dataesr/scientific-paragraphs-categorization huggingface dataset:
DEGs and hub genesderived from the GSE150392 dataset In GSE150392 (mRNA expression dataset of SARS-CoV-2 infected cardiomyocytes), a total of 1,448 DEGs were screened out. Among them, 745 DEGs were up-expressed and 703 DEGs were down-expressed. Notably, OAS1, OAS2, OAS3, and OASL were all up-expressed in the cardiomyocytes with SARS-CoV-2 infection (Figures  1A, B ; Table  S2 ), and their logFCs were 6. 94, 7. 13, 5. 83, and 5. 49, respectively (Figure  1C ). The protein-protein interactions among the DEGs were analyzed using STRING online database and results were presented using cytoscape (Figure  1D ). In this analysis, the algorithms of DMNC in the plugin cyto-Hubba was used to calculate the top 30 hub genes. OAS1, OAS2, OAS3, and OASL were ranked 14, 13, 9, and 28 among the top 30 hub genes, respectively (Figure  1E ).
DEGs and hub genes derived from the GSE120852 dataset In GSE120852 (mRNA expression dataset of HF), four groups were included and their intersection genes (common DEGs) were selected to perform further analysis. Diff-1 indicated the comparison result of LV heart failure (LV-HF) vs. non-failing LV (LV-NF). As a result, 849 DEGs were screened out, 507 were up-expressed and 342 DEGs were down-expressed. Diff-2 was obtained from RV which reflected the RV mRNA difference between LV-HF and LV-NF. In the Diff-2 analysis, 1,128 DEGs were screened out, and among them, 638 were up-expressed and 490 DEGs were down-expressed. Diff-3 was obtained from LV by comparing biventricular heart failure (Bi-HF) and non-failing (NF) hearts. Total 973 DEGs were screened out, in which 614 were up-expressed and 359 DEGs were down-expressed.
SRTING online software was used to explore the relationships among the 239 common DEGs, and results were shown via cytoscape (Figure  2E ). The algorithm of DMNC in plugin cytoHubba was used to calculate the top 30 hub genes. To our speculation, OAS1, OAS2, OAS3, and OASL were all among the top 30 hub genes, and they were ranked 15, 6, 6, and 2, respectively (Figure  2F ).
Experimental validation of high mRNA expressions of OAS genes To verify the expression of OAS genes shown in Figures  1  and 2 , we further analyzed the GSE147507 dataset (SARS-CoV-2 infected NHBE) and the GSE179850 dataset (blood sample of COVID-19 patients) to evaluate the expression of OAS gene family in alternative tissues of COVID-19 patients. Results demonstrated that OAS1, OAS2, OAS3, and OASL were all highly expressed in SARS-CoV-2 infected NHBE (Figure  3  A) (OAS 1-3, P < 0. 01) and in the blood leucocytes of COVID-19 patients (Figure  3B ) (all P < 0. 001). In addition, the qPCR results of 8-week TAC mice and Ang II-stimulated H9C2 cells showed that the mRNA levels of atrial natriuretic peptide (ANP), brain natriuretic peptide (BNP), and myosin heavy chain β (β-MHC) were all significantly increased in the cardiac tissues of TAC mice (Figure  4A , B) and Ang II-challenged H9C2 cells (Figure  4C , D) , and the mRNA levels of OAS1, OAS2, OAS3, and OASL were also significantly elevated in the failing hearts of TAC mice (Figures 4B) and Ang II-treated H9C2 cells (Figures 4D).
GO and KEGG pathway analyses revealing the intersecting signaling between COVID-19 and HF The biological function enrichment analyses of DEGs in GSE150392 and GSE120852 datasets were performed using Metascape. Results of GO analysis from GSE150392 are shown in Figure  5A  and Table  S4. The DEGs of GSE150392 were significantly enriched in GO:0048018 (MF: receptor ligand activity), GO:0030017 (CC: sarcomere), GO:0071345 (BP: cellular response to cytokine stimulus), GO:0003013 (BP: circulatory system process), GO:0044057 (BP: regulation of system process), GO:0030155(BP: regulation of cell adhesion), GO:0001819(BP: positive regulation of cytokine production), GO:0009615 (BP: response to virus), GO:0010817 (BP: regulation of hormone levels), GO:0044706 (BP: multi-multicellular organism process), GO:0043408 (BP: regulation of MAPK cascade), GO:0006936 (BP: muscle contraction), GO:0043269 (BP: regulation of ion transport), GO:0061061(BP: muscle structure development), GO:0007167 (BP: enzymelinked receptor protein signaling pathway), GO:0050900 (BP: leukocyte migration), GO:0032649 (BP: regulation of interferon-gamma production), GO:0010942(BP: positive regulation of cell death), GO:0009725(BP: response to hormone), and GO:0048511(BP: rhythmic process).
