dataesr/scientific-paragraphs-categorization huggingface dataset:
The remaining deregulated genes in patient cells were not altered in parent cells ◀ Figure  5. TAPT1 cellular localization and functional data. A TAPT1 predicted topology: a membrane-spanning protein consisting of 5 transmembrane helices (Uniprot database). B Western blot analyses for TAPT1 (~60 kDa) using cytosolic, Mito/ER/Golgi and nuclear protein extracts from primary dermal fibroblasts of two  WTs (WT1 and WT2)  and two patients (V. 5 (F1) and IV. 1 (F2)). TAPT1 protein is highly enriched in the Mito/ER/Golgi fraction, and to a lower extent in the nuclear fraction. GAPDH, TGN46 and BiP served as a cytosolic, Golgi network and ER markers, respectively. Adenylate Kinase (AK2) was used as a mitochondrial marker. Laminin A/C was used as a nuclear marker. C Immunofluorescence staining of mitochondria using anti-TOM20 (green), ER using anti-CANX (red) and Golgi using anti-GLG1 (red) in primary dermal fibroblasts from WT1 and V.
TAPT1-deficiency resembles a collagenopathy To date, RNA-seq stands out as the gold-standard technique to identify affected signaling pathways underlying a certain disease. To identify cellular processes that are affected upon TAPT1 mutation, we performed an integrated pathway enrichment analysis combining RNA-seq and SI-NET-seq results. RNA-seq and SI-NET-seq uncovered genes with a significant change in transcript levels and nascent transcription in patient cells, respectively. Despite the different types of data, we observed a strong overlap in dysregulated pathways between both datasets. Collagen-and ECM-related pathways standout as the most significant hits from this combined analysis, indicating a dysregulation of these processes in patient cells. This interesting finding is consistent with our patients' phenotype, which manifests with severe bone defects and skin abnormalities.
Sample collection and clinical assessment The affected children were firstly diagnosed at the National Center for Diabetes, Endocrinology and Genetics (NCDEG) (Amman, Jordan) with severe osteogenesis imperfecta. In total, 15 saliva samples were collected from members of the two families including parents, affected and unaffected siblings. Genomic DNA from saliva samples was isolated using the Origene DNA Collection Kit (OG-500, DNAGenotek). Skin biopsies from three affected (V. I (F1), V. 5 (F1) and IV. 1 (F2)) and one unaffected (IV. 3 (F1)) family members were also collected. Informed consent was obtained from all individuals in accordance with local ethical review board requirements in Jordan and Singapore (A*STAR IRB reference code #2019-087, Singapore). Informed consent was also obtained from patients to publish their photos.
Genotyping and homozygosity mapping SNP genotyping was performed on the genomic DNA from 15 affected and unaffected individuals from both families using Illumina HumanCoreExome-12v1 Bead-Chips. Identity-by-descent (IBD) mapping detected common homozygous regions in the 3 affected individuals using Wolfram Mathematica data-analysis software. IBD homozygous blocks were identified as regions > 2 cM. Candidate homozygous regions were refined by excluding the shared homozygous regions with unaffected individuals. Finally, a single identical and homozygous region was revealed on Chr. 4 (4p16. 1-p15. 31) (hg19).
RNA-Sequencing RNA from primary dermal fibroblasts from 2 patients (V. I (F1) and V. 5 (F1)), and 2 unrelated wild types (WT1 and WT2) was extracted using the RNeasy Mini Kit (Qiagen). After measuring RNA quantity and integrity using the Agilent Bioanalyzer 2100 (Agilent Technologies), libraries were sequenced on a Illumina HiSeq/Novaseq sequencer. Reads were aligned to the GRCh38. p12 human reference genome using STAR v2. 5. 3a  (Dobin et al, 2013)  with default parameters in paired-end mode. For differential gene expression analysis, we quantified the transcript abundance of the annotated genes from GENCODE v28  (Frankish et al, 2019 ) using HTSeq v0. 11. 4 (Anders et al, 2015)  in "union" mode. Significant changes between the conditions were tested using DESeq2 v1. 25. 4  (Love et al, 2014).
