Septic shock is a common and often devastating syndrome marked by severe cardiovascular dysfunction commonly managed with vasopressors. Whether markers of heart rate complexity before vasopressor up-titration could be used to predict success of the up-titration is not known.We studied patients with septic shock requiring vasopressor, newly admitted to the intensive care unit. We measured the complexity of heart rate variability  in the 5 min before all vasopressor up-titrations in the first 24 h of an intensive care unit (ICU) admission. A successful up-titration was defined as one that did not require further up-titration  for 60 min.p = 0.003) with success of up-titration. Independent of heart rate variability, MAP was associated (p < 0.001) with success of vasopressor up-titration, while neither Sequential Organ Failure Assessment (SOFA) nor Acute Physiology and Chronic Health Evaluation II (APACHE II) score was associated with vasopressor titration.We studied 95 patients with septic shock, with a median APACHE II of 27 (IQR: 20–37). The median number of up-titrations, normalized to 24 h, was 12.2 (IQR: 8–17) with a maximum of 49. Of the up-titrations, the median proportion of successful interventions was 0.28 (IQR: 0.12–0.42). The median of mean arterial pressure (MAP) at the time of a vasopressor up-titration was 66 mmHg; the average infusion rate of norepinephrine at the time of an up-titration was 0.11 mcg/kg/min. The ratio of fractal exponents was not associated with successful up-titration on univariate or multivariate regression. On exploratory secondary analyses, however, the long-term fractal exponent was associated  contains supplementary material, which is available to authorized users. Septic shock, the life-threatening manifestation of severe infection, is associated with a near-term mortality of 20-30 % –3. CurreCardiovascular failure is common in sepsis, often resulting in multiple organ dysfunction syndrome (MODS), which is frequently fatal. Vascular leakage causes a decrease in cardiac preload, and arterial dilation causes decrease in cardiac afterload. Microvascular dysfunction is common, and cardiac function is often impaired , 10. TogVasopressors, primarily catecholamine hormones such as norepinephrine, are generally administered in shock to elevate the blood pressure (and recruit unstressed volume in capacitance veins to increase cardiac preload), in order to assure perfusion of coronary arteries as well as distal organs. While vasopressors are an important therapy in patients with shock, it is unclear how best to titrate them. While something like consensus supports targeting a mean arterial pressure between 60–75 mmHg, most clinicians adjust infusion rates by hand in hopes of achieving whatever target is locally preferred .Little work has been done to identify best methods of vasopressor titration. Small studies have explored predictors of the safety of vasopressor weaning in patients with septic shock , 13 One We studied patients admitted to either of two ICUs, the 24-bed Shock Trauma ICU, and the 12-bed Respiratory ICU, at Intermountain Medical Center, a 450-bed tertiary-care, academic hospital in Murray, Utah, USA. Typical clinical practice in the study ICUs is to titrate vasopressors for a mean arterial pressure of 60-65 mmHg.We prospectively identified adult (>15 years of age) patients with septic shock (as defined in consensus guidelines ) admitteWe restricted the study to those patients who had at least one increased infusion rate of vasopressors in the first 24 h of their ICU admission for septic shock.We calculated admission APACHE II  and SOFAData were downloaded from Philips Intellivue Monitors via the Research Data Export (RDE) functionality. RDE provides 125-Hz digitized tracings of continuous electrocardiographic monitoring as well as identification of fiducial points (the moment when the QRS complex occurs). Using those fiducial points, we processed the data within Continuous Individual Multiorgan Variability Analysis (CIMVA) . We usedWe only considered one vasopressor up-titration in any five-minute period: if a vasopressor up-titration occurred within 5 min after another, the latter up-titration was used. Only up-titrations ≥ 0.01 mcg/kg/min were considered. We excluded from consideration any up-titration followed by a down-titration within 1.5 min.a priori, successful vasopressor up-titration as maintenance of mean arterial pressure > 60 mm Hg for 60 min after the increased dosage without further titration during those 60 min.We defined, Central tendencies and variance were expressed as mean (standard deviation) or median (inter-quartile range) as indicated by normality of the data. Statistical analysis and hypothesis testing were performed within the R statistical package version 3.1.3 , with OpOf 117 patients with septic shock requiring vasopressor infusions, 95 (81 %) had adequate arterial blood pressure data and interpretable HRV complexity data. Figure A total of 964 up-titrations among the 95 patients were analyzed. The median number of up-titrations per patient, normalized to 24 h, was 12.2 (IQR: 8–17) with a maximum of 49. Of the up-titrations, the median proportion of successful interventions was 0.28 (IQR: 0.12–0.42). In four patients with at least two up-titrations, 100 % of up-titrations were successful. The median of the mean arterial pressure (MAP) at the time of a vasopressor up-titration was 66 mmHg; the average infusion rate of norepinephrine at the time of an up-titration was 0.11 mcg/kg/min.For our pre-specified primary analysis, we evaluated the ratio of fractal scaling coefficients from detrended fluctuation analysis and Sample Entropy. Neither was associated with successful up-titration on univariate analysis or on bivariate analysis controlling for MAP at the time of up-titration. When controlling for age, sex, and admission APACHE II, there was also no significant association. The results for the model evaluating the ratio of scaling coefficients is displayed in Table p = 0.003) and the long-term fractal exponent (p = 0.003) were significantly associated with success of vasopressor titration, effects which persisted after control for APACHE II, MAP, age and sex (p = 0.007 for both). We display these findings in Table In prespecified exploratory analyses, we evaluated the association of all CIMVA complexity measures against the primary outcome. Notably, the overall fractal exponent  and those that were unsuccessful  (p < 0.001). The distributions of the overall fractal exponent also differed between up-titrations that were successful  and those that were unsuccessful  (Wilcoxon rank sum test p < 0.001).The distributions of the long-term fractal exponent differed between up-titrations that were successful (p < 0.001) with success of the up-titration; trends toward association between the size of the up-titration (p = 0.09) and APACHE II score (p = 0.08) and success of up-titration were also observed. None of age, sex, and SOFA score was associated with success of vasopressor up-titration.In an exploratory analysis of other clinical predictors of successful vasopressor up-titration, MAP before the up-titration was strongly associated  over time intervals longer than approximately one minute , with greater variability over longer time intervals. It may be that patients in whom that variability is lower are less likely to respond to vasopressor up-titrations because they have more severe vasoplegia or, contrarily, have a defect in cardiac rather than vascular function. Recent work on the mismatch between ventricular and arterial elastance  in septic shock suggests that certain patients may differentially benefit from vasoconstriction versus fluid loading or inotropic support.  It is poBeyond evaluation of specific predictors, our study provides a useful snapshot of current vasopressor titration practices. On average, doses are adjusted approximately a dozen times in a 24-h period. While the nominal practice in the study ICUs is to target 65 mmHg, many adjustments are made at a moment when the MAP is slightly higher, perhaps representing a desire to never fall below the target MAP. Our study also documents the reality that only about a third of vasopressor up-titrations were successful. This raises the possibility that improved predictive models could facilitate higher-quality approaches to the titration of vasopressors. On the basis of our data, the MAP at the time of up-titration should be incorporated into such predictive models. It may be that the MAP is the most important predictor, and heart rate variability metrics may contribute relatively little. In many if not most control systems, the distance between the current and desired level of a parameter of interest is a key determinant of the required input. Future work should address whether the distance between desired and current blood pressure could guide vasopressor titration.Early work in dogs has suggested the possibility that automated control of vasopressor infusions may be possible . To our With ongoing interest in understanding optimal MAP goals , our resPrior work has suggested that complexity metrics may be useful for clinical management of patients with or at risk for infection. Among premature infants, a complexity-monitoring system focused on entropy measurements demonstrated improved outcomes, primarily as an early identification system for sepsis . A compoA strength of this study is our prespecified analytic approach, including primary predictors and clinical outcomes. Our use of prespecified primary analyses decreases the chance of Type 1 statistical error. Our non-definitive, exploratory analyses suggest that it would be reasonable to evaluate the overall and long-term fractal exponents in another cohort to ensure that the null hypothesis has not been accepted incorrectly.Limitations of this study include the fact that in study ICUs vasopressor titration is predominantly performed by the bedside nurse, at her/his discretion. This practice likely results in unmeasured inter- and intra-clinician variation, which we were unable to control for.In summary, the long-term fractal exponent of heart rate variability and mean arterial pressure at the time of a vasopressor up-titration may be predictive of the success of that vasopressor up-titration. Further work is indicated to improve the rationality and predictability of vasopressor titration for patients with septic shock.


