**Scientific benefits:** 
=========================

-   This screen has provided a thorough analysis of glial functions in ageing

-   Potential to shortcut gene discovery through miRNAs effect. The screening of only \~200 mutant lines potentially targets \>6000 genes.

-   Potential to identify complex regulatory networks that include miRNAs and target genes.

-   Validated the identification of essential genes for the adult nervous system and their functions specifically in motor control.

-   An open-access searchable database for future discoveries upon improved precision of miRNA-target predictions.

**3Rs benefits:** 
==================

-   This screening method provides an alternative approach for studying genes important in glial biology, without the need for animal experiments.

-   Example validation that *Drosophila* can be used to study the biology of GBF1, instead of *in vivo* vertebrate animal models, such as zebrafish or mouse.

**Practical benefits:** 
========================

-   The searchable database can be easily updated upon emergence of updated miRNA target predictions.

-   RNAi lines are publicly available from the Vienna Drosophila Resource Centre stock collection.

-   Genetic studies in Drosophila are quicker and more sophisticated compared to vertebrate studies. They also maintain high conservation of functions.

**Current applications:** 
==========================

-   Uncovered the function of *garz* in glial cells for membrane trafficking, autophagy and mitochondria quality control.

-   Study of genes, such as GBF1, involved in ageing and neurodegeneration

**Potential applications:** 
============================

-   Identification of novel miRNA targets in glia

-   Study of novel miRNA targets in glia

-   Study of glial functions in controlling lifespan and healthspan

Introduction
============

Despite the fact that glial cells were initially identified simply as the connective tissue of the brain ^[@ref-1]^, work developed in the past decades has shed a light on a much more intricate role for these cells in developing and maintaining nervous system homeostasis (reviewed in [@ref-2]). From neuronal nutrient supply ^[@ref-3]^, to neurotransmitter recycling ^[@ref-4]--\ [@ref-6]^, to being the first line of immune response in the brain ^[@ref-7]^, glial cells have been shown to actively contribute to the correct functioning of the brain.

More recently, several studies have been taking advantage of *Drosophila*'s powerful genetic manipulation to better understand the role of glia in the development and maintenance of the nervous system (see [@ref-8] for review).

The use of invertebrate models is also a powerful 3Rs solution to reduce and replace animal experiments. It expressly applies to complex matters in which cross-talk between different cell types (e.g. glia and neurons) is a focal point of the investigation, given that these complex environments are more difficult to model *in vitro* and *in silico*. Popular animal models for studying glial functions are zebrafish, which provide a useful platform for tissue and cell biology, with some capability for genetic manipulation ^[@ref-9]^ and genetically modified mice ^[@ref-10]^. Despite having a different developmental origin, glial cells have converged in *Drosophila* and mammals towards the same key functions of neurotransmission regulation, insulation and immune surveillance/phagocytosis ^[@ref-8]^, making the fruit-fly an organism of choice for studying the function of glial cells.

We have tackled the functions of glial cells in ageing. We have previously screened a large collection of miRNAs regarding their effects on *Drosophila's* lifespan upon ectopic expression in glial cells in adult flies and have validated this screen through the analysis of *repo,* an already-established key glia gene ^[@ref-11]^. The experimental advantage of performing a miRNA-based screen followed by *in silico* identification and ranking of predicted miRNAs target transcripts ^[@ref-11],\ [@ref-12]^ has, however, its bottleneck in the validation of the action of the genes of interest. In principle, the specific knockdown of predicted target genes should mimic, to some extent, the phenotype obtained upon corresponding miRNA overexpression.

In fact, using databases of predicted miRNA-target genes previously allowed us to identify *repo* as an important player for maintaining glial function and, consequently, homeostasis in the adult brain ^[@ref-11]^. We have shown that while the *miR-1*- *repo* axis is physiologically relevant only in the embryo during the glia versus haemocyte cell fate choice ^[@ref-13]^, the miRNA-target relationship can be exploited as a discovery tool to identify the functions of a target gene in a different context, namely adult glial functions ^[@ref-11]^.

While the focus on *repo* was based on its already-established role in glia cell function, here we attempt a global and unbiased systematic *in silico* approach. In order to systematically identify potential target genes that could account for the lifespan phenotype, focusing on the miRNAs that shortened lifespan, we set out to devise a quantitative algorithm. The aim of this algorithm is to identify and rank the predicted target genes so that those ranking on top would be the most relevant for adult glia in lifespan and ageing.

This is followed by experimental validation of the function of these targets in adult glia in the same paradigm used in the miRNAs screen.

We conclude that this approach is valid but has issues of efficiency given the large number of predicted targets that do not recapitulate the expected phenotype. We also establish that there is no significant synergy generated by focusing on the common predictions between all available miRNAs target databases. Nevertheless, the main outcome of our work is a list of candidate genes whose function is essential in glial cells during ageing. These genes can be studied in the future in *Drosophila*, with the tools identified here, rather than in genetically modified mouse models or in zebrafish, providing an incentive towards animal replacement and reduction and advancing the 3Rs. Mouse and zebrafish neuroscientists and geneticists could take advantage of this information to test preliminary approaches and exploratory experiments in *Drosophila*, prior to validation in their system reducing the number of animals used. Alternatively, they may entirely replace vertebrate animals with *Drosophila* to study highly conserved genes and glial functions.

The success of this *in silico* approach is exemplified by our analysis of one of the top predicted targets: *gartenzwerg* ( *garz*), the fly orthologue of GBF1 (golgi brefeldin A resistant guanine nucleotide exchange factor 1), a small GTPase guanine exchange factor. Here, we show that *garz* is an essential factor in glia homeostasis maintenance.

Small GTPases regulate a wide range of cellular events such as proliferation, morphology, nuclear transport and vesicle formation ^[@ref-14]^. The conversion from GDP-bound (inactive) to GTP-bound (active) forms of these enzymes relies on the activity of GTPase activating proteins (GAPs) and guanine nucleotide exchange factors (GEFs). While GAPs are responsible for their inactivation through GTP hydrolysis, GEFs are responsible for their activation promoting the exchange of GDP by GTP ^[@ref-15]^.

GEFs belonging to the Sec7 domain protein family are responsible for the activation of Arf (ADP-ribosylation factor) GTPases which are associated with the recruitment of coat proteins (COP) to vesicle budding sites ^[@ref-16]--\ [@ref-18]^. GBF1 is part of this family ^[@ref-19]^ and is highly conserved in all eukaryotes, conferring significant translatability of the findings obtained using different model organisms.

Strongly localized in the cis-Golgi compartment, GBF1 has been shown to regulate vesicle trafficking between the endoplasmic reticulum (ER) and the Golgi apparatus ^[@ref-20]--\ [@ref-24]^. Mutated versions or knock-down of *garz* expression brings about epithelial morphogenesis defects during development conditioning embryonic trachea and larval salivary gland formation ^[@ref-20],\ [@ref-21]^. Additionally, in accordance with a role in membrane delivery and vesicular trafficking, silencing of *garz* in these glands impairs membrane delivery of adhesion molecules ^[@ref-25]^. Independently from its role in secretion, GBF1/ *garz* has also been implicated in pinocytosis ^[@ref-26]^; intestinal stem cell survival ^[@ref-27]^; cell cycle ^[@ref-28],\ [@ref-29]^; unfolded protein response events ^[@ref-29]^; mitochondria morphology and function ^[@ref-30]^; and autophagy ^[@ref-31],\ [@ref-32]^.

Here we show that *garz* knock-down resulted not only in lifespan reduction but also in motor deficits of adult flies and in subcellular phenotypes indicative of dysfunctions in trafficking, autophagy and mitochondria. Additionally, miRNAs overexpression and *garz* knockdown phenotypes were reverted by expression of its mammalian orthologue GBF1, stressing the conservation of functions and the appropriateness of using *Drosophila* in place of vertebrate models to study the biology of GBF1.

Methods
=======

Online resources and in silico algorithms for target identification and ranking
-------------------------------------------------------------------------------

The following databases were used for the prediction of miRNA targets: MicroCosm ( <https://www.ebi.ac.uk/enright-srv/microcosm/htdocs/targets/v5/>)microRNA.org ( <http://www.microrna.org/microrna/home.do>)TargetScan ( <http://www.targetscan.org/fly_72/>)PicTar ( <https://pictar.mdc-berlin.de/>)

Each of the databases provides for every miRNA a numerical prediction of the likelihood of targeting a given gene (Score). For MicroCosm and PicTar this was used without additional steps. In the case of miRNA.org this score is a negative value and we have squared it to obtain a positive number. In the case of TargetScan a numerical score was calculated on the basis of the information provided by the database as follows: conserved 8mer = 10 points, conserved 7mer-m8 = 6 points, conserved 7mer-1A = 4 points, poorly conserved 8mer = 8 points, poorly conserved 7mer-m8 = 4 points and poorly conserved 7mer-1A = 2 points. A detailed explanation of the 8mer and 7mer species can be found on the TargetScan website and in the original publication ^[@ref-33]^.

The algorithm for ranking targets within each database consists of two steps.

-   Step 1 - column (Score)\*Av(χ ^2^) or (Score ^2^)\*Av(χ ^2^):

    For each miRNA, every target score (or its square value) was multiplied by the Average Chi square (χ ^2^) obtained in the miRNAs screen (from [Table 1](#T1){ref-type="table"}). Information regarding different mRNAs for the same gene, where available, was grouped under the same gene name

-   Step 2 - column Σ(Score)\*Av(χ ^2^)\] or Σ(Score ^2^)\*Av(χ ^2^)\]:

    For each target gene, as defined by its CG number/accession ID, all values resulting from all miRNAs predicted to target the same gene were summed in a final ranking value. Information regarding different mRNAs from the same gene, where available, was grouped under the same gene name.

The algorithm for comparing the ranking between different databases and providing a final common ranking consists of two steps: Step1 - column Normalised Σ\[(Score)\*Av(χ ^2^)\] or Normalised Σ\[(Score ^2^)\*Av(χ ^2^)\]For each database the Σ(Score)\*Av(χ ^2^)\] was normalised to 100 and then weighted for the fraction of miRNAs present in the database, out of the total tested in our miRNAs screen. For TargetScan the groups of miRNAs families were counted as one unit in each case.Step 2 -- column Σ{Normalised Σ\[(Score ^(2)^)\*Av(χ ^2^)\]} For each target gene, all values from all databases were summed into a final ranking number.

