A miRNA screen procedure identifies garz as an essential factor in adult glia functions and validates Drosophila as a beneficial 3Rs model to study glial functions and GBF1 biology

Online resources and in silico algorithms for target identification and ranking

The following databases were used for the prediction of miRNA targets:

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 publication³³.

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

The algorithm for comparing the ranking between different databases and providing a final common ranking consists of two steps:

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, w¹¹¹⁸ flies were used as control. The following lines were acquired from the Bloomington collection: w¹¹¹⁸ (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 library³⁴; 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 described³⁵. 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 equal numbers of female and male flies were 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 CO₂ 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 described¹¹. 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 software³⁶. 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 CO₂ 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 were measured using the 3D Object Counter 2.0.1 plugin³⁷ in the ImageJ 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.

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 (χ²) of each Kaplan Mayer analysis (Table 1).

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) by the strength of the target prediction (Score) given by the database, obtaining the parameter (Score)*Av(χ²). 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(χ²)]. 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(χ²)] that is the sum of three (Score)*Av(χ²) in the other databases, but for TargetScan it would only reflect one (Score)*Av(χ²). 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(χ²)] for EBI MicroCosm (Extended data Table 1)³⁸, PicTar (Extended data Table 2)³⁸, microRNA.org (Extended data Table 3)³⁸ and TargetScan (Extended data Table 4)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).

Figure 1. Effects of garz knock-down in adult glial cell.

(A) Venn diagram referring to the data in Table 2 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 as part of Table 3. (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. (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. (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.

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(χ²)] 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(χ²)]} for all targets, including the 520 that were commonly predicted by all databases (Table 2).

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 w¹¹¹⁸ 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). 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(χ²)IR) with the strength of the prediction in all combined databases (Σ{Normalised Σ[(Score)*Av(χ²)]}).

This parameter (Σ{Normalised Σ[(Score)*Av(χ²)]}*{Av(χ²)IR}) highlighted garz, one of the six targets satisfying the 2-RNAi lines criterion, as the top target (Table 3). 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).

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) and from the differential group (Table 4), we have calculated the database-specific Normalised Σ[(Score)*Av(χ²)] *{Av(χ²)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 GBF1^19,20,39).

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 w¹¹¹⁸ median lifespan control (Figure 1B). Different glial cell types present in the adult fly brain have specific morphology and function⁴⁰. 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). 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), 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). 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 domain²⁴, was important to exercise their rescue activity (Figure 1D). 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).

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 lifespan¹¹, 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). 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 shortening¹¹.

We have previously described an automated unbiased and high-throughput method to analyse fly motor activity¹¹). 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). 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). 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.

Figure 2. 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. (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.

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 development²⁵. 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, 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, whose potential glia localisation effects may, however, be masked by the unaffected CadN localisation in neurons, where CadN is highly expressed (data not shown, the full dataset can be accessed at DOI 10.17605/OSF.IO/7HRZS).

Figure 3. 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. (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.

Conflicting in vitro data has been reported for the effects of GFB1³¹ and garz³² in what concerns autophagy regulation. Looking at the distribution of the Ref(2)p (the orthologue of mammalian p62) autophagy receptor⁴³ revealed Ref(2)p accumulation in puncta, suggesting a potential block in autophagic clearance in glial cells (Figure 3A).

Finally, it has been suggested a role for GBF1 in the regulation of mitochondria morphology and function in yeast, C. elegans muscle and HeLa cells³⁰. Using mito-GFP transgene we were able to identify mitochondrial morphology defects in adult glial cells (Figure 3B, C). 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 results⁴⁴.

Underlying data

Open Science Framework: miRNA-garz. https://doi.org/10.17605/OSF.IO/A5ZST⁴⁴.

This project contains the following underlying data:

Extended data

Open Science Framework: miRNA-garz. https://doi.org/10.17605/OSF.IO/K5HW9³⁸.

This project contains the following extended data:

Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).