Transmembrane transporters seem to influence in a significant manner the disposition of TKIs, especially that of imatinib at both cellular and systemic levels. In particular, members of the ATP-binding cassette (ABC) family (namely ABCB1 and ABCG2) together with solute carrier (SLC) transporters  are responsible for the differences in drug pharmacokinetics. In the case of the newer TKIs, such as nilotinib and dasatinib, the substrate affinity of these drugs for transporters is variable but lower than that measured for imatinib. In this scenario, the investigation of genetic variants as possible predictive markers has led to some discordant results. With the partial exception of imatinib, these discrepancies seem to limit the application of discovered biomarkers in the clinical settings. In order to overcome these issues, larger prospective confirmative trials are needed.Chronic myeloid leukemia was the first haematological neoplasia that benefited from a targeted therapy with imatinib nearly 15 years ago. Since then, several studies have investigated the role of genes, their variants ( Patients affected by chronic myeloid leukemia (CML) were the first ones who benefited from a targeted therapy with imatinib in the late 1990s . NowadayNevertheless, about 30% of the treated patients must interrupt the treatment because of poor tolerability or cytogenetic or molecular failure ,8. ThoseTherefore, the present review will present and discuss possible pharmacogenetic markers predictive of response to TKIs. The published literature has been evaluated through the PubMed database using the following keywords in different combinations: chronic myeloid leukemia, imatinib, nilotinib, dasatinib, bosutinib, ponatinib, pharmacogenetic, pharmacogenomic, polymorphisms, gene expression, pharmacokinetics. Other pertinent articles have been selected among the references of retrieved literature.3 = major molecular response [MR]; MR4/MR4.5/MR5 = deep or complete molecular response) and according to the time required to attain these responses [Many preclinical experiments and clinical trials have focused their efforts to investigate any possible gene variation (in its broadest meaning) that could be predictive of treatment efficacy or toxicity and that could be applied in the clinical practice. Different studies identified at the same time more than one pharmacogenomic determinant, hence revealing the multifactorial characteristics of TKI efficacy and/or toxicity. In other cases, the search for predictive biomarkers was conducted by the aid of a pharmacokinetic approach, through the analysis of plasma concentrations of drugs. In this search for biomarkers, a clear-cut definition of clinical endpoints is an essential requirement. In CML patients, the treatment efficacy may be graded across a hematologic, cytogenetic and molecular response, according to the most recent European guidelines . In partesponses .The above-listed aspects of the TKI investigation may represent some critical issues in clinical trials, while they may explain both discrepancies among different studies and the lack of complete, matched associations between the presence of certain polymorphisms and clinical efficacy.N-desmethyl-imatinib (CPG74588). The latter accounts for approximately 20% of plasma levels of the parent drug and its cytotoxic effect as an active metabolite is estimated to be approximately 3–4 times lower than that of imatinib [Initial studies confirmed the involvement of CYP3A isoforms in the metabolic clearance of imatinib , whose mimatinib . Becauseimatinib , whereasimatinib .in vivo enzymatic activity. This result suggested that biotransformation of imatinib by CYP3A led to the active metabolite. Several clinical trials collected evidence about the association between CYP3A4 and CYP3A5 polymorphisms and response to imatinib [Further evidence strengthened the role of CYP isoforms as determinants of the imatinib response. The use of a probe drug such as quinidine allowed the stratification of patients according to their systemic CYP3A activity , and theimatinib ,15. It iThe CYP3A4 isoform catalyses the biotransformation of the more recent TKIs dasatinib and ponatinib into active metabolites that have an equipotent or a 4-time lower inhibitory activity in comparison to the parent drug, respectively ,17. ConvIn order to bypass the possible negative effect exerted by the transmembrane transporters on TKI efficacy, researchers have recently designed additional drugs whose efficacy is not significantly affected by the ATP-binding cassette (ABC) transporters, as is the case for ponatinib . The impi.e., diminished bioavailability) and increases excretion of imatinib from kidney and liver, while at the cellular level it causes a lower cytoplasmic accumulation of the drug, thus reducing the BCR-ABL1 inhibition capability.Among the most investigated transporters, a prominent role is played by the ABCB1 . Since tHowever, several preclinical and clinical studies reported discordant results about the relationship between the ABCB1 activity and efficacy of imatinib. In the K562 cell line, the expression of ABCB1 variants was not associated with increased resistance against imatinib  while thvs. Asian subjects), and the employed methodologies. In another study, patients who were homozygotes at the three loci for the polymorphic alleles  had a higher imatinib clearance; interestingly, these patients reduced imatinib dose for toxicity less frequently than the other ones [i.e., a more significant inhibition of ABCB1 activity by imatinib in c.3435CC patients, or the presence of other tagged SNPs), but the Authors concluded that confirmatory trials were needed.In order to better clarify the effect of the ABCB1 SNPs in imatinib pharmacokinetics, patients’ genotypes and haplotypes were investigated also by mathematical models including population pharmacokinetic approaches . Resultsher ones . That “aIn vitro experiments demonstrated that nilotinib is not an ABCB1 substrate [N-desmethyl-imatinib and dasatinib [in vitro studies demonstrated that nilotinib is a substrate of ABCB1 [In the case of the second-generation TKIs, a different affinity toward ABCB1 has been reported. ubstrate ,41, and ubstrate ,43,44. Masatinib . Howeverof ABCB1  and thatof ABCB1 . Finallyof ABCB1 , but furin vitro study demonstrated that the use of ABCB1 inhibitors seems not to influence intracellular concentrations of dasatinib in CML-CD34(+) progenitors [Also dasatinib is a substrate of ABCB1 ,41,47, ain vitro experiments on the K562 cell line overexpressing the drug transporters ABCB1, ABCG2, and SLC22A1 showed that only ABCB1 was responsible for the active bosutinib transport, because resistance was overcome by the addition of the ABCB1 inhibitor verapamil [Concerning bosutinib, some authors reported that it is a weak substrate of ABCB1 . Furthererapamil .Finally, some authors reported that ponatinib is able to reverse the ABCG2- and ABCB1-mediated multi-drug resistance . HoweverOverall, the sometime contradictory results can be explained by (a) the relatively lower number of patients treated with second-generation TKIs in respect to those receiving imatinib; (b) the lack of standardized methods and instruments for the measurement of plasma concentrations of TKIs; and (c) the adoption of variable modelling approaches.in vitro studies demonstrated that imatinib is a substrate of this transporter [p = 0.0284, CC as reference) [n = 4, 100%) compared to individuals with other genotypes  [The second transmembrane transporter that caught the attention of the researchers is ABCG2, formerly known as breast cancer resistance protein (BCRP). Several nsporter ,25,52,53nsporter . Therefonsporter . That hynsporter , whereasnsporter . Furthernsporter ,30. Inteference) . Another, 12.3%) , but the, 12.3%) . Overallin vivo study with knock-out mice suggested a partial role for ABCG2 in the transmembrane transport of dasatinib [in vitro experiments failed to confirm a role for ABCG2 in drug disposition [Controversial results have been published regarding the ABCG2 affinity and second-generation TKIs. Nilotinib ,41,53, dasatinib , while oposition ,47. Finaposition . At presSLC22A1, also known as hOCT1 (human organic cation transporter member 1), plays an important role in cellular uptake of imatinib ,60,61. Acis of the c.1222A>G SNP. This still remains a debated issue, because other authors never found the M420del polymorphism in cis with the c.1222A>G SNP [that is only marginally affected by M408V”  [The c.1260-1262delGAT polymorphism (M420del) was associated with an increased risk of treatment failure with imatinib , but tha2A>G SNP . Similar1222A>G) . Beyond 1222A>G) . Second,1222A>G) , thus ex1222A>G) ,68,69,70As reported about ABCB1, population pharmacokinetic analyses were planned to evaluate the role of SLC22A1 in imatinib disposition. The rationale for this approach resides in tissue expression of the transporter, because SLC22A1 is present in the gut mucosa and the liver where it would participate in the absorption and excretion of imatinib, respectively ,71,72. Sin vitro study from Nies and colleagues clearly demonstrated that the uptake and the cytotoxic effect of imatinib in CML cell lines and patients’ leukemic cells were independent from the SLC22A1 expression [Recently, two research teams published interesting findings about the possibility of adopting SLC22A1 as a potential marker of the imatinib effect. In the first study, a low transport activity in peripheral mononuclear cells was associated with poor response rates and the highest probability of transformation to accelerated phase or blast crisis . Therefopression . Interespression . Overallpression ,74 suggepression .The SLC22A1 transporter does not recognize nilotinib as a substrate ,62,77 noIn summary, preclinical and clinical results are strengthening the crucial role of SLC22A1 on imatinib kinetics at both the systemic and cellular level, despite some studies reported contrasting results that should be taken into consideration. Beyond this issue, the question regarding which SLC22A1 polymorphism is the best predictive biomarker of imatinib efficacy or whether a functional assay could be better than pharmacogenetic analyses is still unanswered. In the case of second-generation TKIs, data published in the literature are scarce for both preclinical experiments and clinical trials, and a definitive relationship between these drugs and SLC22A1 may not be demonstrated nor excluded. It is likely that an increasing number of studies evaluating nilotinib, dasatinib, and other most recent TKIs could solve this issue.The expression of other transmembrane transporters on different tissues may account for the unexplained inter-individual variability in both pharmacokinetics and pharmacodynamics. The uptake of several TKIs, other than those directed against BCR-ABL1, was clearly increased by the expression of the SLCO1B members in the human embryonic kidney cell line HEK293 . In partThe involvement of other transmembrane transporters in TKI disposition is still under investigation. In particular, imatinib was demonstrated to be a substrate of ABCC4 and SLCO1A2 in a clinical trial , whereasTherefore, the relationship between imatinib and the three transmembrane transporters SLC22A1, ABCB1, and ABCG2 seems the most promising one to be translated into the clinical practice, but prospective confirmative trials are still needed. At present, the data for second-generation TKIs are discordant and insufficient to identify the best candidate among the different transporters (with their polymorphisms), with the exception of ABCB1 for dasatinib. An improved knowledge about the relationship between BCR-ABL1 inhibitors and transporters is an urgent need, because nilotinib and dasatinib are registered agents for first-line therapy , and thePrevious paragraphs describe the role of pharmacogenetics to predict the treatment efficacy (and perhaps tolerability) in CML patients: among the possible proteins involved in the kinetics of TKIs at the cellular and systemic level, transmembrane transporters play a