dataesr/scientific-paragraphs-categorization huggingface dataset:
Basidiomycete fungi can degrade a wide range of plant biomass, including living and dead trees, forest litter, crops, and plant matter in soils. Understanding the process of plant biomass decay by basidiomycetes could facilitate their application in various industrial sectors such as food & feed, detergents and biofuels, and also provide new insights into their essential biological role in the global carbon cycle. The fast expansion of basidiomycete genomic and functional genomics data (e. g. transcriptomics, proteomics) has facilitated exploration of key genes and regulatory mechanisms of plant biomass degradation. In this study, we comparatively analyzed 22 transcriptome datasets from basidiomycetes related to plant biomass degradation, and identified 328 commonly induced genes and 318 repressed genes, and defined a core set of carbohydrate active enzymes (CAZymes), which was shared by most of the basidiomycete species.
g. transcriptomics, proteomics) plays a key role in exploring and validating new functions of plant biomass degradation related genes and the regulatory mechanisms driving their expression  (Gaskell et al. , 2016; Vanden Wymelenberg et al. , 2009). Meta-analysis of transcriptomic datasets has been a powerful approach to discover new gene expression patterns that are difficult to detect in individual experiments. By combining multiple studies that share a similar experimental setup their statistical power can be increased to reveal a more precise and reliable set of differentially expressed genes  (Ramasamy et al. , 2008). Moreover, cross species integration and comparisons has already been used to identify evolutionarily conserved genes reflecting adaptation of organisms to specific environmental changes  (Shaar-Moshe et al.
, 2015). In animal and plant studies, meta-analysis of transcriptome datasets has been extensively used for finding sets of key genes involved in specific biological processes  (Mustroph et al. , 2010)  and disease mechanisms  (Rhodes et al. , 2004). However, very few meta-analysis of multiple transcriptome studies on basidiomycetes have been reported that go beyond two or three species. The increasing number of fungal plant biomass degradation related transcriptome sets provides options for integration and comparison of these transcriptomes of different basidiomycetes to promote detection of a core set of conserved genes involved in lignocellulose degradation. In this study, we collected a large set of transcriptome datasets associated with plant biomass degradation, which consists of 22 experiments across 10 basidiomycete species.
We aimed to evaluate whether it is possible to compare these heterogeneous studies and still obtain reliable conclusions or leads on fungal lignocellulosic biomass degradation, meaning the identification of a core set of consistently regulated genes/pathways with significant statistical p-values and important biochemical function that could be discovered via integrative analysis of previously published heterogeneous datasets. Through comparing gene expression profiles of these fungi growing on complex lignocellulose media and simple carbon sources, we identified a core set of up-regulated genes that are shared among these basidiomycetes in response to plant biomass degradation (Suppl. Fig. S1 ). In addition, our study revealed that many currently documented CAZy families contain more than one orthologous groups, and significantly different expression patterns among these orthologs were observed during lignocellulose degradation.
Transcriptomic data collection and identification of differentially expressed genes To collect a large transcriptome set related with plant biomass degradation of basidiomycetes, we searched the NCBI GEO database  (Edgar et al. , 2002)  (https://www. ncbi. nlm. nih. gov/geo/) with key words basidiomycete and plant biomass degradation. The matched datasets where manually filtered to select the experiments which shared a similar research focus of comparing fungi grown on complex lignocellulose and simple carbon sources, with at least 2 replicates for each condition. In total, 22 transcriptome datasets from 10 basidiomycete species (including 14 datasets for 6 white rot species, 7 for 3 brown rot species and 1 for litter-degrading species) were collected (Table  1 ). The lignocellulose substrates varied from ball-milled aspen and pine sapwood to cellulose and lignin.
The simple carbon sources included glucose-and maltose-amended Melin-Norkrans (MMN) agar medium, Highley's basal medium, and malt extract agar medium. The experimental platforms included Roche-NimbleGen microarray, Agilent and Illumina RNA-seq (Suppl. Table  S1 ). Due to the heterogeneity of the experimental platforms, the preprocessed gene expression profiling data was downloaded from the Series Matrix File of the GEO database. All the collected gene expression data had been processed with background correction, quality control and normalization in their original studies and was ready for further statistical analysis. For each dataset, samples from fungi grown on lignocellulose were compared with their control samples which contained glucose or maltose as carbon source. Student t-test (p-value ≤0.