Cancer biomarker studies often require nucleic acid extraction from limited amounts of formalin-fixed, paraffin-embedded (FFPE) tissues, such as histologic sections or needle cores. A major challenge is low quantity and quality of extracted nucleic acids, which can limit our ability to perform genetic analyses, and have a significant influence on overall study design. This study was aimed at identifying the most reliable and reproducible method of obtaining sufficient high-quality nucleic acids from FFPE tissues. We compared the yield and quality of nucleic acids from 0.6-mm FFPE prostate tissue cores across 16 DNA and RNA extraction protocols, using 14 commercially available kits. Nucleic acid yield was determined by fluorometry, and quality was determined by spectrophotometry. All protocols yielded nucleic acids in quantities that are compatible with downstream molecular applications. However, the protocols varied widely in the quality of the extracted RNA and DNA. Four RNA and five DNA extraction protocols, including protocols from two kits for dual-extraction of RNA and DNA from the same tissue source, were prioritized for further quality assessment based on the yield and purity of their products. Specifically, their compatibility with downstream reactions was assessed using both NanoString nCounter gene expression assays and reverse-transcriptase real-time PCR for RNA, and methylation-specific PCR assays for DNA. The kit deemed most suitable for FFPE tissue was the AllPrep kit by Qiagen because of its yield, quality, and ability to purify both RNA and DNA from the same sample, which would be advantageous in biomarker studies. The rapid evolution of technologies in cancer research has led to significant advances in our understanding of tumor genetics. Driven largely by high-throughput molecular technologies, there is a growing body of “omics” level data, annotated with cancer phenotypes. Such data permit the molecular profiling of an individual patient’s cancer, which is increasingly becoming more useful as disease management becomes more personalized . MoleculA major consideration in biomarker development is the accrual of sample cohorts of sufficient size to permit rigorous statistical analyses during the discovery and validation phases. Currently, hospital-based pathology laboratories and biobanks are the best repositories of biospecimens linked to robust and relevant clinical and pathological information, enabling retrospective analysis of genotype-phenotype correlations. The ability to preserve morphologic and molecular information within these biospecimens allows one to use histopathological criteria, such as pathologic grade, stage, and histologic subtypes, as the basis for biomarker study design. More specifically, existing techniques for obtaining needle cores from formalin-fixed, paraffin-embedded (FFPE) tissues allow for harvesting of specific tumour grade or type, with minimal contamination by the stroma or other confounding tissue types. The promise of using archived FFPE tissues in biomarker discovery has made it more important than ever before to unlock the potential of this critical resource.However, archived FFPE tissues present many technical challenges in molecular analysis. Formalin fixation leads to crosslinking of nucleic acids to proteins and other cellular constituents, making the extraction of these analytes difficult. In addition, age-related changes in pH can lead to the oxidation of formalin to formic acid, causing base purination and strand breaks . Thus nuEstablishing a reliable and reproducible method of obtaining sufficient amounts of high-quality nucleic acids from limited amounts of FFPE tissue remains a major challenge in many biomarker studies. Currently, several commercial kits are available for the extraction of RNA and DNA from FFPE tissue. While the manufacturer’s quality control process ensures a consistent performance under given experimental conditions, each of these kits has distinct performance characteristics in terms of yield and purity. In this study, we characterize and compare the performance of six RNA, six DNA, and two dual-extraction (RNA+DNA) kits using FFPE prostate cancer tissue as input samples. Furthermore, we highlight the compatibility of the RNA and DNA extracted with these kits as analytes in PCR- and hybridization-based downstream applications.Archived tissue was retrieved from the Department of Pathology archives of the Kingston General Hospital under the approval of the Queen’s University Health Sciences Research Ethics Board. Tumour-rich regions of interest were identified on histopathology slides and harvested from paraffin blocks using a manual tissue microarray punch . Three representative cores from each case were digitally photographed with a phase contrast microscope , and the mean tissue volume per core for each case was calculated based on tissue lengths and diameters measured using ImageJ ,8. The h3 of tissue. The pooled tissue cores were prepared for homogenization by first being deparaffinized in xylene (2 x 5 min at room temperature), washed in 100% ethanol (2 x 5 min), and then air-dried. Cores were then suspended in 150 μL of fresh 100% ethanol per mm3 of tissue and homogenized for 1 min at 10,000 rpm using a Power Gen Model 125 tissue homogenizer .To facilitate direct comparison between 14 commercial nucleic acid purification kits, we pooled and homogenized 120 cores from four different archived tissue blocks, totaling 37.2 mm3 of the homogenized tissue. For each kit, the manufacturer’s protocols were followed  using 0.68 mmTwo of the kits tested, RecAll (ThermoFisher Scientific) and AllPrep (Qiagen), are dual-extraction kits, which permit extraction of RNA followed by DNA from the same input tissue. For these two kits, after the homogenized tissue was treated with Proteinase K and then centrifuged, the resulting tissue pellets were used as input for DNA extraction, while the supernatant was used for RNA extraction.For each extraction, the DNA and RNA yields were quantified on a Qubit 3.0 Fluorometer (ThermoFisher Scientific), using the dsDNA HS (High Sensitivity) and RNA BR (Broad-Range) Assay kits, respectively. The purity of the extracted nucleic acids was assessed by the A260/280 and A260/230 absorbance ratios obtained using a NanoDrop spectrophotometer.HSD11β1 gene  and hot start  steps, then cycled through denaturation  and annealing/extension  steps for 40 cycles on a ViiA7 Real-Time PCR System (Thermo Fisher Scientific). Assays were performed in duplicate for each extraction kit. The cycle thresholds (Cq) across kits were then plotted using the GraphPad Prism v7 software . The inhibitory effect was quantified as the difference in the mean Cq between reactions spiked with the water control and reactions spiked with the extracted nucleic acid. One-way ANOVA with Bonferroni’s corrections was performed to compare for significant differences in Cq values.200).We postulated that the chemicals used in the various extraction kits may affect the size distribution of the final nucleic acid product. The size distribution of RNA fragments within the extracts was assessed using the RNA 6000 Pico kit on a 2100 Bioanalyzer Lab-on-a-Chip platform , and expressed as the percentage of fragments greater than 200 base pairs . The programmed profile of the PCR reaction consisted of initial denaturation at 95 oC for 3 min, followed by 40 cycles of denaturation at 95 oC (30 sec), denaturation at 55 oC (30 sec), and extension at 72 oC (1 min).Endpoint reverse transcriptase PCR (RT-PCR) was also performed to assess the size distribution of RNA fragments. More specifically, it was used to determine the amplifiable fragment length of RNA extracted using five select kits , as a means of further assessing the downstream utility of the RNA. Six PCR primer pairs were designed to span exons 2 to 4 of the human beta-2-microglobulin mRNA (RefSeq NM_004048), with expected amplicon sizes ranging from 92 to 386 base pairs (bp) in approximately 50-bp increments . For eacTo assess amplifiable fragment length of the extracted DNA, four primer pairs were designed flanking the exon 2-intron 2 junction of the human beta-2-microglobulin gene (RefSeq NG_012920.1), with expected amplicon sizes ranging from 102 to 300 bp in approximately 65-bp increments . PCR ampTM DigitalAnalyzer from NanoString Technologies [The NanoString platform was used to further quantify mRNA extracted from four select kits  and one fresh (PC-3) cell line, whose RNA was extracted using RNeasy. Sample preparation and hybridization for the NanoString mRNA assay were performed according to the manufacturer's instructions. Briefly, 100 ng of input RNA was hybridized to NanoString 48-plex Customer Assay Evaluation (CAE) probes at 65°C for 20 hr. The solution-phase hybridization products were then processed on the nCounter Preparation Station for automated removal of excess probe and immobilization of probe-transcript complexes on a streptavidin-coated cartridge. Barcoded signals were acquired using an nCounterData were analyzed using the nCounter™ digital analyzer software Version 2.1.1. Raw data were normalized to the geometric mean of spiked-in exogenous positive controls , and then by subtracting the hybridization background ,12. The PGK1, KRT8, and HPRT1) were quantified by TaqMan-based RT-qPCR gene-expression assay kits (Thermo Fisher Scientific) using the ViiA7 qPCR thermocycler. Statistical analyses, including graphics and multiple comparisons with Bonferroni corrections, the latter using two-way ANOVA, were performed to assess for significant differences in mean Cq values between different kits across the three genes.Like the NanoString mRNA assays, RT-qPCR mRNA assays were used to further quantify mRNA extracted from the four prioritized RNA kits . One hundred nanograms of RNA from each kit was converted to cDNA using SuperScript VILO mastermix (Thermo Fisher Scientific). Three housekeeping genes  with enzyme-based downstream reactions, we performed methylation-specific PCR (MS-PCR), which is frequently employed in biomarker studies . As prepGSTP1, ABCB1 and RASSF1, all known to be hypermethylated in prostate cancer. Alu repeat elements were used as the positive control [MS-PCR assays were carried out as published in our previous study . Briefly control –16. MultThe reproducibility of the AllPrep extraction protocols was tested across three independent laboratories based on serial extractions of RNA and DNA from 12 FFPE prostate cancer samples. Briefly, nine cores were harvested from each of the 12 samples using a 0.6-mm Estigen Punch Set . To ensure uniformity in the quantity and quality of the input tissue, the harvested cores were pooled and homogenized. They were then distributed in equal-volume aliquots to participating laboratories, where RNA and DNA were extracted using the AllPrep protocols and assessed for yield and compatibility with downstream molecular applications (NanoString and MS-PCR) using the methods described above . Inter-lIn this study, we compared the nucleic acid yield and quality from FFPE tissue cores across six DNA and six RNA extraction kits, as well as two dual-extraction kits (DNA/RNA) . Based o3 tissue, respectively. The two RNA-only kits with the highest yields were the PuLink  and RNeasy  kits, followed by HPRNA, EZNRNA, NorRNA, and NucRNA.The two dual-nucleic acid (RNA/DNA) extraction kits, AllPrep and RecAll, yielded 2,512.08 and 2,249.95 ng of RNA per mm3 tissue, respectively. Three dedicated DNA kits produced DNA yields comparable to those of AllPrep and RecAll: DNeasy (1236.03 ng/mm3), QIAamp (980.00 ng/mm3), and GenJet (750.59 ng/mm3). The HPDNA kit produced a lower yield, followed by NorDNA and then NucDNA  was interpreted as the extent of the inhibitory effect. One-way ANOVA with Bonferroni’s correction showed that none of the RNA tested had any effects on the Cq, relative to the control, indicating that the RNA was pure  to 10% (NucRNA). Notably, AllPrep and RecAll, the two kits that permit dual extraction of DNA and RNA, had DV200 values of 54% and 43%, respectively  and four RNA protocols  were prioritised for further quality assessment. Both dual-extraction kits (AllPrep and RecAll) were included based on their capacity to extract both RNA and DNA from the same tissue source, which provides significant advantage in biomarker studies. The prioritised kits were further evaluated using NanoString, RT-qPCR expression, and MS-PCR assays.The NanoString platform has previously been used for direct, digital quantitation of specific mRNA transcripts through hybridization to two sequence-specific, color-coded probes . As thisRNA extracted from four select kits and one fresh (PC-3) cell line were run on the NanoString platform, and the mean differences between total mRNA counts were compared by one-way ANOVA. Pairwise comparisons between RNA extracted from the cell line and each of the four selected kits showed no significant difference in total signal counts .PGK1, KRT8 and HPRT1), showing the overall compatibility of these protocols with downstream gene expression assays. Of these, the AllPrep protocol yielded significantly higher Cq values for all three genes  cannot be attributed to the fragment size. Rather, the data suggest the presence of contaminants in AllPrep RNA that have an inhibitory effect on the PCR reaction, consistent with the delayed Cq also observed with AllPrep DNA in the inhibition assays.By RT-qPCR, RNA from all four prioritized RNA protocols were successfully used to amplify the three house-keeping genes tested . Thus, the AllPrep kit produced nucleic acids that strongly correlated between the three laboratories in yield, quality, and compatibility with downstream applications. These results support a previous observation that the AllPrep kit provides reproducible protocols for the extraction of nucleic acids that are suitable for typical molecular downstream applications [One-way ANOVA analysis did not detect significant differences between laboratories in yield of DNA or RNA  and DNA  extraction protocols were prioritized for further evaluations using methylation-specific PCR, NanoString, and reverse-transcriptase quantitative PCR assays.The data herein provide the necessary metrics to guide the selection of a protocol that best suits the needs of the overall study design in terms of the quantity of available tissue, and the anticipated downstream applications. Overall, the AllPrep protocol reproducibly yields high quantities of matched RNA and DNA from the same tissue source. While the impurities of nucleic acids extracted using the AllPrep kit appear to impact PCR-based methods at high concentration, this effect is negligible at dilutions typical of templates in PCR-based assays. Taken together, the AllPrep kit was our preferred method for preparing nucleic acids for downstream epigenetic and gene expression studies.S1 Fig(A) Electropherograms for the dual-extraction kits AllPrep and RecAll. (B) Electropherograms for RNA-only extraction kits . See supplementary (TIF)Click here for additional data file.S2 FigDistribution of the NanoString mRNA counts were plotted alongside fresh PC-3 RNA (as a control) for all 48 genes from the nCounter CEA code set. Data points from 48 genes are represented as circles with median lines and SD bars. See supplementary (TIF)Click here for additional data file.S3 Fig(A) DNA and (B) RNA yields across the three labs. Results of the (C) MS-PCR and (D) NanoString assays performed using serial extractions of RNA and DNA from 12 FFPE prostate cancer samples. R2 values are Pearson’s correlation coefficients . See supplementary (TIF)Click here for additional data file.S4 Fig(A) Cycle thresholds (Cq) of the control sample (water-spiked) versus two undiluted prostate cancer DNA extracts and their respective 1:10 and 1:20 dilutions. The inhibition assay demonstrates a Cq shift in reactions spiked with undiluted extracts, but a significantly reduced Cq with higher dilutions across the two prostate cancer DNA samples tested. (B) No significant inhibition was seen in one breast cancer DNA extract. See supplementary (TIF)Click here for additional data file.S1 TableB2M) mRNA (RefSeq NM_004048), four primer pairs for B2M DNA (qP1-4), and one primer pair specific for the amplification of HSD11B1 DNA. Also included are the expected PCR amplicon sizes of the respective primer pairs.The table shows six specific primer pairs (CP1-6) for the Homo sapiens β-2-microglobulin ((XLSX)Click here for additional data file.S2 TableThis Excel book contains data used to generate figures, and statistical analyses used in data comparisons. Each tab represents data for each figure or panel, as labeled. All statistical tests reported in this study were done using the GraphPad Prism v7 software .(XLSX)Click here for additional data file.


