Potato mop‐top virus (PMTV), a tri‐partite, single‐stranded RNA virus, is exceptionally low, based on the analysis of sequences obtained from isolates from Europe, Asia and North America. In general, RNA viruses exist as dynamic populations of closely related and recombinant genomes that are subjected to continuous genetic variation. The reason behind the low genetic variation of PMTV remains unclear. The question remains as to whether the low variability is a shared property of all PMTV isolates or is a result of the limited number of isolates characterized so far. We hypothesized that higher divergence of the virus might exist in the Andean regions of South America, the centre of potato domestication. Here, we report high variability of PMTV isolates collected from 12 fields in three locations in the Andean region of Peru. To evaluate PMTV genetic variation in Peru, we generated full‐length cDNA clones, which allowed reliable comparative molecular and pathobiological characterization of individual isolates. We found significant divergence of the CP‐RT and 8K sequences. The 8K cistron, which encodes a viral suppressor of RNA silencing, was found to be under diversifying selection. Phylogenetic analysis determined that, based on the CP‐RT sequence, all PMTV isolates could be categorized into three separate lineages (clades). Moreover, we found evidence for recombination between two clades. Using infectious cDNA clones of the representatives of these two clades, as well as reassortants for the RNA‐CP genomic component, we determined the pathobiological differences between the lineages, which we coined as S (for severe) and M (for mild) types. Interestingly, all isolates characterized previously  fall into the S‐type clade, whereas most of the Peruvian isolates belong to the M‐type. Taken together, our results support the notion of the single introduction of PMTV from the centre of potato origin to Europe, and subsequent spread of the S‐type into Asia and USA. This is also supported by the suggested novel classification of isolates based on genetic constellations.The evolutionary divergence of  Surprisingly, some RNA viruses with multipartite genomes, e.g. Potato mop‐top virus (PMTV), show variability as low as 0%–2% depending on the isolate  or three (RNA‐CP) lineages. Based on the RNA‐CP sequences, the isolates from Peru were classified into three lineages, showing the greatest variability relative to isolates from the rest of the world represented by a single lineage. Pathobiological characterization of the Peruvian isolates using infectious clones revealed two distinct strains of the virus: S (severe) and M (mild). Further reassortment experiments identified the RNA‐CP segment as being responsible for the distinction between S and M types, and the pathobiological properties of the strains were consistent with the phylogeny of the RNA‐CP segment. Both S and M strains are found in Peru, whereas, so far, only the S strain has been reported from the rest of the world, suggesting the single introduction of PMTV into Europe and/or subsequent spread to Asia and North America.To determine the genetic composition of PMTV isolates in Peru Fig. A, leavesin vitro transcription and plant inoculation with the transcripts. Between nine and 30 clones for each full‐length genomic RNA component were sequenced . The genetic variability of three genomic components together accounted for a total of 716 mutations  identified by a single nucleotide polymorphism (SNP) analysis algorithm implemented in the DNASTAR Lasergene Core Suite in 61 clones (Table S2), corresponding to 213 105 nucleotides sequenced. The CP‐RT and 8K cistrons accumulated the largest number of mutations , non‐synonymous mutations per non‐synonymous site (dN) and the dN/dS ratio were calculated by single‐likelihood ancestor counting (SLAC) analysis using Datamonkey  to 1.415 (8K), suggesting different constraints on sequence change depending on the virus gene. Accordingly, the RdRp, CP‐RT and TGB genomic regions contained substitution spectra subjected to negative selection (dN/dS < 1), consistent with the fact that the genes are essential for infectivity and the encoded proteins are involved in virus replication, encapsidation/transmission and movement, respectively  and TGB3 (dN/dS = 0.191) were very similar, suggesting similar stringent constraints on amino acid (AA) residue change as the effect of purifying selection. However, the TGB2 cistron, which significantly overlaps with TGB1 and TGB3, had the lowest dN/dS ratio (dN/dS = 0.082), indicating even further constraints on AA residue change.Notably, the 8K was found to be subjected to strong positive selection , consistent with the fact that 8K is dispensable for virus infectivity and long‐distance movement  selection.In contrast, To determine the phylogenetic relationships of 61 full‐length Peruvian PMTV sequences in relation to the known PMTV genomes from GenBank, we performed multiple sequence alignments and phylogenetic analysis with two statistical methods: maximum likelihood (ML) and neighbour‐joining (NJ) methods. Both ML and NJ approaches yielded virtually identical results (data not shown). Identical sequences derived from a single isolate were excluded. Figure et al., et al., The RNA‐rep bootstrap consensus tree revealed two major clades which have been described previously , with a cysteine residue replacing histidine (CxCnxCxC). Analysis of Nicotiana benthamiana genome sequences identified the presence of the SWIM motif in three mitogen‐activated protein kinases , which differed from the rest of the PMTV RNA‐CP sequences worldwide, all available CP‐RT protein sequences were aligned to identify possible signature AAs for clade I (S‐type) and clade II (M‐type). We found seven AA positions, which were conserved in all clade I (S‐type) isolates, but differed from the corresponding residues of the clade II (M‐type) isolates Fig. A. These CP‐RT, which was previously reported as being deleted in PMTV laboratory isolate Todd, as well as in Swedish isolates propagated by serial passages or identified in the field , contained internal in‐frame deletions , no symptoms were observed on N. tabacum plants inoculated with the Swedish isolate (S‐type) or Peruvian isolate C115 (S‐type) or Peruvian isolate C29 , consistent with the previous reports showing that PMTV causes symptomless systemic infection in N. tabacum  caused much milder symptoms . To further assess whether the differences in pathogenesis were caused by a difference in the CP‐RT sequence, we quantified virus accumulation in the upper systemically infected leaves of N. benthamiana and N. tabacum plants at 14 dpi. Significant differences in virus accumulation were observed  lower in N. benthamiana and 46% (P < 0.01) lower in N. tabacum relative to the ELISA values for the Swedish and C115 isolates  between the Swedish and Peruvian isolate C115. As the differences in virus accumulation between S‐ and M‐types were similar in N. benthamiana and N. tabacum—44% versus 46%, respectively—subsequent experiments were conducted in N. benthamiana.We took advantage of the availability of the complete cDNA clones for all new isolates characterized above. In addition, infectious cDNA clones were available for a Swedish isolate of the virus  higher in the upper leaves than were those of C29  and RNA‐rep + RNA‐TGB + RNA‐CP . Additional inocula were assembled for the Swedish isolate and the Peruvian isolate C115 to serve as controls. Quantitative analysis of progeny virus accumulation at 14 dpi revealed that the ELISA absorbance values of the deletion variant C29Δ progeny were approximately 23%  relative to PMTV isolates containing the S‐type RNA‐CP segment  the virus titre . However, the dN/dS value of the 8K gene was found to be 1.415 (dN/dS > 1), which identified the 8K gene as being under strong positive (diversifying) selection, suggesting acceleration in the divergence of the 8K gene in increasing the adaptability of the virus. It is noteworthy that the previous analysis of the 8K gene from European isolates and some isolates from Colombia did not find that positive selection shapes the evolution of the 8K sequence, as the reported dN/dS value was 1.100 , there were virus isolates from Peru demonstrating greater variability of the virus in Peru relative to other parts of the world. Notably, clade II of RNA‐rep was exclusively represented by isolates from Colombia. This was similar to the previous study, which classified PMTV RNA‐rep into two clades  were found to have consistently greater diversity than those from other parts of the world . Characterization of single‐segment reassortants also demonstrated that reassortant viruses moved systemically in the infected plants, suggesting that the newly formed genomic constellations are genetically and functionally stable.Phylogenetic analysis and molecular characterization of reassortant viruses suggested that the read‐through domain of CP‐RT was a major determinant of the pathobiological properties of different isolates, because all AA differences between S‐ and M‐types were located in this region. In this study, we identified seven signature AA differences in the read‐through region of CP‐RT encoded by M‐ and S‐types. It remains to be determined whether and which of these AA residue substitutions are associated with differences in the accumulation of severe and mild isolates. At this time, we cannot address whether the observed differences in virus accumulation are caused by differences in the rate of virus systemic movement, CP‐RT protein localization or altered protein half‐life/stability/turnover. These questions will be addressed in further studies.Our results highlight the potential for environmental reassortment and/or mixed infections with M‐ and S‐type genome segments that can lead to recombination between M‐ and S‐types. Indeed, as reported in this study, both M‐ and S‐type segments were cloned from one of the field samples, suggesting that mixed infection with at least two RNA‐CP genotypes could be possible. Moreover, an RNA‐CP segment cloned from total RNA extracted from a sample from another field appeared to be a recombinant between M‐ and S‐types. These findings support the potential for the emergence of novel genotypes of the virus in the Andean regions of South America when compared with the rest of the world, from which only S‐type RNA‐CP has been reported so far.S. subterranea , we were unable to detect the presence of untruncated RNA‐CP in one of the fields . This might indicate that the deletions occurred at the very early stages of infection before sampling was conducted or, alternatively that deletion may have already existed in the infected potato plants and the deletion variants were transmitted by vegetative propagation through infected tubers. The second scenario seems to be possible as deletions