Identification of orthologous genes across species To compare the transcriptomic response related to plant biomass degradation across different basidiomycetes, it is necessary to identify the evolutionarily conserved orthologous genes among them. To accomplish this, the predicted proteome of each basidiomycete was downloaded from the JGI MycoCosm database  (Grigoriev et al. , 2014)  and used for an all-against-all BLAST comparison with subsequent clustering of orthologous genes. The OrthoMCL method was used to infer orthologous genes across species  (Li et al. , 2003)  with parameters set as: E-value 1E -5 , inflation level 1. 5 and sequence coverage 60%. The OrthoMCL algorithm generates clusters of proteins where each cluster consists of orthologs or "recent" paralogs across multiple fungal species.
The proteins belonging to the same cluster have putatively evolved from a common ancestor by speciation and share similar biological functions. Orthologous clusters containing at least one detected gene among the collected datasets were extracted. The enrichment of induced and repressed genes in each specific orthologous group were examined by hypergeometric distribution analysis as applied in a previous study  (Mustroph et al. , 2010). All the expressed genes were combined into a matrix according to their orthologous relationships. The relative expression values of each ortholog in each dataset were calculated as fold change between complex lignocellulose containing and glucose-or maltose-amended medium. To visualize gene expression from heterogeneous platforms and make diverse dataset comparable, gene expression features in each species were binned into the following four categories: repressed, induced, absent and no-regulation and labeled respectively in different colors in the heatmap (Figs.
Gene ontology and function catalogue annotation The Gene ontology annotation of consistently induced and repressed orthologous gene clusters were analyzed by BLAST2GO (version 4. 0)  (Gotz et al. , 2008)  based on the best BLASTP hits (with default threshold of E value ≤1e-3 and HSP length of 33) to basidiomycete proteins in the NCBI RefSeq non-redundant database. Since most orthologous clusters contain multiple genes from different species, we randomly picked one gene from each orthologous cluster and annotated its function. 88% of the induced genes (289 out of 328) and 87% of the repressed genes (277 out of 318 genes) have an assigned GO annotation (Suppl. Table  S3A  and S3B ). The GO molecular function terms (level 4) with more than five genes in either induced or repressed gene list were shown on the bar-plot of Fig.
2. In addition, the FunCat annotation available in the FunCat database  (Ruepp et al. , 2004 ) and Pedant3 database  (Frishman et al. , 2003)  was also extracted to provide further gene functional information. Because of the limited number of basidiomycete species that have been well annotated by the FunCat team, only 38% (246 out 646) of consistently regulated genes have mapped with FunCat.
Identification of orthologous genes across diverse basidiomycetes OrthoMCL analysis resolved 34,548 orthologous clusters which contained at least one gene expression value in any of the 22 transcriptomics datasets across 10 studied basidiomycetes. Since our study focused on commonly regulated genes across diverse species, orthologous clusters which were not differentially expressed in any of the collected datasets were excluded from further analysis. In total, 24,950 orthologous clusters which had a differentially expressed gene in at least one dataset were used for statistical analysis. Of these, 56% (14,005) orthologs had expression values in only one basidiomycete species, and 8% (2034) orthologs had expression values in all studied basidiomycete species. Fig. 1  shows the number of organisms distributed in each orthologous cluster which indicates the high specificity of species, substrate and experimental setup, of fungal transcriptomes.
Commonly regulated genes in response to plant biomass degradation To detect commonly regulated genes in response to plant biomass degradation across diverse basidiomycetes, 44,251 induced and 39,770 reduced DEGs from all collected experimental datasets were organized to their corresponding orthologous clusters. The enrichment of regulated genes in specific orthologous groups compared to total regulated genes ratio in all detected genes was tested by hypergeometric distribution analysis (Suppl. Table  S2 ). Using the enrichment p-value ≤0. 01 and presence in more than half of all studied organisms and half of total datasets as criteria, we identified 328 induced genes (Suppl. Table  S3A ) and 318 repressed genes (Suppl. Table  S3B ), which are defined as the consistently differentially expressed genes in response to plant biomass substrates across the tested basidiomycetes (Fig.
