Cholesterol

Volume 2014 (2014), Article ID 639751, 19 pages

http://dx.doi.org/10.1155/2014/639751

Research Article

A Comprehensive In Silico Analysis of the Functional and Structural Impact of Nonsynonymous SNPs in the ABCA1 Transporter Gene

Francisco R. Marín-Martín,¹ Cristina Soler-Rivas,¹ Roberto Martín-Hernández,² and Arantxa Rodriguez-Casado2,3

¹Department of Production and Characterization of New Foods, Institute of Food Science Research (CIAL), UAM-CSIC, Campus de Cantoblanco, 28049 Madrid, Spain

²IMDEA Food Institute, Campus de Cantoblanco, 28049 Madrid, Spain

³Nutritional Genomics of the Cardiovascular Disease and Obesity, IMDEA Food Institute, Carretera Cantoblanco 8, 28049 Madrid, Spain

Received 30 April 2014; Revised 7 July 2014; Accepted 24 July 2014; Published 19 August 2014

Academic Editor: Akihiro Inazu

Copyright © 2014 Francisco R. Marín-Martín et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

Disease phenotypes and defects in function can be traced to nonsynonymous single nucleotide polymorphisms (nsSNPs), which are important indicators of action sites and effective potential therapeutic approaches. Identification of deleterious nsSNPs is crucial to characterize the genetic basis of diseases, assess individual susceptibility to disease, determinate molecular and therapeutic targets, and predict clinical phenotypes. In this study using PolyPhen2 and MutPred in silico algorithms, we analyzed the genetic variations that can alter the expression and function of the ABCA1 gene that causes the allelic disorders familial hypoalphalipoproteinemia and Tangier disease. Predictions were validated with published results from in vitro, in vivo, and human studies. Out of a total of 233 nsSNPs, 80 (34.33%) were found deleterious by both methods. Among these 80 deleterious nsSNPs found, 29 (12.44%) rare variants resulted highly deleterious with a probability >0.8. We have observed that mostly variants with verified functional effect in experimental studies are correctly predicted as damage variants by MutPred and PolyPhen2 tools. Still, the controversial results of experimental approaches correspond to nsSNPs predicted as neutral by both methods, or contradictory predictions are obtained for them. A total of seventeen nsSNPs were predicted as deleterious by PolyPhen2, which resulted neutral by MutPred. Otherwise, forty two nsSNPs were predicted as deleterious by MutPred, which resulted neutral by PolyPhen2.

1. Introduction

Nonsynonymous single nucleotide polymorphisms (nsSNPs) are single base changes in coding regions that cause an amino acid substitution in the correspondent proteins. These missense variants constitute the most identifiable group of SNPs represented by a small (<1%) proportion [1]. The nsSNPs might alter structure, stability, and function of proteins and produce the least conservative substitutions with drastic phenotypic consequences [2–5]. Studies suggest that about 60% of Mendelian diseases are caused by amino acid exchanges [6]. Thousands of associations between Mendelian and complex diseases reveal a phenotypic code that links each complex disorder to a unique set of Mendelian loci [7]. Discriminating disease-associated from neutral variants would help to understand the genotype/phenotype relation and to develop diagnosis and treatment strategies for diseases. Nonetheless, the most important application is the evaluation of functional effect and impact of genomic variation, relating interactions with phenotypes translating the finding into medical practices.

ATP-binding cassette transporter ABCA1 gene also known as the cholesterol efflux regulatory protein (CERP) encodes a 220 kDa protein [8]. This protein is crucial for reverse cholesterol transport and is considered as an important target in antiatherosclerosis treatment. ABCA1 mediates the efflux of cholesterol and phospholipids to lipid-poor apolipoproteins (apoA1 and apoE), which form nascent high-density lipoproteins (HDL). ABCA1 resides on the cell membrane and has an extensive intracellular pathway, with rapid movement of the transporter between the cell membrane and intracellular vesicles [9]. ABCA1 is present in higher quantities in tissues that transfer or are involved in the turnover of lipids such as the liver, the small intestine, and adipose tissue [10–12]. As well, lipid export activity of ABCA1 improves the function of pancreatic cells and ameliorates insulin release [13], reduces biliary cholesterol content protecting against gallstone [14], and plays a key role in lipid homeostasis in the lung [15]. Besides, evidence suggest a causal link between ABCA1 as cholesterol transporter and its antitumor activity [16, 17], as well as its implication in brain cholesterol homeostasis [15–20] founding lipid and myelin abnormalities in schizophrenia and Alzheimer’s disease [18–20].

Although the entire ABCA1 protein 3D-structure remains unknown electron microscopic studies suggest a structural model consisting of a transmembrane domain (TMD) and a nucleotide-binding domain (NBD) (Figure 1) [14, 21, 22], where an NBD-TMD dimer is the minimum unit required for transport function [22, 23]. Also, X-ray structures are available for different domains in the C-terminus protein essential for lipid efflux activity [24, 25]. Many variants disrupting the normal ABCA1 protein function result in modest or no circulating HDL [26–32]. Cholesterol accumulated within cells produces a toxicity that impairs cell function leading to a diversity of phenotypes, from severe disease states to mild impacts on health. In fact, the ABCA1 variability is associated with myocardial infarction, cancer, type 2 diabetes, and metabolic syndrome [33]. Heterozygous states, nearly one-third of them, are associated with hypoalphalipoproteinemia, known as familial HDL deficiency syndrome (FHA). Two copies cause a more severe syndrome Tangier disease (TD) [34–38] described by reduced HDL-c plasma level (<5%), impaired cholesterol efflux, and a trend to accumulate intracellular cholesterol [34–43]. Indeed, loss of function of ABCA1 mutations in TD patients has a major impact on lipoprotein metabolism. A failure to acquire apolipoproteins leads to a rapid catabolism of lipid-poor apoA1 and accumulation of lipids in macrophages, intestinal cells, platelet, and hepatocytes [34–38, 44]. Compared with unaffected family members, heterozygotes and homozygotes have a more prevalent, premature, and severe atherosclerosis [42].

Figure 1: Predicted structure of ABCA1 [21, 23, 105]. The protein consists of 2261 amino acids and comprises 2 halves of similar structure. Each half encodes a transmembrane domain containing 6 helices (1–6 and 7–12) and 1 nucleotide binding domain (NBD-1 and NBD-2), where ATP is bound and utilized as energy for substrate transport across the membrane. Each NBD contains 2 highly conserved peptide motifs known as Walker A and Walker B, which are present in many proteins that use ATP and a Walker C signature unique to ABC transporters. ABCA1 is predicted to have an N terminus oriented into the cytosol and two large extracellular loops that are highly glycosylated and linked by cysteine bonds (Y indicates the glycosylation sites, and S–S indicates predicted disulfide bonds).

Because high levels of HDL-c are atheroprotective there is considerable interest in developing agents that act to increase ABCA1 expression and thereby raise plasma HDL-c levels. The nsSNPs are important indicators of action sites and effective potential therapeutic approaches. Therefore, it is crucial to identify deleterious nsSNPs to characterize the genetic basis of diseases, assess individual susceptibility to these diseases, determinate molecular and therapeutic targets, and predict clinical phenotypes. Beyond the genetic level, a disease depends on the sequence and the structural location of the nsSNPs of the protein. While the nsSNPs occur all through the ABCA1 gene, they tend to cluster in the extracellular loops, the NBD, and the COOH-terminal region (Figure 1). In fact, three structural motifs have been functionally associated with disease: the ARA motif, an interface between NBD and TMD that forms a partially buried α-helix able to interact with the transmembrane helices, the conserved-loop 1, a allosteric loop between the membrane and globular domains, and the conserved-loop 2, an interaction surface for intracellular partners, critical in ATP-binding.