We defined genes as significantly dysregulated when they had an FDR adjusted P-value of <0. 05. For alternative splicing analysis, we focused on alternative exon inclusion and exclusion events between wild-type and patient samples. After read mapping, we identified all exons from GENCODE v28 annotation  (Frankish et al, 2019)  showing an "exon inclusion level" difference of at least 10% using rMATs v3. 1. 0 (Shen et al, 2014). The "exon inclusion level" of an exon describes the fraction of reads accounting for the inclusion of the exon. We defined alternative exon usage as an event between conditions with a significant (FDR < 0. 05) difference in the "exon inclusion level". Splicing events that were supported by less than 5 reads were excluded.
SI-NET-sequencing For spike-in NET-seq (SI-NET-seq), 15 × 10 6 primary dermal fibroblasts were mixed with 3 × 10 6 murine NIH 3T3 cells. Murine NIH 3T3 cells served as spike-in controls. The cells were tested negative for mycoplasma. All subsequent steps of the SI-NET-seq experiments were performed as recently described  (Arnold et al, 2021)  with the following modification. For reverse transcription of nascent RNAs, the SuperScript IV Reverse Transcriptase (ThermoFisher) was used. Processing of SI-NET-seq data was performed as previously described  (Arnold et al, 2021)  with some modifications. Briefly, adaptor sequences and unique molecular identifiers (UMIs) were trimmed by cutadapt v2. 4  (Martin, 2011)  and a custom python script, which preserves information of UMI sequences for the corresponding reads.
The obtained reads were aligned to a joined reference genome from human GRCh38. p12 and mouse GRCm38. p6 using the STAR v2. 5. 3a aligner  (Dobin et al, 2013). For uniquely mapped reads, the position corresponding to the 3 0 -end of the nascent RNA fragment was recorded. We excluded reads that originated from reverse transcription mispriming and from PCR duplication using the UMI sequence information as described previously  (Gajos et al, 2021). Additionally, sequenced splicing intermediates were excluded. We masked regions that were transcribed by Pol I and III, as well as loci of short chromatin-associated RNAs, which were extracted from annotations in GENCODE v28/v29  (Frankish et al, 2019; mouse: M18 and M22), RefSeq v109 (O'Leary et al, 2016) , miRBase v22. 1  (Kozomara et al, 2019)  and the UCSC's RepeatMasker  (Jurka et al, 2005).
In the final step of data processing, we split the spiked-in mouse observations from sample observations. We statistically tested the significance of changes in the Pol II occupancy. First, we quantified the Pol II occupancy at actively transcribed genes using SI-NET-seq data. Active genes had a calculated TPM value of at least one using RSEM v1. 3. 1  (Li & Dewey, 2011)  quantifications from wild-type RNA-seq data. Second, we tested for significant changes in the Pol II occupancy using DEseq2 v1. 25. 4  (Love et al, 2014). For data normalization, we calculated the "Relative Log Expression" on Pol II occupancy measurements from spiked-in mouse cells. Quantification of Pol II occupancy in mouse was calculated as for sample observations. To define actively transcribed genes, we used RNA-seq data available for NIH3T3 mouse cells (ENCODE: ENCSR000CLW)  (Davis et al, 2018).
