dataesr/scientific-paragraphs-categorization huggingface dataset:
72 × 10 -7 ), AMPK signaling pathway (hsa04010, p = 5. 40 × 10 -6 ), and cAMP signaling pathway (hsa04261, p =5. 99 × 10 -5 ), and MAPK signaling pathway (hsa04550, p =6. 44 × 10 -6 ) (Table  1 ). Conversely, in the exosomes of IH-exposed CRL-1675 cells, the miRNAs that were differentially expressed were likely to target pathways such as transcriptional mis-regulation in cancer (hsa05202, p = 2. 37 × 10 -4 ), the ErbB signaling pathway (hsa04012, p = 4. 87 × 10 -4 ) and the Sphingolipid signaling pathway (hsa04071, p = 9. 34 × 10 -4 ) (Table  2 ). In addition, using String software we constructed regulatory gene networks for the predicted gene targets, and in light of the differences in exosome effects on cell proliferation and migration focused on genes involved in AMPK network (Figure  6A ), MAPK (Figure  6B ), and cAMP network (Figure  6C ), respectively.
As shown in Table  1  several pathways were highlighted including Ras signaling, PI3K-Akt signaling, AMPK signaling, MAPK signaling, and cAMP signaling in CLR-1424 cells. Cancers 2021, 13, x FOR PEER REVIEW 16 of 25 In addition, using String software we constructed regulatory gene networks for the predicted gene targets, and in light of the differences in exosome effects on cell proliferation and migration focused on genes involved in AMPK network (Figure  6A ), MAPK (Figure  6B ), and cAMP network (Figure  6C ), respectively. As shown in Table  1  several pathways were highlighted including Ras signaling, PI3K-Akt signaling, AMPK signaling, MAPK signaling, and cAMP signaling in CLR-1424 cells. (A) (B).
Sixty-four new anopheline TE families discovered in An. coluzzii To identify the TE families present in each of the genomes, we used the TEdenovo pipeline from the REPET package  (Flutre et al. 2011). After several rounds of manual curation, we identified between 172 and 294 TE families per genome (Table  1 ; Supplemental Table  S2 ;  Platt et al. 2016). We discovered that differences in sequencing and assembly metrics correlated with the number of families identified in only one of the genomes (Supplemental Table  S1B ). Thus, although using a single reference would have only allowed the identification of a median of 244 TE families, clustering the TE libraries from an increasing number of genomes allowed the identification of 435 well-supported TE families (Fig. 1B ; Methods). Sixty-four of these families are described here for the first time (Fig.
1B ; see below). To annotate the individual copies in each one of the genomes analyzed, besides the 435 families identified by REPET, we also added to our library 85 TE families from the TEfam database that were also present in the seven An. coluzzii genomes (Methods). Although the majority of these families were indeed identified by REPET, we initially discarded them during manual curation (Supplemental Table  S3 ; Methods). The final total of 520 families were classified into 23 superfamilies and then further grouped into four orders (Fig. 1C ). The 82 families initially discovered in all the genomes had a higher copy number (Wilcoxon test, P-value < 0. 001) and were more abundant in euchromatin (two proportion Z-test P-value < 0. 001) compared with the 353 families only discovered in some of the genomes.
Construction of the curated TE library and de novo TE annotation We ran the TEdenovo pipeline  (Flutre et al. 2011)  independently on each of the seven genomes with default parameters. The obtained consensus in each genome were manually curated (for further details, see Supplemental Methods). To ensure that we identified as much of the TE diversity as possible, we also annotated our genomes with the mosquito libraries present in the TEfam database (tefam. biochem. vt. edu). The consensus from the TEfam library identified in our genomes were added to the REPET library, and all the consensuses were clustered using CD-HIT version 4. 8. 1  (Fu et al. 2012)  with the -c and -s parameters set to 0. 8. The sequences belonging to the same cluster were used to perform a multiple sequence alignment and the consensuses were obtained.
The consensuses were classified using PASTEC  (Hoede et al. 2014 ) with default parameters. Next, their bidirectional best hits were calculated using BLAST  (Camacho et al. 2009 ) against the TEfam (tefam. biochem. vt. edu), AnoTExcel  (Fernández-Medina et al. 2011) , and Repbase  (Bao et al. 2015)  databases (for further details, see Supplemental Methods). These classified consensuses were used to reannotate the assembled genomes with the TEannot pipeline using default parameters, and we discarded copies whose length overlapped >80% with satellite annotations  (Quesneville et al. 2005).