pivotal role because they influence both intracellular and plasma concentrations of the drugs. In turn, the variable concentrations of imatinib, nilotinib, and other TKIs may result in significant variations in efficacy and tolerability.Among the different transporters, ABCB1 participates in the drug extrusion and excretion at the cellular level, but it could influence also the intestinal absorption because its characteristic tissue distribution . On the The efflux of imatinib from the cells is dependent also on the presence/activity of ABCG2 ,24, whosin vitro and in vivo models [The introduction of TKIs in clinical settings addressed researchers’ attention toward those transporters that ensure drug uptake within leukemic cells. Several studies confirm the role of SLC22A1 in the cellular uptake of imatinib and, as a consequence, the inhibition of BCR-ABL1 activity. In fact, clinical endpoints, such as cytogenetic or molecular responses, are predicted by polymorphisms of SLC22A1, but also tolerability and changes of drug disposition may be anticipated by the evaluation of allelic variants of the gene. These observations are based on tissue distribution of the transporter, being capable of influencing the drug excretion and/or absorption . Howevero models , suggesto models .TKIs other than imatinib, namely dasatinib and nilotinib, can inhibit members of the SLC22A family , and prei.e., different cell lines) [i.e., each polymorphism) is introduced within the model that in turn quantifies the kinetic and/or dynamic influence of the factor on the whole system .Overall, the landscape of TKI pharmacogenetics is characterized by several points of interest, such as the relationships among gene polymorphisms, altered pharmacokinetics, and drug effects. A number of factors suggest that pharmacogenetic results could be reliable for the prediction of treatment efficacy, but the discrepant data limit the confidence and the application of the biomarker . The real lines) , the ranl lines) ,41, or tAnother possible confounding factor could be the patient’s adherence to the prescribed treatment. The problem of patients’ compliance has been clearly examined in the recent past , and theTwo questions remain to be answered: when pharmacogenetic biomarkers should be used and what is needed to transfer those markers in clinical settings. Biomarkers may have a rational position in the management of CML patients before the start of the treatment. Pharmacogenomic tests may help in the choice of the TKI according to the patient’s genotype of transmembrane transporters and substrate affinity of drugs, but also the dose could be a matter of choice. When treatment has begun, deepest genetic analyses could be useful to investigate possible unexplained relationships between drugs and toxicities or therapy failures. This approach will certainly benefit from next-generation sequencing and whole genome association studies.i.e., complete cytogenetic response, major or complete MR), but their application according to international guidelines will represent an excellent solution, especially for those biomarkers that should predict the earliest drug effects.The second answer is a critical issue for many biomarkers in oncology and beyond. Confirmatory prospective clinical trials, which should be aimed at the final validation of the biomarkers, are lacking and the uncertainty due to discrepancies among studies remains an insurmountable hurdle to set up companion diagnostics. A part of the problem is related to the choice of clinical and molecular endpoints  could lead to interesting results in the close future [In conclusion, the investigation of TKI pharmacogenetics in CML patients has led to important results, with the aim to decipher the relationships between genes and drugs. Therefore, a great part of the inter-individual variability could be due to the transmembrane transporters, whose activity may be evaluated by both genetic analyses, and functional tests . Howevere future . It is l


The eukaryotic initiation factor 4A (eIF4A) is a DEAD box helicase that unwinds RNA structure in the 5′ untranslated region (UTR) of mRNAs. Here, we investigated the role of eIF4A in porcine sapovirus VPg-dependent translation. Using inhibitors and dominant-negative mutants, we found that eIF4A is required for viral translation and infectivity, suggesting that despite the presence of a very short 5′ UTR, eIF4A is required to unwind RNA structure in the sapovirus genome to facilitate virus translation. Eukaryotic initiation factor 4F (eIF4F), comprised of eIF4E, eIF4A, and eIF4G, is essential for initiation of cellular protein synthesis . eIF4E bCaliciviridae family of positive-sense RNA viruses use a novel mechanism of viral protein synthesis that relies on the interaction of initiation factors with a virus-encoded protein, VPg, covalently linked to the 5′ end of viral RNAs  in the 5′ end was experimentally confirmed using selective 2′-hydroxyl acylation and primer extension (SHAPE) analysis. In vitro-transcribed PSaV RNA was modified by incubation with N-methyl isatoic anhydride (NMIA). Highly modified bases, characteristic of unstructured RNA, were detected by stalling in a reverse transcriptase reaction using 32P-labeled primers. Labeled cDNA products were separated on a denaturing 6% acrylamide gel and detected on a phosphorimager   , 12.in vitro was examined in rabbit reticulocyte lysates (RRLs) programmed with viral VPg-linked RNA obtained from PSaV-infected cells (in vitro-transcribed RNA from a dicistronic construct expressing cap-dependent chloramphenicol acetyltransferase (CAT) and porcine teschovirus (PTV)-internal ribosome entry site (IRES)-dependent luciferase were subjected to an in vitro translation reaction. Hippuristanol inhibited PSaV translation in a dose-dependent manner. As expected, cap-dependent CAT expression was also inhibited, but the PTV-IRES-dependent luciferase translation, which is eIF4A independent, was unaffected , and R362Q  or three dominant-negative mutants of eIF4AI, E183Q (DQAD), nd R362Q –17, on Pnslation  to E. Asnslation  to E.The possibility of a direct interaction between the PSaV VPg protein and eIF4A was examined using His tag pulldown assays; however, no interaction was observed (data not shown). To investigate if eIF4A was associated with viral RNA during replication, coimmunoprecipitation of viral RNA with eIF4A was performed. Antibodies to eIF4A and VPg, as a control, were able to coimmunoprecipitate significantly more PSaV viral RNA than the control antibody . While ein vitro analysis, our results demonstrate a functional role for eIF4A in the PSaV life cycle, providing additional insight into the novel mechanism of protein-primed translation initiation and the life cycle of poorly characterized caliciviruses.To examine the functional role for eIF4A in the PSaV life cycle, the effect of hippuristanol on PSaV replication in cell culture was examined. The PSaV titer was reduced by hippuristanol in a dose-dependent manner  at conce


This paper studies the effects of the covering layers on the performance of a cross-like Hall plate. Three different structures of a cross-like Hall plate in various sizes are designed and analyzed. The Hall plate sensitivity and offset are characterized using a self-built measurement system. The effect of the P-type region over the active area on the current-related sensitivity is studied for different Hall plate designs. In addition, the correlation between the P-type covering layer and offset is analyzed. The best structure out of three designs is determined. Besides, a modified eight-resistor circuit model for the Hall plate is presented with improved accuracy by taking the offset into account. The cetc. [As the important characteristics of a Hall sensor, both the current-related sensitivity and the offset have strong impacts on the performance of a Hall plate. A high sensitivity that improves the signal-to-noise ratio of the Hall sensor is often strongly limited by the short-circuit effect . Anotheretc. –18. Durietc. –22. SomeThis work studies the current-related sensitivity and offset of a cross-like Hall plate with different covering layers in detail. We designed multiple Hall cells with different structures. Section 2 introduces these designs and the measurement data. Section 3 analyzes the measurement data from two major aspects: the current-related sensitivity and the offset. Moreover, a modified eight-resistor circuit model with improved accuracy is presented to help the sensor design. Section 4 summarizes all of the work and gives the conclusion.2.2.1.TM technology provided by GLOBALFOUNDRIES. Existing implants are employed to form the Hall devices without additional processes.In this study, we designed three different structures implemented in three Hall chips, and each chip contains five Hall cells in different sizes. All of the Hall chips are fabricated in 0.18-μm BCDliteThe top and sectional view of the first structure (S1) are shown in 2.2.The whole test system for Hall voltage is shown in 3.3.1.I and placed in an orthogonal magnetic field B, the Hall voltage HV appears between two measurement contacts. The Hall voltage HV is defined as:IS is the current-related sensitivity. While the current-related sensitivity IS of Hall device has the following analytical expression [G is the geometrical correction factor, Hr is the Hall factor, efft is the effective thickness of the Hall device's active zone, n is the doping concentration of the active region of Hall plate and q is the elementary charge of an electron.When a Hall device is biased with a current pression :(2)SI=GG depends on not only the shape, but also the size of the Hall plate. For a cross-like Hall plate, the L/W (length/width) ratio works as the major influence on the geometrical correction factor of the Hall plate. The cross-like Hall plates with a high L/W ratio have been analyzed, and the geometrical correction factor G is expressed as follows [Generally, as an important parameter for current-related sensitivity, the geometrical correction factor  follows :(3)G=1−G of the above equation has an accuracy better than 0.5%; while the sizes of our Hall plates are not in this range. The correspondence among length (L), width (W), L/W, the structures and the current-related sensitivity is presented in If L/W ≥ 3.63, effnt can be used to predicate the current-related sensitivity. The areal concentration is obtained by integrating the carrier concentration along the depth [n is the three-dimensional concentration and tx and bx are the top and the bottom of the Hall plate's active region, respectively. On the other hand, both the doping concentration and the effective thickness of the Hall device's active region have a strong influence on the current-related sensitivity. The doping profiles of three structures are shown in he depth :(4)n=∫x3.2.TV contains two parts, the Hall voltage HV and the offset voltage offV. The tested voltage can be expressed by:It is well know that the tested voltage offsetV is almost proportional to the biasing current bI. Therefore, we introduce a parameter offsetR, which is defined as the following equation to express the value of the offset:offsetR of the three proposed structures are presented in offsetR of 0.05 Ω. S2 with a W of 16 μm and an L of 40 μm shows the best performance in the aspect of offset. However, S3 with a W of 18 μm and an L of 40 μm performs best with the smallest offset. It is well known that the offset is influenced by several factors, while L and W are not the most important among them.The values of offsetR is compared among the three structures, S1 is the best and S3 is the worst in the aspect of offset. Although the P-type doping region is necessary for the Hall plate to improve the stability, it increases the offset of the Hall plate. Both S2 and S3 have a lager offset than S1, with one more process on the active region of P-type doping. To our knowledge, the mask misalignment is a major cause of the offset [When the e offset . As a re3.3.To further assess the Hall effect sensor performance of the current-related sensitivity and offset, three different structures of Hall effect devices are modeled by Silvaco TCAD. The simulation tool helps in modeling the specific structures, to obtain the sensitivity and offset of the Hall plate. The simulation results provide useful information to verify the analysis of the experimental results. The three-dimensional representations of the three simulated structures are illustrated in versus biasing current. Meanwhile, in order to simulate the offset, the asymmetry is introduced in the simulated structures by modify the size of the contact. For the analysis of current-related sensitivity, all of the structures were simulated using the biasing currents ramped from 0 to 1 mA, with a magnetic field of 1 T. 3.4.1R and 2R are obtained by the symmetry of the Hall plate and the van der Pauw method of measuring the sheet resistance Rs. 1R and 2R can be expressed as:In order to use the spinning current method to eliminate the offset in the signal conditioning circuit, a circuit model with offset is required in the design of the signal conditioning circuit –27. A reHowever, this model does not take the offset into account. We modified this model by taking the offset into account.