The development of a novel zeolite-based assay for efficient and deep plasma proteomic profiling.

Plasma proteins are considered the most informative source of biomarkers for disease diagnosis and monitoring. Mass spectrometry (MS)-based proteomics has been applied to identify biomarkers in plasma, but the complexity of the plasma proteome and the extremely large dynamic range of protein abundances in plasma make the clinical application of plasma proteomics highly challenging. We designed and synthesized zeolite-based nanoparticles to deplete high-abundance plasma proteins. The resulting novel plasma proteomic assay can measure approximately 3000 plasma proteins in a 45 min chromatographic gradient. Compared to those in neat and depleted plasma, the plasma proteins identified by our assay exhibited distinct biological profiles, as validated in several public datasets. A pilot investigation of the proteomic profile of a hepatocellular carcinoma (HCC) cohort identified 15 promising protein features, highlighting the diagnostic value of the plasma proteome in distinguishing individuals with and without HCC. Furthermore, this assay can be easily integrated with all current downstream protein profiling methods and potentially extended to other biofluids. In conclusion, we established a robust and efficient plasma proteomic assay with unprecedented identification depth, paving the way for the translation of plasma proteomics into clinical applications.

Two-Dimensional Gel Electrophoresis for Protein Separation of Plant Samples.

This chapter provides a description of the procedure for two-dimensional electrophoresis that can be performed for any given gel size and isoelectric focusing range. This will enable the operator to recognize critical steps and gain sufficient information to generate 2D images suitable for computer-assisted analysis of 2D-gel, as well as mass spectrometry analysis for protein identification and characterization.

One-Dimensional Acrylamide Gel Electrophoresis for Analysis of Plant Samples.

Polyacrylamide gel electrophoresis (PAGE) is a widely used technique for separating proteins from complex plant samples. Prior to the analysis, proteins must be extracted from plant tissues, which are rather complex than other types of biological material. Different protocols have been applied depending on the protein source, such as seeds, pollen, leaves, roots, and flowers. Total protein amounts must also be determined before conducting gel electrophoresis. The most common methodologies include PAGE under native or denaturing conditions. Both procedures are used consequently for protein identification and characterization via mass spectrometry. Additionally, various staining procedures are available to visualize protein bands in the gel, facilitating the software-based digital evaluation of the gel through image acquisition.

Sample Preparation and Phosphopeptide Enrichment for Plant Phosphoproteomics via Label-Free Mass Spectrometry.

Phosphopeptide enrichment is the main bottleneck of every phosphorylation study. Therefore, in this chapter, a general workflow tries to overbridge the hurdles of plant sample handling from sample collection to protein extraction, protein solubilization, enzymatic digestion, and enrichment step prior to mass spectrometry. The workflow provides information to perform global proteomics as well as phosphoproteomics enabling the researcher to use the protocol in both fields.

Droplet-based proteomics reveals CD36 as a marker for progenitors in mammary basal epithelium.

Deep proteomic profiling of rare cell populations has been constrained by sample input requirements. Here, we present DROPPS (droplet-based one-pot preparation for proteomic samples), an accessible low-input platform that generates high-fidelity proteomic profiles of 100-2,500 cells. By applying DROPPS within the mammary epithelium, we elucidated the connection between mitochondrial activity and clonogenicity, identifying CD36 as a marker of progenitor capacity in the basal cell compartment. We anticipate that DROPPS will accelerate biology-driven proteomic research for a multitude of rare cell populations.

Gingival proteomics reveals the role of TGF beta and YAP/TAZ signaling in Raine syndrome fibrosis.

Raine syndrome (RNS) is a rare autosomal recessive osteosclerotic dysplasia. RNS is caused by loss-of-function disease-causative variants of the FAM20C gene that encodes a kinase that phosphorylates most of the secreted proteins found in the body fluids and extracellular matrix. The most common RNS clinical features are generalized osteosclerosis, facial dysmorphism, intracerebral calcifications and respiratory defects. In non-lethal RNS forms, oral traits include a well-studied hypoplastic amelogenesis imperfecta (AI) and a much less characterized gingival phenotype. We used immunomorphological, biochemical, and siRNA approaches to analyze gingival tissues and primary cultures of gingival fibroblasts of two unrelated, previously reported RNS patients. We showed that fibrosis, pathological gingival calcifications and increased expression of various profibrotic and pro-osteogenic proteins such as POSTN, SPARC and VIM were common findings. Proteomic analysis of differentially expressed proteins demonstrated that proteins involved in extracellular matrix (ECM) regulation and related to the TGFß/SMAD signaling pathway were increased. Functional analyses confirmed the upregulation of TGFß/SMAD signaling and subsequently uncovered the involvement of two closely related transcription cofactors important in fibrogenesis, Yes-associated protein (YAP) and transcriptional coactivator with PDZ-binding motif (TAZ). Knocking down of FAM20C confirmed the TGFß-YAP/TAZ interplay indicating that a profibrotic loop enabled gingival fibrosis in RNS patients. In summary, our in vivo and in vitro data provide a detailed description of the RNS gingival phenotype. They show that gingival fibrosis and calcifications are associated with, and most likely caused by excessed ECM production and disorganization. They furthermore uncover the contribution of increased TGFß-YAP/TAZ signaling in the pathogenesis of the gingival fibrosis.

