Prospective Study of a Cohort of Russian Nijmegen Breakage Syndrome Patients Demonstrating Predictive Value of Low Kappa-Deleting Recombination Excision Circle (KREC) Numbers and Beneficial Effect of Hematopoietic Stem Cell Transplantation (HSCT)

BackgroundNijmegen breakage syndrome (NBS) is a combined primary immunodeficiency with DNA repair defect, microcephaly, and other phenotypical features. It predominantly occurs in Slavic populations that have a high frequency of carriers with the causative NBN gene c.657_661del5 mutation. Due to the rarity of the disease in the rest of the world, studies of NBS patients are few. Here, we report a prospective study of a cohort of Russian NBS patients.Methods35 Russian NBS patients of ages 1–19 years, referred to our Center between years 2012 and 2016, were prospectively studied.ResultsDespite the fact that in 80% of the patients microcephaly was diagnosed at birth or shortly thereafter, the average delay of NBS diagnosis was 6.5 years. Though 80% of the patients had laboratory signs of immunodeficiency, only 51% of the patients experienced significant infections. Autoimmune complications including interstitial lymphocytic lung disease and skin granulomas were noted in 34%, malignancies—in 57% of the patients. T-cell excision circle (TREC)/kappa-deleting recombination excision circle (KREC) levels were low in the majority of patients studied. Lower KREC levels correlated with autoimmune and oncological complications. Fifteen patients underwent hematopoietic stem cell transplantation (HSCT), 10 of them were alive and well, with good graft function. Three patients in the HSCT group and five non-transplanted patients died; tumor progression being the main cause of death. The probability of the overall survival since NBS diagnosis was 0.76 in the HSCT group and 0.3 in the non-transplanted group.ConclusionBased on our findings of low TRECs in most NBS patients, independent of their age, TREC detection can be potentially useful for detection of NBS patients during neonatal screening. KREC concentration can be used as a prognostic marker of disease severity. HSCT is a viable treatment option in NBS and should be especially considered in patients with low KREC numbers early on, before development of life-threatening complications

Base-Calling Algorithm with Vocabulary (BCV) Method for Analyzing Population Sequencing Chromatograms

<div><p>Sanger sequencing is a common method of reading DNA sequences. It is less expensive than high-throughput methods, and it is appropriate for numerous applications including molecular diagnostics. However, sequencing mixtures of similar DNA of pathogens with this method is challenging. This is important because most clinical samples contain such mixtures, rather than pure single strains. The traditional solution is to sequence selected clones of PCR products, a complicated, time-consuming, and expensive procedure. Here, we propose the base-calling with vocabulary (BCV) method that computationally deciphers Sanger chromatograms obtained from mixed DNA samples. The inputs to the BCV algorithm are a chromatogram and a dictionary of sequences that are similar to those we expect to obtain. We apply the base-calling function on a test dataset of chromatograms without ambiguous positions, as well as one with 3–14% sequence degeneracy. Furthermore, we use BCV to assemble a consensus sequence for an HIV genome fragment in a sample containing a mixture of viral DNA variants and to determine the positions of the indels. Finally, we detect drug-resistant <em>Mycobacterium tuberculosis</em> strains carrying frameshift mutations mixed with wild-type bacteria in the <em>pncA</em> gene, and roughly characterize bacterial communities in clinical samples by direct 16S rRNA sequencing.</p> </div

The shifting patterns in direct sequencing chromatograms.

<p>A. Sequences of 2 mixed DNA types (1 and 2) and their alignment. B and C. Chromatogram sequences (results of base-calling) for both reading directions are named bc-fw and bc-rev. The final Indels are assigned relatively to the main subgroup that is comprised of the DNA type, which has the higher fraction in the mixture. Italic underlined font shows shifting patterns. Bold shows the sequence portions that precede indel positions in each reading direction. Italics show the sequence portions of 2 DNA types that are aligned with the given coordinate shift.</p

BCV-predicted shifting patterns and tandem repeat positions for the human immunodeficiency virus (HIV) sample GEN014DR.01A show the multiple alignment of sequenced clones and consensus sequences for shifting patterns including the main consensus sequence (A) and chromatogram trace images (B).

<p>Tandem repeats were highlighted by a frame on the sequence of clone 3. The beginning of shifting patterns, as for example for the hiv-pf2|shift +12 pattern, is marked by arrows. Sequencing primers are hiv-pf2 (forward) and hiv-pr2 (reverse). HXB2– is a reference sequence.</p

Comparing classification of DNA sequences of sequenced clones and BCV predictions of the 16S rRNA PCR product from a gastric mucosa biopsy.

<p>Each line corresponds to a single taxonomic category. Parentheses contain the number of sequences of clones classified using the RDP Classifier (first value) and the number of best alignments using blastn on the 16S rRNA database Greengenes (second value); brackets contain the number of BCV predictions classified by the method based on STAP (first value) and the number of best alignments using blastn on the 16S rRNA database Greengenes unambiguously assigned to that category (second value, see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0054835#pone.0054835.s004" target="_blank">Table S2</a>). Taxonomic tree represents the RDP classification. The species names of the best blastn hits are marked with circles. Inconsistencies in categorization between BCV and cloning are shown in bold. A. Sample 95. B. Sample97.</p

The comparison of BCV main sequence assembling results with sequences of cloned PCR products.