in the CP‐RT protein, which is encoded by RNA‐CP, have been shown to prevent transmission of PMTV by its natural vector et al., Taken together, we have established that, in Peru, PMTV has undergone continual evolutionary divergence, resulting in the establishment of distinct phylogenetic clades, genotypes and genetic constellations. Our results highlight the potential for environmental reassortment of PMTV genomic segments in South America, which can lead to novel genotype constellations with potential epidemiological risks. Further studies of these isolates and other single‐reassortant viruses are required to understand the constellation of reassortants, which could represent epidemiological risks, and to understand the degree of functional compatibility/superinfection exclusion ‐ELISA to detect and quantify PMTV was performed on equal amounts of leaf samples using anti‐CP monoclonal antibodies and anti‐CP monoclonal alkaline phosphatase conjugate, according to the manufacturer's instructions . Absorbance was measured at 405 nm.Total RNA was extracted from pooled leaf samples using an RNeasy Plant Mini kit  according to the manufacturer's instructions. One microgram of total RNA was used for cDNA synthesis with Superscript III reverse transcriptase . An aliquot of cDNA was subsequently used as a template for PCR. The PCRs were performed using Phusion Flash High‐Fidelity PCR Master mix  with PMTV‐specific primers . In order to generate full‐length infectious clones, the cDNAs were amplified with forward primers containing the sequence of the T7 promoter. PCR was performed with an initial denaturing step for 1 min at 98 °C, followed by 35 cycles consisting of 98 °C for 20 s, 54 °C for 20 s, 72 °C for 1 min 30 s, and final elongation at 72 °C for 7 min. PCR products were incubated with Taq polymerase in the presence of dT to facilitate TA‐cloning. The purified PCR fragments were ligated into a pGEM‐T Easy Vector System . The obtained full‐length infectious cDNA clones were sent for sequencing to Macrogen Europe, Amsterdam, The Netherlands.lustalW using the IUB DNA weight matrix, whereas protein sequences were aligned by MUSCLE implemented in MEGA 7. Phylogenetic analyses were conducted using the NJ method implemented in MEGA 7 . DNA sequences were aligned by CdS) and non‐synonymous substitutions per non‐synonymous site (dN) were calculated using SLAC analysis implemented in Datamonkey , and SpeI‐linearized RNA‐TGB plasmids. In vitro transcription was performed using a T7‐RiboMAX large‐scale RNA production system (Promega) in the presence of m7GpppG cap analogue  according to the manufacturer's instructions. Transcripts were then mixed with GKP buffer  in a 1 : 1 : 1 : 1 ratio and rub inoculated onto N. benthamiana leaves.Additional Supporting Information may be found in the online version of this article at the publisher's website:Fig. S1 Predicted transmembrane domains in the CP‐RT region of the J20 isolate using the Phyre2 server.Click here for additional data file.Fig. S2 Symptoms induced in Nicotiana benthamiana by Potato mop‐top virus (PMTV) isolates carrying either severe (S) or mild (M) type of RNA‐CP segment.Click here for additional data file.Table S1 Geographical locations and GenBank accession numbers of the Peruvian isolates characterized in this study.Click here for additional data file.Table S2 Full‐length cDNA clones (plasmids) of Peruvian isolates of Potato mop‐top virus (PMTV).Click here for additional data file.Table S3 List of primers used in this study.Click here for additional data file.


N6-methyladenosine (m6A) is the most abundant RNA modification. It has been involved in the regulation of RNA metabolism, including degradation and translation, in both physiological and disease conditions. A recent study showed that m6A-mediated degradation of key transcripts also plays a role in the control of T cells homeostasis and IL-7 induced differentiation. We re-analyzed the omics data from that study and, through the integrative analysis of total and nascent RNA-seq data, we were able to comprehensively quantify T cells RNA dynamics and how these are affected by m6A depletion. In addition to the expected impact on RNA degradation, we revealed a broader effect of m6A on RNA dynamics, which included the alteration of RNA synthesis and processing. Altogether, the combined action of m6A on all major steps of the RNA life-cycle closely re-capitulated the observed changes in the abundance of premature and mature RNA species. Ultimately, our re-analysis extended the findings of the initial study, focused on RNA stability, and proposed a yet unappreciated role for m6A in RNA synthesis and processing dynamics. Dozens of RNA modifications decorating coding and non-coding RNA species have been shown to be important determinants in the fate of marked RNA transcripts . m6A is Recently, an RNA metabolic labeling method has been developed that detects nascent RNA molecules based on the incorporation of modified 4sU nucleotides. By allowing the quantification of the kinetic rates governing the various stages of the RNA life-cycle, this method permits to characterize the RNA dynamics and therefore, in our case, to investigate how the m6A epitranscriptome impacts on the dynamics of premature and mature RNA species.Mettl3 had been knocked-out (KO) thus leading to a marked reduction in m6A bulk levels (28% of WT levels). When key transcripts involved in T cell homeostasis and differentiation were considered, m6A was found to heavily influence their stability while having a negligible role in their processing and translationA recent study combining RNA metabolic labeling with m6A profiling demonstrated the relevance of m6A in the control of T cell homeostasis and IL-7 induced differentiation . Its autWe re-analyzed the RNA metabolic labeling data from the same study. To this end, we quantified the kinetic rates of RNA synthesis, processing and degradation using INSPEcT (version 1.8.0), a tool that we had previously developed for the integrative analysis of nascent and total RNA-seq data . Our anaMettl3-KO mouse naive T cells were compared before and after IL-7 stimulation. Nascent RNA was quantified by RNA metabolic labeling with a 15 min pulse of 4sU nucleotides. In WT cells, total and nascent RNA were quantified before IL-7 induction, and at 15, 30, 45, 60, and 75 min following IL-7 exposure. In Mettl3-KO cells, total and nascent RNA were quantified in untreated cells, and after 15, 30, and 60 min of exposure to IL-7.Raw RNA-seq data on total and nascent RNA and raw data for m6A profiling in untreated WT cells were derived from the GEO series GSE100278 , where WWe focused our analysis on the samples obtained from untreated cells and from cells exposed for 15 and 60 min to IL-7 treatment, as these were available for both WT and KO cells. We ignored the samples from 30 min exposure since, in this case, the WT samples gave low numbers of aligned reads  and a low correlation with other samples .Raw data were processed using HTS-flow, a workflow management system for high-throughput sequencing data that we had previously developed . Brieflyp-value cutoff of 1 × 10−5. m6A+ transcripts were identified by requiring at least one MACS peak within each of the two available replicates. m6A− transcripts were identified as those never associated to MACS peaks. Only genes with adequate intronic and exonic expression were retained  with a ined see .BAM alignment files were analyzed using the R/Bioconductor package INSPEcT . The abuThe time-courses of total and nascent RNA-seq data in WT and KO conditions were analyzed with INSPEcT. Due to lack of biological replicates, it was not possible to estimate mean values and variances for the expression levels of the genes. This issue forced us to limit our analysis to first guess rates estimates, which were not subjected to any modeling step. However, first guess and modeled rates are typically well correlated. For example, in Reference  the mediA fraction of the processing and degradation computed rates were not finite . In these cases, we evaluated the missing values by using a linear model to interpolate the rates computed immediately before and after the not finite datum. When this situation occurred at the boundary of the time-course, we returned the closest finite value.p-values were determined by sampling the all and m6A− distributions based on the size of the m6A+ population. The median p-value over 1000 repetitions was reported in the figure.The distributions of the changes in RNA kinetic rates in The distributions of premature and mature RNA abundances and of the RNA kinetic rates in p-values of the hypergeometric and binomial tests were below the threshold of 1 × 10−3. Only p-values of the hypergeometric test were finally reported. The same approach was used to analyze the functional enrichment of gene clusters.Functional enrichments of regulated transcripts were performed using the R/Bioconductor package rGREAT version 1.11.1 , based oMettl3-KO cells.Cluster membership was determined based on the abundance of premature and mature RNA species and on the RNA kinetic rates. Specifically, we considered the absolute values in the untreated condition and the changes that followed IL-7 treatment in WT and The absolute values of mature RNA species were quantified as Z-scores from the joint distribution of untreated WT and KO data by subtracting the median and normalizing over the interquartile value. The same procedure was applied to premature RNA abundances, synthesis rate, processing rate, and degradation rate data.The changes that followed IL-7 treatment were quantified by computing the log2 ratios at 15 and 60 min compared to the untreated condition for premature and mature RNA species and RNA kinetic rates, in both WT and KO cells.We verified that Z-scores and fold changes had comparable medians and interquartile ranges . We thenp-value < 1 × 10−5) and estimated their mean p-value and the median percentage of terms that appeared only in one cluster  minimizing the percentage of clusters with less than 25 genes; (ii) maximizing uniqueness and statistical significance of the Biological Processes related to each cluster. Regarding the second task, we performed a functional enrichment analysis based on the GO Biological Process ontology for each clustering configuration. For all groups of clusters, we selected the statistically significant terms , it reports only a per gene mean value. The complete heatmap is displayed in The R source code for reproducing all the analyses is available in Mettl3-KO and WT T cells . Transcripts with higher processing rates in KO cells were enriched in terms related to energy production , while those with lower processing rates were related to RNA translation, processing, and metabolism . Noteworthy, although m6A depletion did not globally affect the rates of