Results of KEGG pathway analysis from GSE150392 were displayed in Figure  5B  and Table  S5. The DEGs of GSE150392 were significantly enriched in hsa04668 (TNF signaling pathway), hsa04060 (cytokine-cytokine receptor interaction), hsa04260 (Cardiac muscle contraction), hsa05164 (influenza A), hsa05202 (transcriptional misregulation in cancer), hsa05143 (African trypanosomiasis), hsa04010 (MAPK signaling pathway), hsa04080 (neuroactive ligandreceptor interaction), hsa04020 (calcium signaling pathway), hsa05415 (Diabetic cardiomyopathy), hsa04913 (ovarian steroidogenesis), hsa04210 (apoptosis), hsa04750 (inflammatory mediator regulation of TRP chan-nels), hsa05203 (viral carcinogenesis), hsa00350 (tyrosine metabolism), hsa04670 (leukocyte transendothelial migration), hsa04727 (GABAergic synapse), hsa04024 (cAMP signaling pathway), hsa04630 (JAK-STAT signaling pathway), and hsa04713 (circadian entrainment).
Results of GO analysis from GSE120852 were shown in Figure  6A  and Table  S6. The DEGs of this analysis included GO:0031012 (CC: extracellular matrix), GO:0030155 (BP: regulation of cell adhesion), GO:0006935 (BP: chemotaxis), GO:0098552 (CC: side of membrane), GO:0098609 (BP: cell-cell adhesion), GO:0050900 (BP: leukocyte migration), GO:0005539 (MF: glycosaminoglycan binding), GO:0045321 (BP: leukocyte activation), GO:0005518 (MF: collagen binding), GO:0002685 (BP: regulation of leukocyte migration), GO:0002252 (BP: immune effector process), GO:0005178 (MF: integrin binding), GO:0005604 (CC: basement membrane), GO:0002683 (BP: negative regulation of immune system process), GO:0050727 (BP: regulation of inflammatory response), GO:0048525 (BP: negative regulation of viral process), GO:0032103 (BP: positive regulation of response to external stimulus), GO:0019221 (BP: cytokine-mediated signaling pathway), GO:0030934 (CC: anchoring collagen complex), and GO:0046886 (BP: positive regulation of hormone biosynthetic process).
Results of KEGG pathway analysis from GSE120852 were displayed in Figure  6B  and Table  S7. The DEGs of this analysis included hsa05164 (influenza A), hsa04060 (cytokinecytokine receptor interaction), hsa04062 (chemokine signaling pathway), hsa04974 (protein digestion and absorption), hsa04670 (leukocyte transendothelial migration), hsa05171 (coronavirus disease-COVID-19), hsa05340 (primary immunodeficiency), hsa04210 (apoptosis), hsa04064 (NF-kappa B signaling pathway), hsa04929 (GnRH secretion), hsa00260 (glycine, serine and threonine metabolism), hsa04380 (osteoclast differentiation), hsa04926 (relaxin signaling pathway), hsa00480 (glutathione metabolism), hsa03250 (viral life cycle-HIV-1), hsa05221 (acute myeloid leukemia), hsa05120 (epithelial cell signaling in helicobacter pylori infection), hsa04512 (ECM-receptor interaction), and hsa04350 (TGF-beta signaling pathway).
Results of GSE150392 analysis, including GO:0003013 (circulatory system process) and hsa04260 (cardiac muscle contraction), were related to cardiac function in SARS-CoV-2 infected cardiomyocytes. Some results from GSE120852, such as GO:0050727 (regulation of inflammatory response) and hsa05171 (COVID-19), were also related to COVID-19. In addition, some results, including GO:0030155 (BP: regulation of cell adhesion), GO:0006935 (BP: chemotaxis), GO:0050900 (BP: leukocyte migration), hsa05164 (Influenza A), hsa04060 (Cytokine-cytokine receptor interaction), hsa04670 (leukocyte transendothelial migration), and hsa04210 (apoptosis), were common results of GSE150392 and GSE120852, suggesting that the processes of HF and COVID-19 employ the same biological pathways to some extents.