ΔR, as shown in ΔR, when the circuit is biased by a current, the voltage difference between the top and bottom is zero. The offset resistor leads to the asymmetry of the Hall plate, which actually exists in the practical situation. Therefore, even though the model is without the influence of the magnetic field, an offset voltage still appears between the top and bottom due to the offset resistor, when the circuit is biased by a current. ΔR that we defined can be used to model the offset caused by all of the influence factors. By Kirchhoff's circuit law, ΔR can be calculated by:Because the relationship between offset voltage and biasing current is approximately linear, the offset is modeled by an offset resistor The modified model has been written in the behavioral Verilog-A language and tested in a Cadence Spectre simulator. The modified eight-resistor model shows the same accuracy in sensitivity and temperature behavior as the model in , while t4.TM technology provided by GLOBALFOUNDRIES. Each structure is implemented in a Hall chip with multiple Hall cells to study the impact of the L, W and L/W ratio on the performance of the Hall plate. Moreover, we demonstrate that the Hall plate with the MVPLDD layer above the active region is the best structure in both the current-related sensitivity and offset. Besides, we greatly improved the accuracy of the eight-resistor circuit model for the Hall plate by taking the offset into account.In order to analyze the current-related sensitivity and offset of the cross-like Hall plates with different covering layers, we designed and fabricated three structures of Hall plate in 0.18-μm BCDlite


Bos taurus, btOCT1/SLC22A1) with PGE2 analogs, in fluorescently labeled and label-free formats. Using fluorescence polarization (FP), we detected a binding of btOCT1 to the PGE2-Rhodamine conjugate at submicromolar affinity, consistent with affinity data for PGE2 from cells over-expressing the related human OCT1. Using purified native btOCT1 as analyte and biotinylated PGE2 analog as ligand, our data from surface plasmon resonance (SPR) revealed that btOCT1 specifically interacts to PGE2 with KD values in the hundred nanomolar range. BtOCT1 also demonstrated a slow association (ka) in the range of 103 M-1s-1 and an even slower dissociation rate (kd) in the range of 10−4 s-1 for PGE2, suggesting the possibility of a different mode of binding compared to other structurally unrelated transported substrates of low-affinity . Our results complement in vitro transport studies and provide direct evidence that OCT1—which is normally expressed in liver and other tissues—interacts with prostaglandin analogs. While it is not entirely clear from the published literature whether OCTs function as major prostaglandin transporters, the tight binding of the naturally occurring PGE2, as well as the slow dissociation rate, could conceivably affect the transport of lower affinity substrates such as drugs and metabolites by SLC22 transporters. More research is necessary to establish the extent to which individual SLC22 family members actually function as PG transporters in vitro and in vivo and to investigate whether PGs can, independent of being directly transported, alter the ability of SLC22 transporters to handle drugs and other substrates.Organic cation transporter 1 , like many solute carrier 22 (SLC22) family members, is important for the disposition of clinically important drugs, metabolites and signaling molecules. Several studies suggest that SLC22 family (eg. organic anion transporters or OATs and OCTs) bind and possibly transport prostaglandins with relatively high affinity (submicromolar). The affinities of OCT1 and OATs toward PGE2 and PGF2a reported in these cell-based transport studies are considerably greater than for xenobiotics and natural metabolite substrates—in many cases over 100-fold higher. This raises the possibility that prostaglandins are key endogenous substrates and/or that they act on the transporter in a manner different from other substrates such as xenobiotics and lower affinity metabolites. To further investigate OCT1—prostaglandin interactions, we designed biophysical studies using purified bovine OCT1 ( A number of SLC and ABC transporters have broad substrate specificities and are able to transport a wide range of drugs and endogenous metabolites, as well as exogenous and endogenous toxins –6. BecauA considerable amount of evidence also suggests a role for these transporters in normal metabolism and in metabolic diseases  Table 1Table 1. Prostaglandins have well described roles in physiology –16. In tin vitro involving either mammalian cell lines or frog oocytes over-expressing the transporter  ex vivo assays (eg. tissue slices or organ culture) and in vivo knockout data in mice lacking a drug transporter (eg. one of the SLC22 family members) frequently help complement the in vitro data for certain ligands , Vmax = 100% FP, and n = 1) by OriginLab 7.0.D.The btOCT1 PGE2 SPR studies were performed on a BiOptix 404pi SPR machine using either SAHC200m or SAD200m sensor chips (Xantec) made of streptavidin hydrogel. The sensor chips were pre-conditioned, and the SPR unit calibrated according to BiOpitx recommendations. Two hundred and fifty μl of the control ligand PEG11-Biotin and the test ligand PGE2-PEG11-Biotin  each at 40 nM were loaded into two parallel sample flow cells on a SAHC200m chip. The surface of both control flow cells and sample flow cells were then blocked with 350 μl of 0.4% biotin and primed with GF buffer to reach a stable surface prior to btOCT1 analyte injection. A series of 40–840 nM tag-free btOCT1 samples were injected at 50 μl/min into each pair of control and sample flow cells in the 2X2 mode of the Bioptix 404pi instrument. The same btOCT1 analyte solution went through a loop of control flow cell with biotin surface only before a sample flow cell with either PEG11-Biotin control or PGE2-PEG11-Biotin immobilized sample cell surfaces, respectively. An association period of 5 min and dissociation 12 min were used. A blank buffer injection of the same volume was included before and after injection of each concentration of btOCT1 sample, for double subtraction analysis of the software . Steady-state response deduced from Scrubber2 (BioLogic Software Pty Ltd.) analysis for both PEG11-Biotin and PGE2-PEG11-Biotin surface were plotted against analyte concentration. A Hill function fitting of the PGE2-PEG11-Biotin steady-state response using OriginLab 7.0 were performed to yield the k constant as the Kd and ka fitting settings (one binding site) and manual bulk RI corrections to compensate responses caused by increased btOCT1 concentrations in injections. Final graphic presentation of the response data and fitted kinetic curves were presented by OriginLab 7.0.For kinetics studies of btOCT1 binding to PGE2 through the 2-over-2 strategy, 250 μl of two concentrations, 30 and 40 nM, of PGE2-PEG11-biotin were loaded at 50 μl/min onto the surface of the two separate sample flow cells of the SAD200m chip, to form two densities of ligands. Similar to the aforementioned steady state response analysis, both control flow cells and sample flow cells were then blocked by biotin and sensor chip surface primed and stabilized by GF buffer prior to analyte injection. Instead of using the same stock of btOCT1 analyte, two stepwise concentration gradients, ranging from 80 nm to 1.2 μM, were sequentially injected into each pair of control and sample flow cells in the 2X2 mode of the 404pi instrument. An association period of 5 min and dissociation 12 min were used. Also included were blank buffer injections of the same volume as the analyte solution before and after each injection of the btOCT1 sample. SPR kinetics analysis was performed using Scrubber2 with standard kOCT1 was first identified as an organic cation transporter and a wide range of drug and metabolite substrates have since been uncovered , 24. TheThe triple N-glycosylation mutant btOCT1 is highly pure after two chromatography steps . The N-tFP is a technique that has been widely utilized to investigate ligand-protein interactions, including those involving transporters , 27. A fSPR has been extensively used to characterize the interactions between antibodies and antigens, as well as ligands and receptors at a high level of molecular detail. SPR kinetic curves depict the time-frame of the interaction, a useful characteristic that is often masked or imprecisely measured by other methodologies . TherefoFor the steady-state analysis of btOCT1 PGE2 interaction, we immobilized a biotinylated PGE2 with a PEG11 spacer in between the two moieties and used the biotinylated PEG11 spacer only as control to assess the specificity of the interaction. The experimental design is shown in We then designed a 2-over-2 strategy utilizing two ligand densities on two separate sample flow cell surfaces and two series of btOCT1 analyte concentration gradients to titrate a wider range of conditions all in one experimental run . The difSLC22 transporters are important from a physiological, pharmacological and clinical perspective , 9, 29. To our knowledge, there is no report of a structure of a mammalian SLC22 transporter and little, if any, evidence for direct binding of ligand to the transporter. One major difficulty in studying this is the relatively low affinity of most drug and metabolite substrates of multispecific drug transporters of the SLC22 family , 6, 30.in vivo role for PG transport by SLC22 family members remains to be established, and even the in vitro transport data for certain family members such as OCT1 is limited and perhaps not conclusive, the unusually high affinity of PGE2 for OCT1 makes it a much more amenable ligand to study using techniques such as those described here. In addition, it allows one to consider whether there may be aspects of the interaction with SLC22 transporters like OCT1 that are unusual, possibly helping to explain the tight binding between prostaglandins and SLC22 transporters.Unusual among the likely endogenous substrates with accepted physiological importance in the organs in which SLC22 transporters are expressed are prostaglandins, including PGE2 and PGF2a . In cellD values obtained using both techniques are in the range of several hundred nM and are in reasonable agreement with each other, as well as previously published cell-based prostaglandin transport data [i ranges of 22–500 pM) suggesting a potential role of these drugs as modulators of these transporters [We have demonstrated the direct binding characteristics of prostaglandin analogs to pure btOCT1 protein by two different yet complementary approaches: FP and SPR. The Kort data . The binort data . This issporters .More biological studies are needed to conclusively establish the actual role of OCT1 in the in vitro and in vivo transport of prostaglandins as recently published cell-based transport data is not in good agreement, though this may be due to the ligand tested and the experimental conditions . If OCT1


