dataesr/scientific-paragraphs-categorization huggingface dataset:
In keeping with this, JAK1 and STAT1 have been associated with the activation pathways of interferon secretion as for the host antiviral responses  [77]. Moreover, ISG15 and MX1 have antiviral activity like interferon biomolecules  [78] [79] [80] [81]. It has been suggested that they may be participating in the protection against SARS-CoV-2 infection. In our study, we also focused in analyzing the potential interactions of those deregulated molecules at their post-transcriptional level, by performing PPI and co-expression GRN analysis. We identified seventeen genes as the higher interactors among the DEGs shared in both disease conditions. Interestingly, some of the closest interactors are genes that have been previously proposed as potential therapeutics for SAR-CoV-2. For instance, IFNAR2, IMPDH2, ITGA4, JAK1, TLR7, TUBB have been previously identified by computational predictions and annotated in the Reactome database.
All these computationally created events and entities were reviewed by Reactome curators and verified with published experimental data that indicate the existences of differences between the molecular details of the SARS-CoV-1 and SARS-CoV-2 infection pathways. Strikingly, in our computation analysis we determined that among those genes interactors the following five are classified as hub genes TAT1, IRF7, ISG15, MX1, and OAS1. Hence, our hub genes are new molecules with promising clinical potential. Notably, those hub genes are linked to pivotal biological and metabolic pathways that are not only restricted to DKD and COVID-19 conditions, but also include heart disease, angiogenesis disruptions and other metabolic disorders. It is noteworthy that our predictive molecules could have therapeutic potential as targets.
In accordance, the Gene-Disease Associations dataset describes that during acute kidney injury, the herein identified five hub genes are induced  [86]. This suggests their possible involvement in leading CKD. Another hub gene interferon-induced is MX1. It has been reported that this molecule is highly expressed during renal disease like lupus nephritis, and that this could be possible to cause kidney inflammation  [87]. Moreover, another study supports this gene behavior. When analyzing its expression on hamster organotypic kidney cultures infected with SARS-CoV-2, it increased thirteen fold level of its baseline expression compared to the controls  [88]. In this context, other genes of the IFN system that could be stimulating the signaling pathway cascades are IRF7 and STAT1. Notably, STAT1-signaling pathway is associated with renal inflammation and renal injury  [89].
Our co-expression analysis highlights the regulatory role of STAT1 in protein localization, targeting and transport. Notably, OAS1 and ISG15 are upregulated molecules that are both associated with the co-expression of genes linked to cardiac muscle proliferation. Our interactomics approach identified the biological possible effects of severe SARS-CoV-2 infection under DKD condition context, finding affections that are ocurring in other organs in the body. Notably, revealing linkages with cardiovascular diseases, such approaches could represent key steps towards new treatments. In order to validate the potential of those molecules as clinic biomarkers, we performed an in-silico analysis taking advantage of publicly available databases. At the systems level we compared the hub genes expression against datasets in the Coronascape DB.
ISG15, STAT1, and MX1 are the most conserved up-regulated genes across COVID-19 datasets of immunological cells. Among immune cell samples identified to possess induction of this biomolecules in COVID-19 patients, are dendritic, CD4 + T, Natural killer (NK), and CD14 + CD16 + monocyte, as well as in the lung cancer cell line (calu3 and A539). Meanwhile OAS1 is induced in all the immune cells described, to the exception of the CD16 + monocytes. In contrast, the overexpression of IRF7 was found only for the cancer cell line A539 and in dendritic cells. NK and dendritic cells play an important role in responses to viral infections, but during Chronic kidney disease and SARS-CoV-2 infection. These immune cells decrease their circulating levels  [92, 93]. Opposite, the activation the intermediate (CD14-CD16+) monocytes are considered as inflammatory mediators to increase chronic conditions such as cardiovascular disease, diabetes, and DKD  [94, 95].
Basic and clinical research on related genes and drugs is required to provide more strategies for preventing and treating COVID-19 in DKD condition. We hope that the new knowledge generated in this study sheds light that could lead to developing novel therapeutic intervention strategies In addition, by performing interactomes analysis, we determine the following five hub genes STAT1, MX1, ISG15, OAS1, and IRF7and their co-expressed modules, participating in biological events associated with heart disease, virus immune response, and genetic regulation at the transcriptional and posttranscriptional levels. Expression profile system levels analysis reveals that ISG15 and MX1 are the most conserved upregulated genes across COVID-19 datasets of immunological cells. Meanwhile, OAS1 and MX1 are the genes with lower gene expression levels in tissue-specific healthy samples.