synthesis, transcripts with increased synthesis rates in KO cells were enriched in immune system and cytokine response .We used functional enrichment analysis to characterize the transcripts with the largest changes in RNA kinetic rates C. TranscAltogether these analyses indicate that, in unstimulated T cells, m6A exerts a global control on the stability of transcripts. They also suggest that m6A plays a role in determining synthesis and processing dynamics, specifically for classes of transcripts involving co- and post-transcriptional regulation. It remains to be confirmed whether these effects are direct or indirect consequences of m6A.Mettl3-KO T cells, at 15 and 60 min after induction with IL-7 . In WT cells, all kinetic rates became affected after 15 min of IL-7 treatment . These clusters showed similar inductions in terms of synthesis and premature RNA, but different behaviors in terms of total RNA. In fact, following IL-7 treatment in both WT and KO cells, the total RNA abundance decreased for cluster 1 genes while increased for cluster 2 genes. Moreover, these two clusters showed very different patterns in terms of degradation rates. Cluster 1 was characterized by stable transcripts of which half-lives were markedly reduced after IL-7 induction. On the contrary, cluster 2 was characterized by less stable transcripts whose degradation rates partially increased. Noteworthy, the increase in the degradation rates of cluster 2 transcripts was dampened in KO cells, suggesting that m6A depletion partially impaired post-transcriptional regulation.Clusters 1 and 2 were the largest clusters , grouping genes spanning across various functional categories, including: RNA metabolic process, protein metabolic process, chromatin modification, catalytic activity and binding (p = 5 × 10−13), and to RNA processing .Cluster 3 (composed of 93 genes) included highly expressed genes characterized by fast kinetics both in terms of synthesis and degradation rates. Following IL-7 induction, despite a reduction in their rate of synthesis, the total RNA abundance of these transcripts increased due to an increase in RNA stability, which seemed to be more homogeneous in m6A depleted cells. These transcripts were maintained at high level of expression despite high degradation rates, denoting the investment of a significant amount of energy. Due to their low stability, these transcripts were fast responders in case of perturbations and could be expected to encode critical functions in T cells. Indeed, these genes were involved in biological processes related to abnormal T cell differentiation . Differently from cluster 3, cluster 5 transcripts were stable. IL-7 induction resulted in an increase in their degradation, leading to their down-regulation. Cluster 7 (composed of 53 genes) included highly expressed genes that were quickly synthesized and slowly degraded, and which differed from cluster 5 transcripts for their extremely high processing rates. In WT cells, IL-7 induction caused a remarkable decrease in the processing rates of the genes in cluster 7, together with a strong decrease in their stability. Mettl-3 KO cells showed a similar trend but with responses of noticeably smaller magnitudes, highlighting then how the regulation of the processing and degradation kinetics depends on m6A. The genes forming cluster 7 were mainly involved in translation (p = 5 × 10−35).Similar to cluster 3, transcripts in cluster 5 (composed of 410 genes) were highly expressed and synthesized. The corresponding genes were involved in hematologic diseases . This suggests a feedback regulatory strategy, where transcripts involved in RNA processing modulate their own processing dynamics.Finally, cluster 8 (composed of 251 genes) was characterized by the highest processing rates, which showed a decrease in response to IL-7 dampened by m6A depletion. As expected, the modulation of processing did not affect total RNA levels, while it originated the observed increase in premature RNA abundance. Interestingly, genes were enriched for terms involving RNA processing  considering the context-dependent functional role of m6A, (ii) profiling m6A patterning using both base-resolution (miCLIP ) and mor


Vibrio cholerae O395, and key characteristics of their genetic and proteomic content are compared to that of whole cells. DNA analysis reveals enrichment of fragments containing ToxR binding sites, as well as a positive correlation between AT-content and enrichment. Some mRNAs were highly enriched in the vesicle fraction, such as membrane protein genes ompV, ompK, and ompU, DNA-binding protein genes hupA, hupB, ihfB, fis, and ssb, and a negative correlation was found between mRNA enrichment and transcript length, suggesting mRNA inclusion in vesicles may be a size-dependent process. Certain non-coding and functional RNAs were found to be enriched, such as VrrA, GcvB, tmRNA, RNase P, CsrB2, and CsrB3. Mass spectrometry revealed enrichment of outer membrane proteins, known virulence factors, phage components, flagella and extracellular proteins in the vesicle fraction, and a low, negative correlation was found between transcript-, and protein enrichment. This result opposes the hypothesis that a significant degree of protein translation occurs in vesicles after budding. The abundance of viral-, and flagellar proteins in the vesicle fraction underlines the importance of purification during vesicle isolation.Extracellular vesicles secreted by Gram-negative bacteria have proven to be important in bacterial defense, communication and host–pathogen relationships. They resemble smaller versions of the bacterial mother cell, with similar contents of proteins, LPS, DNA, and RNA. Vesicles can elicit a protective immune response in a range of hosts, and as vaccine candidates, it is of interest to properly characterize their cargo. Genetic sequencing data is already available for vesicles from several bacterial strains, but it is not yet clear how the genetic makeup of vesicles differ from that of their parent cells, and which properties may characterize enriched genetic material. The present study provides evidence for DNA inside vesicles from  Xylella fastidiosa inside xylem vessels, enabling wider spread throughout host plants  are membrane-bound bodies regularly secreted by Gram-negative bacteria , and it has been demonstrated that they can deliver their RNA cargo into eukaryotic cells  at approximately 3.0 Mbp, and chromosome 2 (ChII) at 1.1 Mbp, which may give further insight in specific packaging of genetic material into the EVs .All cultures were grown using LB medium or LB agar at 37°C with 200 μg/mL streptomycin, and liquid cultures were grown at 100 rpm shaking. Frozen stock of D600 ≈ 1 , when coD600 ≈ 1 . The proV. cholerae culture was centrifuged  at 4,000 × g for 30 min, transferred to cleaned bottles and centrifuged at 4,000 × g for another 30 min. The bacterial pellets were kept at −20°C until DNA, RNA and protein isolation. The supernatant was filtered through 0.45 μm and then 0.2 μm filters (durapore cat no. HVLP14250 and qvwp14250), using a 142 mm filter holder (Millipore Corporation 01730) and a peristaltic pump. The volume was then concentrated to approximately 25 mL using a 100,000 NMWC hollow cartridge filter  at 15 psi inlet pressure. Three hundred milliliters of PBS was added and this dilution was again concentrated to approx 150 mL. The volume was split in 10 mL aliquots and kept at −20°C (< 7 days) to reduce RNA degradation while performing replicates. The whole volume was filtered through a 0.45 μm filter and then ultracentrifuged at 100,000 × g for 2 h (SW 32 Ti rotor). The supernatant was discarded and another volume of filtrate was added before another centrifugation . The supernatant was discarded and the tubes were filled with PBS and centrifuged again . Each pellet was resuspended in approximately 1.5 mL PBS and kept at −20°C (< 7 days) to reduce RNA degradation until RNA isolation. This isolate is henceforth referred to as the extracellular vesicle fraction (EVf), and each of all three EVf replicates was used for DNA, RNA, and protein analysis, in order to minimize the effect of biological variations between the techniques. The protein content of the isolates was measured to be approximately 11.8 mg/mL. The samples were inspected by negative-stain electron microscopy to assess vesicle integrity and presence of contaminations. A ~ 100 μl portion of the bacterial pellet was resuspended in 5 ml PBS. This isolate is henceforth referred to as the whole cell fraction (WC).The EV isolation protocol is largely the same as described for marine samples . For EVf-DNA isolation, 200 μL aliquots were treated with the Qiagen EZNA mini kit type 1, according to manufacturer's protocol, with the exception of adding four volumes of supernatant lysate to the same HiBind Qiagen, . Therefo® DNA/RNA Mini Kit (Qiagen cat. 80204) treating 200 μL aliquots as starting material, and adding 500 μL RLT buffer. Four to six of these volumes were passed through a single DNA column, and the flowthrough from 2 to 3 of these columns were treated according to protocol, and passed through a single RNA column. RNA was eluted in 30 μL RNase-free water, resulting in approximately 7 ng/μL. This corresponds to approximately 23 ng RNA per mg of protein in the EVf, or 40 ng EVf-RNA per liter bacterial culture. RNA from WC was isolated in the same manner, and yielded approximately 70 ng/μL. The EVf-RNA samples and 1 μg of the each of the WC-RNA samples were prepared with Truseq mRNA stranded kit (Illiumina), quality controlled with NGS kit (Agilent) on Fragment Analyser (ATII), and amplified with KAPA Library Quantification Kit (Roche), all according to manufactures protocol.All RNA quantification was performed using Qubit™ RNA HS assay kit (Thermo Fisher). RNA was isolated from EVf with AllprepDNA and RNA samples were sent to Norwegian Sequencing Centre to be sequenced on Illumina HiSeq 4000, 2x150 paired-end run with 350 bp insert size.de novo using SPAdes (v3.10.1). They were also aligned to an O395 reference genome (GenBank: GCA_000016245.1) with the burrows-wheeler aligner , aligned to the O395 TCP2 genome using BWA, and replicates removed using picard-tools (v2.10.4)TM BCA Protein Assay Kit (Thermo ScientificTM 23225) according to manufacturer's protocol. In-solution digestion and liquid chromatography mass spectrometry (MS) was performed on EV and WC samples as previously described  for 2 min. Excess UA was dried off using a filter paper, and the grids were left to dry for 10 min before imaging.Freshly formvar-coated 200 mesh grids were used for both negative stain- and immunogold electron microscopy, and all micrographs were captured with a JEOL 1400 plus microscope at 100 keV. For negative stain images, grids were placed on droplets of EVf for 1 min, washed 3 times on PBS for 1 min, and fixed