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Microbial Association Networks in Cheese: a Meta-Analysis

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Microbial Association Networks in Cheese: a Meta-Analysis Microbial association networks in cheese: a meta-analysis Eugenio Parente, Teresa Zotta, Annamaria Ricciardi Scuola di Scienze Agrarie, Forestali ed Alimentari, Università degli Studi della Basilicata, Potenza, Italy Supplementary material Index 1. DATA AND SOFTWARE. 2 2. ANALYSIS WORKFLOW. 2 3. INFERENCE OF ASSOCIATION NETWORKS AT DIFFERENT TAXONOMIC LEVELS. 7 4. COMPARING INFERENCE METHODS. 8 5. COMPARING GLOBAL NETWORK PROPERTIES. 12 6. NODE PROPERTIES. 14 7. EDGE PROPERTIES. 14 REFERENCES 19 1 1. Data and software. The metataxonomic data used for the inference of microbial association networks were extracted from DairyFMBN v2.1.6 (Parente et al., 2020, Parente, 2021), a specialized version of FoodMicrobionet (Parente et al., 2019), which is available on Mendeley Data (https://data.mendeley.com/datasets/3cwf729p34/5). The list of studies used in the analysis is shown in Supplementary Table 1. 2. Analysis workflow. A demo of the pipeline used for the inference and analysis of microbial association networks is available from GitHub (https://github.com/ep142/MAN_in_cheese) as a .Rmd notebook which, when run properly, will generate a ready to use report and a number of publication quality images and tables. The analysis workflow is summarized in Supplementary Figure 1. Basically: 1. general options for plotting, saving and inference (including the inference methods to use) are set 2. an arbitrary number of phyloseq objects (McMurdie and Holmes, 2013) containing the data are imported in a list. This can be either objects extracted from DairyFMBN or phyloseq objects generated using a suitable bioinformatics pipeline. Study metadata are also imported in this stage as a tab delimited file; 3. samples with low number of sequences and studies with low number of samples are removed; calculation of diversity indices is also performed at this stage 4. taxonomic filtering (removal of Amplicon Sequence Variants identified as Eukaryotes, mitochondria or chloroplasts, removal of sequences identified above the family level) and agglomeration (no agglomeration, or agglomeration at the species, genus or family level) are performed using mostly functions from package phyloseq 5. prevalence and abundance filtering is performed to remove the least prevalent and abundant taxa (even if this can be performed within the netConstruct() function, see below); prevalence and abundance plots and tables are generated; reports on the effect of filtering on the number of taxa and sequences are generated; 6. network inference is performed for all datasets and, within dataset, all inference methods using the netConstruct() function of the NetCoMi package (Peschel et al., 2020); errors are trapped using try() and the results (as microNet or try objects) are put in a list and a report is generated 7. networks are analysed using netAnalyze() function; global network properties are extracted and joined with metadata and microbial diversity indices and evenness indices (including Pielou J and average Bray-Curtis dissimilarity, see Parente et al., 2018 for details) 8. node statistics (cluster membership and centrality indices) are then extracted from the microNetProps objects and taxonomic and prevalence and abundance information are merged 9. the networks are extracted ad tidygraph objects (Pedersen, 2020) and edge betweenness is calculated; Venn diagrams showing edges in common between different inference methods are optionally generated 10. global properties of the networks are compared using Principal Component Analysis 11. networks are plotted using ggraph (Pedersen, 2021) or NetCoMi and optionally compared in grids 2 12. node plots are generated for each dataset/inference method 13. stable edges (edges identified with more than one method within a given dataset or over different datasets) are identified 14. taxonomic assortativity is tested using epi.2by2() function of the epiR package (Stevenson et al., 2021) Several aspects of the workflow can be personalized by setting options (locations of the input and output files, resolution of the graphs, filtering and taxonomic agglomeration options, network inference methods to be used and their parameters). Supplementary Figure 1. Schematic representation of the workflow used in the meta- analysis. 