Western blot Total cellular protein extracts from primary dermal fibroblasts were obtained using RIPA buffer supplemented with 1X Protease Inhibitor Cocktail (Roche). Nuclear, Mito/ER/Golgi, and Cytoplasmic fractions were prepared using the Cell Fractionation Kit Standard (Abcam, ab109719) following the manufacturer's instructions. Protein concentrations were measured using the Pierce TM BCA Protein Assay Kit (Thermo Fisher Scientific). Samples were reduced in Laemmli loading buffer containing dithiothreitol, and denatured at 95°C for 5 min. Equal amounts of protein were loaded on precast 10% Tris/Glycine/SDS polyacrylamide gradient gels (Bio-Rad), followed by transferring on PVDF membranes (Bio-Rad) using the Trans-Blot â Turbo TM Transfer System (Bio-Rad). Membranes were blocked in 5% milk in TBST for 1 h at room temperature, and subsequently probed with the following primary antibodies diluted in 5% milk in TBST overnight at 4°C: mouse anti-GAPDH (1:1,000; Santa-Cruz, sc-47724), rabbit anti-TAPT1 (1:1,000; Sigma, HPA042567, reacted with TAPT1 sequence covering exon 13-14), rabbit anti-TAPT1 (1:1,000; Sigma, HPA048658, reacted with TAPT1 sequence covering exon 6-8), mouse anti-BIP (1:1,000; BD Biosciences, 610978), rabbit anti-TGN-46 (1:1,000; Abcam, ab50595), rabbit anti-AK2 (1:1,000; Proteintech, 11014-1-AP) and mouse anti-Lamin A/C (1:1,000; EMD Millipore, MAB3211).
Immunofluorescence analysis Primary dermal fibroblasts were cultured on 8-well glass chamber slides (Millicell EZ SLIDES) and fixed for 15 min in 4% paraformaldehyde in PBS at room temperature. The cells were permeabilized with 0. 3% Triton-X100 in PBS for 15 min, and blocked in 1% BSA in PBS for 1 h at room temperature. Samples were then incubated with the following primary antibodies diluted in 1% BSA in PBS overnight at 4°C: rabbit anti-TAPT1 (1:1,000; Sigma, HPA042567), rabbit anti-TAPT1 (1:1,000; Sigma, HPA048658), rabbit anti-TOM20 (1:1,000; Proteintech, 11802-1-AP), rabbit anti-Calnexin (1:2,000; Abcam, ab22595) and mouse anti-GLG1 (1:500; Abcam, ab103439). For visualization, 1:500 secondary antibodies conjugated to Alexa Fluor 568 or Alexa Fluor 488 (Invitrogen, Molecular Probes) were incubated for 1 h at room temperature in the dark.
unbiased list of 172 significantly (P < 0. 05) dysregulated genes in the patients' fibroblasts at the mRNA level(Fig 6A; Dataset EV1). Of these significantly altered genes, a similar fraction was up-and downregulated(Fig 6A; Dataset EV1). Beyond TAPT1 which is the fifth most significantly downregulated gene in mutant cells, the dysregulation of several other target genes such as RARRES2, ZIC1, and ZIC4 was validated by qPCR (Fig6B). Moreover, SI-NET-seq results revealed a total of 317 genes with aberrant Pol II occupancy and dysregulated nascent RNAs in the patient cells(Figs 6C and EV5A  and B; Dataset EV2). For the majority of these genes (70%), the density of transcriptionally engaged Pol II was significantly increased in patient cells(Fig 6C; Dataset EV2). The Pol II occupancy at TAPT1 was not changed in patient cells, indicating that Pol II transcription of TAPT1 was not impacted by the mutation(Fig 6C).
Acknowledgements B. R. is a fellow of the  Branco Weiss Foundation  and  EMBO Young Investigator. This work was also supported by a  Strategic Positioning Fund on Genetic Orphan Diseases (GODAFIT)  and an  Use-Inspired Basic Research (UIBR) grant  from  Agency for Science, Technology and Research (A*STAR ) in Singapore to B. R. This work was also funded by the  Max Planck Society  (to A. M. ) and the  Deutsche Forschungsgemeinschaft (DFG , grant  418415292  to A. M. ). This work was also supported by a  2232 International Fellowship for Outstanding Researchers Program of T € UB _ ITAK  (Project No:  118C318  to N. E. B). Funding for the open access charge was provided by the  Max Planck Society. We are pro- foundly grateful to all patient family members for their participation in this study.
