dataesr/scientific-paragraphs-categorization huggingface dataset:
org/10. 5281/zenodo. 5390383. The new fortnightly climatology at 1-km resolution was compared to the 4-km resolution used in all previous studies. Seasonal variability of Chl-a from the corrected MODIS 1-km Level 2 data (Fig. 6b ) was explored along average cross-shore transects and compared to the MODIS 4-km Level-3 data (Fig. 6a ). A closer look at the shorter distances (0-10 km) (Fig. 6c  and 6d ) shows that, as expected, the improvement is very significant and highlights much stronger cross-shore patterns (Fig. 6e  and 6f for the most contrasted months of March and August), both in terms of Chl-a concentration average and seasonal patterns. The coastal Chl-a (0-10 km) from the 1-km data is 37% higher than that from 4-km data (49% and 46% respectively at distances of 2 and 4-km from the coast).
255 the AB offshore area during the oligotrophic season in the surface layer. Both sources of Chl-a data span nearly the same magnitude. The HPLC data have a slightly lower mean and median (respectively 0. 100 mg m -3 and 0. 094 mg m -3 ) than the satellite data (0. 105 mg m -3 and 0. 104 mg m -3 ). It should be noted that the satellite has a vertically integrating effect (exponentially decreasing) on the estimated Chl-a value. In contrast, the in-situ measurements represent exclusively punctual surface values at 1 m depth and the remaining ones at 2 m depth. The final error associated with the Chl-a satellite data was estimated at 0. 025 (8% of the average) by the RMSD (Root Mean Square Deviation) between Chl-a HPLC and MODIS Level-2 data. No in-situ measurements were available in the coastal areas of the AB.
Conclusion Satellite-based Chl-a is an important proxy of phytoplanktonic biomass that allows us to disentangle very different dynamics between the coastal and offshore domains of the Algerian Basin (AB), characterised by a very narrow continental shelf. We show that a specific fortnightly climatology of 1-km resolution Chl-a generated from MODIS data makes possible this identification. The AB is characterised by two extreme high and low biomass seasons, separated by short 2-month transition periods. The offshore variability is closely related to large-scale processes governed by the influence of Atlantic waters and a progressive eastward decrease in biomass. The coastal domain reveals a very distinct dynamic associated with highly productive hotspots rather than a well-defined seasonality.
Acknowledgements We applied the  SDC (Sequence Determines Credit ) approach for the sequence of authors. We would like to thank the space agency  NASA  for providing the MODIS satellite images used in this paper (https://oceancolor. gsfc. nasa. gov/). The authors are grateful to the whole team of the SOMBA-2014 cruise for providing the in-situ data used in this work. We thank the three anonymous reviewers for their helpful suggestions that greatly improved this manuscript. The combination of three funding sources supported this research: a PhD scholarship from the  MESRS (Algerian government) , a PhD scholarship from the  Algerian-French program  PROFAS  B+ 2018-2019  ( MERS-Algeria ) and a partnership project ( IRD-French ) Fellowship.
Asterisks indicate significant differences from vhRRS benchmark (*P = 0. 023; **P = 0. 005; ****P = 1. 57 × 10 -5 hsPRS-v2, P = 1. 02 × 10 -7 HuSCI; NS, not significant; two-sided Fisher's exact test; center, proportion of positives; error bars, standard error of proportion). Precise P values for all dataset pairs, biological repeats and n for each test are shown in Supplementary Table  3. c, Overlap of SARS-CoV-2 targets identified in HuSCI with previously identified target proteins of other viruses (left) and actual overlap (arrow) compared to n = 10,000 randomized control networks (right) (one-sided, empirical P < 0. 0001). d, Host targets identified in HuSCI overlap with RNA-binding proteins (RBPs) bound to SARS-CoV-2 RNA upon infection (left) and actual overlap (arrow) compared to n = 10,000 randomized control networks (right) (one-sided, empirical P = 0.