###### Average strength of miRNAs that shorten lifespan in adult glia.

To determine the strength of miRNAs in our lifespan assay we have used the averaged χ ^2^ values from each transgenic line used in our previously published analysis ^[@ref-11]^. When only one line was tested for a given miRNA, the value was divided in half, i.e. assuming a neutral value of 0 for a second putative untested line. For the TargetScan database, some miRNAs are grouped in families requiring an amendment to our approach. In this case, we have averaged all miRNAs in the given families. Additionally, some of the lines tested for these grouped miRNAs had, in the original screen the opposite effect of what is here considered, i.e. extending lifespan with respect to the control used. To account for this opposite effect the χ ^2^ values for these miRNAs have been given negative values and have been effectively subtracted, when calculating the Av(χ ^2^) parameter.

  miRNAs   Av(χi ^2^)   Av(χi ^2^) Targetscan
  -------- ------------ -------------------------------------
  1        44.2375      
  3        55.0050      23.8583 (3 + 309 + 318)
  8        11.8700      
  9a       94.4800      89.4917 (9a + 9b + 9c)
  9b       106.5150     
  9c       67.4800      
  10       5.8250       
  12       22.2150      
  31       1.7233       
  34       61.6733      
  79       75.6650      
  92a      95.6050      72.245 (92a + 92b + 310 + 312+ 313)
  92b      28.3000      
  124      82.5700      
  133      48.0200      
  137      42.7400      
  184      27.4050      
  193      47.5300      
  219      2.1550       
  263b     5.5650       
  274      22.6400      
  276b     41.4350      25.02 (276a + 276b)
  277      24.5850      
  278      77.6300      
  279      13.5000      -4.104 (279 + 286 + 996)
  287      2.2550       
  310      102.4833     72.245 (92a + 92b + 310 + 312+ 313)
  312      32.8850      
  313      50.7100      
  315      24.5900      
  316      2.6950       
  318      26.3850      
  375      39.2500      
  932      25.8700      
  958      25.4200      
  968      35.0700      
  977      4.7065       
  978      70.4600      
  980      31.0400      
  989      44.3800      
  992      7.8050       
  995      7.0500       2.695 (285 + 995 + 998)
  999      2.7750       
  1015     3.8550       

Drosophila stocks and husbandry
-------------------------------

Flies were kept on standard cornmeal agar food (0.8% w/v agar, 2% w/v cornmeal, 8% w/v glucose, 5% w/v Brewer's yeast, 1.5% v/v ethanol, 0.22% v/v methyl- 4-hydroxybenzoate, 0.38% v/v propionic acid) at 18°C or room temperature. Unless stated otherwise, *w ^1118^* flies were used as control. The following lines were acquired from the Bloomington collection: *w ^1118^* (RRID:BDSC_3605), *repo-*Gal4 (RRID:BDSC_7415), *NP2222-*Gal4 (RRID:DGGR_112830), *moody-*Gal4, *elav-*Gal4 (RRID:BDSC_8765), *tub-*Gal80 ^ts^ (RRID:BDSC_7019). *alrm-*Gal4 (RRID:BDSC_67031) was kindly provided by M. Freeman (University of Massachusetts) ; *UAS-miR-1*, *UAS-miR-79* and *UAS-miR-315* were generated by E. Lai (Sloan Kettering Institute) for the miR library ^[@ref-34]^; *UAS-garz*-RNAi (42140/GD and 42141/GD) as well as all RNAi lines used are from Vienna *Drosophila* Resource Center (VDRC); *gliotactin-*Gal4 was provided by R. Sousa-Nunes; *UAS-mito*-GFP was provided by J. Bateman; *UAS-garz*; *UAS-garz ^Sec7-^*; *UAS-GBF1* and *UAS-ΔGBF1 ^Sec7-^* were kindly provided by S. Luschnig.

Lifespan
--------

Lifespan analysis was performed as previously described ^[@ref-35]^. Briefly, crosses were maintained at 18°C throughout the whole development of the progeny. Within the first 5 days post-eclosion, adult flies were collected, and female and male flies pooled together. An equal number of flies was distributed in three vials, a total of 60 flies was used. This group size has a power of 0.8 in one tailed survival test at 50% survival for the control group and 29% for an experimental group at 0.05 significance. Lifespan assessment was performed in a controlled environment of 29°C and 60% humidity, three times a week. Upon short CO ~2~ anaesthesia (5 s), the number of dead vs alive flies was counted, and the alive flies transferred into a fresh vial.

Motor behaviour assay
---------------------

Single fly tracking was carried out as previously described ^[@ref-11]^. In each experiment, up to 20 flies per genotype were placed into individual glass tubes. This group size has a power of 0.9 and significance 0.05 for three groups with an effect size of 0.48, as measured for the mean bout length. All the genotypes were positioned on the same platform, having two shaft-less motors placed underneath each subplatform containing each, one genotype. The protocol used consisted of 6 stimuli events equally split during a period of 2 h and 15 min, the first one starting after 30 min of recording and the last one 30 min before the end of the protocol. Each stimuli event was composed of 5 vibrations of 200 ms spaced by 500 ms. The x/y position of each single fly was tracked and analysed using DART software 1.0 (freely distributed upon request to <info@bfklab.com>) in order to evaluate the relative speed and activity before, during and after the stimuli event. The speed analysis was used for the "Stimuli Response Trace" and the general activity used to deduce "Active Speed", "Mean Bout Length" and "Inter-Bout Interval", using a custom-made modification of the DART software ^[@ref-36]^. Raw data were analysed with GraphPad Prism for statistical significance and DART-derived graphs were edited with Adobe Illustrator CC2017 (RRID:SCR_010279).

Immunostaining
--------------

Flies (N=5--10) were briefly (5 s) anesthetized with CO ~2~ and kept on ice, entire fly brains were dissected under a stereoscope and immediately fixed in 4% paraformaldehyde (PFA, from EMS) in Phosphate Buffer Saline (PBS) for 30 min. After washing with PBS, the brains were incubated for blocking in PBS with 0.3% triton-X (BDH 306324N) (PBT) and 10% foetal bovine serum (Sigma F4135) for 1 hr. Primary antibody incubation was done overnight at 4°C and followed by three washes (20 min each) in PBT. Secondary antibody incubation for 1hr at room temperature was followed by three washes. All steps were in 50-µl volume in a 96-well plate on a gentle rocker. Brains were then mounted on a slide in Vectashield with DAPI (Vector Labs). The following primary antibodies, diluted in blocking solution (see above): anti-Repo (1/100, mouse DSHB 8D12, RRID:AB_528448); anti-GFP (1/1000, rabbit, Life technologies, A11122) anti-GFP(1/100, mouse, Roche, RRID:AB_390913), anti-GFP (1/500, chicken, kindly provided by M. Meyer); anti-Ref(2)P (1/2000, rabbit, a gift of Tor Erik Rusten). Secondary antibodies were all from Life technologies (conjugated with Alexa-488, Alexa-555 or Alexa-666) and diluted 1/200 in blocking solution (see above).

Z-stacks at intervals of 0.3 µm or 5 µm were taken at 1024×1024 pixel/inch resolution. For control vs *garz ^IR^* comparisons, microscope settings were established using control flies to have a GFP signal below saturation and kept unchanged throughout all acquisitions. All images were acquired with a Leica TCS SP5 confocal microscope and mitochondria sphericity, volume and surface area in [Figure 3B,C](#f3){ref-type="fig"} were measured using the [3D Object Counter](https://imagej.net/3D_Objects_Counter) 2.0.1 plugin ^[@ref-37]^ in the [ImageJ Fiji](https://imagej.net/Fiji) 1.52n software (RRID:SCR_002285).

Statistical analysis
--------------------

All statistical analysis was performed with Graph- Pad Prism 7 software (RRID:SCR_002798). For all lifespans, the statistical analysis was performed using the log--rank test of the Kaplan and Meier method. For behavioural experiments (DART), the statistical analysis was done by one-way ANOVA using Dunnett's multiple comparisons post hoc test. Significance is shown by asterisks in all figures as follows: \*P\<0.05, \*\*P\<0.01, \*\*\*P\<0.001, and \*\*\*\*P\<0.0001.

Randomization and blinding
--------------------------

In each experiment the desired number of flies were selected haphazardly from a much larger cohort of flies with the same genotype and sex. Blinding was performed in lifespan and behaviour by masking the genotypes with a numerical or alphabetical serial labelling.

Results
=======

Development of an algorithm for ranking miRNA target genes for their relevance in adult glia in lifespan and ageing
-------------------------------------------------------------------------------------------------------------------

Firstly, such algorithm should prioritise the information for the miRNAs that had the strongest effect on the fly lifespan in our miRNA screen. To achieve this, we have quantified the average strength of each miRNA using the Chi square (χ ^2^) of each Kaplan Mayer analysis ( [Table 1](#T1){ref-type="table"}).

To identify potential target genes, we used four different databases available online: EBI MicroCosm, PicTar, microRNA.org and TargetScan. Each database weights the likelihood of every miRNA to target a given gene with a numerical score. Where this is different, for TargetScan, we calculated a numerical score on the basis of the sequence information provided by the database (see *Methods*).

Therefore, to rank target genes within each database taking into account both the likelihood of being targeted by a given miRNA and the strength of the effect of this miRNA in adult glia, we first multiplied the average strength of each miRNA from our screen (values in [Table 1](#T1){ref-type="table"}) by the strength of the target prediction (Score) given by the database, obtaining the parameter (Score)\*Av(χ ^2^). This was done for all miRNAs tested in our screen that were present in each database.

Because a given gene can be targeted by more than one miRNA, to rank its overall importance in adult glia, we have summed all the values obtained for a given gene that were calculated for different miRNAs, obtaining the parameter Σ\[(Score)\*Av(χ ^2^)\]. In the case of TargetScan, some miRNAs are grouped in families and we have considered them as a single unit value. This underweights these miRNAs in comparison to others and the genes targeted by them (for instance a gene targeted by miR-9a, miR-9b and miR-9c would obtain a Σ\[(Score)\*Av(χ ^2^)\] that is the sum of three (Score)\*Av(χ ^2^) in the other databases, but for TargetScan it would only reflect one (Score)\*Av(χ ^2^). Our reasoning was that grouped miRNAs in TargetScan was not taking into account valuable information and this should be reflected in a penalisation in the ranking.

