In gene expression studies, RNA sample pooling is sometimes considered because of budget constraints or lack of sufficient input material. Using microarray technology, RNA sample pooling strategies have been reported to optimize both the cost of data generation as well as the statistical power for differential gene expression (DGE) analysis. For RNA sequencing, with its different quantitative output in terms of counts and tunable dynamic range, the adequacy and empirical validation of RNA sample pooling strategies have not yet been evaluated. In this study, we comprehensively assessed the utility of pooling strategies in RNA-seq experiments using empirical and simulated RNA-seq datasets.The data generating model in pooled experiments is defined mathematically to evaluate the mean and variability of gene expression estimates. The model is further used to examine the trade-off between the statistical power of testing for DGE and the data generating costs. Empirical assessment of pooling strategies is done through analysis of RNA-seq datasets under various pooling and non-pooling experimental settings. Simulation study is also used to rank experimental scenarios with respect to the rate of false and true discoveries in DGE analysis. The results demonstrate that pooling strategies in RNA-seq studies can be both cost-effective and powerful when the number of pools, pool size and sequencing depth are optimally defined.For high within-group gene expression variability, small RNA sample pools are effective to reduce the variability and compensate for the loss of the number of replicates. Unlike the typical cost-saving strategies, such as reducing sequencing depth or number of RNA samples (replicates), an adequate pooling strategy is effective in maintaining the power of testing DGE for genes with low to medium abundance levels, along with a substantial reduction of the total cost of the experiment. In general, pooling RNA samples or pooling RNA samples in conjunction with moderate reduction of the sequencing depth can be good options to optimize the cost and maintain the power. Massively parallel sequencing of cDNA libraries (RNA-seq), is the gold standard for comprehensive profiling of RNA expression . This tySituations like budget constraint, lack of sufficient RNA input or large within-group biological variability are common limiting factors in RNA-seq experiments. Under such circumstances, pooling of RNA samples may provide a solution. Pooling of RNA samples takes place by mixing RNA molecules extracted from independent biological samples from the same population , before library preparation. Consequently, pooling results in a smaller number of replicates, and hence lower cost for the subsequent steps. For microarray studies, the adequacy and experimental validation of pooling has been well studied –13. The In this study, we evaluate the utility of RNA sample pooling strategies in RNA-seq experiments, using both empirical and simulation methods Fig. . Compariq) of randomly selected RNA samples are mixed before library preparation and sequencing. As a result, in pooled experiments, a library represents a pool of q biological samples. In the subsequent sections, we formalize the RNA sample pooling procedure for better understanding of the data generating process.A typical RNA-seq experiment consists of three major steps: RNA sample preparation, library preparation, and sequencing. When there is no pooling of RNA samples in the first step (the standard procedure), a library represents a single biological sample. In pooled RNA-seq experiments, a number  into m non-overlapping pools (m<n), each containing q>1 unique biological samples. First, we assume the pool size q is the same for all pools, and then later we relax this assumption and generalize the theory for pooled experiments with varying pool sizes. To formalize the pooling procedure, we introduce a dummy variable Ajk, which is defined as 1 if biological sample j is in pool k=1,…,m, and 0 otherwise. m-dimensional vector of indicators for biological sample j. We assume Aj∼Multinomial). Thus, each biological sample j can only be assigned to one pool m for all pools. Similarly, we also impose the constraint q biological samples. We further assume that the Ajk are independent of the Uj. This assumption makes sense if one randomly assigns the n biological samples to m pools.Suppose there is no pooling. Let L per pooled library (determined in advance), then pooling of, for example, q=2 biological samples A and B with depths LA and LB, takes place by mixing wALA and wBLB amount of RNA molecules (0≤wA≤1 and wB=1−wA) from sample A and B, respectively. That is, we mix wA and wB fractions of the RNA molecules from biological sample A and B, respectively. We consider the mixing weights as random variables and account for their contribution to the variability of the pooled outcome. To formalize this, let the random variable Wjk denote the mixing weight for biological sample j in pool k. For a given pool k, we have a q-dimensional vector of these fractions, q-component symmetric Dirichlet distribution for mixing weights, i.e. Wk∼Dirichlet(J), where J is a q-dimensional vector of 1s. Consequently, the expected proportion of RNA molecules to be pooled becomes E{Wjk}=1/q. For the previous example of pooling two biological samples A and B, the expected mixing weight is 50% from each sample.If one aims at a sequencing depth of Uj are unobservable random variables, and hence we sometime refer to them as virtual counts. Therefore, the data generating model for the observable gene expression measurement Yk from pool k=1,…,m with pool size q>1 can be written asIn pooled experiments, εk is an error term which represents the extra technical variability introduced by the pooling of RNA samples. We assume that εk is independent of Ajk, Wjk and Uj, with εk∼Normal.where Yk is the weighted sum of the virtual counts Uj from the q biological samples in pool k. Under the assumption that Uj, Ajk and Wjk are independent random variables, the expectation of the gene expression measures in pooled sample k becomesModel  indicateJk is the set the indices j for biological samples included in pool k (i.e. Jk={j: Ajk=1}). This indicates that the expected gene expression measurements in a particular pool is equal to the average of the expected expression levels from the q biological samples included in that pool. The variability of the gene expression levels in pool k, accounting for the sampling variability (Aj), becomeswhere Yk} is inversely proportional to the pool size q, suggesting that pooling reduces the variability of the gene expression measurements, given σ2 is sufficiently small. However, the amount of variability reduction depends on the level of variability among the Uj  between two independent groups. The LFC is a quantity that is commonly used to calibrate the biological effect of interest. The LFC is defined as ρ1 and ρ2 are the relative abundances in groups 1 and 2, respectively. Although pooling results in expression levels with a lower variance, the variance of the estimates of the relative abundance (q/(q+1) times higher than that of the estimates from standard experiments (see H0:θ=0 (no DGE) against the alternative HA:θ≠0 at α level of significance can be lower in pooled experiments than in standard experiments (the full budget experiment). Based on the negative binomial assumption for the virtual counts Uj, we can determine the statistical power of testing the above hypothesis for a particular gene [n1 and n2, respectively), pool size q, the LFC to be detected θ, and the over-dispersion ϕ, the power of the two-sided likelihood-ratio test at significance level α can be calculated as,The mean expression of a gene lar gene . That isΦ(.) is the standard normal cumulative distribution function, Zα/2 is the (1−α/2)100% quantile of the standard normal distribution, and V0 and VA are the variances of the LFC estimate H0 and HA, respectively. The details of the power calculation can be found in where ϕ=0.5) and large LFCs (θ=1), all strategies seem to be equally effective. In such scenarios, it can be suggested that reducing the number of samples (strategy B) or pooling with a large pool size can be used to optimize the cost with comparable power to the reference design.In Fig. qk denote the pool size in pool k, then the variance of the LFC estimate in the pooled experiment q is substituted by the fraction n/m, where, as defined earlier, m and n are the number of pools and RNA samples in a given group, respectively.The same conclusion can be drawn when different pool sizes are used across pools. That is, let function  can be uTo evaluate the pooling strategy compared to the standard procedure, two sets of scenarios were investigated, one starting from the tumor tissue RNA-seq data and one from the cell line RNA-seq data, representing typical data with high and low within-group variability, respectively .n1 = 40) and MYCN non-amplified (n2 = 40). The average sequencing depth per sample in this data is approximately 20 million reads with a range 11−30×106. Subsequently, the data for the test scenarios were generated from the reference scenario according to the data generation model in , respectively. MAD s evaluates the risk associated with using scenario s in terms of losing DE signals that would be detected at the full budget design (A0). The observed LFC bias was generally low across the scenarios  for scenario ios Fig. -panel C.Reducing the within-group variability by pooling RNA samples may enhance the resolution of the biological effect. This can be seen from the standardized LFC estimate 2 groups , and theconcordance with the reference scenario . Scenarios A3 and A4 from edgeR, and scenario A3, A4, B1, B3, C1 and C3 from limma-voom, resulted in more than 75% concordance (q=2) with 10M reads per library and 2 ×20 replicates or C1 (q=4) with 20M reads per library and 2 ×10 replicates) one can increase the chance of recovering the DE genes that would be detected with the full budget design. We will formally examine the false and true positive proportions in a simulation study in the subsequent section. Furthermore, the characteristics of the genes that are exclusively called DE in each test scenario are quite different. Pooling scenarios tend to favor low-abundance genes with high coefficients of variation (as determined based on the data from the reference scenario). In contrast, the non-pooling scenarios are biased towards highly expressed genes with small variability  in the reference scenario, followed by pooling scenarios B1, B3, C1, and C3 (limma-voom) and B1, A3 and A1 (edgeR) . To gaincordance . Overalliability . This req=2, scenario B) and 3 pools of 3 cell line samples . The results indicate that scenarios B and C come with higher power but also with somewhat higher cost (due to extra RNA preparation) compared to scenario A. The benefit is especially true for low abundant genes with a small LFC (θ) (θ≥1), cell line sample pooling is not cost-effective.The results from the comparison of the second set of experimental scenarios with