dataesr/scientific-paragraphs-categorization huggingface dataset:
Data availability The original files, as well as the RmiRTps and RPREps and all the code used in this study are available at: https://github. com/j-solor/Drosoph ila-miRNA-PcG-circuits/ Network available for analysis at NDEx: https://www. ndexbio. org/#/networkset/998959df-1a4e-11ed-ac 45-0ac135e8bacf?accesskey¼6e232187b6745a196eca1d0321b2f8be fd6dbd737e83a4407e6c9ce29e9842a0.
S2 ). Next, we calculated the distance between each pre-miRNA and its closest PRE using the coordinate information of the 260 pre-miRNAs of the miRBase database  (Kozomara et al. , 2019)  and our RPREps. We considered a miRNA to be potentially regulated by PcG/trxG if the 5'end of the pre-miRNA was located 5 kb or less from the closest PRE, a similar strategy to that was used in  Khabiri and Freddolino (2019). We found that 40% (106 out of 260) of the pre-miRNAs are potentially regulated by PREs (Fig. 4 , Table  S2 ). Of note, 16 of the 106 pre-miRNAs overlap with a PRE, i. e. , contain a PRE within the pre-miRNA transcription unit (Fig. 4 ). This is the case for dme-miR-313 that together with dme-miRNA-310, dme-miRNA-311 and dme-miRNA-312 form the dme-miR-310s cluster  (Ryazansky et al. , 2011) , suggesting a coordinated control of all these miRNAs by PcG/trxG proteins.
Acknowledgements We thank  Dr. Peter Freddolino  ( University of Michigan Medical School, USA ) for kindly providing us with the Polycomb Response Element genome-wide predictor (Khabiri and Freddolino, 2019) and  Keith Harshman  for carefully reading the manuscript. This work was supported by  PID2020-114533 GB-C21  grant from  Spanish Agencia Estatal de Investigaci on/Ministerio de Ciencia e Innovaci  on and by institutional grants from  Fundaci on Areces and Banco Santander.
16S rRNA Gene Sequencing and Phylogenetic Analysis. DNA preparation was performed according to the method of Liu et al. [39]. The actinobacterial isolates were grown in a shaker (250 rpm) at 30 °C for 7 days. PCR amplification of the 16S rRNA gene of the isolates was performed using two primers: 27f (5′-AGAGTTTGATCCTGGCTCAG-3′) and 1492r (5′-GTTACCTTGTTACGAC TT-3′). The sequencing reaction was performed by MilleGen Company (Toulouse, France). The same primers as before and an automated sequencer were used for this purpose. The similarities of the 16S rRNA gene sequences between strains were calculated on the basis of pairwise alignment using the EzTaxon-e server  [40]. Phylogenetic and molecular evolutionary analyses were carried out using MEGA version 7. 0  [41]. The 16S rRNA gene sequences of the strains were aligned using the CLUSTAL W program  [42]  against corresponding nucleotide sequences of Nocardiopsis retrieved from GenBank and EzTaxon-e.
The numbers at the nodes indicate the levels of bootstrap support based on neighbor-joining analyses of 1000 resampled data sets; only values over 50% are given. Bar: 0. 005 nucleotide substitution per nucleotide position. Haloactinospora alba YIM 90648 T and Streptomonospora salina YIM 90002 T have been used as outgroups. (a1) (a2) (a) (b1) (b2) (b) (c1) (c2) (c) 3. 4. Phylogenetic Studies. The 16S rRNA gene sequences (1529-1544 bp) of the 23 isolates were determined and deposited in the GenBank data library under the accession numbers JF777508-JF777530. The sequences were aligned with those of Nocardiopsis reference species available in the GenBank database, which confirmed that these 23 strains belong to the genus Nocardiopsis. It is interesting to note that the phylogenetic study corresponded in most cases with phenotypic taxonomy.