Because we observed few significant differences, we continued using the landscape data obtained using the BLAST procedure. We identified the families where the majority of the bases of their insertions were on the peak of identical sequences in the TE landscape (>50% of the bases with >99% base identity) in more than five of the seven genomes we analyzed. Finally, we assessed the ability to actively transpose strong candidates by identifying their intact ORFs, LTRs (in the case of LTR retrotransposons), and target site duplication (TSD), and estimated the percentage identity between the two LTR of each TE copy.
Insertion frequency estimation in rural populations We compared the frequencies of the insertions where the presence/absence status was unambiguously determined in at least four of the seven genomes analyzed with the frequencies of these insertions in two rural populations from the Ag1000G project: Bana in Burkina Faso and Tiassalé in Ivory Coast (Anopheles gambiae 1000 Genomes Consortium 2020). We used the PoPoolationTE2 v-1. 10. 03 pipeline  (Kofler et al. 2016)  to compute the TE insertion frequencies in these two rural populations. Briefly, we used the AcolN1 reference genome and the newly generated TE library for An. coluzzii as reference and mapped the Illumina paired-end data from all samples. Next PoPoolationTE2 detected signatures of TE presence/absence and estimated their frequencies in every sample (for additional information, see Supplemental Material).
Acknowledgments We thank members of the  González laboratory  for comments on the manuscript. We thank the  Ecology of Vectorial Systems team at the Centre Interdisciplinaire de Recherches Médicales de Franceville (CIRMF) (Franceville, Gabon)  for their support in field collections. We thank  Jean Pierre Agbor  and  Serge Donfanck  for their commitment in larvae collections in Douala (Cameroon). This study was supported by grants from the  Ministerio de Economía, Industria y Competitividad, Gobierno de España (MINECO/AEI/FEDER, EU)  ( BFU2017-82937-P ) and grant  PID2020-115874GB-I00  funded by  Ministerio de Ciencia e Innovación/AEI   10. 13039/501100011033  awarded to J. G. D. A. was supported by an  Agence Nationale de la Recherche  grant ( ANR-18-CE35-0002-01-WILDING ). N. M. L. P. was funded by  Agence universitaire de la Francophonie (AUF)  and  CIRMF  scholarships.
Data access The sequencing data generated in this study have been submitted to the NCBI BioProject database (https://www. ncbi. nlm. nih. gov/ bioproject/) under accession number PRJNA676011. TE and gene annotations in each of the seven genomes analyzed are provided as GFF files available at https://digital. csic. es/handle/10261/ 224416 and as Supplemental Files 1-14, respectively. The TE library and the transferred annotations across the seven genomes are also available at https://digital. csic. es/handle/10261/224416 and as Supplemental Files 15, 16, respectively. The TE consensus sequences have also been deposited at Dfam release 3. 6  (Storer et al. 2021 ).
The present study aimed to predict the binding potential of carbon nanotube and nano fullerene towards multiple targets of SARS-CoV-2. Based on the virulent functions, the spike glycoprotein, RNA-dependent RNA polymerase, main protease, papain-like protease, and RNA binding domain of the nucleocapsid proteins of SARS-CoV-2 were prioritized as the molecular targets and their three-dimensional (3D) structures were retrieved from the Protein Data Bank. The 3D structures of carbon nanotubes and nano-fullerene were computationally modeled, and the binding potential of these nanoparticles to the selected molecular targets was predicted by molecular docking and molecular dynamic (MD) simulations. The drug-likeness and pharmacokinetic features of the lead molecules were computationally predicted. The current study suggested that carbon fullerene and nanotube demonstrated significant binding towards the prioritized multi-targets of SARS-CoV-2.
Identification of putative targets and their structural features Based on an extensive literature survey and virulent features of the pathogen, five prominent targets (Fig. 1 ) of SARS-CoV-2 were identified as shown in Table  1. Spike glycoprotein (PDB ID: 6VSB) is involved in promoting the entry through the transmembrane spike into cells and is the main target of antibodies. The transmembrane spike consists of two functional subunits responsible for binding to the host cell receptors and fusion of the viral and cellular membrane. Depending on the viral strain, different viruses use different domains within the host cell receptor subunit to recognize various points of attachment and entry  (Romeo et al. , 2020). The RNA-dependent RNA polymerase (RdRp) (PDB ID: 6M71) is the main component responsible for the replication and transcription of the viral (corona-viral) machinery and is essential for the survival of these viruses  (Gao et al.
