A genome-wide dsRNA library screen for Drosophila genes that regulate the GBP/phospholipase C signaling axis that links inflammation to aging

BMC Research Notes, Dec 2018

Objective Invertebrates are productive models for understanding how inflammation, metabolism and aging are intertwined. We have deployed a dsRNA library screen to search for genes in Drosophila melanogaster—and hence identify human orthologs—that encode participants in a G-protein coupled, Ca2+-signaling pathway that regulates inflammation, metabolism and lifespan. Results We analyzed receptor-dependent, phospholipase C/Ca2+ signaling responses to the growth-blocking peptide (GBP) cytokine in Drosophila S3 cells plated in 384-well plates containing dsRNAs that target approximately 14,000 Drosophila genes. We used Z-scores of < − 3 or > + 3 to define gene hits. Filtering of ‘housekeeping’ genes from these hits yielded a total of 82 and 61 Drosophila genes that either down-regulate or up-regulate Ca2+-signaling, respectively; representatives from these two groups were validated. Human orthologs of our hits may be modulators of Ca2+ signaling in general, as well as being candidates for acting in molecular pathways that interconnect aging and inflammation.

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A genome-wide dsRNA library screen for Drosophila genes that regulate the GBP/phospholipase C signaling axis that links inflammation to aging

BMC Research Notes December 2018, 11:884 | Cite as A genome-wide dsRNA library screen for Drosophila genes that regulate the GBP/phospholipase C signaling axis that links inflammation to aging AuthorsAuthors and affiliations Eui Jae SungStephen B. Shears Open Access Research note First Online: 13 December 2018 90 Downloads Abstract Objective Invertebrates are productive models for understanding how inflammation, metabolism and aging are intertwined. We have deployed a dsRNA library screen to search for genes in Drosophila melanogaster—and hence identify human orthologs—that encode participants in a G-protein coupled, Ca2+-signaling pathway that regulates inflammation, metabolism and lifespan. Results We analyzed receptor-dependent, phospholipase C/Ca2+ signaling responses to the growth-blocking peptide (GBP) cytokine in Drosophila S3 cells plated in 384-well plates containing dsRNAs that target approximately 14,000 Drosophila genes. We used Z-scores of < − 3 or > + 3 to define gene hits. Filtering of ‘housekeeping’ genes from these hits yielded a total of 82 and 61 Drosophila genes that either down-regulate or up-regulate Ca2+-signaling, respectively; representatives from these two groups were validated. Human orthologs of our hits may be modulators of Ca2+ signaling in general, as well as being candidates for acting in molecular pathways that interconnect aging and inflammation. KeywordsCytokine Inflammation Metabolism Calcium-signaling G-proteins Receptor  Abbreviations [Ca2+]T total calcium mobilization GBP growth-blocking peptide GPCR G-protein coupled receptor PLC phospholipase C Introduction A systems-level understanding of cytokine-mediated, inter-tissue signaling can help to generate fundamental insight into links between longevity, metabolism and inflammation [1]. Research with human subjects indicates that aging is affected by the balance between circulating pro- and anti-inflammatory factors [2] which are subject to various environmental influences, among which calorific