007). https://doi. org/10. 1038/s41587-022-01475-z Y2H HIS3 -based human SARS-CoV-2 interactome dataset as HuSCI HIS3. Using Y2H GFP , 28 vORFs were screened against 14,627 human ORFs (70% completeness) (Extended Data Fig. 1a ). After stringent filtering and HIS3-based verification, this yielded 93 interactions involving 13 viral and 84 human host proteins. We refer to this dataset as HuSCI GFP and to the union with HuSCI HIS3 as HuSCI (Supplementary Table  1 ). We also carried out a targeted screen with previously identified SARS-CoV-1 host interactors; of the 62 testable orthologous SARS-CoV-2-human pairs, six were found to interact (HuSCI ORTH ) (Supplementary Table  2 ). Y2H GFP also yielded an intraviral SARS-CoV-2 interactome of 25 binary interactions among 19 vORFs (IntraSCI; Supplementary Table  1 ).
Having collectively identified a contactome of 204 direct virus-host and 25 intraviral interactions among 170 host and 19 viral proteins, we next assessed data quality. Seven interactions were identified in both HuSCI GFP and HuSCI HIS3. Albeit nominally low, this overlap is consistent with the complementary nature of the assays and pipelines. Specifically, the screens interrogated incompletely overlapping protein sets and were each 50%-60% saturated. Each version used for screening has an assay sensitivity of 20%-25%  5  (fraction of detectable interactions); thus, the overlap is consistent with known screening parameters  2  and a low false-discovery rate. Moreover, from these parameters we can estimate that HuSCI covers 15%-22% of the complete contactome between SARS-CoV-2 and host proteins (Methods).
To further assess data quality experimentally, we compared detection rates of our datasets in the yeast-based nanoluciferase complementation assay (yN2H)  6  to those of established human positive and random reference sets (hsPRS-v2 and hsRRS-v2)  5, 6. As additional benchmarks, we derived a set of 55 well-documented binary interactions between human and coronavirus proteins from the curated literature (virus-host literature binary multiple reference set; vhLit-BM) and a virus-host random reference set (vhRRS) (Supplementary Table  3 ). We tested HuSCI, IntraSCI and each benchmark set by yN2H (Fig. 1b  and Extended Data Fig. 1b ). At a stringent scoring threshold of 1% vhRRS, the validation rates of both HuSCI alone and the union of HuSCI with IntraSCI (UnionSCI) were statistically indistinguishable from the two positive control sets (hsPRS-v2, P = 0.
Complementarity of contactome and co-complex datasets Previous studies investigating host and SARS-CoV-2 proteins used either affinity purification followed by mass spectrometry (AP-MS) to identify co-complex associations  9, [12] [13] [14] [15]  or biotin identification (BioID) to find proteins in spatial proximity  [16] [17] [18]. However, co-complex maps capture largely indirect associations in stable complexes that persist through affinity purification  2  and, likely due to experimental differences, the datasets exhibit limited agreement among each other (Extended Data Fig. 2a ). For a subset of such co-complex associations, contacts can be computationally modeled  19. In contrast, binary interactome maps provide direct contact partners and are enriched for regulatory interactions  2.
Despite these differences, 20 of the 204 HuSCI-interacting pairs were found in co-complex and BioID studies, and 58 (34%) of the 170 HuSCI host proteins were associated with a SARS-CoV-2 protein by these studies (Supplementary Table  1 ). Thus, the contactome map is consistent with previous indirect association datasets while providing substantial novelty. Although SARS-CoV-2 primarily infects lung and airway tissues, it can spread to additional tissues and this expanded tropism is characteristic for COVID-19 and important for long COVID symptoms  20. As previous SARS-CoV-2 interaction datasets could only detect host proteins expressed in the specific assay cell lines, we wondered whether HuSCI was also complementary in terms of the tissue specificity of identified host proteins. Using the Human Protein Atlas (HPA)  21  , we defined 'tissue-specific' and 'common' human proteins (Supplementary Table  4 ).