In conclusion we have ranked target genes according to Σ\[(Score)\*Av(χ ^2^)\] for EBI MicroCosm ( *Extended data* Table 1) ^[@ref-38]^, PicTar ( *Extended data* Table 2) ^[@ref-38]^, microRNA.org ( *Extended data* Table 3) ^[@ref-38]^ and TargetScan ( *Extended data* Table 4) ^[@ref-38]^. Surprisingly, this revealed that there was very little agreement among the four databases. The top-ranking genes obtained using the same algorithm were very different and only 5.6% (i.e. 520 genes) of target predictions were common to all four databases ( [Figure 1A](#f1){ref-type="fig"}).

![Effects of *garz* knock-down in adult glial cell.\
( **A**) Venn diagram referring to the data in Table 6 and illustrating the overlap between the four different databases used to predict gene targets of the miRNAs whose expression in the adult glia resulted in a significant reduction in fly lifespan. Only 520 target genes are in common among all four databases, *garz* falls in this group. A remarkably large number of genes as targets were uniquely predicted by the MicroCosm database. ( **B**) Two RNAi lines against *garz* bring about a very significant reduction in fly lifespan in comparison to controls, when expressed in all adult glia. N=60 for each genotype, Error bars SEM, pairwise comparisons: Log-rank (Mantel-Cox) test. The full dataset can be accessed at DOI [10.17605/OSF.IO/8E3NS](https://doi.org/10.17605/OSF.IO/8E3NS) as part of Table 7. ( **C**) Knock-down of *garz* in sub-populations of glial cells, astrocyte-like ( *alrm-Gal4*), Cortex glia ( *NP2222-Gal4*), sub-perineural glia ( *moody-Gal4*), perineural and PNS glia ( *gliotactin-gal4*) or in neurons ( *elav-Gal4*) brings about a significant reduction in lifespan in comparison to controls. N=60 for each genotype, Error bars SEM, pairwise comparisons: Log-rank (Mantel-Cox) test. The full dataset can be accessed at DOI [10.17605/OSF.IO/HQCDG](https://doi.org/10.17605/OSF.IO/HQCDG). ( **D**) Lifespan reduction due to RNAi against *garz* in adult glia is rescued by an exogenous *UAS-garz* transgene and by a transgene expressing the human orthologue GBF1 under UAS control. Note that overexpression of *garz* in an otherwise wt background is highly detrimental to fly lifespan, whereas overexpression of GBF1 in a wt background has no adverse effects. Mutations leading to a non-functional Sec7 domain eliminate or drastically reduce the ability of *garz* or *GBF1* transgenes to rescue fly lifespan. N=60 for each genotype, Error bars SEM, pairwise comparisons: Log-rank (Mantel-Cox) test. The full dataset can be accessed at DOI [10.17605/OSF.IO/5RGEF](https://doi.org/10.17605/OSF.IO/5RGEF). ( **F**) Co-expression of human GBF1 significantly extends the short lifespan caused by overexpression of miR-1, miR-79 and miR-315 in adult glia. N=60 for each genotype, Error bars SEM, pairwise comparisons: Log-rank (Mantel-Cox) test. The full dataset can be accessed at DOI [10.17605/OSF.IO/B37DF](https://doi.org/10.17605/OSF.IO/B37DF).](f1000research-9-25558-g0000){#f1}

To rank these common targets for their predicted overall relevance in adult glia in ageing, we have devised additional steps. First, to make the numerical rankings from each database comparable, we have calculated the Normalised Σ\[(Score)\*Av(χ ^2^)\] parameter by normalising the maximum value to 100. Additionally, we have weighted this number for the fraction of miRNAs present in each database, out of the total tested in our miRNAs screen. Out of 44 miRNAs screened, 31 were present in EBI Microscosm, 28 in PicTar, 43 in microRNA.org and 40 in TargetScan. The rationale for this weighting was to prioritise the databases carrying more information that was relevant to our screen. Then, for each target gene, we have combined all these scores from the four databases generating the final parameter Σ{Normalised Σ\[(Score ^(2)^)\*Av(χ ^2^)\]} for all targets, including the 520 that were commonly predicted by all databases ( [Table 2](#T2){ref-type="table"}).

###### Identification and ranking of target predictions common to all four databases.

The ranking scores from all four databases were pooled to obtain a global rank of all targets predicted by our analysis and a list of targets that are predicted by all four databases. Because the different databases contained information about some, but not all, miRNAs analysed in our screen we have weighted the completeness of each database by normalising the Σ\[(Score)\*Av(χ ^2^)\] by the fraction of miRNAs listed in the database, out of the ones tested in our screen. In addition, to make the ranking from each database equally valued in this analysis, we have normalised each score to 100 as a maximum possible value for each database -- column Normalised Σ\[(Score)\*Av(χ ^2^)\]. Thereafter, all values for each target have been added -- column Σ{Normalised Σ\[(Score)\*Av(χ ^2^)\]} -- for each target and for a specific list of 520 targets that have been predicted by all four databases, albeit with different scores. Only the top 30 rows are shown here. The full table can be accessed at <https://doi.org/10.17605/OSF.IO/QWUAY>.

  ------------------------------------------------------------------------------------------------------------
  Pooled Non Redundant   520 Common elements in \"Targetscan\",\                                    
                         \"microRNA.org\", \"PicTar\" and \"EBI\                                    
                         MicroCosm\":                                                               
  ---------------------- ----------------------------------------- ---------- ----------- --------- ----------
  CG7852                 CG7852                                    206.8084   CG7852      CG7852    206.8084

  CrebA                  CG7450                                    165.9009   CadN        CG7100    148.5861

  CadN                   CG7100                                    148.5861   nerfin-1    CG13906   129.7977

  sha                    CG13209                                   133.6386   Cpr50Ca     CG13338   125.1063

  CG31191                CG31191                                   130.1998   Nak         CG10637   103.4518

  nerfin-1               CG13906                                   129.7977   CG11206     CG11206   95.6901

  CG13338                CG13338                                   125.1063   CG8128      CG8128    94.2880

  CG4297                 CG4297                                    122.1155   up          CG7107    94.0563

  Mef2                   CG1429                                    113.9984   CG3077      CG3077    0.0000

  Khc-73                 CG8183                                    110.5961   porin       CG6647    89.8516

  A2bp1                  CG32062                                   108.8272   CG14015     CG14015   89.2605

  Nak                    CG10637                                   103.4518   CG33090     CG33090   82.8792

  Rbp9                   CG3151                                    102.6802   ttk         CG1856    81.3790

  CG11206                CG11206                                   95.6901    Sbf         CG6939    73.9455

  sinu                   CG10624                                   94.9798    Klp68D      CG7293    72.5983

  CG8128                 CG8128                                    94.2880    CG12024     CG12024   72.3875

  up                     CG7107                                    94.0563    CG10737     CG10737   71.3749

  w                      CG5123                                    91.9243    rau         CG8965    70.9517

  CG3077                 CG3077                                    91.2297    CG9426      CG9426    69.5509

  porin                  CG6647                                    89.8516    salm        CG6464    68.5351

  CG14015                CG14015                                   89.2605    Thd1        CG1981    66.7116

  CG14274                CG14274                                   87.6812    Dysb        CG6856    65.1018

  Eip93F                 CG18389                                   84.3128    rho         CG1004    64.6470

  CG33090                CG33090                                   82.8792    Vha68-1     CG12403   64.4111

  lola                   CG12052                                   81.8661    CG8323      CG8323    64.1433

  ttk                    CG1856                                    81.3790    CG4853      CG4853    63.3083

  srp                    CG3992                                    81.2668    CG9650      CG9650    62.1572

  ck                     CG7595                                    80.9393    SP555       CG14041   61.7540

  CG32767                CG32767                                   78.9667    CdsA        CG7962    61.6698

  sdk                    CG5227                                    74.9601    RhoGAP68F   CG6811    61.4035

  Sbf                    CG6939                                    73.9455    Opa1        CG8479    61.2329
  ------------------------------------------------------------------------------------------------------------

###### Match between experimental RNAi and predictions for targets common to all databases.

Strength of the effect on fly lifespan of RNAi lines against some of the gene targets predicted by all four databases. For most genes two different RNAi lines have been tested. In red are the lines that, in agreement with the miRNA prediction, shorten the lifespan, in comparison to controls ( *w ^1118^*) when specifically expressed in the adult flies with *repo-Gal4* and *tub-Gal80 ^ts^*. In green are the lines that have the opposite effect and prolong lifespan. In black are the lines that had no effect. Highlighted in yellow are the genes for which all lines tested had the same effect and shortened lifespan. Highlighted in pink is the gene for which all lines tested had the same effect and prolonged the lifespan. To combine the target RNAi strength with the strength of the prediction we have averaged the χ² for each miRNA according to the same rules followed in Table 1 and multiplied it for the final score from Table 2 -- column Σ{Normalised Σ\[(Score)\*Av(χ²)\] \*{Av(χ²)IR}. The top rank was achieved by *garz*, which was also one of the 6 genes with all RNAi lines tested having the same effect. We have also repeated the same procedure separately for the four different databases using the final normalised score (Normalised Σ\[(Score)\*Av(χ²)\]) obtained from each database in Tables 2, 3, 4 and 5, and also reported in Table 2-- columns Normalised Σ\[(Score)\*Av(χ²)\] \*{Av(χ²)IR} for each database. The predicting power for the combination of all databases and for each database sums all scores in this column to quantify the global predicting value of each database in comparison to their combination. This is further normalised by the number of predicted targets for the whole screen, to measure the efficiency of each database when predicting target genes. The full dataset can be accessed at DOI [10.17605/OSF.IO/8E3NS](https://www.doi.org/10.17605/OSF.IO/8E3NS).