Protein translation is a fundamental biochemical process and the regulation of this process in response to a variety of changes has been demonstrated to play a key role in cellular functional activity. Recently, the translational control of oncogenes is implicated in many cancers .in vitro and in vivo. In this study, we developed novel computational tools, specifically be tailored to study high throughput ribosome footprint data (Ribo-seq) [We recently reported a translation initiation factor eIF4A RNA helicase-dependent mechanism of translational control that contributes to oncogenesis and underlies the anticancer effects of drug silvestrol . Inhibitibo-seq) , to iden4 motif associated with RNA G-quadruplex (GQ) structures s Figure . Our nov


Pigs are a reservoir for hepatitis E virus (HEV). To determine the relative contribution of game to the risk for human HEV infection in southwestern France, we tested wildlife samples. HEV RNA was in 3.3% of wildlife livers, indicating that in this region, eating game meat is as risky as eating pork. HEV is a nonenveloped, single-stranded, positive-sense RNA virus containing ≈7.2 kb. Its genome contains 3 open reading frames (ORFs)—ORF1, ORF2, and ORF3—which encode nonstructural proteins, the capsid protein, and a small protein involved in virus egress, respectively (Hepatitis E virus (HEV) is a causative agent of acute hepatitis worldwide. According to the Ninth Report of the International Committee on the Taxonomy of Viruses , deer (Cervus elaphus), rabbits (Oryctolagus cuniculus), and coypu (Myocastor coypus) and, thus, the potential for these animals to act as sources of HEV infection for persons living in the Midi-Pyrénées area.HEV is hyperendemic to the Midi-Pyrénées area of southwestern France; annual incidence of cases among humans is 3.2% . Real-time PCR based on ORF3 was used to detect and quantify HEV RNA in plasma samples as previously described (HEV RNA was detected in 5 (5.8%) wild boar livers, 2 (3.2%) deer livers, 1 (5.0%) wild rabbit liver, and no coypu livers . Thus, tThe overall prevalence of HEV RNA in game (3.3%) is similar to that among pigs. A recent nationwide study in France reported that HEV RNA was present in the livers of about 4% of farmed pigs of slaughter age  and Germany (5.0%) are similar  is lower than that found by a previous (2010) study of wild rabbits from the Haute-Garonne Department in southwestern France   may be an alternative reservoir for zoonotic strain HEV3 (In conclusion, the prevalence of HEV RNA in wild boars, deer, and rabbits is similar to that previously reported for pigs. Consumption of the meat of these wild animals, and of pig liver sausage, all contribute to the HEV epidemiology in the Midi-Pyrénées area because of specific local eating habits. Game meat from this part of France should be cooked thoroughly to minimize the risk for HEV infection (


Penicillium digitatum is the most damaging postharvest diseases of citrus fruit. Previously, we have observed that citral dose-dependently inhibited the mycelial growth of P. digitatum, with the minimum inhibitory concentration (MIC) of 1.78 mg/mL, but the underlying molecular mechanism is barely understood.Green mold caused by P. digitatum mycelia after 30 min of exposure were analyzed by RNA-Seq. A total of 6355 genes, including 2322 up-regulated and 4033 down-regulated genes, were found to be responsive to citral. These genes were mapped to 155 KEGG pathways, mainly concerning mRNA surveillance, RNA polymerase, RNA transport, aminoacyl-tRNA biosynthesis, ABC transporter, glycolysis/gluconeogenesis, citrate cycle, oxidative phosphorylation, sulfur metabolism, nitrogen metabolism, inositol phosphate metabolism, fatty acid biosynthesis, unsaturated fatty acids biosynthesis, fatty acid metabolism, and steroid biosynthesis. Particularly, citral exposure affected the expression levels of five ergosterol biosynthetic genes , which corresponds well with the GC-MS results, the reduction in ergosterol content, and accumulation of massive lanosterol. In addition, ERG11, the gene responsible for lanosterol 14α-demethylase, was observed to be the key down-regulated gene in response to citral.In this study, the transcriptional profiling of the control and 1/2MIC-citral treated P. digitatum by the down-regulation of ergosterol biosynthesis.Our present finding suggests that citral could exhibit its antifungal activity against The online version of this article (doi:10.1186/s12864-016-2943-4) contains supplementary material, which is available to authorized users. Penicillium digitatum, and secondarily by blue mold and sour rot caused by P. italicum and Geotrichum citri-aurantii, respectively [P. digitatum is the most economically devastating pathogen causing about 90 % of the total loss of postharvest citrus fruit [Significant losses can occur after harvest during storage and marketing of citrus fruit primarily due to green mold, caused by ectively . Among tus fruit . Currentus fruit , 4.P. digitatum, P. italicum, and G. citri-aurantii [μL of 106 conidia/mL) to puncture wounds, delayed the onset of sour rot at room temperature by 7–10 days and at 5 °C, by 13–30 days [P. italicum and G. citri-aurantii in a dose dependent manner [Citral, one of the volatile constituents in plant essential oils, has been demonstrated to have strong antifungal activity against aurantii –6. Fumig–30 days . Previout manner , and thet manner . Therefo+ carriers [P. italicum and G. citri-aurantii by causing significant losses in total lipids or ergosterol contents [P. digitatum [P. digitatum at molecular level is rather limited, and thus, requires further studies.The antifungal mechanism of volatile compounds has been attributed to its capacity to disturb the cellular membrane, interfere with the cellular metabolism, react with active sites of enzymes, or act as Hcarriers , 11. In contents , 8. In aigitatum . Howeverα-terpinene against Saccharomyces cerevisiae, showing that genes associated with protein analysis, carbohydrate metabolism, and transcription were repressed, whereas genes involved in ergosterol biosynthesis, phospholipid biosynthesis, cell wall organization and detoxification were activated. Rao et al. [p-anisaldehyde can induce the expression levels of genes related to sulphur assimilation, aromatic aldehydes metabolism, and secondary metabolism in S. cerevisiae, suggesting that the normal metabolism of aromatic aldehydes was influenced [μg/mL thymol resulted in a reduction in intracellular thiamine concentration in yeast by impairing thiamin metabolism. In contrast, many genes involving in maintaining cell membrane and cell wall integrity, sulfur assimilation, methionine biosynthesis, and production of AdoMET were drastically induced [Recently, numerous reports regarding the global gene expression in response to essential oils or their volatile components in fungal have been conducted. Parveen et al.  found tho et al.  reportedfluenced . In anot induced . These fP. digitatum mycelia with or without citral treatment by RNA-Seq, in an effort to explore the underlying molecular mechanism and to find some key metabolic pathways or genes involved in.RNA-Seq is an increasingly attractive method for whole-genome expression studies in many biological systems. As compared with traditional microarrays, despite their instrumental role in profiling the global expression of genes, this technique extends the possibilities of transcriptome studies to the analysis of previously unidentified genes and splice variants due to the advantages such as no need to provide a referenced genomic sequence, a fully coverage of three types of RNA, an unlimited dynamic range of quantification at reduced technical variability, the declining cost of sequencing, and so on , 18. It P. digitatum was isolated from infected citrus fruit and preserved on potato dextrose agar (PDA) at 25 ± 2 °C. Two hundred micro liter fungal suspensions (5 × 105 cfu/mL) were added to 40 mL potato dextrose broth (PDB) and incubated in a moist chamber at 28 ± 2 °C for 72 h. The mycelia were vacuum-filtered and weighted at a 6 h interval to make a growth curve.P. digitatum mycelia were grinded to powders in liquid nitrogen and stored at −80 °C until further use.Based on the result of growth curve, 1 g wet mycelia in logarithmic metaphase (48 h of culture) were added to 20 mL PBS (pH 6.8) and incubated with 1/2MIC  or MIC of citral for 0, 30, 60, and 120 min. Samples without citral were severed as a control. The resulting mycelia were vacuum-filtered, weighted, and recorded to select optimal concentration and time for the next analysis. All collected Total RNAs from control, 1/2 MIC or MIC citral-treated samples after 30 min of exposure were extracted with TRIzol regent  according to the manufacturer’s instruction and treated with RNase-free DNase I . RNA integrity was determined by a 2100 bioanalyzer . Poly (A) mRNA from control and 1/2MIC citral-treated samples, designated as CK30 and T30, respectively, was isolated with oligo-dT beads and then treated with the fragmentation buffer. The cleaved RNA fragments were then transcribed into first-strand cDNA using reverse transcriptase and random hexamer primers, followed by second strand cDNA synthesis using DNA polymerase I and RNaseH. The double stranded cDNA was further subjected to end-repair using T4 DNA polymerase, Klenow fragment, and T4 Polynucleotide kinase, followed by a single A base addition using Klenow 3′ to 5′ exo-polymerase. It was then ligated with adapter or index adapter using T4 quick DNA ligase. Adaptor ligated fragments were selected according to the size and the desired range of cDNA fragments were excised from the gel. PCR was performed to selectively enrich and amplify the fragments. Finally, after validating on an Agilent 2100 Bioanalyzer and ABI StepOnePlus Real-Time PCR System, the cDNA library was sequenced on a flow cell using Illumina HiSeq2000 ™ .e value < 0.00001) between unigenes and protein databases (non-redundant protein (NR), Swiss-Prot, Kyoto Encyclopedia of Genes and Genomes (KEGG) and the Clusters of Orthologous Groups of proteins (COG)) were performed, and the best aligning results were selected as a priority order of NR, Swiss-Prot, KEGG and COG to determine the sequence orientation. When a unigene was not aligned to the above databases, ESTScan software [Transcriptome de novo assembly of the short reads was carried out using Trinty software . After fsoftware  was usedThe expression level of unigene is calculated by Fragments Per kb per Million reads (FPKM) method described by Audic and Claverie . An expr1 is the total fragments number of the CK30, and N2 is total fragments number of T30; gene A holds x fragments in CK30 and i fragments in T30. The larger difference of the expression level between the CK30 and T30 is calculated with FDR ≤ 0.001 and log2 Ratio ≥ 1.Where, NP value calculating formula in this hypothesis test is.First, mapping all DEGs to each term of gene ontology database and calculating the gene numbers for each GO term has to get a gene list and gene numbers for every certain GO term, then using hypergeometric test to find significantly enriched GO terms in DEGs comparing to the genome background. The p value goes through Bonferroni Correction, taking corrected-P value ≤ 0.05 as a threshold. In KEGG analysis, N is the number of all genes with KEGG annotation, n is the number of DEGs in N, M is the number of all genes annotated to specific pathways, and m is number of DEGs in M.Where, in GO analysis, N is the number of all genes with GO annotation; n is the number of DEGs in N; M is the number of all genes that are annotated to the certain GO terms; m is the number of DEGs in M. The calculated P. digitatum cells exposure to citral at various concentrations (0 and 1/2MIC) for 0, 30, 60, and 120 min using the Trizol reagent  following the manufacturer’s instructions. Two micrograms of DNA-free RNA were used for reverse transcription by M-MLV  with oligo