restriction receives particular attention [3]. However, little is known concerning the precise nature of the molecular entities and pathways that intertwine these biological phenomena in humans; most work in this area uses animal models. Invertebrates are productive, genetically-tractable models for understanding how inflammation and aging are inter-related in humans [1, 4]. Recent work has established that a Drosophila cytokine, growth-blocking peptide (GBP), interconnects longevity, inflammation and dietary influences, through activation of the phospholipase C (PLC)/Ca2+ signaling cascade [5, 6]. The spatiotemporal control of stimulus-activated Ca2+ dynamics by PLC is a multiplex cellular process that coordinates three fundamental activities: maintenance of basal Ca2+ pools, release of Ca2+ from intracellular stores, and Ca2+ fluxes across the plasma membrane [7]. A complete understanding of the integration of these processes requires characterization of the networking of a multitude of individual regulatory components; this degree of systems-biology insight has not yet been attained. Some important information has been obtained by exploiting the amenability of Drosophila to the application of dsRNA libraries [8, 9]. However, the latter studies were technically limited to identification of the subset of proteins that impact Ca2+ fluxes across the plasma membrane; in the current study, we have sought to widen the knowledge-base through identification of regulatory factors that may impact release of Ca2+ release from intracellular stores and Ca2+ fluxes into the cell, i.e., total Ca2+ mobilization ([Ca2+]T). For this work we deployed a fluorophore-based assay for GBP-mediated Ca2+ mobilization in Drosophila S3 cells in a 384-well, high-throughput screening format [5]. In that latter study, we screened a relative small dsRNA sub-library that targets 1729 genes known (or computationally predicted) to encode transmembrane proteins. In that way, we identified several integral membrane proteins that contribute to GBP-mediated Ca2+ release [5]. Nevertheless, that particular dsRNA sub-library only covered 12% of all Drosophila genes. In the current study, we performed a genome-wide dsRNA screen (almost 14,000 genes) so as to identify a more complete set of regulatory factors. This approach could be particularly beneficial in the context of diseases where changes in Ca2+-signaling are causative, since that can provide identify novel therapeutic targets [7]. Main text MethodsScreening of the dsRNA library We purchased a genome-wide dsRNA library (v2.0) from the DRSC/TRiP Functional Genomics Resources (https://fgr.hms.harvard.edu/). This library contains 66 × 384 well-plates that target almost 14,000 Drosophila genes with either one or two dsRNA constructs. The library is provided in duplicate (i.e., as a total of 132 plates). The biological readout during the screening was the total, GBP-induced change in fluorescence (equivalent to [Ca2+]T) emitted from a genetically encoded Ca2+ sensor in Drosophila S3 cells, in response to the addition of 500 nM GBP (added after 5-days pre-treatment with the dsRNAs [5]. A FLIPRTETRA (Molecular Devices) was used to simultaneously record [Ca2+]T in every well of a 384-well plate. We describe these procedures in more detail in our previous study with a small dsRNA sub-library that targets 1729 genes [5]. Evaluation and deposition of the screening data Z-scores were