Even though many of nsSNP (rare or common) found in human ABCA1 have been identified, mainly in the HapMap project (http://hapmap.ncbi.nlm.nih.gov/), the molecular bases relating these variants and the caused phenotypes have not been studied in detail. To explore the effect of the large number of nsSNPs ABCA1 by experimental approaches would be extremely time-consuming and with low statistical chance. Alternatively, bioinformatic approaches, based on the biophysical severity of the amino acid exchange and the protein sequence and structural information, can offer a more feasible phenotype prediction. As such, MutPred (mutation prediction) [45] and PolyPhen2 (polymorphism phenotyping 2) algorithms [46], were used in this study to investigate the impact of all known nsSNPs on ABCA1 protein function. Besides, based on the results of in vitro, in vivo, and human studies of this gene in the literature we validated the predictions made by reviewing the effect of the most critical nsSNPs in ABCA1 gene and its pathological consequences.

2. Methods

Figure 2 shows the workflow designed to predict the nsSNPs effect on ABCA1 protein. ABCA1 human gene variants including SNPs, short insertions, and deletions were retrieved from Ensemble Variation 72 database 3141. Mutations were annotated using the SnpEff v3.2 toolbox [47] based on the human genome assembly GRCh37.68. Only variants found on the canonical transcript were considered for functional effect prediction, for what we used two different algorithms. PolyPhen2 (http://genetics.bwh.harvard.edu/pph2/) algorithm uses a naive Bayesian classifier to predict allele function based on a combination of sequence and structure-based attributes (if available) [46]. It calculates the probability for a given mutation to be benign, possibly damaging, or probably damaging. Then, we used MutPred (http://mutpred.mutdb.org/) [45] based upon SIFT algorithm [48] and a gain/loss of 14 predicted structural and functional properties. The predicted mutation outcome is based on a random forest (RF) classifier. The MutPred output includes the top 5 property scores and a general score (RF) equal to the probability of amino acid exchange is either deleterious or disease-associated. The ROC curves for both methods were generated using R programming language and the ROCR package (Figure 3) by a variation dataset obtained from VariBench (http://structure.bmc.lu.se/VariBench/) that contains mutations affecting protein tolerance including a neutral set of mutations comprising 17393 human coding nsSNPs and a pathogenic set of 14610 missense mutations obtained by manual curation from the PhenCode database.

Figure 2: Workflow for predicting the effect of nsSNPs on ABCA1 protein. Gene specific variations were retrieved from the main variation databases. Mutations were annotated and nsSNPs belonging to the canonical transcript were selected in order to run the algorithms for functional prediction.

Figure 3: ROC (receiver operating characteristics) curve comparing the performance of MutPred and PolyPhen2 methods in predicting the outcome of nsSNPs. The dashed curve corresponds to MutPred predictions. As the area under the curve (AUC) for the MutPred method predictions is larger than the areas over the curve (AOC) which corresponds to the PolyPhen2 predictions curve, we can confirm that MutPred outperforms PolyPhen2 in predicting the outcome of nsSNPs. The dataset used for the performance comparison was obtained from Varibench as stated in Methods section.

Prediction accuracy accomplished by MutPred and PolyPhen2 depends on their specific criterion. Twelve structural and six sequence-based properties were used in this study (Table 1). About 28% of validated nsSNPs in the Human Genome Variation Database are predicted to affect protein function [49]. Similarly, about 25% of nsSNPs affecting protein activity was predicted by PolyPhen2 [49]. MutPred offers classification accuracy with respect to human disease mutations. Considering conservative thresholds on the predicted disruption of molecular function, MutPred generates accurate and reliable hypotheses on the molecular basis of disease for about 11% of known inherited disease-causing mutations [45].

Table 1: Structural and functional properties used by MutPred and PolyPhen2.

PolyPhen2
MutPred

Sequence based properties

    Bond annotation
X

    Functional site annotation
X
X

    Region annotation
X
X

    PHAT score
X

    PSIC score
X

    SIFT score

X

    Evolutionary attributes
X
X

Structural properties

    Secondary structure
X
X

    Solvent-accessible surface area
X
X

    Phi-psi dihedral angles
X

    Normalized accessible surface area
X

    Change in accessible surface propensity
X

    Change in residue side chain volume
X

    Region of the phi-psi map (Ramachandran map)
X

    Normalized B-factor
X
X

    Ligand contacts
X

    Interchain contacts
X

    Functional site contacts
X

    Molecular Recognition Fragments (MoRFs)

X

Our MutPred and PolyPhen2 predictions were validated by comparing them with previously obtained results from in vitro, in vivo, and human studies of ABCA1 gene in the databases and literature. When a given nsSNP found experimentally to be associated with a remarkable change of phenotype such as altered transporting activity or a disease was predicted by in silico methods as deleterious, it was considered that the prediction on this nsSNPs was correct. The prediction was defined as an error if such a deleterious nsSNP was predicted as tolerant.

3. Results and Discussion

The importance of ABCA1 in cholesterol efflux was demonstrated by the identification of ABCA1 mutations in TD and FHA families [34–38]. This has produced extensive research into the possibility to provide protection from atherosclerosis by increasing ABCA1 expression and thereby to raise plasma HDL-c levels. The identification of the large number of alleles for this transporter gene as target directly involved in HDL-c regulation constitutes a significant therapeutic strategy in reducing the risk for atherosclerosis.

3.1. Accuracy of the Prediction of the Functional Impact of nsSNPs

Out of a total of 3141 SNPs in ABCA1 gene retrieved from dbSNP, we found 233 nsSNPs, 126 sSNPs, 59 mRNA 3′-UTR SNPs, 12 mRNA 5′-UTR SNPs, and 2543 intronic SNPs (Figure 4). Among the 233 nsSNPs, MutPred (RF score > 0.5) predicted 122 (52.36%) as deleterious whereas PolyPhen2 (pph2_prob > 0.5) identified 97 (41.63%) as potentially damaging and damaging. Then, once that MutPred was used to predict the nsSNP disease-association probability, the damaging probability of nsSNPs was validated by PolyPhen2. A total of 80 (34.33%) nsSNPs were found to be deleterious by both methods. Among these 80 deleterious nsSNPs, the 29 (12.44%) targeted (MAF/NA) that resulted with high pathological phenotype (probability > 0.8) are C1477R, W590L, W590S, A1046D, N1611D, M1091T, F2009S, N935H, R2081W, R1068H, N935S, R1068C, D1099Y, D1099N, W1699C, W840R, A937V, I1517R, C1660R, R1680W, P1065S, R1615P, T929I, Y2206D, L1379F, T940M, G1216V, Y2178H, and R1680Q. As shown in Table 2, a good correlation index was obtained between the scores observed from the evolutionary-based approach MutPred and the structural-based approach PolyPhen2. As shown in Figure 5, the overall correlation of the predictions made by both methods is high (~0.57). The majority of mutations classified as pathogenic by PolyPhen2 with the highest score (=1) are also classified as pathogenic by MutPred but within a score range between 0.51 and 1. The prediction accuracy depends not only by limitations of the in silico algorithms such as false positive error and interference of redundant motifs but also by the phenotype data from experimental studies [3].