Funding: This research was funded by  PROFAPI 2022  ( PRO_A2_019 ) and  CONACYT-Paradigms and Controversies 2022  ( 319930 ) projects. K. A. P (CVU: 227919 ) received financial support from  CONACyT  and is a current holder of a fellowship from the  Fulbright García-Robles foundation. Acknowledgments: The authors thank  Octavio Zambada Moreno  for his technical assistance in data visualization. This article is dedicated to the memory of  Dr. Juan Ignacio Sarmiento Sanchez. Conflicts of Interest: "The authors declare no conflict of interest. ".
Download and install (Fiji Is Just) ImageJ (FIJI)  (Rueden et al. , 2017). Install the libraries LOCI (https:// imagej. net/orgs/loci) and MorphoLibJ (https://imagej. net/plugins/morpholibj) (Legland et al. , 2016). Download the Fiji script 'nuclearFRET. py' from the GitHub webpage https://github. com/ phigirard/nuclearFRET. This script was tested in Fiji 2. 1. 0/1. 53f.
Image processing and FRET analysis Timing: varies depending on the number of images acquired We use a custom-made Python script 'nuclearFRET. py' in Fiji version 2. 1. 0/1. 53  (Rueden et al. , 2017)  with the libraries LOCI and MorpholibJ v1. 4. 3  (Legland et al. , 2016). The script with the user guide for image processing is available from the GitHub webpage https://github. com/phigirard/ nuclearFRET. The source code is freely available under GNU General Public License v3. 0. The script can generate a FRET index image of semi-automatically extracted nuclear envelopes and a list of mean FRET index per nucleus ('Nuclei' workflow) when applied on images of cells expressing the CB or CH constructs. Alternatively, the script generates the FRET index image of FRET standard constructs 5AA and TRAF expressed in the cytoplasm ('Whole cell' workflow).
The script is organized in 4 tasks that follow one another. For step-by step visual guide of the protocol please refer to the Figure  5. 14. The script opens the file and checks its dimensions. If the number of Z-slices or frames is >1 (Z or T >1) or if there is only one channel (C=1), the script will abort and warn the user. If the file contains multiple positions, select with the slider in the popup window the series you want to analyze. The script creates a folder with the name '[filename(without extension)]+_S+[selected series number]' to store all the analyzed data files (images and result tables). 15. The script displays the Maximum Intensity Projection (MIP) generated from the multichannel stack. In this image, use the rectangular tool to manually select a ROI that contains the fluorescent protein signal.
The script then displays the mean intensity profile of this ROI through the multichannel image, which reveals the fluorescence emission spectrum of the construct. 16. In the popup, select the channels that correspond to the donor and acceptor intensity maxima based on the emission spectrum. The script applies a Gaussian filter with a radius of 2 pixels to both selected channels in order to reduce noise and assigns Not A Number (NaN) value to saturated and 0-value pixels. TASK 2. 17. In the image window, manually select a line ROI with the freehand line tool in the darkest area of the image. For each channel, the script measures the mean intensity value within the ROI and subtracts it from the whole image. Each background-subtracted channel is saved in the 'analyzed data' folder created in task 1 ('c1': donor, 'c2': acceptor image).
Note: the script bypasses background measurement if you have checked [Choose values for background subtraction] in the dialog of step 13. While background is usually low or even negligible in confocal images of cells expressing reasonable levels of fluorescent protein, background subtraction is critical for unbiased FRET calculation in low expressing conditions. We thus recommend to subtract the background anyway. Note: from here, the script will either follow steps 18 and 19 of the 'Whole cell' workflow or steps 20-24 of the 'Nuclei' workflow. 'Whole cell' workflow: 18. In the popup window, select the min and max intensity values of the pixels for which you want to display the FRET index with the threshold sliders. Press 'OK' to generate thresholded donor and acceptor images in which the selected pixels retain their original values, while the other pixels are set to NaN.