dT18. RTFQ-PCR was performed on a BIO-RAD CFX Connect Thermal Cycler using FastStart Universal SYBR Green Master . All primer pairs for expression assays are listed in Table -△△CT method was used to quantify the value of every sample using actin gene as an internal reference [RNA was extracted from ns 0 and /2MIC foreference .Table 1PP. digitatum cells exposure to citral at a concentration of 1/2MIC for 0, 30, 60, and 120 min were determined by GC-MS method [The sterols compositions of S method , 29 withThe analytical GC was carried out on a Shimadzu QP2010 plus gas chromatograph  equipped with flame ionization detector (FID). A non-polar cross-linked fused-silica capillary column, HP-5MS , was used. The oven temperature was held at 50 °C for 1 min, programmed at a rate of 50 °C/min to 260 °C, then increased to 300 °C at a rate of 4 °C/min where it remained for 10 min. The carrier gas was helium (1.3 mL/min). The injector temperature was 250 °C, detector temperature 310 °C and the volume injected was 2 μL. MS analysis was carried out on the same chromatograph equipped with a Shimadzu QP 2010 GC/MS system, ionisation voltage 70 eV, ion source temperature 200 °C, mass range m/z 50–700, scanning interval 0.5 s and scanning speed 1000 amu/s. The sterol TMS ethers were identified by comparison with commercial references, the NIST™ database or data from literature.P. digitatum cells exposure to citral at a concentration of 1/2MIC for 0, 30, 60, and 120 min were determined by high-performance liquid chromatography (HPLC) as reported by our previous study [Total ergosterol and lanosterol contents of us study , whereasAll data were expressed as the mean ± SD by measuring three independent replicates. Analysis of variance using one-way ANOVA followed by Duncan’s test was performed to test the significance of differences between means obtained among the treatments at the 5 % level of significance using SPSS statistical software package release 16.0 .P. digitatum is shown in Fig. P. digitatum cells was slow and remained in the lag phase. Then P. digitatum cells visibly grew and reached the logarithmic phase at 60 h of incubation. Correspondingly, the mycelia of P. digitatum that grew for 48 h, which was suggested to be in the logarithmic metaphase, were chosen for the next analysis.The growth curve of P. digitatum, the wet weight of P. digitatum mycelia exposed to citral was determined. As shown in Fig. P. digitatum during the whole period, whereas that of the control group remained stable. After 30 min of exposure, the wet weight of 1/2MIC and MIC mycelia were 0.83 ± 0.03 g and 0.71 ± 0.06 g, respectively, which were significantly lower than that of the control . This inhibition was more obvious with the prolonging of the culture time. Considering that a RNA integrated number (RIN) value higher than 6.5 was the prerequisite for the RNA-Seq analysis, we further determined the RIN values in citral-treated or control samples at 30 min incubation. Results showed that the RIN values in 1/2 MIC or MIC citral -treated and control groups were 6.7, 2.6, and 7.9, respectively (data not show), suggesting that MIC citral -treated samples were not suitable for further analysis. Therefore, only samples treated with 1/2MIC citral (T30) or without citral treatment (CK30) were chosen for RNA-Seq analysis.To determine the effects of citral on the growth of P. digitatum were significantly matched to P. digitatum PHI26 (41.1 %), P. digitatum Pd1 (40.2 %), P. chrysogenum Wisconsin 54–1 (14.0 %), and other fungi (4.7 %) , phosphotransferase system , and sesquiterpenoid and triterpenoid biosynthesis (2 unigenes) were also found.2Ration ≥ 1 as a threshold. A total of 6355 differential expression genes were detected between CK30 and T30, including 2322 up-regulated and 4033 down-regulated genes  were mapped to the KEGG database and summed up to 155 pathways. The specific pathways associated with environmental information processing, such as ABC transport , MAPK signaling pathway , two-component system , phosphatidylinositol signaling system ; the pathways belonged to cell membrane, such as fatty acid biosynthesis , biosynthesis of unsaturated fatty acids , steroid biosynthesis ; the pathways involved in energy, such as nitrogen metabolism , oxidative phosphorylation , glycolysis/gluconeogenesis , and TCA cycle ; the pathways related to genetic information, such as ribosome biogenesis in eukaryotes , RNA degradation , RNA polymerase , aminoacyl-tRNA biosynthesis  and ribosome , were all affected by citral treatment  was found to be down-regulated, indicating a blocking in mitochondrial translation and the loss of mitochondrial functions [SDH1, frdA, CS, IDH1, HK, PFK, PK, fbcH, ATPeF1G, ATPeV0C, cysQ, cysJ, aspA, nasB, and glnA, were down-regulated. Previously, we have demonstrated that citral treatment could evidently alter the mitochondrial morphology, reduce the ATP content and inhibit the TCA cycle of P. digitatum [S. cerevisiae cells treated with thymol [To survive from drastic environment changes, eukaryotes must rapidly adjust their gene expression programs to develop a series of protective mechanisms , 31. In unctions . Correspigitatum . These rigitatum . Interesh thymol .PMR1 expression was observed in samples exposed to citral. This gene was an important determinant of resistance to demethylation inhibitor-resistant strains, which is normally suggested to protect the cells against triflumizole [PDR5 and SNQ2, were also up-regulated. These two genes were involved in a robust induction of drug efflux mechanisms, and their expression levels in S. cerevisiae cells have been demonstrated to be strongly induced by thymol [MRP1, MRP4, MRP5, MRP7, and MRP13, were down-regulated. This type of genes generally transports xenobiotic compounds or toxic metabolites that conjugated to glutathione, glucuronide, or sulfur, and the over-expression of MRP1 gene can enhance the multidrug resistance in eukaryotic cells [P. digitatum cell might be impaired by citral.The typical gene subfamilies involved in multidrug resistance in fungal, such as multi-drug resistance , multi-drug resistance-associated protein , and pleiotropic drug resistance , were affected by citral. A slight increase in the MRD gene lumizole . Genes by thymol . Interesic cells . TherefoCHI1, Fks1, two important cell wall-related genes encoding for chitin synthase and 1,3-β-glucan synthase, respectively, were repressed. This finding underlined that citral can block the formation of cell wall architecture and thereby improve the sensitivity of cells to citral. In contrast, Parveen et al. [S. cerevisiae cell exposed to α-terpinene, which in turn activated cell wall compensatory mechanism to overcome the terpinene toxicity. This discrepancy might be, to a large extent, caused by different strains, or by different antifungal mechanisms, as suggested by other reports [Several targets including cell wall, cell membrane, mitochondrion, and genetic material, have been proposed to account for the antifungal activity of essential oils or their volatile compositions –16, 36. n et al.  reported reports .INO1, PI4K, CDIPT, accC, FAS1, FAS2, FAB1, ERG7, ERG11, ERG6, ERG3, ERG5 were down-regulated, suggesting that the integrity of cell membrane might be destroyed. Oppositely, expression of FAD6 was increased by 12.4 folds. These results were consistent with our previous study that citral could induce the disruption of membrane integrity by causing a decrease in total lipid contents of pathogen cells [The lipophilic nature of terpenoid enables it preferentially enter into the lipid membrane, which results in the increased membrane fluidity and eventually leads to the increase of membrane permeability , 39. Theen cells .ERG7), lanosterol 14α-demethylase (ERG11), C-24 sterol methyl-transferase (ERG6), C-5 sterol desaturase (ERG3), and C-22 sterol desaturase (ERG5), were down-regulated by 2.7, 2.1, 2.0, 2.1 and 2.0 folds, respectively.Ergosterol is one of the principal sterol components in fungal membrane. Generally, a decrease in ergosterol contents results in osmotic disturbances, disruption of cell growth and proliferation , 39–41. ERG7 is responsible for the conversion of the last acyclic sterol precursor into lanosterol, the initial cyclic intermediate in the ergosterol biosynthesis [ERG11 is well-known to be the target of antifungal drugs [ERG6 catalyzes the accumulation of fecosterol, the intermediates of ergosterol biosynthesis, and a mutated ERG6 gene makes the C. albicans cell loss the ability to synthesize ergosterol [ERG3 introduces the C5/6 unsaturation in ergosterol biosynthesis [ERG5 catalyzes the biosynthesis of ergosta-5,7,22,24(28)-trienol, a direct precursor for ergosterol biosynthesis [ERG11, ERG6, ERG3, and ERG5, thereby block the ergosterol biosynthesis, lead to the impairment in cell membrane functional and ultimately induce the occurrence of cell disruption [ERG11 is the target site of some commercial chemical fungicides against P. digitatum [To verify the RNA-Seq results, we investigated the gene expression levels of ergosterol biosynthetic genes mentioned above. These genes serve indispensable roles in ergosterol biosynthetic pathway Fig. . ERG7 isynthesis , 46. ERGal drugs , 43, 47.gosterol . ERG3 inynthesis . ERG5 caynthesis . Generalsruption , 43, 44.igitatum , 43.Fig.ERG7 in control and 1/2MIC citral-treated samples decreased before 30 min of treatment, with the expression level higher in 1/2MIC citral-treated samples than that in control samples. In contrast, the gene expressions of ERG11, ERG6, ERG3, and ERG5 at 30 min of treatment were dramatically repressed and the expression levels in 1/2MIC citral-treated groups were significantly lower than those in control groups by 0.96, 0.23, 0.74, and 0.36 folds, respectively  than that of control samples during the entire period. In contrast, the gene expression levels of ERG5 in citral-treated samples were remarkable higher (P < 0.05) than that in control samples at 60 min incubation. Taken these together, it can be concluded that citral can affect the expression levels of genes in ergosterol biosynthetic pathway of P. digitatum, and ERG11 might be an important target site of citral against P. digitatum.As revealed by Fig. P. italicum cells were considerably decreased by citral [P. digitatum by GC-MS. The results are shown in Table ERG5 [ERG3, was absent in 1/2MIC citral-treated groups. Meanwhile, lanosterol and ergosterol were present in all test samples. These results indicate that the target site of citral against P. digitatum might be the upstream genes before ERG 3, which are consistent with the gene expression results.Essential oils have been illustrated to inhibit the growth of fungi by decreasing ergosterol content, affecting membrane structure, and disrupting sterol biosynthesis , 50, 51.y citral . To illuble ERG5 , 29, wasble ERG5 , was excP. digitatum by HPLC-UV. The chromatograms for the ergosterol standard, the ergosterol in control and 1/2MIC citral treated samples were presented in Fig. P. digitatum cells treated with citral continuously decreased throughout the entire period, whereas those in the untreated cells remained stable . This difference became more evident with the increasing of exposure time. The ergosterol contents in treatment cells was 2.80 ± 0.09 mg/g DW at 120 min of exposure, as compared to 5.22 ± 0.66 mg/g DW in control cells.To confirm the results of GC-MS analysis and to illustrate the effect of citral on ergosterol biosynthesis further, we determined the lanosterol and ergosterol contents of ble Fig. . The ergP. digitatum was partially blocked by citral exposure. This blocking was commonly caused by the antifungal substances used in controlling postharvest diseases [The chromatograms for the lanosterol standard, the lanosterol in control and 1/2MIC citral treated samples were presented in Fig. diseases , 54.Fig.P. digitatum cells correspond well with the transcriptional regulation of ERG7, ERG11, ERG6, ERG3, and ERG5 genes. After the initial addition of citral, the gene expressions of ERG11, ERG6, ERG3, and ERG5 were partially inhibited and