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Original article
02 2022
:35;
102474
doi:
10.1016/j.jksus.2022.102474

Prediction of protein aggregation on key proteins involved in ischemic stroke

, , , , , , ,
a Department of Biology, College of Science in Zulfi, Majmaah University, Majmaah 11952, Saudi Arabia
b Department of Medical Laboratory Sciences, College of Applied Medical Sciences, Majmaah University, Majmaah 11952, Saudi Arabia
c Greenlink Analytical and Research Laboratory India Private Limited, Coimbatore 641 014, India
d Department of Medical Laboratory Science, School of Pharmacy and Medical Laboratory Science, Institute of Health, Bule Hora University, Post Box Number – 144, Ethiopia
e Department of Infectious Diseases, St. Jude Children's Research Hospital, Danny Thomas Place, Memphis 38105, TN, United States of America
f Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, 23955, Saudi Arabia

⁎Corresponding author. alaguraj.veluchamy@kaust.edu.sa (Alaguraj Veluchamy)

Received: , Accepted: ,
© The Author(s) 2022
Disclaimer:
This article was originally published by Elsevier and was migrated to Scientific Scholar after the change of Publisher.

Peer review under responsibility of King Saud University.

Abstract

Stroke is a genetic condition comprising multiple subtypes and arising from both genic and other multi factors. Genetic basis of stroke is well established through several studies. Advances in integrating sequencing methods and Genome-wide association studies have shown that genetics of stroke is manifested in several genic disorders. Many of the neurodegenerative disorders show aggravated protein aggregation through amyloid formation. Through the protein aggregation prediction, we observed a higher protein disorder in 46 stroke-associated proteins. Also, we observed a large number of aggregation residues distributed as a pattern in multiple regions of these candidate proteins. Overall, we present a study showing that there is a possible interrelationship between protein aggregation and stroke.

Keywords

Genetic disorder
Ischemic stroke
Protein aggregation
Gene ontology
KEGG pathway
1

1 Introduction

Stroke is a complex heterogenous condition that is one of the major causes of ailment and death in the world. Characterizing stroke involves classification into subtypes. Several classification systems have been proposed to differentiate subtypes of stroke and distinguish between ischemic and hemorrhagic stroke, subarachnoid hemorrhage, cerebral venous thrombosis, and spinal cord stroke (Amarenco et al., 2009). Studies such as the association of monozygotic twins to stroke provide the evidence of implication of genetic factors in stroke pathophysiology. Next generation sequencing (NGS), genome-wide association studies (GWAS) provided evidence that stroke is both a monogenic and a polygenic disorder. Both above classification systems and genetic studies are vital in grouping patients for therapeutic purposes.

Whole-genome sequencing studies have resulted identifying novel variants associated with stroke subtypes. A genome-wide association study through Trans-Omics for Precision Medicine (TOPMed) Program identified 5 novel loci associated with subtypes of stroke in a multi-ancestry population (Hu et al., 2022). Besides this, MEGASTROKE consortium have performed genotyping and GWAS studies that resulted in identification of stroke risk variants such as NKX2-5, ANK2, LRCH1, REEP3, JAZF1(de Vries et al., 2019; Malik et al., 2018).

Although multiple stroke specific risk loci are detected, functional characterization of these loci are challenging as these variants fall mostly on non-coding regions of the genome. Hence recent approaches use data from gene expression, DNA methylation to establish a causal relationship to stroke susceptible genes. Analysis of candidate genes involved in ischemic stroke resulted in identification of at least five susceptibility genes such as factor V Leiden Gln506, ACE I/D, MTHFR C677T, prothrombin G20210A, PAI-1 5G (Bentley et al., 2010).