Whereas the AP-MS and BioID data are biased toward common host proteins, HuSCI is more representative of the human proteome and shows good coverage of proteins expressed in the diverse tissues in which SARS-CoV-2 RNA has been detected  22  (Fig. 2b , Extended Data Fig. 2b ,c and Supplementary Table  4 ). Thus, the HuSCI contactome has unique advantages for understanding tissue-specific perturbations by SARS-CoV-2.
SARS-CoV-2 targeted functions To understand which host functions are directly perturbed by the virus, we investigated SARS-CoV-2 targeted pathways. Broad functions enriched among host proteins include (1) immune response, (2) viral process, (3) protein ubiquitination, (4) cytoskeleton and (5) vesicle-mediated transport (Fig. 2c ). These largely agree with functions identified in association and proximity datasets  9, [12] [13] [14] [15] [16] [17] [18]  (Supplementary Table  5 ). Focusing on immune pathways, we noticed that NSP9, NSP14 and NSP16 contact key regulators of cytokine production such as REL (c-REL proto-oncogene, NF-κB subunit), IKBKG (inhibitor of NF-κB kinase regulatory subunit gamma, also known as IKK or NEMO) and TRAF2 (tumor necrosis factor (TNF) receptor-associated factor 2).
HuSCI interactors of the membrane-spanning NSP6 were enriched   1. b, Proportion of host targets in common and specific expression groups in all (top) and in SARS-CoV-2 RNA-positive organs (bottom) across eight datasets: purple, HuSCI; gray, HPA  21  ; blue, AP-MS datasets from Gordon et al. 12, 13  , Stukalov et al. 9  , Li et al. 14  and Nabeel-Shah et al. 15  ; red, BioID datasets from Laurent et al. 16  , St-Germain et al. 17  and Samavarchi-Tehrani et al. 18. Two-sided Fisher's exact test, Bonferroni adjusted P < 0. 0001. Full statistical details and exact P values are listed in Supplementary Table  4. c, Functions enriched among host proteins found in HuSCI (P = 0. 05, Fisher's exact test with FDR correction). Broad functional groups are indicated in small boxes according to legend in panel a.
In an alternative 'indirect' model, genetic variation in the network neighborhood of targeted host proteins modulates the downstream effects and thereby influences disease outcome. A precedent for this model was observed in a plant system, where pathogen-targeted host proteins tend to interact with proteins relevant to disease severity and fitness (encoded by highly variable genes under balancing selection)  35. The availability of a high-quality contactome map enabled us to address this fundamental question for COVID-19. Because bias toward well-studied proteins in the SARS-CoV-2 literature  36  (Fig. 3b  and Extended Data Fig. 3b ) limits mechanistic understanding and can cause artifacts, we focused our analyses on systematic protein interaction datasets. The direct model was not supported, given that no targeted host protein from HuSCI was encoded from a critical illness associated locus  32, 33  ('critical illness proteins'), and only one (HLA-G, associated with ORF3) was found in a single co-complex study  9.
We then asked whether direct viral target proteins and proteins in each community are encoded by genes associated with human traits of 114 uniformly processed genome-wide association studies (GWASs)  41. Variation in genes encoding direct viral targets was only associated with 'depression' (FDR = 0. 03, MAGMA). In contrast, among the communities, genetic variation associated with severe COVID-19 illness was associated with ten virus-targeted communities, more communities than any other human trait. In contrast, host-protein sets from AP-MS studies were enriched in fewer communities (nominal P < 0. 05, Fisher's exact test; Extended Data Fig. 3e  and Supplementary Table  8 ), and only one host-protein-enriched community each from two AP-MS datasets was enriched for genetic variation associated with severe COVID-19 (refs.