CHMP5 attenuates osteoarthritis via inhibiting chondrocyte apoptosis and extracellular matrix degradation: involvement of NF-κB pathway.

BACKGROUND: Osteoarthritis (OA), the most common joint disease, is linked with chondrocyte apoptosis and extracellular matrix (ECM) degradation. Charged multivesicular body protein 5 (CHMP5), a member of the multivesicular body, has been reported to serve as an anti-apoptotic protein to participate in leukemia development. However, the effects of CHMP5 on apoptosis and ECM degradation in OA remain unclear. METHODS: In this study, quantitative proteomics was performed to analyze differential proteins between normal and OA patient articular cartilages. The OA mouse model was constructed by the destabilization of the medial meniscus (DMM). In vitro, interleukin-1 beta (IL-1ß) was used to induce OA in human chondrocytes. CHMP5 overexpression and silencing vectors were created using an adenovirus system. The effects of CHMP5 on IL-1ß-induced chondrocyte apoptosis were investigated by CCK-8, flow cytometry, and western blot. The effects on ECM degradation were examined by western blot and immunofluorescence. The potential mechanism was explored by western blot and Co-IP assays. RESULTS: Downregulated CHMP5 was identified by proteomics in OA patient cartilages, which was verified in human and mouse articular cartilages. CHMP5 overexpression repressed cell apoptosis and ECM degradation in OA chondrocytes. However, silencing CHMP5 exacerbated OA chondrocyte apoptosis and ECM degradation. Furthermore, we found that the protective effect of CHMP5 against OA was involved in nuclear factor kappa B (NF-κB) signaling pathway. CONCLUSIONS: This study demonstrated that CHMP5 repressed IL-1ß-induced chondrocyte apoptosis and ECM degradation and blocked NF-κB activation. It was shown that CHMP5 might be a novel potential therapeutic target for OA in the future.

Blood-Based Multiomics-Guided Detection of a Precancerous Pancreatic Tumor.

Over a decade ago, longitudinal multiomics analysis was pioneered for early disease detection and individually tailored precision health interventions. However, high sample processing costs, expansive multiomics measurements along with complex data analysis have made this approach to precision/personalized medicine impractical. Here we describe in a case report, a more practical approach that uses fewer measurements, annual sampling, and faster decision making. We also show how this approach offers promise to detect an exceedingly rare and potentially fatal condition before it fully manifests. Specifically, we describe in the present case report how longitudinal multiomics monitoring (LMOM) helped detect a precancerous pancreatic tumor and led to a successful surgical intervention. The patient, enrolled in an annual blood-based LMOM since 2018, had dramatic changes in the June 2021 and 2022 annual metabolomics and proteomics results that prompted further clinical diagnostic testing for pancreatic cancer. Using abdominal magnetic resonance imaging, a 2.6 cm lesion in the tail of the patient's pancreas was detected. The tumor fluid from an aspiration biopsy had 10,000 times that of normal carcinoembryonic antigen levels. After the tumor was surgically resected, histopathological findings confirmed it was a precancerous pancreatic tumor. Postoperative omics testing indicated that most metabolite and protein levels returned to patient's 2018 levels. This case report illustrates the potentials of blood LMOM for precision/personalized medicine, and new ways of thinking medical innovation for a potentially life-saving early diagnosis of pancreatic cancer. Blood LMOM warrants future programmatic translational research with the goals of precision medicine, and individually tailored cancer diagnoses and treatments.

N-glycoproteomic analyses of human intestinal enteroids, varying in histo-blood group geno- and phenotypes, reveal a wide repertoire of fucosylated glycoproteins.