the NGP nutlin dataset (see Table  LFC (θ) . For medSPsimSeq R package (v1.0.0) [SPsimSeq simulates realistic RNA-seq data from a semi-parametrically constructed distribution of gene expression levels from a real RNA-seq data  and true positive rate  associated with testing of DGE in the experimental scenarios shown in Table (v1.0.0) . SPsimSeThe simulation results Fig.  indicateThe level of sensitivity for limma-voom differs among scenarios quite substantially with a range 20-75% and 55-95% at the 5% nominal FDR when the LFC of the DE genes is greater than 0.5 and 1, respectively. In particular, scenarios with equal number of libraries and pool size resulted in almost the same sensitivity, regardless of the sequencing depth difference. For example, scenarios B1 and B3 or C1 and C3 or A1 and A2, attained an equivalent level of sensitivity. For pooling scenarios B1, B3, C1 and C3, higher sensitivity is observed than for the non-pooling scenarios A1 and A2, even though A1 and A3 have twice the number of libraries than C1 and C3, or equal number of libraries to B1 and B3. This result points at the utility of an RNA sample pooling strategy to balance the number of replicates and variability. For edgeR, scenarios A0, A3, A4, B1, B3, C1 and C3 show comparable sensitivities for both LFC thresholds. Although the actual FDR is overall not well controlled by edgeR, it is relatively lower for the pooling scenarios B1, B3, C1, and C3 with maximal sensitivity.Furthermore, for limma-voom, among the pooling scenarios, B1 and B3 are less than 10% off from the maximum sensitivity achieved with the reference scenario Fig. -panel A.Generally, the simulation and empirical results align with each other and support the theoretical predictions in the sense that the reduction in the number of replicates (libraries) comes with the cost of losing sensitivity. However, both results demonstrate that pooling strategies compensate the performance offset caused by sample size reduction. In addition, the choice of DGE tool is a critical factor for DGE analysis with pooled experiments. For example, edgeR focuses on maximizing the sensitivity to detect true DE genes with liberal performance in terms of the FDR control. Therefore, if one aims at only highlighting all possible candidate DE genes, then edgeR can be a good choice for pooling experiments with relatively flexible choice of pooling designs. On the other hand, limma-voom guarantees control of the FDR for all design choices but its sensitivity is strongly dependent to the number of replicates. Therefore, if one aims at maximizing the sensitivity with the actual FDR within the tolerance range and pooling does not result in too much reduction of the sample size, then limma-voom is a good choice for pooled experiments.The experimental scenarios in Table 6) but different numbers of libraries . As a result, A2 is seven ranks below A3 , pooling strategies are robust and cost-effective for genes with low to moderate level of expression, for which most statistical methods fail to perform optimally [W in a given pool follow a Dirichlet distribution. Of note, the parameters of the Dirichlet distribution, α1,…,αq, are all set to 1, resulting in maximum variability representing the worst-case-scenario. The independence assumption between the allocation of RNA samples to pools (denoted by a random variable A) and the gene expression levels U is not a strong assumption either, and can be effectively achieved by random allocation of RNA samples to pools. Similarly, the assumption between W and U holds if the mixing weights in a pool are determined independently of the transcriptome size in each biological sample. As sample pooling should be done at the RNA level, the transcriptome size is not know in advance, and hence, pooling will be random and independent. Consequently, it is reasonable to assume that the random variables A, W and U are independent that drive the mean and variance of a pooled outcome in , hence lower library preparation and sequencing costs. We have also shown that the high level of noise associated with low-abundance genes, which challenge statistical tools for testing DGE, can be mitigated by pooling RNA samples. Pooled experiments can be valid alternatives for DGE analysis, in which the objective is highlighting genes with strong biological effect, such as large LFC. However, pooling in general does not guarantee better results unless the key elements of the pooling experiment are carefully chosen. That is, for pooling to be equally effective to the standard RNA-seq experiment, it is essential to carefully determine the pool size, the number of pools, and sequencing depth depending on the level of variability and the number of RNA samples. The choice of a statistical tool for testing DGE is another essential part of designing pooled experiments. One of the apparent drawbacks of pooling experiments is the reduction of the number of replicates, which most statistical methods strongly rely on for optimal performance. However, our results demonstrate that pooling has the potential to compensate for the loss of the number of replicates by reducing the within-group variability unless the pooling strategy results in too much reduction of the number of replicates. Of note, pooling might not be beneficial when the gene expression levels display low variability, as, for example, in experiments with cultured cells.εk, which, however, showed a negligible effect on the statistical power for testing DGE. Finally, we wish to note that one single experiment with pooling of RNA samples does not have the capacity to confirm or contradict the theoretical findings in our study. In reality, a few dozen experiments should be performed, but this is beyond the scope of this study. We recommend further research with real pooling of RNA samples to verify the theoretical results presented in our computational study.One limitation of our study is that the demonstrated utilities of a pooling strategy were based on proper pooling and sub-sampling from a real read count matrix from a particular experimental design. In practice, however, pooling experiments would involve pooling of the RNA molecules before library preparation, and hence extra technical variability resulting from pooling could be anticipated. This extra variability is represented in the data generation model  by an adWe have shown that the utility of an RNA sample pooling strategy depends on the choice of the pooling parameters, such as the pool size and the number of RNA samples. Since the cost of RNA sample preparation is relatively low, one may consider using as many RNA samples as possible to capture the heterogeneity of the population under study, and using an adequate pooling strategy, one can substantially reduce the cost of the subsequent steps, which are considerably more expensive, and maintain the power of a DGE test. In particular, for scenarios with a high biological variability, a small pool size such as 2 can be effective to optimize the cost of the experiment and maintain the power that one would attain without pooling. Unlike the typical cost-saving strategies, such as reducing the sequencing depth or number of RNA samples (replicates), an adequate pooling strategy is particularly effective for scenarios with many genes with low and moderate levels of expression. We have demonstrated that pooling RNA samples or pooling RNA samples in conjunction with moderate reduction of the sequencing depth can be good options to further optimize the cost of the experiment without much loss of the power of the DGE test. The findings discussed in this paper can be useful for designing future experiments under possible constraints, such as limited budget, large biological variability, or insufficient RNA input.n1 = 91) and MYCN non-amplified (n2 = 81) tumours. Further details about the Zhang data can be accessed in [Two publicly available bulk RNA-seq datasets were used in this study. The first is from Zhang et al.  (GEO accessed in . The secessed in  (GEO accessed in  (v1.7) aplified n = 81 tumlog2-counts per millions of reads. These tools also exhibit different performance with respect to their false-discovery rate control and sensitivity [edgeR [limma [For testing DGE, edgeR  and limmsitivity . edgeR iy [edgeR  R BioconR [limma  R BioconSPsimSeq [We used the Zhang neuroblastoma RNA-seq data as source for the SPsimSeq  simulatiAdditional file 1 Supplementary result. This file constitutes of supplementary figures directly referred in this paper as well as the details of the data generating model for pooled experiments and power calculation.Additional file 2 multiQC report for NGP nutlin dataset. This file contains the quality metrics generated using the MultiQC tool for the NGP nutlin RNA-seq data.


Caenorhabditis elegans to identify putative genes that interact with topoisomerase II during meiosis. Using a temperature-sensitive allele of topoisomerase II, top-2(it7ts), we identified eleven suppressors of top-2-induced embryonic lethality. We used whole-genome sequencing and a combination of RNAi and CRISPR/Cas9 genome editing to identify and validate the responsible suppressor mutations. We found both recessive and dominant suppressing mutations that include one intragenic and 10 extragenic loci. The extragenic suppressors consist of a known Topoisomerase II-interacting protein and two novel interactors. We anticipate that further analysis of these suppressing mutations will provide new insights into the function of Topoisomerase II during meiosis.Topoisomerase II is an enzyme with important roles in chromosome biology. This enzyme relieves supercoiling and DNA and RNA entanglements generated during mitosis. Recent studies have demonstrated that Topoisomerase II is also involved in the segregation of homologous chromosomes during the first meiotic division. However, the function and regulation of Topoisomerase II in meiosis has not been fully elucidated. Here, we conducted a genetic suppressor screen in  Drosophila, and our recent studies in C. elegans demonstrate that Topo II plays a role in homologous chromosome segregation at Meiosis I . At the nonpermissive temperature (24°), top-2(it7ts) disrupts the segregation of homologous chromosomes during the first meiotic division resulting in chromatin bridges during spermatogenesis. Chromatin bridging leads to a failure in the proper segregation of homologous chromosomes during Meiosis I resulting in anucleate sperm and embryonic lethality after fertilization , for our screen. With this approach, we report the identification and characterization of 11 suppressors of top-2(it7ts) embryonic lethality. The suppressors include both dominant and recessive alleles and are found both in intragenic and extragenic loci. We used one-step whole-genome sequencing (WGS) and single-nucleotide polymorphism (SNP) mapping to identify the responsible mutations for all 11 of the suppressor lines. This approach identified genes/proteins that have been previously shown to interact with Topo II in other systems as well as two novel interactors.We sought to identify putative genes that interact with  pathway . In addi pathway . For theC. elegans strains (Table S1) were maintained using standard conditions (nditions .unc-4(e120) top-2(it7ts) L4 hermaphrodites were soaked in 48 mM EMS for 4 h at 15°, washed three times in M9, placed on a fresh 100 mm MYOB plate with OP50 and allowed to recover for 4 h at 15°. 