on 4% paraformaldehyde (PFA) in PBS for 2 min. Finally, the grids were washed 10 times on H2O for 1 min. Finally, the grids were stained on 4% UA for 2 min, after which the grids were picked up with a loop, excess liquid removed and left to dry for 10 min before imaging.10 μL of EVf-sample was diluted in 12% gelatine at 37°C to a total volume of 200 μL. Thirty microliters droplets of the solution were placed on parafilm in room temperature to solidify for 10 min. The solid droplets were fixed in 4% PFA/PBS at 4°C overnight, then submerged in sucrose and cut into ~0.5 mm cubes, which were placed on silver rods and flash-freezed in liquid nitrogen. The samples were sectioned in a cryomicrotome at −120°C to a thickness of approximately 45 nm, and picked up using a loop dipped in 2% methyl cellulose (MC) at 4°C and transferred onto the grids. The grids were suspended on a droplet of PBS on ice for 3 min, and placed on a droplet of 1:1 Antibody (MAB030 anti-dsDNA clone BV16-13 from Sigma-Aldrich)/1% fish skin gelatine (FSG) for 50 min. The grids were then washed 5 times on PBS for 3 min, before being transferred to a droplet of 1%FSG/proteinA-gold for 20 min. The grids were washed 3 times on PBS for 3 min, followed by 10 times on HV. cholerae contains both double- and single membrane vesicle structures     . Aside fenriched , which iOne of the few highly enriched regions that do not contain the ToxR binding motif, harbors a 12× repeat of the motif 5′-TCTAGAATCC-3′ . This seV. cholerae sequence data from fecal samples  and the rest of the genome (p = 1.5·10−99)  . There a5·10−99) , and a h5·10−99) , and theThe EVf-RNA sequencing resulted in 23.6M–78.2M reads per sample, whereof 99.5–99.9% were mapped to the genome. WC-RNA sequencing resulted in 23.7–96.3M reads per sample, of which 96.8–99.6% were mapped to the genome. The mapped read coverage reveals that similar to the DNA data, certain regions display higher coverage across all replicates, some being characteristic to either EVf- of WC-samples . AccordiV. cholerae reveal that genes on ChI are more frequently transcribed than those on ChII during growth in rich media, although the difference is less pronounced when grown in rabbit intestine , the difference is not that pronounced, but EVf maintains a significantly lower coverage in ChII than in ChI . ResultsEscherichia coli, which could partly explain its abundance in EVf , which enables the bacteria to survive under lower pH , which is associated with the membrane through the SRP receptor (Akopian et al., A third mechanism likely to affect enrichment is size, as a portion of the vesicles are smaller than many mRNA sequences. While vesicles can be as small as 20 nm, typical RNAs can vary in size from ~7 to 33 nm for 0.7–8.9kbp sequences, respectively (Borodavka et al., The results from genetic sequencing prompted us to map the proteomic profile of EVs in order to asses a possible correlation between enrichment of genes and protein products. By LC-MS, a total of 1312 proteins were detected, 670 of which in common while 222 and 420 were exclusively detected in the EVf or WC, respectively.Proteins associated with the extracellular milieu, outer membrane and periplasm are significantly enriched in EVf compared to cytoplasmic proteins . The var580 genes were detected both as mRNA and their protein products in both WC and EVf. Only genes destined for the periplasm demonstrated a statistically significant mRNA–protein enrichment correlation, that being negative . These rS. enterica and E. coli to bind to sRNA and mRNA, increasing their stability (Smirnov et al., cspE mRNA (Holmqvist et al., An interesting EVf-enriched protein in the context of RNA enrichment is the methionine ABC transporter substrate-binding protein . This meV. cholerae as a tailocin (Ghequire and De Mot, A number of flagellar and extracellular proteins were the most abundant in the extracellular fraction compared to whole cells. Of the 48 known flagella-associated proteins in the O395 proteome, a total of 39 were detected in the samples. 9 were found only in the bacterial fraction, including regulators such as FleN and FleQ, motor switch proteins such as FliG and FliM, and synthesis proteins such as FliS and FlgN. 17 flagellar proteins were uniquely detected in the EVf, mostly structural components such as FliD, FlaG, FlaF, FlgL, FlgK, FlgG, FlgC, FlgB, FlgF, but also the biosynthesis protein FlhF, L- and M-ring proteins FlgH and FliF, and the hook-length control protein FliK (Kim and McCarter, V. cholerae previously (Altindis et al., Beyond flagellar-, and phage constituents, there were many proteins highly enriched and depleted in EVf . ExpecteThere are several proteins associated with iron uptake that are enriched significantly in EVf. For instance, a ferrichrome-iron receptor was only found in EVf, while heme-, and sidophore receptors HutA (Henderson and Payne, acrB or tolC is associated with hypersensitivity to a range of antibiotics in S. enterica (Buckley et al., ycf operon, YcfL, was among the most abundant only detected in EVf. Its location on the same operon could imply an association with drug resistance, but little is known about this lipoprotein (Alam et al., While the bacteria need to effectively absorb iron compounds in low-iron conditions, antimicrobials are preferably effluxed out. Several relevant acriflavin-resistance proteins are highly enriched in EVf . For insV. cholerae are RmbA and RmbC (Altindis et al., Staphylococcus aureus (He et al., Two other proteins previously detected in EVs fromhupA and hupB mRNA were some of the most enriched mRNAs in EVf. Similarly, H-NS protein is depleted by 80% in EVf, while hns mRNA was enriched >7× on average. Ssb is depleted by 80% while ssb mRNA was enriched >11×, and transcription termination factor Rho is only detected in WC while rho was enriched >3× in EVf. While the mechanisms behind this is unknown, it could be that these mRNAs are more stable than the average, or that they somehow localize to the membrane, maybe by protein binding. DNA-binding protein HU has been found to bind to both tmRNA and RNase P RNA in E. coli (Macvanin et al., A thorough analysis of proteins depleted in EVf is beyond the main scope of this study, but some interesting observations deserve comment. As expected, cytoplasmic proteins were significantly depleted in EVf , but manV. cholerae, to that of its extracellular vesicles. Many interesting observations shed light on the general composition of the vesicle fraction, including their cargo of DNA, functional RNA, mRNA, RNA-binding proteins and virulence factors. While the observed nucleic acid enrichment patterns seem to have possible explanations in membrane protein binding, specific investigations are required to confirm the specific mechanisms involved. For instance, a future research objective is to investigate the effect of ToxR and its binding sites on the enrichment of DNA in vesicles. An alternative study could be on the effect of ProQ or other RNA-binding proteins on RNA enrichment. A series of known virulence proteins were enriched in EVf, which is to be expected, as the host-pathogen interaction is modulated in part by the outer membrane proteins of V. cholerae. Tail tube and tail fiber proteins of several prophages were abundant in EVf, while no complete phage structures were visible in electron microscopy. This could imply function as tailocins, but further research is required to assess this hypothesis. A discrepancy between transcript enrichment and the enrichment of their protein products in EVf argues against significant translation occurring after vesicle isolation, but the effects of mRNA turnover in this context is unclear. A future prospect is therefore to investigate the effects of translation-, transcription-, and RNase inhibitors on the bacterial culture before vesicle isolation. An interesting observation in the EVf proteome is that enrichment of genes for several DNA-binding proteins contrasted the depletion of their protein product, while for a number of outer membrane proteins, mRNA enrichment mirrored the proteomic profile. This could suggest either a certain level of co-translational mRNA localization, or perhaps differing turnover requirements for the transcripts of DNA-binding- and membrane proteins. A research prospect is to locate these mRNAs within the living bacteria, and identify the affinities of RNA-binding membrane proteins. If the mechanisms behind the observed enrichment in DNA, RNA and protein are well-characterized, this would be a big step on our way to produce customized vesicles. In turn, this could greatly expand our possibilities when it comes to the utilization of vesicles as vaccines or vessels for gene-, or drug delivery.The present study is the first exploratory work that directly compares the DNA, RNA and protein content of PRJNA586749 and PRJNA587223.The datasets generated for this study can be found in NCBI, accession numbers: PL has planned and performed most of the experiments and contributed in writing of the manuscript. AK has planned and performed some of the experiments and contributed in writing of the manuscript. HW-L had contributed in planning of the experiments and in writing of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Full RNA-Seq is a fundamental research tool for whole transcriptome analysis. However, it is too costly and time consuming to be used in routine clinical practice. We evaluated the transcript quantification agreement between RNA-Seq and a digital multiplexed gene expression platform, and the subtype call after running the PAM50 assay in a series of breast cancer patients classified as triple negative by IHC/FISH. The goal of this study is to analyze the concordance between both expression platforms overall, and for calling PAM50 triple negative breast cancer intrinsic subtypes in particular.N = 96). In four cases, digital multiplexed gene expression analysis and RNA-Seq were discordant. The Spearman correlation to each of the centroids and the risk of recurrence were above 0.89 in both platforms while the agreement on Proliferation Score reached up to 0.97. In addition, 82% of the individual PAM50 genes showed a correlation coefficient > 0.80.The analyses were performed in paraffin-embedded tissues from 96 patients recruited in a multicenter, prospective, non-randomized neoadjuvant triple negative breast cancer trial (NCT01560663). Pre-treatment core biopsies were obtained following clinical practice guidelines and conserved as FFPE for further RNA extraction. PAM50 was performed on both digital multiplexed gene expression and RNA-Seq platforms. Subtype assignment was based on the nearest centroid classification following this procedure for both platforms and it was concordant on 96% of the cases  contains supplementary material, which is available to authorized users. Gene expression