I α-keratin 14	14	1155.4
	(−)	XP_025006915.1	α-1-antitrypsin isoform X1	10	611.66
	(−)	XP_015130883.1	eIF-2-α kinase activator GCN1 isoform X1	16	420.96
	(−)	AAA48630.1	B-G	12	50.99
shared between normal and abnormal	(−)	NP_990592.2	albumin precursor	355	35948.54
	(−)	AAA48592.1	apolipoprotein A-I precursor	265	30932.8
		XP_024998571.1	α-2-macroglobulin isoform X1	156	13563.35
	(+)	XP_015128103.2	keratin, type II cytoskeletal 8	15	549.28
	(−)	NP_990736.3	complement C3	36	2155.21
		BAU68261.1	keratin, type II cytoskeletal cochleal	13	549.28
		NP_001161155.1	fibrinogen β chain precursor	12	869.42
		NP_990340.2	keratin, type I cytoskeletal 19	15	1163.59
		XP_015155859.1	keratin, type II cytoskeletal 6B-like	17	1400.32
		NP_001258840.1	fibrinogen α chain isoform 1 precursor	14	639.03
unique to abnormal	(+)	XP_024998605.1	α-2-macroglobulin-like	58	4909.92
	(+)	XP_001233972.1	keratin, type I cytoskeletal 19	40	3118.05
	(+)	NP_001026562.2	ATP synthase subunit β, mitochondrial precursor	9	335.35
	(+)	XP_025008170.1	isocitrate dehydrogenase [NADP] cytoplasmic	7	552.07
	(+)	JAS03214.1	Z-linked ATP synthase, H+ transporting,	7	491.9
	(+)	NP_001001312.2	mitochondrial	14	1102.05
	(+)	NP_001268427.1	keratin, type I cytoskeletal 15	17	738.56
	(+)	Q01406.1	transthyretin isoform 2	8	388.04
	(+)	XP_015131556.2	Src substrate protein p85, Cortactin, P80	11	702.97
	(+)	XP_015139455.1	arf-GAP with SH3 domain, ANK repeat and PH domain	10	509.57
			centrosomal protein of 170 kDa

Proteins identified using the G. gallus protein database (Downloaded from NCBI on September 2020) were filtered for high confidence peptides. Comparison was made on Proteome Discoverer 2.0. A/N = relative abundance ratio between abnormal and normal plasma, + = increase in abnormal, and – = decrease in abnormal.

Table 4

Capillary Electrophoresis Fraction Comparison between Normal and Abnormal Penguin Plasma Using S. demersus NCBI Protein Database