The miRNAs regulating the expression of OAS gene family To explore the regulatory mechanism of OAS gene family expression, we analyzed the miRNAs derived from GSE104150 (miRNA expression dataset of HF) and took intersection with the predicting results from Targetscan. Total 88 different miRNAs were obtained from GSE104150, and among them, 22 miRNAs were upregulated and 44 miRNAs were downregulated (Figure  7A , B; Table  S8 ). From the intersection of Targetscan and GSE104150, we found that 6 miRNAs regulated OAS1, including hsa-miR-1225-5p, hsa-miR-1229-5p, hsa-miR-15a-3p, hsa-miR-4270, hsa-miR-6751-3p, and hsa-miR-7106-5p. There were 33 miRNAs that regulated OAS2, including hsa-miR-1225-5p, hsa-miR-1229-5p, hsa-miR-1275, hsa-miR-181a-5p, hsa-miR-197-5p, hsa-miR-2392, hsa-miR-23a-5p, hsa-miR-26b-5p, hsa-miR-3149, hsa-miR-4433a-3p, hsa-miR-4459, hsa-miR-4484, hsa-miR-4499, hsa-miR-4632-5p, hsa-miR-4721, hsa-miR-4769-3p, hsa-miR-5196-5p, hsa-miR-548aq-5p, hsa-miR-548d-5p, hsa-miR-576-5p, hsa-miR-5787, hsa-miR-580-3p, hsa-miR-6124, hsa-miR-671-5p, hsa-miR-6751-3p, hsa-miR-6752-5p, hsa-miR-6756-5p, hsa-miR-6785-5p, hsa-miR-6812-5p, hsa-miR-6850-5p, hsa-miR-6893-5p, hsa-miR-7106-5p, hsa-miR-7641.
Predicted chemicals andingredientsinteracting with OAS gene family CTD and SymMap databases were used to predict the chemicals and ingredients that may interact with the OAS genes. CTD results showed that 12, 14, 10, and 6 chemicals interacted with OAS1, OAS2, OAS3, and OASL, respectively. Notably, estradiol and tetrachlorodibenzodioxin were found the common chemicals regulating the four OAS genes (Figure  8A ). SymMap results showed that 4, 3, 5, and 4 ingredients acted on OAS1, OAS2, OAS3, and OASL, respectively. Among them, 17-β-estradiol, hydrargyrum and saccharose were the common chemicals or ingredients that regulated the OAS gene family (Figure  8B ). The 17-β-estradiol, also named estradiol  (Dubey & Jackson, 2001) , were commonly recommended by CTD and SymMap database.
Results ofdocking analysis The 3D structure of OAS1 was obtained from PDB database (PDB ID: 4IG8), and the 3D structures of OAS2, OAS3, and OASL were downloaded from UniProt database. The binding energies of 17-β-estradiol with OAS1, OAS2, OAS3, and OASL were -7. 1 kcal•mol -1 , -7. 6 kcal*mol -1 , -8. 4 kcal*mol -1 , and -8. 7 kcal*mol -1 , respectively, suggesting that the bonding between 17-β-estradiol and the four "receptors" (OAS proteins) were strong. From the results of ligand-receptor protein interaction, we found that 17-β-estradiol could form hydrophilic binding with SER63, GLN229, and THR19, and had hydrophobic interactions with the amino acid residues ASP77, GLY62, GLN194, LEU150, and THR188 of OAS1 (Figure  9A ). In addition, 17-β-estradiol exhibited hydrophilic force with LEU340 and LYS556, while had hydrophobic force with PHE341, TRP663, GLU552, PRO339, LEU340, GLN235, GLU659, and MET266, of OAS2 (Figure  9B ).