Tumor-released RNA may mediate intercellular communication and serve as biomarkers. Here we develop a protocol enabling quantitative, minimally biased analysis of extracellular RNAs (exRNAs) associated with microvesicles, exosomes , and ribonucleoproteins (RNPs). The exRNA complexes isolated from patient-derived glioma stem-like cultures exhibit distinct compositions, with microvesicles most closely reflecting cellular transcriptome. exRNA is enriched in small ncRNAs, such as miRNAs in exosomes, and precisely processed tRNA and Y RNA fragments in EVs and exRNPs. EV-enclosed mRNAs are mostly fragmented, and UTRs enriched; nevertheless, some full-length mRNAs are present. Overall, there is less than one copy of non-rRNA per EV. Our results suggest that massive EV/exRNA uptake would be required to ensure functional impact of transferred RNA on brain recipient cells and predict the most impactful miRNAs in such conditions. This study also provides a catalog of diverse exRNAs useful for biomarker discovery and validates its feasibility on cerebrospinal fluid. While circulating DNA has been extensively explored as a potential cancer biomarker, RNA potential has been overlooked so far. Here the authors present a comprehensive analysis of extracellular RNA secreted by glioblastoma cells that could prove a valuable resource for biomarker discovery and a means of intercellular communication. Extracellular RNAs (exRNAs) are packaged into various extracellular complexes, including microvesicles (MVs), exosomes, and non-vesicular ribonucleoprotein complexes (RNPs)3. MVs and exosomes, broadly called extracellular vesicles (EVs), are released and taken up by various cells, thereby transferring their content. This process likely plays a role in cancer development and manipulation of its microenvironment4. However, methodologies are only beginning to emerge for characterizing the exRNA landscape and monitoring levels of individual coding and regulatory exRNAs. exRNA mostly consists of small RNA species (<200 nt); and the majority of reports to date focus on miRNA6. As a critical step toward understanding the biological impact of exRNA release and transfer, we investigated the complete spectrum of cancer-derived exRNAs, and the enrichment of specific RNA classes and individual species. By creating cDNA libraries of both small and long exRNA, and reducing the ligation bias favoring miRNAs, we identified a diverse and highly distinct composition of exRNA in MVs, exosomes, and RNP complexes. Furthermore, semi-absolute quantification of RNAseq, performed using RNA spike-in molecules, allowed us to monitor the levels of various RNA classes and species in these extracellular complexes.Intercellular communication within complex biological systems, such as cancer and its host microenvironment, via “horizontal” RNA transfer, is an expanding area of research4. Communication between tumor cells and diverse normal cells in the brain is nevertheless one of the least investigated aspects of glioma biology. We employed low-passage patient-derived tumorigenic GBM cell cultures that represent the most therapy-resistant stem-like cell population (GSC), and are considered the core cell type within the tumor. Analysis of GSC cellular and extracellular RNA, along with the transcriptome of primary human and mouse cells of the brain microenvironment  enables us to predict the most impactful miRNAs and expand the repertoire of potentially transferred exRNAs far beyond the classes of miRNAs and mRNAs. We also demonstrate that MVs, large vesicles of 0.2–0.8 μm, most closely mirror the cellular transcriptome and thus present a highly promising but yet poorly explored source of liquid biopsy biomarkers.This work focused on glioblastoma (GBM), the most common and aggressive brain tumor, as an important model for investigation of cancer-derived exRNA. As proliferating and invading GBM cells migrate through brain parenchyma, they interact with the changing landscape of extra-tumoral stimuli and actively modulate this landscape7. Since specific markers or physical parameters for the various types of EVs and extracellular RNPs are still not clearly defined, UC remains the most commonly used approach to isolate the entire vesiculome8. However, based on nanoparticle tracking analysis  and fluorescent dye-binding quantification (RiboGreen), the yield of EVs and exRNA isolated by this technique is relatively low (20–40%)  protocol , precipitation using chemical polymers (PP), such as polyethylene glycol, and fractionation, including density gradient UC and gel filtration (DG&GF)col Fig. . This prvely low 0–40%  and T4 polynucleotide kinase (T4 PNK) to create more uniform 5′ and 3′ ends for various types of transcripts, leading to their more accurate representation in the cDNA libraries17. The caveat is that this end-modifying procedure leads to an overwhelming abundance of rRNA reads in cellular and exRNA samples, and reduces the sequencing depth for other RNA classes. Therefore, we included an rRNA depletion step in the protocol that reduced rRNA reads remarkably  contain RNA that is co-isolated with cell-derived exRNA19, we also assessed the interference of FM RNA with downstream analysis of GSC exRNA. With this goal, we isolated three RNA fractions from the corresponding FM, using the same filtration procedure, and subjected them to RNAseq in parallel with the GSC exRNA. Approximately 1 ng/ml total exRNA was isolated from FM, and it was largely associated with the RNP fraction , were classified as either mRNA or non-coding RNA. Although in three out of four GSC cultures, cellular mRNA reads accounted for 20–35% of long RNA libraries, the mRNA proportion in EV fractions was below 10%, and even lower in RNPs Fig. . The rea23. Considering the small size of EVs, the maximum length of an mRNA that could be packaged is still an open question. Previous high-throughput studies have not discriminated between full-length mRNAs and fragments in exRNA25. On average, our RNAseq detected 17,148 intracellular mRNAs in GSCs, whereas 17,219, 11,592, and 11,819 mRNAs were detected in MV, exosome, and RNP fractions, respectively. Many of the most abundant extracellular mRNAs corresponded to relatively short transcripts, including mRNAs for various ribosomal proteins . Even fewer, 19 miRNA species, accounted for 80% of the total miRNome in GBM8 exosomes, suggesting a higher inequality of miRNAs in exRNA. Such comparison, however, relies on a randomly selected cutoff (80% used above). To compare the inequality of intracellular and extracellular transcripts more objectively, we developed two alternative strategies. The first one is an improved version of traditional evaluation named the evenness factor (ε), which defines that ε% of RNA species can account for (100−ε)% of total abundance. The second strategy is based on the Gini coefficient, commonly used for evaluation of inequality in economics. Of note, although both are objective and use no preset cutoff, higher ε and lower Gini coefficient correspond to a more equal representation . The detailed illustration of the concepts behind these evaluation systems can be found in Supplementary Fig. A relatively small number of the most abundant miRNA species usually account for the majority of total miRNA in a given cellular contextχ2 value). The higher χ2 value reflects the higher diversity/heterogeneity of GSC cultures in terms of RNA composition. As shown in Fig. Despite the genetic diversity of GSCs, cellular RNA class composition was similar among the four different GSC cultures analyzed, with the exception of MGG75 cells that expressed more mRNA than long ncRNA Fig. . However30  appear to be a good source for RNA biomarker discovery, and have the potential to outperform the more studied smaller exosomes.Cancer-derived exRNA may serve as clinical biomarkers for disease diagnostics, prognostics and monitoring. An extracellular fraction that closely mirrors the deranged cellular transcriptome would be the most valuable for such applications. To date, mostly unfractionated, precipitated or 100,000 g-pelleted EVs have been explored as a source for potential biomarkers. To examine what type of extracellular complexes  might serve as the closest proxy of the GSC transcriptome, we performed correlation and clustering analysis of their RNA composition. Overall, clustering analysis based on small RNA libraries demonstrated the highest similarity between the cellular transcriptome and MV content, and to a lesser extent to exosome content, while the RNP fraction had a highly distinct RNA composition  samples obtained from four GBM patients. As shown in Fig. 33. In total, 2267 mRNA species interact with at least one of the impactful miRNAs. Based on the ingenuity pathway analysis (IPA), these targets are significantly enriched in many canonical cancer-related pathways and bioterms, including the molecular mechanism of cancer, ERK/MAPK signaling, PI3K/AKT signaling, and NGF signaling , and the levels of mRNAs most commonly mutated in GBM  were not higher than one copy per 100,000 EVs approximately. Based on these data, continuous barrage, massive and/or highly selective uptake of EVs might be required to affect signaling or alter the phenotype of the recipient cells via their exRNA content.A substantial amount of transferred exRNA might be required to exert a functional effect in a recipient cell; however, the quantitative data for the levels of specific RNA classes and individual transcripts in exRNAs is very limited thus far. To address this issue, we performed a stoichiometric analysis of the EV-associated RNA. On average, GSC EVs (MVs and exosomes collectively) provided ~8.9 ng total RNA per ml of conditioned media. Considering the concentration 2 × 1035. These data support intercellular transfer of miRNA via exRNA and suggest that, despite the apparently low levels in EVs, miRNAs may exert regulatory functions in recipient cells, albeit with specific conditions and highly sensitive methodologies required for detection.To examine the functionality of transferred GSC-derived miRNAs, we established 3D transwell co-cultures of GBM8 neurospheres with primary mouse astrocytes, a system more physiological than the commonly used exposure of recipient cultures to super-concentrated EVs isolated from donor cell conditioned medium. In line with the low copy number of individual miRNAs per EV, we were unable to detect significant elevation of either miR-21 or miR-10b (top GBM-promoting miRNAs) in astrocytes co-cultured with GBM8. However, miR-21 became readily detectable in primary astrocytes established from miR-21 knockout mice upon their co-culture with GBM8 Fig. . Further36, and in contrast to others41, we found that miRNA species are relatively enriched in exosomes, but not in MVs or RNPs. Of note, the isolation approaches, the library preparation strategies, and the normalization methods utilized in these different studies were diverse, making the results not directly comparable. Other non-coding RNA species are more abundant, and some of them are enriched in EVs or RNPs. Notable among them are precisely processed tRNA and Y RNA fragments, associated with both EVs and extravesicular RNPs, and supported by observations of other cultured cells37 and body fluids42.exRNA studies have expanded rapidly and multidirectionally in this decade. Revealing the composition of exRNA complexes released by defined cell types remains one of the most fundamental milestones toward understanding the role of exRNA in intercellular communication, as well as discovery of RNA biomarkers for disease. This study provides the first minimally biased quantitative analysis of the exRNA released by tumor-derived cells from GBM patients. Despite the high heterogeneity of GSC cultures established from different tumors, and even higher heterogeneity of the exRNA they release, extracellular complexes share key characteristics. The RNA profiles of MVs, exosomes, and