Table S1. STRING (Search Tool for the Retrieval of Interacting Genes) software (v.11) (http://stringdb.org/) for extract functional relationships between cytokines and growth factors.

**Repository: https://stringdb-downloads.org/download/protein.physical.links.v12.0.txt.gz **Reference: Szklarczyk, D. et al. The STRING database in 2023: protein–protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Research 51, D638–D646 (2023).

Snapshot of 9606.protein.links.full.v10.5.experiments.abc.txt from https://string-db.org/

Protein–protein interaction network of the top differentially expressed genes between the patient’s samples and the Ctrl cohort. Edges represent protein–protein associations. Confidence ≥0.700; maximum number of interactors ≤20. Edge confidence: high (0.700) and highest (0.900) (see https://string-db.org/cgi/network).

Proteome of peripheral mononuclear cells (PBMCs) from asymptomatic malaria and uninfected individuals and the ensuing febrile malaria episodes

Proteins were extracted from peripheral mononuclear cells (PBMCs), pooled using Tandem Mass Tags (TMT) (10-plex) and injected into the LC-MS/MS for proteomics analysis. The output raw files were loaded into MaxQuant software v2.0.3.0 for protein quantification. The output from MaxQuant was then read using PERSEUS software v2.05.0 and differential protein abundance analysis performed. The Proteomics_metadata file contains the metadata that links each sample to the raw data files and the treatment group (condition).

Description of the data and file structure

The RAW data files provided contains the output data from the LC-MS/MS per each pool. The pools serve as the input data for MaxQuant software.

The Proteomics_metadata contains the metadata information that links each sample to the condition/treatment group (i.e. asymptomatic, uninfecte...

Pathway model based on hub miRNAs and their putative targets from network analysis. * From a set of differentially expressed genes in both chronic HCV (hepatitis C virus) and HCC (hepatocellular carcinoma) samples, a protein-protein network was constructed using STRING and GeneMANIA. * After topological analysis and network visualization in Cytoscape, the top hub genes were identified. * miRNAs related to hub genes were identified using miRTarBase server and combined with the PPI network to constructed a miRNA-Hubgene network. Based on Figure 4 from Poortahmasebi et al, How Hepatitis C Virus Leads to Hepatocellular Carcinoma: A Network-Based Study. Proteins on this pathway have targeted assays available via the CPTAC Assay Portal.

All the IDs and sequences in StringDB version 12 https://string-db.org/cgi/download

This is the file used to create the PPI that was used for the RWR analysis in the paper. These files were downloaded from the STRING website:https://string-db.org/cgi/download?sessionId=bds8SFD97XFN&species_text=Mus+musculus&settings_expanded=0&min_download_score=0&filter_redundant_pairs=0&delimiter_type=txt