  Common to all databases                                           EBI MicroCosm   PicTar          microRNA.org   Targetscan                                                                                                                         
  ----------------------------------------------------------------- --------------- --------------- -------------- --------------- --------------- ---------- ----------- --------- ----------- --------- ----------- --------- ----------- --------- -----------
  Blimp-1                                                           108374/KK       27              3.9090         0.0480          2.1158          49.9482    105.6803    15.8908   33.6217     3.4973    7.3996      14.1685   29.9777     16.3916   34.6813
  34978/GD                                                          33              0.3226          0.5700                                                                                                                                            
  Bx                                                                106495/KK       24              4.1660         0.0412          5.7270          58.9239    337.4571    12.5711   71.9946     10.0509   57.5614     21.5938   123.6680    14.7081   84.2331
  2971/GD                                                           27              7.2880          0.0069                                                                                                                                            
  CadN                                                              101644/KK       31              0.0939         0.7593          -2.1481         148.5861   -319.1725   20.0306   -43.0271    46.4332   -99.7416    38.4779   -82.6531    43.6443   -93.7507
  1092/GD                                                           31              4.3900          0.0361                                                                                                                                            
  CG10737                                                           106383/KK       32              0.1800         0.6714          18.2600         71.3749    1303.3054   35.5040   648.3023    5.7567    105.1168    13.7227   250.5759    16.3916   299.3104
  8996/GD                                                           19              36.3400         \<0.0001                                                                                                                                          
  CG11206                                                           42943/GD        35              8.4800         \<0.0001        -3.7402         95.6901    -357.9003   9.0084    -33.6933    8.5247    -31.8840    34.9136   -130.5839   43.2434   -161.7391
  42945/GD                                                          28              0.9996          0.3174                                                                                                                                            
  CG12918                                                           105503/KK       35              10.6900        0.0011          -5.3436         52.1314    -278.5678   20.8109   -111.2046   3.3907    -18.1183    18.0949   -96.6912    9.8350    -52.5538
  38082/GD                                                          31              0.0029          0.9573                                                                                                                                            
  CG13606                                                           109997/KK       31              3.1620         0.0754          1.6099          52.6384    84.7431     14.5549   23.4320     4.7616    7.6658      16.6199   26.7566     16.7020   26.8887
  45481/GD                                                          29              0.0578          0.81                                                                                                                                              
  CG3077                                                            109618/KK       31              3.1620         0.0754          1.6012          91.2297    146.0771    23.2887   37.2898     6.4559    10.3372     41.8153   66.9546     19.6699   31.4955
  25630/GD                                                          29              0.0404          0.8301                                                                                                                                            
  CG32105                                                           108747/KK       28              2.5080         0.1132          2.5570          43.3198    110.7687    10.3112   26.3658     2.2363    5.7182      20.9373   53.5367     9.8350    25.1480
  51267/GD                                                          28.5            2.6060          0.1064                                                                                                                                            
  CG33090                                                           23407/GD        29              0.5904         0.4423          0.3904          82.8792    32.3560     21.1135   8.2427      4.0624    1.5859      16.7976   6.5578      40.9057   15.9696
  28033/GD                                                          28              0.1904          0.6626                                                                                                                                            
  CG3376                                                            12226/GD        29              0.3743         0.5406          0.1872          42.0182    7.8637      8.3405    1.5609      5.9978    1.1225      17.8450   3.3397      9.8350    1.8406
  CG3534                                                            109666/KK       35              8.7280         0.0031          -4.3640         48.6526    -212.3199   17.7977   -77.6690    3.4824    -15.1971    17.5376   -76.5341    9.8350    -42.9197
  41276/GD                                                          30              4.522E-07       0.9995                                                                                                                                            
  CG3624                                                            36304/GD        27              0.4244         0.5148          3.1832          55.4638    176.5522    16.9653   54.0040     2.6852    8.5476      23.3858   74.4417     12.4274   39.5589
  956/GD                                                            26              5.9420          0.0148                                                                                                                                            
  CG4360                                                            105000/KK       25              2.6370         0.1044          4.7780          47.9093    228.9108    18.0506   86.2459     2.4584    11.7464     11.0087   52.5996     16.3916   78.3190
  26520/GD                                                          25              6.9190          0.0085                                                                                                                                            
  CG4984                                                            10057/GD        31              0.4919         0.4831          -2.9691         59.4745    -176.5827   18.0490   -53.5884    7.6279    -22.6477    7.6745    -22.7861    26.1230   -77.5605
  107854/KK                                                         33              6.4300          0.0112                                                                                                                                            
  CG5599                                                            106456/KK       29              0.9585         0.3276          22.6593         40.5821    919.5590    8.8509    200.5549    1.0127    22.9470     12.8686   291.5939    17.8498   404.4632
  16505/GD                                                          18              44.3600         \<0.0001                                                                                                                                          
  CG6129                                                            110171/KK       23              12.2400        0.0005          14.1150         41.6798    588.3099    6.8082    96.0981     2.8427    40.1250     14.9821   211.4727    17.0467   240.6141
  22094/GD                                                          21              15.9900         \<0.0001                                                                                                                                          
  CG7510                                                            105469/KK       31              1.4210         0.2333          6.9305          49.2790    341.5279    14.8554   102.9554    2.4046    16.6651     12.5453   86.9454     19.4736   134.9621
  8532/GD                                                           24              12.4400         0.0004                                                                                                                                            
  CG8121                                                            105866/KK       33              5.6770         0.0172          -0.7240         54.1836    -39.2289    7.7300    -5.5965     2.9878    -2.1632     8.4767    -6.1371     34.9891   -25.3321
  43952/GD                                                          30              2.2380          0.1347                                                                                                                                            
  43953/GD                                                          31              1.2670          0.2603                                                                                                                                            
  CG8128                                                            107574/KK       31              0.0685         0.7936          -8.1508         94.2880    -768.5205   19.9651   -162.7308   3.0917    -25.1994    54.8397   -446.9861   16.3916   -133.6042
  97740/GD                                                          34.5            16.3700         \<0.0001                                                                                                                                          
  CG8303                                                            107101/KK       28              7.3890         0.0066          11.0345         49.7826    549.3262    15.8126   174.4840    3.3083    36.5052     14.2702   157.4640    16.3916   180.8730
  4918/GD                                                           25              14.6800         0.0001                                                                                                                                            
  CG8323                                                            4861/GD         29              3.5680         0.0589          1.7840          64.1433    114.4316    18.4807   32.9695     4.3704    7.7968      13.6601   24.3696     27.6321   49.2957
  CG8360                                                            23461/GD        34              4.8070         0.0283          -2.4035         56.7583    -136.4185   16.8753   -40.5598    5.7226    -13.7542    11.2122   -26.9484    22.9482   -55.1561
  CG8417                                                            106461/KK       38              23.1700        \<0.0001        -11.5805        52.5854    -608.9652   15.8653   -183.7277   4.3959    -50.9072    15.9326   -184.5075   16.3916   -189.8227
  49509/GD                                                          31              0.0090          0.9244                                                                                                                                            
  CG9376                                                            106062/KK       34              20.1300        \<0.0001        -10.0650        15.8820    -159.8522   7.2495    -72.9661    0.5563    -5.5996     3.1890    -32.0968    4.8872    -49.1895
  CG9650                                                            104402/KK       33              0.8672         0.3517          0.4336          62.1572    26.9522     7.6917    3.3352      7.0478    3.0560      23.3065   10.1060     24.1113   10.4549
  23170/GD                                                          29.5            2.53E-05        0.996                                                                                                                                             
  Cpr                                                               107422/KK       38              31.0800        \<0.0001        -15.5400        47.5051    -738.2286   14.3186   -222.5113   9.1431    -142.0838   14.2084   -220.7984   9.8350    -152.8352
  Dysb                                                              106957/KK       32              1.6390         0.2005          1.6958          65.1018    110.3974    19.4330   32.9539     4.5585    7.7301      20.0348   33.9743     21.0755   35.7391
  34354/GD                                                          29              0.1023          0.7491                                                                                                                                            
  34355/GD                                                          34              3.3460          0.0674                                                                                                                                            
  endoB                                                             104712/KK       38              39.1400        \<0.0001        -16.4445        24.5428    -403.5939   0.1061    -1.7449     6.1164    -100.5814   7.4080    -121.8216   10.9122   -179.4460
  29291/GD                                                          24              6.2510          0.0124                                                                                                                                            
  Ero1L                                                             11045/KK        31              0.0187         0.8912          3.1058          45.3898    140.9738    14.8674   46.1758     5.0706    15.7486     15.6169   48.5035     9.8350    30.5458
  51169/GD                                                          25              6.1930          0.0128                                                                                                                                            
  garz                                                              42140/GD        17              42.4800        \<0.0001        45.3800         31.3660    1423.3876   4.7120    213.8301    0.5962    27.0568     6.4735    293.7671    19.5843   888.7336
  42141/GD                                                          16              48.2800         \<0.0001                                                                                                                                          
  Gfat2                                                             105129/KK       31              0.9625         0.3265          0.9978          41.9788    41.8843     15.8440   15.8083     3.1358    3.1287      13.1641   13.1345     9.8350    9.8128
  17187/GD                                                          27              1.0330          0.3094                                                                                                                                            
  Myd88                                                             106198/KK       31              1.7490         0.186           1.9035          6.4706     12.3167     4.1638    7.9258      0.0121    0.0231      2.0501    3.9023      0.2446    0.4656
  25402/GD                                                          28              2.0580          0.1515                                                                                                                                            
  Nak                                                               109507/KK       29              0.4342         0.5099          0.2233          103.4518   23.1023     19.0669   4.2579      16.3947   3.6612      36.5532   8.1629      31.4370   7.0203
  35482/GD                                                          29              0.0124          0.9112                                                                                                                                            
  pdm2                                                              102126/KK       31              2.1180         0.1455          1.0590          25.7957    27.3176     6.5096    6.8937      0.9607    1.0173      4.8823    5.1704      13.4431   14.2362
  porin                                                             101336/KK       38              15.4000        \<0.0001        -7.7000         89.8516    -691.8569   62.5375   -481.5385   2.5203    -19.4065    2.9915    -23.0343    21.8023   -167.8776
  Ptp69D                                                            27091/GD        27              2.4770         0.1155          1.4233          52.3718    74.5408     14.2880   20.3361     6.8889    9.8050      19.0121   27.0599     12.1828   17.3398
  40631/GD                                                          29              0.3696          0.5432                                                                                                                                            
  RASSF8                                                            105823/KK       31              0.9831         0.3214          0.8924          34.0327    30.3690     4.4412    3.9631      0.7073    0.6312      8.3362    7.4388      20.5480   18.3360
  26520/GD                                                          27              0.8016          0.3706                                                                                                                                            
  raw                                                               101255/KK       35              17.5900        \<0.0001        10.6350         43.2677    460.1515    11.3523   120.7318    5.8320    62.0234     15.1805   161.4449    10.9028   115.9513
  24532/GD                                                          14              38.8600         \<0.0001                                                                                                                                          
  regucalcin                                                        105509/KK       31              1.0220         0.312           9.8860          56.0884    554.4895    15.5743   153.9677    15.7528   155.7325    14.9263   147.5610    9.8350    97.2283
  39945/GD                                                          22              18.7500         \<0.0001                                                                                                                                          
  RhoGAP68F                                                         107775/KK       28              3.0870         0.0789          1.5819          61.4035    97.1323     14.2428   22.5303     1.8217    2.8817      19.0121   30.0747     26.3269   41.6457
  34520/GD                                                          31              0.0767          0.7818                                                                                                                                            
  Sbf                                                               22317/GD        35              8.9180         0.0028          -4.4590         73.9455    -329.7229   28.9451   -129.0661   4.3431    -19.3659    17.0058   -75.8288    23.6515   -105.4621
  sens                                                              106028/KK       34              7.399          0.0065          -3.6995         49.4627    -182.9872   14.7634   -54.6171    2.6482    -9.7972     8.9293    -33.0340    23.1217   -85.5388
  Sirt2                                                             103790/KK       30              5.7360         0.0166          -4.8680         44.5340    -216.7917   15.6316   -76.0947    5.3448    -26.0184    13.7227   -66.8020    9.8350    -47.8766
  21999/GD                                                          31              4.0000          0.0455                                                                                                                                            
  SP555                                                             39821/GD        19              19.5700        \<0.0001        9.7850          61.7540    604.2626    20.8951   204.4589    7.8086    76.4074     17.9622   175.7604    15.0880   147.6358
  T48                                                               100334/KK       11              21.3000        \<0.0001        10.6500         49.6111    528.3581    4.3790    46.6361     1.1091    11.8117     10.2365   109.0189    33.8865   360.8914
  Thd1                                                              110439/KK       29              0.7217         0.3956          0.3609          66.7116    24.0729     19.9882   7.2127      2.1996    0.7937      24.2192   8.7395      20.3047   7.3269
  Tm1                                                               34119/GD        19              10.7400        0.001           5.3700          37.4083    200.8823    9.7281    52.2401     3.4708    18.6380     9.6231    51.6761     14.5862   78.3281
  toe                                                               107893/KK       23              15.4400        \<0.0001        13.0900         47.8611    626.5024    17.7450   232.2815    3.2504    42.5476     17.0308   222.9338    9.8350    128.7395
  46515/GD                                                          27              10.7400         0.001                                                                                                                                             
  up                                                                27853/GD        25              7.8600         0.0051          3.9300          94.0563    369.6411    32.8545   129.1183    24.6085   96.7114     24.4104   95.9330     12.1828   47.8784
  Vha68-1                                                           17102/GD        28              0.7032         0.4017          0.6250          64.4111    40.2570     9.5403    5.9627      3.4957    2.1848      26.8446   16.7779     24.5305   15.3316
  46397/GD                                                          28              0.5468          0.4596                                                                                                                                            
  **Predicting Power**                                              **4843.1509**   **1178.4057**   **279.9651**   **1284.1503**   **2100.6298**                                                                                                      
  **Predicting power normalised for number of targets predicted**   **0.3227**      **0.1704**      **0.1556**     **0.3902**      **0.6990**                                                                                                         