RNPs are highly distinct. They all display selectivity in their RNA loading compared with the cells, with the MVs being the most like the cells and the RNPs the least. Small and fragmented RNAs account for the majority of exRNA in all three types of complexes. Between 64 and 93% of all exRNA consists of fragmented rRNA Fig. , althoug43 produced by angiogenin (ANG), a member of ribonuclease A family44. This multifunctional ribonuclease regulates angiogenesis, cell proliferation and viability of cancer cells, as well as neuronal survival and stress response43. Specific 5′-tRFs are known to perform crucial functions, often associated with regulation of gene expression in stress response. They repress protein translation by displacing eukaryotic translation initiation factors eIF4E and eIF4G45; and modulate stress response by inducing formation of stress granules—cytoplasmic foci where untranslated mRNAs are transiently stored44. Importantly, specific 5′-tRFs  selectively regulate translation of subsets of mRNAs, both capped and uncapped, and therefore reprogram protein synthesis45. They may also function in a miRNA-like manner46. ANG is upregulated in GBM48 and is one of the proteins most highly secreted by glioma cells49. Despite its high abundance, functions of ANG, including its extracellular activity and its exRNA products remain to be investigated. Remarkable enrichment of both ANG and 5′-tRFs observed in GSC-derived exosomes , also called 5′-tRNA halves or tiRNAs, are evolutionarily conserved moleculeses Figs.  suggests51. Their lower stem domain, which recruits chaperone Ro60 and exoribonuclease PNPase, may play a role in RNA quality control and degradation of misfolded RNAs50. The upper stem domain may participate in initiation of chromosomal DNA replication52. With biogenesis and activity independent of Dicer and Ago253, Y RNA fragments do not appear to silence gene expression in a miRNA-like manner54. Recent studies reported that Y RNA fragments may be involved in histone mRNA processing55 and cell damage56. Interestingly, Y5 fragment, the most abundant in exRNA, is proposed to specialize in surveillance of ribosomal RNA57. Studying the functions of these precisely processed, highly enriched extracellular transcripts represents an exciting new avenue in RNA biology.Another highly abundant but poorly studied classes of non-coding exRNAs are Y RNAs and their specific 5′ fragments. The majority of information about Y RNA has come from studies of bacteria and invertebrates. In vertebrates, Y RNAs are expressed in all tissues and cells and have been proposed to participate in many important cellular functions59 might cause differential sorting or stability, leading to enrichment in exRNAs. UTRs and their fragments might function as molecular sponges for various regulatory molecules, including miRNA, translation factors , other RNA-binding proteins, and thereby exert functions in recipient cells. Altogether, our data indicate the strong preference of processed fragments for multiple classes of RNA, protein-coding and non-coding, in exRNA  albumin-depletion protocol, we were able to compare protein content in MVs, exosomes, and non-vesicular RNPs using immunoblots. Interestingly, two nuclear proteins PCNA and Ro60 appeared enriched in RNPs but not in EVs. While the co-localization of Ro60 with its binding Y RNA partners in RNPs may point to the functionality of this extracellular complex, the role of extracellular PCNA, a protein involved in DNA replication, remains to be elucidated. Although the possibility of direct non-vesicular, RNP-mediated RNA uptake and function in recipient cells is currently unexplored, it is supported by the utility of RNPs for RNA and drug delivery62. Regardless of the potential biological function of RNPs, their associated RNAs expand the repertoire of potential biomarkers that should be further explored in body fluids, in parallel to EV transcripts.Although several studies have explored the RNA bound to specific secreted or circulating proteins, such as high-density lipoprotein (HDL)RNA Fig. . We presRNA Fig.  and 6, cThe key outcome of our work, overall, is an expansion of the repertoire of exRNAs released by GSCs in different vehicles, with functional and biomarker potential, far beyond the class of miRNA. This conclusion challenges the commonly assumed predominant role of miRNA in exRNA-mediated intercellular communication. It further points to the need for in-depth investigation of other classes of exRNAs and their impact on the physiology of recipient cells and use as biomarkers. The future should bring the development of novel experimental techniques and computational resources for integrating complex expression data sets into comprehensive biologic networks and biomarker discovery.5 dissociated cells were seeded per 10 cm dish (Corning) in 10 ml fresh media, and 1/3 volume of fresh medium was added every 3 days. Mature neurospheres, typically formed in 7–10 days, were dissociated and replated. All cells were tested for mycoplasma. Human cells were used in accordance with the policies of institutional review boards at Brigham and Women’s Hospital.Human low-passage (below 20) GBM cells  were cultured as neurospheres in Neurobasal medium (Gibco) supplemented with 3 mM GlutaMAX (Gibco), 1× B-27 supplement (Gibco), 0.5× N-2 (Gibco), 20 ng/ml EGF , 20 ng/ml FGF  and 0.5% Antibiotic-Antimycotic Solution (Corning), and passaged by NeuroCult Chemical Dissociation Kit (Mouse)  following the manual. Approximately 5 × 10d-lysine-coated T25 flasks or 24-well plates at ~80,000 cells per cm2, in the seeding medium consisting of DMEM-F12 (Corning), 10% FBS (Gibco), and 1% Antibiotic-Antimycotic Solution (Corning). For neuronal cultures, the media was exchanged to Neurobasal (Gibco), 2% B-27 (Gibco), 1% Antibiotic-Antimycotic Solution (Corning) and 0.5 mM Glutamax (Gibco) 1 day after plating. Mature neurons at 21 days in culture have been utilized for the RNAseq. For glial cultures, the flasks were shaken (200 rpm at 37 °C) three times overnight to remove microglia, and astrocytes trypsinized and further cultured in 24-well plates. For microglia cultures, the media was supplemented with the recombinant M-CSF mouse protein . Floating microglial cells were collected from the conditioned media by gentle spin , and re-plated in 24-well poly-d-lysine-coated plates at ~100,000 cells per cm2. For human primary neuroglial cultures, fetal cortical tissues  were provided by Advanced Bioscience Resources, Inc. , dissociated with papain , seeded with neuronal media plus 2% FBS (Gibco), and cultured as mouse neurons63. Neuronal cultures at 30 days in vitro have been utilized for the RNAseq. Human (HBEC) and mouse (MBEC) primary brain microvascular endothelial cells were purchased from Cell Biologics, IL  and cultured accordingly to the manufacturer. All animal experiments have been approved by the Harvard Medical Area Standing Committee on Animals.Brain cortices of E18 and P1 C57BL/6 mice were dissected for primary cultures of neurons and glial cells, respectively. The tissues were dissociated with 0.25% Trypsin (Gibco) and 0.1 mg/ml DNaseI (Roche) for 15 min at 37 °C. The cells were plated in poly-g, 4 °C for 10 min, following by the additional centrifugation at 2000×g, 4 °C for 15 min, to remove cells and cell debris. To monitor EVs, the samples were diluted in DPBS and examined using the Nanoparticle Tracking Analysis system , and EV concentrations were quantified within the optimal linear range (2–10 × 108 particles per 1 ml).Approximately 100 ml conditioned medium was used as input for exRNA isolation. Conditioned media was centrifuged at 300×g, 4 °C, for 60 min. The concentrates were collected and lysed with six volumes of the same lysis solution (Exiqon). Total RNA was then isolated from all fractions as recommended by miRCURY protocol, with on-column DNase treatment . The corresponding 1.2 ml of the source neurospheres were span down at 300×g, 4 °C for 5 min, and total cellular RNA was isolated from them and analyzed in parallel. The same protocol was carried out for RNA isolation from fresh media, with 500 ml media input. For RNA isolation from primary cells cultured in 24-well plates, the cells were lysed with 350 μl lysis solution per well. The concentrations of cellular and extracellular RNA were determined using the NanoDrop 2000 Spectrophotometer and Quant-iT RiboGreen RNA Assay Kit (Thermo Fisher Scientific), respectively. The RNA quality was examined using Agilent 2100 Bioanalyzer  and the RNA Integrity Number (RIN) estimated.For RNA preparation, 5 μl of SUPERase In RNase Inhibitor (Ambion) was added to the supernatants per 10 ml media. The media was then filtered sequentially through the 2 μm filter , 0.8 μm filter , and 0.22 μm filter (EMD Millipore), with no/minimal pressure applied. The filtrate was split to 15 ml per sample and further filtered through the 0.02 μm filter  with up to 75 psi pressure applied. To facilitate the 0.02 μm filtration, a mechanical syringe pump was designed and manufactured , and images recorded by the AMT 2k CCD camera  at the Harvard Medical School EM Facility.The material collected on the filters was resuspended using DPBS (Corning), and further pelleted by 100,000×Two sets of spike-in RNAs were added to the samples prior to library preparation: ERCC RNA Spike-In Control Mixes , and miRCURY Spike-in kit, part 1, with UniSp2’s final concentrations of 1.25 and 5.0 fmol per 1 μg of total RNA for cellular and extracellular RNA, respectively. Total RNA, either 40–200 ng of exRNA, or 2 μg of cellular RNA, was rRNA-depleted using the Ribo-Zero rRNA Removal Kits . One quarter of the rRNA-depleted RNA was fragmented to 100–500 nt using the 5× First-Strand Buffer , and utilized for the long RNA library construction by SMARTer Stranded RNA-Seq Kit (Clontech). The remaining 75% of the rRNA-depleted RNA was treated sequentially with Tobacco Acid Pyrophosphatase  and T4 Polynucleotide Kinase  to create more uniform 5′- and 3′-ends for various classes of transcripts. The RNA was then used as input for the NEBNext Multiplex Small RNA Library Prep Set for Illumina (New England Biolabs), with size selection of 15–65 nt inserts for small RNA libraries. The quality of libraries was examined using the Agilent DNA 1000 kit at the Agilent 2100 Bioanalyzer instrument, and cDNA quantified by qRT-PCR. The libraries were sequenced on HiSeq 2000 (Illumina) with single read 50 cycles by Beijing Genomics Institute .Sequencing reads were treated using the BGI pipeline that included multiple filtering steps, as follows: (1) removing reads with adapters; (2) removing reads with >10% of unknown bases; and (3) removing low-quality reads . After filtering, the remaining clean reads were subjected to the bioinformatics analysis. Generally, 20 and 10 M clean reads were generated per long RNA library and small RNA library, respectively. The clean reads produced from the long RNA libraries were first mapped to the Homo sapiens rRNA database using the SOAPaligner/SOAP2 short read alignment software, to remove the remaining rRNA reads. The non-rRNA reads were used to perform the transcriptome assembling and quantification. First, non-rRNA reads were mapped to human reference genome hg19 using an improved version of TopHat2, which aligns reads across splice junction without relying on gene annotation. Default -g/--max-multihits was used to allow up to 20 multimapping. Next, the reads mapped to genome were assembled using Cufflinks. Reference Annotation Based Transcript (RABT) assembly was performed with the reference gene annotation to compensate incompletely assembled transcripts caused by read coverage gaps in the regions of reference gene. The set of transfrags generated was then compared with the reference transcripts to remove transfrags that were approximately equivalent to the whole or a portion of a reference transcript. After the assembling, the whole parsimonious set