(Choudhary, Sharma, & Silakari, 2021; Zhang C. , Wu, Li, Zhao, & Wang, 2020; Ritchie & Singanayagam, 2020). Our study on COVID-19 dataset reveals that inflammation-related pathways, such as hsa04668 (TNF signaling pathway), hsa04210 (Apoptosis), and hsa04630 (JAK-STAT signaling pathway), are consistent with the above viewpoint. These signaling pathways also have important biological functions in HF  (Feldman et al. , 2000; Sabbah & Sharov, 1998; Booz, Day, & Baker, 2002). In addition, our results show that after cardiomyocytes being infected by SARS-CoV-2, some HF-associated biological pathways became prevalent, such as hsa04260 (cardiac muscle contraction) and hsa05415 (diabetic cardiomyopathy), which directly indicates the signaling by which COVID-19 causes HF. By analyzing the DEGs in HF dataset GSE120852, we found that some signaling pathways associated with the DEGs, such as GO:0002252 (BP: immune effector process), GO:0002683 (BP: negative regulation of immune system process), GO:0050727 (BP: regulation of inflammatory response), hsa05171 (coronavirus disease-COVID-19), hsa04210 (apoptosis), and hsa04064 (NF-kappa B signaling pathway), are COVID-19 or inflammation related pathways.
Especially, the term hsa05171 (coronavirus disease-COVID-19) from the HF dataset fully illustrates the close relationship of COVID-19 with HF. Currently, there is no specific and effective drug for COVID-19. Thus, development of this kind of drugs is in urgent need. Some antiviral drugs have the potential to treat COVID-19, such as remdesivir, lopinavir/ritonavir, interferon β-1a, and hydroxychloroquine/chloroquine  (Atzrodt et al. , 2020). In the present study, we predicted some potential chemicals or ingredients which regulate the expression of OAS genes by analyzing the CTD and SymMap databases (shown in Figure  8 ). Notably, we found that estradiol is one of them. Except for regulating the sexual system, estradiol plays an important role in antiinflammation and suppression of virus-induced innate immune inflammatory response.
(C ) Venn diagram of DEGs in GSE120862. There are 239 common different gene in diff1-4, 169 were up-expressed and 70 were down-expressed. (D ) OAS gene family expression in GSE120862. (E ) PPI network of common DEGs wereshowed with dots. Yellow dot is hub genes showed as F. (F ) Top 30 hub genes calculated by cytohubba. In these genes, OAS1, OAS2, OAS3, and OASL rank is 15,2,6, and 6, respectively.
Data collection and structure determination A selenium single-wavelength anomalous diffraction (Se-SAD) dataset was collected on a SeMet-DdrC crystal at 100 K on beamline ID23-1 at the European Synchrotron Radiation Facility (ESRF, Grenoble, France), on a Pilatus 6M detector (Dectris) to a resolution of 2. 5 Å (Table  1 ). A total of 500 images were collected at a wavelength of 0. 978 Å with 100 ms exposure and an oscillation angle of 0. 15 • per frame. For the native DdrC, data collection was performed at 100 K on beamline ID30A-1/MASSIF-1 at ESRF, on a Pilatus3 2M detector (Dectris) to a resolution of 2. 5 Å (Table  1 ). A total of 1150 images were collected at a wavelength of 0. 966 Å with 220 ms exposure and an oscillation angle of 0. 1 • per frame. In both cases, data were integrated, indexed and scaled with XDS  (32)   Å respectively (Table  1 ).
The Se-SAD dataset was processed with the CRANK2 suite  (33). Briefly, SHELXC  (34)  was used to calculate structure factor estimates from merged intensities, after what heavy-atom search was performed using SHELXD  (34)  and a resolution cutoff of 3. 69 Å. Two selenium sites were found. The substructure refinement and phasing were performed with REFMAC5  (35) , then experimental phases were improved using density modification with PARROT  (36) , which enabled automatic determination of the correct hand. Automatic model building and structure refinement were performed with Buccaneer  (37, 38)  and REFMAC5  (35) , and were followed by manual building in Coot  (39)  and refinement in PHENIX  (40). The native DdrC structure was solved by molecular replacement with Phaser MR (41) using the SeMet-DdrC structure as a search model (Table  1 ).