Molecular docking AutoDock vina  (Trott and Olson, 2010)  was used for docking the nanoparticles against the identified SARS-CoV-2 targets, and interactions of the docked complexes were analyzed. The obtained binding energy (kcal/mol), root mean square deviation (RMSD), and the number of hydrogen bonds and residues of the targets interacting with the nanoparticles are shown in Table  3  and depicted   Carbon nano fullerene, also known as buckminsterfullerene (C60) exhibited promising binding affinity towards SARS-CoV-2 targets. It showed a binding affinity of -13. 7, -12. 9, -11. 4, -10. 6, and -10. 1 kcal/mol towards spike glycoprotein, RNA dependent RNA polymerase, main protease, papain-like protease, and RNA binding domain of nucleocapsid protein, respectively. The amino acid residues of spike glycoprotein (PDB ID: 6VSB) interacting with fullerene include Tyr380, Pro412, Pro987, and Val991 as shown in Fig.
3a. These residues are present at the binding sites of the protein as predicted by CastP and Depth servers. RNA-dependent RNA polymerase (PDB ID: 6M71) -carbon nano fullerene docked complex shows interactions of fullerene with the residues Pro323, Pro461, Leu460, and Pro677 as shown in Fig. 3b. These interacting residues are present at the helix and beta-strand regions of the target and therefore, this interaction could suppress the function of the protein. The interacting residue of the main protease (PDB ID: 6LU7) includes Phe223 as shown in Fig. 3c. Ala39 and Ile44 present at the active sites of papain-like protease (PDB ID: 6W9C) interact with fullerene in the docked complex as depicted in Fig. 3d. The interacting residues of the RNA binding domain of nucleocapsid protein (PDB ID: 6M3M) include Ile75, Pro81, and Pro163 as shown in Fig.
3e. These residues are involved in binding at the phosphate groups of RNA of the virus which makes it an essential and promising interaction to inhibit the target. Carbon nanotube (C182), modeled with 6, 5 chirality docked against the SARS-CoV-2 targets showed potential interactions that could inhibit the pathogenic mechanism of the virus. The binding energies of -26. 7, -19. 7, -15. 8, -21. 1, and -20. 7 kcal/mol were exhibited towards spike glycoprotein, RNA dependent RNA polymerase, main protease, papain-like protease, and RNA binding domain of nucleocapsid protein, respectively. Interactions observed between the spike protein (PDB ID: 6VSB) and nanotube involved residues such Leu303, Lys310, Tyr313, Asp663, Arg765, Thr768, Val772, Lys776, Gly946, Asp950, and Asn953 as shown in Fig. 4a.
These residues form a prominent part of structural integrity and this interaction could alter the structure thereby suppressing the pathogenic function of the spike protein. Interacting residues of RNA-dependent RNA polymerase (PDB ID: 6M71) include Phe35, Ile37, Lys41, Lys50, Lys73, Asp208, Asp218, and Asn713 as depicted in Fig. 4b. These residues are present at the binding sites of the protein as predicted by CastP and Depth servers. Asp245, Ile249, Pro252, Phe294, and Val297 are the residues of main protease (PDB ID: 6LU7) interacting with the nanotube as shown in Fig. 4c. These residues are present near the binding sites of Lopinavir and Ritonavir  (Nutho et al. , 2020) , but with better binding energy could provide better results than the conventional antiviral drugs employed. The interacting residues of papain-like protease (PDB ID: 6W9C) are Leu36, Ala39, Ile44, Thr74, Thr75, Asp76, Lys92, Asn156, Gln174, and His175 shown in Fig.
4d. The residues Thr74, Gln174, and His175 are known to interact with conventional antiviral drugs such as nelfinavir and remdesivir  (Mothay and Ramesh, 2020). Similarly, the residues of the RNA binding domain of nucleocapsid protein (PDB ID: 6M3M) interacting with carbon nanotube include Ala51, Ala91, Arg93, Arg150, Pro152, Tyr173, and Ala174 as shown in Fig. 4e. The residues involved in these docked interactions are essential to be targeted to inhibit the function of the virus.
Molecular dynamic simulation The best binding poses of the SWCNT and nano-fullerene with the five selected targets predicted by molecular docking studies were selected and simulated using the Desmond module of Schrödinger's suite, to interpret the stability and dynamics of the interaction between the nanoparticles and SARS-CoV-2 targets. The interaction stability was confirmed by analyzing the simulation trajectories such as the changes and fluctuations in protein RMSD and root mean square fluctuations  (RMSF), ligand RMSF, interactions between the protein residues and ligands, changes occurring in the protein backbone, and ligand atoms throughout 100 ns. The trajectories obtained for spike glycoprotein (PDB ID: 6VSB) in complex with nano fullerene confirmed that the interactions exhibited on docking were stable.