Table 2: Functional effects of nsSNPs for ABCA1 gene (UniProt: O95477) predicted by PolyPhen2 and MutPred. A score >0.5 means that the mutation is classified as damaging by the algorithms. In addition, MutPred algorithm formulates hypotheses about structural and functional impact of the mutation; the most statistically significant hypothesis is reported in this table. The MAP column includes Minor Allele Frequency. The deviation column represents how close the two predicted scores are; a value of 0 corresponding to identical values.

Mutation
pph2_prob
Polyphen class
RFscore
Mutpred hypothesis
MAF
Deviation

A1010V
0,104
neutral
0,816
Loss of ubiquitination at K1009
NA
0,50346003

A1046D
0,999
deleterious
0,938
Loss of MoRF binding
NA
0,04313351

A1182T
0,001
neutral
0,425
Loss of sheet
0,00045914
0,29981328

A1407T
0,119
neutral
0,277
Gain of helix
0,00045914
0,11172287

A1631V
0,401
neutral
0,321
Loss of sheet
NA
0,05656854

A1670T
0,997
deleterious
0,566
Gain of glycosylation at A1670
NA
0,30476302

A1756T
0,81
deleterious
0,418
Gain of glycosylation at A1756
NA
0,27718586

A2028V
0,146
neutral
0,564
Gain of methylation at K2023
0
0,29557064

A255T
0
neutral
0,755
Loss of helix
NA
0,53386562

A343V
0,001
neutral
0,227
Loss of disorder
NA
0,15980613

A391S
0,318
neutral
0,34
Gain of disorder
NA
0,01555635

A594T
0,02
neutral
0,406
Loss of catalytic residue at A594
NA
0,27294322

A697T
0,416
neutral
0,508
Gain of sheet
0,00045914
0,06505382

A746G
1
deleterious
0,608
Loss of stability
0,00045914
0,27718586

A779T
0,411
neutral
0,468
Loss of helix
NA
0,04030509

A795S
0,07
neutral
0,494
Loss of helix
NA
0,29981328

A937V
0,991
deleterious
0,921
Loss of disorder
NA
0,04949748

C1477F
1
deleterious
0,496
Loss of catalytic residue at C1477
NA
0,35638182

C1477R
1
deleterious
0,852
Gain of methylation at C1477
NA
0,10465181

C1660R
0,954
deleterious
0,917
Gain of methylation at C1660
NA
0,02616295

C3S
0,005
neutral
0,48
Gain of disorder
NA
0,33587572

C887F
0,002
neutral
0,604
Gain of phosphorylation at S884
0,00137741
0,42567828

D1018G
0,992
deleterious
0,535
Loss of catalytic residue at D1018
NA
0,3231478

D1099N
1
deleterious
0,906
Gain of sheet
NA
0,06646804

D1099Y
1
deleterious
0,946
Loss of disorder
NA
0,03818377

D1263E
0,003
neutral
0,214
Gain of helix
NA
0,14919953

D1289N
0,339
neutral
0,573
Gain of sheet
NA
0,16546299

D1553E
0,001
neutral
0,273
Loss of helix
NA
0,19233304

D1706N
1
deleterious
0,611
Loss of helix
NA
0,27506454

D2243E
0,078
neutral
0,491
Gain of relative solvent accessibility
0,00459137
0,2920351