19. The script generates a FRET index image (in %) from the background-corrected and thresholded donor and acceptor images by applying the following formula to each non-NaN pixel: FRET index ðin %Þ = 100 3 I FRET I FRET + I Donor ; where I FRET and I Donor are the intensity values in the acceptor and donor channels, respectively. The script will then display: a. an image where every selected pixel displays a FRET index value in % with a 'Fire' color map. The resulting image is saved as 'FRET index (%). tif', b. a single row Result Table called 'Mean FRET index (%)' with the area (in the unit of the original image) of the analyzed region, the mean FRET index and its standard deviation. This table is not saved. This is the end of the 'Whole cell' workflow, you can now repeat the Image processing and FRET analysis from TASK 1 on through the 'Nuclei' workflow and/or go to the expected outcomes section.
'Nuclei' workflow: 20. The script generates a FRET index image (in %) from the background-corrected donor and acceptor images obtained in step 17 by applying the same formula as in step 19 of 'Whole cell' workflow. The resulting image is saved as 'FRET index (%). tif'. TASK 3. 21. In the MIP image, manually point with the multi-point selection tool all nuclei. The coordinates of these markers are saved as a set of ROIs under the name 'RoiSet_Markers. zip'. The script segments all selected regions with the Marker-controlled Watershed plugin from the MorphoLibJ library. 22. In the segmented image, select background regions to fuse with the multi-point selection tool. The function 'Merge Label and Kill border' of the MorphoLibJ library then merges together the selected background regions and the nucleus regions touching the border of the image.
The resulting image with the 'Jet' color map and white background is saved as 'LabelBordersRGB. tif'. 23. The script then interpolates labeled nuclei in closed polygon ROIs with points spaced one pixel apart by applying a 3-point running average. Polygons with less than 10 points are discarded. The set of resulting ROIs are saved as 'RoiSet_NucleiContour. zip'. A 3-pixels-thick band is then generated from each ROI. The set of resulting ROIs is saved as 'RoiSet_NucleiBand. zip'. A Result Table is generated with one row per pixel within the band of each nucleus contour and the following columns: the index of the nucleus ('IObject'), the ROI name of the nucleus given by ROI Manager ('Iname'), the index of the pixel within the band ('IcontourPoints'), the coordinates of this pixel ('PointX' and 'PointY') and the FRET index in % of this pixel ('FRET index (%)').
24. The script sets all pixels excluded from the 3-pixels thick nucleus contour to NaN in the FRET index image obtained in step 20 of 'Nuclei' workflow and displays: a. an image where the pixel values in the nucleus contour are the FRET indices in % with a 'Fire' color map. This image is saved as 'FRET index Nuclei. tif'. b. a Result Table called 'Mean FRET index (%)' with, for each nucleus, its area (in the unit of the original image), the mean and standard deviation of the FRET index (in %) and the shape descriptors (circularity, aspect ratio, roundness and solidity) of the nucleus contour. This table is saved as 'MeanFRETMeasurements. csv'. Note: This script was successfully run on laptop computers (both Windows and Mac) with at least 1. 80 GHz Processor and 8 GB of RAM. Note: The segmentation step will highly depend on the signal-to-noise ratio of the initial image and the transfection homogeneity across the cell population.
QUANTIFICATION AND STATISTICAL ANALYSIS Quantification of treatment effect on nesprin tension 1. Repeat the protocol from steps 'Cell seeding on imaging coverslips' to 'image processing and FRET analysis' at least 3 times for the nesprin tension sensor CB and its control CH. 2. In Prism, pool mean FRET indices output from the script from all images of the same construct (CB, CH), condition (treated, untreated), and experimental repeat in the same group of a column table. Repeat for every construct, condition and experimental repeat. 3. To assess for inter-experimental repeat variability, run a Kruskal-Wallis test between experimental repeats of the same construct and condition. Pool groups that are not statistically different. Repeat for each construct and condition. 4. To assess for an effect of the treatment, run a Mann-Whitney test between treated and untreated conditions, for each construct.
ACKNOWLEDGMENTS This work was supported in part by the  Centre national de la recherche scientifique (CNRS) , the  French National Research Agency (ANR)  grants ( ANR-17-CE09-0019 ,  ANR-18-CE13-0008 ,  ANR-19-CE14-0045 ), the  LabEx ''Who am I?''  ( ANR-11-LABX-0071 ), and the  Universitede Paris IdEx  ( ANR-18-IDEX-0001 ) funded by the  French Government  through  its ''Investments for the Future'' program. This work was in part performed at the  ImagoSeine core facility  of the  Institut Jacques Monod , member of the '' Infrastructures en Biologie Santeet Agronomie'' (IBiSA) and France-Bio-Imaging infrastructure  ( ANR-10-INBS-04 ).