| Habitat and protection effects on megafauna densities A permutational multivariate analysis of variance (PERMANOVA)  (Anderson, 2017)  with 9999 permutations and dissimilarity matrices constructed with Euclidean distance was computed to assess the effects of habitat (six categories) and protection (inside/outside the no-take MPA) and their interaction on log-transformed megafauna densities. The PERMANOVA is a geometric partitioning of variation across a multivariate dataset. Statistical inferences are made in a distribution-free setting using a permutation-based algorithm. An F-statistic was used to compare variability within groups and among different groups and to test the null hypothesis that the centroids and dispersion of the groups are equivalent for all groups  (Anderson, 2017). For all taxa with detected significant effects of habitat and/or protection, pairwise multilevel t-tests were further performed.
sand and seagrass) for all shallow (up to 10 m deep) tropical reefs. Since the Allen Coral Atlas only provides habitat classification for waters shallower than 10 m, waters with depth greater than 10 m were lumped into an 'open sea' category. Densities per cell were intersected with these habitat categories and the location of the Poé MPA obtained from the world database on protected areas (IUCN, UNEP-WCMC, 2022).
ACKNOWLEDGEMENTS We are grateful to all students for their help with annotation of aerial images. We thank  Monaco Explorations  for funding part of the aerial surveys and  Air Paradise  for their continuing collaboration in data collection. This project received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement no. 845178  (' MEGAFAUNA ').
Metagenomic sequencing of semen pools After quality control and filtering, the sequencing of eight semen pools generated between 4. 4 -15. 7 million reads per sample with a mean read length of 130 bp. Of these reads, an average of 24% mapped against the host genome (Sus scrofa domesticus) and were removed. Of the remaining reads, approximately 98% could be taxonomically classified. A majority of the classified reads, approximately 55%, were of microbial origin, mainly bacterial. Less than 1% of the reads were classified as viral (Table  1 ). The specific alignment against the APPVgenome identified APPV-sequences in one of the eight semen pools (pool 4; Fig. 1 ). At the contig level, approximately 94% of the de novo assembled contigs could be taxonomically classified. The majority of the contigs, on average 60% in each dataset, were of chordate origin, mainly Sus scrofa domesticus.
About 35% of the contigs were of microbial origin, mainly bacterial. Less than 1% of the classified contigs were of viral origin. The datasets generated and analysed during the current study are available in the RJEB51770 repository at the European Nucleotide Archive. [https:// www. ebi. ac. uk/ ena/ brows er/ view/ PRJEB 51770, accession number: SAMEA13468669 -SAMEA13468676].
Limited virus diversity in boar semen Across the eight pools, we detected three viral species which generated > 300 reads per sample: Equine infectious anemia virus, Porcine type-C oncovirus, and Posavirus-1. Other detected viral species generated < 25 reads per pool. Atypical Porcine Pestivirus generated 16 reads in one of the pools (pool4; Fig. 1 ). Reads classified as Equine infectious anemia virus were most abundant, detected in all pools, with one pool containing > 15 000 reads. When reads classified as Equine infectious anemia virus were extracted and blasted, they generated Equine infectious anemia virus genome hits. These contigs were the most common contigs, present in all pools, and generated in total 16 contigs. However, it should be noted that virus classified as Equine infectious anemia virus is commonly detected in metagenomic dataset due to reagent contamination by viruses highly similar to Equine infectious anemia virus  [30]  and are therefore not considered a clinically relevant finding and thus not further investigated.
Phages In total, 121 reads classified as 20 different phages were detected in seven of eight datasets (6-28 phage reads in pool 1-7). Escherichia phage vB_EcoM_KAW1E185 was the most abundant species with a total of 29 reads detected in four out of eight datasets (pool 2, 3, 5, and 7; 1-19 reads per pool) followed by Thermus phage phiLo with a total of 13 reads detected in two out of eight datasets (pool 4 and 5; 2-11 reads per pool), and Escherichia phage vB_EcoM_G4500 with a total of 12 reads detected in three out of eight datasets (pools 2, 3, and 4; 2-8 reads per pool) (Fig. 4 ). Two contigs could be assembled from the reads, classified as Escherichia phage PBECO 4 and Synechococcus phage S-CAM7.