D434E
0,005
neutral
0,413
Gain of disorder
NA
0,28849957

D441N
0
neutral
0,348
Loss of helix
NA
0,24607316

D444E
0
neutral
0,338
Loss of helix
NA
0,23900209

D446E
0
neutral
0,301
Loss of helix
NA
0,21283914

D457E
0,001
neutral
0,566
Loss of helix
NA
0,39951533

D462G
0,071
neutral
0,555
Loss of catalytic residue at D462
NA
0,34223968

D581E
0,38
neutral
0,346
Loss of helix
NA
0,02404163

D677E
0,158
neutral
0,846
Loss of loop
NA
0,48648947

D831N
0,999
deleterious
0,589
Gain of helix
NA
0,28991378

D917N
0,1
neutral
0,577
Gain of MoRF binding
NA
0,33728994

E1005K
0,026
neutral
0,542
Gain of MoRF binding
NA
0,3648671

E1172D
0,003
neutral
0,253
Loss of sheet
0,0509642
0,1767767

E1172G
0,003
neutral
0,281
Loss of sheet
0,00091827
0,19657569

E1253K
0,048
neutral
0,453
Gain of ubiquitination at E1253
NA
0,28637825

E1916A
0,027
neutral
0,34
Gain of MoRF binding
0,00183655
0,22132442

E226G
0,019
neutral
0,47
Loss of stability
NA
0,31890516

E284K
0,206
neutral
0,842
Gain of methylation at E284
NA
0,44971991

E815G
0,072
neutral
0,361
Loss of disorder
NA
0,20435386

E868K
0,051
neutral
0,69
Gain of methylation at E868
0,00275482
0,45184123

F1285L
0,001
neutral
0,235
Loss of loop
NA
0,16546299

F1573S
0,065
neutral
0,532
Gain of disorder
NA
0,33021887

F2009S
0,811
deleterious
0,932
Gain of disorder
NA
0,08555992

F2082V
0,266
neutral
0,442
Gain of MoRF binding
NA
0,12445079

F2163S
0,939
deleterious
0,727
Loss of sheet
NA
0,14990664

F346L
0
neutral
0,239
Gain of disorder
NA
0,16899852

F426L
0,94
deleterious
0,549
Gain of helix
0,00045914
0,27647875

F632C
0,998
deleterious
0,694
Loss of stability
0,00045914
0,21496046

F950S
0,995
deleterious
0,664
Gain of disorder
NA
0,23405235

G1049R
1
deleterious
0,638
Gain of MoRF binding
NA
0,25597266

G1216V
0,951
deleterious
0,844
Loss of ubiquitination at K1214
NA
0,07566043

G1321A
0,936
deleterious
0,715
Gain of catalytic residue at G1321
NA
0,15627061

G156V
0,017
neutral
0,389
Loss of helix
NA
0,26304372

G2147E
0,49
neutral
0,397
Loss of catalytic residue at S2148
NA
0,06576093

G315W
0,997
deleterious
0,526
Loss of disorder
NA
0,33304729

G616V
0,998
deleterious
0,444
Loss of disorder
NA
0,39173716

G788W
1
deleterious
0,664
Loss of catalytic residue at P784
NA
0,23758788

G851R
0,998
deleterious
0,71
Gain of catalytic residue at G851
NA
0,20364675

H263D
0
neutral
0,399
Loss of helix
NA
0,28213561

H551D
0,613
deleterious
0,468
Gain of sheet
NA
0,10253048

I1084V
0,241
neutral
0,511
Gain of MoRF binding
NA
0,19091883

I1239V
0,078
neutral
0,376
Loss of catalytic residue at L1244
NA
0,21071782

I1517R
0,962
deleterious
0,917
Gain of catalytic residue at I1517
NA
0,03181981

I1555T
0,439
neutral
0,484
Loss of stability
NA
0,03181981

I1911T
0,006
neutral
0,437
Loss of stability
0,00045914
0,30476302

I35V
0,066
neutral
0,31
Loss of helix
0,00045914
0,17253406

I546V
0
neutral
0,31
Gain of MoRF binding
NA
0,2192031

I560T
0,829
deleterious
0,496
Loss of stability
NA
0,23546656

I649F
0,97
deleterious
0,669
Loss of catalytic residue at I649
NA
0,21283914

I659V
0,034
neutral
0,405
Loss of methylation at K663
NA
0,26233662

I883M
0,002
neutral
0,296
Gain of disorder
0,365473
0,20788939

K1250R
0,118
neutral
0,377
Loss of methylation at K1250
0,00045914
0,18314066

K1587R
0,034
neutral
0,377
Gain of helix
0,410927
0,24253763

K166R
0
neutral
0,251
Loss of ubiquitination at K166
NA
0,1774838

K1761T
0,038
neutral
0,545
Loss of methylation at K1761
0,00045914
0,35850314

K1974R
0,158
neutral
0,679
Loss of ubiquitination at K1974
NA
0,36840263

K2040E
0,243
neutral
0,459
Loss of MoRF binding
0,00045914
0,15273507

K401Q
0,007
neutral
0,322
Loss of ubiquitination at K401
NA
0,22273864

K613E
0,002
neutral
0,428
Loss of ubiquitination at K613
0,00045914
0,30122749

K776N
0,985
deleterious
0,568
Loss of methylation at K776
0,00183655
0,29486353

L1041V
0,999
deleterious
0,625
Loss of ubiquitination at K1040
0,00137741
0,26445794

L1379F
0,987
deleterious
0,866
Loss of catalytic residue at L1379
NA
0,08555992

L1408F
0,443
neutral
0,418
Loss of stability
0,00045914
0,01767767

L1648P
0,961
deleterious
0,749
Loss of stability
NA
0,14990664

L184S
0,007
neutral
0,576
Loss of sheet
0,00045914
0,40234376

L184W
0,928
deleterious
0,54
Gain of helix
0,00045914
0,27435743

L2032M
0,897
deleterious
0,688
Gain of methylation at K2036
NA
0,14778532

L2168P
0,984
deleterious
0,693
Gain of disorder
NA
0,20576807

L2187Q
0,995
deleterious
0,698
Gain of disorder
0,00045914
0,21001071

M1012I
0,008
neutral
0,595
Loss of catalytic residue at V1008
NA
0,41507168

M1091T
0,98
deleterious
0,955
Gain of disorder
NA
0,01767767

M233V
0
neutral
0,417
Loss of stability
NA
0,29486353

M415L
0,002
neutral
0,423
Gain of relative solvent accessibility
NA
0,29769196

M674L
0,534
deleterious
0,599
Loss of catalytic residue at M674
0,00045914
0,04596194

N1185K
0,01
neutral
0,583
Gain of MoRF binding
NA
0,40517219

N1185S
0,002
neutral
0,358
Gain of helix
NA
0,25173001

N1406K
0,006
neutral
0,408
Gain of ubiquitination at N1406
NA
0,28425693

N1611D
0,968
deleterious
0,921
Loss of MoRF binding
NA
0,03323402

N1800H
0,758
deleterious
0,826
Loss of stability
NA
0,04808326

N1800S
0,241
neutral
0,84
Loss of stability
NA
0,42355696

N2119Y
0,96
deleterious
0,727
Loss of MoRF binding
NA
0,16475588

N820S
0,001
neutral
0,348
Loss of catalytic residue at N820
NA
0,24536605

N935H
0,996
deleterious
0,966
Gain of disorder
NA
0,02121321

N935S
0,831
deleterious
0,969
Gain of disorder
NA
0,09758074

P1065S
0,998
deleterious
0,906
Gain of MoRF binding
NA
0,06505382

P1475S
1
deleterious
0,235
Gain of phosphorylation at P1475
NA
0,54093669

P1878T
0,019
neutral
0,482
Gain of helix
NA
0,32739044

P2150L
0,068
neutral
0,826
Loss of disorder
NA
0,53598694

P248A
0
neutral
0,207
Loss of helix
NA
0,1463711

P250L
0
neutral
0,244
Loss of glycosylation at P250
NA
0,17253406

P641L
1
deleterious
0,603
Gain of MoRF binding
NA
0,28072139

P855S
0,97
deleterious
0,59
Loss of catalytic residue at W856
NA
0,26870058

P85L
0,394
neutral
0,618
Loss of disorder
0,00045914
0,15839192

Q1279K
0,032
neutral
0,715
Gain of MoRF binding
NA
0,48295393

Q188K
0,001
neutral
0,543
Gain of ubiquitination at Q188
0
0,38325188

Q205E
0,01
neutral
0,255
Loss of loop
NA
0,17324116

Q205R
0
neutral
0,279
Gain of helix
NA
0,19728279

Q2196H
0,011
neutral
0,798
Loss of MoRF binding
NA
0,55649304

Q2210H
0,984
deleterious
0,388
Gain of catalytic residue at D2214
NA
0,42143564

Q597R
1
deleterious
0,71
Gain of MoRF binding
NA
0,20506097

Q849R
0,689
deleterious
0,662
Gain of methylation at Q849
NA
0,01909188

R104C
0,985
deleterious
0,589
Loss of MoRF binding
NA
0,28001429

R1068C
1
deleterious
0,962
Loss of MoRF binding
NA
0,02687006

R1068H
1
deleterious
0,972
Loss of MoRF binding
NA
0,01979899

R1082C
1
deleterious
0,694
Loss of MoRF binding
NA
0,21637468

R1195Q
0,044
neutral
0,364
Loss of loop
NA
0,22627417

R1195W
0,952
deleterious
0,589
Gain of catalytic residue at A1194
NA
0,25667976

R126H
0
neutral
0,467
Loss of MoRF binding
NA
0,33021887

R1273L
0,007
neutral
0,35
Loss of loop
NA
0,24253763

R1283C
0,001
neutral
0,449
Gain of ubiquitination at K1278
NA
0,31678384

R1341T
0,016
neutral
0,604
Loss of methylation at R1341
0,001
0,41577879

R1344W
0,993
deleterious
0,64
Loss of methylation at K1345
0,00045914
0,24960869

R1417H
0,837
deleterious
0,457
Loss of phosphorylation at T1416
0,00183655
0,26870058

