All human protein interactions were obtained from STRING (https://string-db.org/, version 11.0). Interactions were then filtered to those involving only BM zone proteins. Related to Fig. S6B.

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.

CD200 and LAIR1 proteins are coexpressed across multiple experiments

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

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).

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.

CD200 and LAIR1 proteins are mentioned together in at least one PubMed abstract

Cumulative malaria parasite exposure in endemic regions often results in the acquisition of partial immunity and asymptomatic infections. There is limited information on how host-parasite interactions mediate maintenance of chronic symptomless infections that sustain malaria transmission. Here, we have determined the gene expression profiles of the parasite population and the corresponding host peripheral blood mononuclear cells (PBMCs) from 21 children (<15 years). We compared children who were defined as uninfected, asymptomatic and those with febrile malaria. Children with asymptomatic infections had a parasite transcriptional profile characterized by a bias toward trophozoite stage (~12 hours-post invasion) parasites and low parasite levels, while earlier ring stage parasites were characteristic of febrile malaria. The host response of asymptomatic children was characterized by downregulated transcription of genes associated with inflammatory responses, compared to children with ..., Proteins were extracted from PBMCs by resuspending the pellet with 5µl of 6M UREA (Thermo scientific). The protein samples were then adjusted with 50mM Triethylamonium bicarbonate (TEAB, Sigma-Aldrich) to 100µl and the protein concentration determined using the Bicinchoninic acid (BCA) protein assay (Thermo scientific). The protein samples were then reduced with 40mM dithiothretol, alkylated with 80mM iodoacetamide in the dark, and quenched with 80mM iodoacetamide at room temperature, followed by digestion with1µg/µl of trypsin (57). Nine pools, each containing 9 samples and 1 control for batch correction, were prepared by combining 1µl aliquots from each sample. The samples were pooled using a custom randomization R script. The pooled samples were then individually labelled using the Tandem Mass Tag (TMT) 10-plex kit (Thermo Scientific) according to the manufacturer’s instructions. One isobaric tag was used solely for the pooled samples and combined with peptides samples labelled with ..., The files can be opened using MaxQuant software, specifically version 2.0.3.0 was used for analysis. Differential protein abundance analysis of MaxQuant output was done using PERSEUS version 2.05.0 software. Protein-protein interaction and Gene ontology analyses was perforened using STRING database version 11.5 (https://string-db.org/)., # 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...

Data from the Gene Expression Omnibus (GEO) database (GSE119600) for PSC were downloaded and analyzed using R software to identify differentially expressed genes (DEGs). Online analysis tools were employed for Gene Ontology (GO) functional analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. The STRING database (https://www.string-db.org) was used for protein-protein interaction (PPI) analysis to identify key genes in PSC, and ssGSEA was used to analyze immune cell infiltration.

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

CD200 and LAIR1 proteins cooccur across multiple genomes

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.

This archive contains the supplementary datasets supporting the analyses in the manuscript“MSC1 Cells Suppress Colorectal Cancer Cell Growth via Metabolic Reprogramming, Laminin–Integrin Adhesion Signaling, Oxidative Stress Resistance, and a Tumor-Suppressive Secretome.” by Galliou PA, et al. All files are provided in Excel format and structured to reproduce the protein-protein interaction and transcription factor (TF) target analyses downstream of TLR4 activation by LPS in WJ-MSCs.

Contents of this ZIP:

1. STRING_Input_Protein_List-for_PPI_Network_Construction.xlsx
• Curated list of proteins reported in the literature as downstream effectors of TLR4 upon LPS stimulation.
2. PPI_Network_from_STRING-Downstream_of_TLR4_Activation.xlsx
• Protein–protein interaction network generated by STRING using the input list from File 1, including interaction scores and network metrics.
3. MCODE_Clusters-In_PPI_Network_Downstream_of_TLR4_Activation_by_LPS.xlsx
• Module clusters identified within the PPI network (File 2) via the MCODE Cytoscape plugin, with cluster IDs and constituent proteins.
4. WJ-MSC_Expressed-TF_Target_Genes-of_Enriched_TF_Targets_in_PPI_Network_Downstream_of_TLR4_Activation_By_LPS.xlsx
• List of transcription factor target genes (from MSigDB) of enriched transcription factor targets in the PP network (File 2) (from Metascape) that are also expressed in WJ-MSCs. Includes gene names, function, genomic location, and annotated GO terms derived from Uniprot.