3 Supplementary Table 1. Details of the studies used for inference of microbial association networks. The studies were extracted from DairyFMBN 2.1.6 (Parente et al., 2020, Parente, 2021). R ead R lenGth NCBI SRA S Study T arGet egion (bp) seq. accn. amples Description Type1 DesiGn Reference Commercial hiGh-moisture Mozzarella cheese Guidone et ST1 16S RNA Gene V1-V3 504 SRP052240 29 produced with different acidification methods cs (1) descriptive al., 2016 Undefined strain starters (milk cultures) for hiGh- Parente et ST2 16S RNA Gene V1-V3 498 SRP057506 24 moisture Mozzarella cheese cs (1) descriptive al., 2016 Undefined strain starters (whey cultures) and cheese curds for water-buffalo Mozzarella, Grana Padano 3x2 Groups De Filippis ST3 16S RNA Gene V1-V3 465 SRP033419 50 (and ParmiGiano ReGGiano cheese cs (1) (small) et al., 2014 time series De Milk, curd and ewe's milk Canestrato cheese durinG + Pasquale et ST6 16S RNA V1-V3 485 SRP038100 22 ripeninG ts (11) descriptive al., 2014a Milk (from different lactation staGes), curd and 3x2 Groups Dolci et al., ST8 16S RNA V1-V3 490 SRP040575 27 Fontina cheese from three different dairies mx (3) (small) 2014 time series Alessandri Piedmont hard cheese made from raw milk: milk, curd + a et al., ST9 16S RNA V1-V3 469 SRP044294 39 and cheese throuGhout ripeninG mx (7) descriptive 2016 2x2x2 16S RNA Gene Caciocavallo Silano cheese manufacture: starter groups De Filippis ST10 and 16S RNA V1-V3 601 SRP061555 67 culture, milk, curd and cheese throuGhout ripeninG ts (5) (small) et al., 2016 Environmental swabs from an Italian dairy plant and different kind of cheeses (Mozzarella; Ricotta; time series Scamorza; Caciocavallo; Grancacio) produced in the + Stellato et ST18 16S RNA Gene V1-V3 454 SRP058584 45 same plant cs (1) descriptive al., 2015 Environmental swabs from an Italian dairy plant and different kind of cheeses (Caciotta; Caciocavallo Calasso et ST22 16S RNA Gene V1-V3 465 -- 48 PuGliese) and cow milk produced in the same plant mx (10) 2 Groups al., 2016 Grana Padano cheese samples with or without blowinG Bassi et al., ST23 16S RNA Gene V3-V4 398 SRP055798 37 defect, from different factories, at different ripeninG cs (1) descriptive 2015 4 times, produced with and without lysozyme Continental (Swiss type cheese) produced early and late in the day, core and rind samples included, O'Sullivan ST25 16S RNA Gene V4-V5 232 ERP009223 31 different ripeninG times cs (1) descriptive et al., 2015 descriptive, Artisanal soft, semi-hard and hard cheeses from raw possibly 3 QuiGley et ST32 16S RNA Gene V4 500 -- 93 or pasteurized cow, Goat, or sheep milk cs (1) groups al., 2012 InvestiGatinG the role of the microbiota in Pink Cheese. Cheddar, Emmental and cheese coloured with 2x2 Groups QuiGley et ST33 16S RNA Gene V4 500 ERP006630 58 Annatto, either unspoiled or spoiled. cs (1) small al., 2016 Bovine ricotta cheese (two lots, winter and sprinG) without or with pink discoloration, throuGhout storaGe 2 Groups Sattin et al., ST34 16S RNA V3-V4 422 SRP060430 46 at 8°C. mx (7) small 2016 Raw cow milk and burrata cheese protective 3 Groups, Minervini ST36 16S RNA V1-V3 424 SRP110830 50 lactobacilli cheese with or without dietary fibres an mx (4) small et al., 2017 Teat skin, raw cow milk and Cantal cheese) 4+ Groups, Frétin et ST39 16S RNA Gene V3-V4 435 SRP126475 48 microbiota, as a function of GrazinG system mx (3) small al., 2018 4+ or 2 DuGat- groups, Bony et al., ST41 16S RNA Gene V3-V4 425 SRP071345 95 Microbiota of core and rind of 12 French cheeses cs (1) small 2016 4 Groups Guzzon et ST43 16S RNA Gene V1-V3 420 SRP051167 26 Cheese with brown defect and cheese environment cs (1) (small) al., 2017 Caciocavallo cheese, throuGhout ripeninG, with milk 2 Groups Giello et al., ST44 16S RNA Gene V1-V3 371 SRP070077 38 obtained under different cow's feedinG reGimes mx (7) (small) 2017 Gouda cheese (15 brands), from pasteurized or raw milk. Spatial (core, surface, middle) variability was assessed. Cheese age (2-18 mo.) confounded with Salazar et ST45 16S RNA Gene V3-V4 463 SRP103624 92 brand. cs (1) 2 Groups al., 2018 HiGh moisture Mozzarella cheese (cow or buffalo Marino et ST48 16S RNA Gene V3-V4 427 SRP156292 39 milk), produced with different types of starters cs (1) descriptive al., 2019 Artisanal raw milk cheeses from Brazil (11 different Kamimura ST49 16S RNA Gene V3-V4 412 SRP165151 196 types from 5 GeoGraphical areas) cs (1) descriptive et al., 2019 Cow milk, cheese (4 types: Brie, Cheddar, Gruyere, cs (1) descriptive JarlsberG) and environmental samples, from farm to Falardeau ST74 16S RNA Gene V3 151 SRP170819 375 fork for an artisanal cheese production facility et al., 2019 cs (1) descriptive De Active microbiota from Italian PDO ewe's cheese Pasquale et ST106 16S RNA V1-V3 369 SRP059382 30 (Pecorino toscano; Pecorino siciliano; Fiore sardo) al., 2016 ST110 16S RNA Gene V3-V4 427 SRP212264 112 Dynamics of the microbiota of semisoft caciotta cheese mx (3) 4+ Calasso et 5 produced a washed rind protocol, with or without al., 2020 attenuated adjuncts and surface inoculants Microbiota of core and rind of Cheddar, Provolone and 3x2 Swiss type cheese produced in OreGon from groups, Choi et al., ST115 16S RNA Gene V4 245 SRP233045 63 pasteurized milk cs (1) small 2020b Microbiota
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