Overall, the docked complex and the interactions involved were observed to be stable in the applied dynamic conditions. The RdRp (PDB ID: 6M71) and fullerene complex trajectories obtained from the MD simulation are given in Fig. 6. The RMSD of the receptor protein was observed to be stable till about 80 ns which was less than 3. 0 Å, thereafter a sudden increase up to 9. 0 Å is observed as shown in Fig. 6a. From 80 to 100 ns the RMSD fluctuates between 3. 0 and 9. 0 Å, which is high indicating the severe changes undergone by the protein as a result of interacting with fullerene. The RMSF of the receptor was also obtained with high fluctuations, that is, in the range of 0. 8-6. 5 Å as shown in Fig. 6b. These fluctuations imply that the ligand has a significant effect on suppressing the receptor thereby inhibiting its function.
6f , and Trp268, Pro323, Leu460, Pro461, and Pro677 are the residues involved. The structural and chemical changes that the target and ligand undergo throughout 100 ns are shown in Supplementary figs. S3 & S4. Altogether, it is evident that the docked complex and the dynamic interactions involved are relatively stable. The docked complex of the main protease (PDB ID: 6LU7) and fullerene, when simulated, produced trajectories that are shown in Fig. 7. The deviation in the protein RMSD was obtained to be between 1. 2 and 3. 6 Å as shown in Fig. 7a. The RMSD reached the highest peak around 45 ns to 3. 6 Å, thereafter, for about 30 ns, it remained stable in the range of 2. 2-2. 8 Å, and it came back to 1. 2 Å in the last 10 ns. These large variations are a result of the ligand binding to the protein backbone.
3) is given in Fig. 7d. The Ligand RMSF, in this case, was about 4. 0 Å as the atoms break as and when they interact with the target molecule which is shown in Fig. 7e. The residues such as Phe223 and Leu271, that interact with the ligand for more than 10% of the simulation period are represented in Fig. 7f. The changes that occur to the structures of protein and ligand are shown in Supplementary figs. S5 & S6. Overall, the docked complex interactions and energies are observed to be dynamically stable. The simulation results obtained for the papain-like protease (PDB ID: 6W9C) and fullerene complex are shown in Fig. 8. The changes in the RMSD of the protein backbone are depicted in Fig. 8a , and it can be observed that it increases stably up to 60 ns and thereby stabilizes between 6. 0 and 7.
S9 & S10. Therefore, this confirms that the interactions are dynamically stable in the applied simulation conditions. Similarly, when the docked interaction complex of the spike protein (PDB ID: 6VSB) and carbon nanotube was simulated, the trajectories were obtained as shown in Fig. 10. The RMSD changes in the protein structure increase constantly from 3. 2 to 5. 7 Å over 100 ns as a result of being involved in the binding event, as depicted in Fig. 10a. The plot of protein RMSF which is given in Fig. 10b , shows very high fluctuations throughout the protein structure ranging from 2. 0 to 8. 0 Å, indicating the effect of a nanotube on the receptor structure. The interaction plots are given in Fig. 10c , where the top panel represents the contacts and the bottom panel represents the 19 interacting amino acids at a given point of time.
The trajectories obtained for RdRp (PDB ID: 6M71) in complex with nanotube confirmed that the interactions exhibited on docking were stable. The protein RMSD, as shown in Fig. 11a , deviated between 2. 0 and 4. 0 Å throughout the simulation. The RMSD was below 4. 0 Å till about 70 ns, there on it increased and then decreased gradually, as a result of the binding event that occurs between the nanoparticle and the macromolecule. Protein RMSF showed extremely high fluctuations ranging from 0. 7 to >5. 6 Å along the entire length of the receptor backbone, which represents the effect of the drug interaction with the target and is depicted in Fig. 11b. The interactions that occurred during the simulation are shown in Fig. 11c , to time. The top panel represents total contacts that occurred throughout the simulation which is depicted to be an average of 5 to 8 interactions at any given point in time.