Walnut is a significant woody oil tree species that has been increasingly affected by anthracnose in recent years. Effective anthracnose control is crucial for walnut yield and quality, which requires a comprehensive understanding of the response mechanisms to Colletotrichum gloeosporioides. The PR10/Bet v1-like proteins are involved in defense against various diseases, therefore, in this study, 7 JrBet v1s were identified from the walnut transcriptome (named JrBet v1-1~1-7), whose open reading frame (ORF) was 414~483 bp in length with isoelectric point ranging from 4.96 to 6.11. These JrBet v1s were classified into three groups, with the MLP/RRP and Dicot PR-10 subfamilies each comprising three members (the largest ones), indicating that the proteins within these two subfamilies may have evolved from a shared ancestral gene within the broader PR10/Bet v1 protein family. The cis-elements in the promoters of JrBet v1s were involved in response to hormones, coercive, and plant growth metabolism. Most JrBet v1s could be significantly upregulated by walnut anthracnose, JrBet v1-1, JrBet v1-2, JrBet v1-4, and JrBet v1-6 peaked at 12 days of anthracnose stress, showing a 2.85- to 63.12-fold increase compared to the control, while JrBet v1-3, JrBet v1-5 and JrBet v1-7 peaked at 9 days, with a 3.23- to 27.67-fold increase. Furthermore, the significant correlation of the upregulation under anthracnose stress of JrBet v1s and JrChit02-1 as well as JrChit02-2, the genes encoding chitinase, suggesting that during the long process of microevolution in walnut-C. gloeosporioides interactions, walnut has developed a Bet v1-chitinase defense mechanism to counteract pathogen invasion. Methods 1 plant materials and treatments Two-year-old ‘Xiangling’ walnut grafted seedlings were potted and used as materials (the growth temperature was 22±2℃, relative humidity was 70±5%, light cycle 14 h light/10 h dark). The C. gloeosporioides was isolated by the Laboratory of Walnut Research Center in Northwest A&F University, and cultivated to the conidia concentration of 105~106 cells/mL. Then spray the C. gloeosporioides spore re-suspension on the leaves for treatment after slight friction. The leaves were harvested after being treated with 1, 3, 6, 9, 12, and 15 days, and saved in a -80℃ refrigerator for further RNA isolation. Each treatment contained 6 plants. 2 Identification, collinearity, and structure analysis of JrBet v1s “Pathogenesis-related protein Bet v1” was used to search for walnut Bet v1 family genes (JrBet v1s) in walnut transcriptome. The ORF Finder (https://www.ncbi.nlm.nih.gov/orffinder/) was used to find the open reading frame (ORF) of JrBet v1s. Basic biological information, including amino acid number, theoretical isoelectric point (pI), and molecular weight were predicted by ExPASy (https://web.expasy.org/protparam/). The sub-cellular location of JrBet v1s was predicted using the WoLF PSORT tool (http:∥wolfpsort.seq.cbrc.jp/). The BLASTP was applied to the double-direction alignment of the walnut genome and obtained all the collinear sequences. The TBtools (program: MCScan X) was used to visualize Bet v1 collinear genes in the walnut genome (Chen et al., 2020), and TBtools was utilized to calculate the synonymous (Ks) and non-synonymous (Ka) nucleotide substitution rates between all JrBet v1s gene pairs. The online software Gene Structure Display Server2.0 (GSDS 2.0: http://gsds.gao-lab.org/) (Hu et al., 2015) was applied to construct the gene structure map. 3 The cis-elements analysis of the JrBet v1s promoter The 2,000 bp upstream promoter sequences for each JrBet v1 gene were extracted from the walnut genome. Potential cis-elements within these promoters were identified using the PlantCARE database (http://bioinformatics.psb.ugent.be/webtools/plantcare/html/). The identified elements were then screened and organized using Excel, and subsequently visualized using TBtools (Chen et al., 2020) for further analysis. 4 Conserved domain, multiple sequence alignment, and evolutionary analysis CD-Search (https://www.ncbi.nlm.nih.gov/Structure/cdd/wrpsb.cgi) was used to analyze the conserved domains of the JrBet v1 protein. Multi-sequence alignment was applied using ESPript3.0（https://espript.ibcp.fr/ESPript /cgi-bin/ESPript.cgi）. MEME online tools (http://alternate.meme-suite.org/) were adopted to uncover the conservative motifs. The setting parameters were as follows: the number of motifs was 10 and the motif width was 6 to 50. The PRANK software (http://wasabiapp.org/software/prank/#About_PRANK_alignments) was used for sequence alignment and phylogenetic tree construction. The WAG model (Whelan and Goldman, 2001) was applied for protein alignments. MAFFT (Katoh et al., 2005) was employed to generate a preliminary alignment and infer the relationships tree based on evolutionary distances, with optimization scores derived from maximum likelihood inference. And iTOL v6 (https://itol.embl.de/) was used for visualization. 5 Interaction protein prediction and protein structure analysis The interaction network of the JrBet v1 protein was analyzed using the STRING protein interaction database (http://string-db.org/). The three-dimensional structure and interaction sites of the JrBet v1 protein, particularly those associated with potential disease responses, were predicted using the AlphaFold Server (https://golgi.sandbox.google.com/), and the predicted results were visualized using PyMOL. 6 RNA extraction, reverse transcription, and expression analysis The total RNA of each sample was isolated using the CTAB (cetyltriethyl ammonium bromide) method (Yang et al., 2018). Then the RNA was digested with RNase-free DNase I (Fermentas, Germany) for quality evaluation by agarose gel electrophoresis and ultra-micro UV spectrophotometer (Thermo, USA). The RNA was reversely transcribed into cDNA using PrimeScript™ RT reagent Kit (CWBIO, Beijing, China). The cDNA was diluted 10-fold by sterile water and used as the template of qRT-PCR. The 20 μL reaction system for qRT-PCR includes 10 μL SYBR Green Real-time PCR Master Mix (CWBIO), 2 μL cDNA, 1.0 μL forward primer and 1.0 μL reverse primer. The reaction program was 94°C for 30 s; 94°C for 12 s, 60°C for 45 s, 72°C for 45 s, 44 cycles; 81°C for 1 s. 18S rRNA was used as an internal reference gene (Xu et al., 2012). The primers are shown in Table S1, the quantitative results were calculated based on the threshold cycle using the 2−ΔΔCT method (Livak et al., 2001). The expression was related to the internal reference gene and 0 d. Data analysis was performed using SPSS (SPSS, Chicago, USA). 7 Yeast two-hybrid assay To determine whether JrBet v1 proteins interact with chitinase 2-like proteins JrChit02-1 and JrChit02-2, the yeast two-hybrid (Y2H) assay was employed. JrChit02-1 and JrChit02-2 were cloned into the pGBKT7 vector (denoted as BD) to create bait recombinants BD-JrChit02-1 and BD-JrChit02-2. Independently, JrBet v1s (JrBet v1-1, JrBet v1-2, JrBet v1-3, JrBet v1-4) were inserted into the pGADT7_Rec vector (denoted as AD) to generate prey recombinants AD-JrBet v1s. Additionally, JrChit02-1 and JrChit02-2 were cloned into pGADT7_Rec to form AD-JrChit02-1 and AD-JrChit02-2, while each JrBet v1 gene was independently cloned into pGBKT7 to create BD-JrBet v1s. The interactions between BD-JrChit02-1, BD-JrChit02-2, and AD-JrBet v1s, as well as between AD-JrChit02-1, AD-JrChit02-2, and each BD-JrBet v1, were then tested using Y2H assays on SD/-Ade/-His/-Leu/-Trp/X-α-Gal/Aureobasidin A (QDO/X/A) medium plates. Empty AD and BD vectors served as negative controls. The specific primers used in this study are listed in Table S2. 8 Statistical analysis All the data were organized and analyzed using Excel 2023 and SPSS (Chicago, Illinois, USA). The sample variability was described by standard deviation (S.D.) from three repeated assays. References Chen C, Chen H, Zhang Y, Thomas HR, Frank MH, He Y, Xia R. TBtools: an integrative toolkit developed for interactive analyses of big biological data. Molecular plant. 2020, 13(8): 1194-1202. doi: 10.1016/j.molp.2020.06.009. Hu B, Jin J, Guo AY, Zhang H, Luo J, Gao G. GSDS 2.0: an upgraded gene feature visualization server. Bioinformatics. 2015, 31(8): 1296-1297. doi: 10.1093/bioinformatics/btu817. Katoh K, Kuma KI, Toh H, Miyata T. MAFFT version 5: improvement in accuracy of multiple sequence alignment. Nucleic acids research. 2005, 33(2): 511-518. doi: 10.1093/nar/gki198. Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods. 2001, 25(4): 402-408. doi: 10.1006/meth.2001.1262. Whelan S, Goldman N. A general empirical model of protein evolution derived from multiple protein families using a maximum-likelihood approach. Molecular biology and evolution. 2001, 18(5): 691-699. doi: 10.1093/oxfordjournals.molbev.a003851. Xu F, Deng G, Cheng S, Zhang W, Huang X, Li l, Cheng H, Rong X, Li J. Molecular cloning, characterization and expression of the phenylalanine ammonia-lyase gene from Juglans regia. Molecules. 2012, 17(7): 7810-7823. doi: 10.3390/molecules17077810. Yang G, Gao X, Ma K, Gao X, Su L, Peng S, Zhai M. The walnut transcription factor JrGRAS2 contributes to high temperature stress tolerance involving in Dof transcriptional regulation and HSP protein expression. BMC Plant Biol. 2018, 18(1): 367. doi:10.1186/s12870-018-1568-y.