calculated for every unique dsRNA. A ‘positive’ gene-hit for any single dsRNA was defined by a reduction in [Ca2+]T that yielded a Z-score of less than − 3; the Z-score of a dsRNA = ([[Ca2+]T of each dsRNA] − [average [Ca2+]T of plate])/[SD of plate [Ca2+]T]. This strict disambiguation approach is designed to avoid false positives. Additionally, we describe some unusual, ‘negative’ gene-hits: dsRNAs that yielded an increase in [Ca2+]T (with a Z-score greater than 3). We encountered more plate-to-plate variability than in our previous study with the transmembrane dsRNA sub-library [5]. In the current study, for a number of plates, the cells in individual wells exhibited values for [Ca2+]T that clustered atypically close to that of the control, raising the possibility that knock-down efficiencies were abnormally low. We did not establish the cause of this variability, although it was not specific to any particular plate barcodes. We resolved this problem in the following manner: the Itp-r83A gene was one of our initial, and statistically most significant hits (Table 1); we independently validated this hit using alternate dsRNA constructs (Fig. 1a). Thus, we decided to use this as an internal control. This led us to establish that an adequate range of [Ca2+]T signals within a plate was typically observed whenever our Itp-r83A dsRNA caused at least 60% inhibition of GBP-mediated Ca2+ signaling. Thus, all data were discarded from any plate in which the inhibition by the internal Itp-r83A dsRNA control did not reach the 60% cut-off. We were able to procure new plates from the vendor to replace most of those plates that we discarded, and eventually we performed enough assays to screen every dsRNA in the genome-wide library at least once. Previous studies that used similar plates to screen for gene knock-downs that target Ca2+ entry into the cell [8, 9] do not state if a similar problem was experienced. However, a different version of the dsRNA library (i.e., v1) was deployed in those earlier studies; we used v2. Table 1 Filtered list of gene knock-downs that reduced GBP-mediated Ca2+ mobilization Flybase ID Gene Amplicon # Mean Z-score FBgn0033246 ACC DRSC06059 − 3.15 FBgn0025725 alphaCOP DRSC08706 − 5.27 FBgn0003884 alphaTub84B DRSC12622; DRSC25011 − 3.32; − 3.09 FBgn0013749 Arf102F DRSC17195 − 4.18 FBgn0033062 Ars2 DRSC04893 − 3.06 FBgn0010217 ATPsynbeta DRSC17194 − 3.48 FBgn0014127 barr DRSC03488 − 3.52 FBgn0025724 beta’COP DRSC03492; DRSC26650 − 4.27; − 5.03 FBgn0259876 Cap-G DRSC21805 − 4.04 FBgn0022213 Cas DRSC26857 − 3.13 FBgn0022942 Cbp80 DRSC18450 − 3.12 FBgn0012058 Cdc27 DRSC11112 − 3.3 FBgn0030510 CG12177 DRSC19437 − 7.01 FBgn0033429 CG12929 DRSC06242 − 6.12 FBgn0031023 CG14200 DRSC19555 − 3.02 FBgn0266917 CG16941 DRSC15166 − 3.92 FBgn0031498 CG17260 DRSC00497 − 8.46 FBgn0083978 CG17672 DRSC09230 − 3.49 FBgn0035205 CG2469 DRSC08562 − 3.14 FBgn0031266 CG2807 DRSC00535 − 5.48 FBgn0031493 CG3605 DRSC00619 − 5.79 FBgn0058198 CG40198 DRSC21068 − 3.12 FBgn0035983 CG4080 DRSC10398 − 6.77 FBgn0086758 chinmo DRSC28547 − 3.26 FBgn0259993 CR42491 DRSC25025 − 4.52 FBgn0028836 CSN7 DRSC06807; DRSC06808 − 3.03; − 3.07 FBgn0025455 CycT DRSC11124 − 3.6 FBgn0086687 Desat1 DRSC23578 − 5.23 FBgn0260635 Diap1 DRSC11404 − 4.59 FBgn0039183 Dis3 DRSC16034 − 3.99 FBgn0004638 drk DRSC07606 − 3.52; − 3.54 FBgn0034975 enok DRSC04096 − 3.07 FBgn0033859 fand DRSC39027 − 4.62 FBgn0004656 fs(1)h DRSC29017 − 3.39 