these led to the decrease in total ergosterol content before 30 min of exposure. Correspondingly, the absence of ergosterol might induce the gene expression of ERG7 at this time, as convinced by the up-regulation of this gene and the accumulation of lanosterol content. The gene expressions of ERG7 in both groups have the same changing tendency after 60 min of exposure, but the expression levels in 1/2MIC citral-treated samples were significantly higher (P < 0.05) than those in control group. This up regulation, together with the reduction of ergosterol content, might stimulate the gene expressions of ERG6, ERG3 and ERG5 to provide more ergosterol to meet the demand of cells, which is thought to be a general response to decreased ergosterol levels [S. cerevisiae cells exposed to α-terpinene [ERG6 and ERG3 in 1/2MIC group were still lower than the control groups, which were supposed to contribute a lot to the inhibition of ergosterol biosynthesis, as illustrated by other documents [ERG5 in 1/2MIC group was lower than that in the CK group at 120 min of treatment, which might be caused by the reduction in its precursor -trienol). Interestingly, the expression levels of ERG11 continuously declined in 1/2MIC citral-treated groups and were always lower than those of CK group. Considering that the down-regulation of ERG11 can block the ergosterol biosynthesis and lead to the decrease of ergosterol content to inhibit the growth of fungi [ERG11 is a critical target of citral against P. digitatum.The ergosterol and lanosterol contents in 1/2MIC citral-treated l levels . This reerpinene . Neverthocuments , 55–57. of fungi , 47, ourP. digitatum hyphae, including ABC transport, steroid biosynthesis, amino sugar and nucleotide sugar metabolism, starch and sucrose metabolism, TCA cycle, oxidative phosphorylation, RNA degradation, and ribosome, can be greatly affected by citral exposure. RTFQ-PCR verified that citral can affect the expression level of five genes  in ergosterol biosynthetic pathway of P. digitatum, showing that ERG11 is a critical targeted gene. In addition, the ergosterol and lanosterol contents in citral-treated samples correspond well with the coordinated regulation of these five genes and GC-MS results. These results suggest that citral could exhibit its antifungal activity against the mycelial growth of P. digitatum by disrupting ergosterol biosynthesis.In conclusion, our present research revealed that the global gene expressions involved in many important pathways in MIC, minimum inhibitory concentration; PDA, potato dextrose agar; PDB, potato dextrose broth; DEGs, differentially expressed genes; FPKM, fragments per Kb million fragments; FDR, false discovery rate; RTFQ-PCR, real-time fluorescence quantitative PCR; RIN, RNA integrated number; CDS, coding sequence; NR, non-redundant protein; NT, non-redundant nucleotide sequence; KEGG, Kyoto Encyclopedia of Genes and Genomes; COG, clusters of orthologous groups of proteins; GO, gene ontology; NCBI, National Center for Biotechnology Information; GC-MS, gas chromatography-mass spectrometry; HPLC, high-performance liquid chromatography; MSTFA, N-methyl-N-trimethylsilyltrifluoroacetamide; TSIM, N-trimethylsilylimidazole; MRD, multi-drug resistance; MRP, Multi-drug resistance-associated protein; PDR, Pleiotropic drug resistance; 1/2MIC, Half of minimum inhibitory concentration


RNA-seq is a powerful technique for identifying and quantifying transcription and splicing events, both known and novel. However, given its recent development and the proliferation of library construction methods, understanding the bias it introduces is incomplete but critical to realizing its value.in vitro transcription sequencing (IVT-seq), for identifying and assessing the technical biases in RNA-seq library generation and sequencing at scale. We created a pool of over 1,000 in vitro transcribed RNAs from a full-length human cDNA library and sequenced them with polyA and total RNA-seq, the most common protocols. Because each cDNA is full length, and we show in vitro transcription is incredibly processive, each base in each transcript should be equivalently represented. However, with common RNA-seq applications and platforms, we find 50% of transcripts have more than two-fold and 10% have more than 10-fold differences in within-transcript sequence coverage. We also find greater than 6% of transcripts have regions of dramatically unpredictable sequencing coverage between samples, confounding accurate determination of their expression. We use a combination of experimental and computational approaches to show rRNA depletion is responsible for the most significant variability in coverage, and several sequence determinants also strongly influence representation.We present a method, These results show the utility of IVT-seq for promoting better understanding of bias introduced by RNA-seq. We find rRNA depletion is responsible for substantial, unappreciated biases in coverage introduced during library preparation. These biases suggest exon-level expression analysis may be inadvisable, and we recommend caution when interpreting RNA-seq results. High-throughput sequencing of RNA (RNA-seq) is a powerful suite of techniques to understand transcriptional regulation. Using RNA-seq, not only can we perform traditional differential gene expression analysis with better resolution, we can now comprehensively study alternative splicing, RNA editing and allele-specific expression, and identify novel transcripts, both coding and non-coding RNAs -3. In coPrevious efforts at understanding bias identified several contributing sources, including GC-content and PCR enrichment ,9, primiin vitro transcribed (IVT) from cDNA clones, where the nucleotide sequence at every base is known, the splicing pattern established and inviolate, and the expression level is known to be uniform across the transcript. Thus, any observed biases in coverage or expression must be technical rather than biological. This is the experimental equivalent of simulated data that computational researchers commonly use to develop and assess alignment algorithms [Bacillus subtilis or the deep-sea vent microbe Methanocaldococcus jannaschii genomes [Given that researchers are continually developing new sequencing methodologies and library generation protocols , we needgorithms -15. Jian genomes , organisin vitro transcription. Next, this RNA was mixed with complex mouse total RNA at various concentrations, and sequenced using the two most common RNA-seq protocols, polyA-seq or total RNA-seq, on the Illumina platform. We found coverage bias in most IVT transcripts, with over 50% showing greater than two-fold changes in within-transcript coverage and 10% having more than 10-fold differences attributable to library preparation and sequencing. Additionally, we found greater than 6% of IVT transcripts contained regions of high, unpredictable sequencing coverage, which varied significantly between samples. These biases were highly reproducible between replicates and suggest that exon-level quantification may be inadvisable. Furthermore, we created sequencing libraries from the original plasmid templates and using several different RNA selection methods . We found that both rRNA depletion and polyA selection are responsible for a significant portion of this coverage bias, and computational analysis showed that poorly represented regions of transcripts are associated with low complexity sequences. Taken together, these results show the utility of the IVT-seq method for characterizing and identifying the sources of coverage bias in sequencing technologies.Here we present and apply IVT-seq at scale to better understand bias introduced by RNA-seq. In brief, individual plasmids were produced, pooled, and subjected to Materials and methods section), we produced individual glycerol stocks each harboring a single, human, fully sequenced plasmid from the Mammalian Gene Collection (MGC) [in vitro transcription of the cloned cDNA sequences  . Next, ws Figure B. FollowTo approximate what happens in a total RNA-seq reaction, we subjected this IVT RNA to rRNA depletion and then prepared libraries using the Illumina TruSeq protocol  using the RNA-seq Unified Mapper (RUM) . For all9 using tWhile we do see reads aligned to the human IVT transcripts in the mouse-only data, these transcripts collectively represent approximately 2% of reads Table . Those tMaterials and methods section.Consider first the IVT-only data. Given that these transcripts were generated from an IVT reaction using cDNA sequences, these data are unaffected by splicing or other post-transcriptional regulation. Thus, most regions of transcripts should be ‘expressed’ and present at similar levels. The exceptions would be repetitive sequences that map to multiple genome locations and may be poorly represented, and the ends of the cDNAs, which are subject to fragmentation bias. To account for this, we created a simulated dataset that models the fragmentation process and deviates from uniform data only by the randomness incurred by fragmentation. We generated two such datasets using the Benchmarker for Evaluating the Effectiveness of RNA-Seq Software (BEERS) . The firin vitro transcription. Furthermore, we sequenced the IVT products directly and found the vast majority were transcribed with little to no bias. Taken together, these data suggest that these coverage patterns are primarily the result of technical biases introduced during library construction, rather than biology. These results are consistent with a previous study that used IVT RNA as standards in RNA-seq experiments [Using IVT data derived from the BC015891 transcript as a representative example, the ideal, theoretical coverage plot from the simulated data shows near-uniform coverage across the transcript’s entire length, with none of the extreme peaks and valleys characteristic of biological datasets Figure A. Howeveeriments , suggestIn addition to this variability within transcripts, we also found many transcript regions showing extreme variability in coverage across samples Figure . For exaMaterials and methods section for full details), it is likely that this is an underestimate. To address the possibility that mouse RNAs may interact with homologous human RNAs and interfere with them in trans, we assayed the sequences surrounding these regions using the MEME Suite [Including BC003355, we found 86 regions of high, unpredictable coverage (hunc) spread across 65 transcripts , RNA selection method , and sequencing-specific molecular biology . To identify biases introduced solely by sequencing-specific molecular biology, we created a DNA-seq library from the same MGC plasmids used as templates for the IVT-seq libraries . Furthermore, the rRNA-depleted and polyA libraries were more than 60% more variable on average than the plasmid library . These data suggest that sequencing-specific molecular biology common to all libraries we prepared  is also responsible for a portion of the observed coverage variability and sequencing bias.To quantify the variability for each selection method, we calculated the coefficient of variation at the single base level in coverage for all IVT transcripts across each of these datasets Figure B. Using y Figure C. This sy Figure A; black.Materials and methods for a detailed description of these metrics). For each sequencing strategy , we tested if any of the three sequence characteristics had a significant effect on variability in sequencing coverage, as measured by the coefficient of variation. While we are primarily focused on coverage variability as an indicator of sequencing bias, we also looked at depth of coverage, as measured by FPKM.Given these significant differences in coverage variability, we sought to identify sequence features that might contribute to this bias. We considered three quantifiable sequence characteristics: hexamer entropy, GC-content, and sequence similarity to rRNA  between coverage variability and depth, which indicates sequence features could be affecting coverage through variability (or vice versa). The rRNA-depleted data showed