Microbiome Bacterial reads were found in all pools (range 627 031 -3 241 146 bacterial reads per pool) and the taxonomic classification result showed that the overall microbiome was comparable between the eight pools. The vast majority of the reads were classified, using the nucleotide non redundant (nt) NCBI database, into three Gram-negative, bacterial families; Burkholderiaceae (approximately 20% of the bacterial reads), Comamonadaceae (approximately 9% of the bacterial reads), and Enterobacteriaceae (approximately 5% of the bacterial reads). The species that generated the highest number of classified reads were Variovorax. sp HW608 (747,316), Leptothrix cholodnii (473,624), and Ralstonia pickettii (417,795). When blasted, the reads generated Ralstonia pickettii, Variovorax. sp, and Leptothrix cholodnii hits.
As part of analysing the microbiome, a bioinformatic screening for antibiotic-resistant genes (ARGs) was done. In the datasets from the eight pools, 24 breeding boars in total, no ARGs were detected. Globally, the knowledge of the occurrence of ARGs in commercial boar semen is limited, and only a few studies comprising a low number of individuals are available for comparison. One highthroughput sequencing study on boar semen from Spain (Godia et al. , 2020) identified a low occurrence of three different ARGs. In contrast, a study using MALDI-TOF (matrix-assisted laser desorption/ionization time-offlight) mass spectrometry and API 20 E on boar semen from Romania  (Costinar et al. , 2021)  found more than half of the isolated bacteria to be resistant to gentamycin and penicillin  [49]. Although the number of Swedish boars investigated in the current study was limited to 24 individuals, the absence of ARGs is a positive finding for Sweden's policy on antibiotic use.
Conclusion In conclusion, APPV was detected in one semen sample from a boar in a boar stud delivering sperm to most Swedish sow herds. In addition, a sparse and individual RNA virome was identified in the extended semen; mainly consisting of retrovirus, phages, and some stool-associated contaminants. The seminal microbiome, however, was large and more homogenous, being characterized by Gram-negative bacteria from the phylum Proteobacteria and mainly apathogenic or opportunistic bacteria. The proportion of potentially pathogenic bacteria was low, and no ARGs could be detected in the datasets. Overall, these results indicate a good health status among Swedish breeding boars, although, we would like to encourage the boar studs to include screening for APPV in their routine testing of breeding boars.
Bioinformatics analysis of the metagenomics datasets A NextFlow pipeline  [51]  including the following steps: read quality control and trimming using FASTP version 0. 19. 5  [52] , removing of the host reads by mapping on Sus scrofa domesticus genome using Bowtie2 version 2. 3. 5. 1  [53] , and de novo assembly of the reads using MEGAHIT version 1. 2. 9  [54]  was used to perform the analyses on each dataset. The pipeline is fully available online (https:// github. com/ jhayer/ nf-metav ir). The taxonomic classification of the reads was performed using Kraken 2 version 2. 0. 8-beta  [55]  run against the nucleotide non-redundant (nt) NCBI database. For the taxonomic classification of the assembled contigs, Kraken2 was run against the non-redundant protein database (nr)  [56]. The produced reports were visualised using Pavian  [57].
Analysis of Antimicrobial Resistance Genes (ARGs) All assembled contigs that were classified as bacterial by Kraken2 were extracted using KrakenTools (https:// github. com/ jenni ferlu 717/ Krake nTools) and submitted to an ARGs screening using two different methods: the NCBI AMRFinderPlus (version 3. 10. 45, database release 2022-10-11), and the Resistance Gene Identifier (RGI, version 6. 0. 0) using the CARD database (release September 2022).
Phylogenetic analysis All available Posavirus-1 and APPV sequences were mined from GenBank and aligned using MAFFT and the E-INS-i algorithm, implemented in Geneious prime (https:// www. genei ous. com). As the contigs recovered spanned different parts of the genome, we took an approach of removing the regions of the alignments between contigs. Specifically, to capture all three Posavirus contigs, we concatenated three alignment blocks ranging from position 1072-3987 (nonstructural region spanning the RNA helicase) relative to NC_023637, for a final alignment length of 1446 bp. To capture both Atypical Porcine Pestivirus contigs, we concatenated the 5'UTR and a region spanning the end of Erns and first half of E1, for a final alignment length of 692 bp. Maximum likelihood trees were estimated using PhyML, implementing the best nucleotide substitution model as determined by smart model selection, integrated into PhyML (Guindon et al.