of transcripts was obtained. These transcripts were blasted with the NONCODE database using the filter set (identity >0.9 and coverage >0.8) to identify known long non-coding RNA. Then, the rest of assembled transcripts were aligned to the reference annotation utilizing Cuffcompare. Thereafter, Cuffmerge was utilized to merge several assemblies from different samples together, which automatically filtered out a number of transfrags that probably were artifacts and produced a single annotation file for downstream gene expression analysis. Two mismatches were allowed for annotation generally, and only one mismatch was allowed for the ERCC spike-ins. mRNA and lncRNA expression analyses were performed by Cuffdiff with parameter -u/--multi-read-correct and normalized based on the ERCC spike-in controls using the cyclic loess robust local regression.Clean reads produced from the small RNA libraries were first aligned to carrier RNA (Enterobacteria phage MS2) of miRCURY Spike-in kit with bowtie . The unmapped reads were aligned against human rRNA sequences with bowtie  to remove the remaining rRNA reads. The remaining reads were mapped to human miRNA precursors (miRBase V19) with bowtie , and the miRNA precursors reads were separated according to the mapping position. The mapped reads with more than 10% mismatched nucleotides were excluded. The remaining reads were mapped to spike-ins , and other non-coding RNA classes including tRNAs (gtRNAdb), piRNAs (RNAdb), snoRNA (snoRNA-LBME-db), scRNAs (Genbank), and others (Rfam database) . Finally, the remaining reads were mapped to the hg19 human genome, and the reads mapped to exons, introns, intergenic regions, and repeats  identified. The SAMtools was used to calculate the reads depth for each base position and R package barplot was used to draw the corresponding bar plot.χ2 value) of species composition was calculated using MS Excel Macro . Venn diagrams were plotted using Venny 2.1.Based on the known amounts of spike-in RNAs, the read count for each RNA species was first normalized to spike-ins’ reads, to quantify the absolute amounts in fmol per μg of total RNA. Next, the obtained values were corrected to the corresponding fresh media as the blank control, using the equation shown in Supplementary Fig. Maxima Reverse Transcriptase  was used to reverse transcribe 20 ng RNA with both oligo(dT) and random hexamers in a 10 μl reaction system. Next, 0.5 μl cDNA was used in 10 μl PCR reactions based on Phire Hot Start II PCR Master Mix (Thermo Fisher Scientific) and primers (0.5 μM each), and amplified using a touchdown program with either 15 or 70 s extension step for short or long amplification, respectively. The PCR products were examined on 0.8–1.2% agarose gels (Thermo Fisher Scientific). Primer sequences are provided in Supplementary Table Generally, for small RNA qRT-PCR, 10 ng of total RNA was used in 10 μl reverse transcription reaction with Universal cDNA Synthesis kit II (Exiqon). The cDNA was diluted 80 times, and 4 μl was used in 10 μl qPCR reactions using the ExiLENT SYBR Green master mix and custom-designed LNA primers (Exiqon). For mRNA qRT-PCR, 10 ng of total RNA was used in 10 μl reverse transcription reaction with PrimeScript RT Master Mix . One microliter cDNA was used in 10 μl qPCR reactions using the ExiLENT SYBR Green master mix and synthesized primers , and concentrated using Pierce SDS-PAGE Sample Prep Kit . Protein concentrations were quantified using the Micro BCA Protein Assay Kit (Pierce). Equal amounts of the total protein (50 μg) were loaded per lane in Novex WedgeWell 14% Tris-Glycine Mini Gel (Thermo Fisher Scientific). Proteins were transferred to 0.45 μm PVDF membrane (Thermo Fisher Scientific). After blocking with 5% (wt/vol) fat-free milk in Tris-buffered saline with 0.075% Tween-20 (TBST), membranes were incubated with 1:1000 diluted primary antibodies  overnight at 4 °C. The membranes were washed and incubated with horseradish peroxidase–conjugated secondary antibodies  for 1 h at room temperature. The blots were developed by the Amersham ECL Reagent  and, if required, stripped using One Minute Western Blot Stripping Buffer . Uncropped scans of blots are shown in Supplementary Fig. Mouse cortical astrocytes established from WT or miR-21 KO P1 pups, passage 1 or 2, were cultured in 24-well plates. Small GBM8 neurospheres grown in GSC conditions were transferred to the upper chamber of the Millicell hanging insert with 1.0 μm pore size (Millipore), and co-cultured with the pre-established astrocytes. The co-culture media was adjusted to contain DMEM-F12 (Corning), 1× B-27 supplement (Gibco), 0.5× N-2 (Gibco), and 1% Antibiotic–Antimycotic Solution (Corning), enabling co-culturing of healthy astrocytes and GSC neurospheres over a period of at least 6 days. The corresponding control “mono-cultures” of astrocytes were cultured in the identical media under the same conditions. Three days later, astrocytes were washed with cold DPBS (Corning) three times, and cellular RNA isolated as in other experiments.g at room temperature for 10 min, immediately after collection to remove cells. The supernatants were filtered through the 0.8 μm filters (Millipore), aliquoted into cryotubes (Thermo Fisher Scientific), and stored at −80 °C for 12–24 months. The aliquots of 3 ml were thawed on ice, and used to isolate MV, exosomes, and RNP fractions by sequential filtration, as described above.Cerebrospinal fluid (CSF)samples from four patients diagnosed with primary GBM were collected during surgery, with informed consent obtained according to the appropriate protocol, approved by the UC San Diego Institutional Review Board. The samples were centrifuged at 1500×t-test. Before performing t-test, normal distribution was verified by one-sample Kolmogorov–Smirnov test and equality of variances was verified by Levene’s test using SPSS . A p-value < 0.05 is considered as statistical significance. All bars represent mean ± SEM.Values are given as mean ± SEM. Numbers of experimental replicates are given in the figure legends. When two groups were compared, significance was determined using an unpaired two-sided RNAseq fq files and processed data are available through GEO under GSE93143. MS Excel Macros used to calculate inequality and heterogeneity are available in Supplementary Data Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4


Salmonella and Legionella species, respectively, may harbor novel biochemical activities. Our study presenting the first systematic evaluation of the RNA-targeting potential of bacterial effectors offers a basis for discussion of whether or not host RNA is a prominent target of secreted bacterial proteins.Many pathogenic bacteria utilize specialized secretion systems to deliver proteins called effectors into eukaryotic cells for manipulation of host pathways. The vast majority of known effector targets are host proteins, whereas a potential targeting of host nucleic acids remains little explored. There is only one family of effectors known to target DNA directly, and effectors binding host RNA are unknown. Here, we take a two-pronged approach to search for RNA-binding effectors, combining biocomputational prediction of RNA-binding domains (RBDs) in a newly assembled comprehensive dataset of bacterial secreted proteins, and experimental screening for RNA binding in mammalian cells. Only a small subset of effectors were predicted to carry an RBD, indicating that if RNA targeting was common, it would likely involve new types of RBDs. Our experimental evaluation of effectors with predicted RBDs further argues for a general paucity of RNA binding activities amongst bacterial effectors. We obtained evidence that PipB2 and Lpg2844, effector proteins of  The characterization of such effectors promises both, a better understanding of the infection processes and new applications in cell biology and biotechnology. To date, almost all of the identified targets of effectors are host proteins3, prominently involved in infection-related processes such as cytoskeletal manipulation, immune-evasion, apoptosis signaling, and vesicle trafficking5. Effector proteins from widely divergent pathogens may target similar proteins and pathways in the host6, with the NFκB innate immune pathway7 being a prominent example. Similarly, small GTPases are commonly targeted to reroute vesicular trafficking avoiding lysosomal degradation and favoring intracellular replication of bacteria8. Understanding the functions of effector proteins is essential to unravel the mechanisms of bacterial survival in the host.Many bacterial pathogens depend on virulence factors, translocated into the host by specialized secretion systems, to subvert distinct cellular functions. The number of these secreted proteins, known as effectors, encoded by a pathogen varies greatly from one to several hundred depending on the speciesXanthomonas (a plant pathogen) in which nearly-identical repeats containing a hypervariable pair of residues confer specificity to a particular base pair, allowing them to bind to promoters in host DNA11. In addition to TALEs, effectors harboring SET-like domains influence host gene expression by modifying chromatin state14.By contrast, targeting of host nucleic acids by effectors remains little explored. One example are transcription activator-like effectors (TALEs) of 15. Therefore, it was conceivable that some bacterial effectors possess RNA-binding domains (RBDs), which would allow them to selectively target coding and non-coding RNAs to modulate host gene expression, similarly to viral encoded RNA-binding proteins18. Additionally, viruses are known to directly manipulate the host microRNA pathway by this mechanism19. However, in contrast with the importance of hijacking host RNA-mediated regulation by viral proteins, no RNA-targeting bacterial effectors have been identified so far. The only identified RNA-binding effector to date is the Yersinia effector YopD, which regulates T3SS genes by binding directly to bacterial mRNAs at short AU-rich sequences21.Beyond DNA, cellular RNA would offer a wide spectrum of potential targets for effectors to interfere with host gene expression post-transcriptionally at the levels of RNA splicing, maturation, export, silencing, and storage. Endogenous RNA-binding proteins (RBPs) play an essential role in these processes and there is an increasing number of new proteins with RNA-related function2. This suggests that RNA-targeting effectors may be identified by homology searches for conserved RBD domains. Indeed, automated homology-based approaches have enabled RBP discovery in various organisms22. With the recent characterization of hundreds of RBPs, a large collection of RBDs is now available. For instance, the Pfam database now contains ~800 RBDs including RNA-binding protein families23. This rich collection of domains lends itself as a reference set for the prediction of potential RNA-binding effectors.Many characterized effectors target specific host pathways by mimicking host proteins, hence very often they contain eukaryotic-like domains25. Collectively, these studies have identified ~1,500 RBPs in human cells, which contain ~600 distinct RBDs, many of them widely conserved whereas the rest are novel domains with unique architecture15. Methods using protein or RNA as bait combined with high-throughput RNA sequencing and mass spectrometry have accelerated RBP discovery26. Moreover, in vivo UV crosslinking combined with immunoprecipitation (CLIP) not only permits the capture of endogenous RNA-protein interactions but also pinpoint interaction sites30. This approach allows the purification of the cross-linked RNA-protein pairs under harsh washing conditions, which reduces false-positive interactions with non-specific RNA31.The recent increase in known RBPs was much driven by novel methods for the exploration of RNA-protein interactions32P] and co-purification of proteins, if not considered with adequate