R1615P
0,947
deleterious
0,895
Loss of methylation at R1615
0,001
0,03676955

R1615Q
0,187
neutral
0,679
Gain of helix
NA
0,34789654

R1680Q
0,997
deleterious
0,807
Gain of ubiquitination at K1683
NA
0,13435029

R1680W
1
deleterious
0,911
Loss of disorder
NA
0,06293251

R1839H
0,006
neutral
0,48
Loss of phosphorylation at S1842
0,00045914
0,33516861

R1851Q
0,015
neutral
0,897
Gain of catalytic residue at R1851
NA
0,62366818

R1897W
0,009
neutral
0,408
Probably damaging
NA
0,28213561

R1901S
0,091
neutral
0,81
Gain of phosphorylation at R1901
NA
0,50840978

R1925Q
0,006
neutral
0,69
Loss of MoRF binding
0,00229568
0,48366104

R2004K
0,911
deleterious
0,699
Gain of ubiquitination at R2004
NA
0,14990664

R2030Q
0,032
neutral
0,442
Loss of MoRF binding
NA
0,28991378

R2081W
0,998
deleterious
0,936
Loss of MoRF binding
NA
0,04384062

R2173Q
0,008
neutral
0,382
Gain of sheet
NA
0,26445794

R2189G
0,142
neutral
0,443
Loss of MoRF binding
NA
0,21283914

R219K
0
neutral
0,41
Gain of ubiquitination at R219
0,419192
0,28991378

R230C
0
neutral
0,68
Loss of MoRF binding
0,00734619
0,48083261

R306G
0,04
neutral
0,463
Loss of helix
0,001
0,29910617

R306H
0,899
deleterious
0,431
Loss of helix
NA
0,33092597

R369H
0,937
deleterious
0,433
Loss of MoRF binding
NA
0,35638182

R437Q
0,088
neutral
0,48
Loss of helix
NA
0,27718586

R437W
0,991
deleterious
0,562
Gain of catalytic residue at L435
NA
0,30334881

R443K
0
neutral
0,44
Loss of stability
0,00045914
0,31112698

R496W
0,209
neutral
0,576
Loss of loop
NA
0,25950819

R500H
0,013
neutral
0,519
Loss of helix
NA
0,35779603

R587W
1
deleterious
0,688
Loss of disorder
NA
0,22061732

R638Q
0,957
deleterious
0,664
Loss of methylation at R638
NA
0,20718229

R638W
0,999
deleterious
0,671
Loss of methylation at R638
0,00045914
0,23193102

R666Q
1
deleterious
0,437
Gain of ubiquitination at K668
NA
0,39810112

R666W
1
deleterious
0,56
Gain of catalytic residue at R666
NA
0,31112698

R672Q
0,832
deleterious
0,501
Loss of MoRF binding
NA
0,23405235

R965C
0,302
neutral
0,608
Loss of disorder
0,001
0,21637468

R999C
0,127
neutral
0,766
Loss of solvent accessibility
NA
0,45184123

R999L
0,208
neutral
0,748
Loss of solvent accessibility
NA
0,38183766

S107A
0,002
neutral
0,342
Gain of helix
NA
0,24041631

S1141Y
0,805
deleterious
0,306
Loss of phosphorylation at S1141
NA
0,35284628

S1157N
0,022
neutral
0,355
Gain of sheet
0,00045914
0,23546656

S116N
0,196
neutral
0,241
Loss of phosphorylation at S116
NA
0,03181981

S1181F
0,042
neutral
0,542
Loss of disorder
0,00045914
0,35355339

S1255R
0,027
neutral
0,326
Loss of glicosilation at S1255
0,00183655
0,21142493

S1280R
0,009
neutral
0,351
Gain of sheet
0,00045914
0,24183052

S1376G
0,007
neutral
0,407
Gain of methylation at K1373
0,001
0,28284271

S139G
0
neutral
0,284
Gain of helix
NA
0,20081833

S1506L
0,996
deleterious
0,733
Loss of helix
NA
0,18596908

S1536F
0,998
deleterious
0,594
Loss of disorder
NA
0,28567114

S1731C
0,893
deleterious
0,526
Loss of helix
0,001
0,25950819

S212T
0,028
neutral
0,375
Gain of helix
0,00459137
0,24536605

S2182F
0,578
deleterious
0,498
Loss of disorder
NA
0,05656854

S2186F
0,862
deleterious
0,458
Loss of disorder
NA
0,28567114

S442R
0,001
neutral
0,449
Loss of helix
NA
0,31678384

S713G
1
deleterious
0,576
Loss of stability
NA
0,29981328

S780N
0,996
deleterious
0,559
Loss of stability
NA
0,30900566

T1175M
0,432
neutral
0,331
Loss of sheet
0,00091827
0,07141779

T1242M
0,993
deleterious
0,62
Loss of catalytic residue at T1242
NA
0,26375083

T1399M
0,003
neutral
0,279
Loss of glycosylation at T1399
NA
0,19516147

T1401I
0,003
neutral
0,368
Loss of glycosylation at T1401
NA
0,25809398

T1427M
0,005
neutral
0,352
Gain of catalytic residue at T1427
0,00091827
0,24536605

T2073A
0,852
deleterious
0,657
Loss of phosphorylation at T2073
0,001
0,13788582

T459P
0,03
neutral
0,534
Gain of helix
0,001
0,35638182

T515A
0,04
neutral
0,449
Gain of helix
NA
0,28920667

T774P
0,003
neutral
0,61
Gain of methylation at K776
0,00183655
0,42921382

T929I
0,946
deleterious
0,889
Loss of disorder
NA
0,04030509

T940M
1
deleterious
0,844
Loss of methylation at K939
NA
0,11030866

V1054I
0,951
deleterious
0,51
Loss of ubiquitination at K1052
NA
0,31183409

V1096I
0
neutral
0,467
Gain of helix
NA
0,33021887

V1158I
0,065
neutral
0,226
Loss of sheet
NA
0,11384419

V1674I
0,053
neutral
0,376
Loss of catalytic residue at V1674
0,00137741
0,22839549

V1704D
0,752
deleterious
0,876
Loss of helix
NA
0,08768124

V1806M
0,98
deleterious
0,417
Gain of ubiquitination at K1804
NA
0,39810112

V1858M
0,305
neutral
0,472
Loss of catalytic residue at V1858
NA
0,11808683

V2035M
0,042
neutral
0,38
Loss of methylation at K2036
NA
0,23900209

V2244I
0,002
neutral
0,386
Gain of sheet
NA
0,271529

V304M
0,823
deleterious
0,387
Gain of disorder
NA
0,30829856

V380I
0,001
neutral
0,353
Gain of methylation at K376
NA
0,24890159

V399A
0,071
neutral
0,563
Gain of disorder
0,00183655
0,34789654

V408G
0,101
neutral
0,431
Loss of stability
NA
0,23334524

V481L
0,001
neutral
0,374
Gain of sheet
0,00045914
0,26375083

V589I
0,004
neutral
0,334
Gain of catalytic residue at V589
0,00091827
0,23334524

V654G
0,433
neutral
0,438
Loss of stability
NA
0,00353553

V702A
0,002
neutral
0,408
Loss of stability
NA
0,28708535

V724M
0,833
deleterious
0,462
Loss of catalytic residue at V724
0,00045914
0,26233662

V771M
0,034
neutral
0,421
Loss of ubiquitination at K776
0,0606061
0,27365032

V825I
0,001
neutral
0,401
Loss of catalytic residue at V825
0,128099
0,28284271

W1699C
1
deleterious
0,915
Gain of catalytic residue at L1700
NA
0,06010408

W590L
0,841
deleterious
0,888
Loss of helix
0,001
0,03323402

W590S
0,889
deleterious
0,857
Gain of disorder
0,001
0,02262742

W840R
0,998
deleterious
0,923
Gain of methylation at W840
NA
0,05303301

Y1921H
0,986
deleterious
0,752
Gain of disorder
NA
0,16546299

Y2178H
0,982
deleterious
0,826
Gain of disorder
NA
0,11030866

Y2206D
0,992
deleterious
0,878
Gain of sheet
NA
0,08061017

Y482C
0,03
neutral
0,826
Gain of loop
NA
0,56285691

Y835H
0,967
deleterious
0,653
Loss of stability
NA
0,22203153

Figure 4: Distribution of ABCA1 nonsynonymous SNPs (nsSNPs), synonymous SNPs (sSNPs), 3′-UTR and 5′-UTR, and intronic SNPs.