	A/N	NCBI Acc #	protein description	#PSMs	score MS Amanda
unique to normal	(−)	KAF1443202.1	hypothetical protein FQV21_0001894, partial	66	3243.8
	(−)	KAF1460030.1	Apolipoprotein A-IV, partial	41	2831.5
	(−)	KAF1440412.1	Vitamin d-binding protein, partial	35	2665.58
	(−)	KAF1457930.1	Glutathione peroxidase 3, partial	48	4027.82
	(−)	KAF1451635.1	α-1-antiproteinase 2, partial	29	2240.39
	(−)	KAF1462588.1	Complement C4, partial	12	704.55
	(−)	KAF1462355.1	Antithrombin-III, partial	17	749.18
	(−)	KAF1448111.1	IgGFc-binding protein, partial	7	419.08
	(−)	KAF1445884.1	Lumican, partial	12	705.35
	(−)	KAF1448559.1	Kininogen-1, partial	9	607.52
	(−)	KAF1440420.1	Immunoglobulin J chain, partial	6	404.59
	(−)	KAF1436065.1	α-tectorin, partial	5	446.56
	(−)	KAF1442536.1	α-2-antiplasmin, partial	7	418.29
	(−)	KAF1461885.1	Vitellogenin-2, partial	15	250.62
	(−)	KAF1435349.1	Fibrinogen α chain, partial	6	215.12
		KAF1437685.1	Vitronectin, partial	3	0
	(−)	KAF1450162.1	Ceruloplasmin, partial	16	1011.78
	(−)	KAF1433771.1	α-2-macroglobulin, partial	16	298.93
	(−)	KAF1461872.1	Vitellogenin-1, partial	16	551.09
	(−)	KAF1463509.1	Hemoglobin subunit β, partial	6	602.3
	(−)	KAF1463870.1	Angiotensinogen, partial	23	698.96
	(−)	KAF1455435.1	Heparin cofactor 2, partial	4	290.54
		KAF1452016.1	Gelsolin, partial	4	0
	(−)	KAF1448583.1	α-2-HS-glycoprotein, partial	2	155.5
	(−)	KAF1460517.1	Keratin, type II cytoskeletal cochleal, partial	11	562.27
shared between normal and abnormal	(−)	KAF1440393.1	Serum albumin, partial	882	90551.6
		KAF1461539.1	Ovotransferrin, partial	428	29264.01
	(−)	KAF1455946.1	Apolipoprotein A-I, partial	478	49003.21
	(−)	KAF1433561.1	α-2-macroglobulin, partial	117	12637.97
	(−)	KAF1451546.1	α-1-antiproteinase 2, partial	129	11830.1
	(−)	KAF1464563.1	α-2-macroglobulin, partial	41	5923.86
	(−)	KAF1464562.1	Pregnancy zone protein, partial	48	4907.35
	(−)	KAF1433885.1	α-2-macroglobulin, partial	75	6403.21
		KAF1446481.1	Transthyretin, partial	31	847.92
		KAF1432482.1	hypothetical protein FQV21_0005409	22	2007.11
		KAF1460522.1	Keratin, type II cytoskeletal 4, partial	28	1337.94
		KAF1460299.1	Immunoglobulin heavy variable 3–23, partial	12	1941.7
		KAF1432484.1	Keratin, type I cytoskeletal 19, partial	29	2282.34
		KAF1447822.1	α-1-acid glycoprotein, partial	22	2467.13
		KAF1435377.1	hypothetical protein FQV21_0002155, partial	13	869.42
		KAF1432480.1	Keratin, type I cytoskeletal 19, partial	20	1163.59
	(+)	KAF1433649.1	Ovostatin, partial	12	0
		KAF1460518.1	Keratin, type II cytoskeletal 75, partial	23	1688.01
		KAF1460516.1	Keratin, type II cytoskeletal 75, partial	24	1637.88
	(+)	KAF1463905.1	Vascular non-inflammatory molecule 2, partial	6	529.87
unique to abnormal	(+)	KAF1442749.1	Haptoglobin, partial	79	6603.76
	(+)	KAF1460527.1	Keratin, type II cytoskeletal 6A, partial	40	3009.32
	(+)	KAF1440571.1	Isocitrate dehydrogenase [NADP] cytoplasmic,	6	547.32
		KAF1435572.1	partial	20	1251.63
	(+)	KAF1443465.1	Fibrinogen γ chain, partial	7	491.9
	(+)	KAF1455495.1	ATP synthase subunit α, mitochondrial, partial	7	148.21
	(+)	KAF1457308.1	hypothetical protein FQV21_0013721, partial	13	404.11
	(+)	KAF1450900.1	Src substrate protein p85, partial	11	292.36
	(+)	KAF1437491.1	ATP synthase subunit β, mitochondrial, partial	8	491.06
	(+)	KAF1437567.1	hypothetical protein FQV21_0002520, partial	59	2059.02
	(+)	KAF1440713.1	Protein bassoon, partial	13	633.47
	(+)	KAF1462098.1	Titin, partial	30	669.19
	(+)	KAF1440406.1	Ras GTPase-activating protein nGAP, partial	10	248.84
	(+)	KAF1458515.1	Ankyrin repeat domain-containing protein 17, partial	15	511
	(+)	KAF1446351.1	TBC1 domain family member 8B, partial	11	19.3
	(+)	KAF1445301.1	Transmembrane protein 51, partial	2	10.58
			Prostaglandin E2 receptor EP4 subtype, partial

Proteins identified using the S. demersus protein database (Downloaded from NCBI on September 2020) were filtered for high confidence peptides. Comparison was made on Proteome Discoverer 2.0. A/N = relative abundance ratio between abnormal and normal plasma, + = increase in abnormal, and – = decrease in abnormal.