4 BioMed Research International Table 2 :Figure 3 : 23 Figure 3: Neighbor-joining tree based on the 16S rRNA gene sequences showing the relationships between the 23 halophilic Nocardiopsis strains of Saharan soils and the type species of the genus Nocardiopsis. The accession numbers of strain sequences are given in parentheses. Asterisks indicate branches that are conserved when the neighbor-joining, maximum parsimony, and maximum likelihood methods were used in reconstructing phylogenetic trees. The numbers at the nodes indicate the levels of bootstrap support based on neighbor-joining analyses of 1000 resampled data sets; only values over 50% are given. Bar: 0. 005 nucleotide substitution per nucleotide position. Haloactinospora alba YIM 90648 T and Streptomonospora salina YIM 90002 T have been used as outgroups.
DNA Sequencing and Phylogenetic Analyses Conventional PCR amplicons generated for ticks and target microorganisms were purified using shrimp alkaline phosphate and exonuclease I (Thermo Fisher Scientific, Melbourne, Australia) and subjected to Sanger sequencing using PCR primers. Nucleotide sequences obtained for target loci of ticks (cox1 and 16S rRNA) and tick-borne microorganisms/pathogens (outer membrane protein B (ompB), ompA, gltA, and 17 kDa) were assessed for various quality parameters (such as the desired sequence length, base quality score, etc. ) and assembled in Geneious Prime 2019. 0. 4 software using the de novo assembly function (Biomatters Ltd. , Auckland, New Zealand; www. geneious. com). Unique sequences for each locus were identified via the "find duplicates" function in Geneious.
Subsequently, these unique sequences were subjected to online searching within the National Center for Biotechnology Information (NCBI) (https://blast. ncbi. nlm. nih. gov/Blast. cgi) database to match their identities with known published reference sequences. All protein-coding nucleotide sequences were assessed for open reading frames, and then all unique nucleotide sequences were submitted to the NCBI GenBank. Pairwise comparisons were also conducted for each sequence dataset using BioEdit  [44]. The reference sequences for each locus (of ticks or tick-borne microorganisms) were retrieved from GenBank and aligned in MEGA 11  [45]  using MUSCLE v. 3. 8. 31  [46]. Aligned sequences were trimmed to uniform lengths of 612 (cox1), 370 (16S rRNA), 352 (gltA), 434 (ompA), 391 (17 kDa), and 267 (ompB) bp.
Phylogenetic analyses were performed on aligned sequence datasets of individual loci for ticks (cox1 and 16S rRNA) and tick-borne microorganisms (ompA, ompB, gltA, and 17 kDa) using Bayesian Inference (BI), Maximum Likelihood (ML) and Neighbour Joining (NJ) methods. The NJ and ML analyses were performed in MEGA 11, whereas BI was performed using the MrBayes plugin  [47]  in Geneious. The jModelTest v. 3. 7  [48]  was used to estimate the best-fit evolutionary models for individual sequence alignments based on the Akaike information criteria (AIC). The best-fit models for nucleotide sequences of ticks included the Tamura-Nei with Gamma distribution (cox1) and Tamura 3-parameter with Gamma distribution (16S rRNA)  [49, 50]. For nucleotide sequences of the microorganisms, the best-fit models were the Tamura 3-parameter (ompB and gltA), Tamura 3-parameter with Gamma distribution (ompA), and Kimura-2 parameter (17-kDa) datasets  [49, 51].
The nodal supports in the ML and NJ phylogenies were tested using the bootstrap method (10,000 replicates,) whereas the posterior probabilities of the BI analyses were calculated for 2,000,000 generations (ngen = 2,000,000) while saving every 200th tree (samplefreq = 200). Corresponding reference sequences of Ornithodoros moubata (GenBank accession: KJ133594), Rickettsia bellii (LAOIO1000001) and R. felis (CP000053, AF210692 and AF210694) were used as outgroups for the phylogenetic trees of ticks and microorganisms, respectively.