Acknowledgements We would like to acknowledge funding from 'The  Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning, Formas ' under the project " Neurotropic viruses in pigs: the role in congenital disease"  (Grant number  2016-00979 ) and  Sveriges Grisföretagares  forskningsstiftelse. We would also like to thank the sequencing facilities. Sequencing was performed by the  SNP&SEQ Technology Platform in Uppsala. The facility is part of the  National Genomics Infrastructure (NGI) Sweden and Science for Life Laboratory. The SNP&SEQ Platform is also supported by the  Swedish Research Council  and the  Knut and Alice Wallenberg Foundation. The data handling was enabled by resources provided by the  Swedish National Infrastructure for Computing (SNIC)  at UPPMAX partially funded by the  Swedish Research Council  through grant agreement no.
datasets Pool id Number of reads before QC Read length before QC Number of good quality reads Read length good quality reads % Host mapped Total nr of contigs Viral reads Viral contigs Bacterial reads Bacterial contigs Pool 1 1628 769 883 1349 758 1418 2229 1042 3. 2 M 0. 96 M 0. 63 M 1 M 1. 1 M 3 M 2. 9 M 1. 2 M 7 6 4 10 10 5 4 5 16,021 10,776 3890 6048 6419 15,707 12,622 4550 5280 5013 7976 3216 2810 5252 4050 4621 20,85% 39. 55% 47,09% 13,98% 20,59% 12,83% 14,04% 24,43% 133bp, 133bp 125bp, 125bp 122bp, 122bp 135bp, 135bp 132bp, 132bp 131bp, 131bp 128bp, 128bp 130bp, 130bp 15. 7 M 7. 2 M 6. 1 M 4. 4 M 5. 6 M 13. 4 M 12. 7 M 6. 8 M 151bp, 151bp 151bp, 151bp 151bp, 151bp 151bp, 151bp 151bp, 151bp 151bp, 151bp 151bp, 151bp 151bp, 151bp 17. 3 M 8. 7 M 6. 6 M 4. 8 M 6. 2 M 14. 6 M 13. 6 M 7. 0 M Pool 2 Pool 3 Pool 4 Pool 5 Pool 6 Pool 7 Pool 8.
Authorization and ethical approval The study was conducted according to the Declaration of Helsinki after written informed consent was obtained from the healthy subjects, the patients or their legal guardians. All experiments were approved by the local institutional review boards. All information is subject to the provisions of the local data protection regulations. JMML samples were obtained from the Hilda Biobank Freiburg, and fetal samples were obtained from the Wellcome Trust-funded Human Developmental Biology Resource (HDBR; http://www. hdbr. org). HDBR is regulated by the UK Human Tissue Authority (HTA; www. hta. gov. uk) and operates in accordance with the relevant HTA Codes of Practice. Healthy BM donors at the Heidelberg University Hospital received financial compensation. All mouse experiments were approved by German local authorities (Regierungspräsidium Karlsruhe and Regierungspräsidium Freiburg).
Flow cytometry analysis of JMML patients For validation experiments of epitype-specific CD45RA/CD90 expression profiles, single cell suspensions prepared from JMML spleen tissue were subjected to density gradient centrifugation (Ficoll) to obtain mononuclear cell fractions (MNCs). An aliquot of the isolated MNCs was stained for flow cytometry (Supplementary Table  24 ) and data acquired using BD LSRFortessa. For validation experiments of epitype-specific CD52 expression profiles, JMML samples were thawed as described above ("Single-cell RNA-seq and WGBS of JMML patients") and stained with fluorescence-coupled antibodies for flow cytometry (Supplementary Table  25 ). Cells were analyzed with BD FACSAria Fusion.