Usage Notes:

• To reproduce the PPI analysis, first load File 1 into STRING (https://string-db.org).
• Cluster definitions (File 3) were obtained with default MCODE parameters in Cytoscape v3.10.3.
• Trascriptional factor target enrichment and filtering (File 4) were performed using Metascape (https://metascape.org) against the MSigDB TF target gene sets, then cross-referenced to WJ-MSC expression data (https://www.stemformatics.org/datasets/view?id=6393).

Version: v1 (2025-05-28)

License: CC-BY 4.0

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

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).

PKT Human Disease Knowledge Graph Benchmark Builds (v1.0.0)

Build Date: September 03, 2019

The KG Benchmark Builds can also be downloaded from Zenodo:
👉 KGs: https://doi.org/10.5281/zenodo.7030200
👉 Embeddings: https://zenodo.org/record/7030189

Required Input Documents

• resource_info.txt
• class_source_list.txt
• instance_source_list.txt
• ontology_source_list.txt

Data

Data Download Date: November 30, 2018

Ontologies

• Gene Ontology
• Human Phenotype Ontology

Classes

• Human Disease Ontology
• Gene Ontology: gene associations
• Reactome: gene associations
• Human Phenotype Ontology: all source annotations - genes to phenotypes
• Human Phenotype Ontology: all source annotations - diseases to genes to phenotypes

Instances

• CTD: chemicals-genes
• CTD: chemicals-pathways
• CTD: chemicals-diseases
• CTD: genes-pathways
• CTD: diseases-pathways
• STRING DB: Proteins
• String DB: entrez gene mappings

Knowledge Graphs

Knowledge Representation
We worked with a PhD-level biologist to develop a knowledge representation (see the figure below) that modeled mechanisms underlying human disease.

To do this, we manually mapped all possible combinations of the following six node types:

• Humans Diseases
• Human Phenotypes
• Human Genes
• Gene Ontology concepts
• Reactome Pathways
• Chemicals

As shown in the figure above, the Basic Formal Ontology and Relation Ontology ontologies were then used to create edges between the node types.

As shown in this figure, the following edge-types were created:

• Phenotypes-Genes: The Human Phenotype Ontology (HP) provides phenotype-Entrez gene annotations that were used to map 6,651 HP classes to 120,288 Entrez genes.
• Phenotypes-Diseases: The HP provides HP-DOID-Gene annotations that were used to map 5,438 HP concepts to 43,817 DOID concepts.
• Biological processes, Molecular Functions, and Cellular Locations-Genes: The Gene Ontology (GO) provides GO-Gene annotations that were used to map 17,505 GO concepts to 265,002 Entrez genes.
• Biological processes, Molecular Functions, and Cellular Locations-Pathways-Pathways: Reactome provides GO-Gene links that were used to map 17,906 pathways to 1,910 biological processes, molecular functions, and cellular locations.
• Chemicals-Pathways: The Comparative Toxicogenomics Database (CTD) provides Chemical-pathway links that were used to map 8,886 MESH concepts to 711,043 Reactome pathways.
• Chemicals-Genes: The Comparative Toxicogenomics Database (CTD) provides Chemical-Gene links that were used to map 8,881 MESH concepts 410,379 Entrez genes.
• Chemicals-Diseases: The Comparative Toxicogenomics Database (CTD) provides Chemical-Disease links that were used to map 14,238 MESH concepts 1,216,900 DOID concepts.
• Genes-Genes: TheSTRING Database provides Gene-Gene links that were used to create 594,100 gene-gene interactions. When generating these mappings, only the inferred protein-protein relationships considered to be high confidence were used (score of 700 or better).
• Genes-Disease: Mappings between genes and diseases were retrieved from DisGeNet via SPARQL endpoint and used to map 6,051 Entrez genes to 20,452 DOID concepts.
• Genes-Pathways: The Comparative Toxicogenomics Database (CTD) provides Gene-Pathway links that were used to map 110,370 Entrez genes to 107,029 Reactome pathways.
• Pathways-Disease: The Comparative Toxicogenomics Database (CTD) provides Pathway-Disease links that were used to map 1,818 Reactome pathways to 106,727 DOID concepts.