The cox1 sequences of I. tasmani showed a 90. 4-98. 04% similarity to those of the same species (KX676867 and MN106731) from Australia. There was no reference sequence available for I. antechini in the GenBank; the sequences determined herein showed the highest similarity (91. 33%) to that of I. brunneus (KX360364) from Canada. For the 16S rRNA sequences, I. kohlsi had the highest similarity (99. 7%) to those of I. eudyptidis/kohlsi (KM488490 and KM488491) from Australia, while the I. antechini sequences showed the highest similarity (97. 1%) to a sequence (AF113929) of the same species from an unknown origin. No 16S rRNA reference sequence was available for Ap. auruginans and I. trichosuri; however, their sequences determined herein showed the highest similarities (90. 4% and 92%, respectively) to those of Bothriocroton concolor (JN8663727) and I.
Preparation of sequencing libraries, high-throughput sequencing, and read pre-processing MNase-seq libraries were prepared from purified nucleosomal DNA using the kit NEBNext Ultra DNA Library Prep Kit for Illumina (cat. # E7370L New England BioLabs) following the manufacturer's instructions. The NEBNext Ultra Directional RNA Library Prep Kit for Illumina (cat. # E7420L New England BioLabs) was used to prepare all RNA-seq libraries, following the manufacturer's instructions. Sequencing was performed by the GenomEast platform, a member of the 'France Génomique' consortium (ANR-.
Transcriptome analyses RNA-seq reads were mapped against their respective reference genomes (see Table  1 ) using STAR v2. 5. 1  (33). TPM counts (Transcripts Per Million reads, (34)) were computed using the count TPM tool provided with the MNase-Seq Tool Suite (MSTS; Supplementary Figure  1 ) that was developed in-house to analyse genome-wide nucleosome positioning data combined with RNA-seq data (https://github. com/nlapalu/MSTS).
MNase-seq analyses MNase-seq paired-end reads were mapped using Bowtie2 software ran in very-sensitive mode  (35). MSTS (MNase-seq Tool Suite) was used to compute all phasograms, dinucleotide composition, as well as nucleosome density profiles of genomic compartments and/or gene lists (Supplementary Figure  1 ). Lists of near-universal single copy orthologs were obtained by running BUSCO3 for Fungi  (36, 37)  on each reference genome studied. Graphical visualisations were computed with MSTS and Matlab R2020b (MathWorks).
ACCESSION NUMBERS All sequenced reads have been deposited with the Short Read Archive (SRA; https://www. ncbi. nlm. nih. gov/sra) under project accession number PRJNA580372. RNA-Seq data have been deposited in the Gene Expression Omnibus Database (GEO) (http://www. ncbi. nlm. nih. gov/geo/) under the entries GSE150127, GSE162838, and GSE162839.
ACKNOWLEDGEMENT Sequencing was performed by the GenomEast platform, a member of the "France Génomique" consortium (ANR-10-INBS-0009). We particularly thank B. Jost for his help with setting up this project. We are grateful to the Genotoul bioinformatics platform Toulouse Midi-Pyrenees for providing computing resources. funded by a "Contrat Jeune Scientifique" grant from INRA. The BIOGER Unit benefits from the support of Saclay Plant Sciences-SPS (ANR-17-EUR-0007).
lactis reference genome. A total of 1,767,970 reference-based polymorphic positions were detected in the population, 1,594,125 being related to SNPs and 173,845 to small indels. The SNP dataset was used to evaluate the overall genetic diversity within the species. The average pairwise difference between strains π reaches 2. 8 × 10 -2 (θ w = 3. 3 × 10 -2 ), which is almost 10-fold higher compared with S. cerevisiae (π = 3 × 10 -3 )  (Peter et al. , 2018). This population-level genetic divergence is, to our knowledge, the highest reported to date within a yeast species. While several species such as Saccharomyces uvarum and Lachancea kluyveri showed greater diversity compared with S. cerevisiae (π = 1. 2 × 10 -2 , θ w = 1. 2 × 10 -2 and π = 1. 7 × 10 -2 , θ w = 2. 1 × 10 -2 , respectively), none were ever mentioned as exceeding 2 × 10 -2  (Almeida et al.
