MOESM6 of Gene activity in primary T cells infected with HIV89.6: intron retention and induction of genomic repeats

Additional file 6: Estimating relative abundance of HIV 89.6 message size classes using RNA-Seq data. A) RNA-Seq coverage of the HIV89.6 genome for the replicates in this study. Each replicate is indicated by a different color. The HIV genome is shown on the x-axis and the number of reads that aligned to each position is shown on the y-axis. Black line indicates the 0.021 % coverage decrease per base distance from the 3′ end of the mRNA estimated from a least squares fit on the read counts in the first intron. B) Diagram of the segments of the HIV89.6 RNA present in each of 9 kb, 4 kb, 2 kb and 1 kb size class. C) The proportion of reads mapped to each of the segments of the HIV89.6 genome shown in B adjusted by the length of the segment. Each replicate is shown by a different color. D) Corrected representation of RNA segments from the different size classes. Because cDNA synthesis was primed from the polyA tail, more 3′ sequences are recovered preferentially. Using the bias estimate from A, we adjusted each genome segment by the inverse of the bias predicted based on its distance from the 3′ end of the mRNA. Corrected proportions for the indicated RNA segments are shown colored by replicate. The first exon can not be adjusted using this method since multiple lengths of messages will include it. E) The proportion of each size class was inferred using the estimates in D by calculating the difference between segments. Replicates are indicated by color

A Reverse Transcription Loop-Mediated Isothermal Amplification Assay Optimized to Detect Multiple HIV Subtypes

<div><p>Diagnostic methods for detecting and quantifying HIV RNA have been improving, but efficient methods for point-of-care analysis are still needed, particularly for applications in resource-limited settings. Detection based on reverse-transcription loop-mediated isothermal amplification (RT-LAMP) is particularly useful for this, because when combined with fluorescence-based DNA detection, RT-LAMP can be implemented with minimal equipment and expense. Assays have been developed to detect HIV RNA with RT-LAMP, but existing methods detect only a limited subset of HIV subtypes. Here we report a bioinformatic study to develop optimized primers, followed by empirical testing of 44 new primer designs. One primer set (ACeIN-26), targeting the HIV integrase coding region, consistently detected subtypes A, B, C, D, and G. The assay was sensitive to at least 5000 copies per reaction for subtypes A, B, C, D, and G, with Z-factors of above 0.69 (detection of the minor subtype F was found to be unreliable). There are already rapid and efficient assays available for detecting HIV infection in a binary yes/no format, but the rapid RT-LAMP assay described here has additional uses, including 1) tracking response to medication by comparing longitudinal values for a subject, 2) detecting of infection in neonates unimpeded by the presence of maternal antibody, and 3) detecting infection prior to seroconversion.</p></div

Performance of the AceIN-26 primer set with different starting RNA concentrations.

<p>Tests of each subtype are shown as rows. In each lettered panel, the left shows the raw accumulation of fluorescence signal (y-axis) as a function of time (x-axis); the right panel shows the threshold time (y-axis) as a function of log RNA copy number (x-axis) added to the reaction. In the right hand panels, values were dithered where two points overlapped to allow visualization of both.</p

Examples of time course assays, displaying replicate tests of RT-LAMP primer set ACeIN-26 tested over six HIV subtypes, used in Z-factor calculations.

<p>A total of 5000 RNA copies were tested in each 15 μL reaction. Time is shown on the x-axis, Fluorescence intensity on the y-axis. Replicates are distinguished using an arbitrary color code. Z-factor values and standard deviations are shown on each panel.</p

Bioinformatic analysis to design subtype-agnostic RT-LAMP primers.

<p>A) Conservation of sequence in HIV. HIV genomes (n = 1340) from the Los Alamos National Laboratory collection (file HIV1_ALL_2012_genome_DNA.fasta; <a href="http://www.hiv.lanl.gov/content/sequence/NEWALIGN/align.html#web" target="_blank">http://www.hiv.lanl.gov/content/sequence/NEWALIGN/align.html#web</a>) were aligned and conservation calculated. The x-axis shows the coordinate on the HIV genome, the y-axis shows the proportion of sequences matching the consensus for each 21 base segment of the genome (red points). The black line shows a 101 base sliding average over these proportions. The vertical red shading shows the region targeted for LAMP primer design that was used as input into the EIKEN primer design tool. Numbering is relative to the HIV_89.6 sequence. B) Aligned genomes, showing the locations of the ACeIN-26 primers. Sequences in the red shaded region in A are shown, with DNA bases color-coded as shown at the lower right. Each row indicates an HIV sequence and each column a base in that sequence. Horizontal lines separate the HIV subtypes (labeled at right). Arrows indicate the strand targeted by each primer. Primers targeting the negative strand of the virus are shown as reverse compliments for ease of viewing.</p

Summary of amplification results for all the RT-LAMP primer sets tested in this study.