Figure 5: Scatter plot shows the correlation between the predictions made by PolyPhen2 (-axis) and MutPred (-axis) for 233 amino acid substitutions on ABCA1. The diagonal line represents a perfect correlation (=1) between both prediction methods for every mutation. The overall correlation of the predictions made by both methods is high (0.57). The majority of mutations classified as pathogenic by PolyPhen2 with the highest score (=1) are also classified as pathogenic by MutPred but within a score range between 0.51 and 1.

Equally important is to consider the incorrect predictions in order to know the limitations of both algorithms and to suggest how they might be improved. Where MutPred predicts P2150L variant as deleterious, PolyPhen2 indicates a benign amino acid exchange. Conversely, MutPred predicted P85L to be probably damaging, while PolyPhen2 indicates it as neutral. Conflicting results were observed for a few other nsSNPs included in Table 2. A total of seventeen deleterious nsSNPs predicted by PolyPhen2 resulted neutral by MutPred. In contrast, forty two deleterious nsSNPs by MutPred result neutral by PolyPhen2. We have observed (Figure 5, Table 2) that some mutational characteristics of nsSNPs such as C1477F, R666Q, P1475S, G616V, Q2210H, V1806M, and V304M show high PolyPhen2 values but very low MutPred scores due, at least in part, to loss or gain of catalytic residues and disorder and gain of ubiquitination and phosphorylation to the protein. On the other hand, some mutational characteristics of nsSNPs included T459P, A2028V, T774P, Q1279K, N1185K, D917N, E1005K, C887F, D1289N, Q188K, D462G, M1012I, R965C, S1181F, A255T, D457E, R496W, R1341T, R1925Q, R230C, L184S, R999L, and K1974R with low PolyPhen2 values but high MutPred scores produce, however loss of solvent accessibility and of disorder, gain of phosphorylation, and both loss and gain of molecular recognition features (MoRFs) binding, loss and gain of methylation, and loss and gain of helix structure. Both, loss and gain of catalytic residues are actively involved in human inherited disease. Also, the small ubiquitin—a 76 residue β-grasp protein—is about 95% conserved from yeast to human. Overall, both gain and loss of a phosphorylation site in a target protein may be important features for predicting cancer-causing mutations and may represent a molecular cause of disease for a number of inherited and somatic mutations. Changes in secondary structure impair large functional alterations, as well as the solvent accessibility degree. Therefore, inaccurate predictions occurred at these sites could be explicated not only for the limited effects of genetic variant but also for gene-environment interactions. Since the MutPred is based on a predicted structure of the protein under study rather than a solved structure as PolyPhen2 and considering the fact that nowadays the ABCA1 protein structure is only partially solved, it makes sense to prioritize the MutPred predictions. This fact was confirmed after evaluating the performance of both methods using a curated nsSNPs dataset with known outcome as a benchmark as shown in Figure 3. We have also observed that mostly, nsSNPs with verified functional effect in experimental studies are correctly predicted as damage variants by MutPred and PolyPhen2 tools. Still, controversial experimental data are obtained for those nsSNPs predicted as neutral by both of these methods.

3.2. Functional Assessment of ABCA1 Variants

Disease-causing variants are under strong selective constraints, which determines if mutation frequency will increase, decrease, or change randomly during evolution. Most alterations are deleterious and so are finally removed during purifying selection. Benign mutations can sweep through the population and become fixed contributing to species differentiation. The ABCA1 gene is highly conserved between species. Human ABCA1 is 95.2% identical to mouse, 85.3% to chicken, 25.5% to drosophila, 21.6% to C. elegans, and 10.2% identical to fugu. In humans, there is an abundance of common nsSNPs that disrupt sites highly conserved across species and likely to be deleterious [50]. The information of nsSNPs can be used to outline the migration patterns of ancient humans and the ancestry of modern humans. Causal nsSNPs in single gene disorders are sufficient to impart large effects. Instead, complex traits are due to a much more complicated system of causative mechanisms that in aggregate increase the probability of disease. Genome-wide association studies reveal common genetic variants effects (common disease/common variant hypothesis) in complex traits. However, where common nsSNPs account for a relatively slight heritability of the traits, rare variants might produce large effects on the phenotype (rare variant/common disease hypothesis). The frequency range includes alleles that are exceptionally rare and even unique to an individual genome to be extremely common. Most deleterious nsSNPs are retained at low-population frequencies due to negative selection. Thus, variants with large effect tend to be rare and those that exert weak effects are more common. It is worthy to note that rare alleles can also have weak effect or no effect. A specific locus may contain numerous rare alleles, so there may be many rare variants with large effect and a few common variants with weak effects. Although it has not yet been possible to determine whether other variables are associated with specific nsSNPs frequencies, variants within metabolic genes are not randomly distributed along the human population but follow diverse ethnic and/or geographic-specific patterns. It has been reported [51] that a significant proportion (~16%) of individuals with low HDL-c from the general population has the rare sequence of 25 variants in ABCA1 gene (Table 2, MAF ≤ 0.01). However, consistent with MutPred and PolyPhen2 only nine of them, N1800H, W590L, S1731C, C1477R, D1706N, R1615P, R638Q, T2073A, and A1670T, are predicted as functionally impaired. Some deleterious mutations from some other genes have reached intermediate to high frequencies. Specifically, the ancestral APOE4 allele, remains higher in populations like Pygmies (0.41), Khoi San (0.37), Papuans (0.37), some Native Americans (0.28), Lapps (0.31) and aborigines of Malaysia (0.24), and Australia (0.26) [52]. The exposure of APOE4 to the current environmental conditions could have rendered it a susceptibility allele for cardiovascular and Alzheimer diseases. However, the prediction for variant within ABCA1 gene indicates lack of harmful alleles to MAF ≥ 0.01. Therefore we have evaluated and contrasted the predictions made for nsSNPs (rare/common) most widely studied for their role in cholesterol pathway by reviewing the effect of the most significant nsSNPs in ABCA1 gene and its pathological consequences.

3.2.1. Accurate Prediction of the Functionally Deleterious nsSNPs in the ABCA1 Gene

The N1800H ABCA1 has been fully characterized showing a complete lack of protein function in terms of cholesterol efflux and HDL production [53, 54]. Unlike the WT (wild-type), which is found at the endoplasmic reticulum and plasma membrane, N1800H is accumulated intracellularly [54]. Even similar physicochemical properties (polar, medium size) of exchanged residues the N1800H nsSNP, located between transmembrane domains [54], is a critical site for protein function. Scores from in silico methods predict the N1800H variant as highly deleterious.

The W590L was never studied, but the W590S ABCA1 variant affecting the same position is extensively known [54]. Distribution of W590S is identical to WT [55] as well as apoA1 binding activity [54–57]; however it shows defective lipid transport [54, 56, 58, 59]. Since multiple alignments often show a leucine residue in this position, it could be assumed that W590L had a similar behavior or even a lower impact than W590S on the protein function. Both W590S and W590L were predicted as deleterious nsSNPs with loss of functionality.