controls, can mislead in the interpretation of the results. Nonetheless, our results also suggest that the long-studied Salmonella effector PipB2 and the uncharacterized Legionella effector Lpg2844 may harbor a yet unidentified nucleotide-binding domain.In this study, we have assembled from the literature a searchable collection of 1,022 individual effector proteins of 35 animal and plant pathogens or symbionts. To identify RBPs in this effector dataset, we applied a biocomputational approach that used as a reference all currently identified unambiguous RBDs. This analysis predicted several classical and non-classical RBDs in a small subset of effector proteins. We applied the CLIP method to interrogate 33 selected effector proteins with putative RBDs for their RNA binding potential. Our combined biocomputational/experimental approach unraveled important limitations and challenges both in the automated prediction of RBDs in bacterial effector proteins using sequence-based homology, as well as in the CLIP approach when used with a broad diversity of proteins. For example, UV irradiation, use of ATP  were added to the reaction and incubated at 37 °C for 30 min. Labeling was preceded with a dephosphorylation step using Calf Intestinal Alkaline Phosphatase CIP . The beads were then washed in PNK buffer and in High-salt buffer. Crosslinked RNAs and proteins were eluted in gel loading buffer by boiling for 6 min at 90 °C. The eluted complexes were loaded on Bis-Tris 10% polyacrylamide gel and transferred on nitrocellulose membranes, and exposed. The same blots were blocked in 10% milk and incubated with the anti-FLAG antibody to detect the proteins in the corresponding complexes.After washing with PBS, transfected cells of ~80–90% confluency were placed on ice and irradiated with 0.2 J/cmFor Western blots, aliquots were collected from the lysates before incubation with the beads (input) and from the eluted fraction (eluate). These were loaded on Tris-glycine 10% gel for protein analysis, and transferred on PVDF membranes, the proteins were subsequently detected using the FLAG antibody.66. For structure analysis, Chimera USCF67 and Phyre268 tools were used. Protein structures were collected from the Protein Data Base (PDB).Sequence alignment was performed using Clustal Omega softwareCells growing on glass coverslips were washed in 1X PBS, fixed with 4% paraformaldehyde (PFA) for 15 min at RT, and permeabilized with 0.5% Triton-X-100 in PBS for 10 min. Blocking was performed for 30 min in 1% Bovine Serum Albumin (BSA) in PBS. Cells were stained with a primary antibody anti-FLAG  diluted in blocking buffer. Cells were further washed and incubated with the corresponding secondary antibody conjugated with Alexa Flour 594 . Cell nuclei were stained with Hoechst for 15 min at RT.7 cells were collected after crosslinking as described above and cells were pelleted by centrifugation at 1500 g for 3 min at 4 °C. The pellet was resuspended in 1 ml buffer A , SuperaseIn was added, and the suspension was incubated on ice for 5 min. The nuclei were collected by low-speed centrifugation at 1300 g at 4 °C for 4 min, the pellet (nuclear fraction) was washed once in 2ml buffer A, and lysed in buffer B  on ice for 30 min. The nuclear fraction was cleared from the chromatin by centrifugation at 1700 g for 4min at 4 °C, and the supernatant was saved (nuclear fraction). The supernatant from the low-speed centrifugation was collected (cytosolic fraction), and cleared by high-speed centrifugation at 20 000 g for 15 min at 4 °C. The cytosolic and the nuclear fractions obtained were subsequently used for immuno-precipitation as described above. LaminB and tubulin were detected by western blot and used to normalize the protein concentrations of nuclear and cytoplasmic fractions, respectively.For cytosolic and nuclear fractionation of 293 T cells transfected with plasmids, approx. 4 × 1032. These candidate RBPs are further annotated by sequence-based features like chemical properties, protein compositions and structural properties, which are compared with the references in order to calculate similarity scores for each of these features. These similarity scores are further used for the ranking of selected candidates in order to differentiate proteins that consist of RBDs with high biological conservations than the proteins that are predicted with RBDs of lower conservations. The source-code and related documentations for APRICOT are freely available online at https://github.com/malvikasharan/APRICOT.APRICOT was established as an automated pipeline to carry out a sequence-based identification of functional motifs including RNA-binding domains in the bacterial effectors compiled from various studies. APRICOT uses two main data sources, namely Conserved Domain Database (CDD) and InterPro that consist of conserved motifs, signatures and functional domains from various databases. CDD comprise of 50,648 entries (February 2016) as Position Specific Scoring Matrix (PSSM) and InterPro comprise of 28926 entries (February 2016) as Hidden Markov Models (HMM) or position weight matrix. As shown in the Table Combined Supplementary materialSupplementary Table S2Supplementary Table S3Supplementary Table S4Supplementary Table S5Supplementary Table S7


Drosophila, A-to-I editing is prevalent in the brain, and mutations in the editing enzyme ADAR correlate with specific behavioral defects. Here we demonstrate a role for ADAR in behavioral temperature adaptation in Drosophila. Although there is a higher level of editing at lower temperatures, at 29°C more sites are edited. These sites are less evolutionarily conserved, more disperse, less likely to be involved in secondary structures, and more likely to be located in exons. Interestingly, hypomorph mutants for ADAR display a weaker transcriptional response to temperature changes than wild-type flies and a highly abnormal behavioral response upon temperature increase. In sum, our data shows that ADAR is essential for proper temperature adaptation, a key behavior trait that is essential for survival of flies in the wild. Moreover, our results suggest a more general role of ADAR in regulating RNA secondary structures in vivo.In  In this work, we study one of the most abundant, yet poorly characterized genomic phenomena that has the potential to change the basic biological dogma–RNA editing, which creates transcriptome diversity by transforming adenosine into guanosine in RNA sequences. Such alteration, which is performed by ADAR family of deaminases, does not damage the original genomic version, and can be revised when circumstances change. Our analysis demonstrates that ADAR plays an important role in temperature adaptation by sensing and acting globally on RNA secondary structure. We suggest that ADAR has evolved to be highly efficient at cold temperatures, where RNA secondary structure is more prevalent. On the contrary, at high temperatures, where the secondary structure is more labile, ADAR may have negative effects, as it increases the chance of substitution in exonic sequences. Moreover, we observed behavioral defects in the ADAR hypomorphs at high temperatures. Control of RNA function is essential for all living organisms. Gene expression is regulated at the transcriptional, co-transcriptional and post-transcriptional levels. Regulation at the post-transcriptional level is achieved by alternative pre-mRNA processing, regulation of RNA turnover, control of RNA localization or translation. Post-transcriptional RNA modifications also serve to regulate the functions of specific coding or non-coding RNA molecules without any impact at the DNA level.Drosophila melanogaster[A prevalent RNA modification is A-to-I editing. The reaction is catalyzed by the ADAR family of enzymes. ADAR stanogaster–12. Hundanogaster,14. Mostanogaster and micranogaster.Drosophila, especially in the brain[Identifying novel editing sites is not easy, since A-to-G mismatches can occur due to somatic mutations, genomic polymorphisms, and sequencing or alignment errors. Traditithe brain.Drosophila[Drosophila melanogaster has only a single ADAR protein (dADAR), an ortholog of the mammalian ADAR2. ADAR1 was lost during the evolution of insects[RNA editing is critical to the function of the nervous system. Indeed, A-to-I editing re-coding events are found mainly in neuronal genes, and functional effects of editing on ion channels and G-protein coupled receptors have been demonstrated–21. Editrosophila as mutanrosophila. There af insects.Interestingly, most A-to-I editing events in flies do not seem to impact the coding sequence and do not alter directly gene expression by altering splicing or miRNA binding sites, leading researchers to postulate that most RNA editing events are non-functional. HoweverDrosophila that do not regulate internal temperature. Indeed, temperature is a strong regulator of fly physiology and behavior[Drosophila is strongly influenced by temperature[adar expression and by a decrease in ADAR activity. Interestingly, at higher temperatures adar mRNA is auto-edited, resulting in a protein with diminished editing activity. The fact that ADAR activity is tightly regulated by temperature suggests that A-to-I editing is important for the adaptation of flies to temperature changes[Therefore, it is not surprising that ADAR activity appears to be modulated in situations in which RNA secondary structure is altered. One of these situations is temperature change, in particular in poikilotherm organisms like  behavior. The pat behavior. Interesmperature. At a sue changes, perhapsDrosophila. We first fully characterized the A-to-I editing landscape in heads of flies adapted to temperatures of 18, 25, and 29°C. By utilizing our recently developed approach[Here we investigate the role of ADAR in temperature adaptation in  approach, we idende novo RNA editing sites in this dataset, we utilized our previously published algorithm[To explore the global effect of temperature on RNA-editing, we generated RNA-seq datasets from heads of flies entrained at three different temperatures . To identify algorithm. Our appalgorithm, 3580[32algorithm editing algorithm, 1,181 oalgorithm,32), butalgorithm. ImportaWhen comparing editing events in flies entrained at different temperatures, we observed that the editing index  was lower at 29°C than at 18 or 25°C , as prev−4, Surprisingly, and despite the lower editing index at 29°C compared to lower temperatures, there were significantly more hyper-edited sites at 29°C  than at 25°C or 18°C  oTo identify whether transcripts from different functional classes are edited at different temperatures, we evaluated enrichment of gene ontology (GO) terms for mRNAs edited at the different temperatures using the PANTHER classification system. We obseIt is assumed that edited sites are located in double-stranded structures as ADAR binding and enzymatic activity is specific for these regions,36. To dADAR mRNA expression decreased as temperature increased . These data again suggest that although there are more editing sites at 29°C, ADAR is less specific at this temperature and may modify RNA even when it is not involved in a strong secondary structure. It is possible that at higher temperatures labile double-stranded RNA structures also serve as ADAR substrates. As expected based on previously results, we founncreased , implyinAlthough the results obtained above suggest the presence of little or no secondary structure on ADAR targets which are unique to 29°C, it is possible that secondary structure is present and was not detected by our aligning approach. Therefore, we determined the percentage of editing sites engaged in evolutionarily conserved secondary structures as predicted by EvoFold. Consist+ selection, we observed a significant fraction of editing in intronic sequences. Indeed, we observed that at 18°C, 40% of the editing sites are located in intronic sequences. This is likely an underestimation, as we