, 2014; Friedrich et al. , 2015). Interestingly, the diversity within the closely related species, K. marxianus, in which dairy and wild strains also coexist, is more than twice lower and has been estimated at π = 1. 2 × 10 -2 (Ortiz-  Merino et al. , 2018). This SNP dataset was used for a neighbor-net (SplitsTree) analysis that clusters the strains into five highly separated lineages (fig. 1A ), within which individuals are closely related (supplementary Table  S2 , Supplementary Material online). This topology was confirmed by the inference of the phylogenetic relationships between the strains, through the construction of a maximum-likelihood tree, as well as a neighbor-joining tree (supplementary fig. S1 , Supplementary Material online). The largest population corresponds to the K. lactis var.
2B ). This growth advantage is most likely related to the presence of this gene cluster; however, we cannot exclude that some other genes contribute to the phenotype. While the main driver of lactose uptake is obviously related to the presence of this gene cluster, the availability of sequencing data for a large K. lactis collection paves the way for a more systematic view of the variants that discriminate populations at the genomic level. To this end, gene CNV were first investigated based on read mapping to the K. lactis var. lactis reference with Control-FREEC  (Boeva et al. , 2012). Within the whole population, we detected 1,406 gene gains and 2,027 gene losses related to 937 unique genes annotated on the reference genome (supplementary fig. S4A , Supplementary Material online). However, the distribution of CNVs is highly affected by segmental duplications, in particular, for the UCD 50-81 and UCD 50-80 strains, for which 270 and 146 specific gene gains are, respectively, reported (supplementary fig.
Studied Strains A collection of 42 K. lactis strains was compiled for this study (supplementary Table  S1 , Supplementary Material online) with the aim to maximize the ecological and geographical origins of the strains. The samples were mostly purchased from the Phaff and the CIRM collections. Our final dataset contains 17 strains directly related to dairy processes, which originate mostly from Europe. While the number of dairy strains is high, some share closely related origins, which could impact the diversity among these isolates. The wild strains were mostly isolated from insect and tree exudates in North America and Asia.
Illumina Sequencing and Polymorphisms Detection To obtain genomic DNA for 41 strains sequenced in the context of this study, isolates were grown overnight at 30 °C in 20 mL of YPD medium to early stationary phase before cells were harvested by centrifugation. Total genomic DNA was subsequently extracted using the MasterPure Yeast DNA purification kit (Cat No MPY80200) according to the manufacturer's instructions. For all genomes, 280-bp insert libraries were produced and sequenced on an Illumina HiSeq 2000 platform. A 100-bp paired-end reads were generated. For each strain, reads were mapped to the reference genome of the K. lactis var. lactis type strain CBS 2359 (GenBank accession numbers CR382121 to CR382126) using BWA mem v. 0. 7. 15  (Li & Durbin, 2009)  with default parameters. Alignments were then successively cleaned using the samtools fixmate function v.
1. 3. 1  (Li et al. , 2009 ), GATK v. 4. 0. 11 realignment function (McKenna et al. , 2010) , and Picard MarkDuplicates function v. 1. 140 (broadinstitute. github. io/picard). The resulting BAM files were then used to detect SNPs and small indels using GATK HaplotypeCaller with a requested ploidy set to one. GVCF files obtained were finally concatenated into one single GVCF using GATK CombineGVCFs and GenotypeGVCFs functions, allowing to produce correct genotype likelihoods and regenotype the newly merged record.
Tree Building, Divergence Calculation and Structure Analysis We inferred phylogenetic relationships among the 41 isolates using the dataset of 1,594,125 SNPs in a maximumlikelihood framework with IQ-Ttree2  (Minh et al. , 2020) , with TVM + F as a model. This latter was chosen as the bestfit model by BIC through ModelFinder  (Kalyaanamoorthy et al. , 2017). These polymorphic positions were also used for the neighbor-net analysis which was performed with SplitsTree5 software via the SplitsNetworkViewer. Isolate relationships were also assessed through the construction of a distance tree using the BioNJ algorithm  (Gascuel, 1997)  provided in the SplitsTree4 software  (Huson & Bryant, 2006). To that end, a sequence representative of each strain was constructed by inferring SNPs within the reference chromosomes, that were then merged into a single 10.