Homophily/heterophily evaluation, expressed in terms of z-score values, is related to the human Protein-Protein Interaction Network (PPI), obtained from the STRING v11.5 database (https://string-db.org) setting standard threshold on edge score (T=700). Each protein occurring in the PPI was assigned to a class corresponding to the chromosome the related gene belongs to.

A total of 23 classes (chr1, chr2, ..., chr22, chrX) were considered (excluding the class corresponding to chromosome Y because of the small number of genes occurring in the network).

The homophily/heterophily nature of the network, with respect to chromosome classes, was evaluated through HONTO tool (https://github.com/cumbof/honto).

In other words, the tendency of proteins to preferentially interact with proteins whose genes are physically located on the same chromosome (homophily) or on different chromosomes (heterophily) was investigated and evaluated in terms of z-scores.

Values related to intra (along the diagonal) and inter chromosomal interactions (other than the diagonal) are also reported as a heatmap.

As one can observe, values occurring in the diagonal are clearly higher than values out of the diagonal, leading to assess a homophilic nature of the network, confirming the link between shared chromosome and interaction in the PPI.

This data collection contains the data sets related to human (9606) that were previously deposed as separate datasets in STRING ver.10.5 before changing the download files structure with release of ver.11.0.

In this study we construct lists of candidate genes for articulate language. Analysis of coding regions of over 100 candidate genes for the effects of natural selection (directional episodic selection and relaxed/intensified selection) in the various lineages of primates (thirty-four nonhuman primate species, plus Homo sapiens Neanderthals and Denisovans) revealed a burst of increased selection effects on neural genes at the node leading to the Homo sapiens-Neanderthal-Denisova triad, followed by bursts of selection effects on neural genes related to language in both the Denisovan and Neanderthal lineages. Those latter increases in involvement of neural genes in Neanderthals and Denisovans can be contrasted with the missing or slight response to selection on those same genes in the H. sapiens lineage. The genes involved in these bursts can mostly be classified as involved in synapse structure and maintenance. We develop a hypothesis for how synaptic efficiency could be related to langua..., , # Dryad dataset

Dataset DOI: 10.5061/dryad.sbcc2frk5

Description of the data and file structure

The data in this collection are supplemental to the paper "Natural Selection and Language Genes in Humans". DNA sequence matrices for 175 genes for 42 taxa can be found in "Lang_Supplemental_File1.zip". All sequences used to construct these matrices were obtained from GENBANK. These matrices were used to perform all tests for selection accomplished in the paper. We used DATAMONKEY (https://www.datamonkey.org/). The study involves an examination of natural selection in these genes some of which are involved in language acquisition. Once we obtained measures of selection, genes obviously under selection were examined for interactions using STRING (https://string-db.org/).

Files and variables

File: Language_Supplemental_File1.zip

Description:Â This zip file holds phylogenetic matrices fo..., There are no PPI data in this paper. Individuals are NOT identified.