Human noroviruses, globally the main cause of viral gastroenteritis, show strain specific affinity for histo-blood group antigens (HBGA) and can successfully be propagated ex vivo in human intestinal enteroids (HIEs). HIEs established from jejunal stem cells of individuals with different ABO, Lewis and secretor geno- and phenotypes, show varying susceptibility to such infections. Using bottom-up glycoproteomic approaches we have defined and compared the N-linked glycans of glycoproteins of seven jejunal HIEs. Membrane proteins were extracted, trypsin digested, and glycopeptides enriched by hydrophilic interaction liquid chromatography and analyzed by nanoLC-MS/MS. The Byonic software was used for glycopeptide identification followed by hands-on verifications and interpretations. Glycan structures and attachment sites were identified from MS2 spectra obtained by higher-energy collision dissociation through analysis of diagnostic saccharide oxonium ions (B-ions), stepwise glycosidic fragmentation of the glycans (Y-ions), and peptide sequence ions (b- and y-ions). Altogether 694 unique glycopeptides from 93 glycoproteins were identified. The N-glycans encompassed pauci- and oligomannose, hybrid- and complex-type structures. Notably, polyfucosylated HBGA-containing glycopeptides of the four glycoproteins tetraspanin-8, carcinoembryonic antigen-related cell adhesion molecule 5, sucrose-isomaltase and aminopeptidase N were especially prominent and were characterized in detail and related to donor ABO, Lewis and secretor types of each HIE. Virtually no sialylated N-glycans were identified for these glycoproteins suggesting that terminal sialylation was infrequent compared to fucosylation and HBGA biosynthesis. This approach gives unique site-specific information on the structural complexity of N-linked glycans of glycoproteins of human HIEs and provides a platform for future studies on the role of host glycoproteins in gastrointestinal infectious diseases.

StellarPath: Hierarchical-vertical multi-omics classifier synergizes stable markers and interpretable similarity networks for patient profiling.

The Patient Similarity Network paradigm implies modeling the similarity between patients based on specific data. The similarity can summarize patients' relationships from high-dimensional data, such as biological omics. The end PSN can undergo un/supervised learning tasks while being strongly interpretable, tailored for precision medicine, and ready to be analyzed with graph-theory methods. However, these benefits are not guaranteed and depend on the granularity of the summarized data, the clarity of the similarity measure, the complexity of the network's topology, and the implemented methods for analysis. To date, no patient classifier fully leverages the paradigm's inherent benefits. PSNs remain complex, unexploited, and meaningless. We present StellarPath, a hierarchical-vertical patient classifier that leverages pathway analysis and patient similarity concepts to find meaningful features for both classes and individuals. StellarPath processes omics data, hierarchically integrates them into pathways, and uses a novel similarity to measure how patients' pathway activity is alike. It selects biologically relevant molecules, pathways, and networks, considering molecule stability and topology. A graph convolutional neural network then predicts unknown patients based on known cases. StellarPath excels in classification performances and computational resources across sixteen datasets. It demonstrates proficiency in inferring the class of new patients described in external independent studies, following its initial training and testing phases on a local dataset. It advances the PSN paradigm and provides new markers, insights, and tools for in-depth patient profiling.

Proteomic biomarkers in seminal plasma as predictors of reproductive potential in azoospermic men.

Introduction: Azoospermia, characterized by an absence of sperm in the ejaculate, represents the most severe form of male infertility. While surgical sperm retrieval in obstructive azoospermia (OA) is successful in the majority of cases, patients with non-obstructive azoospermia (NOA) show retrieval rates of only about 50% and thus frequently have unnecessary surgery. Surgical intervention could be avoided if patients without preserved spermatogenesis are identified preoperatively. This prospective study aimed to discover biomarkers in seminal plasma that could be employed for a non-invasive differential diagnosis of OA/NOA in order to rationalize surgery recommendations and improve success rates. Methods: All patients signed written informed consent, underwent comprehensive andrological evaluation, received human genetics to exclude relevant pathologies, and patients with azoospermia underwent surgical sperm retrieval. Using label-free LC-MS/MS, we compared the proteomes of seminal plasma samples from fertile men (healthy controls (HC), n=8) and infertile men diagnosed with 1) OA (n=7), 2) NOA with successful sperm retrieval (mixed testicular atrophy (MTA), n=8), and 3) NOA without sperm retrieval (Sertoli cell-only phenotype (SCO), n=7). Relative abundance changes of two candidate markers of sperm retrieval, HSPA2 and LDHC, were confirmed by Western Blot. Results: We found the protein expression levels of 42 proteins to be significantly down-regulated (p ≤ 0.05) in seminal plasma from SCO NOA patients relative to HC whereas only one protein was down-regulated in seminal plasma from MTA patients. Analysis of tissue and cell expression suggested that the testis-specific proteins LDHC, PGK2, DPEP3, and germ-cell enriched heat-shock proteins HSPA2 and HSPA4L are promising biomarkers of spermatogenic function. Western blotting revealed a significantly lower abundance of LDHC and HSPA2 in the seminal plasma of men with NOA (SCO and MTA) compared to controls. Discussion: The results indicate that certain testis-specific proteins when measured in seminal plasma, could serve as indicators of the presence of sperm in the testis and predict the success of sperm retrieval. Used in conjunction with conventional clinical assessments, these proteomic biomarkers may assist in the non-invasive diagnosis of idiopathic male infertility.