Colony growth was scored using a custom dilated convolutional neural network  68. For training, previous datasets of more than 1,500 images of biochemically and functionally validated binary Y2H studies were used  3. Each image was scaled to achieve equal pixel distance between the yeast spots of different images. The images were cropped and sliced, and the mean grayscale image of all spots on a plate was calculated. With this dataset, a simple front-end prediction module was trained consisting of six dilated convolutional layers with exponential increasing dilation rate and two dense layers at the end. After each layer except the last, a Leaky-ReLU activation was added  69. The model was optimized with a combination of Softmax and Cross entropy and an Adam Optimizer  70. The model achieved an accuracy >0.
After primer-dimer removal, DNA was quantified by qPCR, and the pooled NGS library was sequenced on an Illumina NextSeq using a mid-or high-output 150-cycles kit. Read counting based on expected barcodes. The sequencing data were demultiplexed using bcl2fastq2 (v2. 20. 0. 422) provided by Illumina with the following command: 'bcl2fastq -r 10 -p 20 -w 10 -no-lane-splitting -barcode-mismatches 1 -adapter-stringency 0. 7ignore-missing-bcls -ignore-missing-filter -ignore-missing-positions'. After demultiplexing, the fastq files were aligned to the group specific reference files using bowtie2  71  with the following parameters: For read 1: -q -norc -local -very-sensitive-local -t -p 23 -reorder. For read 2: -q -nofw -local -very-sensitive-local -t -p 23 -reorder. Reference files contained expected barcode sequences for the ORFs in each group.
15 × 0. 6 = 9%. It has been previously shown that using high-copy C-terminal fusions increases sensitivity by ~33% without affecting precision  26. Thus, screening sensitivity of Y2H GFP was modeled from that of lcnY2H GFP as S GFP = S lcnGFP × 1. 33 = 9% × 1. 33 = 12%. Given that Y2H GFP covered 70% (T GFP = 70%) of all possible virus-human protein combinations, the completion level of the Y2H GFP dataset is C GFP = T GFP × S GFP = 0. 70 × 0. 12 = 8. 4%. Only 4 out of 28 (14. 2%) hsPRS-v1 pairs detected by the union of Y2H HIS3 and lcnY2H GFP were detected with both methods, indicating a high degree of orthogonality (that is, different detection profiles of the methods used). In addition, Y2H GFP implemented in this study includes further differences such as high-copy and C-terminal fusion constructs for human proteins.
Pairwise Y2H testing with SARS-CoV-2 variants Lineage-defining mutations for the SARS-CoV-2 'variants of concern' as defined by the Centers for Disease Control and Prevention (Alpha, Beta, Gamma and Delta) were obtained from CoV-Spectrum  72, 73  and mapped to the SARS-CoV-2 reference genome (NCBI accession number NC_045512. 2). To generate variant ORFs, Y2H HIS3 plasmids were used as template for mutation PCR (primers in Supplementary Table  12 ). Mutation PCR reaction products were transformed and sequence verified. Plasmids containing the desired mutation were directly transformed into yeast and processed in pairwise mating as described above. A complete list of mutations generated is shown in Supplementary Table  12. SARS-CoV-2 proteins for which interactions were identified in AD-fusions (N and E) were tested only against the identified interactors.
GO enrichment analysis gProfiler  74  (database versions: Ensembl 104, Ensembl Genomes 51 and Wormbase ParaSite 15) was applied to identify enriched functional categories in HuSCI, AP-MS  9, [12] [13] [14] [15]  and BioID studies  [16] [17] [18]. The hORFeome9. 1, which was used for contactome mapping, served as the background for HuSCI, otherwise the universal annotated human genes. 'Inferred from electronic annotations' annotations were excluded. Adjusted P values were calculated using the Benjamini-Hochberg procedure. Functional terms with a hypergeometric P < 0. 05 and term size between 5 and 1,000 were collected and enrichment calculated as the ratio between observed and expected gene counts. To categorize HuSCI host proteins, five meta categories inspired by the functional enrichment analysis results were used, namely 'immune response' (GO:0006955), 'viral process' (GO:0016032), 'protein ubiquitination' (GO:0016567), 'cytoskeleton' (GO:0005856) and 'vesicle-mediated transport' (GO:0016192).