Orbitrap Fusion (Thermo Fisher Scientific) LC-MS/MS analyses were performed on an Easy-nLC 1000 liquid chromatography system (Thermo Fisher Scientific) coupled to an Orbitrap Fusion via a nano-electrospray ion source. Tryptic peptides were dissolved with a loading buffer (acetonitrile and 0.1% formic acid), and were eluted with a flow rate of 350 nL/min. Survey scans were acquired after an accumulation of 5×105 ions in the Orbitrap for m/z 300-1,400 using a resolution of 120,000 at m/z. The top speed data-dependent mode was selected for fragmentation in the cell at a normalized collision energy of 32%, and fragment ions were then transferred into the ion trap analyzer with the AGC target at 5×103 and maximum injection time at 35 ms. The dynamic exclusion of previously acquired precursor ions was enabled at 18 s. The Proteome Discoverer 1.4.1.14 was used for analysis of the protein spectrum. Oxidation (Methionine) and acetylation (Protein-N term) were chosen as variable modifications, cysteine carbamidomethylation was chosen as a fixed modification. Two missed cleavage sites for trypsin were allowed. The intensity-based absolute quantification (iBAQ)-based protein quantification were performed by an in-house software. The interaction of SNT-related differentially expressed proteins was investigated by STRING 11.0 (https://string-db.org). The differentially expressed protein interaction network (high reliability, interaction score > 0.4, PPI enrichment P-value < 1.0×10 -16) was selected for the analysis.

Proposed working model and protein–protein interaction map. (A) Working model. In the absence of growth factors (top left), Gαi3 preferentially interacts with AGS3, its GDI, which maintains it inactive (Gαi3–GDP). The Gαi3–AGS3 complex localizes to LC3-positive structures via a direct interaction between AGS3 and LC3, thereby promoting autophagy (i.e., maturation of preautophagosomal structures into mature LC3-positive structures; green panel, bottom). In the presence of growth factors (top panel, right), Gαi3 is activated (Gαi3–GTP) by GIV, its GEF, which 1) enhances the anti-autophagic class I PI3K-Akt-mTOR signaling pathway at the PM and 2) releases active Gαi3 from Gαi3–AGS3–LC3 complexes assembled on membranes, thereby directly inhibiting autophagosome formation and maturation. It is possible that signaling programs modulated by GIV’s GEF function at the PM also influence the equilibrium between Gαi3–AGS3 and Gαi3–GIV as a regulatory feedback loop (interrupted arrow). Further studies will be required to pinpoint the specific LC3-positive compartment where Gαi3 localizes and the precise role of this G protein in regulating autophagosome maturation. (B) Proposed protein–protein interaction map for Gαi3, GIV, and AGS3 showing the shortest functional links that integrate pro- or anti-autophagic signaling by growth factors and G protein pathways in balancing the process (initiation and reversal) of autophagy. Functional interactions between Gαi3 and its modulators analyzed in this (GIV and AGS3) and other studies (RGS19/GAIP; Ogier-Denis et al., ) in the context of autophagy are shown. These are based on interactions listed in the I2D (Brown and Jurisica, ; Brown et al., ), STRING (http://string-db.org/), and MitoCheck (www.mitocheck.org) databases and validated in the literature. For simplicity, this interaction map includes only the experimentally validated functional interactions that are relevant to G protein and growth factor signaling during autophagy. The proteins of the autophagic pathway (green) are linked to Gαi3 by a direct interaction (identified in this work and highlighted with a bold red line) between the GDI AGS3 (GPSM1) and LC3 (ATG8). The proteins of the anti-autophagic growth factor/nutrients (growth factor receptors, G protein–coupled receptors) and mTOR signaling pathways (red) are linked to G protein signaling via GIV (Girdin), the GEF that directly interacts with and activates Gαi3 (resulting in Gαi3–GTP). The protein network also reveals that processes such as apoptosis (blue) and asymmetric cell division (yellow) are likely to be influenced by modulation of G protein activity by either this or other such pairs of GDI(s) and GEF(s).

BackgroundAdvancements in modern medicine have extended human lifespan, but they have also led to an increase in age-related diseases such as Alzheimer’s disease (AD) and atherosclerosis (AS). Growing research evidence indicates a close connection between these two conditions.MethodsWe downloaded four gene expression datasets related to AD and AS from the Gene Expression Omnibus (GEO) database (GSE33000, GSE100927, GSE44770, and GSE43292) and performed differential gene expression (DEGs) analysis using the R package “limma”. Through Weighted gene correlation network analysis (WGCNA), we selected the gene modules most relevant to the diseases and intersected them with the DEGs to identify crosstalk genes (CGs) between AD and AS. Subsequently, we conducted functional enrichment analysis of the CGs using DAVID. To screen for potential diagnostic genes, we applied the least absolute shrinkage and selection operator (LASSO) regression and constructed a logistic regression model for disease prediction. We established a protein-protein interaction (PPI) network using STRING (https://cn.string-db.org/) and Cytoscape and analyzed immune cell infiltration using the CIBERSORT algorithm. Additionally, NetworkAnalyst (http://www.networkanalyst.ca) was utilized for gene regulation and interaction analysis, and consensus clustering was employed to determine disease subtypes. All statistical analyses and visualizations were performed using various R packages, with a significance level set at p