25 L4s (P0s) were picked to each of 40 100 mm MYOB plates with OP50 and incubated at 15°. ∼60 h later the P0s were removed from the plates. Gravid F1 adult progeny  were subjected to hypochlorite treatment to isolate F2 embryos. The F2 progeny were grown at 24° and their progeny were screened for viable animals. Eleven stable lines from independent P0s were identified.top-2(it7ts) suppressors were determined by Hawaiian single-nucleotide polymorphism (SNP) mapping and whole-genome sequencing (WGS) , SAMtools  suppressor was either a dominant or recessive mutation, each suppressor line  was crossed with top-2(it7ts); him-8(e1489) males at 15°. Non-unc L4 cross progeny  were picked to individual plates for embryonic viability assays at 24°. Nonviable progeny (<10% viable) indicated that the suppressing mutation was recessive and viable progeny indicated that the suppressing mutation was dominant (>50% viable) or semi-dominant (10–49% viable).To determine whether each unc-4(e120) top-2(it7ts)/unc-4(+) top-2(it7ts); sup/+] from the above cross were picked to new plates and allowed to produce self-progeny at 15°. 16-48 Unc F2 progeny  were picked to individual plates for embryonic viability assays. Dominant suppressors were determined to be unlinked from top-2 if 3/4 of F2 animals had viable progeny. Recessive suppressors were determined to be unlinked from top-2 if 1/4 of F2 animals had viable progeny. Suppressors were determined to be linked if all F2 animals had viable progeny. These results were confirmed by WGS/SNP analysis.To distinguish between linked and unlinked suppressor mutations, F1 progeny .RNA was introduced into worms using the feeding method of (dpy-10 as a co-CRISPR marker (ude5 and ude6 candidate mutations in KK381 [unc-4(e120) top-2(it7ts)]. All injections were done using an injection mix of Cas9 protein (25 μg), dpy-10 crRNA , dpy-10(cn64) repair oligonucleotide (0.2 μg), universal tracrRNA , an allele-specific crRNA (8 μg) and an allele-specific repair oligonucleotide (3.8 μg). To recreate the top-2(ude6) allele, which changes Asp809 to Asn, a crRNA targeting the sequence AATCTCGCTCAAGATTACGTTGG and a repair template (GGAGAACAGTCGCTTATGGGAACAATTGTGAATCTAGCTCAGAATTATGTCGGCTCCAACAACATCAACCTGCTTCTTCCAATCGGAC) with sequence to edit codon 809 (bold), mutations to prevent additional cleavage by Cas9 , and an AluI recognition site (underlined) for genotyping were used. tdpt-1(ude5), which changes Gly270 to Asp, was recreated using a crRNA targeting the sequence CCGGGCGCCCTCGTCTTTTT and a repair template (GTCCGTGAAATCATCGCTCAAAACCCGGGCGCCCTGGTGTTCTTTGGCGACGATTTAAACTTACGAGACGAGGAGGTCAGCCGTGTGCCTGACG) with sequence to edit codon 270 (bold), mutations to prevent additional cleavage by Cas9 , and a DraI recognition site (underlined) for genotyping.CRISPR-mediated genome editing via the clone-free method  plus 0.1% Tween 20. Following dissection and extrusion of the gonads, 15 μl of the Egg buffer was removed and discarded. 15 μl of 2% paraformaldehyde solution in 1X Egg buffer plus 0.1% Tween 20  was then added to the slide. This was followed by removal of 15 μl of the paraformaldehyde/Egg/Tween solution and a Superfrost Plus microscope slide (Fisher Scientific) was placed on top. The samples were allowed to fix for 5 min and then frozen on dry ice. The slides were then placed in -20° methanol for 1 min and washed three times in PBST  for five min each. Slides were incubated in the dark with DAPI (2 μg/ml) for 5 min followed by a final wash in PBST for 5 min. Slides were mounted with Vectashield (Vector Laboratories) and the edges sealed with clear nail polish.Hermaphrodites were synchronized by picking L4s and incubating at either 15° or 24° for 16-24 h. Gonads were dissected on a coverslip in 30 μl 1X Egg buffer . For each sample, the appropriate range of focal planes for z-stack projections was chosen with a constant slicing of 0.2 μm. Image processing and analysis was done via Fiji Is Just ImageJ  as a suppressor of top-2(it7ts) embryonic lethality . This could be due to insufficient knockdown of top-3. We delivered the double-stranded RNA (dsRNA) to the animals using the RNAi feeding method  top-2(it7ts) background did not suppress embryonic lethality  top-2(it7ts). The combined knockdown of viln-1 and nlp-38 in unc-4(e120) top-2(it7ts) at 24° resulted in 6.9% embryonic viability, however, this was not statistically significant  animals did not decrease embryonic viability . From these data, RNAi in the unc-4(e120) top-2(it7ts) background did not positively identify either viln-1 [R552C] or nlp-38 [G77R] as the suppressing mutations. However, as the ude16 suppressing mutation is dominant, we reasoned that it may also be a gain-of-function mutation. To that end we performed RNAi in the suppressed line to determine if gene knockdown would reverse suppression. Using this approach, we found that RNAi of viln-1, nlp-38, and the viln-1/nlp-38 combination did not reverse suppression (Table S2). This suggests that WGS has not identified the correct suppressing mutations or that RNAi knockdown of these genes is not efficient in eliciting the suppressing phenotype. Additional studies will need to be performed to determine the identity of the suppressing mutations of the ude16 suppressor.For tdpt-1 result in top-2(it7ts) suppression of embryonic lethality, we used CRISPR/Cas9 genome editing to recreate one of the identified tdpt-1 mutations. We identified three suppressors that resulted in missense alleles at amino acid 270 of TDPT-1, suggesting that this site is very important for TDPT-1 function. For this reason, we recreated the tdpt-1(ude5) and tdpt-1(ude24) G270D missense mutation in the unc-4(e120) top-2(it7ts) strain originally used for the suppressor screen. We generated one line and assigned it the allele designation ude22. While unc-4(e120) rol-1(e91) WT control viability was 94.2%, embryonic viability of unc-4(e120) top-2(it7ts) was 0.6% at 24° (tdpt-1 G270D mutation  was 72.2% (tdpt-1 can suppress top-2(it7ts)-induced embryonic lethality.As further validation that mutations in % at 24° . Embryonas 72.2% . We conctop-2(ude6) D809N allele in the unc-4(e120) top-2(it7ts) line. We recovered two independent, edited lines, one linked and one unlinked from dpy-10, and assigned them the allele designation ude20. Embryonic viability of unc-4(e120) top-2(it7ts) was 0.6% (at 24°) while embryonic viability of the recreated lines were 56.5% and 88.7% respectively  have a round and compact morphology (unc-4(e120) top-2(it7ts) post-meiotic sperm have abnormal chromosome morphology with chromatin bridges  mutation. Even though the primary defect of top-2(it7ts) is a failure to localize to meiotic chromosomes, this study demonstrates that top-2(it7ts) also has a defect in TOP-2 protein stability and that some of the suppressors can restore TOP-2 protein stability. Future studies will examine the mechanisms of suppression for the validated suppressors.Next, we assessed trol N2,  and 4B. n levels  and 4B. top-2(it7ts) embryonic lethality. We identified the molecular lesion responsible for suppression of nine of the suppressor candidates via whole-genome sequencing and single-nucleotide polymorphism mapping and verified either through RNAi phenocopy or through CRISPR/Cas9 recreation of the suppressing mutation. The genes identified in this screen comprise different functional classes including chromatin remodeling, DNA repair, and protein structure/stability. The intragenic mutation, ude6, most likely helps restore protein structure and stability as TOP-2 protein levels are decreased in the top-2(it7ts) mutant line and restored in the top-2(it7ude6) suppressor line (MEP-1 and NURF-1 are both involved in chromatin remodeling and interact genetically. TDPT-1 is involved in the removal of TOP-2 protein complexes and DNA repair. How these proteins interact with TOP-2 in the context of homologous chromosome segregation during meiosis is unknown. Future studies will help determine the mechanism of suppression of these different mutations and will elucidate the genetic pathways in which these genes interact with top-2 during meiosis.In summary, we identified 11 genetic suppressors of sor line . MEP-1 a


Cur-PLGA-DPPs were prepared using a top-down fabrication method. The prepared Cur-PLGA-DPPs had a mean particle size of 2.5 ± 0.4 μm and a zeta potential value of −34.6 ± 4.8 mV. Ex vivo biodistribution results showed that the Cur-PLGA-DPPs mainly accumulated in the lungs and liver after intravenous injection. Treatment with Cur-PLGA-DPPs effectively suppressed the infiltration of inflammatory cells in bronchoalveolar lavage fluid, and reduced bronchial wall thickening and goblet-cell hyperplasia compared to those in the phosphate-buffered-saline-treated control group. No significant changes in hematology and blood biochemistry parameters were observed after treatment with Cur-PLGA-DPPs. At equal curcumin concentrations, treatment with Cur-PLGA-DPPs exhibited better therapeutic efficacy than treatment with free curcumin. Our results suggest that the microscale Cur-PLGA-DPPs can be potentially used as a lung-targeted asthma therapy.Curcumin is considered a potential anti-asthmatic agent owing to its anti-inflammatory properties. The objective of the present study was to prepare curcumin-containing poly(lactic- Asthma is a chronic inflammatory disease of the lungs that is characterized by reversible airway obstruction, airway hyper-responsiveness, and airway inflammation ,2. ChronCurcuma longa Linn.). Previous studies have demonstrated its antioxidant, antimicrobial, anti-inflammatory, and anticarcinogenic properties. These functions are achieved via the modulation of transcriptional factors, inflammatory cytokines, protein kinase, enzymes, signaling receptors, growth factors, cell-surface adhesion molecules, and anti-apoptotic proteins [Curcumin is a natural polyphenol extracted from the rhizomes of turmeric  (PLGA) particles are the most commonly applied vehicles for drug delivery, owing to their biodegradability and biocompatibility [Various drug-delivery systems, including liposomes, nanoparticles, microemulsions, cyclodextrin complexes, solid dispersions, and micelles, have been tested to improve the in vivo therapeutic applications of curcumin ,16,17,18tibility . Previoutibility ,21.The size and shape of particles can affect their biological behavior in vivo ,23. Decuco-glycolide) ((PLGA); Resomer® RG503H, acid terminated, molecular weight: 24–38 kDa, lactide:glycolide ratio = 50:50), poly (PVA) , ovalbumin , and curcumin  were purchased from Sigma-Aldrich . Polydimethylsiloxane (PDMS) (Sylgard 184 Silicone Elastomer Kit) was purchased from Dow Corning . Cy7-labeled PLGA  was obtained from RuiXi Biological Technology .All reagents