The prevalence of, and molecular defects underlying, inherited protein S deficiency in the general population

The molecular basis of protein S (PS) deficiency was investigated in seven of eight donors identified with persistently low plasma PS levels from a survey of PS levels in 3788 Scottish blood donors. PROS1 gene analysis identified at least one defect in six donors. Five were heterozygous for the Heerlen polymorphism predicting a Ser460Pro substitution. Haplotype analysis revealed the possibility that this allele was inherited with the same haplotype in four of the five donors, suggesting a founder effect for the Heerlen allele in this population. One Heerlen allele carrier was also heterozygous for a 3 bp deletion 68–72 bp upstream of exon 2. Platelet PROS1 transcript analysis showed no reduction in mRNA expression from the affected allele in this donor. A T to G transversion 3 bp upstream of exon 12 was identified in one donor, which is predicted to reduce the efficiency of PS mRNA splicing. However, PROS1 transcript analysis showed no evidence of exon skipping or cryptic splicing. No PROS1 gene defect was detected in the remaining donor. This genetic information enabled us to refine our estimate of the prevalence of heritable PS deficiency in the Scottish population to between 0·16% and 0·21%, predominantly resulting from the presence of the Heerlen allele

Phylogenetic trees and MGD comparisons of domains of Tat between selected X4 and R5 sequences.

<p>The final nine selected X4 sequences and nine selected R5 sequences were utilized in different Tat domain analyses by phylogenetic construction as well as MGD analysis. The different 15 domains of Tat were selected based on both structural and functional properties. The phylogenetic trees were constructed using the maximum-likelihood method; a simple Python script was also written to automate the process of generating the hundreds of trees. The branch distance was calculated between all pairs of X4 and R5 sequences. These groups of distances were tested for a significant difference using a two-tailed student t-test. The MGD, SD, and <i>P</i> value of each group are shown. The three best <i>P</i> values are shown in green, and the least three significant comparisons in purple.</p

Quantitative PSSM score analysis of LANL-derived Env-V3 sequences.

<p><b>(A)</b> A total of 11,866 HIV-1 subtype B Env-V3 sequences with a complete 35-amino-acid sequence were retrieved from the LANL database. PSSM scores were obtained and results were plotted. Black columns represent predicted R5 viral sequences; white columns represent predicted X4 viral sequences; and gray columns represent an area of mixture of Env-V3 sequences, which were predicted as either X4 or R5. <b>(B)</b> A total of 10,600 Env-V3 sequences that contained 35-amino-acid residues with PSSM scores below −6.96 and therefore classified as R5 (black column) with scores above −2.88 classified as X4 (white column). The gray column area was eliminated from the subsequent analysis. <b>(C)</b> Only the final 79 LANL-derived Env-V3 sequences were included in this study. Of these, 67 sequences were predicted to utilize CRR5, while 12 sequences were predicted to be CXCR4 utilizing. The frequencies of PSSM scores were analyzed and the distributions were compared between the two groups.</p

Comparative analysis of full-length X4 and R5 HIV-1 gp120 phylogenetic trees.

<p>A total of 11,010 full-length gp120s derived from LANL and the co-receptor usage was analyzed using the PSSM algorithm, giving 10,477 of R5 (blue circles) and 533 of X4 sequences (red circles). The phylogenetic tree is subsequently constructed using the maximum-likelihood method.</p

Phylogenetic trees and MGD comparisons of different LTR regions between selected X4 and R5 sequences.

<p>A total of nine regions of LTR were chosen based on the known functions. Utilizing a maximum-likelihood algorithm, phylogenetic trees were created based upon a total of 18 sequences, which included nine sequences of X4 and nine sequences of R5. A simple Python script was generated to automate the process of creating the hundreds of trees; additionally, each branch distance was calculated between all pairs of X4 and R5 sequences. Each group of distances was tested for significant differences using a two-tailed student t-test <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0107389#pone.0107389-Mathelier1" target="_blank">[115]</a>. The MGD, SD, and <i>P</i> value of each group are shown. The three most significant comparisons are shown in green and the three least significant ones in purple.</p

Primers used for amplification and sequencing.

<p>The position of each primer is indicated according to the HXB2 sequence and the direction of the primer is designated as sense (+) or antisense (-).</p><p>Primers used for amplification and sequencing.</p

HIV-1 LTR regions and binding sites have differential X4/R5 MGD with Sp binding sites demonstrating altered predicted binding phenotype.

<p><b>(A)</b> Phylogenetic trees were created based on identified transcription factor binding sites within HIV-1 LTR from a total of 18 final selected samples: nine X4 and nine R5 sequences, using a maximum-likelihood method. A simple Python script was written to automate the process of generating the hundreds of trees. The branch distance was calculated between all pairs of X4 and R5 sequences. These groups of distances were tested for significant differences using a two-tailed student t-test. <b>(B)</b> The JASPAR position weight matrix was utilized to examine the predicted binding scores of the sequences in the X4 and R5 groups for each of the three Sp binding sites. The sequence logos for the JASPAR matrix and each Sp binding site in the X4 and R5 groups are shown. Mean binding scores (MBS) are also presented. p values were calculated using a student t-test with p<0.05 being considered statistically significant.</p

Quantitative PSSM score analysis of Drexel Medicine (DM)-derived Env-V3 sequences.

<p><b>(A)</b> A total of 92 PSSM scores were classified into two groups based on their co-receptor usage predictions, R5 (black column) or X4 (white column). <b>(B)</b> Twenty-four sequences were selected based on the predetermined PSSM score cut off values and elimination of redundant patient sequences. The frequency of each score value was analyzed and the distributions were compared between the X4- and the R5-predicted groups. <b>(C)</b> DM-derived sequences (24), along with LANL-derived sequences (79), were quantitated for the frequency distribution of PSSM score values. The analysis predicted 16 X4-utilizing sequences (white column), which exhibited high PSSM scores and were located separately from a group of 87 sequences predicted to be R5 (black column), according to their PSSM score values.</p

Comparison of the Env-V3, Vpr, LTR, and Tat MGD between the X4 and R5 populations.

<p>MGD of each sequence from the two groups was compared by 2-tailed student t-test¹ and the corrected <i>P</i> value, which was determined by a two-tailed t-test using 1000 random iterations, selecting 8 of 16 X4 sequences and 8 of 87 R5 sequences.² The variances between two groups were tested with the F-test; <i>P</i><0.05 was considered as unequal variance comparison.</p><p>Comparison of the Env-V3, Vpr, LTR, and Tat MGD between the X4 and R5 populations.</p