Osteosarcoma (OS) is the common primary bone cancer that affects mostly children and young adults. To augment the standard-of-care chemotherapy, we examined the possibility of protein-based therapy using mesenchymal stem cells (MSCs)-derived proteomes and osteosarcoma-elevated proteins. While a conditioned medium (CM), collected from MSCs, did not present tumor-suppressing ability, the activation of PKA converted MSCs into induced tumor-suppressing cells (iTSCs). In a mouse model, the direct and hydrogel-assisted administration of CM inhibited tumor-induced bone destruction, and its effect was additive with Cisplatin. CM was enriched with proteins such as Calreticulin, which acted as an extracellular tumor suppressor by interacting with CD47. Notably, the level of Calr transcripts was elevated in OS tissues, together with other tumor-suppressing proteins, including histone H4, and PCOLCE. PCOLCE acted as an extracellular tumor-suppressing protein by interacting with amyloid precursor protein (APP), a prognostic OS marker with poor survival. The results supported the possibility of employing a paradoxical strategy of utilizing OS transcriptomes for the treatment of OS. Methods Here are the procedures for collecting and analyzing in vitro and in vivo data and conducting bioinformatics analysis. In vitro assays

MTT-based metabolic activity (Figs. 1A, 1D, 2A, 2B, 2D, 2E, 2F, 4G, 4H, 5H, 6B, 7E, and Figure 1-figure supplement 1A, 1C, Figure 5-figure supplement 4): The activity was evaluated using three osteosarcoma cell lines. The optical density was determined at 562 nm using a multi-well spectrophotometer (EL808, BioTek, VT, USA). Data were analyzed in Excel. Transwell invasion (Figs. 1C, 1F, 6D, 7C, and Figure 1-figure supplement 1A, 1D): Images were taken with an inverted optical microscope (magnification, 100x, Nikon, Tokyo, Japan). The average number of stained cells was determined with Image J (National Institutes of Health, Bethesda, MD, USA). Data were analyzed in Excel. Two-dimensional motility (Figs. 1B, 1E, 6E, 7D): Images were taken with an inverted optical microscope. The two-dimensional motility scratch areas were quantified by Image J software. Data were analyzed in Excel. Western blot analysis (Figs. 2C, 4A, 5D, 5E, 5G, 6C, 7A, 7B, 7F, and Figure 5-figure supplement 2B, Figure 5-figure supplement 3, Figure 5-figure supplement 5): A luminescent image analyzer (LAS-3000, Fuji Film, Tokyo, Japan) was used to determine signal intensities for Western blot images. The relevant bands are labeled in PowerPoint. ELISA assay (Figs. 2B, 2C, 2D, 2E, 2F): According to the procedure provided by the manufacturer, protein levels in CW008-treated CM were determined using the ELISA kits (MyBioSource, San Diego, CA, USA). The absorbance of each well was measured at 450 nm using a multi-well spectrophotometer (EL808). Data were analyzed in Excel. Alizarin Red assay (Figure 5-figure supplement 2): Alizarin Red staining was used to visualize calcium deposits. The optical density was measured at 562 nm using a multi-well spectrophotometer (EL808). Data were analyzed in Excel.

In vivo assays (mouse model)

X-ray images (Fig. 5A): X-ray imaging was performed using a Faxitron radiographic system (Faxitron X-ray Co., Tucson, AZ, USA). Data were analyzed in PowerPoint. microCT images (Figs. 3, 5B, 5C): Micro-computed tomography was performed using Skyscan 1172 (Bruker-MicroCT, Kontich, Belgium). Scans were performed at pixel size 8.99 μm, and the images were reconstructed using a pair of software tools (nRecon v1.6.9.18, and CTan v1.13). Data were analyzed in Excel. Histology (Figure 3-figure supplement 1 and Figure 3-figure supplement 2): H&E staining was conducted on the sagittal sections, and images were taken with a microscope (U-TV0.63XG, OLYMPUS, Tokyo, Japan). The distribution of tumor cells in the tibial bone cavity was quantified by Image J software. Data were analyzed in Excel. Immunohistochemistry (Figure 3-figure supplement 3 and Figure 5-figure supplement 1): Immunohistochemistry staining was conducted on the sagittal sections, and images were taken with a microscope (U-TV0.63XG, OLYMPUS, Tokyo, Japan). The immunostained area was quantified in the tumor-invaded area by Image J software. Data were analyzed in Excel.

Bioinformatics

Survival rate (Figs. 5F, 6F, 6H): Patient survival analyses were obtained from a web-based tool, GEPIA (Gene Expression Profiling Interactive Analysis). Transcript levels (Figs. 6A, and Suppl. File 1): The TCGA (The Cancer Genome Atlas) database was used to predict tumor-suppressing protein candidates via GEPIA. Protein interactions (Fig. 6G): The target protein-protein interaction network was shown by String (String Consortium; string-db.org/network/) via the Uniprot (Universal Protein Resource; uniport.org).