The structural changes undergone by the target and ligand are shown in Supplementary figs. S13 & S14. Overall, the docked complex and the interactions involved were observed to be stable in the applied dynamic conditions. The main protease (PDB ID: 6LU7) and nanotube complex trajectories obtained from the MD simulation are given in Fig. 12. The RMSD of the receptor protein was observed to be increasing linearly from 1. 3 to 3. 2 Å along the course of the simulation, as shown in Fig. 12a. The RMSF of the receptor was also obtained with fluctuations as high as 5. 5 Å in N -terminal region, whereas the remaining structure fluctuated between 0. 6 and 2. 9 Å as shown in Fig. 12b. These fluctuations imply that the ligand has a significant effect on suppressing the receptor thereby inhibiting its function.
12e. The binding contact that occurs for more than 10% of the simulation period is pictorially represented in Fig. 12f , and Val212 was the residue involved. The structural and chemical changes that the target and ligand undergo throughout 100 ns are shown in Supplementary figs. S15 & S16. Altogether, it is evident that the docked complex and the dynamic interactions involved are relatively stable. The simulation results obtained for the papain-like protease (PDB ID: 6W9C) and nanotube complex are shown in Fig. 13. The RMSD changes in the protein structure increase constantly from 1. 4 to 5. 6 Å over 100 ns with an unusual peak between 15 and 20 ns, as a result of being involved in the binding event, as depicted in Fig. 13a. The plot of protein RMSF which is given in Fig. 13b , shows very high fluctuations throughout the protein structure ranging from 0.
The data and conclusions suggested in the present study are based on the web-based database and bioinformatics software and server with the lack of solid molecular biology experiments to confirm these carbon nanomaterials binding targets, SARS-COV-2 Spike, RdRp, main protease, papain-like protease, and nucleocapsid protein, thus, further experimental studies and validations are essential to conclude. However, the current study provides a fundamental basis for carrying out in vitro assays and further experimental validations.
Identification of putative targets Five major virulent proteins were analyzed and selected as targets based on their involvement in the pathogenicity of SARS-CoV-2. Literature from databases such as PubMed and UniProt (The UniProt Consortium, 2019) was utilized for the identification of these probable putative targets. Experimentally solved three-dimensional structures of the identified protein targets were retrieved from the RCSB PDB  (Berman et al. , 2020). The targets that were retrieved included Cryo-electron microscopy structure of Spike glycoprotein (PDB ID: 6VSB)  (Wrapp et al. , 2020) , electron microscopy structure of RNA dependent RNA polymerase (PDB ID: 6M71) (Gao et al. , 2020b), X-ray diffraction structures of main protease (PDB ID: 6LU7), papain-like protease (PDB ID: 6W9C)  (Jin et al.
Modeling and retrieval of nano-structures The 3-D chemical structure of carbon nano-fullerene, C60 (PubChem ID: 123591) was retrieved from the PubChem database  (Kim et al. , 2019). The 3-D chemical structure of carbon nanotube was modeled using Nanotube Modeller (www. jcrystal. com/products/wincnt/). This carbon nanotube was modeled based on the literature evidence of the dimensions used for medicinal purposes. Therefore, carbon nanotube (C182) with (6, 5) chirality of length 20. 0 Å, the diameter of 7. 474 Å, the bond length of 1. 421 Å, and 261 bonds were selected.
Molecular docking Autodock vina  (Dai et al. , 2020)  was employed to perform docking of the carbon nanoparticles against the identified target proteins. The binding sites of the protein targets were predicted using Depth  (Tan et al. , 2011)  and CastP  (Binkowski et al. , 2003)  servers. The 3-D structures of the protein targets and the carbon nanoparticles were prepared using the Autodock tool of the MGL tools package  (Morris et al. , 2009)  according to the Autodock vina protocol (Schrödinger Release 2019-3: Desmond Molecular Dynamics System, 2019). The torsion, number of torsions, and root atoms for the ligands were assigned and the file was saved as pdbqt. Similarly, the targets were prepared by removing all the heteroatoms and ligands from the PDB files and polar hydrogen atoms were added to the targets.
Molecular dynamic simulation The best-docked complexes were subjected to molecular dynamic simulation by the Desmond module of Schrödinger's suite  [77]  to test the stability of the interactions. Hydrogen bonds were assigned to the protein and nanoparticles in the complex. The MD simulation was carried out by Optimized Potentials for Liquid Simulations (OPLS) force field and solid polymer electrolyte (SPE) water models at 300 K using the constant number of particles, pressure, and temperature (NPT) ensemble class for 100 ns. The trajectories of RMSD, RMSF, protein-ligand contacts, ligand properties, and ligand torsion profile were obtained and analyzed. The obtained trajectories of RMSD and RMSF were calculated using the following formula: RMSD X = ̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅ ̅ 1 N ∑ N i=1 ( r t i (t x ).