###### Match between experimental RNAi and predictions for targets not in common to all databases.

Strength of the effect on fly lifespan of RNAi lines against some of the gene targets predicted by some, but not all, databases. In red are the lines that, in agreement with the miRNA prediction, shorten the lifespan, in comparison to controls ( *w ^1118^*) when specifically expressed in the adult flies with *repo-Gal4* and *tub-Gal80 ^ts^*. In green are the lines that have the opposite effect and prolong lifespan. In black are the lines that had no effect. Highlighted in yellow is the gene for which all lines tested had the same effect and shortened lifespan. To combine the target RNAi strength with the strength of the prediction we have averaged the χ² for each miRNA according to the same rules followed in Table 1 and multiplied it for the final normalised score (Normalised Σ\[(Score)\*Av(χ²)\]) obtained from each database in *Extended data* Tables 1, 2, 3and 4 \[49\], and also reported in Table 2 -- columns Normalised Σ\[(Score)\*Av(χ²)\] \*{Av(χ²)IR} for each database. The predicting power for each database sums all scores in this column to quantify the global predicting value of each database. This is further normalised by the number of predicted targets for the whole screen, to measure the efficiency of each database when predicting target genes, as in Table 3. The full dataset can be accessed at DOI [10.17605/OSF.IO/QTASN](https://doi.org/10.17605/OSF.IO/QTASN).

                                                                                                                                           EBI MicroCosm   PicTar     microRNA.org   Targetscan                                  
  ----------------------------------------------------------------- --------------- ------------- -------------- -------------- ---------- --------------- ---------- -------------- ------------ --------- ---------- --------- ----------
  CG15544                                                           39997/GD        28            0.2505         0.6167         0.1253     1.2446          0.1559     0.0137         0.0017                            0.1564    0.0196
  CG1623                                                            107655/KK       26            3.0500         0.0808         2.9325     14.6090         42.8407                                20.3535   59.6866    13.6183   39.9357
  32665/GD                                                          27              2.8150        0.0934                                                                                                                         
  CG17712                                                           105119/KK       31            0.3146         0.5749         4.5853     16.4402         75.3831                                12.5865   57.7130    9.8350    45.0962
  32987/GD                                                          26              8.8560        0.0029                                                                                                                         
  CG3678                                                            26267/GD        22            35.9900        \<0.0001       24.6750    22.2661         549.4160                               15.6470   386.0907   9.8350    242.6775
  49793/GD                                                          24              13.3600       0.0003                                                                                                                         
  CG4893                                                            110188/KK       31            1.0850         0.2977         5.8825     19.6009         115.3024                               21.6375   127.2824   20.3047   119.4422
  22356/GD                                                          26              10.6800       0.0011                                                                                                                         
  fray                                                              101058/KK       38            22.8600        \<0.0001       -11.2926                                                                               1.0625    -11.9986
  27944/GD                                                          31              0.2749        0.6001                                                                                                                         
  Ggamma1                                                           28894/GD        32.5          3.4180         0.0645         1.7090                                10.0845        17.2344      3.4702    5.9305     18.7955   32.1215
  inx2                                                              102194/KK       33            0.2422         0.6226         0.1211     0.0986          0.0119                                 10.2664   1.2433               
  Nek2                                                              103408/KK       33            0.6786         0.4101         3.1163     29.1504         90.8415                                13.6972   42.6847    9.8350    30.6487
  40052/GD                                                          26              5.5540        0.0184                                                                                                                         
  nord                                                              39901/GD        31            0.0068         0.9341         0.0034                                0.0120         0.0000       3.5694    0.0122     0.3669    0.0013
  Paf-AHalpha                                                       101683/KK       33            6.2970         0.0121         -3.1485    2.4176          -7.6118                                                               
  Pif1B                                                             49782/GD        17            36.0700        \<0.0001       18.0350    8.5626          154.4266                               5.5158    99.4768    18.7342   337.8716
  sna                                                               50003/GD        32            3.0820         0.0791         2.1454     0.5810          1.2464                                 0.3121    0.6696     6.6337    14.2319
  50004/GD                                                          33              0.1852        0.6670                                                                                                                         
  6232/GD                                                           31.5            3.1690        0.0751                                                                                                                         
  tor                                                               101154/KK       31            2.5570         0.1098         1.5355     16.4688         25.2879                                11.3850   17.4817    16.3916   25.1693
  36280/GD                                                          31              0.5140        0.4734                                                                                                                         
  **Predicting Power**                                              **1047.3007**   **17.2361**   **798.2716**   **875.2168**                                                                                                    
  **Predicting power normalised for number of targets predicted**   **0.1515**      **0.0096**    **0.2426**     **0.2913**                                                                                                      

Systematic experimental testing of the prediction, ranking and effectiveness of different databases
---------------------------------------------------------------------------------------------------

To test these predictions, we decided to screen for the lifespan effect, a number of RNAi lines from Vienna *Drosophila* Resource Center (VDRC) that were already present in our stock collection. These corresponded to a random selection of approximately 10% (51 out of 520) of commonly predicted target genes. Adopting a similar strategy used for the miRNA screen, we have used the *repo-Gal4, tub-Gal80* ^ts^ inducible system to trigger the RNAi expression in all glial cells in adult flies. As negative control, we used the offspring of crossing *repo-Gal4, tub-Gal80* ^ts\ ^ to *w ^1118^* throughout the screen. The expectation was that RNAi against these target genes in adult glia, would phenocopy the effect of the miRNAs that are predicted to target them, therefore shortening lifespan.

The gold standard commonly used by the *Drosophila* community to gain confidence about the effects of RNAi knock-down is to obtain a similar effect when testing two RNAi lines against the same gene (2-RNAi lines criterion). Remarkably, only in six cases at least two different RNAi lines tested for the same gene delivered the shorter lifespan phenotype that was predicted ( [Table 3](#T3){ref-type="table"}). In another case both RNAi lines tested had the same effect, but it was the opposite of the predicted one, extending lifespan with respect to the control flies.

In other cases (11/51) there was an overall confirmation of the prediction, but the two RNAi lines tested for one given target did not share the same effect or we were able to test only one line. The largest group (19/51) was made by cases in which there was no effect and surprisingly in a remarkable number of cases (14/51) there was an overall effect opposite to that predicted, albeit either the two RNAi lines tested for one given target did not share the same effect or we were able to test only one line.

In addition to the 2-RNAi lines criterion we have devised a quantitative index for ranking these targets by combining their effect in the RNAi screen (averaging the Chi square for the RNAi lines targeting each gene, Av(χ ^2^)IR) with the strength of the prediction in all combined databases (Σ{Normalised Σ\[(Score)\*Av(χ ^2^)\]}).

This parameter (Σ{Normalised Σ\[(Score)\*Av(χ ^2^)\]}\*{Av(χ ^2^)IR}) highlighted *garz*, one of the six targets satisfying the 2-RNAi lines criterion, as the top target ( [Table 3](#T3){ref-type="table"}). However, there was incomplete agreement with respect to the rest of the ranking between the two criteria, i.e. our scoring system and the rule of 2-RNAi lines, with only four of the ten top scores coming from target genes satisfying the 2-RNAi lines criterion.

We also tested 14 additional targets that were differentially predicted by the different databases. We were able to further identify five targets that confirmed the predicted phenotype, one satisfying also the 2-RNAi lines criterion, while two had the opposite overall effect ( [Table 4](#T4){ref-type="table"}).