Dear Juan Francisco, Dear Pablo we finished analyzing your latest order. In short, the data looks great! Your neg. controls behave as expected and cluster tightly. The probe (JT-195) enriches for a lot of prey proteins. Many are shared with the cysteinome control. Replicates are very similar. Kind regards, Tobias *** Always reply to workflow results by using the order comments function in b-fabric! Please do NOT send emails to proteomics@ or individual people involved in workflow processing (order coach). *** 1 Downloading results packaged in a b-fabric workunits [https://fgcz-bfabric.uzh.ch/wiki/tiki-index.php?page=WorkunitDownload] 2 General data processing logic LC-MS raw data => Dia-NN => proLFQ/SAINTexpress => Interaction proteomics report |-> ScaffoldDIA 3 Workunit guidance 3.1 Dia-NN The main output of Dia-NN is called

macwiatrak/bacbench-ppi-stringdb-protein-sequences-small dataset hosted on Hugging Face and contributed by the HF Datasets community

The free database mapping COVID-19 treatment and vaccine development based on the global scientific research is available at https://covid19-help.org/.

Files provided here are curated partial data exports in the form of .csv files or full data export as .sql script generated with pg_dump from our PostgreSQL 12 database. You can also find .png file with our ER diagram of tables in .sql file in this repository.

Structure of CSV files

*On our site, compounds are named as substances

compounds.csv

Id - Unique identifier in our database (unsigned integer)

Name - Name of the Substance/Compound (string)

Marketed name - The marketed name of the Substance/Compound (string)

Synonyms - Known synonyms (string)

Description - Description (HTML code)

Dietary sources - Dietary sources where the Substance/Compound can be found (string)

Dietary sources URL - Dietary sources URL (string)

Formula - Compound formula (HTML code)

Structure image URL - Url to our website with the structure image (string)

Status - Status of approval (string)

Therapeutic approach - Approach in which Substance/Compound works (string)

Drug status - Availability of Substance/Compound (string)

Additional data - Additional data in stringified JSON format with data as prescribing information and note (string)

General information - General information about Substance/Compound (HTML code)

references.csv

Id - Unique identifier in our database (unsigned integer)

Impact factor - Impact factor of the scientific article (string)

Source title - Title of the scientific article (string)

Source URL - URL link of the scientific article (string)

Tested on species - What testing model was used for the study (string)

Published at - Date of publication of the scientific article (Date in ISO 8601 format)

clinical-trials.csv

Id - Unique identifier in our database (unsigned integer)

Title - Title of the clinical trial study (string)

Acronym title - Acronym of title of the clinical trial study (string)

Source id - Unique identifier in the source database

Source id optional - Optional identifier in other databases (string)

Interventions - Description of interventions (string)

Study type - Type of the conducted study (string)

Study results - Has results? (string)

Phase - Current phase of the clinical trial (string)

Url - URL to clinical trial study page on clinicaltrials.gov (string)

Status - Status in which study currently is (string)

Start date - Date at which study was started (Date in ISO 8601 format)

Completion date - Date at which study was completed (Date in ISO 8601 format)

Additional data - Additional data in the form of stringified JSON with data as locations of study, study design, enrollment, age, outcome measures (string)

compound-reference-relations.csv

Reference id - Id of a reference in our DB (unsigned integer)

Compound id - Id of a substance in our DB (unsigned integer)

Note - Id of a substance in our DB (unsigned integer)

Is supporting - Is evidence supporting or contradictory (Boolean, true if supporting)

compound-clinical-trial.csv

Clinical trial id - Id of a clinical trial in our DB (unsigned integer)

Compound id - Id of a Substance/Compound in our DB (unsigned integer)

tags.csv

Id - Unique identifier in our database (unsigned integer)

Name - Name of the tag (string)

tags-entities.csv

Tag id - Id of a tag in our DB (unsigned integer)

Reference id - Id of a reference in our DB (unsigned integer)

API Specification

Our project also has an Open API that gives you access to our data in a format suitable for processing, particularly in JSON format.

https://covid19-help.org/api-specification

Services are split into five endpoints:

Substances - /api/substances

References - /api/references

Substance-reference relations - /api/substance-reference-relations

Clinical trials - /api/clinical-trials

Clinical trials-substances relations - /api/clinical-trials-substances

Method of providing data

All dates are text strings formatted in compliance with ISO 8601 as YYYY-MM-DD

If the syntax request is incorrect (missing or incorrectly formatted parameters) an HTTP 400 Bad Request response will be returned. The body of the response may include an explanation.

Data updated_at (used for querying changed-from) refers only to a particular entity and not its logical relations. Example: If a new substance reference relation is added, but the substance detail has not changed, this is reflected in the substance reference relation endpoint where a new entity with id and current dates in created_at and updated_at fields will be added, but in substances or references endpoint nothing has changed.

The recommended way of sequential download

During the first download, it is possible to obtain all data by entering an old enough date in the parameter value changed-from, for example: changed-from=2020-01-01 It is important to write down the date on which the receiving the data was initiated let’s say 2020-10-20

For repeated data downloads, it is sufficient to receive only the records in which something has changed. It can therefore be requested with the parameter changed-from=2020-10-20 (example from the previous bullet). Again, it is important to write down the date when the updates were downloaded (eg. 2020-10-20). This date will be used in the next update (refresh) of the data.