were commercially available and were used as purchased. Poly aqueous PVA solution to the surface of the PDMS template. Subsequently, it was dried at 30 °C and peeled off after 15 h. PVA templates showed the same patterns as the silicon master template. PLGA (60 mg) was dissolved in 100 μL of dichloromethane–chloroform . To this solution, 60 mg of curcumin (dissolved in 100 μL DMSO) was added, followed by stirring to ensure homogenization. The curcumin–PLGA mixture was evenly applied to the wells on the PVA template using a razor and then dried at room temperature for 5 min. The PVA template was dissolved in deionized water for 3 h, and the solution was passed through a 100 µm nylon filter to remove large debris. The pellets were collected by centrifugation at 1000× g for 2 min.Microsized Cur-PLGA-DPPs were prepared using a top-down fabrication method, as described previously ,26. BrieParticle size distribution was measured using a Multi-Sizer Coulter Counter . The zeta potential and particle size of the Cur-PLGA-DPPs were determined using a Zetasizer Nano ZS90  instrument after dilution with deionized water (1 mg/mL). The surface morphology of the Cur-PLGA-DPPs was examined using a JEOL-7800F field emission scanning electron microscope , with an accelerating voltage of 0.8 kV. Fluorescence images of Cur-PLGA-DPPs were acquired using a Zeiss LSM-700 confocal laser scanning microscope .Drug loading (percent) and release of Cur-PLGA-DPPs were measured using a Synergy™ HTX multimode microplate reader . Before measurement, a standard calibration curve of curcumin was prepared using six standard solutions (7.8–250 μg/mL). To measure loading efficiency, 1 mg of lyophilized curcumin DPPs was dissolved in 1 mL DMSO. The suspension was vigorously vortexed, and the absorbance of the mixture was measured at 420 nm. The percentage of drug loading (%DL) was determined using the following formula:Drug release of Cur-PLGA-DPPs was determined using a dialysis method in phosphate-buffered saline ((PBS); pH 5.5 and pH 7.4) containing 2% Tween-80. The pH of the PBS solution was adjusted to 5.5 using 0.1 M acetate buffer. Tween-80 was added to the PBS solution to counteract the low aqueous solubility of curcumin. One milligram of lyophilized Cur-PLGA-DPPs, dissolved in 1 mL PBS, was placed in a 10 kDa cut-off dialysis membrane tube . The bag was immersed in 10 mL PBS containing 2% Tween-80 and was kept at 37 °C under constant stirring at 140 rpm. At designated time intervals, 100 μL samples were removed from the incubation medium, and the same volume of PBS solution was added. The amount of curcumin released from Cur-PLGA-DPPs was analyzed using a multimode microplate reader, as detailed above.v/v) mouse serum at 37 °C was assessed for up to 72 h. As a control, the in vitro stability of Cur-PLGA-DPPs in PBS (pH 7.4) was recorded. Samples were collected at 0.5, 1, 3, 6, 12, 24, 48, and 72 h, and the concentration of Cur-PLGA-DPPs was monitored using a Multi-Sizer Coulter Counter (Beckman Coulter Inc.).The stability of Cur-PLGA-DPPs in 50%  for up to 2 h. Particle size and distribution were measured by dynamic laser light scattering (DLS) at 25 °C using a Zetasizer Nano ZS90  instrument.Female Balb/c mice, aged 6–8 weeks, were obtained from Orient Bio Inc.  and kept under standard conditions with air filtration. The murine asthma model was established using ovalbumin (OVA), as reported previously, with minor modifications . BrieflyOn Day 21, OVA-induced asthmatic mice were injected in the tail vein with 500 μg Cy7-labeled Cur-PLGA-DPPs and were sacrificed at 1 h, 3 h, 6 h, 1 day, 3 days, 5 days, and 7 days (three mice at each time point) after injection. Liver, muscle, spleen, heart, lung, and kidney tissues were collected, and ex vivo imaging was performed using the in vivo Imaging System (IVIS) Spectrum . Images were analyzed using the IVIS imaging software (Perkin Elmer). OVA-induced asthmatic mice were fed an alfalfa-free diet for 14 days before bioluminescence imaging to reduce intestinal autofluorescence.Mice were assigned to OVA-induced asthma groups with different treatments and a PBS-treated control group. OVA-induced asthmatic mice were randomly assigned to three treatment groups : (i) an OVA group, (ii) an OVA + Cur-PLGA-DPP group (at a dose of 5 or 25 mg/kg), and (iii) an OVA + curcumin group . Starting from Day 21, the animals received weekly injections of 0.9% NaCl, Cur-PLGA-DPPs, or curcumin via the tail vein, for six weeks. The mice in the control and OVA groups were treated with the corresponding volumes of 0.9% NaCl solution. After 1 h, all OVA-induced asthmatic mice received 1% OVA intranasally to maintain sensitivity.® EDTA-coated tubes and serum separator tubes . Hematological assays were performed using the HEMAVET® 950 hematology system . Serum levels of aspartate aminotransferase (AST), alkaline phosphatase (ALP), alanine aminotransferase (ALT), creatinine (CRE), and blood urea nitrogen (BUN) were measured using an automated biochemistry analyzer .After the six week treatment period, blood was collected via cardiac puncture and was placed in Microtainerg at 4 °C for 10 min. Cell pellets were resuspended in PBS, and the number of nucleated cells was counted using a hematocytometer. To determine the differential cell count, BALF was centrifuged at 800 rpm for 3 min using a Cytospin cytocentrifuge® 3 . Slides were stained using a Diff-Quik® staining kit . In total, 400 nucleated cells per slide were examined, in a minimum of two different fields of view, using an Olympus BH-2 microscope .After six weeks of Cur-PLGA-DPP treatment, bronchoalveolar lavage fluid (BALF) was collected as described in a previous study, with a minor modification . BALF waAfter the Cur-PLGA-DPP treatment, the left lung of each mouse was harvested and was then fixed using 4% paraformaldehyde, embedded in paraffin, and sectioned at 5 μm thickness. Lung sections were stained using hematoxylin and eosin (H&E) and periodic acid-Schiff (PAS), using standard histological methods. Stained histology slides were scanned using an Aperio AT2 Digital Whole Slide scanner .t-test. Statistical significance is reported at p < 0.05.Data are shown as the mean ± standard deviation. Statistical analysis was performed using the GraphPad Prism 7 software . Treatment effects were tested using one-way analysis of variance and Student’s The mean sizes of PLGA-DPPs and Cur-PLGA-DPPs were 2.5 ± 0.7 μm and 2.5 ± 0.4 μm, respectively, and their zeta potential values were −27.7 ± 4.7 mV and −34.6 ± 4.8 mV, respectively . In geneA scanning electron micrograph of Cur-PLGA-DPPs revealed that the produced Cur-PLGA-DPPs were discoidal and had a smooth surface a. The moThe drug-loading efficiency in Cur-PLGA-DPPs of the optimized batch was 16.3 ± 0.1% . In vitrp < 0.001) in the mouse serum, with 51.6 ± 1.5% of Cur-PLGA-DPPs traceable in mouse serum after incubation for 3 h, and only 14.3 ± 0.7% traceable after incubation for 72 h. These in vitro stability data suggest that certain components of the mouse serum exerted a stronger effect on Cur-PLGA-DPPs than PBS.The stability of Cur-PLGA-DPPs in mouse serum and PBS was assessed at different time points after particle incubation at 37 °C. Cur-PLGA-DPPs were present in PBS at 87.5 ± 0.9%, 50.1 ± 0.6%, and 45.5 ± 1.1% after incubation for 1, 24, and 72 h, respectively b. HoweveCur-PLGA-DPPs were incubated at different pH values to examine the potential effects of pH on stability. DLS was used to monitor the size of Cur-PLGA-DPPs at 25 °C for up to 2 h. Our results confirmed that Cur-PLGA-DPPs remained stable at pH 6, 7, and 8 . HoweverTo investigate the ex vivo organ distribution of Cur-PLGA-DPPs, we injected OVA-induced asthmatic mice with Cy7-labeled Cur-PLGA-DPPs. Mice were sacrificed at predetermined time points, and their organs were imaged using an optical imaging system . Cy7-labTo investigate the potential toxic effects of Cur-PLGA-DPPs, hematological and biochemical analyses were conducted on normal Balb/c mice. Changes in serum AST, ALP, ALT, CRE, and BUN levels were measured. Each group was treated with different doses of Cur-PLGA-DPPs (5 and 25 mg/kg) and curcumin (4 mg/kg) for six weeks, as shown in p < 0.05). The total number of BALF cells in the OVA + 25 mg/kg Cur-PLGA-DPP-treated group was significantly lower than that in the OVA + 5 mg/kg Cur-PLGA-DPP-treated group (p < 0.05). Administration of 4 mg/kg curcumin also significantly reduced the total number of BALF cells, compared with that in the OVA group. However, asthmatic mice treated with OVA + 25 mg/kg Cur-PLGA-DPPs produced lower total cell numbers than mice in the 4 mg/kg curcumin-treated group (p < 0.05).The effects of Cur-PLGA-DPPs on the BALF total cell count and histological changes in the lung tissue were examined in OVA-induced asthmatic mice. Bronchoalveolar lavage is a useful method for monitoring inflammatory processes during various lung diseases . InfiltrHistological staining with H&E was performed to assess the infiltration of inflammatory cells into the lung tissue of asthmatic mice. OVA-challenged mice showed extensive inflammatory cell infiltration, with prominent eosinophil infiltration in the peribronchial and perivascular areas. In addition, peribronchiolar smooth muscle hyperplasia and fibrosis were observed a. HoweveTo investigate the anti-inflammatory effects of blank PLGA-DPPs, OVA-induced asthmatic mice were injected with 25 mg/kg blank PLGA-DPPs. H&E staining of lung sections showed massive infiltration of inflammatory cells in the peribronchial and perivascular areas . PAS staThe physical stability of polymeric particles is critical, because low stability can reduce drug bioavailability. In this study, in vitro serum stability and ex vivo organ distribution data indicated that the physical stability of Cur-PLGA-DPPs was relatively low. Various factors, including zeta potential, pH, particle size, ionic strength, molecular weight, and physicochemical properties, can affect the physical stability of polymeric particles ,35,36. ZThe loading amount of drugs and their chemical properties can also affect the physical stability of polymeric particles . In the A previous study suggested that microsized PLGA-DPPs are promising as a lung-targeted drug-delivery system . In the In the present study, microscale Cur-PLGA-DPPs were designed as a polymeric vehicle of curcumin to enhance in vivo efficacy. Cur-PLGA-DPPs were successfully produced using a top-down fabrication method. The in vitro release experiment showed sustained release of curcumin from Cur-PLGA-DPPs, and ex vivo biodistribution experiments indicated that Cur-PLGA-DPPs accumulated mainly in the lungs, liver, and spleen. Administration of Cur-PLGA-DPPs effectively reduced the number of inflammatory cells in BALF and lung tissue, and also reduced the thickness of the bronchial wall and goblet-cell hyperplasia in mice with OVA-induced asthma. No signs of toxicity were observed in mice treated with Cur-PLGA-DPPs. These findings demonstrate the potential of Cur-PLGA-DPPs as a drug-delivery system for lung-targeted asthma treatment.