FBgn0004435 Galphaq DRSC07432 − 3.36 FBgn0001105 Gbeta13F DRSC20247 − 5.32 FBgn0014189 Hel25E DRSC03342 − 3.76 FBgn0053818 His3:CG33818 DRSC21267 − 4.05 FBgn0015393 hoip DRSC03546 − 5.3 FBgn0001218 Hsc70-3 DRSC25105 − 3.05 FBgn0266599 Hsc70-4 DRSC29729 − 3.32 FBgn0010051 Itp-r83A DRSC12354 − 6.95 FBgn0004378 Klp61F DRSC28179 − 3.31 FBgn0001491 l(1)10Bb DRSC20346 − 3.32 FBgn0001986 l(2)35Df DRSC03560 − 4.51 FBgn0011640 lark DRSC11362; DRSC25108 − 4.28; − 5.71 FBgn0035889 mkg-p DRSC10777 − 3.13 FBgn0032921 Mpp6 DRSC03169 − 3.77 FBgn0035132 mthl10 DRSC39158 − 3.58 FBgn0086707 ncm DRSC02179 − 5.76 FBgn0026401 Nipped-B DRSC07815; DRSC29151 − 3.27; − 3.01 FBgn0014366 noi DRSC12383 − 3.03 FBgn0005648 Pabp2 DRSC07501 − 3.32 FBgn0259214 PMCA DRSC17154 − 4.59 FBgn0010590 Prosbeta1 DRSC07159 − 7.45 FBgn0026380 Prosbeta3 DRSC16801 − 3.72 FBgn0032006 Pvr DRSC36840 − 5.7 FBgn0003189 r DRSC19813; DRSC19814 − 4.38; − 4.44 FBgn0020255 Ran DRSC28160 − 4.51 FBgn0003205 Ras85D DRSC39132 − 3.42 FBgn0031868 Rat1 DRSC02044 − 3.31 FBgn0011704 RnrS DRSC07533; DRSC23541 − 3.50; − 3.79 FBgn0010173 RpA-70 DRSC16830 − 3.63 FBgn0015805 Rpd3 DRSC08696 − 3.32 FBgn0262955 RpII140 DRSC16831 − 4.09 FBgn0003277 RpII215 DRSC20280 − 3.11 FBgn0028694 Rpn11 DRSC03422 − 5.4 FBgn0028689 Rpn6 DRSC07541 − 4.56 FBgn0028688 Rpn7 DRSC16841 − 4.81 FBgn0002787 Rpn8 DRSC04624 − 3.37 FBgn0028684 Rpt5 DRSC16842 − 3.02 FBgn0038269 Rrp6 DRSC16223 − 3.87 FBgn0262601 SmB DRSC03437 − 3.07 FBgn0261789 SmD2 DRSC12536 − 3.88 FBgn0261790 SmE DRSC02680 − 4.03 FBgn0028982 Spt6 DRSC18836 − 4.58 FBgn0038810 Srp72 DRSC15800 − 3.69 FBgn0045073 Stim DRSC20158 − 4.54 FBgn0003575 su(sable) DRSC18839 − 3.15 FBgn0030365 Tango4 DRSC23475 − 3.85 FBgn0035713 velo DRSC08841 − 3.45 FBgn0003978 vls DRSC02101 − 6.77 The genes listed are the ‘hits’ that reduced GBP-mediated Ca2+ mobilization (i.e., Z-score > 3), after filtering of housekeeping genes (see “Methods”) Open image in new window Fig. 1 Identification and validation of genes that regulate GBP/PLC-mediated Ca2+ signaling. a Representative Ca2+-signaling responses from Drosophila S3 cells in plate 116B88 that were pre-treated with either control dsRNAi (traces ‘a’ and ‘c’) or our dsRNAi construct against Itp-r83A (trace ‘b’) before addition of either vehicle (trace ‘a’) or 500 nM GBP (traces ‘b’ and ‘c’). b reproducibility of dsRNA replicates obtained from 23 plates assayed in duplicate. c Z-scores from all amplicons. d Effects upon [Ca2+]T after treatment of cells with our dsRNA constructs against the indicated genes; n = 4; *p < 0.001 The complete list of Z-scores is available at http://www.flyrnai.org/. Here, we tabulate two filtered hit-lists (Tables 1 and 2), from which we have removed all hits that could reasonably be predicted to non-specifically affect protein synthesis, by virtue of their having the following categorizations in the Gene Ontology Database (http://www.geneontology.org/): mRNA splicing (GO:0000398); structural constituent of ribosome (GO:0003735); translation initiation factor activity (GO:0003743); transcription factor activity, sequence-specific DNA binding (GO:0003700); RNA polymerase II transcription cofactor activity (GO:0001104); RNA polymerase II activity (GO:0001055); DNA-directed RNA polymerase activity (GO:0003899); transcription factor binding (GO:0008134); transcriptional activator activity, RNA polymerase II core promoter proximal region sequence-specific binding (GO:0001077); translation (GO:0006412); translation elongation factor activity (GO:0003746). Table 2 Filtered list of gene knock-downs that increased GBP-mediated Ca2+ mobilization Flybase ID Gene name Amplicon # Mean Z-score FBgn0027084 Aats-lys DRSC25618 3.62 FBgn0000043 Act42A DRSC04835 4.46 FBgn0000044 Act57B DRSC04042 5.98 FBgn0000042 Act5C DRSC17723; DRSC25024 7.32; 8.13 FBgn0000045 Act79B DRSC11604 3.69 FBgn0087035 AGO2 DRSC10847; DRSC40785 4.89; 3.95 FBgn0041188 Atx2 DRSC40788 4.40 FBgn0000212 brm DRSC11330; DRSC26226 4.33; 9.95 FBgn0029856 CG11700 DRSC23384 3.67 FBgn0033507 CG12909 DRSC27706 3.07 FBgn0039601 CG1523 DRSC15033; DRSC26315 3.49; 3.10 FBgn0035569 CG15876 DRSC08484 3.71 FBgn0067622 LSm-4 DRSC02845 3.44 FBgn0032240 CG17768 DRSC02845 3.44 FBgn0034325 CG18539 DRSC06766 3.12 FBgn0034326 CG18540 DRSC06767 3.10 FBgn0011824 CG4038 DRSC25331 3.79 FBgn0036991 CG5872 DRSC11785 3.64 FBgn0035872 CG7185 DRSC10781 3.49 FBgn0259236 comm3 DRSC09995 3.24 FBgn0031831 COX5BL DRSC27300 6.17 FBgn0263093 CR43361 DRSC23926 3.19 FBgn0033260 Cul4 DRSC27185 4.46 FBgn0086901 cv-c DRSC27016 4.33 FBgn0002413 dco DRSC16929 3.26 FBgn0034246 Dcr-2 DRSC29436 3.53 FBgn0260049 flr DRSC09787 3.11 FBgn0034964 IntS1 DRSC04343; DRSC27942 9.26; 5.92 FBgn0026679 IntS4 DRSC17810 5.00 FBgn0036570 IntS9 DRSC10493 3.52 FBgn0004419 me31B DRSC03569 3.30 FBgn0035473 mge DRSC08721 3.58 FBgn0027378 MRG15 DRSC16731 4.68 FBgn0085417 natalisin DRSC23390 3.78 FBgn0032725 Nedd8 DRSC02092 6.04 FBgn0041102 ocn DRSC17020 3.25 FBgn0020626 Osbp DRSC16779 3.21 FBgn0044826 Pak3 DRSC26832 4.48 FBgn0050382 CG30382 DRSC07515 3.16 FBgn0263121 Prosalpha1 DRSC07515 3.16 FBgn0086134 Prosalpha2 DRSC28078 5.52 FBgn0261394 Prosalpha3 DRSC04644 3.19 FBgn0004066 Prosalpha4 DRSC20271 4.03 FBgn0020618 Rack1 DRSC03405; DRSC23796 5.15; 6.14 FBgn0033897 Rcd1 DRSC07116; DRSC23713 3.57; 5.29 FBgn0015283 Rpn10 DRSC11876 4.34 FBgn0028686 Rpt3 DRSC23412 3.58 FBgn0266666 Sem1 DRSC02282 3.50 FBgn0003392 shi DRSC20373; DRSC29498 5.20; 5.03 FBgn0019890 Smg5 DRSC03124 3.63 FBgn0264357 SNF4Agamma DRSC16847; DRSC40676 4.95; 4.12 FBgn0011715 Snr1 DRSC12369 6.12 FBgn0034175 ste24b DRSC07315 3.24 FBgn0033902 Tango7 DRSC07142; DRSC26908 3.95; 5.70 FBgn0024921 Trn DRSC25104 4.88 FBgn0011726 tsr DRSC04718; DRSC40832 6.64; 6.60 FBgn0035124 ttm2 DRSC08268 3.84 FBgn0039530 Tusp DRSC15838 3.17 FBgn0023143 Uba1 DRSC07567 4.15 FBgn0263697 Uba3 DRSC06377 3.27 FBgn0035853 UbcE2M DRSC10828 6.22 The genes listed are the ‘hits’ that increased GBP-mediated Ca2+ mobilization (i.e., Z-score < − 3), after filtering of housekeeping genes (see “Methods”) Independent validation and additional screening Follow-up GBP-mediated Ca2+ mobilization in S3 cells, using unique dsRNA constructs different from those used in the high-throughput assays, were performed exactly as described previously [5], using 50 nM GBP, and dsRNAs constructed using the following primers (for cDNA fragment and T7-cDNA, respectively): Act5c-f, ATGTGTGACGAAGAAGTTGCTG; TAATACGACTCACTATAGGGTGTGACGAAGAAGTTGCTGC Act5c-r, AAGCCTCCATTCCCAAGAACG; TAATACGACTCACTATAGGGCTGGGTCATCTTCTCACGGT Itp-r83A-f, CCTCAAGCGTTTGCATCATGC; TAATACGACTCACTATAGGGGGGCACCTCAATCCAATATG Itp-r83A-r, CTGTTTTCCCTTGGGTTTGTCATTTATG; TAATACGACTCACTATAGGGTATGGTGGAGTTCATGGTCG Mthl10-f GCCATAGGCTCTTTCCCAAC; TAATACGACTCACTATAGGGTAGTTCCCGCAGAATTGGTC Mthl10-r CGTTGACGTATGTCGGAACC; TAATACGACTCACTATAGGGTATGTCGGAACCATGCAGAA NorpA-f, GCGGACTCCTCAAACTATATGC; TAATACGACTCACTATAGGGCTGCCAGATGGTCTCACTCA