dataesr/scientific-paragraphs-categorization huggingface dataset:
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 ). 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(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  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 (w1 118 ) 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 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. 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 (w1 118 ) 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. 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.
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 lifespan  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 ). 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.
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  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, 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). 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. Conflicting in vitro data has been reported for the effects of GFB1  31  and garz  32  in what concerns autophagy regulation. 1 Looking at the distribution of the Ref(  2 )p (the orthologue of mammalian p62) autophagy receptor  43  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 cells  30.
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  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.
Nevertheless, GBF1 was 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  [27] [28] [29] [47] [48] [49]  has targeted such molecules for health and disease studies  18. Recent y, it has been shown that siRNA knockdown of GBF1 causes intracellular Amyloid Precursor Protein (APP) accumulation in primary cortical neurons; overexpression of GBF1 contributes to APP trafficking and is dependent on its GEF activity  50   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 1. 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.
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.
(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.
(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.
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.
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. 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 Table1) 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.
some values specifically generated averaging all miRNA grouped in a single family by TargetScan) by the Score obtained according to our abovementioned 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. Data are available under the terms of the Creative Commons Attribution 4. 0 International license (CC-BY 4. 0). 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.
doi. org/10. 6084/m9. figshare. 12162393. v1 42 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. video file https://doi. org/10. 6084/m9. figshare. 12162351. v1 41 Video 2. CD8-GFP in garz knock-down brains. 3D reconstruction of confocal stacks imaging of panglial (repo-Gal4) CD8-GFP expression in a 1 video file https://.
(nm) (nm) (nm) (nm) (nm) (nm) (nm) (nm) (nm) (nm) (nm) (nm) (nm) (nm) (nm) (nm) (nm) (nm) (nm) (nm) (nm) (nm) (nm) (nm) ACF callose ACF CBM3 ACF PDM (nm) (nm) (nm) (nm) (nm) (nm) concomitant, and not sequential (Figure  4A ). Deviation from the linear dependence, especially between callose and cellulose (CBM3 staining), reflects the cell plate maturation stage, going from a ''fluid'' and wrinkled lamella to a stiff, flat primary cell wall. Callose is digested away during maturation, but cellulose accumulation keeps increasing or levels off (Figure  3 ; plate, 7). The ''pointillist'' dSTORM data permit the quantification using spatial point pattern statistical descriptors to characterize the spatial arrangement of different epitopes  (Sengupta et al. , 2011; Shivanandan et al. , 2016). We developed a boundary-corrected 3D bivariate pair-correlation function (PCF) analysis, commonly used in cosmology.
Resource Availability Lead Contact Kalina Tamara Haas kalina. haas@inrae. fr. A) Representative primary cell wall region with crystalline cellulose tagged using the CBM3 probe, presenting a fibrous pattern. B) The CBM3 filaments were segmented using 3D Voronoi diagrams and 3D Delaunay triangulation, as described in the Transparent Methods. The lateral resolution was evaluated as a cellulose microfiber's width, estimated as a median radial distance from every point in a connected graph to the graph centroid (orange dot in the bottom zoomed-in graph). C) Bean plot showing the distribution of estimated cellulose microfiber width. D) Cross-sectional analysis of the thin cell plate width. The cross-section was calculated from the Z-projected plots binned to 2D image with a pixel size 10 nm and smooth using ImageJ software.
and we thank  Nick Barry ,  Jonathan Howe ,  Michel Biocco , and  Marc Koch  for their support. We thank  Herman Ho  ¨fte for help with the fundraising and discussion of the results. Funding: A. P. has received the support of the  French National Research Agency (ANR)  GoodVibration  ANR-17-CE13-0007  and of the EU in the framework of the  Marie-Curie   FP7 COFUND People Program , through the award of an  AgreenSkills+ fellowship  (under grant agreement no. 201310 ). The IJPB benefits from the support of  Saclay Plant Sciences-SPS  ( ANR-17-EUR-0007 ).
Supplementary figures and captions Supplementary fig. 1  Comparison of 3D dSTORM data representation. Related to Figure  1. The 3D dSTORM data set is comprised of the XYZ coordinates of localized molecules. XY coordinates can be binned to 2D histogram (pixelated image) and represented as (A) super-resolved dSTORM image, here showing Arabidopsis meristem stained against mannan (PDM-CF568, orange), callose-A647 (green), and cellulose (CBM3-FITC, violet). Two superimposed image patches correspond to a low-resolution image. Scale bar, 1 µm. (B) Three-dimensional scatter plot represents two cell plates marked in A) as 1 and 2. Plate #2 is shown in three views: side-on 3D view, top XY view, and top XY view rotated by 58° around X-axis. Z-axis is oriented perpendicular to the image plane shown in A).
dSTORM data analysis The molecular localization lists from Nikon N-STORM and Vutara SRX were imported to custom-written Matlab based single-molecule data analysis software available at https://github. com/inatamara/Grafeo-dSTORM-analysis-. The single-molecule localization data was first filtered based on the number of photons emitted, as described previously  (Haas et al. , 2018; Haas, et al. , 2020a). Typically, the minimum number of photons detected was set to 1000-1500 photons. To minimize the background contribution, we employed a 3D Voronoi diagram-based filtering, as described previously  (Haas et al. , 2018). Typically, points forming Voronoi Polygons larger than ~10 -12 ~10 -16 nm 3 were discarded.