Statistical Analysis

To determine the variability between samples, a multivariable dimension-reduction analysis was implemented. To compute total (common and unique) variance and fit each component variance in a linear model, a principal component analysis (PCA) was done using proteomic results from the G. gallus analysis (Figure A, left panel). From this analysis, two features become prominent: (1) the cluster of individual samples represents the variability within that dataset (abnormal plasma contains a higher variability when compared to normal) and (2) the 95% confidence region between the normal and abnormal samples does not overlap, indicating that these samples are compositionally distinct. To validate a categorical distinction (normal vs abnormal), a bilinear factor model analysis was implemented. The partial least squares discriminant analysis (PLS-DA) demonstrated that there is a distinct categorical separation between the two datasets, as no overlap is observed between the normal and abnormal samples (Figure A, right panel).

Figure 2

Fibrinogen β chain (FGB, F1NUL9) elevation is associated in disease. (A) Multivariate principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) for plasma samples demonstrating unique samples with no overlapping variance. (B) Volcano plot comparing normal and abnormal samples using UniProt database. Data normalization = log transformation, ratio of comparison: abnormal/normal, p-value threshold = 0.001, and group variance = equal. Significant proteins highlighted in magenta (F1NUL9, Fibrinogen β chain). (C) Volcano plot comparing normal and abnormal samples using NCBI database. Data normalization = log transformation, ratio of comparison: abnormal/normal, p-value threshold = 0.001, and group variance = equal. Significant proteins highlighted in magenta. (D) PANTHER 15.0 pathway analysis. Fisher’s exact test, correcting for the False Discovery Rate (FDR). Blood coagulation pathway, Fold Enrichment (47.56), raw p = 3.75 × 10–5, FDR = 6.04 × 10–3. The image created using BioRender.

Plasma Inflammatory Markers

Several proteins with significant changes in the abnormal plasma sample were identified using the chicken and African penguin databases (UniProt and NCBI). These changes included a significant increase in the fibrinogen β chain (FGB: F1NUL9, Q02020) in the abnormal plasma (Figure B). Although not statistically significant, fibrinogen α chain was also elevated in the abnormal plasma.

Additional changes included a significant decrease in albumin. As an important negative acute phase protein, decreases have been previously observed in birds with inflammatory processes and reported in African penguins with aspergillosis.[7] Decreased ceruloplasmin was also noted; as it is a positive acute phase protein (APP) in the chicken, the low levels here may be related to the severity of the chronic infection in this penguin.[8] This is contrasted by the increased levels of the positive APP haptoglobin. Glutathione peroxidase 3 levels were significantly decreased. In humans, this protein was found to be significantly decreased with severe inflammatory response syndrome.[9] Changes in keratin levels are likely consistent with liver injury related to the disease process.[10] Decreased levels vitellogenin and apolipoprotein may signify changes in lipid metabolic processes related to the disease process and/or inflammatory processes.[11] Increased isocitrate dehydrogenase levels may also be associated with lipogenesis.[12] The increase in prostaglandin E2 receptor EP4 also requires further investigation. Prostaglandin E2 is known to increase with inflammation. The EP4 receptor can be expressed in many tissues including platelets where it helps with controlling homeostasis.[13] The presence of the EP4 receptor in the plasma may reflect tissue or cellular damage related to the aspergillosis disease process. A decrease in kininogen was observed; this protein has a role in coagulation and inflammation.[14] Additional studies should be undertaken to examine repeated measures during infection to better understand the kinetics of these potential inflammatory biomarkers.

To understand the potential pathways over-represented in the abnormal sample, the PANTHER classification system was utilized using both the UniProt and NCBI G. gallus databases. These revealed that the abnormal sample has an increased activation of the blood coagulation pathway (raw p-value = 3.75 × 10–5 and FDR = 6.04 × 10–3), suggesting the potential role of clotting proteins in contributing to the shift observed in the CE electropherogram (Figures A and 2D).

Aspergillus Protein Analysis

Examination of the expression of protein relative to the Aspergillus protein database revealed several uncharacterized proteins and many be involved in cellular processes (Figure C, right panel). Of possible note is a decrease in isocitrate lyase. This enzyme has been reported to aid the persistence of Mycobacterium infection and other infectious agents that persist in macrophages.[15] In addition, increases were also observed in macrophages containing A. fumigatus conidia.[16] A more detailed examination of the other proteins would be best studied in in vivo models.