Here, we show that long non-coding RNA Hotairm1 converts MDSCs from an activator to a repressor state. Mechanistically, increased Hotairm1 expression in MDSCs in mice converted S100A9 from a secreted proinflammatory mediator to an immune repressor by binding to and shuttling it from cytosol to nucleus during late sepsis. High Hotairm1 levels were detected in exosomes shed from MDSCs from late septic mice. These exosomes inhibited lipopolysaccharide-stimulated secretion of S100A9 from early sepsis Gr1+CD11b+ cells. Importantly, Hotairm1 knockdown in late sepsis Gr1+CD11b+ MDSCs prevented S100A9 cytosol to nuclear transfer and decreased repression of proimmune T cells. Notably, ectopic expression of Hotairm1 in early sepsis Gr1+CD11b+ cells shuttled S100A9 to the nucleus and promoted the MDSC repressor phenotype. In support of translating the mechanistic concept to human sepsis, we found that Hotairm1 binds S100A9 protein in CD33+CD11b+HLA-DR− MDSCs during established sepsis. Together, these data support that Hotairm1 is a plausible molecular target for treating late sepsis immune suppression in humans and its immune repressor mechanism may be cell autonomous.Myeloid-derived suppressor cells (MDSCs) expand during mouse and human sepsis, but the mechanism responsible for this is unclear. We previously reported that nuclear transport of S100A9 protein programs Gr1 Life threatening sepsis dysregulates the host response to uncontrolled infection . The imm+CD11b+ myeloid cells generated during acute sepsis are proinflammatory , 150 mM NaCl, 1% NP-40, 0.25% sodium deoxycholic acid, and 1 mM EDTA  plus 1X protease inhibitor cocktail. After 30 min on ice, cell lysates were cleared by centrifugation for 5 min at 4°C and 14,000 rpm. Protein concentrations were determined by Bradford assay (Bio-Rad), and aliquots were kept at −20°C.For whole cell lysates, the Gr1Cytoplasmic and nuclear protein extracts were prepared using the NE-PER nuclear and cytoplasmic extraction kit  per the manufacturer's instructions. Immediately after harvesting, the cells were washed in PBS and resuspended in CER1 lysis buffer with protease inhibitor cocktail and incubated on ice for 1 min. CER2 buffer was added, and the incubation continued for 5 min. Supernatants (cytoplasmic proteins) were removed by centrifugation for 5 min at 4°C and 14,000 rpm. The nuclear pellets were resuspended in NER lysis buffer with protease inhibitor cocktail and incubated for 40 min on ice with occasional vortexing. The nuclear proteins were recovered by centrifugation for 10 min at 4°C and 14,000 rpm. Protein aliquots were kept at −20°C.+CD11b+ cells (~10 × 106) were washed with warm PBS and incubated with 0.2% formaldehyde (in PBS) for 10 min at room temperature, to preserve RNA-protein complexes. Cross-linked cells were incubated for 30 min on ice in lysis buffer containing: 250 mM sucrose, 10 mM Tris-HCl [pH 7.5], 25 mM KCl, 5 mM MgCl2, 2 mM DTT, 0.5% NP-40, 0.5% deoxycholate, 30 U/ml RNase inhibitor, and 1x protease inhibitor cocktail. After DNase I treatment for 10 min at 37°C, cell lysates were cleared by centrifugation at 10,000 rpm for 10 min at 4°C. Cell lysates were immunoprecipitated with anti-S100A9 antibody  according to a published method [RNA immunoprecipitation was performed to detect Hotairm1 RNA binding to S100A9 protein. Briefly, Gr1d method . AliquotProtein extracts or immunoprecipitated protein complexes were resolved by electrophoresis using SDS-10% polyacrylamide gel  and transferred to nitrocellulose membranes . Membranes were blocked with 5% milk in Tris-buffered saline/Tween-20 for 1 hr at room temperature, and then probed overnight at 4°C with anti-S100A9 antibody . After washing, blots were incubated with the appropriate HRP-conjugated secondary antibody for 2 hr at room temperature. Proteins were detected with the enhanced chemiluminescence detection system , the bands were visualized using the ChemiDoc XRS System (Bio-Rad), and the images were captured with the Image Lab Software V3.0. Membranes were stripped and reprobed with b-actin or nucleoporin antibody (Sant Cruz Biotechnology).Hotairm1 was cloned in pReceiver-M02 expression vector downstream of the CMV promoter. An empty control vector (pReceiver-M02CT) served as a negative control .+CD11b+ cells were transfected according to the manufacturer's instructions and incubated with RPMI-1640 medium for 24-36 hr.For Hotairm1 expression, Hotairm1 plasmid was suspended in HiPerFect reagent at a 0.5 μg/ml final concentration . For Hotairm1 knockdown, Hotairm1-specific or scrambled siRNAs were suspended in HiPerFect reagent at a 0.5 μM final concentration. Gr1+CD11b+ cells, or S100A9 immunoprecipitates. Exosomes were purified from plasma or cell culture supernatants, and exosomal RNA was isolated using exoRNeasy Starter Kit (Qiagen). Total cellular RNA was isolated from Gr1+CD11b+ cells using TRIzol reagent (Invitrogen). Levels of Hotairm1 expression were measured using QuantiTect PCR Mastermix and RT2 lncRNAqPCR Assay primers (Qiagen). The expression level was calculated using the 2−ΔΔCt cycle threshold method. Values were normalized to GAPDH RNA for total RNA or 18S RNA for exosomal RNA, amplified with QuantiTect Primer Assays. The PCR was performed in duplicate.Real-time qPCR (RT-qPCR) was used to determine the levels of Hotairm1 in exosomes, Gr1+ T cells were purified from splenocytes by positive selection using biotinylated anti-CD4 magnetic beads (Myltenyi) and labeled with carboxyfluoresceindiacetate, succinimidyl ester (CFSE) dye using the Vybrant CFDA SE Cell Tracer Kit . The cells were incubated for 10 min at room temperature with 10 μM CFSE dye. Gr1+CD11b+ cells were isolated from the bone marrow of sham and septic mice and cultured for 24 hr in serum-free medium, and exosomes were purified from the culture supernatants as described above. The labeled CD4+ cells were cultured in 12-well plates with naive Gr1+CD11b+ cells in the presence of exosomes (50 μg/ well). In other experiments, CD4+ T cells were co-cultured in a 1:1 ratio with late sepsis Gr1+CD11b+ cells in which Hotairm1 was first knocked down for 36 hr with a pool of siRNAs. Anti-CD3 antibody plus anti-CD28 antibody (1 μg/ml each) were added to the cultures to stimulate T cell activation and proliferation. After 3 days, the culture supernatants were collected and used to measure IFN-ϒ levels by ELISA. The cells were harvested and CD4+ T cell proliferation was determined by the stepwise dilution of CFSE dye in dividing CD4+ T cells using flow cytometry.We used a co-culture system to test the effects of MDSC-derived exosomes or MDSCs lacking Hotairm1 on spleen CD4 T cell proliferation and activation. Splenocytes were isolated from naive wild-type mice. CD4+CD11b+cells was determined by measuring urea concentration (a by- product of arginase 1 activity) in the cell lysates using an arginase assay kit  as described previously [Arginase 1 activity in Gr1eviously . One uniEnzyme-linked immunosorbent assay (ELISA) kits were used to measure the levels of S100A9  and IFNϒ (eBioscience) in cell culture supernatants. Samples were run in duplicate.Data were analyzed with Microsoft Excel, V3.0. Data are expressed as mean ± SD. Differences among groups were analyzed by a two-tailed student's t-test for two groups and by one-way ANOVA for multiple groups. Statistical significance with p-values <0.05 are reported.in vivo and in vitro [+CD11b+ cells during late sepsis is associated with the development of MDSCs [+CD11b+ MDSCs could be used to identify mediators that promote S100A9 nuclear localization.Exosomes are nano-sized 30-100 nm) vesicles that are released to the extracellular space when fused with the plasma membrane ,30. Exos-100 nm vin vitro ,32. We hof MDSCs . Becauseof MDSCs ,33, we w+CD11b+ cells from bone marrow of early septic mice - where S100A9 resides in cytosol and is readily secreted [+CD11b+ cells isolated from sham or septic mice. Then, the cells were stimulated with gram-negative bacterial lipopolysaccharide (LPS) to induce S100A9 secretion. We used Gr1+CD11b+ cells from bone marrow of early septic mice because, unlike cells from late septic mice, they can secrete S100A9 protein following stimulation with LPS [+CD11b+ cells . Sixteen lncRNAs simultaneously increased or decreased by ~2- to 4-fold in early and late sepsis Gr1+CD11b+ cells. Notably, lncRNA Hotairm1  markedly increased in Gr1+CD11b+ cells after early sepsis. Using qPCR, we observed an 18-fold increase in Hotairm1 in exosomes from late sepsis Gr1+CD11b+ cells versus 2-fold in early sepsis cells  throughout sepsis, Gr1+CD11b+ cells that expand during early/acute sepsis for up to 5 days are not immunosuppressive [+CD11b+ cells become immunosuppressive, with higher levels of S100A9 protein in the nucleus [+CD11b+ cells as well as in exosomes derived from their culture compared with early sepsis Gr1+CD11b+ cells. In addition, late sepsis Gr1+CD11b+ MDSCs with Hotairm1 knockdown could not inhibit T cells activation and proliferation. These findings suggest that Hotairm1 plays a pivotal role in generating immunosuppressive Gr1+CD11b+ MDSCs after acute sepsis in mice and in established chronic sepsis in humans.S100A9 expression markedly increases during early/acute sepsis, but significantly decreases in late septic mice concurrently with its translocation to the nucleus in Gr1b+ MDSCs . In thisof MDSCs . Althougpressive ,25. In c nucleus . Importa+ MDSCs from patients with lung cancer [Hotairm1 is located at the 3' end of the HOXA cluster between HOXA1 and HOXA2 genes . Limitedg cancer . In thatg cancer ,39. In ag cancer . Togetheg cancer ,40.+CD11b+ MDSC repressor phenotype. Since genetic targeting of S100A9 in mice limits MDSC expansion and sepsis-associated immunosuppression, molecular targeting of Hotairm1 is a plausible drug target for reversing sepsis-induced chronic immunosuppression.In summary, we identified a novel molecular pathway leading to MDSC expansion during late/chronic sepsis. We discovered that following its increased expression, lncRNA Hotairm1 binds to and transfers S100A9 protein from the cytosol to the nucleus, where it promotes the development of Gr1Supplementary data