<p>Phylogenetic tree shows relationships between consensus sequences (black squares) assembled from direct reads of the HIV protease gene fragment with sequences of clones (black circles) for sample GEN014DR.01A. The consensus assembled from two opposite direct reads with trimmed degenerate parts is denoted as D.vqa01; the one that is assembled by the BCV indel detection script is FR.main. F.main is the dominating DNA type extracted from a direct read in the forward direction by the BCV indel detection script; R.main is the same read in the opposite direction. H61 is the blastn best hit to sequence D.vqa01 used for scaling quasispecies variation (black circles).Reads in forward and reverse directions have different fractions of non-degenerate positions: F: 56/503 = 11%; R –430/492 = 87%. B: a node in the tree corresponding to HIV subtype B branch. The phylogenetic tree is constructed by the Minimum Evolution method <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0054835#pone.0054835-Rzhetsky1" target="_blank">[66]</a> for the Maximum Composite Likelihood <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0054835#pone.0054835-Tamura1" target="_blank">[67]</a> distance matrix by the MEGA 5 software <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0054835#pone.0054835-Tamura2" target="_blank">[68]</a>.</p

DNA types predicted by BCV for the sample composed from 2 components of D and F hepatitis B virus (HBV) genotypes.

<p>Black squares show predicted DNA types; black circles show actual sample components (identical to the GenBank sequences X02496, and X69798). Suffixes of sequence names correspond to HBV subtypes. Branches containing a mixture component are shown in bold. Right square brackets mark branches that contain predicted DNA types. The tics below the panels show the time scale. A and B correspond to two different vocabularies. A. Tree with DNA types predicted by BCV using the HBVRT vocabulary composed from 639 sequences of HBV genotypes A–H. B. Tree with DNA types predicted by BCV with vocabulary composed from 2 sequences approximately 0.028 substitution per site distant from components of the df7 sample. Phylogenetic trees are constructed by the Minimum Evolution method <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0054835#pone.0054835-Rzhetsky1" target="_blank">[66]</a> for the Maximum Composite Likelihood <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0054835#pone.0054835-Tamura1" target="_blank">[67]</a> distance matrix by the MEGA 5 software <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0054835#pone.0054835-Tamura2" target="_blank">[68]</a>.</p

BCV dataflow.

<p>Rectangles depict software applications; rolls depict files; black arrows are the pipeline input and output streams with the corresponding input and output file extensions shown in italic bold. The file extensions are as follows: The input ABIF (*.ab1) file contains the chromatogram itself and the ABI base-calling. TraceTuner files (PHRED compatible): *.scf contains the chromatogram; *.phd.1 is the chromatogram sequence, and *.poly is the secondary peak calling results. PolyScan files: *.fpoly contains minor peak calls around the primary sequence, and *.bqs contains the peak likelihoods. BCV pipeline output files: *.viterbi.fasta contains the chromatogram sequence; *.cluster.fasta is the DNA type reconstruction and *.indels.txt is the indel report. The configuring and calling of TraceTuner, BCV::PolyScan and BCV::proc applications is enveloped in the bcv_run.pl script. For indel detection functionality the call of the bcv_indels.pl script is followed of the bcv_run.pl. The bcv_run.pl prepares an alignment of raw predicted DNA variants (from the *.strains.fasta file) with similar sequences from the vocabulary that are listed in the *.decomplog.gfas file. Both files are generated by the BCV::proc application. The input file for the indel detection script bcv_indels.pl has the grouped FASTA format and corresponding.gfas file name extension.</p

Comparison of the base-calling accuracy statistics of Base-Caller with Vocabulary program (BCV) and other programs.

<p>Sequences predicted by one of the following basecallers – BCV, ABI Basecaller 3100, TraceTuner v. 3.01 and PolyScan – are compared with manually assembled sequence datasets of genome fragments of Hepatitis A (HAV) and Hepatitis D (HDV) Viruses. The reference sequences in the HAV dataset do not have ambiguous IUPAC symbols and have moderate portion (3–14% ) of SNV in the HDV dataset. The standard measures of sensitivity, specificity and identity are shown.</p

The guide for selection of the BCV usecase.

<p>The table explains empirical rules that could be used for choosing appropriate BCV usecese depending on the expected diversity of DNA variants in a sample study. Expected diversity (eDiv) is the mean divergence of DNA variants expected in the sample. Prevailed mutation type is the most frequent type of mutations expected for sequenced DNA locus. Expected vocabulary distance (evDist) is maximal identity for DNA variants in the mixture with the sequences in the vocabulary that we expect: e.g. for human genome we can expect evDist = 0.001, for HBV surface antigen evDist<0.03. The vocabulary is considered as approximate if evDist≈eDiv; we cannot deconvolute mixture of DNA variants but still can detect indels if components of the mixture are similar. The vocabulary is considered as representative if evDist</p