Knowledge Graph
The knowledge graph represented above was built using the following steps: Merge Ontologies: Merge ontologies using the OWL Tools API
Express New Ontology Concept Annotations: Create new ontology annotations by asserting a relation between the instance and an instance of the ontology class. For example to assert the following relations:

Morphine --> is substance that treats --> Migraine

We would need to create two axioms:

• isSubstanceThatTreats(Morphine, x1)
• instanceOf(x1, Migraine)

While the instance of the HP class hemiplegic migraines can be treated as an anonymous node in the knowledge graph, we generate a new international resource identifier for each newly generated instance.

Deductively Close Knowledge Graph: The knowledge graph is deductively closed by using the OWL 2 EL reasoner, ELK via Protégé v5.1.1. ELK is able to classify instances and supports inferences over class hierarchies and object properties. inference over disjointness, intersection, and existential quantification (ontology class hierarchies).

Generate Edge List: The final step before exporting the edge list is to remove any nodes that are not biologically meaningful or would otherwise reduce the performance of machine learning algorithms and the algorithm used to generate embeddings.

🚨 AVAILABLE FILES 🚨Available KG benchmark files are zipped and listed below. For additional details on what each file contains, please see the associated Wiki page 👉 here.

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.

Dataset for protein-protein interaction prediction across bacteria (Protein sequences)

A dataset of 10,533 bacterial genomes across 6,956 species with protein-protein interaction (PPI) scores for each genome. The genome protein sequences and PPI scores have been extracted from STRING DB. Each row contains a set of protein sequences from a genome, ordered by their location on the chromosome and plasmids and a set of associated PPI scores. The PPI scores have been extracted using the… See the full description on the dataset page: https://huggingface.co/datasets/macwiatrak/bacbench-ppi-stringdb-protein-sequences.

This data accompanies the paper "Ecological network analysis reveals cancer-dependent chaperone-client interaction structure and robustness", by Geut Galai, Xie He, Barak Rotblat, Shai Pilosof. Published in Nature Communications. Please cite the paper when using the data.All users must read the paper to understand how the data were obtained and processed, and their limitations. Data comes without warranty. Licence is CC BY-NC-SA (Attribution-NonCommercial-ShareAlike): This license lets you remix, tweak, and build upon this work non-commercially, as long as you credit the authors and license the new creations under the identical terms.All the computational processes related to data derivation and analysis are in the GutHub repository that accompanies the paper.Raw data (raw.zip)Gene level transcriptome profiling (RNA-Seq) data (in the form of HTSeq - FPKM) that was download from The Cancer Genome Atlas (TCGA) using the Genomic Data Commons Data Portal https://portal.gdc.cancer.gov).Human protein expression data that was downloaded from the string-db.org data base, and from published papers as follows.File: 12192_2020_1080_MOESM4_ESM.xlsx. Source: Bie AS, Cömert C, Körner R, Corydon TJ, Palmfeldt J, Hipp MS, et al. An inventory of interactors of the human HSP60/HSP10 chaperonin in the mitochondrial matrix space. Cell Stress Chaperones. 2020;25: 407–416. doi:10.1007/s12192-020-01080-6File: 41467_2013_BFncomms3139_MOESM481_ESM.xls. Source: Chae YC, Angelin A, Lisanti S, Kossenkov AV, Speicher KD, Wang H, et al. Landscape of the mitochondrial Hsp90 metabolome in tumours. Nat Commun. 2013;4: 2139. doi:10.1038/ncomms3139File: 12915_2020_740_MOESM8_ESM.xlsx Source: Joshi A, Dai L, Liu Y, Lee J, Ghahhari NM, Segala G, et al. The mitochondrial HSP90 paralog TRAP1 forms an OXPHOS-regulated tetramer and is involved in mitochondrial metabolic homeostasis. BMC Biol. 2020;18: 10. doi:10.1186/s12915-020-0740-7File: mmc2.xlsx Source: Ishizawa J, Zarabi SF, Davis RE, Halgas O, Nii T, Jitkova Y, et al. Mitochondrial ClpP-Mediated Proteolysis Induces Selective Cancer Cell Lethality. Cancer Cell. 2019;35: 721–737.e9. doi:10.1016/j.ccell.2019.03.014Processed data (processed.zip)The network data. Rows are chaperones, columns are clients.Source dataFile: Source Data for Figures and Tables.zipThis is the source data underlying the figures and tables, as requested by Nature Communications.

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.