Services for entities

List of endpoint URLs:

/api/substances

/api/references

/api/substance-reference-relations

/api/clinical-trials

/api/clinical-trials-substances

Format of the request

All endpoints have these parameters in common:

changed-from - a parameter to return only the entities that have been modified on a given date or later.

continue-after-id - a parameter to return only the entities that have a larger ID than specified in the parameter.

limit - a parameter to return only the number of records specified (up to 1000). The preset number is 100.

Request example:

/api/references?changed-from=2020-01-01&continue-after-id=1&limit=100

Format of the response

The response format is the same for all endpoints.

number_of_remaining_ids - the number of remaining entities that meet the specified criteria but are not displayed on the page. An integer of virtually unlimited size.

entities - an array of entity details in JSON format.

Response example:

{

"number_of_remaining_ids" : 100,


"entities" : [


  {


    "id": 3,


    "url": "https://www.ncbi.nlm.nih.gov/pubmed/32147628",


    "title": "Discovering drugs to treat coronavirus disease 2019 (COVID-19).",


    "impact_factor": "Discovering drugs to treat coronavirus disease 2019 (COVID-19).",


    "tested_on_species": "in silico",


    "publication_date": "2020-22-02",


    "created_at": "2020-30-03",


    "updated_at": "2020-31-03",


    "deleted_at": null


  },


  {


    "id": 4,


    "url": "https://www.ncbi.nlm.nih.gov/pubmed/32157862",


    "title": "CT Manifestations of Novel Coronavirus Pneumonia: A Case Report",


    "impact_factor": "CT Manifestations of Novel Coronavirus Pneumonia: A Case Report",


    "tested_on_species": "Patient",


    "publication_date": "2020-06-03",


      "created_at": "2020-30-03",


    "updated_at": "2020-30-03",


    "deleted_at": null


  },


]

}

Endpoint details

Substances

URL: /api/substances

Substances endpoint returns data in the format specified in Response example as an array of entities in JSON format specified in the entity format section.

Entity format:

id - Unique identifier in our database (unsigned integer)

name - Name of the Substance (string)

description - Description (HTML code)

phase_of_research - Phase of research (string)

how_it_helps - How it helps (string)

drug_status - Drug status (string)

general_information - General information (HTML code)

synonyms - Synonyms (string)

marketed_as - "Marketed as" (string)

dietary_sources - Dietary sources name (string)

dietary_sources_url - Dietary sources URL (string)

prescribing_information - Prescribing information as an array of JSON objects with description and URL attributes as strings

formula - Formula (HTML code)

created_at - Date when the entity was added to our database (Date in ISO 8601 format)

updated_at - Date when the entity was last updated in our database (Date in ISO 8601 format)

deleted_at - Date when the entity was deleted in our database (Date in ISO 8601 format)

References

URL: /api/references

References endpoint returns data in the format specified in Response example as an array of entities in JSON format specified in the entity format section.

Entity format:

id - Unique identifier in our database (unsigned integer)

url - URL link of the scientific article (string)

title - Title of the scientific article (string)

impact_factor - Impact factor of the scientific article (string)

tested_on_species - What testing model was used for the study (string)

publication_date - Date of publication of the scientific article (Date in ISO 8601 format)

created_at - Date when the entity was added to our database (Date in ISO 8601 format)

updated_at - Date when the entity was last updated in our database (Date in ISO 8601