NF-κB reporter assays HEK293 (RRID: CVCL_0045, DSMZ) were cultured in complete DMEM (high glucose) supplemented with 10% fetal calf serum, 100 U ml -1 penicillin and 100 µg ml -1 streptomycin and maintained in humidified atmosphere at 5% CO 2 at 37°C. For the reporter assay, 1 × 10 6 HEK293 cells were seeded in a 60-mm cell culture dish one day before transfection. Transfection was done using the calcium phosphate protocol using 10 ng NF-κB reporter plasmid (6 × NF-κB firefly luciferase pGL2), 50 ng pTK reporter (Renilla luciferase) and expression vectors (Flag-IKKb (pRK5), Flag-A20 (pEF4) and SARS-CoV-2 constructs (pMH)) using a total of up to 6 µg DNA. Briefly, the DNA was diluted in 200 µl 250 mM CaCl 2 solution (Carl Roth, 5239. 1), vortexed and added dropwise to 200 µl 2 × HBS (50 mM HEPES (pH 7.
0), 280 mM NaCl, 1. 5 mM Na 2 HPO 4 × 2 H 2 O, pH 6. 93) while gently vortexing. After 15-min incubation at room temperature, the mix was added dropwise to cell culture dishes. Transfection media was replaced after 6-h incubation with complete DMEM. Then, 24 h after transfection cells were stimulated with 20 ng ml-1 TNF-α for 4 hours. Luciferase activity was measured https://doi. org/10. 1038/s41587-022-01475-z using the dual luciferase reporter kit (Promega, E1980) according to the manufacturer's protocol. The firefly and Renilla luminescence was determined with a luminometer (Berthold Centro LB960 microplate reader, software MikroWin 2010) and quantified in relative light units (RLU). NF-κB induction was specified as the ratio of firefly luminescence (RLU) to Renilla luminescence (RLU). Significance of relative NF-κB transcriptional activity was assessed via one-way ANOVA with Dunnett's multiple comparisons.
After 24 h, the transduction was repeated with the same steps as above. Cell surface expression of hACE2 was monitored by FACS using the AttuneNxT Flow Cytometer (Thermo Fisher Scientific) and results were analyzed with FlowJo v10 Software (BD Life Sciences). The resulting cells are referred to as A549-hACE2. Generation of KO cell lines. KO cells were generated using the target-specific CRISPR-Cas9-HDR (homology-directed recombination) KO directed technology developed by Santa Cruz Biotechnology, which enables selection of KO cells with puromycin and red fluorescent protein (Supplementary Table  15 ). Briefly, A549-hACE2 cells were seeded at 2. 5 × 10 6 cells in T25 flasks and standard media. After 24 h, cells were cotransfected with 7. 5 µg each of KO and HDR plasmids for the previously described targets and 15 µg KO plasmid for the mock KO, from Santa Cruz Biotechnology using FuGene (Promega, E2312).
Tissue specificity analysis The Tissue Atlas dataset was obtained from the HPA database 21 (version 2021. 04. 09). The HPA categories 'tissue enriched', 'group enriched' and 'tissue enhanced' were combined with 'tissue-specific', 'low tissue specificity' was denoted as 'common' and the 'not detected' category was not included in this analysis. A total of 11,069 of 19,670 genes (56. 3%) in the HPA dataset were defined as tissue specific, and 8,385 of 19,670 genes (42. 6%) showed common expression profiles. Tissue distribution differences were determined using Fisher's exact test with Bonferroni correction. SARS-CoV-2 organotropism data were obtained from post mortem examinations  22, 82. The RNA tissue-specific NX value (normalized transcripts per million) was extracted and used to denote whether the gene is specifically expressed in a given tissue.