THOC5, which encodes a nuclear mRNA export protein implicated in M-CSF-driven macrophage differentiation. Knockdown of APOBEC3A reduces IL6, IL23A and IL12B gene expression, CD86 surface protein expression, and TNF-α, IL-1β and IL-6 cytokine secretion, and increases glycolysis. These results show a key role of APOBEC3A cytidine deaminase in transcriptomic and functional polarization of M1 macrophages.Pro-inflammatory M1 macrophage polarization is associated with microbicidal and antitumor responses. We recently described APOBEC3A-mediated cytosine-to-uracil (C > U) RNA editing during M1 polarization. However, the functional significance of this editing is unknown. Here we find that APOBEC3A-mediated cellular RNA editing can also be induced by influenza or Maraba virus infections in normal human macrophages, and by interferons in tumor-associated macrophages. Gene knockdown and RNA_Seq analyses show that APOBEC3A mediates C>U RNA editing of 209 exonic/UTR sites in 203 genes during M1 polarization. The highest level of nonsynonymous RNA editing alters a highly-conserved amino acid in  Alqassim et al find that RNA editing, known to be induced by IFN-1 in macrophages by the enzyme APOBEC3A, is required for the transcriptional, pro-inflammatory and metabolic responses that drive M1 macrophage polarization. APOBEC3A-mediated editing is also induced by IFN-1 exposure in tumor-associated macrophages isolated from ovarian cancer-related ascites fluid, pointing to a wide role for APOBEC3A in macrophages. Through phagocytosis, macrophages contain bacterial and fungal infection, clear debris and apoptotic cells, and modulate anti-viral and anti-tumor immunity2. Macrophages have a wide range of diversity in their phenotype and functions in response to environmental cues, such as microbial products, products of cellular injury, cytokines, and hypoxia3. The biological functions of macrophages are mediated by specific subpopulations that are polarized phenotypically by exposure to specific mediators. Interferon-γ (IFN-γ), TNF-α, or pathogen-associated molecular patterns (PAMPs) can activate pro-inflammatory M1 or classical polarization. M1 macrophages have potent microbicidal activity and release pro-inflammatory cytokines such as IL12 and TNF-α5. M1 macrophages augment T cell immunity required for control of various intracellular infections , and can enhance anti-tumor immunity. Alternatively activated (M2) macrophages are involved in anti-inflammatory and immunosuppressive activity through the release of IL10 and synthesis of mediators  that promote angiogenesis, tissue remolding and wound repair. M2 macrophages can be induced by fungal infections, various parasites, allergy, interleukin-4 (IL4), IL10, IL13 and tumor growth factor beta (TGF-β)8. While the M1/M2 classification does not capture the plasticity of macrophage phenotypes, it is a widely used designation to study mechanisms for macrophage polarization.Macrophages are tissue-localized myeloid cells that originate during embryonic development or from recruited circulating monocytes. Macrophages are known to be active secretory cells that regulate host defense, inflammation, and homeostasis. They can function as antigen-presenting cells that are involved in initiating specific T cell responses10. M1 polarization is generally characterized by induction of aerobic glycolysis (Warburg effect) along with high glucose uptake and pyruvate-to-lactate conversion rate. On the other hand, M2 macrophages use oxidative phosphorylation as the predominant source of energy with a lower rate of glycolysis compared to M1 macrophages. Furthermore, forced oxidative phosphorylation in M1 polarized macrophages can skew them to M2 polarization13.Mitochondrial function, reactive oxygen species (ROS), and metabolic changes modulate macrophage polarization. In general, M1 macrophages depend on glycolysis for ATP production, produce more ROS and accumulate succinate compared to resting macrophage14. microRNA (miRNAs), DNA methylation and histone modification influence macrophage polarization15. In addition to these well-characterized pathways for epigenetic modification, enzyme-regulated RNA editing may be another mechanism regulating macrophage responses. RNA editing is a posttranscriptional mechanism that alters transcript sequences, without affecting the encoding DNA sequences and increases protein sequence diversity16. By changing RNA sequences, RNA editing can generate protein diversity in single-cell organisms, plants, and animals16. Two main types of RNA editing have been recognized in mammals. One type involves adenine conversion to inosine (A>I) by adenosine deaminase enzymes and occurs in hundreds of thousands of sites located mostly in non-coding, intronic regions17. The other dominant type of RNA editing is C>U RNA editing which is catalyzed by the apolipoprotein B-editing catalytic polypeptide-like (APOBEC) family of cytidine deaminase enzymes19.Macrophage responses to environmental cues  are modulated by complex signaling pathways that include transcriptional regulation20. APOBEC3 proteins have a well-recognized role in inhibiting retroviruses and other viruses in in vitro systems and in restricting the intracellular transposition of retro-elements. This inhibition may be dependent or independent of the deaminase functions of APOBEC3s21. Recently, we identified the cellular RNA editing functions of APOBEC3A (A3A)22 and APOBEC3G (A3G)23. A3A and A3G each target specific cytidines located in putative stem-loop structures in distinct sets of transcripts24. RNA editing by A3A and A3G can be induced by exogenous over-expression in cell lines27. A3A-mediated RNA editing is endogeneously induced by IFN-γ during M1 macrophage polarization and by type 1 interferons (IFN-1) in monocytes/macrophages22. In addition to IFNs, hypoxia and cellular crowding induces endogenous C>U RNA editing by A3A in monocytes and by A3G in natural killer cells28. Hypoxia and IFN-1 additively increase A3A-mediated RNA editing in monocytes, leading to RNA editing levels of ~80% in transcripts of certain genes. A3A-induced or A3G-induced RNA editing by cellular crowding and hypoxia can be mimicked by the inhibition of mitochondrial respiration29. A3G-mediated RNA editing triggers a Warburg-like metabolic remodeling in HuT78 T cell lymphoma line23. These findings suggest that APOBEC3-mediated RNA editing may play a role in hypoxia-induced or IFN-induced cell stress response. Humans have 10 APOBEC genes, but only A3A mediates RNA editing in monocytes and macrophages22.The initially described activity of APOBEC-mediated C>U editing involved site-specific mRNA editing of ApoB, a protein required for the assembly of very low-density lipoproteins from lipids, by APOBEC1In this study, we hypothesized that A3A plays an essential role in macrophage functions during M1 polarization and in response to viral infections. Using primary human monocyte-derived macrophages, we observed A3A-mediated RNA editing during viral infections and M1 polarization with LPS and IFN-γ, and had broad effects on transcriptome, pro-inflammatory and metabolic responses that drive M1 polarization. These results demonstrate a key role for A3A-mediated RNA editing in modulating human macrophage function under ex vivo conditions modeling sepsis and viral infection, and set the foundation for investigating the role of A3A in patients during sepsis and other diseases associated with pathologic macrophage responses.22. Here, M0 cells were treated for 2 days with 20 ng/ml recombinant human IFN-γ and 50 ng/ml Escherichia coli LPS, or 20 ng/ml recombinant human IL4, for M1 or M2 polarization, respectively. In KD experiments, M0 macrophages were transfected with negative control scrambled (SC) or A3A siRNAs 1 day before induction of M1 polarization. To examine the induction of A3A-mediated RNA editing, we used the previously developed a RT-qPCR assay for the c.C136U (R46X) event in succinate dehydrogenase  subunit SDHB mRNA28. We first confirmed that the induction of SDHB c.C136U RNA editing occurs in M1 macrophages but not in M0 or M2 macrophages  of A3Ages Fig. . To evalles Fig. . These fhi expression and by cytology. Compared to untreated samples, we found that editing level was induced significantly in TAMs treated with IFN-1 . Paired RNA_Seq analysis was performed comparing M1 A3A SC and M1 A3A KD macrophages from the same donor.We next tested the extent that A3A mediated C>U RNA editing during M1 polarization. Monocyte-derived macrophages from three normal donors were transfected with A3A KD and A3A control SC siRNAs followed by induction of M1 polarization. Non-polarized macrophages (M0) were used as specificity controls. RNA editing of We first determined the distribution of RNA mismatches upregulated >2-fold in SC M1 vs. M0 and A3A KD M1 vs. M0, and found that KD of A3A caused greater decreases in C>T/G>A events than the other types of RNA mismatches  alters the last amino acid arginine, which is conserved in all sequences from 100 vertebrate genomes  of non-synonymous RNA editing in M1 A3A SC compared to M1 A3A KD macrophages  of RNA editing in ing Fig. . TogetheIL6, IL23A, and IL2B were among the most downregulated genes upon A3A KD in M1 macrophages , a marker of M2 polarization. The expression of inflammatory cytokine genes TNF, IL6, IL23A, IL18, and CXCL8 was also statistically significantly reduced by A3A KD  RNA editing. We found no differences in SDHB protein expression levels between M1 A3A KD and M1 A3A SC macrophages  replication Table . IFN-1 pses Fig. . MG1 MarTNF, IL6, IL18, and CXCL8, while also limiting the glycolysis in M1 macrophages. GO analysis showed that A3A down-regulated the glycolysis pathway and metabolic pathways involved in protein translation, glucose, amino acid, and lipid metabolism, and HIF-1 in M1 macrophages. These results demonstrate