Single-base substitutions are generally enriched in non-B DNA structures in human cancers To obtain a general view of somatic mutagenesis in non-B DNA structures in human cancer and use it further as a baseline for comparison with the APOBEC mutagenesis, we first calculated the densities of all somatic single-base substitutions (SBS) in non-B and B-DNA genome regions using mutational data from the Pan-Cancer Analysis of Whole Genomes (PCAWG) project  (Campbell et al. , 2020). Seven types of Non-B DNA structures were considered using genomic coordinates of non-B DNA motifs from the non-B DB  (Cer et al. , 2013) : direct repeats (DR), inverted repeats (IR), mirror repeats (MR), short tandem repeats (STR), G-quadruplexes (G4), A-phased repeats (APR), and Z-DNA. As was observed earlier for a smaller cancer dataset  (Georgakopoulos-Soares et al.
, 2013). It can be seen that samples with low APOBEC activity have an increased density of APOBEC-signature mutations in comparison with its density in B-DNA. This agrees with the fact that the density of somatic mutations in human cancer in non-canonical DNA motifs is generally higher than in canonical regions, as confirmed by PCAWG data in the previous section. It should be noted that most mutations with the APOBEC signature in these samples are likely associated with non-APOBEC mutagenesis, as the absence of APOBEC enrichment reflects a low level of APOBEC mutagenesis. Meanwhile, Figures  2A  and S3  show that as the APOBEC activity increases, the APOBEC-signature mutation density in the four non-canonical DNA genome regions (DR, STR, MR, and G4) decreases to the level of the APOBEC-induced mutational density in B-DNA genome regions and to even lower level for cancer samples highly enriched in the APOBEC-signature mutations.
Here, we analyzed the activity of mutagenesis induced by APOBEC enzymes in non-B DNA genome regions, compared its level with the APOBEC mutagenesis in B-DNA, and elucidated the impact of structural parts of non-B DNA structures, focusing on ssDNA regions. Using the large PCAWG dataset of mutations in cancer genomes, we confirmed that, in general, the density of somatic mutations is relatively larger in non-B DNA genome regions, as was found in other datasets of cancer mutagenesis  (Georgakopoulos-Soares et al. , 2018; McKinney et al. , 2020). With most of the mutagenic sources, mutations in cancer genomes originate from either misincorporation during DNA copying or from unrepaired lesions in dsDNA. Within this paradigm, the increased density of mutations in non-B DNA could be explained by a higher rate of replication errors or/and by lower access to error-prone DNA repair systems.
Limitations of the study The limitations of this study are linked to the properties of the input data. Although the information on noncanonical DBA structures that we obtained from the non-B DB database  (Cer et al. , 2013)  is comprehensive and widely used in many studies, there are new experimental data on non-B DNA structures scattered over diverse publications, and these additional data are difficult to combine and normalize. Indeed, there is a need for a shared repository that consolidates experimental data and computational predictions on all types of non-canonical DNA structures.
ACKNOWLEDGMENTS We thank  Dmitry Gordenin  for thoughtful discussion, critical reading of the article, and insightful suggestions,  Irina Ponomareva  for the non-B DNA structures and graphical abstract artworks. This study was supported by  Russian Science Foundation  (grant  22-14-00132 ) to M. D. K. The authors would like to acknowledge the dbGaP repository for providing access to the TCGA dataset (the accession number is phs000178. v11. p8).
Slides were incubated with primary antibodies, mouse anti-biotin (clone BTN. 4, ThermoFisher Scientific) and rabbit anti-FANCJ (cat. B1310, Merck). Specific probes that cross-react with the primary antibodies were used for rolling circle amplification and the related SiRF signals were visualized as discrete PLA (proximity ligation assay) spots in cell nuclei that were also counter-stained with DAPI. Images were acquired with a confocal microscope (Zeiss LSM 700) using a 63x magnification objective (Nikon). Signals were quantified using ImageJ software (1. 52v) and the Find Maxima tool with variable values of prominence depending on each experiment. Each cell was analyzed and quantification (number of points identified), done using the software, was also verified by eye inspection. Control experiments were also carried out to detect sites of DNA synthesis in cell nuclei by EdU-EdU PLA spot analysis for the same aforementioned lines.
