Log ratios of telomere length estimates by Computel and TelSeq compared to qPCR for five neuroblastoma (D) and matched normal tissue (N) samples.

<p>* the absolute values of the mean telomere lengths for tumor and paired healthy tissue computed by Computel and TelSeq are available in the <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0125201#pone.0125201.s002" target="_blank">S2 Information</a>, section <i>Absolute mean telomere length estimation for neuroblastoma samples</i>.</p><p>Log ratios of telomere length estimates by Computel and TelSeq compared to qPCR for five neuroblastoma (D) and matched normal tissue (N) samples.</p

Mean telomere length estimates for osteosarcoma and matched normal tissues by qPCR, Computel and TelSeq.

<p>SJOS002_D, SJOS004_D—osteosarcoma tissue samples; SJOS002_N, SJOS004_N—paired healthy tissue samples.</p

Comparison of performance of Computel and TelSeq in mean telomere length estimation from synthetic data.

<p>^a The actual telomere length was 30 kb attached to the Chromosome 1.</p><p>^b The default TelSeq read length.</p><p>^c For 36 nt read lengths, estimation of telomere length by TelSeq was performed with <i>k</i> (threshold of telomeric repeats in short-reads) equal to 4, 5 or 6; For all other read lengths, the default value of <i>k</i> = 7 was used.</p><p>^d TelSeq fails to output results for 150 nt read length.</p><p>^e SimSeq is available at <i><a href="https://github.com/jstjohn/SimSeq" target="_blank">https://github.com/jstjohn/SimSeq</a></i>.</p><p>Comparison of performance of Computel and TelSeq in mean telomere length estimation from synthetic data.</p

An example of sequence structure of telomeric index for telomeric read alignment.

<p>Telomeric pattern is “TTAGGG” (human); read length = 20 nt; seed length (<i>min</i>.<i>seed</i> option) = 10 nt. The top read contains a non-telomeric region, which will be aligned to the non-telomeric tail of the index, the rest of the reads are six possible cyclic permutations of pure telomeric repeats.</p

Computel: Computation of Mean Telomere Length from Whole-Genome Next-Generation Sequencing Data

<div><p>Telomeres are the ends of eukaryotic chromosomes, consisting of consecutive short repeats that protect chromosome ends from degradation. Telomeres shorten with each cell division, leading to replicative cell senescence. Deregulation of telomere length homeostasis is associated with the development of various age-related diseases and cancers. A number of experimental techniques exist for telomere length measurement; however, until recently, the absence of tools for extracting telomere lengths from high-throughput sequencing data has significantly obscured the association of telomere length with molecular processes in normal and diseased conditions. We have developed Computel, a program in R for computing mean telomere length from whole-genome next-generation sequencing data. Computel is open source, and is freely available at <i><a href="https://github.com/lilit-nersisyan/computel" target="_blank">https://github.com/lilit-nersisyan/computel</a>.</i> It utilizes a short-read alignment-based approach and integrates various popular tools for sequencing data analysis. We validated it with synthetic and experimental data, and compared its performance with the previously available software. The results have shown that Computel outperforms existing software in accuracy, independence of results from sequencing conditions, stability against inherent sequencing errors, and better ability to distinguish pure telomeric sequences from interstitial telomeric repeats. By providing a highly reliable methodology for determining telomere lengths from whole-genome sequencing data, Computel should help to elucidate the role of telomeres in cellular health and disease.</p></div

Schematic representation of the Computel algorithm for mean telomere length estimation.

<p>Computel takes whole-genome NGS short-reads as input; maps them to the telomeric index built based on user-defined telomeric repeat pattern and the read length; and calculates the mean telomere length based on the ratio of telomeric and reference genome coverage, the number of chromosomes, and the read length.</p

Correlation between actual and estimated mean telomere lengths.