7 Mb sequence. All strain sequences were given as input to SplitsTree. These sequences were also leveraged to estimate the pairwise divergence between each strain as the ratio between the number of nonequal positions between the two considered strains and the total size of the genome. Finally, these sequences were used for the inference estimation of the number of population clusters through STRUCTURE software, version 2. 3. 4  (Pritchard et al. , 2000). We ran the software independently with a number of populations, K ranging from 2 to 6, using the admixture model with a burn-in period of 100,000 and 500,000 replicates.
Transposable Element Detection The strategy used in Bleykasten-Grosshans et al. (  2021 ) was adapted for this study. Briefly, a set of 23 sequences originating from K. lactis and K. marxianus was defined as the representative dataset. Among them, nine sequences belong to the Class II Rover elements from the hAT superfamily described in  Sarilar et al. (2015). The other 14 query sequences are similar to Class I LTR elements from the Copia superfamily. The Class I query sequences were named Tkl or Tkm (for Ty K. lactis and Ty K. marxianus, respectively), according to the nomenclature used in  Neuvéglise et al. , (2002). Similarly, the Class II sequences were named Rkl or Rkm (for Rover K. lactis and Rover K. marxianus, respectively). The Illumina reads of the 41 strains were mapped against this dataset and the coverage profiles were manually inspected.
Whole-Genome Assembly Construction Illumina paired-end reads assemblies were constructed with Abyss (v. 2. 0. 2)  (Simpson et al. , 2009)  with the option "-k 64". For the long-read assemblies, the Oxford Nanopore fastq files were downsampled with Filtlong (v0. 2) (https://github. com/rrwick/Filtlong) to get a 40X coverage per strain (options -min_length 1000 -mean_q_weight 10). The downsampled datasets were then independently assembled with SMARTdenovo  (Liu et al. , 2021) , with the options "-c 1 -k 16 -J 5000 -e zmo".
Acknowledgments We are grateful to the  IT and Bioinformatics core facility  of the  Institut de biologie moléculaire des plantes (IBMP-CNRS, Strasbourg, France)  for providing computing and storage resources. This work was supported by the  Agence Nationale de la Recherche  ( ANR-18-CE12-0013-02 ) and the  European Research Council  ( ERC Consolidator Grant   772505 ). This work of the  Interdisciplinary Thematic Institute IMCBio , as part of the  ITI 2021-2028 program  of the  University of Strasbourg ,  CNRS , and  Inserm , was supported by  IdEx Unistra  ( ANR-10-IDEX-0002 ) and by  SFRI-STRAT ' US  project ( ANR-20-SFRI-0012 ) and  EUR IMCBio  ( ANR-17-EURE-0023 ) under the framework of the  French Investments for the Future Program. J. -S. G. was supported by a grant from the  French "Ministère de l'Enseignement Supérieur et de la Recherche.
Figures legends Figure 1 The ∆lcpB mutant (JMV4) presents curved, inflated, longer and larger cells than the parental strain JMV1. A. JMV3 (∆lcpA) and JMV4 (∆lcpB) single mutants were analyzed in optic microscopy and complemented with pMTL84222 (vector), plcpA (plasmid carrying lcpA expressed with its own promoter) or plcpB (plasmid carrying lcpB expressed with its own promoter). Scale bar represents 20µm. B. The percentage of abnormal (curved, thick, or inflated) cells among total cells was calculated by measuring more than 100 cells for each strain. C. Cell length of the parental strain JMV1, JMV3 and JMV4 complemented with the vector (pMTL84222), plcpA or plcpB. D. Cell width of the parental strain JMV1, JMV3 and JMV4 complemented with the vector (pMTL84222), plcpA or plcpB. For B, C and D, the number above each group data represents the number of cells counted.