that A3A enhances the pro-inflammatory responses in M1 macrophages, likely at the expense of genes mediating metabolic responses. Given that A3A is located in the cytoplasm without translocating to the nucleus in normal monocytic cells36, and no evidence yet exists that A3A can selectively deaminate nuclear DNA in normal cells, these findings strongly suggest that A3A’s RNA editing function is responsible for metabolic, gene expression, cytokine secretion and surface marker expression changes associated with M1 polarization. Since A3A-mediated RNA editing is also induced by cellular crowding and hypoxia in normal monocytes22, we speculate that C>U RNA editing plays a general role in monocyte/macrophage stress response under a variety of physiologically relevant conditions.RNA-Seq analysis showed that A3A increases gene expression of pro-inflammatory cytokines like 37. Constitutive activation of IFN-1 signaling can cause tissue dysfunction and organ failure in certain diseases, such as Aicardi–Goutières Syndrome38. Our results support distinct roles for A3A in the adaptation of macrophages to specific stressors. In response to stressors LPS+IFN-γ, IFN-1 or viruses, A3A augments M1 polarization and pro-inflammatory responses  and APOBEC3A in immunity. AID and APOBEC3A are evolutionarily related cytidine deaminases43. We previously examined genomic DNAs corresponding to 23C>U RNA editing sites in primary human monocytes treated with hypoxia/IFN1 (n = 2 donors) and in 293T/APOBEC3A overexpression system (n = 2 replicates)22. Separately, we cloned PCR-amplified exon 2 of SDHB genomic DNA, which contains the RNA editing site, in 293T/APOBEC3A overexpression system and sequenced individual clones25. In both of these studies, we found no evidence of DNA mutations at sites corresponding to APOBEC3A-induced RNA-editing sites. Therefore, we consider cellular RNA editing as the principle mechanism for A3A modulating macrophage function.A3A-mediated DNA mutations were previously identified in human cancer cellsn = 209) than initially suggested by analysis of publicly available RNA_Seq data (n = 120) in M1 macrophages22, including the identification of THOC5 as the most nonsynonymously edited gene. THOC5 encodes a member of the THO complex that is part of the mRNA transcription/export complex44. THOC5 protein is phosphorylated by several tyrosine kinases such as the M-CSF receptor45. Tran et al. 46 showed that depletion of THOC5 in mouse bone marrow-derived macrophages downregulated 99 genes and suppressed M-CSF-mediated M2-like macrophage differentiation by inhibiting the export of M-CSF inducible genes. It is therefore conceivable that one mechanism by which A3A-mediated RNA` editing promotes M1 polarization is through physiologic suppression of THOC5, which leads to reduced M-CSF-regulated gene export. Our study opens new avenues of investigation of the molecular basis by which A3A regulates inflammation and metabolism in macrophages and the potential of A3A as a therapeutic target for diseases associated with pathologic inflammation.The mechanism by which cellular RNA editing by A3A promotes M1 polarization requires further investigation. C>U RNA editing by A3A may alter certain gene products or affect the stability or translatability of mRNAs, which in turn causes a cascade of downstream effects to facilitate the M1 phenotype polarization. Using high-coverage RNA_Seq data, we find a greater number of exonic/UTR C>U RNA editing sites (6/well in six-well plates with 50 ng/ml recombinant human macrophage colony-stimulating factor (M-CSF) , 1× GlutaMAX-I (Life Technologies), and 1 mM sodium pyruvate  to generate M0 macrophages. For M1 or M2 macrophage polarization, M0 cells were treated for 2 days with 20 ng/ml recombinant human IFN-γ (Life Technologies) and 50 ng/ml Escherichia coli LPS , or 20 ng/ml recombinant human IL4 (Life Technologies), respectively. RNA was isolated from cells using TRIzol™ Reagent 22. Research protocol for tissue collection and subsequent work for RNA editing was approved by RPCCC’s IRB.Normal human donor CD14+ monocytes were isolated from TRIMA leukoreduction filters following platelet apheresis at the blood donor center at Roswell Park Comprehensive Cancer Center (RPCCC). Since these cells were remnant products of platelet apheresis and contained no identifiers, their use was considered for non-human research. Mononuclear cells were recovered with lymphocyte separation media and sepMate tubes  by centrifugation. Monocytes were isolated from mononuclear cells using CD14 microbeads and AutoMACS . The purity of monocytes was confirmed by cytology and flow cytometry (>90% based on CD33+ CD14+ expression). Cells were cultured for 1 week at a density of 1022.A day before induction of M1 polarization, M0 macrophages were transfected with 100 nM of negative control  or equimolar mix of two human APOBEC3A siRNAs  using Lipofectamine RNAiMAX (Life Technologies), following the manufacturer’s protocol. IFN-γ and LPS were added with 2 ml medium to each well, to induce M1 polarization, and cells were harvested at 48 hSDHB probe on a Light Cycler 480 System (Roche), as previously described28. Quantification cycle (Cq) values were calculated by the instrument software using the maximum second derivative method, and the mean Cq value of duplicate or triplicate PCR reactions was used for analysis22.RNA was reverse-transcribed with random DNA hexamers and oligo-dT primers using material and methods provided with the Transcriptor First Strand cDNA Synthesis . PCR typically employed 35 cycles of amplification and an annealing temperature of 60 °C. Primers  used for PCR of cDNA templates were designed such that the amplicons spanned multiple exons. A blend of Taq and high-fidelity Deep VentR DNA polymerases  was used in PCR to generate products for Sanger sequencing. KD of A3A is confirmed by RT-qPCR using allele-specific primers and ® Cell (Bio-Rad). Membranes were incubated in Tris-buffered saline (TBS) with 0.05% v/v TWEEN 20 (Sigma Aldrich) and 5% w/v nonfat dry milk . Primary antibodies were applied to the PVDF membrane using the indicated dilutions: Rabbit polyclonal Anti-PHO1 (APOBEC3A) antibody , Rabbit polyclonal anti-THOC5 antibody , Mouse monoclonal anti-SDHB antibody , and mouse monoclonal anti-beta actin antibody . Horseradish peroxidase-conjugated, donkey anti-rabbit  or goat anti-mouse . IgG antibodies were used at 1:2000 dilution. Pierce ECL western blotting substrate  was used for chemiluminescent detection. Signals were visualized and imaged using the ChemiDoc XRS+ System and Image Lab Software (Bio-Rad).Cells were washed with 1× phosphate-buffered saline (PBS) and whole-cell lysates were collected using Pierce RIPA buffer  with 1× Halt protease and phosphatase inhibitor cocktail . Protein concentrations were quantified using Pierce Coomassie (Bradford) Protein Assay Kit . 20 μg proteins were mixed in Laemmli buffer, denaturized at 95 °C for 5 min and run on SDS–PAGE using a pre-cast, 4–20% gradient polyacrylamide gels . Proteins were then transferred to polyvinylidene difluoride membrane  at a constant voltage of 100 V for 70 min at 4 °C using Mini Trans-Blot5 cells/ml, and 3 ml was dispensed into each well of six-well tissue culture plates. M-CSF, 50 ng/ml (Life Technologies), was added and the cultures were incubated at 37 °C, 95% relative humidity, and 5% CO2 for 7 days. Influenza inoculum was prepared by thawing a stock vial of influenza A/Brisbane/10/2007 (H3N2) , sonicated for 45 s in an ultrasonic water bath , and diluted to 2 × 105 PFU/ml (as determined in MDCK plaque assay) with ice cold PBS. The plated cells were washed 2× with 1 ml warm 3% BSA in DMEM after which 0.3 ml was added to prevent drying of the cells. 100 µl of influenza inoculum (MOI = 0.02) or PBS control was added to appropriate wells and adsorbed for 1 h at 37 °C, 95% relative humidity, and 5% CO2 while gently rocking. Warm medium, 2.6 ml, was added to each well and incubation continued. After 24 or 48 h, the medium was collected, centrifuged at 500 × g for 5 min at 4 °C, and the supernatant was quick frozen in ethanol and dry ice and stored at −80 °C for influenza MDCK plaque assay analysis. The cells remaining in the well were harvested by adding 1  ml of Trizol to each well, incubating at room temperature for 5 min and transferring to a microfuge tube and storing at −80 °C for RNA analysis. In vitro studies with influenza virus was approved by University at Buffalo’s Institutional Biosafety Committee.Monocytes were isolated from freshly drawn blood, suspended in 1× GlutaMAX-I (Life Technologies) and 1 mM sodium pyruvate (Mediatech) medium at 3.3 × 105 cells/well in 2 ml of MEM with Earle’s salts + 0.1 mM non-essential amino acids + 1 mM pyruvate + 50 U/ml penicillin + 50 µg/ml + streptomycin + 20 µg/ml gentamicin + 10% fetal calf serum and incubated at 37 °C, 95% relative humidity, and 5% CO2. When cells reached 80–90% confluency (≈3 days) the cells were rinsed 2× with 1 ml 0.3% BSA in DMEM and 0.3 ml was added to prevent drying of the cells. Influenza samples were prepared, as described above, 10-fold serially diluted in 0.3% BSA in DMEM and kept on ice. Diluted samples were added to appropriate wells, 100 µl/well, and adsorbed for 1 h at 37 °C, 95% relative humidity, and 5% CO2 while gently rocking. The samples were aspirated and the cells rinsed 1× with PBS + 50 U/ml penicillin + 50 µg/ml + streptomycin. 