<p>S—single-end reads, P—paired-end reads. Estimation of mean telomere length was performed with reads generated from 200 kb length region of human chromosome 1, with telomeres attached to both its ends with lengths sampled from a normal distribution with mean 10 kb and SD 7 kb. The minimum-maximum range of the generated telomere lengths were: 194.5–21138 bp for single-end reads, and 387.5–24169.5 bp for paired-end reads. The read length, insert size and fold coverage ranges are described in the <i>Materials and Methods</i>.</p

table_2_Footprints of Sepsis Framed Within Community Acquired Pneumonia in the Blood Transcriptome.xlsx

<p>We analyzed the blood transcriptome of sepsis framed within community-acquired pneumonia (CAP) and characterized its molecular and cellular heterogeneity in terms of functional modules of co-regulated genes with impact for the underlying pathophysiological mechanisms. Our results showed that CAP severity is associated with immune suppression owing to T-cell exhaustion and HLA and chemokine receptor deactivation, endotoxin tolerance, macrophage polarization, and metabolic conversion from oxidative phosphorylation to glycolysis. We also found footprints of host’s response to viruses and bacteria, altered levels of mRNA from erythrocytes and platelets indicating coagulopathy that parallel severity of sepsis and survival. Finally, our data demonstrated chromatin re-modeling associated with extensive transcriptional deregulation of chromatin modifying enzymes, which suggests the extensive changes of DNA methylation with potential impact for marker selection and functional characterization. Based on the molecular footprints identified, we propose a novel stratification of CAP cases into six groups differing in the transcriptomic scores of CAP severity, interferon response, and erythrocyte mRNA expression with impact for prognosis. Our analysis increases the resolution of transcriptomic footprints of CAP and reveals opportunities for selecting sets of transcriptomic markers with impact for translation of omics research in terms of patient stratification schemes and sets of signature genes.</p

Data_Sheet_1_Footprints of Sepsis Framed Within Community Acquired Pneumonia in the Blood Transcriptome.docx

<p>We analyzed the blood transcriptome of sepsis framed within community-acquired pneumonia (CAP) and characterized its molecular and cellular heterogeneity in terms of functional modules of co-regulated genes with impact for the underlying pathophysiological mechanisms. Our results showed that CAP severity is associated with immune suppression owing to T-cell exhaustion and HLA and chemokine receptor deactivation, endotoxin tolerance, macrophage polarization, and metabolic conversion from oxidative phosphorylation to glycolysis. We also found footprints of host’s response to viruses and bacteria, altered levels of mRNA from erythrocytes and platelets indicating coagulopathy that parallel severity of sepsis and survival. Finally, our data demonstrated chromatin re-modeling associated with extensive transcriptional deregulation of chromatin modifying enzymes, which suggests the extensive changes of DNA methylation with potential impact for marker selection and functional characterization. Based on the molecular footprints identified, we propose a novel stratification of CAP cases into six groups differing in the transcriptomic scores of CAP severity, interferon response, and erythrocyte mRNA expression with impact for prognosis. Our analysis increases the resolution of transcriptomic footprints of CAP and reveals opportunities for selecting sets of transcriptomic markers with impact for translation of omics research in terms of patient stratification schemes and sets of signature genes.</p

table_1_Footprints of Sepsis Framed Within Community Acquired Pneumonia in the Blood Transcriptome.xlsx

<p>We analyzed the blood transcriptome of sepsis framed within community-acquired pneumonia (CAP) and characterized its molecular and cellular heterogeneity in terms of functional modules of co-regulated genes with impact for the underlying pathophysiological mechanisms. Our results showed that CAP severity is associated with immune suppression owing to T-cell exhaustion and HLA and chemokine receptor deactivation, endotoxin tolerance, macrophage polarization, and metabolic conversion from oxidative phosphorylation to glycolysis. We also found footprints of host’s response to viruses and bacteria, altered levels of mRNA from erythrocytes and platelets indicating coagulopathy that parallel severity of sepsis and survival. Finally, our data demonstrated chromatin re-modeling associated with extensive transcriptional deregulation of chromatin modifying enzymes, which suggests the extensive changes of DNA methylation with potential impact for marker selection and functional characterization. Based on the molecular footprints identified, we propose a novel stratification of CAP cases into six groups differing in the transcriptomic scores of CAP severity, interferon response, and erythrocyte mRNA expression with impact for prognosis. Our analysis increases the resolution of transcriptomic footprints of CAP and reveals opportunities for selecting sets of transcriptomic markers with impact for translation of omics research in terms of patient stratification schemes and sets of signature genes.</p