Emerging evidence shows that protein aggregates formed in Ischemic stroke. Misfolded proteins tend to form fibers of aggregates (Hu et al., 2001). In particular ischemic stroke (Luo et al., 2013); stroke and aggregation (Zhang et al., 2020). Protein aggregates are found to be form deposits in degenerate cells and are involved in cellular toxicity (Tutar et al., 2013). Several neurological disorders such as Parkinson’s Disease (PD), Huntington’s disease (HD), prion diseases, Amyotrophic lateral sclerosis (ALS) are associated with the formation of protein aggregate (Pedersen & Heegaard, 2013). Aβ-peptide (1–40/1–42) forms amyloid plaque in regions such as cortex, hippocampus and forebrain. Proteins such as Tau, α-synuclein, Ataxins, superoxide dismutase (SOD1) and RNA binding proteins TDP43, FUS, TAF15 are found to form lewy bodies, intranuclear inclusion, axonal spheroids and cytoplasmic aggregates (Kumar et al., 2016).

Efforts on analysis of protein aggregation and characterization shown significant improvement in the development of protein aggregation prediction methods. More than 20 different computational algorithms are available now for the prediction of protein aggregation based on amino acid sequences (Santos et al., 2020). Tools such as TANGO, PASTA2.0, AGGRESCAN uses either protein features or experimental data to predict protein aggregation (Conchillo-Solé et al., 2007; de Groot et al., 2005; Walsh et al., 2014). Taking advantage of the available methods of protein aggregation prediction and sequences available on the stroke dataset, here we explored the connection or common theme between the above stroke and amyloid formation.

2

2 Methods

2.1

2.1 Dataset of candidate genes associated with disorders related to stroke

We performed text mining and sequence database search through pubmed to obtain a base dataset of genes associated with Ischemic stroke. As stroke is linked to both monogenic and polygenic disorders, we obtained list of genes reported earlier (Ekkert et al., 2022). Each of these genes in the dataset have certain impact on stroke pathogenesis. Mutiple literature and database search resulted in a dataset of 46 genes linked to risk of stroke.

2.2

2.2 Functional annotation of genes linked to stroke disorders

Curated canonical protein sequences are obtained from UniProtKB/Swiss-Prot protein sequence database (The UniProt Consortium, 2021). Only full-length protein sequences are used for each of these genes. Alternative sequences for each ids are avoided to remove redundancy and only unique sequences are further analyzed. For functional annotation of gene list, DAVID knowledgebase which is a webserver for bioinformatics resource providing functional enrichment analysis is utilized (Sherman et al., 2022).

2.3

2.3 Prediction of protein aggregation in stroke disorder associated protein sequences

To evaluate the tendency of proteins associated with stroke to form protein aggregate, we performed analysis whether these sequences forms β-sheet enriched secondary structure conformation. Using a pairwise energy potential, intrinsic disorder and secondary structure, protein aggregation calculation for the candidate protein sequences were performed in PASTA2.0 webserver (Walsh et al., 2014).

2.4

2.4 Amyloidogenic region in the protein sequences

Smaller fragments of regions in the protein sequences responsible for the amylodogenesis (Ivanova et al., 2004). These regions are composed of aminoacids which are unique and distinct from other non-aggregating regions or peptides. Using expected contact of the residues in the protein sequences, amyloidogenic regions are predicted (Garbuzynskiy et al., 2010).

3

3 Results

We have shown here that the protein aggregation might occur among the candidate proteins involved in stroke associated disorders. The pathophysiology between the neurodegenerative disorders and protein aggregation are shown to be shared. Our approach has shown that there could be a significant overlap between the pathophysiology of amyloid formation and ischemic stroke.

3.1

3.1 Genes involved in monogenic and polygenic disorders associated with stroke

Around 46 genes related to stroke associated disorders are retrieved from different databases. These genes are found to have around 687 splice variants in the UniprotKB. We used full length canonical protein sequences for further analysis. Annotation of genes retrieved through DAVID shows that most genes are related to signaling function including receptors (Table1). Multiple candidate genes functions have stroke phenotypic manifestation.