Fibrinogen as an Inflammatory Marker

Despite the various potential markers identified using the NCBI database, the UniProt databased identified fibrinogen as the only statistically significant marker, which highlights the advantage of using the UniProt database as fibrinogen was manually curated and annotated in the database. The presence of fibrinogen also corroborates the PANTHER pathway findings as both the NCBI and UniProt databases provided the same results.

Fibrinogen is known to migrate in the β globulin fraction of agarose gel electrophoresis and may be, in part, responsible for the electrophoretic shift seen in penguins with aspergillosis.[7] It is notable that fibrinogen β chain along with fibrinogen α chain is involved in the elevation of the blood coagulation pathway. In humans with infection by A. fumigatus, tissue necrosis and subsequent thrombosis (blood coagulation) have been reported.[17−20] Coagulation is the process in which a series of molecular events leads to the formation of a blood clot, whose primary role is to maintain homeostasis, prevent excessive hemorrhage, and allow tissue repair. This process requires an intricate network of interactions between different molecular components and is separated into three major pathways: (1) intrinsic, (2) extrinsic, and (3) common. The activation of the intrinsic and extrinsic pathways intersects by the activation of prothrombin to thrombin, which in turn cleaves fibrinogen into fibrin monomers (Figure D). Mediation of the coagulation pathway is important in maintaining homeostasis but upon disruption pathological manifestations occur, which often results in disease.

There are many cellular and molecular components within this pathway but during fungal infection platelet activation can be induced by secreted fungal factors, which in turn stimulates platelet’s antifungal properties. These include increased sensitivity to foreign particles, inhibit fungal growth, and increase host’s immune response. Despite the benefits provided by platelet activation, some harmful effects can be considered, which include excessive inflammation and the production of thrombotic factors. Such factors include fibrinogen, a soluble glycoprotein synthesized in the liver composed of three separate polypeptide chains (Aα, Bβ, and γ). Fibrinogen plays a crucial role in the regulation and propagation of pathological conditions, given that different biological functions have been attributed to this protein.[21] This diversity in biological function has been attributed to unique epitopes within fibrinogen that are associated with unique interactions and molecular cascades. This highlights fibrinogen as a pivotal regulator in pathological conditions making fibrinogen a potential target for therapeutic intervention.[21]

Fibrinogen has previously been described in experimental models in chickens and identified as a positive acute phase protein in this species.[8] This study is inclusive of increases with turpentine injection and in response to infection with Escherichia coli, Eimeria tenella, and Streptococcus. However, no increases were observed with infectious bursal disease virus. Despite the association of fibrinogen with inflammatory lesions,[22] due to the lack of accurate fibrinogen detection methods, increases in fibrinogen in Gentoo penguins with an inflammatory lesion named bumblefoot were undetected.[23] In addition, no increases were observed in recently captured Humboldt penguins and in Magellanic penguins, which were in rehabilitation facilities.[24,25] This calls for the development of epitope-specific detection methods, such as the generation of antibodies that accurately recognize fibrinogen across multiple species (Figure ). The area highlighted in red indicates a potential region that is conserved between multiple species and can be targeted for the development of antibody detection methods. By focusing on antibodies that recognize this region, an early detection method can be beneficial for monitoring the health of endangered species.

Figure 3

Fibrinogen conserved regions between multiple species. Sequence alignment using CLUSTAL Omega (1.2.4) demonstrates a region conserved between species and a potential target region for the development of antibodies against fibrinogen. Asterisk (*) = positions that have a single, fully conserved residue; colon (:) = conservation between groups of strongly similar properties - scoring >0.5 in the Gonnet PAM 250 matrix; and period (.) = conservation between groups of weakly similar properties - scoring ≤ 0.5 in the Gonnet PAM 250 matrix.

Conclusions

Our findings support the premise that monitoring thrombotic factors such as fibrinogen and in particular fibrinogen β chain is of important clinical relevance in monitoring the health status of African penguins in captivity. Currently, there are no standardized methods of fibrinogen measurement in avian species although an imprecise method used in older veterinary research studies is a manual heat precipitation method, which presents with many inaccuracies.[23] Importantly, several other hosts and Aspergillus proteins were also identified that also require further study.