A comparison between these two groups, the common to all databases and the differentially predicted, highlights that the fraction of validated prediction is similar, but the chance of finding false positives (i.e. targets that had the opposite effect to that predicted) is paradoxically higher in the commonly predicted group (15/51 in the common and 2/14 in the differential).

Considering the lack of tangible benefits of focusing on the commonalities between the different databases, we have then exploited our validation analysis to quantify the prediction capability of each of the four databases to identify the most valid for our screen. For all targets tested, both from the common group ( [Table 3](#T3){ref-type="table"}) and from the differential group ( [Table 4](#T4){ref-type="table"}), we have calculated the database-specific Normalised Σ\[(Score)\*Av(χ ^2^)\] \*{Av(χ ^2^)IR} parameter by combining the quantification of the lifespan effect of the RNAi lines (average Chi square in the RNAi screen) with the normalised predicted score from each database. Then, to rank databases we have summed all these results (with a negative value for false positives) to determine the predicting power score. TargetScan had the highest predicting power for the list of common targets, while MicroCosm had the highest capacity for target identification among the differential targets. PicTar had the lowest predicting power in all cases. However, MicroCosm also predicted the largest number of genes as targets of our miRNA screen, with over 44% of them not shared by the other databases. We reasoned that this lack of efficiency in EBI Microcosm had to be considered and when normalising for the total number of predicted targets from each database, as a measure of the predicting power efficiency, TargetScan showed a greater efficiency in both cases, followed by miRNA.org.

Fly lifespan and motor behaviour are affected by *garz* knockdown in adult glia
-------------------------------------------------------------------------------

As mentioned, we ranked the target genes from the RNAi confirmed predictions and decided to further investigate the top ranked target, *garz*, the fly orthologue for *GBF1* ^[@ref-19],\ [@ref-20],\ [@ref-39]^).

Pan-glial knockdown of *garz* with *repo-Gal4* specifically during adulthood strongly reduced lifespan. This was true for both RNAi lines tested when compared to *w ^1118^* median lifespan control ( [Figure 1B](#f1){ref-type="fig"}). Different glial cell types present in the adult fly brain have specific morphology and function ^[@ref-40]^. In order to test if a specific glial sub-population could account for the observed phenotype, we targeted the knockdown of *garz* using established Gal4 driver lines: astrocyte-like ( *alrm-Gal4*), cortex ( *NP2222-Gal4*), subperineural ( *moody-Gal4*) and peripheral ( *gliotactin-Gal4*) glia. In all sup-populations tested, the downregulation of *garz* caused a reduction in lifespan, albeit not as strong as the pan-glial knockdown ( [Figure 1C](#f1){ref-type="fig"}). This suggests that a combination of multiple functions is affected by *garz*.

We also analysed the effects of pan-neuronal ( *elav-Gal4*) knockdown of *garz*. This also led to a significant shortening of lifespan although the effect was milder than the one obtained with pan-glial *garz* knockdown ( [Figure 1B vs 1C](#f1){ref-type="fig"}), either because of differences in Gal4 line strength or because of a higher impact of *garz* function in glial cells for maintenance of the brain homeostasis.

We then focused on rescuing the glia-related shorter lifespan phenotype using exogenous transgenes for *garz* and human *GBF1*. Although the overexpression of *garz* alone in adult glia had a very toxic effect, when combined with the *garz-*RNAi overexpression, promoted a modest but significant rescue of the lifespan ( [Figure 1D](#f1){ref-type="fig"}). This suggests that *garz* levels need to be tightly controlled in the fly. On the other hand, overexpression of the human *GBF1* was entirely neutral for fly lifespan when expressed on its own and fully rescued the lifespan phenotype when co-expressed with *garz* RNAi. This indicates a remarkable conservation in functions between *garz* and *GBF1*.

For both garz and GBF1, the presence of a functional Sec7 domain, which is responsible for the catalytic activity of GEF proteins domain ^[@ref-24]^, was important to exercise their rescue activity ( [Figure 1D](#f1){ref-type="fig"}). In the case of *UAS-garz*, a mutation of the Sec7 domain entirely eliminated the rescue of *garz* knock down, actually aggravating toxicity. This also indicates that the toxicity of *garz* overexpression is not dependent on the catalytic GEF function of *garz*, possibly suggesting a dominant negative effect by sequestration of binding partners in catalytically inactive complexes. Additionally, in the case of *UAS-GBF1*, the rescue effect was significantly reduced, albeit not entirely eliminated, by an inactive Sec7 domain ( [Figure 1D](#f1){ref-type="fig"}).

Human GBF1 showed a remarkable capability to fully rescue lifespan shortening upon *garz* knockdown in glia. We then asked whether it would also be able to rescue the lifespan shortening induced by miRNAs predicted to target *garz*. From our database analysis, miR-1, miR-79 and miR-315, all causing a strong reduction of lifespan ^[@ref-11]^, were among the miRNAs predicted to target *garz* and may be rescued by *GBF1*. Indeed, *UAS-GBF1* was able to significantly rescue the phenotypes caused by the overexpression of these miRNAs in glia ( [Figure 1E](#f1){ref-type="fig"}). *GBF1* co-overexpression was able to rescue the lifespan for miR-79 and miR-315 to what would be commonly observed in wild-type flies. These results confirm our initial predictions and establish *garz* as the main mediator of the effect on lifespan caused by overexpression of miR-79 and mir-315 in adult glia. The partial rescue of the miR-1 phenotype indicates that *garz* is only partially responsible for the effect of miR-1 in adult glia and is in accordance with the previously reported role of *repo* in miR-1-mediated lifespan shortening ^[@ref-11]^.

We have previously described an automated unbiased and high-throughput method to analyse fly motor activity ^[@ref-11]^). When using this paradigm, we unravelled an impact of glial *garz* knockdown on the amplitude of the response to a train of stimuli and GBF1 co-overexpression rescued this response ( [Figure 2A](#f2){ref-type="fig"}). When looking at spontaneous activity parameters, i.e. non-stimulus driven, in the same experiment, flies expressing *garz*-RNAi showed a reduced average speed and an increased interval between bouts of movement without reflecting in the overall bout movement duration. Both average speed and inter-bout interval were fully rescued by the co-overexpression of GBF1 ( [Figure 2B--D](#f2){ref-type="fig"}). This analysis indicates that *garz* knock down affects not only lifespan but also the healthspan and motor activity both exogenously stimulated and internally generated, making flies slower and also pausing more.

![Knock down of *garz* in adult glial cells leads to significant impairment of fly motor functions.\
All data in this figure represent a grouping of two independent experiments with a total number of flies analysed (N) of 35--40. Error bars represent SEM in all graphs. Untreated track data can be accessed at DOI [10.17605/OSF.IO/UNJX7](http://dx.doi.org/10.17605/OSF.IO/UNJX7). ( **A**) Stimulus response curve for control flies (black), *garz* RNAi (red) and co-expression of GBF1 and *garz* RNAi (green). The graph is an average of 6 tracks for each of the stimuli received at 15 min intervals (See Methods). All genotypes also include *repo-Gal4* and *ubi-Gal80 ^ts^* to express the transgenes in all adult glia. In control flies the presence of *tub-Gal80* blocks any expression of UAS-transgenes. The graph to the right reports the mean amplitude of the response to a train of stimuli, which is significantly reduced by RNAi against *garz*, and this reduction is reverted to normal level by co-expression of human GBF1. One-way ANOVA, Dunnett's multiple comparisons post hoc test. ( **B**) Average speed analysis of the same flies as in A. RNAi against *garz* significantly slows down fly motility and this is rescued by human GBF1. One-way ANOVA, Dunnett's multiple comparisons post hoc test. ( **C**) Mean bout length analysis of the same flies as in A. No significant difference is detected in this parameter. One-way ANOVA, Dunnett's multiple comparisons post hoc test. ( **D**) Mean interbout interval analysis of the same flies as in A. RNAi against *garz* significantly increases the time spent in inactivity by flies and this is rescued by human GBF1. One-way ANOVA, Dunnett's multiple comparisons post hoc test.](f1000research-9-25558-g0001){#f2}

Subcellular effects of *garz* knockdown in adult glia
-----------------------------------------------------

We next set out to determine the effects *garz* knock down had inside the glial cells that would correlate with behavioural and lifespan dysfunctions.

It has been reported that *garz* knockdown impairs vesicle transport and membrane delivery during fly development ^[@ref-25]^. Thus, we analysed membrane distribution in the presence of *garz*-RNAi in adult brains. Driving the expression *CD8*- *GFP* in glia showed aberrant membrane distribution upon *garz* knockdown when compared to a more homogeneous distribution of the GFP signal in glia from control brains ( [Figure3A](#f3){ref-type="fig"}, Videos 1 and 2). Such data suggests that overall membrane trafficking in glia may be impaired although we have not been able to detect failure in membrane delivery of the cell adhesion cadherin molecule CadN (data not shown, the full dataset can be accessed at DOI [10.17605/OSF.IO/7HRZS](http://dx.doi.org/10.17605/OSF.IO/7HRZS)).

![Sub-cellular dysfunctions caused by *garz* knock-down in adult glial cell.\
( **A**) Representative single confocal sections of adult fly brains stained for DAPI (blue), GFP (green), Repo (magenta) and Ref (2)P (red). Pan glial knock-down of *garz* with *repo-Gal4* and *ubi-Gal80 ^ts^* leads to abnormal distribution of the plasma membrane targeted CD8-GFP protein (expressed from a *UAS-CD8-GFP* transgene in all glial cells) leading to gaps and blebs (arrows, see also Videao1 and 2), and to accumulation of Ref(2)P puncta (arrowheads). The full dataset can be accessed at DOI [10.17605/OSF.IO/96TS3](https://doi.org/10.17605/OSF.IO/96TS3). ( **B**) Representative single confocal section of adult fly brains stained for DAPI (blue), GFP (green) and Repo (red). The GFP signal also in back and white (lower panels) is due to the presence of a *UAS-mitoGFP* transgenes and detects mitochondria. ( **C**) Quantification of mitochondria parameters based on the GFP signal in B. Pan glial knock-down of *garz* with *repo-Gal4* and *ubi-Gal80 ^ts^* leads to significant increases in the volume, surface area and sphericity of mitochondria. Mann-Whitney non-parametric test. N=300 objects, randomly selected from 4 brains. Error bars represent SEM. The full dataset can be accessed at DOI [10.17605/OSF.IO/EXMTG](https://doi.org/10.17605/OSF.IO/EXMTG).](f1000research-9-25558-g0002){#f3}

Video 1. CD8-GFP in control brains.3D reconstruction of confocal stacks imaging of panglial (*repo-Gal4*) CD8-GFP expression in a control brain. Note the smooth appearance of the glial membranes highlighted by the green signal. The full dataset can be accessed at DOI 10.17605/OSF.IO/96TS3.Click here for additional data file.Copyright: © 2020 Gonçalves-Pimentel C et al.2020

Video 2. CD8-GFP in garz knock-down brains.3D reconstruction of confocal stacks imaging of panglial (*repo-Gal4*) CD8-GFP expression in a *garz* knock-down brain. Note the clustering and blebs formed by the glial membranes highlighted by the green signal. The full dataset can be accessed at DOI 10.17605/OSF.IO/96TS3.Click here for additional data file.Copyright: © 2020 Gonçalves-Pimentel C et al.2020

Conflicting *in vitro* data has been reported for the effects of GFB1 ^[@ref-31]^ and *garz* ^[@ref-32]^ in what concerns autophagy regulation. Looking at the distribution of the Ref(2)p (the orthologue of mammalian p62) autophagy receptor ^[@ref-43]^ revealed Ref(2)p accumulation in puncta, suggesting a potential block in autophagic clearance in glial cells ( [Figure 3A](#f3){ref-type="fig"}).