Studies indicate that S1731C variant alters the activity of ABCA1 protein [27, 51, 60]. This allele is present in French-Canadian families with low HDL-c levels [27] but not in subjects with normal [60] or high [51] HDL-c levels. Compared with WT, heterozygous show decreased ~60% the cholesterol efflux activity [27, 51, 61]. Interestingly, some but not all families harboring S1731C also carried the 2144X stop mutation [60] able to produce the most severe effects on HDL-c levels and on cholesterol efflux [62]. These data along with our in silico predictions indicate that conserved S1731C is highly likely to affect protein function.

S1506L, Q597R, and C1477R variants are linked to TD and FHA and found in tumor cancer [54]. Normal function of ABCA1 inhibits tumor growth in human cancer cells [54]. However, although expressed to similar levels as WT, these alleles show deficient cellular cholesterol efflux and HDL production and do not decrease tumor growth [17]. The three are located intracellularly but C1477R is also found in membrane [54], which indicates that membrane localization is essential but not sufficient for apoA1 binding [54, 63]. In fact, ApoA1 binds to ABCA1 protein oligomers but not with monomers [64]. Thus, conformation changes in binding sites might be produced by these nsSNPs found as deleterious by our in silico analysis.

The R587W reaches the cell surface but reduces the apoA1 binding efficiency ~50% [56]. Others studies indicate that this allele is mainly retained intracellularly decreasing cholesterol efflux and apoA1 binding ~75% [54]. Severe HDL deficiency [34] and premature CVD is caused by R587W [65]. This variant is highly conserved during evolution, and the in silico analysis predicts it as strongly damaging and disease-associated. Besides to be related with TD, the R587W as well as W590S variants are linked to AD. As the WT, these mutants significantly reduce Aβ-peptide synthesis ~45% [66], but increase by ~2-fold (R587W) and by 25% (W590S) amyloid precursor protein intracellular domain, a major cytotoxic of AD [66].

The A1046D, localized between conserved motifs [54, 67], shows an intermediate phenotype caused by its limited presence in the plasma membrane. This variant shows reduced apoA1 binding efficacy, poor HDL-c, and folding protein alteration. Both in silico methods predict A1046D as a functional residue with a probability to be deleterious very close to 1.

According to literature, N1611D is associated to probable atherosclerosis [62, 68]. The mutated protein expression was comparable to WT although cholesterol efflux from the cells was markedly reduced. Our theoretical results indicate very high probabilities of this nsSNP being deleterious, which indicate an adverse and potential harmful effect on ABCA1 function.

The M1091T variant exerts a dominant-negative impact on ABCA1 function with severe phenotype observed in subjects carrying this variant [42, 54, 62]. It is retained intracellularly preventing the protein from reaching the membrane [54]. In heterozygous, M1091T is lowered by 50% HDL and inhibits apoA1 binding and cholesterol efflux [54]. From evolutionary path, the inherited residue at this position has been methionine. Among related homologues ABCA2 and ABCA4 share a methionine at this position, while ABCA7 substitutes a leucine. Consistent with this fact, ABCA1 and ABCA7 are functionally divergent, with ABCA7 easing the efflux of phospholipids but not cholesterol [69]. Despite the modest conservation at this position, located in a critical cluster at the C-terminal region, in silico data suggest a severe-negative impact.

The F2009S is conserved between human and mouse, that along with the exchange from large size and aromatic (F) to small size and polar (S) explicates its reduced cholesterol efflux, low HDL-c, and apoA1 levels [70]. The functional effect produced by F2009S variant is consistent with our prediction made by PolyPhen2 and MutPred indicating a deleterious mutant.

The N935S variant is found intracellularly [54] in subjects without risk of premature atherosclerosis but with extremely low levels of HDL and signs of severe dementia and amyloid depositions in the brain [71, 72]. This variant was predicted as deleterious by the used methods.

R1068H mutation is located within the first ATP-binding domain. It is identified in TD homozygous [73]. Since the R1068H mutation is likely to produce a dysfunctional protein, one would expect it to be associated with FHA in the heterozygous state [73]. Residue R1068 is located in an α-helix of the Walker B motif in the NBD, vulnerable to interaction with D1092 and E1093 [74]. Homology modeling of the ABCA1 protein showed that the R1068H mutation disrupts the conformation of NBD. Functional studies of R1068H showed a lack of cholesterol efflux activity due to defective transference to the plasma membrane, mainly caused by impaired oligomerization [74]. The in silico analysis predicts a high possibility for R1068H to be damaging. Besides, a different mutation of this position, R1068C, predicted as a deleterious by our methods, has been reported in a compound heterozygote with almost no HDL [31].

D1099Y is located at possible interaction site and exchanges the medium size and acidic residue to the large and aromatic tyrosine. Surface residues not at defined interfaces are usually preserved. Still, a moderate to highly conserved domain on the surface of the structure includes this nsSNP, which is associated to familial HDL deficiency [70, 75] and predicted as deleterious in our analysis.

The W1699C, located within the transmembrane domain, is accumulated within the cytoplasm and a small proportion reaches the plasma membrane [76]. It introduces a cysteine residue, which stimulates the formation of a new disulphide bridge able to disrupt the ABCA1 protein structure preventing its oligomerization and transference to the plasma membrane. Probably, W1699C retains some residual functions, as shown by the plasma HDL-c levels found in members carrying this mutation which were not as low as might be expected in carriers [76]. In silico analysis with PolyPhen2 and MutPred indicate a deleterious effect of this nsSNP on ABCA1 function.

3.2.2. Controversial Results for Prediction of the Neutral nsSNPs in the ABCA1 Gene

The mutant R1897W that induces a change from basic (R) to aromatic (W) is predicted functionally neutral in this analysis. This variant was identified in the mother and the brother of an FHA patient, who had plasma HDL levels in the lower range of the normal values [77].

Both the D1289N and P2150L variants identified in TD patients are considered as disease causative [42, 78, 79]. Further experimental evidences disagreeing with these results suggest that both could be nonfunctional variants [51, 54, 80]. Indeed, they showed a lipid transport activity, apoA1 binding, and distribution similar to WT [54, 80]. Interestingly, P2150L is only found in patients who also harbor a second variant, the deleterious R587W described above [54]. Besides, TD patients with D1289N variant were homozygous for a second mutation R2081W that could cause the shown pathological phenotype [79]. R2081W is missed at the plasma membrane and instead accumulated intracellularly [54]. Our results suggest that mutations R2081W and R587W are highly deleterious. For D1289N and P2150L variants, PolyPhen2 predicts a neutral impact on protein function contrary to MutPred predictions that indicates a high probability for these mutants to be deleterious. The positions 1289 and 2150 are conserved among all ABCA1 orthologs but with the close-related ABCA7 and ABCA4. Since conservation patterns in ABCA1 protein endure for a relatively short time in evolutionary path, it is hard to determine if the conservation at these positions is due to functional constraint or simply reflects random chance. Along with experimental data, this suggests that R2081W is a major responsible of ABCA1 protein dysfunction found in TD patients.