signatures are becoming a key tool for decision-making in oncology, and especially in breast cancer. In 2000, Perou et al. identified 4 intrinsic subtypes of breast cancer with clinical implications from microarray gene expression data: Luminal A (LumA), Luminal B (LumB), HER2-enriched and Basal-like –3. TheseInitially developed on microarray data, PAM50 is being successfully used in digital multiplexed gene expression platforms such as NanoString nCounter®, which is the basis for the Prosigna® test. The latter includes the PAM50 assay in combination with a clinical factor (i.e. tumor size) and has been approved for the risk of distant relapse estimation in postmenopausal women with hormone receptor-positive, node negative or node positive early stage breast cancer patients; and is a daily-used tool assessing the indication of adjuvant chemotherapy .The NanoString nCounter® system enables gene expression analysis through direct multiplexed measurements. This technology is based on 2 probes specific to each gene of interest, a capture probe and a reporter probe, consisting of a complementary sequence to the target messenger RNA (mRNA) coupled to a color-coded tag . Unique On the other hand, RNA-Seq has become the cornerstone of modern whole transcriptome analyses. It represents a useful tool for discovery and validation of biomarkers. The use of FFPE has been a concern in the past but several studies observed that this kind of samples are suitable to be used in RNA-Seq platforms assessing for gene expression analyses, and comparable to fresh frozen tissue . From thOverall, 96 samples were successfully processed and had sufficient RNA for both NanoString nCounter® and RNA-Seq transcript quantification. The mean RNA concentration from the FFPE samples was 146.9 ng/μl, mean RNA integrity number (RIN) value was 2.015  and its mean A260/A280 ratio was 1.98    with RNA-Seq and NanoString nCounter®, respectively. On the other hand, Basal-like and LumA centroids had the strongest inverse correlation  , and considering the role of the Proliferation Score (ROR + PS), had a Spearman’s rho of 0.90 and 0.97, respectively. Thus, in terms of ROR, the results show an extremely high correlated scenario. We observed high agreement between techniques in the Bland-Altman plots displayed in Fig. Additional measures such as expression level of HER2, along with the Proliferation Score, also showed a high degree of correlation between both platforms with a Spearman’s rho 0.96 and 0.97, respectively.We lastly evaluated the correlation coefficients for each of the 50 genes in the PAM50 gene list. We measured the expression levels in log2 scale in both platforms. We observed that in our dataset 23 genes had a correlation greater than 0.9, 18 genes between 0.8 and 0.9, 7 genes between 0.7 and 0.8 and only 2 genes had a correlation lower than 0.7. The median ICC was 0.90 (mean = 0.88)  patient cohort. We noticed that the PAM50 subtype calling was concordant on 96% of the cases and the expression in genes that comprise the PAM50 assay had a median ICC of 0.90.PAM50 was originally developed and validated using microarray data from 1753 genes, but since then it has been transferred into a wide variety of platforms. Interestingly, PAM50 performance has been evaluated by comparing quantitative real-time reverse-transcription-PCR (qRT-PCR) and NanoString nCounter® . That stIn this TNBC cohort 4 samples out of 96 were misclassified in the subtype calling. While this might be concerning from the patient care perspective, it is strongly suggested in these cases to evaluate the ROR and ROR + PS, because from the clinical point of view the ROR-score group is more important to select therapy (chemotherapy vs no chemotherapy) than the plain subtype calling. The PAM50 assay provides numeric and categorical values for both scores and we observed in the misclassified samples the assigned risk group remained the same except in one patient with discordant low/medium ROR Table .Table 2RPerou, Sørlie, Hu, Nielsen et al. evaluated the prognostic effect of PAM50 genes using the qRT-PCR from FFPE samples, and demonstrated its superiority to standard clinicopathological factors in predicting long-term survival of estrogen receptor positive tumors , 13. TheThe kind of samples to be processed is often a major factor in deciding which technology should be used to quantify transcripts and perform the PAM50 assay. In medical research the FFPE are the most common sources of archived material because they are cheap, easy to process and stable for a very long time. The PAM50 PCR-based classifier has been validated and translated into the NanoString nCounter® platform, because it previously demonstrated higher performance than PCR for FFPE data . Since tR2 = 0.90 for FFPE samples in idiopathic pulmonary fibrosis) [R2 = 0.99 for technical replicates), high correlation with FF matched samples (R2 = 0.874 for NanoString) and a high correlation between both platforms (R2 = 0.838) [Several studies for both oncologic and non-oncologic diseases have shown a good correlation between gene expression data from RNA-Seq and NanoString nCounter® (ibrosis) . ParticuWhile RNA-Seq technologies can be used in multiple research scenarios  a digitaFinally, our study shows that PAM50 intrinsic subtype in TNBC patients, which are mostly basal-like, is reproducible between different platforms. This encourages the solidity of the classification. Moreover, our comparison comes from the same FFPE-extracted RNA, directly comparing the platform with no RNA quality bias.The PAM50 subtype calling agreement between RNA-Seq and NanoString nCounter® transcript quantification technologies was evaluated in FFPE tissue samples obtained from a TNBC patient cohort. We observed that the subtype call was concordant in most of the samples and the potential discordances had reduced clinical implications in terms of prognosis. Although PAM50 can be used with RNA-Seq data and it shows similar results to NanoString nCounter®; the former is still difficult to use in daily clinical practice due to its processing time requirements and economic costs, unlike the discussed digital multiplexed option.The analyses were performed in FFPE samples from 96 patients recruited in a previously described multicenter, prospective, non-randomized neoadjuvant TNBC trial (NCT01560663) . This coThis trial included patients from 7 centers across Spain and Peru. Pre-treatment core biopsies were performed and conserved as FFPE following each institution protocol for further RNA extraction.This study was reviewed and approved by the Ethical Board at all the participating institutions, and all patients included were required to sign a written informed consent. All clinical data and samples were anonymized.FFPE blocks were centralized and included in a biologic sample bank at the Translational Oncology Laboratory (LAOT), which belongs to the Medical Oncology Department at the Hospital General Universitario Gregorio Marañón, registered at the Instituto de Salud Carlos III; in Madrid, Spain.2: 6 unstained 10 μm slides; 20–99 mm2: 3 unstained 10 μm slides; ≥100 mm2: 1 unstained 10 μm slide. RNA was then extracted using the RNeasy FFPE Kit (Qiagen), which is specially designed for purification of total RNA from FFPE tissue sections by isolating RNA molecules longer than 70 nucleotides. Firstly, all paraffin is removed from freshly cut FFPE tissue sections by treating with deparaffinization solution or using an alternative deparaffinization method. Next, samples are incubated in an optimized lysis buffer, which contains proteinase K, to release RNA from the sections. A short incubation at a higher temperature partially reverses formalin crosslinking of the released nucleic acids, improving RNA yield and quality, as well as RNA performance in downstream enzymatic assays. This is followed by DNase treatment that is optimized to eliminate all genomic DNA, including very small DNA fragments that are often present in FFPE samples after prolonged formalin fixation and/or long storage times. Next, the lysate is mixed with Buffer RBC. Ethanol is added to provide appropriate binding conditions for RNA, and the sample is then applied to a RNeasy MinElute spin column, where the total RNA binds to the membrane and contaminants are efficiently washed away. RNA is then eluted in a minimum of 14 μl of RNase-free water. RNA concentration and quality were assessed with Nanodrop 2000 Spectophotometer (Thermo Scientific NanoDrop Products) according to A260/280 ratio. RIN was evaluated with the TapeStation 2200 .RNA extraction and PAM50 intrinsic subtyping on the nCounter® platform were performed at the LAOT. Invasive tumor from haematoxilin and eosin stained slides was delimited by a pathologist at the LAOT and was subsequently microdissected in 10 μm slides. The number of slides recommended based on the measured tumor surface area on the hematoxylin and eosin (H&E) stained slide; 4-19 mmFrom an initial panel of 110 genes, we analyzed the expression of the 50 genes included in the PAM50 assay and 5 additional housekeeper genes described by Parker et al . In the https://github.com/mozack/ubu), respectively. Samples were normalized to a fixed upper quartile (1000 genes) followed by log2 transformation. The PAM50 algorithm was run according to the scripts provided by Parker et al [Sequencing was performed at the University of North Carolina (UNC) at Chapel Hill . 300-1000 ng of total FFPE RNA was used to create an RNA-Seq library using the Illumina TruSeq Ribo-Zero Gold Kit (RS - 1 22–2301 or RS - 122 - 2302). Libraries were then sequenced 2 per lane on a HiSeq2500 with 48x7x48 bp configuration. Alignment against GRCh37 and transcript quantification was done using MapSplice [fastq files in FastQC v0.11.8 [The quality control for the RNA-Seq samples was assessed using the . v0.11.8 . We also v0.11.8  using Fa v0.11.8 . The QC  v0.11.8 .We used R  v3.3.3 iAdditional file 1:Table S1. PAM50 output for RNA-Seq and NanoString nCounter. PAM50 table of results for RNA-Seq and NanoString nCounter®. It is reported for each sample and platform: the centroid correlation, the subtype call with its confidence, the ROR and ROR + PS values and groups, the Proliferation Score; and the ER/HER2 gene expression. (XLSX 90 kb)Additional file 2:Table S2. RNA Sample Quality Control. The RNA concentration measured in each sample, absorbance values for A260 and A280, the A260/A280 ratio and the measured RIN. Descriptive statistics are provided for A260/A280 ratio and RIN. (XLSX 16 kb)Additional file 3:Table S3. PAM50 genes correlation and inter-rate agreement analyses. Correlation analysis using the Spearman’s rho where the gene expression measured in RNA-Seq and NanoString nCounter® are compared. The inter-rater agreement analysis for the PAM50 genes was based on the Cohen’s kappa calculation using two-way mixed effect single measures. The statistics and p-values are provided in both analyses. (XLSX 17 kb)Additional file 4:QC – Fastq. RNA-Seq data Quality Control. FastQC and FastQ Screen reports summarized using MultiQC. All the information is summarized in an interactive .html. Additionally, a table is provided to associate the sample IDs mentioned in the manuscript with the IDs generated during the sequencing process. (ZIP 5962 kb)Additional file 5:QC – NanoString. NanoString nCounter data Quality Control. NanoStringQCPro reports in .html files. Technical, control and count-based metrics are reported. Additionally, a table is provided to associate the sample IDs mentioned in the manuscript with the IDs generated during the NanoString nCounter® quantification process. (ZIP 15743 kb)Additional file 6:Code. Normalization and PAM50 scripts for both platforms. Scripts used to perform the PAM50 assay in RNA-Seq and NanoString nCounter® platforms, along with additional technique-specific and transcript ID files. (ZIP 91 kb)