The methodology implemented in this study demonstrates that there is a very distinct protein profile that contributes to the electropherogram shift between normal and abnormal penguin plasma. The CE system was able to accurately replicate the shift previously documented and was also able to collect the constituents comprising this shift. A shotgun proteomics approach was able to identify fibrinogen as the constituent most likely responsible for this shift; however, this shotgun proteomics approach comes with its limitations. These include a general protein profile that only takes into consideration major protein changes, which does not explain minor changes that can lead to the overall effect. To elucidate a more detailed proteomic profile, the isobaric tags for relative and absolute quantitation (iTRAQ) protein mass spectrometry is required for more specific and confident results. This method was implemented in a previous study comparing African penguins with aspergillosis at different disease time points.[26] However, fibrinogen was not able to be detected, which can be due to the variation in disease severity between samples.

Additional studies should be undertaken with other repeated measures from African penguins with aspergillosis. Of note, the current sample was obtained after the start of antifungal and very close to death. Samples provided earlier in the disease process may provide information on other biomarkers. Overall, while many of these observed protein changes are not specific for aspergillosis, they do reflect a greater understanding of the pathogenesis of aspergillosis and the acute phase response in this species. Although mass spectrometry is able to elucidate protein changes in avian plasma samples, the amount of time required to process and analyze samples often comes at the cost of the lives of these endangered species. The African penguin along with other avian species that are endangered require a quick and precise approach for monitoring their health decline and conservation. Mass spectrometry is not a cost-effective method that can be used in the field, but it only provides an avenue for the development of methods that can focus on a quick and accurate approach for monitoring health decline in endangered avian species.

Experimental Section

Acquisition of African Penguin (S. demersus) Samples

A 15 year old female penguin under managed care (Brookfield Zoo, Chicago, IL) was presented with weight loss over a 3 month period. A complete blood count revealed a significant leukocytosis and radiographs supported the presence of a respiratory infection. The animal received treatment with antibiotics and antifungal medication. It was noted that the appetite improved and attitude remained good. On recheck approximately 1.5 weeks later, a mild weight gain was present, the body condition was still poor, and the leukocytosis was improved but still present. Three days later, the animal was observed to have respiratory difficulty but still ate; the following day, the animal was found dead. Necropsy confirmed the presence of systemic aspergillosis. The samples that were used in this project included plasma obtained on the recheck date and a banked plasma sample approximately one year previous when the animal was in good health.

Separation via Capillary Electrophoresis (CE)

Fractions were separated using the Agilent Capillary Electrophoresis 7100 coupled with a bare fused silica capillary tube (Agilent, G1600–63311).[27] CE buffer consisted of 100 mM formic acid, 5 mM ammonium acetate, and 100 mM Tris at a pH of 7.3. The sample was prepared at a 1:10 ratio of raw penguin plasma to CE buffer. Parameters for the CE system included sample injection at 50 mbar for 10 s, high voltage at 30 KV, current set at 10 μA, power set at 6 W, and cassette temperature of 25 °C, coupled with a high-pressure system of 10 mbar for the duration of the separation (60 min). Signal was detected using UV absorbance at 280 nm (bandwidth 4 nm) and no reference wavelength. Fractions were collected in separate collection tubes from the region represented by brackets in the CE electropherogram (Figure ).