Though dependent on genetic anomalies, clinical manifestations of the human autoimmune disease systemic lupus erythematosus (lupus) can be triggered by environmental exposures including inhalation toxicants such as crystalline silica dust (cSiO2), tobacco smoke, and ambient air particles. Prednisone, a glucocorticoid (GC), is a keystone therapy for managing lupus flaring and progression, however, long-term use is associated with many adverse side effects. Here, we characterized the dose-dependent immunomodulation and toxicity of prednisone in a preclinical model that emulates onset and progression of cSiO2-triggered lupus. Two cohorts of 6-wk-old female NZBWF1 mice were fed either control AIN-93G diet or one of three AIN-93G diets containing prednisone at 5, 15, or 50 mg/kg diet which span human equivalent oral doses (HED) currently considered to be low (PL; 5 mg/d HED), moderate (PM; 14 mg/d HED), or high (PH; 46 mg/d HED), respectively. At 8 wk of age, mice were intranasally instilled with either saline vehicle or 1 mg cSiO2 once weekly for 4 wk. The experimental plan was to 1) terminate one cohort of mice (n=8/group) 14 wk after the last cSiO2 instillation for pathology and autoimmunity assessment and 2) to maintain a second cohort (n=9/group) to monitor glomerulonephritis development and survival. Mean blood concentrations of prednisone’s chief active metabolite, prednisolone, in mice fed PL, PM, and PH diets were 27, 105, 151 ng/ml, respectively, which are consistent with levels observed in human blood ≤ 12 h after single bolus treatments with equivalent prednisone doses. Results from the first cohort revealed that consumption of PM but not PL diet significantly reduced cSiO2-induced pulmonary ectopic lymphoid structure formation, nuclear-specific AAb production, and inflammation/autoimmune gene expression in the lung, splenomegaly, and glomerulonephritis in the kidney. Relative to GC-associated toxicity, PM but not PL diet elicited muscle wasting, but these diets did not affect bone density or cause glucosuria. Importantly, neither PM nor PL diet influenced latency of cSiO2-accelerated death. PH-fed mice in both cohorts displayed robust GC-associated toxicity including body weight loss, reduced muscle mass, and hyperglycemia 7 wk after the final cSiO2 instillation requiring their early removal from the study. Taken together, our results demonstrate that while moderate doses of prednisone can reduce certain pathological endpoints of cSiO2-induced autoimmunity in lupus-prone mice, these ameliorative effects come with unwanted GC toxicity and, crucially, none of these three doses extended survival time. Methods NanoString Autoimmune Profiling RNA was extracted from lungs, kidneys, and blood with RNeasy Mini Kits with DNase treatment (Qiagen, Valencia, CA). RNA was dissolved in nuclease-free water, quantified with Qubit (Thermo Fisher Scientific), and integrity verified with a TapeStation (Agilent Technologies). Samples (RNA integrity > 8) were analyzed with NanoString Autoimmune Gene Expression assay (XT-CSO-MAIP1-12, NanoString Technologies, Seattle, WA) at the MSU Genomics Core. Assays were performed and quantified on the nCounter MAX system, sample preparation station, and digital analyzer (NanoString Technologies) according to the manufacturer’s instructions. Raw gene expression data were analyzed using NanoString’s software nSolver v3.0.22 with the Advanced Analysis Module v2.0. Background subtraction was performed using the eight negative controls included with the module. Genes with counts below a threshold of 2σ of the mean background signal were excluded from subsequent analysis. Data normalization was performed on background-subtracted samples using internal positive controls and selected housekeeping genes that were identified with the geNorm algorithm (https://genorm.cmgg.be/). Differential gene expression analyses were performed using the nSolver Advanced Analysis Module, which employs several multivariate linear regression models (mixture negative binomial, simplified negative binomial, or log-linear model) to identify significant genes. Resulting p values were adjusted using the Benjamini-Hochberg (BH) method to control the false discovery rate. A statistically significant difference in gene expression was defined as 1.5-fold change in expression (log2 > 0.58 or < -0.58) with BH q < 0.05. Four pairwise comparisons within each time point for each tissue examined were determined a priori, as follows: cSiO2/P0 vs VEH/P0, cSiO2/PL vs cSiO2/P0, cSiO2/PM vs cSiO2/P0, and cSiO2/PM vs cSiO2/PL. Venn diagrams of significant differentially expressed genes were generated using BioVenn. To assess the impact of experimental diets on annotated gene sets, global and directed significance scores were calculated for each pathway at each time point. The global score estimates the cumulative evidence for the differential expression of genes in a pathway. Directed significance scores near zero indicate that a pathway may have many highly regulated genes, but no apparent tendency for those genes to be over- or under-expressed collectively. As a complementary method for comparing pathways and discriminating between experimental groups, pathway Z scores were calculated as the Z-scaled first principal component of the pathway genes’ normalized expression. ClustVis was used to perform unsupervised hierarchical cluster analyses (HCC) and principal components analyses (PCA) using log2 transcript count data for DEGs. Spearman rank correlations were performed to examine overall patterns in the gene expression profiles using the pathway Z score compared to other biomarkers of disease in lung or kidney tissues at 14 weeks PI. A significant correlation was inferred when ρ > 0.5 or <-0.5 and p < 0.05. Network analyses for interactions among significant genes were performed using STRING database version 11.5 (http://string-db.org/), with a minimum interaction score > 0.05 and cluster identification using the Markov Cluster (MCL) algorithm with inflation parameter of 1.5. Networks generated by STRING were visualized with Cytoscape v. 3.9. The NanoString nSolver Advanced Analysis software employs the method described by Danaher to measure the abundance of various immune cell populations using marker genes that are expressed stably and specifically in particular cell types. Cell type scores were calculated as the average log-scale normalized expression of their characteristic genes. Relative cell type measurements were based on the total population of infiltrating lymphocytes, which is useful in a sample of heterogenous mix of cell types. Only cell types that exceeded the quality control analysis for correlation of marker gene expression are reported. Statistical Analysis All data were analyzed, and statistical tests were performed using Prism 9 (GraphPad Prism v 9.2, San Diego, CA) except for the NanoString gene expression data discussed above. Data were assessed for outliers using the Grubb’s outlier test (with Q = 1%) and for normality using the Shapiro-Wilk test (p < 0.01). Data of histopathological endpoints were analyzed using an unpaired one-tailed t-test to detect cSiO2-induced inflammation and autoimmunity in lupus-prone mice (VEH/P0 vs cSiO2/P0) and a One-Way ANOVA with Dunnett’s post-hoc test to address our hypothesis that dietary prednisone would dose-dependently suppress cSiO2-triggered responses (cSiO2/P0 vs cSiO2/PL or cSiO2/PM). Non-normal and semi-quantitative data were analyzed using the nonparametric Mann-Whitney U test (for VEH/P0 vs cSiO2/P0) and the nonparametric Kruskal-Wallis test with a Dunn’s post-hoc test (cSiO2/P0 vs cSiO2/PL or cSiO2/PM). Data are presented as mean ± standard error of the mean (SEM), with a p-value ≤ 0.05 being considered as statistically significant.