Consistently up-regulated CAZymes With the special interest in the up-regulated CAZyme encoding genes, we further focused on the functional analysis of these plant biomass degradation related genes. To recognize commonly expressed CAZymes across basidiomycetes, the orthologous groups that were significantly enriched with up-regulated CAZymes were separated from the above identified commonly regulated gene list. In total, 23% (75 out of 328 genes) consistently up-regulated genes encoded CAZymes (Suppl. Table  S3A ). Compared to 2% (392) CAZymes in all collected 24,950 orthologous clusters (Suppl. Table  S2  higher enrichment of CAZymes in the consistently up-regulated gene set, which indicates the high specificity of our method to detect key genes from large heterogeneous datasets. By including enzyme-substrate information provided by CAZyme database (http://www.
, 2012; Gomez-Cabrero et al. , 2014; Miyauchi et al. , 2016; Zhang et al. , 2010). Studies have been performed to identify key genes involved in plant cell wall degradation in e. g. Phanerochaete chrysosporium  (Vanden Wymelenberg et al. , 2009) , Neurospora crassa  (Tian et al. , 2009) , Dichomitus squalens  (Rytioja et al. , 2016)  and Agaricus bisporus  (Patyshakuliyeva et al. , 2015) , by combining evidence from proteomics and transcriptomics data. Now that more fungal functional genomics data associated with plant biomass degradation is accumulating, a deeper understanding of lignocellulose degradation could be achieved via an integrated analysis of "omics" data from these different species and platforms. The 50 CAZymes involved in plant biomass degradation for which the corresponding genes were considered to be consistently upregulated GSE39569_wheatStraw_vs_MMN GSE14736_Aspen_vs_Glucose GSE14736_Cellulose_vs_Glucose GSE30230_cellulose_vs_MMN GSE30230_lignin_vs_MMN GSE30230_pinesBark_vs_MMN GSE34636_Aspen_vs_Glucose GSE34636_Cellulose_vs_Glucose GSE39805_pineHeart_vs_MaltExtract GSE39805_pineSap_vs_MaltExtract GSE39805_pinesBark_vs_MaltExtract GSE53112_driedPine_vs_glucose GSE53112_freshPine_vs_glucose GSE74234_aspen_vs_maltose GSE74234_pine_vs_maltose GSE27839_wood10d_vs_MMN GSE27839_wood30d_vs_MMN GSE78007_Aspen_vs_Glucose GSE78007_cellulose_vs_Glucose GSE78007_pine_vs_Glucose GSE12540_Aspen_vs_Glucose GSE12540_Cellulose_vs_Glucose Reduced & orthologs>1 Reduced & orthologs=1 in the presence of plant biomass, cover a range of activities acting on various plant polymers.
However, when these are ranked based on the number of datasets they are upregulated in, an interesting pattern emerges. Only three CAZyme orthologous groups are upregulated in 19 of the 22 datasets and two in 18 of the 22 datasets (Suppl. Table  S3C ). The first three encode a putative β-glucosidase, β-xylosidase, and endoxylanase, while the last encode a putative endomannanase and α-glucuronidase. The first two are crucial enzymes for the complete hydrolysis of cellulose and xylan, while the third and fourth are highly important enzymes for degradation of xylan and mannan, respectively, by hydrolyzing the backbone of these polysaccharides into oligosaccharides  (Rytioja et al. , 2014). Surprisingly, the latter, α-glucuronidase, is an enzyme that removes the 4-O-methyl-glucuronic acid side groups from xylan thus providing access for endoxylanases to the backbone of xylan  (Chong et al.
, 2011). The consistent upregulation of this gene suggests that the tested basidiomycetes all commonly encounter glucuronoxylans and that 4-O-methyl-glucuronic acid side groups significantly hinder xylan hydrolysis by these fungi. The next set of most prevalently upregulated genes in the presence of plant biomass are mainly a set of cellulases, followed by a diverse number of (hemi-)cellulases and pectinases (Fig. 3 ). The presence of the cellulases is expected as a high amount of cellulose is present in all plant biomass substrates. However, the relative amount of pectin in plant biomass is highly variable, from highly abundant in some dicots (e. g. soy beans) to very low (e. g. in wood)  (Edwards and Doran-Peterson, 2012). The common upregulation of a basic set of pectinases in most of the datasets (even on substrates with low pectin content), suggests that the presence of pectin is a significant barrier for enzymes targeting other plant polymers.