did not use nascent RNA for this assessment. Interestingly, at 29°C we observed a significant shift toward exons . Howeverperatures. The decperatures. These r[in vivo ,41. The [in vivo .The presence of strong secondary structures during transcription would have strong consequences both on transcription and other co-transcriptional events like splicing. As A-to-I editing happens co-transcriptionally, the possible disruption of specific RNA structures involving intronic sequences can influence splicing as well as polyadenylation efficiencies. Others and us recently showed that circular RNA (circRNA) production is regulated by ADAR in mouse (ADD); dThe arguments laid out above might explain the necessity for high dADAR activity at 18°C. As putative disruptive structures in RNA transcripts must be quickly removed in order not to affect mRNA production, ADAR has evolved to be highly efficient at 18°C. The specificity of ADAR becomes problematic then at high temperatures as secondary structures are more labile. In this situation, ADAR activity increases the chance of mutations in exonic sequences, which we indeed found are more heavily edited at 29°C compared to 18°C. It seems plausible that ADAR is not completely necessary at 29°C; however, the behavioral defects observed in the ADAR hypomorphs suggest a more complicated mechanism. First, these mutant flies are generally less active than wild-type flies as previously described, and their activity levels are not temperature dependent. Second, after three days of entrainment ADAR hypomorphs adapt well to 18°C but not to 29°C. The lack of effect at 18°C might be related to the fact that the most important structures to be disrupted (and re-coding sites to be edited) likely engage in strong secondary structures at this temperature and that the remaining ADAR activity might suffice to disrupt them. The lack of adaptation to the higher temperature could be explained either by the presence of temperature-dependent re-coding events of important behavior-related genes or to the lack of disruption of structures that form at this temperature.fruitless[cacophony[Previous studies have addressed roles for dADAR in fly behavior. While ADAR5G1 have completely disrupted locomotor behavioral patterns, they are likely of developmental origin, as dADAR hypomorph flies display more modest behavioral abnormalities,44,45. dfruitless, cacophocacophony.per se resulted in an immediate behavioral response . We also utilized a wt and ADAR hypomorph strains[Adar5G1).For the identification of temperature dependent editing events, we utilized a h strains  and treated with DNase I (NEB) following the manufacturer's protocol.+ selected (using Oligo(dT) beads, Invitrogen), fragmented in FastAP buffer (Thermo Scientific) for 3min at 94°C and then dephosphorylated with FastAP, cleaned  and then ligated to a linker1 , using T4 RNA ligase I (NEB). Ligated RNA was cleaned-up by Silane beads  and pooled into a single tube. RT was then performed for the pooled sample, with a specific primer (5´-CCTACACGACGCTCTTCC-3´) using AffinityScript Multiple Temperature cDNA Synthesis Kit (Agilent Technologies). Then, RNA-DNA hybrids were degraded by incubating the RT mixture with 10% 1M NaOH (e.g. 2ul to 20ul of RT mixture) at 70C for 12 minutes. pH was then normalized by addition of corresponding amount of 0.5M AcOH (e.g. 4ul for 22 ul of NaOH+RT mixture). The reaction mixture was cleaned up using Silane beads and second lygation was performed, where 3’end of cDNA was ligated to linker2 (5Phos/AGATCGGAAGAGCACACGTCTG/3ddC/) using T4 RNA ligase I. The sequences of linker1 and linker2 are partially complementary to the standard Illumina read1 and read2/barcode adapters, respectively. Reaction Mixture was cleaned up (Silane beads) and PCR enrichment was set up using enrichment primers 1 and 2  and Phusion HF MasterMix (NEB). 10–12 cycles of enrichment were performed. Libraries were cleaned with 0.7X volume of SPRI beads. Libraries were characterization by Tapestation. RNA was sequenced as paired-end samples, in a NextSeq 500 sequencer (Illumina).For 3’ Digital gene expression libraries preparation was similar, with one exception- PolyA+ selection was not done before fragmentation, but after linker1 ligation and samples pooling (before the RT reaction step).RNA  extracted from flies entrained at 18°C/ 25°C/ 29°C was reverse transcribed using random primes . The resulting cDNA, and genomic DNA from the above flies were amplified by PCR  using the following primers:Sh-RO (intronic): Fwd: 5’-CTCCGGACCCCAAATCTAAC-3’, Rev: 5’-CGTTTCGCGGTGATAGAAGT-3’;Calx-RF: Fwd: 5’-GGACAAGAACTACCGGGTCA-3’, 5’-AGTACATTCGGATGGGATCG-3’;Ca-beta: Fwd: 5'-ACTCCCAGTCCCACTCTCAGTAT-3', Rev: 5'- GTGGATGTATCTGTGTCGCTGTA -3';Tut1: Fwd: 5'- TTAGTATCGCACGAATCGGTATC -3', Rev: 5'- GACCTACTACTTCCGCGTGCT -3';nAChRa1pha6: Fwd: 5'- ATAACCGATGAATCGAACTGATG -3', Rev: 5'- TTTGCTGTGTATTTTGTCGTTTG -3';NaCP60E: Fwd: 5'- AATATTCCTTCCAGCCCGTTT -3', Rev: 5'- CCATCTGATTTACTTGCAGATACG -3';Unc80: Fwd: 5'- GGCGGATTCTGAGACATGAG -3', Rev: 5'- CTGCTAAAGGTGTCGCCCTAT -3';CG8481: Fwd: 5'- GAGGCAATCACACATGCACTT -3', Rev: 5'- ATATATCTGTCCACCATCGACCA -3';Para: Fwd: 5'- TACCAAACTCCAAACCCCTTATT -3', Rev: 5'- TCTTTGTATAACGCTCACCGACT -3';DopEcR: Fwd: 5'- GAGAACAACATGACGCACATTT -3', Rev: 5'- ATGATCAGAATTTTCCAAAACGA -3';Amplified PCR products were cleaned from agarose gels and subjected to sanger sequencing.All reads at each file were aligned to dm3 genome, using BWA aln (0.6.2) with default parameters, and then BWA mem (0.7.4) with minimum seed length of 50. We kept only uniquely aligned reads (using samtools). The unaligned reads were used for detection of hyper-editing events, and the aligned reads were used for further analysis.We used the recently published pipeline for deteThe analyses of editing levels were done using REDItools script, which sχ2 test followed by 5% FDR correction. Then we compiled a list of 55 significantly altered editing sites in coding sequence, with at least 50 reads coverage and over 20% editing difference between temperatures.To detect RNA-editing sites in coding regions, we annotated all sites using ANNOVAR and thenFurther validation on those sites was done using fly RNA-seq datasets downloaded from NCBI Gene Expression Omnibus database (accession number—GSE37232).In order to detect formation of double-stranded RNA structures, we searched for alignment of 40 bases centered on the hyper-edited sites with sequence 2 kilobases upstream and downstream of the regions. We used bl2seq with -G The number of aligned reads to each gene was calculated using featureCounts, with thRNA-seq reads from 3’ Digital gene expression (DGE) experiments were aligned to the genome and transcriptome (dm3) using TopHat. ESAT toDrosophila Activity Monitors using 1 minute bins. Flies were entrained 5 days in 12:12 Light:Dark cycles (LD). Before temperature shift (to 18 or 29°C), flies were entrained for 3 days at 25°C. Analyses were performed with a signal processing toolbox[Male flies were monitored using Trikinetics g toolbox. All theDrosophila Activity Monitors  system. During the experiment, flies were supplemented with agar food (2% Agar and 5% sucrose). The sleep assay was performed at fixed humidity and temperature (25 ± 1°C). Data were collected according to pySolo manual and analyzed via pySolo sleep analysis software[Adult male flies (3–5 days old) were placed in glass tube and monitored for 5–6 days in constant light-dark (LD) condition via using Trikinetics  software.All raw sequencing data are available for download from National Center for Biotechnology Information GEO database under accession number GSE95313.S1 Fig(A) Count of hyper-editing events in drosophila melanogaster. Most of the detected mismatches were of A-to-G type. A total of 30,190 unique A-to-G editing sites were discovered, more than 74% of all the detected unique sites. 10,300 (25%) of the detected sites were of A-to-C type substitution, next was proven to be due to a sequencing error. (B) Distribution of hyper-editing events along read's positions. Each bar represents the number of editing events (A-to-G or A-to-C) found at every specific position within the read. We expect that real editing events to have no position preference along the read, whereas A-to-C substitution (background mismatch) tended to localize in specific position. Importantly, the distribution of A-to-G hyper-editing sites is not equal along the read since we discarded clusters too close to the end of the read to overcome improper alignment due to splicing [(C) Distribution of editing events across the reads strands. Each bar refers to a specific type of mismatch, and shows the fraction of editing sites in each strand (+ or -) as well as the specified mismatch . Only the A-to-G sites show the expected behavior from true editing sites (~50/50%), while A-to-C substitution was detected only in the sense (+) strand, strongly suggesting that these mismatches are the result of technical error during the sequencing reaction.splicing . (C) Dis(TIF)Click here for additional data file.S2 Fig(A) Venn diagram describing the number of detected hyper-editing sites in each temperature. 10,122 unique A-to-G editing events were discovered at 18°C, 5,406 at 25°C and 15,724 at 29°C. 681 (2%) of the detected hyper-editing sites were shared between all three temperatures. (B) The fraction of inosines among all expressed adenosines of previously detected hyper-editing sites, shows a lower editing levels at 29°C. (C) dADAR deferential expression levels distributions at different temperatures is shown. ADAR transcript levels decrease as temperature increases. (D) Illustration of hyper-edited region. Hyper-edited cluster defined as the number of nucleotides between the first and the last high-quality A-to-G mismatch (cluster should cover at least 10% of the read). All overlapping hyper-edited clusters were merged (the genomic coordinates) to create a hyper-edited region, maintaining a maximum distance of 20 bases between edited clusters. Region's boundaries were set from the first base of the upstream cluster, to the last base of the downstream cluster.(TIF)Click here for additional data file.S3 Fig(A) Each row represents one of the 55 editing sites in CDS ((B) Validation of Calx  and Sh  differential editing using direct Sanger sequencing. (C) Genomic validations of eight significantly altered editing sites: Ca-beta (chr2L:11159274), tut1 (chr2L:4314586), nAChRalpha6 (chr2L:9809350), NaCP60E (chr2R:20802317), unc80 (chr3R:23500290), CG8481 (chr3R:5595147), para (chrX:16398273), DopEcR (chr3L:4369459).s in CDS  that wer(TIF)Click here for additional data file.S4 Fig(A) The distribution of hyper-editing detected sites, normalized to the aligned number of reads, shows a different editing behavior for hypomorfic and WT flies. (B) ADAR hypomorph flies have lower expression of Adar mRNA comparing to WT flies. The data presents normalized read counts from 3' RNA-seq experiment (C) IGV snapshot of RNA-seq data from WT and ADAR hypomorph flies 3'. The single peak in both strains demonstrates no production of a new truncated protein in the ADAR hypomorph flies (D) Cumulative distribution plot for genes differentially expressed in 29°C vs. 18°C that are affected by ADAR hypomorph in both 18°C and 29°C. (E) Cumulative distribution plot for genes that are affected by ADAR hypomorph only in 29°C. For the comparisons presented in B and C Kolmogorov-Smirnov test showed that there are no significant differences between the curves .(TIF)Click here for additional data file.S5 Fig(A) No difference in average sleep between ADAR hypomorph and control flies, both at 18 and 29°C. (B) No difference in night or day sleep between ADAR hypomorph and control WT flies at 18 or 29°C. (C-D) Temperature transition (marked in an arrow) does not affect total sleep of ADAR hypomorph flies, in respect to their controls.(TIF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.