Finally, it has been suggested a role for GBF1 in the regulation of mitochondria morphology and function in yeast, *C. elegans* muscle and HeLa cells ^[@ref-30]^. Using *mito-GFP* transgene we were able to identify mitochondrial morphology defects in adult glial cells ( [Figure 3B, C](#f3){ref-type="fig"}). Quantification of the main morphological parameters has unravelled an overall increased mitochondrial volume, surface and sphericity upon *garz* knockdown. These parameters may indicate a defect in mitochondria quality control and are in agreement with an impaired autophagic clearance, which has the potential to also affect mitophagy.

*Underlying data* contains the raw data behind these results ^[@ref-44]^.

Discussion
==========

We have previously screened a library of miRNAs for effects on *Drosophila's* lifespan when expressed in adult glia and already established that this strategy can identify factors important for nervous system health in adult life ^[@ref-11]^. We aimed here at developing a generalizable global approach that would allow to identify the key target genes that mediate the actions of miRNAs in a given context. Focusing on miRNAs that shortened the lifespan, we devised an *in-silico* strategy to unravel a potential list of genes relevant for glial function and consequently brain homeostasis in adult flies. The outcome of this strategy had efficiency issues and highlighted the little overlap in the predictions made on the basis of four different databases for miRNA target prediction in *Drosophila.*

To put to the test the outcome of these *in silico* predictions, we have silenced individual genes by inducing the expression of specific RNAi in adult glia. The assumption being that RNAi downregulation of the top target genes would phenocopy the effect observed when expressing the miRNAs targeting them, i.e. lifespan reduction. Overall, however, the number of genes that, upon knockdown, reduced lifespan was remarkably low, and we could observe no tangible benefit of focusing on predictions in common to all four databases, versus targets differentially predicted in the different databases. It was also evident from our analysis that, among the databases, TargetScan and miRNA.org were considerably more efficient in delivering predictions that withstood the RNAi tests.

Therefore, the benefits of using miRNAs-based screens and *in silico* identification of targets, in place of much larger screens based on targeting single genes, have to be carefully evaluated and *in silico* selection of target genes should be based primarily on the TargetScan and miRNA.org databases. Nevertheless, the fraction of validated positive target genes by two criteria (7/65) and by at least one (22/65) is much larger than what usually expected in siRNA screens and suggests a 3/5-fold enrichment in positive hits. Thus, our method makes *Drosophila* screens a more appealing platform with reduced workload in comparison to traditional single gene targeted screens, whether by RNAi or genomic mutagenesis. This may have 3Rs benefits, facilitating the use of *Drosophila* as a model for preliminary studies on the genetic factors that influence a given biomedical process.

Our screen has also highlighted a number of genes that are strong, and in most cases unexpected, candidates for essential functions in adult glia in ageing. This list of genes provides a useful tool for scientists studying glial functions in ageing. In particular, all identified genes that have been validated by two RNAi lines have clear mammalian orthologues. *Drosophila* can therefore be used to study in detail the functions of these genes in the adult glial cells, in place of genetically modified mouse models.

To validate our findings, we focused on the top target of the genes commonly predicted by all databases and also by TargetScan, i.e. *garz*, the fly orthologue of the human GBF1.

The analysis of *garz* confirmed that this gene is absolutely required in adult glia, and also in neurons, for fly survival. Using our automated behavioural set up we could also establish that *garz* is essential in glia for locomotor activity in response to a stimulus or endogenously generated. Analysing the effects of silencing *garz* in different glial sub-populations showed that the strong reduction in lifespan could not be accounted for by one specific type of glia but rather due to a combined effect of silencing *garz* in all glial cells simultaneously, indicating that *garz* is essential for any glial cell type.

Our subcellular analysis suggests that the locomotor and lifespan defects correlate and possibly originate from a number of cellular defects in protein trafficking, autophagy and mitochondria quality control.

In *Drosophila*, mutated versions or knockdown of *garz* resulted in developmental epithelial morphogenesis defects ^[@ref-20],\ [@ref-21]^ and impaired membrane delivery of adhesion molecules ^[@ref-25]^. We have been able to identify membrane defects in glial membrane distribution, although not all membrane proteins seemed to be affected by *garz* knockdown. *garz* and *GBF1* have been identified as a positive autophagy regulator in *Drosophila* primary cultured muscle cells ^[@ref-32]^ and mammalian cells ^[@ref-31]^. An accumulation of Ref(2)P upon *garz*-RNAi expression in adult glia suggests an autophagic clearance deficits, in agreement with these studies.

*GBF1*-RNAi has been shown to affect mitochondrial morphology and function ^[@ref-30]^. Chemical inhibition of GBF1 in mammalian cells also showed condensed mitochondria and mislocalisation in the cell ^[@ref-45]^. Although mislocalisation of mitochondria is difficult to assess due to glial cell morphology in the *Drosophila* brain, *garz*-RNAi strongly affected mitochondria morphology suggesting a more condensed state which may be a reflection of an unbalanced fission/fusion regulation and mitochondria quality control ^[@ref-46]^.

Our analysis further suggested that there was remarkable functional conservation between *garz* and human GBF1, with the latter being able to fully rescue, partially in a Sec7-domain dependent manner, the shorter lifespan and motor behaviour phenotypes caused by the silencing of *garz*. GBF1 was also able to rescue the lifespan shortening by three different miRNAs, miR-1, miR-79 and miR-315, validating that in our screen their effect is at least partially, and in some cases almost entirely, due to downregulation of *garz*.

Thus, these data validate both the logic and principles of miRNA screens, despite inefficiencies, and the use of *Drosophila* as a valid organism to study the biology of *garz*/ *GBF1*.

The identification of major cellular events regulated by *garz*/ *GBF1* ^[@ref-27]--\ [@ref-29],\ [@ref-47]--\ [@ref-49]^ has targeted such molecules for health and disease studies ^[@ref-18]^. Recently, it has been shown that siRNA knockdown of GBF1 causes intracellular APP accumulation in primary cortical neurons; overexpression of GBF1 contributes to APP trafficking and is dependent on its GEF activity ^[@ref-50]^. Inhibition of GBF1 with brefeldin A was also shown to lead to a new form of cellular degeneration and death in neurodegenerative diseases, based on destruction of the nuclear lamina ^[@ref-51]^.

Gbf1 conditional mutant mice have been generated in the Wellcome Trust Sanger Institute and are being phenotyped by the International Mouse Phenotyping Consortium ( <https://www.mousephenotype.org/data/genes/MGI:1861607>). We demonstrate here that *Drosophila* would constitute an ideal organism to put forward 3Rs-compliant alternatives and, at least partially, replace this mouse line in studies aiming at understanding the role of GBF1 in health and disease.

Data availability
=================

Underlying data
---------------

Open Science Framework: miRNA-garz. <https://doi.org/10.17605/OSF.IO/A5ZST> ^[@ref-44]^.

This project contains the following underlying data: [Table 2](#T2){ref-type="table"} (XLSX). (The complete [Table 2](#T2){ref-type="table"}.)[Table 2](#T2){ref-type="table"} Data -- Pimental *et al.,* 2020 (XLSX). (Data underlying [Table 2](#T2){ref-type="table"}.)[Table 3](#T3){ref-type="table"} Data -- Pimental *et al.,* 2020 (XLSX). (Data underlying [Table 3](#T3){ref-type="table"}.)[Table 4](#T4){ref-type="table"} Data -- Pimental *et al.,* 2020 (XLSX). (Data underlying [Table 4](#T4){ref-type="table"}.)Figure 1C Data - Pimentel *et al.,* 2020 (XLSX). (Data underlying [Figure 1C](#f1){ref-type="fig"}.)Figure 1D Data - Pimentel *et al.,* 2020 (XLSX). (Data underlying [Figure 1D](#f1){ref-type="fig"}.)Figure 1E Data - Pimentel *et al.,* 2020 (XLSX). (Data underlying [Figure 1E](#f1){ref-type="fig"}.)Figure 2 Data - Pimentel *et al.,* 2020 (XLSX). (Data underlying [Figure 2](#f2){ref-type="fig"}.)Figure 3A and videos. (TIFF images and ZIP files containing data underlying [Figure 3A](#f3){ref-type="fig"}.)Figure 3B-C. (ZIP files containing raw images underlying [Figure 3B, C](#f3){ref-type="fig"}.)Extended data Table 1- Data - Pimentel *et al.,* 2020 (XLSX). (Data underlying *Extended data* Table 1.)Extended data Table 2- Data - Pimentel *et al.,* 2020 (XLSX). (Data underlying *Extended data* Table 2.)Extended data Table 3- Data - Pimentel *et al.,* 2020 (XLSX). (Data underlying *Extended data* Table 3.)Extended data Table 4- Data - Pimentel *et al.,* 2020 (XLSX). (Data underlying *Extended data* Table 4.)Data not shown. (ZIP files containing images of membrane delivery of the cell adhesion cadherin molecule CadN.)

Extended data
-------------

Open Science Framework: miRNA-garz. <https://doi.org/10.17605/OSF.IO/K5HW9> ^[@ref-38]^.