The rare R219K polymorphism is located on an N-terminal extracellular loop, which mediates ABCA1 protein interaction with apoA1 [39, 56, 58, 59]. Despite high number of case-control studies conducted to investigate the functionality of R219K variant the results have been inconclusive [60, 81–83]. While some reports suggest an association of R219K is with risk of CVD [84], other research indicates a decreased atherosclerosis progression in general population [60, 85]. Conversely, large prospective studies found no association with HDL-c levels or atherosclerosis susceptibility [82, 86]. A meta-analysis indicates that R219K polymorphism is protective against CVD in Asians but not in Caucasians [87]. Unexpectedly, the K219 allele was associated with a decreased risk of myocardial infarction [18, 60, 84]. Also, this variant has effect on triglycerides [60] but not with HDL-c [84] or with apoA1 levels [85]. Otherwise, a study indicates that blood lipid levels do not seem to be R219K dependent [88]. Whether this variant confers major susceptibility to CVD is for clarification. The association of R219K variant to risk of AD has been studied in diverse ethnic groups [18–20, 89, 90]. Although conflicting results were noted, a study observed a protective dependence in delaying the risk of late-onset AD [18]. Equal to other cases, experimental results inconclusive and contradictories result prediction of the R219K polymorphism was predicted to be neutral in our in silico analysis.

Some studies [60, 91–93] but not all [39, 94] indicate that I883M variant severely increases the risk of atherosclerosis and AD [20]. The I883M has been reported as a milder phenotype with a significant reduction of HDL-c and cholesterol efflux (~70% of WT) [51]. In contrast, others studies [28, 60, 82, 83, 88] did not find any difference in lipid levels in I883M carriers. Studies among different healthy people [95, 96] as well as population with T2D [97] correlated the I883M variant with higher HDL-c concentration. Also, a stepwise regression approach identified I883M as one the key predictors of ischemic heart disease, whereas additive effects were found for V771M/I883M and I883M/E1172D pairs [82, 98]. As well, several studies have reported associations between V825I/I883M and increased plasma HDL-c levels [39, 67, 99]. Despite the controversial experimental results on the influence on cholesterol efflux activity observed of this polymorphism, our data predict that the I883M variant is functionally neutral. Interestingly, both alleles are found in the human population and the minor allele, methionine, is likely to be the ancestral allele at this position. Along with the human ABCA1orthologs, murine aligns valine at this position and the chimpanzee sequence aligns methionine. This divergence could explain why a simple conservation-based approach predicts the I883M change as neutral.

The R1851Q variant exchanges the large size and basic arginine (R) residue to medium size and polar glutamine predicted deleterious by MutPred and neutral by PolyPhen2. R1851Q occurs within the extracellular loop proximal to the transmembrane [68, 100]. Heterozygotes states show low HDL-c and apoA1 levels compared with those related to WT protein.

The R230C variant, found in Native American groups but not in European, Asian, or African individuals, has been associated with low levels of HDL-c and apoA1 [101]. These results are confirmed after adjusting for gender, BMI, and waist circumference [102]. Besides, the C230 allele is associated with obesity, metabolic syndrome, and T2D in Mexican population [101]. Still, R230C may have conferred resistance against certain infectious diseases [101]. R230C has been reported as a rare variant causing FHA in an Oji-Cree individual [67]. MutPred predicts a high probability of functional impairment of R230C, while that the PolyPhen2 program predicts the variant as neutral. Other facts that suggest functionally damage are (1) R230C occurs at the first extracellular loop, where TD and FHA mutations are clustered; (2) the arginine at position 230 is conserved between species; and (3) very different nature of residues involved; whereas arginine is basic and hydrophilic, the hydrophobic cysteine is vulnerable to disulfide bond.

The variants, R1901S that induces a change from large size and basic (R) to small size and polar (S); Q2196H that exchanges residues with similar physicochemical property (medium size, polar); and E284K that exchanges a medium size and acidic (E) to large size and basic (K), are predicted to be deleterious by MutPred and neutral by PolyPhen2. The R1901S and Q2196H variants occur within the C-terminal domain, close to the NBD, and E284K was located in the first extracellular loop, all of them associated to FHA [76]. The A594T, I659V, T1512M, and R2004K polymorphisms display different degrees of mislocalization to the plasma membrane and slight impacts on cholesterol efflux [103]. These nsSNPs were identified in low-HDL subjects [29]. The A594T, I659V, and T1512M were predicted to be functionally neutral and the R2004K mutation possibly damaging [29]. Finally, the novel mutation (P85L) in ABCA1 was identified in one family with low HDL but was not detected in over 400 chromosomes of healthy subjects [104]. Our in silico prediction indicated this variant as possible damaging by MutPred and neutral by PolyPhen2.

4. Conclusion and Future Directions

The practice of medicine, including health promotion and disease prevention, is primarily based on phenotype-based approaches. Most of them are proximal phenotypes achieved through biochemical markers. Finding genetic determinants of the phenotypes could not only clarify biological and functional consequence of variants but also might translate and extend to clinical phenotype. This focus would consider the large locus heterogeneity and numerous nongenetic factors to contribute to the phenotype. Since high levels of HDL-c are atheroprotective, there is extensive interest in developing agents that enhance ABCA1 expression and thereby raise plasma HDL-c levels. Amino acid exchange variants are crucial indicators of action sites and effective potential therapeutic approaches. In fact, nsSNPs represent disease modifiers capable of altering drug/nutrient response and potential targets vulnerable to environmental factors.

Evaluation of 233 nsSNPs (rare or common) found in ABCA1 transporter indicates that the rare 29 (12.44%) of them resulted to be highly deleterious with a probability >0.8. From 20 sequence variants found in about 16% of individuals with low HDL cholesterol only nine of them, D1706N, R1615P, W590L, C1477R, N1800H, R638Q, T2073A, A1670T, and S1731C, were predicted by MutPred and PolyPhen2 as functionally impaired. We have observed that mostly nsSNPs with verified functional effect in all experimental studies made are correctly predicted as damage variants by MutPred and PolyPhen2 tools. However, controversial experimental data are obtained for those nsSNPs predicted as neutral by both methods. Presumably clinical phenotype is the result of the additive effects and interactions among multiple alleles with different effect degree. Multiple rare alleles in ABCA1 contribute to plasma HDL-c levels in the general population.

Predicting the phenotypic consequence of nsSNPs using computational algorithms provides a better understanding of genetic differences in susceptibility to diseases and drug/nutrient response. These methods predict whether an amino acid altering mutation is deleterious or disease-causing based on physicochemical properties, population frequency, protein structure, and cross-species conservation. However, computational prediction tools are generally based on machine learning algorithms, which need to be trained before classifying a mutation as either neutral or deleterious. A major obstacle of these approaches is the lack of experimentally validated and impartial data sets. A further complication is that mutations in highly conserved sequences do not always produce phenotypes that are easily noticeable. Besides, knowledge of protein structure is crucial to accurately predict functional nsSNPs and understand their linkage with disease. Severe limitation arises thus when protein 3D-structure is not available as the ABCA1 case. Thus, an accurate, efficient, and generally applicable approach is needed to establish a genotype/phenotype correlation. Whole genome sequencing is likely to become a commodity that could be readily available at a reasonable cost and be easily accommodated into the decision making tree of health care of every individual. The challenging task will be to identify variants that are disease-causing or likely disease-causing and develop strategies to prevent and attenuate the evolving phenotype. Likewise, various complementary studies, genetic and biological, would be necessary to discern the associated alleles from the true disease-causing variants. Moreover a better understanding of genome components, such as functional, large intergenic noncoding RNAs, small noncoding RNAs, and primary transcripts, would be essential. An integrated approach that utilizes genomics, transcriptomic, proteomics, and metabolomic would be expected to facilitate identification and characterization of the mechanisms involved in the pathogenesis of the phenotype.

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publishing of this paper.

Acknowledgment

This work has been supported by European Union Structural Funds.

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