Immune checkpoint inhibitors (ICIs) have dramatically improved outcomes in cancer patients. However, ICIs are associated with significant immune-related adverse events (irAEs) and the underlying biological mechanisms are not well-understood. To ensure safe cancer treatment, research efforts are needed to comprehensively detect and understand irAEs.We manually extracted and standardized irAEs from The U.S Food and Drug Administration (FDA) drug labels for six FDA-approved ICIs. We compared irAE profile similarities among ICIs and 1507 FDA-approved non-ICI drugs. We investigated how irAEs have differential effects on human organs by classifying irAEs based on their targeted organ systems. Finally, we identified broad-spectrum (nontarget-specific) and narrow-spectrum (target-specific) irAEs.A total of 893 irAEs were extracted. 31.4% irAEs were shared among ICIs as compared to the 8.0% between ICIs and non-ICI drugs. irAEs were resulted from both on- and off-target effects: irAE profiles were more similar for ICIs with same target than different targets, demonstrating the on-target effects; irAE profile similarity for ICIs with the same target is less than 50%, demonstrating unknown off-target effects. ICIs significantly target many organ systems, including endocrine system (3.4-fold enrichment), metabolism (3.7-fold enrichment), immune system (3.6-fold enrichment), and autoimmune system (4.8-fold enrichment). We identified 21 broad-spectrum irAEs shared among all ICIs, 20 irAEs specific for PD-L1/PD-1 inhibition, and 28 irAEs specific for CTLA-4 inhibition.Our study presents the first effort toward building a standardized database of irAEs. The extracted irAEs can serve as the gold standard for automatic irAE extractions from other data resources and set a foundation to understand biological mechanisms of irAEs. Paradigm-shifting checkpoint inhibition immunotherapies have transformed outcomes for patients with melanoma and other metastatic cancers.The population of cancer patient taking ICIs is large and fast growingIn this study, we manually extracted irAEs for six FDA-approved ICIs from their FDA drug labels, standardized and mapped the extracted terms to the Medical Dictionary for Regulatory Activities (MedDRA), a widely used terminology for encoding drug side effects,Currently there are six FDA-approved ICIs: atezolizumab (PD-L1 inhibitor), avelumab (PD-L1 inhibitor), durvalumab (PD-L1 inhibitor), ipilimumab (CTLA-4 inhibitor), nivolumab (PD-1 inhibitor), and pembrolizumab (PD-1 inhibitor).We then mapped extracted irAE terms to terms in MedDRA, an international medical terminology of drug side effects used by FDA and other regulatory authorities in the pharmaceutical industry during the regulatory process.A and B are sets of AEs associated with drug A and drug B, respectively. For example, ipilimumab is associated with 100 AEs, and nivolumab is associated with 208 AEs. The number of overlapping terms between these two drugs is 57, and the Jaccard similarity is 0.229 [57/(10 057)].We calculated the pairwise irAE profile similarities among ICIs using extracted irAEs. We also calculated the similarities between ICIs and 1507 FDA-approved drugs using extracted irAEs and drug-AE associations from SIDER.To understand which organ systems were significantly affected by ICIs, we classified irAEs based on MedDRA classification scheme [18]. The MedDRA classification system is organized by the System Organ Classes (SOC), divided into High-Level Group Terms (HLGT), High-Level Terms (HLT), Preferred Terms (PT), and finally into LLT. We classified extracted irAEs into SOC. For instance, the 208 extracted irAEs for nivolumab were classified into 21 SOC classes, including “immune system,” “skin,” “metabolism,” and “endocrine.” For each SOC class, we assessed its probability of being associated with a ICI (or the set of irAEs associated with the ICI) as compared to its probability of being associated with the same number of randomly selected AEs. The random process was repeated 1000 times, and a t-test was used to assess the statistical significance. For example, among the 21 SOC classes associated with nivolumab, 9 were significantly enriched, including 4-fold enrichment for “immune system.” We then performed finer-grained classification based on the next-level HLT classification. For example, the 208 irAEs for nivolumab were classified into 87 HLT classes, including “autoimmune diseases,” “thyroid gland disorders,” and “heart failures.” Among the 87 HLT classes, 70 were significantly enriched, including 4.34-fold enrichment for the class “autoimmune disorders” and 5.3-fold enrichment for the class “thyroid gland disorders.”To understand the relationships between irAEs and genetic targets of immune checkpoint inhibition, we identified irAEs that were shared among all six ICIs. We then identified target-specific irAEs for PD-L1/PD-1 inhibition and for CTLA-4 inhibition. The irAEs unique for PD-L1/PD-1 are irAES that were associated with all PD-L1/PD-1 inhibitors , but not with CTLA-4 inhibitor (ipilimumab). Similarly, we identified irAEs specific for CTLA-4, which were irAEs associated with CTLA-4 inhibitor but absent from PD-L1/PD-1 inhibitors.We manually extracted irAEs from FDA drug labels and mapped extracted irAEs to unique preferred terms (PTs) based on the MedDRA terminology. As shown in The numbers of irAEs were not necessary correlated with the year the drugs were first approved. For example, ipilimumab was first approved in 2011 and was associated with 100 irAEs. Nivolumab was first approved in 2014 and was associated with 208 irAEs. Drugs with the same genetic targets tend to have similar numbers of irAEs as compared to drugs targeting different targets. The number of irAEs for three PD-L1 inhibitors ranges from 108 to 133, the number of irAEs for the two PD-1 inhibitors ranges from 191 to 208, and the number of irAEs for the CTLA-4 inhibitor is 100. The fact that ICIs with the same targets have similar number of irAEs than ICIs with different targets, indicates that certain irAEs may be caused by on-target effects of ICIs.We compared the pairwise similarities of irAE profiles between PD-L1 inhibitors  and others drugs (other ICIs and 1507 FDA-approved drugs). As shown in The similarity between ipilimumab, a CTLA-4 inhibitor, and other ICIs ranges from 0.2 to 0.3 , which iPembrolizumab and nivolumab are PD-1 inhibitors. The irAE profile of PD-1 inhibitors are more similar to other PD-1 or PD-L1 inhibitors (ranging from 0.28 to 0.38) than to ipilimumab (ranging from 0.19 to 0.22) . The aveWe investigated which body systems were significantly targeted by ICIs. For each ICI, we classified its associated irAEs based on organ systems (SOC groups in MedDRA) and performed enrichment analysis to examine which categories of irAEs were significantly enriched. For example, the 100 irAEs associated with ipilimumab were classified into 23 SOC groups, among which 11 groups were significantly enriched, including 6-fold enrichment for the class “immune system disorders” . We performed finer-grained classification based on the next-level HLT classes. To understand the relationships between irAEs and immune checkpoint inhibition targets, we investigated irAEs shared among all ICIs. http://nlp.case.edu/public/data/irAEs_FDA.In this study, we manually extracted irAEs for six FDA-approved ICIs from FDA drug labels, performed comparative analysis of irAE profiles among ICIs and 1506 FDA-approved drugs, and investigated which organ systems were significantly targeted by ICIs. The extracted irAEs can serve as the gold standard to evaluate automatic irAE extractions from other data resources and set a foundation to develop computational approaches to understand biological mechanisms of irAEs. To the best our knowledge, our study presents the first effort towards building a highly accurate, standardized irAE data resource and performed comparative analysis to characterize and understanding extracted irAEs. We have made the data publicly available at: ,,Future efforts are needed for comprehensively characterizing and understanding irAEs in cancer patients. First, it will be necessary to extract irAEs from other data resources, such as published biomedical literature, clinical trial reports, FDA postmarket drug safety surveillance systems, patient electronic health records, among others. It is known that FDA drug labels only capture a small portion of drug side effects.Second, not all patients taking ICIs will develop the same irAEs, therefore it is necessary to extract patient-level irAEs, which may be affected by patient genetics, disease characteristics, demographics, co-occurrent drugs, among others. These patient-level irAEs are important for tailored personalized ICI treatments and irAE management. Unlike FDA drug labels, many other data resources contain rich information of patient population information, such as patient electronic health records, FDA-post-market surveillance system and published biomedical literature.Third, our study demonstrated that the majority of irAEs were not caused by drugs’ on-target effects, therefore it is important to identify and understanding how off-targets of ICIs are involved in irAEs.This work was supported by the NIH Director’s New Innovator Award under the Eunice Kennedy Shriver National Institute Of Child Health & Human Development of the National Institutes of Health (DP2HD084068); NIH National Institute of Aging (1 R01 AG057557-01); American Cancer Society Research Scholar (RSG-16-049-01 – MPC); NIH Clinical and Translational Science Collaborative of Cleveland (1UL1TR002548-01).Conflict of interest statement. None declared.Xu and Wang have jointly conceived the idea, designed and implemented the algorithms and prepared the manuscript. All authors read and approved the final manuscript.