Acknowledgements The authors acknowledge  Hisao Masai  ( Tokyo Metropolitan Institute of Medical Science, Tokyo, Japan ) for the pCSII-EF-MCS plasmid expressing Flag-tagged FANCJ WT;  Masato Kanemaki  ( National Institute of Genetics, Mishima, Japan ) for the plasmid pMK240-TetOne AAVS1-MCS (+);  Luca Pellegrini  ( University of Cambridge, Cambridge, United Kingdom ) for the plasmid pRSF-Duet-1 expressing 6xHis SepB. This study has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement n. 859853 (acronym: AntiHelix; to A. B. , D. S. , F. M. P. , L. M. R. N. and S. O. ), and grant agreement n. 722729 (acronym: Syntrain; to N. K. J. and D. B. ), from the Cross-border Cooperation Programme Italy-Slovenia 2014-2021 by the  European Regional Development Fund and national funds (TransGlioma) , and from  Associazione Italiana per la Ricerca sul Cancro  ( AIRC IG n.
associated FANCJ mutant derivatives, we carried out co-immunoprecipitations experiments with anti-Flag agarose beads in whole extracts of FJ-KO HeLa cells transiently transfected with plasmids expressing Flag-tagged versions of the FANCJ mutants of interest. Through data mining of cancer genomics databases (cBioPortal, http://cbioportal. org, Cosmic-3D, https://cancer. sanger. ac. uk/cosmic3d, and gnomAD, https://gnomad. broadinstitute. org)wepinpointed FANCJ variants expected to give rise to mutations of CIP box amino-acid residues in various malignancies, including uterine endometrial carcinoma (N734H), invasive breast carcinoma (D736H), breast and ovarian cancer (Q740H), glioblastoma multiforme (Y743C) and colorectal adenocarcinoma (A745T) (Figure7A). To test AND-1-binding by the above cancer-.
Spheroid Growth and Viability To measure the spheroid growth, a brightfield picture was taken for each spheroid, and the area was determined using thresholding with ImageJ software (version 1. 53c, Bethesda, MD, USA), as previously described by Guyon et al. [25]. To explore cell viability in spheroids, 4 spheroids were dissociated and incubated for 20 min in PI Propidium Iodide (PI)/Calcein staining dead and alive cells, respectively. Stained cells were analyzed by cytometry. For qualitative evaluation of live/dead cell status in spheroids themselves, whole spheroids were incubated with PI/Calcein for 20 min and imaged in a fluorescence microscope (Nikon Eclipse Ti-U). Spheroid formation assays with GG6 and GG16 resulted in spheroids of various sizes, probably linked to cell adhesion properties.
First, we confirmed that under 5-ALA treatment, they accumulated porphyrins, as evidenced by flow cytometry (Figure  5B ). As for P3 spheroids, untreated cells showed very little fluorescence signals. 5-ALA induced the appearance of highly fluorescent fluorocytes. As for P3 spheroids, porphyrins were cleared after 24 h. Next, we evaluated the radiosensitization property of 5-ALA treatment in GG16 and GG6 spheroids. Both cell lines were radiosensitive, in a dose-response manner, as evidenced by the reduced spheroid growth under radiotherapy (Figure  6A ,B for 0 Gy, Figure  6C ,D for 4 Gy and Figure  6E ,F for 10 Gy, black curves). Taken together, this set of data confirms that despite porphyrin accumulation, to higher levels than what was obtained in vivo, 5-ALA treatment did not radiosensitize P3 cell-derived spheroids.
, 1996 , 1986; Chen & Hillman, 1999). This negative correlation helps maintain synaptic transmission between aging neurons thus indicating a compensatory mechanism of dendritic deafferentation  (Monteiro et al. , 1992). We reasoned that intrinsic plasticity could adjust PCs' response during aging, thus operating as a local homeostatic mechanism by adapting PC membrane capacity. The membrane capacitance of modeled PCs decreased as a function of age (Fig. 5B ; see Methods). This led to increased tonic firing rates in older PCs (Fig. 5C ), consistent with electrophysiological data  (Zhang et al. , 2010). We also assessed the relationship between the duration of post-complex spike pauses and the duration of pre-complex spike ISIs (Inter Spike Intervals) in model PCs. We realized this measure by incrementally increasing PF inputs while maintaining the CF stimulation constant (i.