<p>The data is shown as a heat map, with more intense yellow coloring indicating shorter amplification times (key at bottom). Primer sets tested are named along the left of the figure. Primer sequences, and their organization into LAMP primer sets, are cataloged in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0117852#pone.0117852.s002" target="_blank">S1</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0117852#pone.0117852.s003" target="_blank">S2</a> Tables. The raw data and averaged data are collected in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0117852#pone.0117852.s004" target="_blank">S3</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0117852#pone.0117852.s005" target="_blank">S4</a> Tables. ACeIN-26 primer set (highlighted) had one of the best performances across the subtypes and a relatively simple primer design.</p

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原著和名: [記載なし]科名: ラン科 = Orchidaceae採集地: タイ カオヤイ国立公園 (タイ国 カオヤイ国立公園)採集日: 1984/10/12採集者: 萩庭丈壽整理番号: JH049911国立科学博物館整理番号: TNS-VS-99483

Reference genomes used for pangenome analysis.

BackgroundThe gut microbiome is believed to contribute to bloodstream infection (BSI) via translocation of dominant gut bacteria in vulnerable patient populations. However, conclusively linking gut and blood organisms requires stringent approaches to establish strain-level identity.MethodsWe enrolled a convenience cohort of critically ill patients and investigated 86 bloodstream infection episodes that occurred in 57 patients. Shotgun metagenomic sequencing was used to define constituents of their gut microbiomes, and whole genome sequencing and assembly was done on 23 unique bloodstream isolates that were available from 21 patients. Whole genome sequences were downloaded from public databases and used to establish sequence-identity distribution and define thresholds for unrelated genomes of BSI species. Gut microbiome reads were then aligned to whole genome sequences of the cognate bloodstream isolate and unrelated database isolates to assess identity.ResultsGut microbiome constituents matching the bloodstream infection species were present in half of BSI episodes, and represented >30% relative abundance of gut sequences in 10% of episodes. Among the 23 unique bloodstream organisms that were available for whole genome sequencing, 14 were present in gut at the species level. Sequence alignment applying defined thresholds for identity revealed that 6 met criteria for identical strains in blood and gut, but 8 did not. Sequence identity between BSI isolates and gut microbiome reads was more likely when the species was present at higher relative abundance in gut.ConclusionIn assessing potential gut source for BSI, stringent sequence-based approaches are essential to determine if organisms responsible for BSI are identical to those in gut: of 14 evaluable patients in which the same species was present in both sites, they were identical in 6/14, but were non-identical in 8/14 and thus inconsistent with gut source. This report demonstrates application of sequencing as a key tool to investigate infection tracking within patients.</div

Mapping stool reads against BSI whole genome sequences (WGS).

Each available BSI isolate was subjected to whole genome shotgun sequencing and then assembly into WGS (mean 97.49% coverage; mean 78.81X depth). Stool short reads were then aligned with multiple genomes (indicated on the left of the graph), which included the cognate BSI WGS (“subject”), the WGS of other subjects’ BSI organisms of the same species if any (“cohort”), and unrelated species-matched WGS downloaded from Genbank (“database”). Sequence similarity was calculated based on mismatches defined as single nucleotide variant per megabase pair (SNV/Mbp), reflected along the X axis for each stool sample compared with multiple genomes. (A) Results for six BSI episodes where stool and BSI WGS showed sequence-based strain identity. The SNV/Mbp is indicated along the X axis for each alignment; for clarity, the value is shown for those less than 1000 SNV/Mbp followed by the proportion genome aligned. The red arrow indicates the cognate BSI WGS matched to the stool sample. Each bar is colored to indicate the proportion of genome aligned with stool short reads (because of the short bar, the proportion of genome aligned is shown as a fraction next to the SNVs for the low-SNV matched sample. (B) Representative example of a stool/BSI WGS that did not match. Stool alignment with the cognate Achromobacter BSI WGS showed SNV/Mbp that was no lower than when compared with unrelated WGS of Achromobacter (which was identified only at the genus level by the clinical microbiology lab) downloaded from GenBank.</p

Clinical characteristics of enrolled patients.

Major diagnosis refers to most common principal acute and underlying reasons for ICU stay; full details are in S1 Table. Numbers add to greater than 100% due to concomitant processes. There are no significant differences in any clinical characteristics between BSI-positive subjects and BSI-negative subjects or between BSI-positive subjects and BC-available subjects (chi-square test if categorical, t-test if continuous; applying 0.05 significance threshold).</p