NorpA-r, GCTCTGCTCCTCAATGCCAAG; TAATACGACTCACTATAGGGGAAGTCCTCAAAGCCGTCA. Plc21C-f, ACGGGAACTGACCTCGATTAG; TAATACGACTCACTATAGGGCACTGCTAAGGGGAATCCAA Plc21C-r, TTGGAGCTTTGTAACGACTAGG; TAATACGACTCACTATAGGGCTGCCAGATGGTCTCACTCA Tsr-f, AGAAATGCGGACCTGGAGAG; TAATACGACTCACTATAGGGCCCAGACCCATCGAAACTAA Tsr-r, CAAATTGGCGATCTCAACAGG; TAATACGACTCACTATAGGGAGGATACGTGTTTCCATCGC Results and discussion The GBP/PLC signaling axis, which acts through of a G-protein coupled receptor (GPCR), stimulates Ca2+ mobilization by a biphasic process; first, Ca2+ is released from the endoplasmic reticulum, which secondarily stimulates Ca2+ entry into the cell [5, 7]. To screen novel genes that regulate the entire Ca2+-signaling process, we deployed a genome-wide dsRNA library (the DRSC/TRiP Functional Genomics Resources; https://fgr.hms.harvard.edu/). These dsRNAs were tested using a strain of Drosophila S3 cells that encode a fluorescent Ca2+ sensor, the GCaMP3 gene [5]. We screened all 66 library plates at least once, and obtained data for dsRNA knockdown of every gene in the screen (see “Methods”). A scatter-plot of those data that had replicates (see “Methods”) indicate that most amplicons show good reproducibility (Fig. 1b). From the entire data set (Fig. 1c), we found that total Ca2+ mobilization ([Ca2+]T) was inhibited by 103 amplicons (Z score < − 3; Table 1). A separate group of 80 amplicons (Table 2) increased [Ca2+]T (Z score > 3). All of these data for each individual dsRNA are available at: http://www.flyrnai.org/screensummary). These numbers of amplicons in the two categories were reduced to 82 and 61, respectively (Tables 1 and 2), after we filtered out housekeeping genes (see “Methods”). Any high throughput screen is susceptible to false positive and false-negative hits. We were concerned that plc21C and NorpA may have been false-negatives; neither of these genes were hits in our screen, yet being that they are orthologs of mammalian PLC-β, one or both of these gene products was expected to mediate GBP-dependent, GPCR-coupled Ca2+ mobilization. Thus, we performed additional validation assays using our own, unique dsRNA constructs. Both plc21C and NorpA were hits in these follow-up assays (Fig. 1d); knock-down of either significantly reduced Ca2+ mobilization. The simultaneous knockdown of both genes elicited an approximately additive effect (Fig. 1d). These data suggest partial functional redundancy of the two PLC-β genes, which can account for their being false negatives in a dsRNA screen. As mentioned above, we separated our hits into two groups, based on whether [Ca2+]T was either decreased (Table 1) or increased (Table 2). We selected representatives from each group for validation. We used our unique dsRNAs at an early stage of this project to validate that knockdown of the Itp-r83A reduced [Ca2+]T (see “Methods”, and Figs. 1a, d); we subsequently deployed Itp-r83A as a positive control to interrogate dsRNA plate integrity (see “Methods”). A second hit, Mthl10 (Table 1), was also validated in secondary assays with our own, independent dsRNAs (Fig. 1d). Among hits that elevate [Ca2+]T (Table 2), we selected two—Tsr and Act5C—for further testing with our independent dsRNAs; in both cases, we confirmed that knockdown of either gene significantly increased [Ca2+]T. These data indicate that both of these genes normally constrain [Ca2+]T; it should be interesting to study further the