Conclusions In this study, transcriptomic profiling of plant biomass degradation in 10 basidiomycetes was systematically evaluated to identify consistently regulated genes. Despite the variations in growth conditions and species, a core set of commonly up-regulated plant biomass degradation related genes was discovered. Further investigation of this core gene-set could help to determine their specific biological function and regulatory mechanisms during the process of plant biomass degradation. Also, a more extensive study including a larger dataset of more fungal species would likely result in an even more robust analysis and be able to distinguish between basic core genes and genes that are consistently upregulated on specific plant biomass substrates. While our study focused on consistent patterns between fungi and datasets, this approach could also be used to identify significantly different patterns in either different datasets from the same species, or in different species grown under similar conditions.
Funding The comparative genomics study was carried out on the Dutch national e-infrastructure with the support of  SURF Foundation  (e-infra140010). MVAP was supported by a grant of the  Dutch Technology Foundation STW, applied science division of NWO , and the  Technology Program  of the Ministry of Economic Affairs  016. 130. 609  to  RPdV. BH  and MH were funded by  ANR  grant  N°21000602.
(2012) medium Pycnoporus coccineus White Aspen, pine VS maltose Illumina RNA-seq 12,690 GSE74234 Couturier et al. (2015) Serpula lacrymans Brown Wood VS MMN medium Roche-NimbleGen 12,797 GSE27839 Eastwood et al. (2011) Postia placenta Brown Cellulose, ball-milled aspen VS glucose Roche-NimbleGen 12,438 GSE12540 Martinez et al. (2009) Wolfiporia cocos Brown Aspen, cellulose, pine VS glucose Illumina RNA-seq 12,748 GSE78007 Gaskell et al. (2016).
), there was a 12-fold The distribution of the expressed orthologous clusters among the 10 different basidiomycete species studied. In total 34,548 orthologous clusters were collected in this study. Among them, 24,950 orthologs were differentially expressed in at least one dataset (highlighted in dark grey), and 9598 orthologs were not significantly changed in any of dataset (highlighted in light grey). 25000 Orthologous clusters without any differentially expressed gene in 22 collected datasets 02000 Orthologous clusters with >=1 differentially expressed gene in 22 collected datasets Frequency 01500 01000 0500 0 1 2 3 4 5 6 7 8 9 1 0 Number of species presented in each of expressed orthologous cluster Fig. 1.
Acknowledgements The authors are grateful to two reviewers for their constructive comments. Eric Machu (IRD) provided the ROMS-PISCES plankton biomass used for the carrying capacity estimates. The second, the third and the fifth authors have been supported by the project  AWA  ( BMBF -IRD; 01DG12073E ) "Ecosystem approach to the management of fisheries and the marine environment in West African waters" and the  European Preface project  ( DG Env. ,  FP7)  under Grant Agreement number  603521. This paper was partially done while the first author was visiting at the  Vietnam Institute for Advanced Study in Mathematics (VIASM). The author is grateful to VIASM for the hospitality.
Sequences and phylogenetic analyses Newly gained sequences were analysed as described earlier  (Pantó et al. , 2015) , assembled with the Staden Sequence Analysis Package, and translated to amino acid (aa) sequences. After removing the primer sequences, a 253-bp (84 aa) useful sequence was obtained. Phylogeny was inferred on partial IVa2 protein alignments by using the ProtDist and PhyML algorithms of the PHYLIP package (v3. 5c) and Montpellier bioinformatics platform, respectively. The ProtDist analysis was run with the JTT substitution model, followed by Fitch-Margoliash analysis, applying the global rearrangements option  (Harrach and Benkő, 2007). Bootstrap analysis with 100 sampling replicates was applied in the PhyML analysis which was based on a user tree obtained by the PHYLIP package.
The resulting phylogenetic tree was visualised by the MEGA6 program. For construction of the primates' phylogenetic tree, nucleotide sequences of cytochrome c ox-Acta Veterinaria Hungarica 66, 2018 idase subunit I gene of each primate group representatives were used in a maximum likelihood analysis (GenBank accession numbers J01415, D38113, D38114, D38115, D38484, AF312703, AF312704, AF312708 and AF312709). Similarity plots and bootscanning analyses were performed with SimPlot 3. 5. 1 with window size 140 bp, step size 10 bp.
g. lemur or marmoset) was given (Table  2 ). DNA fragments with identical sequences were also recovered from different animals from the same place, marked as black lemur AdV-3 and lemur AdV, or, more often, from the same enclosure (red-fronted lemur AdV-2, marmoset AdV, tufted capuchin AdV-3, red-handed tamarin AdV-2). Partial IVa2 gene sequences of the newly detected AdVs were submitted to GenBank (accession numbers from MG574566 to MG574593). The multiple alignment of the partial IVa2 aa sequences suggested a clear difference between the prosimian and NWM AdVs in certain amino acids (Fig. 1 ).