The DdrC structure was then refined using iterative cycles of manual building in Coot  (39)  and refinement in REFMAC5  (35). The final R work and R free are 18. 3% and 24. 6%, respectively. The structure of DdrC was validated in MolProbity  (42)  with no Ramachandran outliers (Table  1 ) and deposited in the Protein Data Bank with accession number 7QVB. Analysis of dimerization interface and crystal contacts was carried out using PISA  (43). Electrostatic surfaces (at pH 7. 5 and 200 mM NaCl) were produced by APBS  (44)  from structures protonated by PDB2PQR (45) after structure-based titration of protonatable residues using PROPKA  (46).
DdrC structure prediction The sequence of DdrC was submitted to AlphaFold2 (47) via the Colaboratory service from Google Research (https://colab. research. google. com/github/sokrypton/ ColabFold/blob/main/beta/AlphaFold2 advanced. ipynb) and to RosettaFold (48) (https://robetta. bakerlab. org). Of note, the mmseq2 method  (49, 50)  was employed for the multiple-sequence alignment. The first five models predicted by each program were overlaid using the align tool in PyMOL  (51)  with overall root mean square deviation (rmsd) values of 0. 312-0. 498 Å for RosettaFold and of 0. 266-0. 735 Å for AlphaFold2. The best-ranked model from AlphaFold2 in its entirety, or as isolated N-and C-terminal domains, was used as a search model (looking for either one or two copies of the protein) to phase the native crystallographic data by molecular replacement with Phaser MR  (41).
Atomic force microscopy pUC19sc was diluted in milli-Q water to a final concentration of 0. 5 nM for all samples. 'Glycerol-free' DdrC was diluted in milli-Q water to a final concentration of either 2, 5, 10 or 20 nM. For the protein-DNA samples, pUC19sc was incubated with DdrC for 30 min at 30 • C before sample deposition on the mica sheet. Topographic data were acquired by a multimode 8 microscope equipped with a Nanoscope V controller (Bruker, Santa Barbara, USA). Before use, a freshly cleaved V-1 grade muscovite mica (Nanoandmore, Wetzlar, Germany) sheet was pre-treated with 10 l 5 mM NiCl 2 and dried under nitrogen gas. 5 l of each sample solution was deposited on the mica, after which the mica was incubated for 2 min, then dried under a gentle stream of nitrogen gas. All imaging was conducted with the PeakForce Tapping mode and ScanAsyst mode at a rate of ∼1.
Fluorescence polarization Equilibrium fluorescence polarization DNA binding assays were performed on a Clariostar (BMG Labtech) microplate reader, fitted with polarization filters. Reactions were performed in black 386-well medium-binding plates (Greiner). 0-100 M DdrC (dimer) were titrated into 10 nM 5 -FAM labeled dsDNA 20 mer or 50 mer substrates (Table  S2 ) in binding buffer composed of 20 mM Tris pH 8. 0, 100 mM NaCl, 1 mM MgCl 2 and 0. 2 mg. ml -1 BSA. Reactions were performed in a final volume of 40 l at room temperature. After subtracting the polarization values obtained for DNA alone, the mean data from at least three independent measurements were fitted to one of the following equations using GraphPad Prism 8: (a) a one-site specific binding model with Hill coefficient (Y = (B max *X h )/(K D h + X h )), or (b) a two-site spe- cific binding model (Y = [(B max (Hi)*X)/(K D (Hi) + X)] + [(B max (Lo)*X)/(K D (Lo) + X)] ), where Y is the difference between the anisotropy of completely bound and completely free oligo, B max is the maximal polarization signal, X is the DdrC concentration, K D is the equilibrium dissociation constant and h is the Hill slope.
Analytical ultracentrifugation Sedimentation velocity experiments were performed at 42 000 rpm and 4 • C, on a Beckman XLI analytical ultracentrifuge using a AN-60 Ti rotor (Beckman Coulter, Brea, USA) and double-sector cells with optical path lengths of 12 and 1. 5 mm equipped with sapphire windows (Nanolytics, Potsdam, DE). Buffer C was used as a reference. Measurements were made on 1, 4 and 8 mg. ml -1 DdrC using absorbance at 280 nm and interference optics. Data were processed with the REDATE software (https: //www. utsouthwestern. edu/labs/mbr/software/) and the parameters were determined with SEDNTERP and SEDFIT  (53). Analysis of sedimentation coefficients and molecular weights were performed using SEDFIT  (53)  and GUSSI  (54).