a slight, significant correlation (P = 0.04933). It is possible some feature of RNA selection affects both variability and coverage, given that we saw no significant correlations for the two remaining samples. This indicates that coverage variability and depth are independent for the plasmid and no selection data.To check for any confounding effects between coverage depth and variability, we tested the least and most expressed transcripts for any correlations with variability in coverage , and lower coverage depth in the no selection and polyA libraries  and lower variability in the no selection library  between a transcript sequence’s similarity to rRNA and the magnitude of the difference in coverage between the no selection and rRNA-depleted libraries . If this is the case, it could prove difficult to account for, given the challenges we have already encountered making predictions for miRNA targets and RNA secondary structure. Based on these results, we strongly recommend caution in interpreting exon-level quantification data, particularly for identifying and quantifying alternative splicing events, without further understanding of these biases.In this study, we present IVT-seq as a method for assessing the technical variability of RNA-seq technologies and platforms. We created a pool of IVT RNAs from a collection of full-length human cDNAs, followed by high-throughput sequencing Figure . BecauseUsing simulated data and by sequencing at various stages of the process , we found each step introduced bias. Regions of certain IVT transcripts were under-represented in both DNA and RNA, suggesting something inherent in their structure may resist cloning and sequencing properly. The IVT reaction had its own biases; however, by and large, it worked extremely efficiently with 90% of the input templates producing transcripts at detectable levels. PolyA sequencing revealed the well-described 3′ bias. Finally, we saw extreme bias introduced by the rRNA depletion step. Though we have yet to find the majority of the sources for this extreme bias, knowing that it occurs and that it is at least partially due to rRNA sequence similarity is an important first step. By making this data available to the community, we hope that new experimental and analysis methods can be developed to account for the biases inherent in various aspects of RNA-seq.Moreover, IVT-seq could be more broadly employed. By itself, the MGC collection has cDNAs derived from more than 16,000 mouse and human genes, including hundreds of genes for which there are more than one form. Therefore, in principle, it is possible to generate sequence profiles for representatives for nearly two thirds of the mammalian transcriptome, or spike in datasets to develop new and better methods for splice form detection and quantification. Similar profiling approaches could do the same for other organisms. In addition, IVT-seq is also immediately relevant to RNA-seq method development, for example, developing new protocols or refining existing ones. Finally, the method is not specific to Illumina sequencing and could be used to account for bias in other sequencing chemistries and methods .Importantly, we are not suggesting that current generation RNA-seq is not a fantastic new technology or that quantification data is incorrect, particularly given the validated, reproducible results researchers have been able to gain through its use. Rather, we wish to provide a cautionary note that our understanding of this technology is still relatively new and incomplete. It is our hope that through the use of this data and IVT-seq, we will develop the means to minimize or account for bias in RNA-seq and truly realize the vision of digital gene expression.2O was added to each well and incubated at 37°C for 10 min to resuspend plasmid DNA in water. Plasmid DNAs were collected and combined in 1.5 mL tubes with the aid of a multichannel pipette and concentrated by ethanol precipitation. To amplify the library, 10 ng of plasmid library was transferred into E. coli DH5α cells . The heat shock method was used to transform E. coli. Briefly, cells were incubated with plasmid library for 5 min on ice and were subjected to 42°C for 30 s. Cells were transferred back to ice and incubated for 2 min. Next, 0.95 mL S.O.C. medium  was added to the cells before incubation at 37°C for 1 h with shaking at 225 rpm. Cells were plated on LB-agar  plates containing 100 μg/ml ampicillin. Plates were incubated for 16 h at 37°C to grow the colonies and 3,500 (approximately three times the library size) colonies were collected with liquid LB . Cells were transferred into 100 mL liquid LB and incubated at 37°C for 2 h. Plasmids were purified using Qiagen  maxiprep kit , according to the manufacturer’s protocol.Glycerol stocks containing individual cDNAs (cloned into pCMV-Sport 6 plasmid) from the MGC  were pro2O. The reaction was incubated at 37°C for 2 h to achieve complete digestion. We assessed the complete digestion of plasmid DNA using DNA gel electrophoresis. To eliminate NotI and possible RNase in reaction mixture, the sample was subjected to Proteinase K treatment. SDS and Proteinase K were added to the reaction mixture to a final concentration of 0.5% and 100 μg/mL, respectively. The sample was incubated at 37°C for 30 min. After Proteinase K treatment, the sample was subjected to phenol/chloroform extraction, followed by ethanol precipitation. The pellet was dissolved in 50 μl of RNase-free water. Next, in vitro transcription was carried out using MAXIscript® SP6 Kit . The reaction was composed of 1 μg of library plasmid, 1X transcription buffer, 0.5 mM of nucleoside triphosphates , 40 U of SP6 RNA polymerase, and 10 μl of RNase-free water. The reaction was incubated at 37°C for 30 min. Next, the sample was treated with TURBO DNase to remove the plasmid templates. Briefly, 10 U of TURBO DNase (included with the MAXIscript SP6 Kit) were added to the reaction mixture and incubated at 37°C for 15 min. To stop the reaction, 1 μL of 0.5 M EDTA was added. To remove unincorporated nucleoside triphosphates and other impurities, the sample was precipitated with ammonium acetate/ethanol. The following reagents were added to the DNase -treated reaction mixture: 30 μL RNase-free water to bring the volume to 50 μL, 5 μL of 5 M ammonium acetate, and three volumes of 100% ethanol. The sample was chilled at -20°C for 30 min and then centrifuged at maximum speed in a 4°C table-top microcentrifuge. The supernatant was discarded and the pellet washed with ice-cold 70% ethanol. The pellet was dissolved in 50 μL of RNase-free water and the quality of RNA was assessed by agarose gel electrophoresis. In addition, PCR was carried out with IVT RNA to confirm total depletion of plasmid DNA.Plasmids were linearized by NotI enzyme so that the SP6 polymerase promoter site was upstream of the sequences to be transcribed. Reactions consisted of 5 U NotI , 5 μg library plasmid DNA, 1 X NEBuffer 4 (supplied with enzyme), and 90 μl of dHWild-type, six-week old male C57/BL6 mice were acquired from Jackson Laboratories . Mice were sacrificed and liver samples were quickly dissected and snap-frozen in liquid nitrogen. RNA was isolated from frozen mouse liver samples by TRIzol reagent according to the manufacturer’s protocol . All animal experiments were performed in accordance with the approval of the Institutional Animal Care and Use Committee.IVT RNA  was pooled with mouse liver RNA  to a final quantity of 2.5 μg. Each pool was split into two replicate samples of 1 μg each. RNA pools were treated with Ribo-Zero Gold Kit  and converted into Illumina RNA-seq libraries with the TruSeq RNA Sample Preparation Kit . Briefly, rRNA was removed from 1 ug of pooled RNA using Ribo-Zero Gold Kit and purified via ethanol/sodium acetate precipitation according to the manufacturer’s protocol. After drying, the RNA pellet was dissolved in 18 μL of Elute, Prime, Fragment mix (provided with the TruSeq RNA Sample Preparation Kit). RNA was fragmented for 8 minutes and 17 uL of this fragmented RNA was used to make the RNA-seq library according to Illumina TruSeq RNA Sample Preparation Kit protocol. After fragmentation and priming, first strand cDNA synthesis with SuperScript II , second-strand synthesis, end-repair, a-tailing, and adapter ligation, the library fragments were enriched with 15 cycles of PCR. The quality and size of the library was assessed using Agilent  2100 BioAnalyzer. The five libraries from each replicate were pooled together and sequenced using a single lane from an Illumina HiSeq 2000 (paired 100 bp reads).2O was loaded into a microTUBE . The ultrasonicator was de-gassed and prepared according to the manufacturer’s protocol. Linearized plasmids were sonicated using the following conditions: intensity 5, duty factor 10%, cycles per burst 200, time 120 s, and water bath temperature 7°C. Fragmented plasmids were gel-purified using a 1% agarose gel  and TAE running buffer . A slice between 100 bp and 700 bp was excised from this gel. DNA was purified from this gel slice using the MinElute Gel Extraction Kit  according to the manufacturer’s protocol. Fragmented DNA was converted into a sequencing library using the TruSeq DNA Sample Preparation Kit . End repair, adenylation, adapter ligation, gel size-selection, and PCR enrichment were performed according to the manufacturer’s protocol. During the gel size-selection, a band between 300 bp and 500 bp was excised. The quality and size of the library was assessed using Agilent 2100 BioAnalyzer. This library was sequenced using an Illumina MiSeq (paired 100 bp reads).MGC plasmids were linearized by NotI-HF enzyme as before. These linearized plasmids were then fragmented using a Covaris  S220 Focused-ultrasonicator. Briefly, 1.2 μg of linearized plasmid in a final volume of 60 uL of HAs with the other RNA-seq libraries, these libraries were prepared using the TruSeq RNA Sample Preparation Kit . For the polyA sample, 1 μg of IVT RNA was treated with polyA selection reagents included with the TruSeq RNA Sample Preparation Kit according to the manufacturer’s protocol. The remainder of the library preparation was carried out using the same conditions as for the other IVT RNA samples. For the no selection sample, 100 ng of IVT RNA at a concentration of 100 ng/μL was diluted with 17 μL of Elute, Prime, Fragment mix (provided with the TruSeq RNA Sample Preparation Kit). Again, the remainder of the library preparation was carried out as with the other samples. These samples were sequenced in a single Illumina HiSeq 2500 lane (paired 100 bp reads).Raw reads from all sequencing samples were aligned to the human genome GRCh37/hg19) using the RNA-seq Unified Mapper  . These processivity ratios were calculated for all transcripts in the plasmid and no selection data, with expression >5 FPKM.Coverage data for each of the IVT transcripts was extracted from the coverage files for the IVT-only, polyA, and no selection samples. The first and last 200 bp were trimmed from each transcript to prevent edge effects from interfering with the calculations. Due to this trimming, all IVT transcripts with less than 500 bp were discarded. All IVT transcripts expressed with FPKM <5 in any of the samples were discarded from further analysis. Nucleotide-level coverage data was grouped into percentiles based on depth of coverage. Average coverage was calculated across the 10th percentile and the 90th percentile. Fold-change differences in within-transcript coverage were calculated by dividing the 90th percentile average by the 10th percentile average. The list of transcripts with associated fold-change values is included in Additional file mad function with constant = 1. Next, a sliding window was used to calculate the average MAD in the 100 bp windows centered on each nucleotide in the transcript. The first 300 and last 250 windows were trimmed from each transcript to avoid confounding variability due to edge effects or fragmentation artifacts. All analysis up until this point was carried out separately on the two replicate datasets. The 95th percentile of MAD scores was