This project contains the following extended data: **Extended Data Table 1. MicroCosm target prediction and ranking tables.** For each miRNA, ranking of target prediction - column (Score)\*Av(χ ^2^) - was made by multiplying the Average χ ^2^ obtained in the screen (from [Table 1](#T1){ref-type="table"}) by the Score predicted in the MicroCosm database. In the total table, all values from a given target, resulting from all miRNAs were summed in a final ranking value in column Σ(Score)\*Av(χ ^2^)\]. This table and the full dataset can be accessed at DOI [10.17605/OSF.IO/R3ZX9](https://doi.org/10.17605/OSF.IO/R3ZX9).**Extended data Table 2. PicTar target prediction and ranking tables.** For each miRNA, ranking of target prediction - column (Score)\*Av(χ ^2^) - was made by multiplying the Average χ ^2^ obtained in the screen (from [Table 1](#T1){ref-type="table"}) by the Score predicted in the PicTar database. In the total table, all values from a given target, resulting from all miRNAs were summed in a final ranking value in column Σ(Score)\*Av(χ ^2^)\]. This table and the full dataset can be accessed at DOI [10.17605/OSF.IO/MDKHR](https://doi.org/10.17605/OSF.IO/MDKHR).**Extended data Table 3. miRNA.org target prediction and ranking tables.** For each miRNA, ranking of target prediction - column (Score ^2^)\*Av(χ ^2^) - was made by multiplying the Average χ ^2^ obtained in the screen (from [Table 1](#T1){ref-type="table"}) by the square value of Score predicted in the miRNA.org database. The square value was used in this case as the scoring system used by miRNA.org delivers negative values, differently from the other databases. In the total table, all values from a given target, resulting from all miRNAs were summed in a final ranking value in column Σ(Score ^2^)\*Av(χ ^2^)\]. This table and the full dataset can be accessed at DOI [10.17605/OSF.IO/539J8](https://doi.org/10.17605/OSF.IO/539J8).**Extended data Table 4. TargetScan target prediction and ranking tables.** The TargetScan database does not provide a scoring system for its predictions, rather a list of 8mer or 7mer sequences matched by the miRNA on the target and an information on the conservation of these sequences. We have attributed a numerical score to these sequences privileging the importance of 8mer vs 7mer and of conservation according to the scheme described in the Methods section. For each miRNA, ranking of target prediction - column (Score)\*Av(χ ^2^) - was made by multiplying the Average χ ^2^ obtained in the screen (from [Table 1](#T1){ref-type="table"}, some values specifically generated averaging all miRNA grouped in a single family by TargetScan) by the Score obtained according to our above-mentioned scheme. In the total table, all values from a given target, resulting from all miRNAs were summed in a final ranking value in column Σ(Score)\*Av(χ ^2^)\]. This table and the full dataset can be accessed at DOI [10.17605/OSF.IO/WD6ZR](https://doi.org/10.17605/OSF.IO/WD6ZR).

Data are available under the terms of the [Creative Commons Attribution 4.0 International license](https://creativecommons.org/licenses/by/4.0/legalcode) (CC-BY 4.0).

We thank M. Freeman, E.C. Lai, R. Sousa-Nunes, J. Bateman, S. Luschnig, the VDRC, the DSHB and the BDSC for fly stocks and reagents.

10.5256/f1000research.25558.r63653

Reviewer response for version 1

Barclay

Jeff W.

1

Referee

Department of Cellular and Molecular Physiology, Institute of Translational Medicine, University of Liverpool, Liverpool, UK

**Competing interests:**No competing interests were disclosed.

22

6

2020

Copyright: © 2020 Barclay JW

2020

This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Version 1

This manuscript uses *Drosophila* to screen by miRNAs for essential glial genes, identifying and briefly investigating one of the candidates ( *gartenzweg* or *garz*). The screen appears to be conducted well, the data logically presented and the outcomes interesting and novel. The screening method and methodological algorithm is straight-forward and described well. I have no negative issues with the experiments and believe this is a valid topic for publication that will prove useful to the community. In addition, I agree whole-heartedly that the 3Rs benefits are well demonstrated for studying miRNAs, glia and GBF1 biology specifically.

I have only minor suggestions for improvement, where the authors may want to reconsider wording to accurately reflect outcomes. The overall interpretations of the manuscript are that this screen is identifying glial functions. From the manuscript, however, it is not clear what these functions actually are? The experiments manipulate expression in glial cells -- and then measure broad outcome phenotypes such as lifespan and locomotion. However, I don't think the authors are necessarily suggesting that the function of glia is lifespan or locomotion.  Later there are experiments that infer potential alterations to intracellular trafficking in glia, but I find these experiments more representative of *garz* function in glia rather than glial function itself.The glia are targeted by miRNA expression and RNAi in the glia themselves. As miRNAs can be potentially released by exocytosis, it would be worthwhile to discuss the possibility for effects originating in other cells. The authors have done a nice control with the RNAi in neurons; however, this has partially replicated the glial RNAi effects and thus overall may reflect some transcellular effects.The manuscript indicates remarkable conservation in function between *garz* and GBF1; however, I would suggest tempering that conclusion given that they do have divergent effects (e.g. one is toxic, one is not).It isn't entirely clear why CD8 trafficking was selected for investigation over other possibilities. Given the lack of effect on cadherin trafficking, the speculation that overall trafficking in glia is impaired seems premature.

Is the rationale for developing the new method (or application) clearly explained?

Yes

Are the 3Rs implications of the work described accurately?

Yes

Is the description of the method technically sound?

Yes

If applicable, is the statistical analysis and its interpretation appropriate?

Yes

Are the conclusions about the method and its performance adequately supported by the findings presented in the article?

Yes

If any results are presented, are all the source data underlying the results available to ensure full reproducibility?

Yes

Are a suitable application and appropriate end-users identified?

Yes

Are sufficient details provided to allow replication of the method development and its use by others?

Yes

Reviewer Expertise:

Invertebrate models, genetics, neuroscience. Referee suggested by the NC3Rs for their scientific expertise and experience in assessing 3Rs impact.

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

10.5256/f1000research.25558.r62978

Reviewer response for version 1

Bjedov

Ivana

1

Referee

UCL Cancer Institute, University College London, London, UK

**Competing interests:**No competing interests were disclosed.

1

6

2020

Copyright: © 2020 Bjedov I

2020

This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Version 1

This manuscript by Gonçalves-Pimentel et al. describes an impressive series of very elegant and demanding longevity experiments used to develop an innovative methodological algorithm to identify and rank candidate genes that are targeted by miRNAs and that shorten lifespan when downregulated in adult glial cells. The work presented offers a comprehensive comparison of different miRNA target databases and link those to the longevity analysis screen. Advantage of miRNA is targeting multiple genes at once. For instance by screening 200 miRNA lines, this examines effect of down-regulation of approximately 6000 genes. However this subsequently presents a challenge to determine which genes are targeted with a particular miRNA and which gene is accountable for a given phenotype. Therefore, here, Gonçalves-Pimentel et al. produce an algorithm in which they link their miRNA lifespan screen results to multiple miRNA databases, with the final aim to rank genes that are predicted targets by these miRNA and that affect lifespan and ageing of the glial cells. Briefly, within each data base, each target score was multiplied by values of the lifespan screen results, and then ranking value for each target gene obtain for different data bases. Values obtained from all miRNA databases for each gene were summed for final ranking. This approach combined the effect of the miRNA on lifespan with the prediction of a gene being targeted by particular miRNA, using a variety of databases to strengthen the approach. Combining different databases is particularly important given a surprising difference in their target prediction.

The top candidate from the screen is a gene *garz*, (mammalian orthologue of GBF1), for which they predict that its down-regulation in the glia shorten lifespan. Advantage of such approach is that it offers possibility for screening in an invertebrate organism to uncover genes potentially important in mammalian glia.

*garz* is a target for three miRNA, *miRNA-1*, *miRNA-79*, *and miRNA-315*. The authors carefully examine its affect in different population of glial cells, replicate shorter lifespan using two different *garz*RNAi lines, overexpress human GBF1 to rescue short lifespan by these different miRNAs. Downregulation of *garz* in adult glial cells also leads to significant impairment of fly motor functions. The authors expanded their characterisation of *garz-RNAi* overexpressor flies further to show accumulation of Ref(2)P and likely consequent alterations/enlargement of mitochondria.

Overall this is a detailed and exhaustive study. The algorithm is clearly explained and well presented, and will certainly be useful in other miRNA studies and will inspire its adaptation to other miRNA screens. Moreover the authors developed a valuable list of genes that when down-regulated in glia impact lifespan. This is a really significant resource and dataset that the authors present and should be commended for. I only have a few minor points:

Were the longevity analysis done using males or females flies?

Why do the authors think that some of their predictions actually resulted in lifespan extension rather than shortening? Could the down-regulation of given gene have different outcomes when it occurs in concert with other miRNA gene target downregulation? Could this be commented in discussion perhaps?

How easily can their method be adapted for a different screen using a different output, such as for instance miRNA screen for stress resilience?

In the ageing field, lifespan extension is a gold standard to detect anti-ageing genes and interventions. Could the authors comment on finding genes that extend lifespan in glia rather than shorten it?

The authors say" Recently, it has been shown that siRNA knockdown of GBF1 causes intracellular APP accumulation in primary cortical neurons" , could they please define APP.

  

In Figure 1 and 2 driver names are not very visible, could this be improved for clarity or genotypes be written in full perhaps?

In Figure 3A, it is not very visible what the arrows is pointing at, at least not in my downloaded version of the article.

Overall, this is a very valuable resource for anyone working on miRNA and glial cell. This research is an excellent example how screens in invertebrate organisms can lead to discoveries of important biological functions of mammalian orthologues.

Is the rationale for developing the new method (or application) clearly explained?

Yes

Are the 3Rs implications of the work described accurately?

Yes

Is the description of the method technically sound?

Yes

If applicable, is the statistical analysis and its interpretation appropriate?

Yes

Are the conclusions about the method and its performance adequately supported by the findings presented in the article?

Yes

If any results are presented, are all the source data underlying the results available to ensure full reproducibility?

Yes

Are a suitable application and appropriate end-users identified?

Yes

Are sufficient details provided to allow replication of the method development and its use by others?

Yes

Reviewer Expertise:

ageing, mTOR, autophagy, Drosophila

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

[^1]: **Competing interests:**B.K. is co-founder of BFK Lab LTD