2× L-15 (Sigma Aldrich) with 25 mM HEPES + 0.15% NaHCO3 + 100 U/ml penicillin + 100 µg/ml + streptomycin + 0.5 µg/ml gentamicin + 2 µg/ml TPCK-trypsin (Sigma Aldrich) was combined with an equal volume of liquefied 1.0% agarose in water then 2 ml/well was dispensed into the culture wells. After incubating the plates for 2 days at 37 °C, 95% relative humidity, and 5% CO2 the L-15/agar was removed and the cells fixed with 2 ml/well 90% ethanol for 30 min on an orbital shaker at room temperature. The ethanol was replaced with 2 ml/well 0.3% crystal violet (Sigma Aldrich) in 5% isopropanol + 5% ethanol in water and incubated for 20 min at room temperature on an orbital shaker. Finally, the crystal violet was removed, the cells rinsed with 2 ml/well of water, and the cultures allowed to air dry prior to counting the virus plaques.MDCK cells  were seeded on six-well tissue culture plates at 4 × 1047. Maraba virus was prepared and titered at McMaster University, shipped on dry ice to RPCCC and stored at −80 °C48.The attenuated strain MG1 Maraba virus has been previously describedCytokines production were measured in supernatants of the cultured cells using ELISA kits for TNF-α, IL-1β, IFN-α, and IL6  following the instructions of the manufacturer.The following antibodies were used: anti-human CD33 PE-Cyanine7 , anti-human CD86 APC , and anti-human CD206 FITC (BioLegend). Annexin V FITC (BioLegend) and propidium iodide solution (Sigma) were used for detection of cell death and apoptosis. Flow cytometry analysis was conducted on a LSRII flow cytometer . Forward scatter versus side scatter gating was set to include all non-aggregated cells from at least 20,000 events collected per sample and data were analyzed using Winlist 3D version 8.0 . CD33+ cells from the total cells were gated to obtain CD33+CD86+ or CD33+CD206+ cell populations based on unstained controls.5 cells per well. The assay plates were spun for 5 min at 1000 rpm and incubated at 37 °C without CO2 before performing the assay on the SeaHorse Bioscience XFe96. The Mitochondrial Stress Test was conducted in XF media containing 10 mM glucose, 1 mM sodium pyruvate, and 2mM l-glutamine and the following inhibitors were added at the final concentrations: oligomycin (2 µM), carbonyl cyanide 4-(trifluoromethoxy) phenylhydrazone (FCCP) (2 µM), and rotenone/antimycin A (0.5 µM each). The glycolytic stress test was performed in XF media containing 2 mM l-glutamine, and the following reagents were added at the final concentrations: glucose (10 mM), oligomycin (2 µM), and 2-deoxyglucose (50 mM). The data was analyzed using Wave software.For all extracellular flux assays, cell were plated on cell-take coated SeaHorse XF96 cell culture microplates at a density of 0.5 × 10E. coli, is used to evaluate RNA editing induction in TAMs and was obtained from PBL Assay Science .Ascites was collected at the time of primary surgery from patients with newly diagnosed stage III epithelial ovarian cancer (EOC) under an protocol approved by RPCCCs’s IRB. All subjects signed informed consent before surgery. Ascites cells were pelleted by centrifugation (500 g for 10 min at 4 °C). Cells were either used within 24 h of harvest for flow cytometry and functional studies or frozen in liquid nitrogen in media containing 20% FBS and 5% DMSO. PE-Cy7-CD33, PE-CD15  and APC-H7-CD14 (Biolegend), anti-human mAb along with L/D Aqua were used for sorting of TAM (CD33+CD15–CD14+) from cryopreserved peritoneal cells and the post-sort purity was >90%. Universal type IFN-1, a hybrid of amino-terminal IFNα-2 and carboxy-terminal IFNα-1, produced in 23. Three experimental conditions including M0 macrophages, control SC siRNA, and A3A siRNA (KD) are used. SC and KD samples were derived from the same three donors, whereas the M0 macrophages were derived from three unrelated donors. RNA libraries were prepared using the Illumina TruSeq RNA Exome protocol. For each sample, a total of ~90 million paired-end reads were obtained and QCed using fastqc (v0.10.1)49. The reads were mapped to GRCh38 human reference genome and GENCODE (v25) annotation database using TopHat (v2.0.13)50 with a maximum of 1 mismatch per read. The aligned reads were further checked with RSeQC (v2.6.3)51 to identify potential RNASeq-related problems. Gene level read counts were estimated with featureCounts from Subread (v1.6.0)52 using—fracOverlap 1 option. Differential expression analyses were performed using DESeq2 (v1.18.1)53 and the heatmaps were generated using pheatmap (v1.0.8) R library54. Pathways analyses were performed running GSEA (v3.0b3)55 pre-ranked mode with genes ordered by DESeq2’s test statistic and MiSigDB canonical curated gene sets (c2).We used a similar approach for RNA_Seq analysis as previously described24. Sequence data from RNA_Seq experiments are deposited to GEO repository at https://www.ncbi.nlm.nih.gov/geo/ under accession number GSE146867.RNA reads were mapped to reference genome GRCh38 using a lenient alignment strategy allowing at most two mismatches per read. Each mapped sample was run through the GATK best practices pipeline to obtain a list of variant sites. A filtered list of candidate editing events was collected based on selecting only variants that match the editing event of interest . Returning to the mapping results, samples are ‘piledup’ on the union of all potential candidates. Piledup samples were formatted and compiled for statistical comparisons based on a series of specific conditional filtering. Potential candidates for RNA editing required the following features: (A) variant spots with at least two piledup records per group  and (B) at least two samples from the experimental condition SC have >5% of editing level (el). A generalized linear model (GLM) was used to model and call editing events within the populations. The number of alternative nucleotides was compared to the reference between the two groups (Group). The first filter was designed to remove any possible known SNPs. The resulting editing candidates were annotated according to dbSNP144. A first layer of filtering removed those events where at least one sample had >0.95 event level, corresponding to homozygous SNPs. Then a second layer was performed where an event was removed if all of the samples had any of the following SNP features: el < 0.05; 0.4 < el < 0.6 and el > 0.95 and if this event was matched in dbSNP database. The next filtering step removed all C>T (or G>A) events that were not preceded by C or T in the +strand and G or A in the −strand. A resulting table specifying the editing site, the type of editing event, editing level and number of reads on a reference and alternative bases on each sample for each group was generated. The editing events were annotated using ANNOVAR’s gene-based annotation to identify gene features and protein amino acid sequence changes. Finally, a manual stringency filter was employed to retain only the exonic or UTR sites where (a) the editing level increases 2-fold or more in SC relative to M0 and (b) the edited C is located at the 3′-end of a putative tri- or tetra loop that is flanked by a stem that has at least 2 bp long perfect complementarity, or at least 4 bp long imperfect complementarity in which 1 nucleotide mismatch or bulging was allowedPCR-amplified cDNA fragments were sequenced to examine candidate RNA-editing sites. PCR reactions were treated with an ExoSAP-IT exonuclease  and then directly used for sequencing on 3130 XL Genetic Analyzer (Life Technologies) via RPCCC Genomic Core Facility. Major and minor chromatogram peak heights at a nucleotide position of interest were visualized using Sequencher 5.0 software .No explicit power analysis is used for sample size estimation. We tested whether APOBEC3A has a large effect size on inflammatory marker secretion/expression. The following points, not provided in the manuscript, were considered to enhance the reproducibility of our RNA-Seq findings. The sample numbers in the experiments were limited by the availability of donors.Three paired biological replicates for M1 macrophages, each transfected by either SC control or APOBEC3A KD siRNAs, are used to compare gene expression and RNA-editing patterns. Another three biological replicate was used for M0 macrophages. To confidently detect an RNA editing event in a single sample with 5% editing level, we need a coverage of 106 reads at that site with a minimum of three reads with editing. Given that the approximate size of all human transcripts is 50 Mbp, using 100 bp single end reads, we will need 53 million reads mapped to transcriptome. Since RNASeq is different from exome sequencing, in that it depends heavily on the expression level, a higher number of reads (>50 million) is thus desired. In these experiments, we used ~90 million paired-end reads. Stringent C>U RNA editing detection filters included (a) removal of genomic SNPs, (b) removal of C>U sites that are not preceded by a C or T at −1 location, (c) at least 2-fold increase in M1 SC vs. M0 in a candidate editing site, and (d) removal of edited sites that are not located within a putative stem-loop structure. A fourth biological replicate is used to confirm selected RNA editing by Sanger Sequencing Fig. .https://www.ncbi.nlm.nih.gov/geo/ under accession number GSE146867 (publicly available).Each experiment reporting novel findings is performed using independent biological replicates. This information is included in figure legends. Sequence data from RNA_Seq experiments are deposited to GEO repository at t-test in experiments comparing the impact of APOBEC3A KD on a cellular phenotype. No data point was excluded from analyses. We employed two normality tests on paired samples: Shapiro–Wilk and Kolmogorov–Smirnov with Dallal–Wilkinson–Liliefor P value, as implemented in GraphPad Prism 7.03 . The N values were too small to run D’Agostino and Pearson normality test. Most data passed both normality tests, and we ran paired t-test on them. This is consistent with general ad hoc rule of thumb that the paired t-test (unlike the unpaired t test) generally meets the approximate normality assumptions in that differences of random variables tend to be more symmetrical even if the raw observations are asymmetrical. Unpaired data are compared by non-parametric Mann–Whitney test. All reported P-values are two-tailed. Data are displayed in scatter plot by bars including standard deviation.Statistical methods used are explained in figure legends. We used two-tailed paired Further information on research design is available in the Peer Review FileSupplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Reporting Summary