Sample Preparation for Protein Mass Spectrometry

Samples from CE fraction were added 4 times their volume of acetone at −20 °C and incubated overnight (about 16–18 h) at −20 °C. Samples were centrifuged at 10 000 g at 4 °C for 10 min (Beckman Microfuge 18). The supernatant was discarded and the pellet (very small) was air-dried for 10 min. The pellet was resuspended in 8 μL of 50 mM ammonium bicarbonate, pH 7.8. The sample was denatured in 6 M urea in 50 mM ammonium bicarbonate, pH 7.8 and reduced with 10 mM dithiothreitol in 50 mM ammonium bicarbonate, pH 7.8 for 1 h. Following reduction, the sample was alkylated in 15 mM iodoacetamide in 50 mM ammonium bicarbonate, pH 7.8 for 30 min while kept away from the light. Alkylation was quenched in 20 mM dithiothreitol in 50 mM ammonium bicarbonate, pH 7.8 for 1 h while kept away from the light. Urea in the sample was diluted to 1 M using 50 mM ammonium bicarbonate, pH 7.8 before enzymatic digestion. The sample was digested using trypsin (Promega, V5111) at a ratio of 1:30 (w/w) of enzyme to protein sample and incubated overnight (about 16–18 h) at 37 °C. Enzymatic reaction was terminated by the addition of 50% (v/v) formic acid in ultrapure water, corresponding to a ratio of 5:100 (v/v) of 50% formic acid to sample volume. Samples were either stored at −20 °C or immediately processed for desalting and protein enrichment. Desalting and protein enrichment was carried out using the Pierce Graphite Spin Columns (Thermo, 88302) using the manufacturer’s recommendations. Samples were evaporated using a CentriVap concentrator system (Labconco, 7810016) coupled with a CentriVap cold trap (Labconco, 7811020) and resuspended in 30 μL of 0.1% (v/v) formic acid, 2% (v/v) acetonitrile, in mass spectrometry grade ultrapure water.

High-Performance Liquid Chromatography-Mass Spectrometry

Mass spectrometry proteomics was carried out as described in detail in our previous publication for a total of 3 technical replicates.[28] In brief, mass spectrometry was performed on a Q-Exactive instrument after fractionation on a coupled Easy nLC 1000 nano-liquid chromatography system (Thermo Fisher Scientific). This was equipped with the Acclaim PepMap C18 RSLC Analytical column, Nano Viper, 75 μm x 15 cm diameter and length, 2 μm particle size (Cat. # 164534, Thermo Fisher Scientific). The following gradient was set up using Thermo Scientific Xcalibur (Version 4.1.31.9, Released 2017), 20 min of 2% solvent B, 20 min of 30% solvent B, 15 min of 40% solvent B, 20 min of 70% solvent B, and 30 min of 98% solvent B, all at a constant rate flow of 350 nL/min. Data was acquired using Thermo Scientific Xcalibur software (Version 4.1.31.9, Released 2017), proteins were identified and analyzed using Proteome Discoverer 2.2 (Version 2.2.0.388, Released 2017) and MS Amanda 2.0 (Version 2.0, Released 2017). UniProt and NCBI sequence databases were used for the identification of proteins G. gallus, S. demersus, and A. fumigatus (UniProt: downloaded January 2020, NCBI: downloaded September 2020). Proteome Discoverer search parameters for trypsin-digested enzymes (max missed cleavage sites: 2, min. peptide length: 6, max peptide length: 144), dynamic post-translational modifications: oxidation +15.995 Da (M), acetylation +42.011 Da (N-terminus), static post-translational modifications: carbamidomethylation +57.021 Da (C), max modification per peptide: 3, precursor mass tolerance: 10 ppm, fragment mass tolerance: 0.02 Da, signal/noise threshold for spectra: 1.5, false discovery rate: strict for PSMs 0.01, strict for peptides 0.01. In brief, false discovery rates are calculated as follows: first, the software ascertains whether there are q-values and PEPs available for PSMs. If so, the software uses them and assigns the PSM confidences based on Target FDRs for PSMs. Next, the software calculates q-values and PEPs for peptides engaging the Qvality algorithm. Peptide confidence is then assigned based on Target FDRs for peptides. If there are no q-values and PEPs available for PSMs, the PSM confidence is set based on our defined Target FDRs for PSM employing the respective search engine scores.

Data from Proteome Discoverer was analyzed using MetaboAnalyst 4.0.[29] Data was normalized to a generalized logarithm transformation. The multivariate principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) displayed a 95% confidence region with specific principal component 1 on X1-axis and principal component 2 on X2-axis. The volcano plot analysis had a fold change threshold of 1.0, a comparison type ratio of abnormal to normal, nonparametric test, p-value threshold of 0.001 (raw data), and equal group variance. A PANTHER overrepresentation test for pathway analysis was carried out using the PANTHER classification system (PANTHER version 15, released February 2020).[30,31] The G. gallus reference database was used for this analysis using Fisher’s exact test, correcting using the calculated false discovery rate and setting threshold for a false discovery rate of p < 0.05.