Table 1 Candidate gene ids and their functional annotation of genes involved in stroke associated disorders.
SI From Species David Gene Name
1 NOTCH3 Homo sapiens notch receptor 3(NOTCH3)
2 FOXC1 Homo sapiens forkhead box C1(FOXC1)
3 CASZ1 Homo sapiens castor zinc finger 1(CASZ1)
4 WNT2B Homo sapiens Wnt family member 2B(WNT2B)
5 LINC01492 Homo sapiens long intergenic non-protein coding RNA 1492(LINC01492)
6 HTRA1 Homo sapiens HtrA serine peptidase 1(HTRA1)
7 ADCY2 Homo sapiens adenylate cyclase 2(ADCY2)
8 PRPF8 Homo sapiens pre-mRNA processing factor 8(PRPF8)
9 HDAC9 Homo sapiens histone deacetylase 9(HDAC9)
10 ABO Homo sapiens ABO, alpha 1–3-N-acetylgalactosaminyltransferase and alpha 1–3-galactosyltransferase(ABO)
11 ZCCHC14 Homo sapiens zinc finger CCHC-type containing 14(ZCCHC14)
12 EDNRA Homo sapiens endothelin receptor type A(EDNRA)
13 SH3PXD2A Homo sapiens SH3 and PX domains 2A(SH3PXD2A)
14 CBS Homo sapiens cystathionine beta-synthase(CBS)
15 PITX2 Homo sapiens paired like homeodomain 2(PITX2)
16 ZNF566 Homo sapiens zinc finger protein 566(ZNF566)
17 NKX2-5 Homo sapiens NK2 homeobox 5(NKX2-5)
18 SH2B3 Homo sapiens SH2B adaptor protein 3(SH2B3)
19 HABP2 Homo sapiens hyaluronan binding protein 2(HABP2)
20 RGS7 Homo sapiens regulator of G protein signaling 7(RGS7)
21 FGA Homo sapiens fibrinogen alpha chain(FGA)
22 ZFHX3 Homo sapiens zinc finger homeobox 3(ZFHX3)
23 FOXF2 Homo sapiens forkhead box F2(FOXF2)
24 TREX1 Homo sapiens three prime repair exonuclease 1(TREX1)
25 ABCC6 Homo sapiens ATP binding cassette subfamily C member 6(ABCC6)
26 ANK2 Homo sapiens ankyrin 2(ANK2)
27 PDZK1IP1 Homo sapiens PDZK1 interacting protein 1(PDZK1IP1)
28 TBX3 Homo sapiens T-box transcription factor 3(TBX3)
29 MMP12 Homo sapiens matrix metallopeptidase 12(MMP12)
30 COL3A1 Homo sapiens collagen type III alpha 1 chain(COL3A1)
31 LRCH1 Homo sapiens leucine rich repeats and calponin homology domain containing 1(LRCH1)
32 CDK6 Homo sapiens cyclin dependent kinase 6(CDK6)
33 GAL Homo sapiens galanin and GMAP prepropeptide(GAL)
34 COL4A2 Homo sapiens collagen type IV alpha 2 chain(COL4A2)
35 COL4A1 Homo sapiens collagen type IV alpha 1 chain(COL4A1)
36 PDE3A Homo sapiens phosphodiesterase 3A(PDE3A)
37 KCNK3 Homo sapiens potassium two pore domain channel subfamily K member 3(KCNK3)
38 LOC100505841 Homo sapiens zinc finger protein 474-like(LOC100505841)
39 FBN1 Homo sapiens fibrillin 1(FBN1)
42 ILF3 Homo sapiens interleukin enhancer binding factor 3(ILF3)
43 CDKN2A Homo sapiens cyclin dependent kinase inhibitor 2A(CDKN2A)
44 ZNF318 Homo sapiens zinc finger protein 318(ZNF318)
45 FURIN Homo sapiens furin, paired basic amino acid cleaving enzyme(FURIN)
46 TM4SF4 Homo sapiens transmembrane 4 L six family member 4(TM4SF4)
47 PMF1 Homo sapiens polyamine modulated factor 1(PMF1)
48 SMARCA4 Homo sapiens SWI/SNF related, matrix associated, actin dependent regulator of chromatin, subfamily a, member 4(SMARCA4)