Acknowledgments We thank  Johann Peltier  for providing the pMSR plasmid for allelic exchange in C. difficile,  R. P. Fagan  for the gift of pRPF185,  Afi Akofa Diane Sapa  for the purification of SlpA,  Assilina Parfut ,  Marie-Emeline Zielinski  and  Mathieu Rodriguez  for plasmids constructions. We also thank  Valerie Nicolas  of the platform  MIPSIT of Paris Saclay University  for immunofluorescence imaging. Finally, we want to thank  Frederic Eghiaian  from  Abberior company  for his help for super-resolution microscopy experiments.
We also included an observation-level random effect  to correct model overdispersion. Model selection was performed based on the AICc and we kept the most parsimonious model with the lowest AICc. We used packages lme4 (procedures 'glmer';  Bates et al. , 2015) ; MuMIn ('dredge';  Bartoń, 2022) , ggplot2  (Wickham, 2016)  and interactions for the figures  (Long, 2019)  The model residuals were visually checked for normality and homoscedasticity using the package DHARMa  (Hartig, 2022). Ther was no multicollinearity between explanatory variables as all Variation Inflation Factors (VIFs) values were below 3. The Pvalues and model parameters were recovered from the 'Anova' procedure (package 'car') using a type = "III" analysis-of-variance tables with Wald chi-square. Finally, for significant effects, paired comparisons were performed using the estimated marginal means with the package emmeans  (Lenth, 2020).
Visual observations were made every minute for a duration of 4 h, always at the same time of the day, from 11:00 a. m. to 03:00 p. m. , considered the hours of highest sexual activities in R. ferrugineus  [36]. Because pre-mating interactions do not necessarily lead to successful mating, the occurrence of insemination was assessed by visual observation of the aedeagus in the female genital opening for a time longer than 30 s  [36]. The following behavioral data were recorded:.
(2)  In the case of the response variable 'first mating', two models were considered: In the first model a binomial distribution was applied within the glmer model. Thus, the response variable consisted if the first mating occurred with an irradiated male or not. In this model was considered also the case where one of the two males did not mate at all during the 4 h of the experiment. The second model was performed in order to analyze the case when both irradiated and healthy males were competitive enough to mate once at least. Thus, in the second model, only the cases where both males mated once at least were analyzed. The number of minutes elapsed before the 1st mating were used as response variable and a negative binomial distribution was applied into the glmer model. The Tukey's test for mean separation was performed in the statistical environment R by the multcomp package  [40].
Fixed Table  14. Glmer model (family = negative binomial) with the time elapsed before the first mating as outcome variable. Data were selected when both males mated once at least. The applied treatment is nested into the applied doses (0, 60 and 80 Gy) and the model estimates are referred to the zero hypothesis of no difference between irradiated and fertile. The males at the dose zero were both not irradiated and were used as a further control. Significant p-values are highlighted in bold.
Acknowledgments: The authors are grateful to all colleagues and friends who contributed to the study in various ways:  F. Di Cristina ,  S. Barlattani ,  F. Marini  ( BBCA onlus, Rome, Italy ) for technical support;  G. Tonne  for the English language editing;  R. Cappadona  ( Cooperativa Capperi Pantelleria, Italy ),  S. Anelli ,  A. Biddittu  ( Parco Nazionale di Pantelleria, Italy ),  G. Lasagni , and  F. Maggiore  ( Bonomo  and  Giglio srl ) for facilitating the field collections in the Island of Pantelleria, Italy,  C. Gigleux-Spitz  and  G. Hoen  ( FREDON Corse, France );  S. Arnone  ( ENEA C. R. Casaccia, Rome Italy ),  S. Colazza ,  E. Peri  ( University of Palermo, Italy ) and  S. Guarino  ( CNR, Palermo, Italy ), for their support in RPW collections in Rome province and in Sicily, respectively.