calculated for each of the replicates using R’s quantile function . Only those regions with at least 20 contiguous windows having MAD scores above the appropriate 95th percentile values were retained for further analysis. Next, the BEDTools [Coverage data for each of the IVT transcripts was extracted from the coverage files from each of the rRNA-depleted datasets . These coverage plots were normalized between zero and one to allow comparison between different dilutions. For each nucleotide position in a transcript, the deviation in coverage between each of the samples was calculated using the median absolute deviation (MAD), due to its resistance to outliers. MAD scores were calculated across the different dilutions using R’s Sequences for each transcript were collected in R using the BSgenome, GenomicRanges, and GenomicFeatures packages. Hexamer entropy for each transcript was calculated as follows: occurrences of all possible hexamers in a given transcript were counted. These counts were converted into frequency space, and these frequency values were used to calculate the Shannon entropy. Shannon entropy is commonly used to represent complexity in nucleotide sequences or multiple alignments -21. Simiwilcox.test function). This entire analysis was repeated using transcript-level FPKM values instead of the coefficients of variation. All boxplots were prepared using R.The list of IVT transcripts was sorted by transcript-level coefficients of variation for each library method . All transcripts with transcript-level FPKM ≤5 were excluded from further analysis. From this sorted list, the transcripts with the 100 least and 100 most extreme coefficients of variation were collected for each of the above sequencing samples. The values for hexamer entropy, GC-content, and rRNA sequence similarity were compared between every pair of 100 least and 100 most extreme coefficients of variation using a Wilcoxon signed-rank test  Correlation plots for log10 transcript-level FPKM values between replicate IVT-seq samples. Pearson R2 values for the correlations are included as inserts in each plot. (B) Distribution of FPKM values in both replicates of the IVT-only sample. FPKM values are plotted on the x-axis in log10 space. The y-axis is plotted in arbitrary density units.Expression comparison between replicates. Click here for file(A) simulated data or (B) QM-simulated data, and replicate one of the IVT-only data. Pearson R2 values for the correlations are included as inserts in each plot.Expression comparison between simulated and IVT data. Correlation plots for log10 transcript-level FPKM values between Click here for file(A) BC015891, (B) BC009037, and (C) BC016283 are displayed according to the gene model (green), as it is mapped to the human reference genome. Blocks correspond to exons and lines indicate introns. The chevrons within the intronic lines indicate the direction of transcription. Numbers on y-axis refer to RNA-seq read-depth at a given nucleotide position. All transcripts are displayed in the 5ʹ to 3ʹ direction.Coverage patterns are reproducible across replicates. Coverage patterns from both replicates for all transcripts in Figure Click here for fileMaterials and methods section for full details on the fold-change calculations.Fold-change in within-transcript coverage across libraries. The cumulative distribution functions for fold-change in within transcript coverage are displayed for the rRNA-depleted (red), polyA (orange), no selection (green), plasmid (blue), QM-simulated (gray), and simulated (black) datasets. Curves toward the left side of the plot indicate fewer genes contain high fold-change differences in coverage. Curves toward the right side of the plot indicate many genes contain high fold-change differences in coverage. The dotted lines indicate the y-axis values for none of the data (0.0) and all of the data (1.0). This plot is focused on the fold-change values between 1 and 10. See the Click here for fileList of regions with high coverage variability (hunc regions).Click here for filein vitro transcription reaction. IVT RNA is then pooled with mouse RNA, rRNA is removed from pool via Ribo-Zero Gold kit, rRNA-depleted pool is fragmented via metal-ion hydrolysis, and fragmented RNA is converted to cDNA via reverse transcription with random-hexamer priming. The resulting cDNA fragments are then taken through the TruSeq protocol.Plasmid sequencing protocol compared to IVT-seq. The protocol for preparing MGC plasmids for DNA-sequencing library generation is displayed alongside the protocol for preparing IVT transcripts for RNA-seq library generation. Both protocols start by linearizing the plasmids. For DNA-sequencing, linearized plasmids are fragmented via Covaris sonication, and the resulting fragments are taken through the TruSeq protocol. For RNA-sequencing, the linearized plasmids are used as templates for an Click here for fileNucleotide frequency as a function of read position for sequencing reads at the 5ʹ ends of cDNA fragments. Frequencies are plotted for plasmid, no selection, rRNA-depleted, and polyA datasets.Click here for file(A) hexamer entropy, (B) GC-content, and (C) rRNA sequence similarity for the 100 transcripts with the highest and lowest transcript-level FPKMs from the plasmid, no selection, rRNA-depleted, and polyA libraries. Asterisks indicate the significance of a Wilcoxon signed-rank test comparing values for the listed sequence characteristics between each pair of groups from the same sample. **P <0.01; ***P <0.001.Effects of sequence characteristics on coverage depth. Distributions of Click here for fileP <0.05; ***P <0.001.Confounding effects between coverage depth and variability. Distributions of transcript-level coefficients of variation for the 100 transcripts with the highest and lowest transcript-level FPKMs from the plasmid, no selection, rRNA-depleted, and polyA libraries. Asterisks indicate the significance of a Wilcoxon signed-rank test comparing values for the listed sequence characteristics between each pair of groups from the same sample. *Click here for filerRNA sequence similarity and coverage bias in rRNA-depleted data. Correlation plot between Smith-Waterman alignment score to rRNA sequences and the magnitude of the decrease in coverage depth between no selection and rRNA-depleted samples. A coverage drop of 1.0 indicates a large decrease in coverage between the no selection and rRNA-depleted samples. A coverage drop of 0 indicates no difference between the two samples. For full details on this analysis, see Additional file Click here for fileDescription of window analysis of rRNA sequence similarity.Click here for fileAlignment statistics for all sequencing datasets.Click here for fileList of transcripts with associated fold-change values in within-transcript coverage.Click here for file


This review focuses on the mobility of small RNA (sRNA) molecules from the perspective of trans-kingdom gene silencing. Mobility of sRNA molecules within organisms is a well-known phenomenon, facilitating gene silencing between cells and tissues. sRNA signals are also transmitted between organisms of the same species and of different species. Remarkably, in recent years many examples of RNA-signal exchange have been described to occur between organisms of different kingdoms. These examples are predominantly found in interactions between hosts and their pathogens, parasites, and symbionts. However, they may only represent the tip of the iceberg, since the emerging picture suggests that organisms in biological niches commonly exchange RNA-silencing signals. In this case, we need to take this into account fully to understand how a given biological equilibrium is obtained. Despite many observations of trans-kingdom RNA signal transfer, several mechanistic aspects of these signals remain unknown. Such RNA signal transfer is already being exploited for practical purposes, though. Pathogen genes can be silenced by plant-produced sRNAs designed to affect these genes. This is also known as Host-Induced Genes Silencing (HIGS), and it has the potential to become an important disease-control method in the future. Caenorhabditis elegansSince the discovery of gene silencing induced by inverse transcripts in the 1980s Saccharomyces cerevisiae (Unikonta), Trypanosoma cruzi and Leishmania major (Excavata), Cyanidioschyzon merolae (Archaeplastida), and Plasmodium falciparum  sRNAs are typically 19–25 nt long, and they are produced from larger dsRNA or hairpin RNA (hpRNA) molecules by DICER (DCR) or DICER-like (DCL) proteins. They bind to complementary mRNA targets with the help of an Argonaute (AGO) protein, leading to transcriptional and post-transcriptional gene silencing. This complex of an sRNA and an AGO protein is called the RNA-Induced Silencing Complex (RISC). The components of the RNA silencing machinery are widely conserved in eukaryotes -1 As indicated, sRNA not enveloped in a membrane can be found in the extracellular space. It is still unknown how this free sRNA is secreted, not to mention how it is selected for secretion. However, outside the cells it can be associated with HDL and proteins, such as AGO RNA in extracellular vesicles has attracted increasing interest as a means of intercellular communication. When first discovered, extracellular vesicles were merely considered to result from stressed cells shedding waste products RNA signals in extracellular vesicles are envisaged to enter target cells in one of two ways. The intact vesicle can be endocytosed at the plasma membrane, after which the RNA will end up being behind two membranes in an endosome . RNA escT. cruzi–produced vesicles are loaded with an Argonaute protein and sRNAs, which potentially influence host gene expression RNA secretion is believed to generate the extracellular RNA that is transported between hosts and parasites. For instance, Trans-kingdom RNAi could also depend on transfer of non-vesicular RNA. However, to our knowledge, functional homologs of SID-1 and SID-2, for instance, or an alternative direct RNA uptake system, have not been described in plants. Therefore, given the accumulation of vesicular material in the interphase between the plant and pathogen, we deem it likely that the dissemination of gene silencing RNA during HIGS in plants relies on vesicle-mediated transport, much like in mammalian circulation, where the spread of membrane-enveloped endogenous miRNA signals through the bloodstream requires the selective uptake of these signals by the recipient cells B. cinerea exploits sRNAs to silence defense genes in Arabidopsis and tomato, this process is dependent on plant AGO1  sRNA production, (2) RNA secretion in MVB-based exosomes, (3) fusion of RNA containing exosomes to the plasma membrane, (4) RdRP-dependent amplification integrated with transcript cleavage and inhibition . We thinIt is now documented by many examples that eukaryotic organisms of different kingdoms exchange RNA sequences as signals affecting gene expression, and we may only have seen the tip of the iceberg of this phenomenon. Future studies that investigate the mechanisms of this trans-kingdom RNA transfer more systematically, will most likely identify “the usual suspects” of the canonical silencing machinery  as being required for production of mobile RNA and the hijack of the target-cell RNA silencing machinery. The biggest revelations may come in the form of factors that are involved in RNA export from the producing cell, its physical extracellular transport and its import into target cells. These mechanisms are very enigmatic at this point, and we can only speculate by comparison to analogous phenomena within organisms. So far, HIGS has focused on the function of target genes, but we foresee that it could be used to dissect the process of trans-kingdom RNA-silencing transfer by setting up carefully designed screens. We think that HIGS systems, in which plant expression of hpRNA directed against genes in the pathogen, hold a big promise as a mechanism for pest control, since the system has been described to work effectively in an increasing number of species