Discussion We successfully accomplished our primary aim of obtaining insights into the abundance, genetic diversity and evolutionary relationships of adenoviruses of the most ancient primate host lineages. Samples from every or almost every family of NWMs and the prosimian suborder Strepsirrhini were screened for the presence of AdVs, respectively. For the screening, we used primer pairs targeting the IVa2 gene of mastadenoviruses specifically, since these primers had proved to be the most robust for the detection of primate AdVs in our laboratory. For the sake of better detection performance, we had to accept the obvious drawback of the scarcer availability of the sequences of IVa2 gene in the GenBank, compared to the partial sequences from the pol and hexon genes of NWM and other AdVs  (Hall et al.
Acknowledgements We would like to thank  Eric J. Kremer  from the  Institute of Molecular Genetics of Montpellier (National Center for Scientific Research)  and  Nadine Mestre-Francés  from the  University of Montpellier (France)  for kindly arranging the possibility and providing the samples from mouse lemurs. This work was supported by the  Hungarian Scientific Research Fund Grant OTKA  (grant number  NN107632 ), the  EU grants ADVance  ( FP7-290002 ) and  AD-VEC (FP7-324325 ), the  NWO/ALW  and  PE&RC grant  (grant number  1208 , project number  5120873001 ). Iva I. Podgorski  was awarded with a travel grant to Hungary by the  Hungarian Balassi Institute (Tempus Public Foundation ). Funds to conduct the fieldwork were provided by the  KNAW Academy Ecology Fund , the  Dr. J. L. Dobberke Foundation , the  Treub Foundation , the  FONA Foundation for Research on Nature Conservation , the  Dutch Royal Botanical Society  and the  Dutch Royal Zoological Society.
Data collection methods Aggregate data on the total number of malaria cases in the Kavango region from 2010 to 2014 were obtained from Namibia's Health Information System (HIS), which houses country data on all diseases at district level. Among the confirmed cases, the following data variables were extracted from the case investigation forms: age, residence, sex, pregnancy status, occupation, ownership and usage of long-lasting insecticide-treated nets (LLINs), whether the household had been sprayed and whether the cases were classified as local or imported. A local case was defined as any individual who, based on his/her reported 2-week travel history, was found to have contracted malaria within the borders of Namibia. An imported case was defined as any individual who, based on his/her reported 2-week travel history, was found to have contracted malaria outside the borders of Namibia.
2002; Wagner et al. 2010). This example illustrates that biostabilisation research still requires fundamental input from various perspectives to investigate the most relevant conditions and processes to deliver variables that would allow implementation in models at a later stage. In recent times, there has been some progress in implementing biological cohesion and adhesion forces into sediment transport models. However, whether it is about mimicking cohesion  (Lick et al. 2004)  or adding a combination of cohesion and adhesion coefficients  (Righetti and Lucarelli 2007) , there is still a significant lack of suitable data and appropriate measuring devices to strengthen the empirical dataset and validate the models  (Grabowski et al. 2011). Moreover, the complex nature of biological processes and the numerous variables identified  (Paterson 1997)  would increase model complexity beyond reliability so the challenge to the biological community is to detect and parameterise a limited number of master variables that can be used in models.
3 ). Now, metagenomic analysis of environmental DNA is helping to build a database of operational taxonomic units (OTUs, molecular analogue for species) that reveals the diversity of entire microbial assemblages in immense detail. In recent work, nearly 200,000 bacterial OTUs have been recorded from a laboratory incubation experiment using natural sediments  (Hicks et al. pers. comm). Having to interpret this level of microbial biodiversity will become common in the next decade, but in itself will be of less interest than the study of the processes (metabolomics) that the bacterial assemblages drive  (Nicholson and Lindon 2008). Part of this research will be the analysis of polymer production and secretion into the environment, as this medium is probably the major factor in mediating the response of the sediment to environmental forcing.