3.2

3.2 Prediction of protein aggregation

Formation of amyloid aggregates is implicated in several neurodegenerative disorders. We use protein disorder as a scale for predicting protein aggregation. Propensity of aggregation remains relatively similar across multiple methods for candidate stroke related genes (Fig. 1). We further used PASTA2.0 to determine the percentage of protein disorder, number of amyloid within the protein sequence, percentage of α-helix, percentage of β-strand etc. Percentage disorder of proteins vary ranging from 1 to upto 80 for stroke associated genes. For a random dataset of non-stroke related genes this range from 1.5 to 63. Median value differs significantly between these two groups of proteins (Fig. 2). Statistical test (t-test) using R between the above two groups of proteins was performed. This test reveals a significantly lower p-value (p-value = 0.007744). This is highly significant and percentage disorder is higher for stroke associated genes.

Consensus methods predicting same amyloid regions.
Fig. 1
Consensus methods predicting same amyloid regions.
Boxplot showing the differences in the disorder among two groups of proteins.
Fig. 2
Boxplot showing the differences in the disorder among two groups of proteins.

3.3

3.3 Residue based prediction of aggregation specific protein region

Each protein sequences are predicted to have atleast 20 poteintial amyloid forming short amino acid sequence pattern (Table 2). Consensus pattern derived from multiple amyloid predicting tools such as TANGO, AGGRESCAN, WALTZ through FoldAmyloid shows that these patterns are detected in all methods (Fig. 1). For example, in protein WNTB-2B, these aggregation forming residues are distributed in 14 different sites (Fig. 3). Most of these patterns are 4–14 aminoacid length (Fig. 4).

Table 2 Protein aggregation propensity for the genes involved in stroke associated disorders.
Protein name (from fasta header) Length # Amyloids Best energy % disorder % α-helix % β-strand % coil
spQ08462 1091 20 −39.221871 4.216 64.25 8.07 27.68
spO95255 1503 20 −19.941325 8.715 59.41 6.25 34.33
spQ9Y2L9 728 20 −19.362124 27.33 42.58 10.03 47.39
spP09958 794 20 −18.84564 18.26 15.49 25.06 59.45
spQ13113 114 20 −17.268009 16.66 48.25 8.77 42.98
spO14649 394 20 −17.165792 28.17 65.48 2.79 31.73
spP25101 427 20 −16.917126 7.259 64.64 6.32 29.04
spQ9UM47 2321 20 −15.802264 13.09 17.36 16.59 66.05
spP48230 202 20 −15.752635 7.92 54.46 2.97 42.57
spQ6P2Q9 2335 20 −14.995744 1.498 46.55 13.28 40.17
spQ14432 1141 20 −14.680867 33.74 41.89 7.01 51.1
spP16442 354 20 −14.397722 3.672 33.9 16.95 49.15
spP02671 866 20 −10.907301 35.33 16.17 24.02 59.82
spQ01484 3957 20 −10.281311 42.91 26.38 14.43 59.19
spP35520 551 20 −10.195829 15.06 37.75 14.7 47.55
spQ5TCZ1 1133 20 −9.781373 45.18 11.92 21.27 66.81
spQ15911 3703 20 −9.775551 52.17 32.19 8.37 59.44
spQ93097 391 20 −9.619822 10.48 45.78 14.07 40.15
spQ8N726 132 20 −9.546539 66.66 11.36 14.39 74.24
spQ12906 894 20 −9.535643 47.2 28.75 10.18 61.07
spQ00534 326 20 −9.230796 13.49 43.86 16.87 39.26
spQ92743 480 20 −8.959704 13.33 18.96 30.83 50.21
spQ9UQQ2 575 20 −8.630576 45.04 24.35 14.61 61.04
spQ12947 444 20 −8.290021 52.02 22.52 3.83 73.65
spP51532 1647 20 −8.254159 50.15 46.63 5.22 48.15
spQ9UKV0 1011 20 −8.19617 29.57 40.26 8.51 51.24
spP35555 2871 20 −7.913752 3.065 4.25 31.8 63.95
spP39900 470 20 −7.626874 4.042 23.83 23.83 52.34
spP49802 495 11 −7.622278 11.11 52.93 2.42 44.65
spP02462 1669 20 −7.454205 83.1 2.64 8.63 88.74
spQ14520 560 20 −7.387258 6.607 14.46 26.43 59.11
spQ9NSU2 314 20 −6.854083 27.7 40.45 6.69 52.87
spP08572 1712 20 −6.646477 62.44 2.69 9.35 87.97
spO15119 743 9 −6.556453 32.03 28.4 11.84 59.76
spQ12948 553 6 −6.366574 65.82 22.78 6.69 70.52
spQ5VUA4 2279 11 −6.276438 50.89 27.29 10.79 61.91
spQ969W8 418 1 −5.915617 3.588 22.73 18.66 58.61
spP42771 156 7 −5.761273 28.2 50 0 50
spQ86V15 1759 20 −5.735543 44.11 19.56 17.45 62.99
spQ8N6F7 178 4 −5.629332 28.08 21.91 13.48 64.61
spP22466 123 3 −5.60644 80.48 52.03 0 47.97
spQ99697 317 3 −5.452123 34.7 37.54 3.79 58.68
spQ8WYQ9 949 1 −5.372969 50.36 18.02 16.97 65.02
spP02461 1466 2 −5.361421 77.96 3.96 6.48 89.56
spQ6P1K2 205 1 −5.021246 22.43 76.59 0 23.41
spP52952 324 0 −4.668274 15.12 30.56 11.11 58.33
Contact frequency predicted for different residues across the protein sequence is shown here.
Fig. 3
Contact frequency predicted for different residues across the protein sequence is shown here.
Amyloidogenic residues predicted for human Wnt-2b protein.
Fig. 4
Amyloidogenic residues predicted for human Wnt-2b protein.