Formalin-fixed paraffin-embedded (FFPE) tissues are among the most widely available clinical specimens. Their potential utility as a source of RNA for transcriptome studies would greatly enhance population-based cancer studies. Although preliminary studies suggest FFPE tissue may be used for RNA sequencing, the effect of storage time on these specimens needs to be determined. We conducted this study to determine whether RNA in archived FFPE high-grade ovarian serous adenocarcinomas from Surveillance, Epidemiology and End Results (SEER) registries was present in sufficient quantity and quality for RNA-Seq analysis. FFPE tissues, stored from 7 to 32 years, were obtained from three SEER sites. RNA was extracted, quantified, quality assessed, and subjected to RNA-Seq (a whole transcriptome sequencing technology). FFPE specimens stored for longer periods of time had poorer RNA sample quality as indicated by negative correlations between specimen storage time and fragment distribution values (DV). In addition, sample contamination was a common issue among the RNA, with 41 of 67 samples having 5% to 48% bacterial contamination. However, regardless of specimen storage time and bacterial contamination, 60% of the samples yielded data that enabled gene expression quantification, identifying more than 10,000 genes, with the correlations among most biological replicates above 0.7. This study demonstrates that FFPE high-grade ovarian serous adenocarcinomas specimens stored in repositories for up to 32 years and under varying storage conditions are a promising source of RNA for RNA-Seq. We also describe certain caveats to be considered when designing RNA-Seq studies using archived FFPE tissues. Advances in molecular technologies have enabled more comprehensive molecular characterization of pathology tissues beyond what can be achieved using morphological or clinical factors. The wide application of next-generation sequencing of both DNA and RNA has revolutionized cancer research. Comprehensive analyses of different cancer types in The Cancer Genome Atlas (TCGA) have revealed many genomic changes that can be used as markers of tumor subtypes and may represent new therapeutic targets. For example, gene expression-based subclassification of glioblastoma into subtypes appears to be more predictive of treatment response than traditional methods of using clinical or morphological factors –3.Fresh frozen tissue is the desired sample type for all molecular analyses. However, tissue that is normally left after surgery and diagnosis is typically formalin fixed and paraffin embedded (FFPE) due to the cost effectiveness of storing tissues preserved in this manner versus freezing tissues at ultra-low temperatures . TherefoRNA from FFPE tissues may be more prone to being chemically modified, cross-linked and degraded over time due to fixation and archiving methods than DNA, and therefore, whole transcriptome analyses may be more difficult than DNA analysis . DespiteThe Surveillance, Epidemiology, and End Results (SEER) cancer registries cover approximately 28% of the United States population, providing high quality demographic, clinical, pathologic, and survival data. In three of the SEER registries, annotated FFPE tumor tissue specimens were made available for research through established Residual Tissue Repositories (RTR) . The FFPWe performed this study to determine whether it is feasible to perform RNA-Seq assays using FFPE-derived RNA obtained from 60 high-grade ovarian serous adenocarcinomas (plus 7 replicates) from SEER Residual Tissue Repositories (RTR) that were stored up to 32 years, and to develop some criteria to help understand which FFPE tissues may not yield useful RNA-Seq data.This study was approved by institutional review boards (IRB) at participating cancer registries  and at the National Cancer Institute (NCI). The study was determined to be exempt from IRB review under 45 CFR 46.101(b)(4) for the use of anonymized and coded or coded but unlinked tissue blocks from the SEER registries. No contact with subjects was made for this study.The same FFPE tissue sections used in our previous analysis of SEER FFPE tissues for whole exome sequencing were used in this study  and fromEach SEER registry conducted a pathology review of lead and trail sections flanking the 5 sections from each tissue block to determine whether tissue was consistent with the selection criteria ; approximately 30 cases from each registry were reviewed to ultimately select 20 cases that met study criteria. For each case identified as meeting the study criteria, five 10-micron sections were placed in a sterile tube. Additional details regarding the pathology review are described previously [2 RNA QC PCR Array from Qiagen that tests for the presence of transcripts of two housekeeping genes, ACTB and HPRT1, in addition to testing for inhibitors of Reverse Transcription and PCR amplification reactions, and the presence of genomic DNA contamination.The FFPE tissues were deparaffinized using xylene and ethanol according to the procedures detailed by Qiagen for the AllPrep DNA/RNA FFPE Kit. RNA was eluted in 60 microliters EB buffer (Qiagen). Qiagen All Prep FFPE kit was used to purify DNA and RNA from each specimen; the entire tissue section sent was used for nucleic acid purification (i.e. no macrodissection was performed). RNA quality was checked by running on the Agilent Bioanalyzer (RNA Nano chip) and RNA Integrity Number (RIN) values were obtained. The RNA quality was also assessed using the RTNGS libraries were prepared using the NEBNext Ultra II RNA Library Prep Kit for Illumina. The input amounts of RNA used for library preparation ranged from 21 ng to 1000 ng. The input amount of RNA  was deteLibraries were prepared per manufacturer recommendations. Briefly, the ribosomal RNA (rRNA) was depleted by hybridization to rRNA probes, followed by RNase H digestion of the hybridized RNA. RNase H is a better method than RiboZero for depleting ribosomal RNA when low quality RNAs, such as from FFPE tissues, are anticipated . Excess The libraries were sequenced on Illumina NextSeq 500 using NextSeq High Output v2 kit. Three samples were sequenced using the pair-end run mode with run setup as 2x76 bps. The rest of the 64 samples were sequenced on NextSeq 500 using the 1x151 bps single-read run with 10 samples pooled per run; this was done as recommended by Illumina tech support when the initial pair-end 2x76bps runs did not work for Read 2. In addition, 25 samples  with low sequencing yield were re-sequenced on NextSeq 500 using the 1x151 bps runs to get additional sequencing depth.https://www.bioinformatics.babraham.ac.uk/projects/fastq_screen) software tool. The mapping step in the software filtered out the bacteria reads.Bacterial contamination of the RNA was determined for all samples using the FastScreen (https://www.bioinformatics.babraham.ac.uk/projects/fastqc) to detect any abnormalities in the raw sequencing reads. FastqScreen was also used to detect any reads originating from other species indicating bacterial contamination. The sequencing adapters and low quality bases were trimmed using Trimmomatic [https://broadinstitute.github.io/picard/command-line-overview.html#MarkDuplicates) was used to evaluate the library complexity by determining the amount of unique or non-duplicated reads in the samples. Picard’s CollectRnaSeqMetrics program (https://broadinstitute.github.io/picard/command-line-overview.html) was used to collect mapping percentages on coding, intronic, intergenic and UTR regions as well as gene body coverage. In addition, RSeQC [Illumina’s HiSeq Real Time Analysis software (RTA) was used for processing images files, and the Illumina bcl2fastq_v2.17 was used for demultiplexing. The quality of reads was investigated using FastQC  were calculated to estimate the percentage of fragments longer than 200 nt (DV200), 100 nt (DV100), and 50 nt (DV50). The DV200 (percentage of RNA fragments > 200 nt in size) was between 1% and 50%, with a median value of 13%. The DV100 (percentage of RNA fragments > 100 nt in size) was between 11% and 86%, with a median value of 56%. DV50 (percentage of RNA fragments > 50 nt in size) was found to be between 76% and 100%, with a median value of 98%; see RNA extracted from the 67 FFPE samples obtained from the SEER repositories was assessed for quality before preparing NGS libraries . The RIN100 and DV200 values, there was a negative correlation between the storage time and DV values (-0.44 for DV200 and -0.39 for DV100) indicating that longer storage time is associated with more degraded RNA ; specimens stored for 7 to 12 years yielded an average library of 500 ng, stored 13 to 22 years yielded an average library of 87ng, and those stored 23–32 years yielded an average library of 41ng. This was expected since degraded samples have less amplifiable RNA and therefore, have lower library yields compared to non-degraded RNAs when using the same PCR conditions. We did not find the RIN values to be a useful measure of quality for the highly degraded FFPE RNA samples , while samples with DV100 > 60 were more likely to perform better (percent mapping >60). With the caveat that DV value is not the sole determinant of sequencing outcome, we suggest, for future analyses of similar quality samples, proceeding with samples with DV100 > 40; we also suggest starting with higher input amounts of total RNA for all samples with DV100 < 60. The current Illumina recommendations for their TruSeq RNA Access / RNA Exome kit (the recommended kit for FFPE RNA samples) are based on DV200; values