Phylogenetic analyses identified two distinct major CRF01_AE lineages and a Shanghai-based sub-lineage. The estimated tMRCAs for lineage 1 and 2 were 1996.0 (1992.9–1999.2) and 1997.8 (1994.3–2001.4), respectively. Of the 1, 152 MSM, 681 (59.1%) were identified as belonging to 241 separate networks. Of these 681 individuals in networks, 74.2% were linked to cases diagnosed in different years, 4.3% were linked to heterosexual women, and 0.7% were linked to persons who inject drugs. A total of 71 networks including 180 individuals diagnosed in Shanghai with the same domicile were found. Recent infection (P = 0.022) and sampling year after 2011 (P < 0.001) were significantly associated with potential transmission links among the networks. Besides, a significant transmission of viruses with drug resistant mutations at V179D/E were found in the networks. Given these findings, we propose that genetic transmission analysis is a useful tool in HIV intervention strategies to curb the spread of virus and promoting public health.To explore the evolutionary dynamics and molecular transmission patterns of HIV-1 CRF01_AE in depth among men who have sex with men (MSM) in Shanghai, we constructed phylogenetic tree and genetic transmission networks based on 1, 152  HIV-1 CRF01_AE has become the most prevalent strains in China12445Comprehensive surveillance strategies are necessary to monitor the rapid spread of HIV among MSM. However, the silence and secrecy associated with societal stigma and discrimination as well as the clinical latency of HIV-1 are barriers for timely surveillance of HIV-1 transmission among this population. The recent advancement in genetic transmission network analyses techniques, together with the quickly expanding HIV sequence database represent an opportunity to better understand temporal and spatial transmission characteristics8pol gene sequences from Shanghai HIV-1-infected MSM and 282 other risk groups’ sequences derived from 2008 through 2013, and 795 CRF01_AE reference strains. We then re-constructed the relatively deeply sampled sub-networks of CRF01_AE to depict the temporal and spatial transmission of networks and explore the linking-associated factors among genetic transmission networks.In the present study, we describe the molecular evolutionary dynamics of HIV-1 CRF01_AE among Shanghai MSM using a phylogenetic inference analysis based on 1, 152 A total of 1, 152 MSM infected with HIV-1 CRF01_AE, newly diagnosed in Shanghai between 2008 and 2013, were enrolled in this retrospective study. The group had an average age of 31.45 ± 9.63 years, with a predominance of young MSM i.e. 832/1, 152 or 72.2% were <35 years. All participants were Han ethnicity, and 64.5%  were domestic migrants. Approximately half  had a college or higher (≥13 years) level of education. Most were single . Almost one third  had ≥6 sex partners in the past 6 months. Median CD4 + T cell counts in the first follow-up (<3 months after infection confirmation) was 360.9 (IQR: 238.3–499.0) cells/μl. These demographic data are consistent with other reports in Shanghai101112−3 nucleotide substitutions/site/year. The demographic characteristics did not differ substantially between two lineages (P = 0.028), while other demographic characteristics did not differ.Bayesian skyline plot (BSP) deduced that CRF01_AE strains among Shanghai MSM started its initial growth phase around 1998, followed by an exponential expansion during 2000–2007, and reached a steady growth afterwards . Phylogelineages . SH-L1 clineages . A dozenlineages . Of intelineages . CompareP < 0.001), Of 2, 229 individuals, 1, 211 (54.3%) were segregated into 276 networks , with siOf the 681 Shanghai MSM in networks, 74.2% (505) were linked to cases diagnosed in different years. As shown in To explore the potential transmission between recent infection and long-standing infection in the networks, 517  recent infections (<1 year) were identified using a molecular algorithm (See Methods), of which 417 (80.7%) were interlinked among recent infections, and 162 (31.3%) were linked to the long-standing infections (>1 year). Of the 164 long-standing infection, 71 (43.3%) were interlinked among long-standing infections, and 117 (71.3%) were linked to recent infections.To better understand the role that the migrants  played in the formation of networks, we investigated the influence of their domicile on transmission. We found the transmission linkage existed not only in the individuals with different domiciles and diagnosed in Shanghai (intra-province), but also in the individuals with same domiciles (townsmen) but diagnosed in the domicile places and Shanghai (inter-province). Seventy-one networks including 180 individuals diagnosed in Shanghai (dots in different colors based on domicile) and those who were diagnosed in their hometowns (squares in different colors based on domicile) are shown in P < 0.001) and recent infection (P = 0.022) were factors associated with potential transmission links among networks. These persons with ≥2 links were involved in 474 (69.6%) persons through multiple links in the established genetic transmission network. Of note, 17 individuals with ≥5 links were involved in 112 (16.4%) persons  of persons had 1 link, and 19.1%  of persons had ≥2 links in our network analysis. Chi-square test  showed t persons .P = 0.441). Overall, four main network-related drug resistant mutations (some were non-tansmitted drug resistance mutations) were discovered at V179D/E (n = 53), M46L (n = 15), T69N (n = 8), and P225H (n = 2), P < 0.001. V179D/E mutation distributed in 26 networks in lineage 1B (n = 8), 1C (n = 33), lineage 2 (n = 3), and small lineage (n = 9). Only 2 networks for M46L, 6 networks for T69N, and 1 network for P225H were found in lineage 1C and 1B, lineage 1D, 1B, 1 A, small lineage, lineage1D, and lineage 1D, respectively.Among the 1, 152 studied individuals, 59 (5.1%) harbored drug resistance mutations conferring resistance to at least one class of antiretroviral (ARV) drugs. Only one individual harbored three mutations including M46I, A62V and T69N. The rate of drug resistant mutation did not differ between sampling years  and had a close match with China-lineage 4, the most common CRF01_AE lineage in MSM epidemic nationwide, while SH-L2 was closely associated with China-lineage 5, a common lineage related to heterosexuals and injection drug users epidemicAs a coastal international metropolis, Shanghai attracted an increasing numbers of domestic migrant people in recent years. As these migrants constantly face difficulties in accessing employment, social welfare, education, and health services locally under the current household registration system, they usually flow between hometowns and different cities1116172022An interesting observation in this study was that V179D/E, an NNRTI-associated mutation was dominating Shanghai MSM. Sequences with V179D/E were distributed and networked in most sub-lineages in SH-L1, suggesting that several independent CRF01_AE strains with V179D/E were involved in ongoing transmission in this city. Besides, we found a higher proportion of V179D/E mutation in Shanghai CRF01_AE strains than in all others China CRF01_AE strains. This observation constitutes an alternative molecular evidence for determination of HIV-1 transmission among Shanghai MSM.Although the present study was intended to include all first-time follow-up MSM after diagnosed, there was still ~20% loss to follow. Besides a misclassification as heterosexuals could be present among MSMIn conclusion, we elucidated the molecular evolutionary dynamics of HIV-1 CRF01_AE circulating among Shanghai MSM. Genetic transmission network analysis further revealed a complexity of transmission pattern. We thus suggest that network analysis based on a molecular approach, combined with social science and public health approaches could be helpful in effective HIV intervention strategy to curb the spread of virus and promote public health.bp were selected for further analysis. The corresponding demographic characteristics and CD4 + T cell counts were also collected for analysis. None of the individuals was exposed to antiretroviral treatment (ART) at the time of enrolment. In addition, 130 heterosexual women sequences, 134 heterosexual men sequences and 18 PWID sequences were acquired from individuals diagnosed in Shanghai. We also isolated all closely related publicly available sequences with 1, 152 CRF01_AE strains of MSM from HIV databases of Los Alamos National Laboratory. We performed a BLAST searchhttp://sierra2.stanford.edu/sierra/servlet/JSierra?action=sequenceInput) and the last updated (Oct-Nov 2015) guidelines from the International AIDS Society Resistance Testing-USA panelhttp://hivdb.stanford.edu/pages/WHOResistanceList.html).A total of 2, 252 MSM blood samples were collected at the first follow-up after HIV diagnosis during 01/2008-12/2013, representing >80% of newly HIV-1 diagnosed MSM in Shanghai. A total of 1, 836 sample sequences were acquired after RNA extraction and PCR amplification, among which, 1, 152 CRF01_AE isolate sequences with >1, 000 pol sequences using the GTR + G + I nucleotide substitution model. The phylogenetic tree’s reliability was determined with local support values based on the Shimodaira-Hasegawa (SH) testhttp://beast.bio.ed.ac.uk). Monophyletic groups with bootstrap support ≥0.9 were identified as a lineage.FastTree 2.3Bayesian skyline plot (BSP) analysis was conducted to explore the changes in the effective population size of CRF01_AE among MSM over time in Shanghai. The models selected were GTR + Relaxed clock (uncorrelated) + Bayesian skyline. The tMRCAs of CRF01_AE lineages were estimated using a Bayesian inference approach to explore the timescale of CRF01_AE expansion among Shanghai MSM. Rates of evolution (in units of nucleotide substitutions/site/year) were estimated simultaneously. Phylogenies were inferred using BEAST v.1.7.2. The Markov chain Monte Carlo (MCMC) analysis was computed for 200 million generations and sampled every 1,000 steps and output was assessed for convergence by means of effective sampling size (ESS) after a 20% burn-in using Tracer. To minimize the effects of standard errors, only ESS ≥200 were accepted.The flowchart of genetic transmission networks was depicted as 3132pol gene under 0.5% to define a recent infection event <1 year prior to clustering analysis35Three groups were compared including (1) individuals with no link to others, (2) individuals who linked to another one, and (3) individuals who linked to ≥2 others. Chi-square test was used to determine linking-associated factors among networks. The demographic and laboratory information retrieved included sampling year, recent infection, age, domicile, education, marital status, number of sex partner in the past 6 months, and CD4 + T cell counts. We used a molecular algorithm of a frequency of ambiguous calls in bulk sequencing of The study protocol was reviewed and approved by the Institutional Review Board at the Human Medical Research Ethics Committee of the Shanghai CDC. No additional informed consent from participants was obtained for this special investigation as the data were analyzed retrospectively and anonymously. All research methods in this study were carried out in accordance with the approved guidelines.How to cite this article: Li, X. et al. Evolutionary Dynamics and Complicated Genetic Transmission Network Patterns of HIV-1 CRF01_AE among MSM in Shanghai, China. Sci. Rep.6, 34729; doi: 10.1038/srep34729 (2016).