Identification of genetic variation in host targets and network communities Host network communities were identified using the OCG hierarchical community clustering algorithm on the Human Reference Interactome  26, 83  as implemented in the linkcomm R package (V1. 0-13) using 'centered cliques' as initial class system  84. A total of 3,603 communities with a minimum size of 4 were found, of which 204 contained a significant number of virus interactors (that is, were significantly targeted) (nominal P < 0. 05, Fisher's exact test; Supplementary Table  8 ). A community was annotated to a function if a GO term was enriched (FDR < 0. 05) or if ≥20% or ≥30% of the annotated constituent proteins shared an annotation  85  (Supplementary Table  8 ). From AP-MS-based association studies  9, [12] [13] [14] [15]  , 57, 43, 18 and 17 significantly targeted communities were found, respectively (nominal P < 0.
showing COVID-19 critical illness associated loci from two GWAS metaanalyses  32, 33. Locus-associated proteins present in HuRI are marked in bold. b, Genes in indicated COVID-19 datasets ranked across the human genome by number of publications. Error bars are 95% confidence intervals of the mean, calculated by 1,000 bootstrap samples (from top to bottom n = 45, 170, 383, 876,  29, 75, 25, 71, 233, 46, 49, 45, 15, 33, 10, 71, 97, 47, 58, 9, 46, 22, 20, 23, 39). c, Virusinteractor enrichment in contactome: number of direct SARS-CoV-2 protein interacting HuSCI proteins in a HuRI subnetwork formed by proteins encoded by COVID-19 critical illness associated loci  33  (marked in bold in table (a)) and their first level interactors (arrow) compared to n = 10,000 randomized control networks (gray distribution).
One-sided, empirical P = 0. 012. d, Virus-interactor enrichment in co-complex associations: number of SARS-CoV-2 associated human proteins in two AP-MS based studies  9, 13  in a subnetwork formed by proteins encoded by COVID-19 critical illness associated loci  32, 33  (marked in bold in table in (a)) and their first level interactors (arrow) either in HuRI or BioPlex 3. 0. The comparisons are against n = 10,000 randomized control networks (gray distribution). One-sided, empirical P values are shown for each dataset. e, Upset plots showing number of communities targeted by SARS-CoV-2 (left) and associated with severe COVID-19 (right) in HuSCI and AP-MS based datasets. f, Table  showing  15 traits for genetic variation identified within targeted network communities. An association with severe COVID-19 comorbidities is indicated, as well as trait references: T2D_UKBS  [91] [92] [93]  , BMIA  92, 94  , FAT_UKBS  95  , HRET  96  , RET  96  , HC_UKBS  94, 97  , ADPN  98  , HYPOTHY_UKBS  43, 99, 100  , SCZ_UKBS  44, 101  , GIANT_HIP  92, 102  , IBD_UKBS  103  , OST_UKBS  104  , EGG_PHF, GIANT_HEIGHT, NEUROT_UKB.
[62][63][64]ent SARS-CoV-2 vORF collections were constructed in Gateway entry vectors. The Y2H GFP collection 60 includes all but one (NSP11 was too short for Gateway cloning) codon-optimized ORF of SARS-CoV-2, synthesized based on a published genome61, which were cloned with and without stop codon, to enable C-terminal fusions. The Y2H HIS3 entry clone collection is based on National Center for Biotechnology Information (NCBI) accession number NC_045512. 2 and annotation62. Y2H HIS3 vORFs were synthesized by Twist Bioscience without codon optimization and included 5´ and 3´ linkers with SfiI restriction sites. The 5´ linker incorporates a translational start ATG flanked by BamHI sites; the 3´ linker provides a stop codon flanked by PacI and AsiSI restriction sites. For Y2H HIS3 , vORFs were cloned into pENTR223.