biological significance of such a phenomenon. Another aspect of our data that is of interest is the determination that GBP-dependent Ca2+ signaling is regulated by a family of genes that encode proteins that are components of the proteosome (PSMA2, PSMB6, PSMD6, Rpn11/PSMD14; Tables 1, 2). This multiprotein complex can regulate PLC/GPCR signaling by controlling the cell-surface levels of the receptor [10]; our data highlight the likely participation of the proteosome in regulating the activity of the GBP/Mthl10 signaling axis. Another hit, Gqα (Table 1), is a subunit of a heterotrimeric G-protein that couples GPCRs to the activation of PLC-β. These are all data that underscore the value of a systems-level approach to fully understanding all aspects of the Ca2+-signaling process. We propose that human orthologs of our complete list of filtered gene hits (Tables 1 and 2) are not only candidates for acting in molecular pathways that interconnect aging and inflammation, but also potential new modulators of Ca2+ signaling in general. Thus, our data may drive several new, future research directions. Limitations A limitation in this study—as is the case for all high throughput screening exercises—is the possibility of false positives and false negatives. The possibility that gene redundancy may lead to false negatives is highlighted by Fig. 1d. In a genome-wide study such as this, it is not feasible to validate every hit, so false positives remain a possibility for future studies that pursue our data. Notes Authors’ contributions SBS designed the study. EJS performed experiments and analyzed data. EJS and SBS wrote the manuscript. Both authors read and approved the final manuscript. Acknowledgements We thank Drs. Gary Bird and Yoichi Hayakawa for helpful advice and discussions. Competing interests The authors declare that they have no competing interests. Availability of data and materials The data supporting our findings are either included in the current study, or are publically accessible at the DRSC Functional Genomics Resources repository (http://www.flyrnai.org/screensummary); the unique identifier associated with that dataset is the Pubmed ID for the current study. Consent to publish Not applicable. Ethics approval and consent to participate Not applicable. Funding This research was supported by the Intramural Research Program of the NIH, National Institute of Environmental Health Sciences (ES080046-28). The DRSC Functional Genomics Resources (Harvard Medical School) is supported by NIH Funding NIGMS R01 GM067761. Neither of these funding bodies played any role in the design of the study, the collection, analysis and interpretation of the data, and the writing of the manuscript. Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. References 1. 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The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Authors and Affiliations Eui Jae Sung1Stephen B. Shears1Email authorView author's OrcID profile1.Inositol Signaling Group, Signal Transduction Laboratory, National Institute of Environmental Health SciencesNational Institutes of HealthResearch Triangle ParkUSA


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Eui Jae Sung, Stephen B. Shears. A genome-wide dsRNA library screen for Drosophila genes that regulate the GBP/phospholipase C signaling axis that links inflammation to aging, BMC Research Notes, 2018, 884, DOI: 10.1186/s13104-018-3996-z