above 70% are considered good with input recommendations as low as 20 ng, while 20–50 ng and 50–100 ng are recommended for samples with medium (DV200 50–70%) and low (DV200 30–50%) quality. Illumina does not recommend proceeding with samples with DV200 < 30%. It is noteworthy that in the current set of samples, there were no samples with DV200 >50%, and only 8 had DV200 between 30% and 50% . The assay also included controls for the presence of inhibitors of reverse transcription and PCR amplification, and the presence of DNA contamination. We were unable to detect the presence of either of the two housekeeping genes in any of the FFPE-RNA samples tested, even though no inhibition of reverse transcription or PCR amplification was detected in any of the tested samples. The non-FFPE samples showed good amplification of both housekeeping genes. This indicates that the RT2 RNA QC PCR Array is not a good assay to assess the integrity of these samples. This could be either because of inherent incompatibility of some assay component(s) with FFPE samples, or because the genes or amplicons tested may not be optimum and another set of housekeeping genes / amplicons  may be more suited for FFPE-RNA.In order to further assess the quality of extracted RNA, we performed qPCR assays on a subset of SEER samples, along with some non-FFPE total RNA and some non-SEER FFPE RNA samples . We testTotal RNA libraries were prepared from all the samples after rRNA depletion. Because the degraded FFPE-RNA fragments are likely to lack an intact polyA tail, rRNA depletion, followed by random priming during the reverse transcriptase reaction was chosen as the method of choice over the use of poly(dT) for mRNA selection and for priming first strand cDNA synthesis.100  collected from same patient and stored at the same SEER site; only SEER_065 had reads mapped to mouse genome in the 4–11% range. This suggests that samples were not contaminated during the initial sample collection, but may have been introduced in somewhere along the sample preparation process . While we cannot conclude where the contamination was from, we do know that contamination with the bacterial or mouse genomes was not from a single SEER site; rather, it was found in 56 samples from all 3 SEER sites.100 has positive correlations with the percentage of reads mapped on human genome (0.58), non-duplicated reads (0.53), as well as total gene detected (0.43). DV100, together with bioanalyzer library molarity values after library prep, can be used as a metrics parameter to screen poor quality samples before sequencing.The correlation analysis  between 100 and sample mRNA content. The longer storage time was associated with lower RNA quality (DV100) and lower mRNA content from NGS analysis. However, no significant correlation was observed between SEER site and DV100 or mRNA content  were included in this study . The samIn order to compare our sequencing data with pre-existing ovarian cancer data, we selected TCGA ovarian prognostic signature gene set  which inhttps://portal.gdc.cancer.gov/projects/TCGA-OV) and one normal ovary tissue RNA-Seq data set from Human BodyMap Illumina project (https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-513). We then examined gene expression profiles of the 193 signature gene panel using the three data sets. The expression level was higher overall in a majority of the 193 genes in the TCGA fresh-frozen ovary tumor samples and Human BodyMap normal ovary tissue data sets compared to the SEER FFPE data set. This may be due to higher sequencing depth in the TCGA/Human BodyMap data sets than SEER FFPE RNA-Seq data. While SEER FFPE samples were targeted for 50 million reads per sample, TCGA-OV data set had 100–300 million reads per sample and Human BodyMap ovary tissue sample had about 243 million reads. Among the 67 SEER FFPE RNA-Seq samples, 26 SEER samples . Th100 for initial sample QC, combined with library quality analysis using bioanalyzer and qPCR to avoid sequencing of samples that are not likely to yield useful data. We further expanded upon previous studies by using a larger number of FFPE tissues from a single cancer site. Our study leveraged the SEER registries to obtain FFPE tissues from 60 ovarian cancer cases, which was more than triple what previous studies used for examining the utility of FFPE for RNAseq using a single cancer site; these previous studies had used between two [Our study complements and expands upon several published studies that examined the use of FFPE tissues for RNAseq. As noted in a recent review article on the use of FFPE for high throughput genomic studies, reliable gene expression data can be obtained from RNAseq using FFPE tissues, provided they are not stored for a long periods of time . Our stuween two , 24 to 1Another strength of our study was that the samples are reflective of what epidemiologists might anticipate having access to for translational clinical research studies. Residual tissues from surgical resections are typically preserved as FFPE blocks, and subjected to a variety of storage conditions. Our study shows that it is not essential to have pristine FFPE blocks in order to conduct RNA-seq analyses and obtain useable results.200 < 30%) based on the protocol. Only 8 of the 53 SEER FFPE samples for which we could measure DV200, had DV200 > 30%. Therefore, initial QC checks for RNA degradation and selection of a suitable library preparation protocol based on the RNA degradation profile is highly recommended. Our analysis showed that ribosomal RNA depletion based whole transcriptome analysis protocol such as NEBNext Ultra II RNA Library Prep Kit for Illumina works very well with sample input far lower in quantity and quality than typically recommended. However, it still showed limitations for handling highly degraded RNA samples. This observation is consistent with the recent study by Schuierer and colleagues [Sample quality was one of the limitations of our study. We found that 40% of the samples had very low mRNA and gene counts due to highly degraded RNAs, and about 80% of the samples were contaminated with bacterial or mouse RNA. Larger library size was correlated with a lower percentage of total mapped reads to the human reference sequence (-0.78). Samples with heavier bacterial and mouse contamination had larger library sizes , which can explain the lower mapping percentages. As previous studies , 25 sugglleagues . CarefulFrom our study, we observed that gene body coverage along the transcript length was fairly even for a majority of the RNA samples. Data from this study agree with a previous study done by Graw and colleagues  that ranAnother limitation to this study was the lack of fresh frozen (FF) tumor tissue from the same patients to compare the results. However, several studies have provided evidence showing FFPE as an acceptable alternative when FF tissue is not available for NGS , 24, 26.The results collectively suggest that the NGS data produced from the FFPE SEER archive tissue can be reproducible when samples meet specific quality control criteria or the following caveats are taken into account:200 or RIN alone may not provide an accurate measurement for highly degraded RNA. This is in agreement with several earlier studies that showed RIN may not be an accurate RNA quality assessment measure [Sample quality assessment using the normal QC metrics such as DV measure . Therefo200 is not very informative for highly degraded samples, but DV100 provides some resolution to assess the sample quality. Based on this study of FFPE-RNA, we suggest proceeding with samples with DV100 > 40. For all samples with DV100 < 60, it is beneficial to use higher sample input amounts, whenever possible. Using DV100 for initial sample QC, combined with library quality analysis using bioanalyzer and qPCR are often necessary to avoid sequencing samples that are not likely to yield useful data.DVAnticipate more degradation of the RNA in older FFPE tissues. However, RNA-Seq can still yield reliable gene expression data for degraded RNA. Due to FFPE having relatively heavily degraded RNA, we suggest rRNA depletion using RNase H and random priming of the reverse transcription reaction for preparing sequencing libraries from FFPE-RNA samples, over the use of poly(dT) for mRNA selection and conversion to cDNAAssess potential contamination of the RNA as part of the standard post sequencing RNA-Seq QC protocols. Bacterial contamination and genomic DNA contamination are common for some of the FFPE samples independent of storage condition of sites and sample preparation procedures. Contamination can lead to misinterpretation of results, especially when working with other species (such as the microbiome). A recent article by Heintz-Buschart et al  showed cGene expression data from samples that have low quality cDNA, few mapped mRNA reads, and generate low gene counts should be used with caution. In our study, expression levels of the 193 signature gene panel in the 28 SEER samples with low gene counts Click here for additional data file.S2 Fig(TIFF)Click here for additional data file.S3 Fig(TIFF)Click here for additional data file.S4 FigPCA biplot with samples plotted in two dimensions using their projections onto the first two principal components. Each pair of replicates have color keys displayed on the upper right legend.(TIFF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 TableA: Conserved genes in samples across specimen storage times and SEER sites. B: KEGG Pathway of 189 Highly Expressed Genes.(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.