The NTD of DdrC exhibits a classic wHTH motif (␤3-␣1-␣2-␤4-␤5), although the additional ␣-helix (␣3) is located downstream of the wHTH motif in the sequence (Figure  2A ). The wHTH motif of DdrC is also preceded by a hairpin structure composed of ␤1 and ␤2. Surprisingly, the electrostatic surface potential of the NTD, calculated with the APBS program  (44) , indicates that the surface is mainly negatively charged, which would likely prevent DNA binding to this motif (Figure  2B ). A DALI search with the NTD alone confirmed the structural homology of DdrC NTD with other wHTH-containing proteins. The proteins with the highest Z scores were the human Dachshund protein (PDB code 1L8R, Z score 7. 0), the Dachshund-homology domain of human SKI protein (SKI-DHD, PDB code 1SBX, Z score 5. 5) and Bacillus subtilis RacA (BsRacA, PDB code 5I44, Z score 4.
DdrC dimer possesses two distinct DNA binding sites To identify a potential DNA binding site on DdrC, we analyzed the charges displayed at the surface of the DdrC dimer with the APBS program (Figure  3A ). A large positive groove involving mostly arginine residues contributed by the four ␣-helices of the CTD and ␣4-␣5 of the NTD is present on both sides of the dimer, suggesting that DdrC could possess two distinct DNA binding sites. To test this hypothesis, we performed fluorescence polarization assays with fluorescein-labelled dsDNA oligonucleotides of either 20 (20d5 F) or 50 (50d5 F) base-pairs. DdrC was able to bind efficiently to both DNA substrates and the experimental data were fitted to either a one-site or a two-site DNA binding model. In both cases, the fits to the two-site specific binding model were significantly better than those obtained for the simpler, one-site specific binding model (Figure  3B  and C ).
Further studies will be needed to explore the molecular mechanisms underlying this second putative role of DdrC in the response of D. radiodurans to severe radiation-induced DNA damage. used the platforms of the Grenoble Instruct-ERIC center (ISBG; UMS 3518 CNRS-CEA-UGA-EMBL) within the Grenoble Partnership for Structural Biology (PSB), supported by FRISBI (ANR-10-INBS-0005-02) and GRAL, financed within the University Grenoble Alpes graduate school (Ecoles Universitaires de Recherche) CBH-EUR-GS (ANR-17-EURE-0003). We thank Christine Ebel for access to the AUC and SEC-MALLS platforms. This work acknowledges the AFM platform at the IBS. All the calculations were performed on the local LPCT computing cluster.
ACKNOWLEDGEMENTS IBS acknowledges integration into the  Interdisciplinary Research Institute of Grenoble (IRIG, CEA ). This work la Recherche) and  CGI (Commissariat à l'Investissement d'Avenir ) are gratefully acknowledged for their financial support of this work through  Labex SEAM (Science and Engineering for Advanced Materials and devices)  [ ANR 11 LABX 086  and  ANR 11 IDEX 05 02 ]. Funding for open access charge: CEA Radiobiology.
Our synthesis investigation of extensive and diverse empirical datasets shows no evidence that NHL permafrost regions have become net sources of CO 2 to the atmosphere; rather, we find that NHL permafrost regions appear to be gaining C through complex interactions between above and belowground processes that respond differently to seasonal climate. However, current DGVMs are unable to reproduce the different trends of net CO 2 uptake along permafrost-vegetation gradients, potentially calling into question current projections of CO 2 dynamics in the NHL.
e. , productivity and respiration). All data (EC, ACIs, and TRENDY simulations) showed that net CO 2 uptake was more strongly regulated by ecosystem productivity, which was itself primarily controlled by spring Air T and %TC. This was consistent with previous analyses indicating that temperature-controlled photosynthetic activity and increase in woody vegetation cover were among the major drivers of productivity and net CO 2 uptake in the EGS  4, 24. In the LGS (Fig. 5d , f), we tested whether net CO 2 release was controlled by moisture stress-induced productivity declines  11  or increased respiration  25  , either from warming-enhanced respiration rate or increased labile carbon carried over from enhanced productivity early in the growing season (PreGPP)  10, 26. Observationally-constrained estimates of net CO 2 fluxes (EC and ACIs) showed that respiration had a stronger control on net CO 2 uptake in the LGS.