4

4 Discussion

Analysis of protein aggregation from 46 protein sequences implicated in stroke manifestation shows that large number of proteins form amyloids with varying degree of protein disorder. The role of protein aggregation and amyloid formation in the neurodegenerative diseases are well established (Pedersen & Heegaard, 2013; Tutar et al., 2013). Also, earlier studies have shown that there is higher levels or induction of protein aggregation after cerebral Ischemia (Wu & Du, 2021). Compared to the random datasets of proteins from UniprotKB, these proteins are found to have higher percentage of protein disorder. This could arise from the β-strand composition of these sequences. Furthermore, structural variation including SNP and small indels in these genes could possibly contribute to the amyloid formation. Our work contributes to the evidence that protein aggregation could be implicated in stroke disorder. Further research following our findings would improve our knowledge on the molecular level overlap between these two related process and disorder. More experimental evidences are needed for implicating the above listed genes (their products) in the protein aggregation. Establishing mouse models for stroke and investigating the aggregation through electron microscopy, laser-scanning confocal microscopy, and Western blotting could help further.

5

5 Conclusion

In the present study, we analyzed the aggregation properties of stroke related proteins. We selected a set of stroke-associated candidate proteins and a set of random control dataset. Overall, we observed that most proteins associated with stroke have higher protein disorder compared to a random dataset of protein sequences. These amyloids forming aggregating residues are distributed anywhere between the N-terminal and C-terminal part of the sequence of these candidate proteins. We found the contact frequency profile value of multiple residues are higher than average expected value and is part of disordered region related to protein conformation. Our study suggests that there is an overlap in pathophysiology of protein aggregation, neurological disorders and stroke related disorders.

Acknowledgment

The authors extend their appreciations to the deputyship for Research & Innovation, Ministry of Education in Saudi Arabia for funding this research work through the project number (lFP-2020-38).

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Appendix A

Supplementary material

Supplementary data to this article can be found online at https://doi.org/10.1016/j.jksus.2022.102474.

Appendix A

Supplementary material

The following are the Supplementary data to this article:

Supplementary data 1


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