Impact of inflammatory preconditioning on murine microglial proteome response induced by focal ischemic brain injury.

Preconditioning with lipopolysaccharide (LPS) induces neuroprotection against subsequent cerebral ischemic injury, mainly involving innate immune pathways. Microglia are resident immune cells of the central nervous system (CNS) that respond early to danger signals through memory-like differential reprogramming. However, the cell-specific molecular mechanisms underlying preconditioning are not fully understood. To elucidate the distinct molecular mechanisms of preconditioning on microglia, we compared these cell-specific proteomic profiles in response to LPS preconditioning and without preconditioning and subsequent transient focal brain ischemia and reperfusion, - using an established mouse model of transient focal brain ischemia and reperfusion. A proteomic workflow, based on isolated microglia obtained from mouse brains by cell sorting and coupled to mass spectrometry for identification and quantification, was applied. Our data confirm that LPS preconditioning induces marked neuroprotection, as indicated by a significant reduction in brain infarct volume. The established brain cell separation method was suitable for obtaining an enriched microglial cell fraction for valid proteomic analysis. The results show a significant impact of LPS preconditioning on microglial proteome patterns by type I interferons, presumably driven by the interferon cluster regulator proteins signal transducer and activator of transcription1/2 (STAT1/2).

Multimodal joint deconvolution and integrative signature selection in proteomics.

Deconvolution is an efficient approach for detecting cell-type-specific (cs) transcriptomic signals without cellular segmentation. However, this type of methods may require a reference profile from the same molecular source and tissue type. Here, we present a method to dissect bulk proteome by leveraging tissue-matched transcriptome and proteome without using a proteomics reference panel. Our method also selects the proteins contributing to the cellular heterogeneity shared between bulk transcriptome and proteome. The deconvoluted result enables downstream analyses such as cs-protein Quantitative Trait Loci (cspQTL) mapping. We benchmarked the performance of this multimodal deconvolution approach through CITE-seq pseudo bulk data, a simulation study, and the bulk multi-omics data from human brain normal tissues and breast cancer tumors, individually, showing robust and accurate cell abundance quantification across different datasets. This algorithm is implemented in a tool MICSQTL that also provides cspQTL and multi-omics integrative visualization, available at https://bioconductor.org/packages/MICSQTL .

Dynamics of CD44+ bovine nucleus pulposus cells with inflammation.

Intervertebral Disc (IVD) degeneration has been associated with a chronic inflammatory response, but knowledge on the contribution of distinct IVD cells, namely CD44, to the progression of IVD degeneration remains elusive. Here, bovine nucleus pulposus (NP) CD44 cells were sorted and compared by gene expression and proteomics with the negative counterpart. NP cells were then stimulated with IL-1b (10 ng/ml) and dynamics of CD44 gene and protein expression was analyzed upon pro-inflammatory treatment. The results emphasize that CD44 has a multidimensional functional role in IVD metabolism, ECM synthesis and production of neuropermissive factors. CD44 widespread expression in NP was partially associated with CD14 and CD45, resulting in the identification of distinct cell subsets. In conclusion, this study points out CD44 and CD44-based cell subsets as relevant targets in the modulation of the IVD pro-inflammatory/degenerative cascade.

Proteomic studies in VWA1-related neuromyopathy allowed new pathophysiological insights and the definition of blood biomarkers.

Bi-allelic variants in VWA1, encoding Von Willebrand Factor A domain containing 1 protein localized to the extracellular matrix (ECM), were linked to a neuromuscular disorder with manifestation in child- or adulthood. Clinical findings indicate a neuromyopathy presenting with muscle weakness. Given that pathophysiological processes are still incompletely understood, and biomarkers are still missing, we aimed to identify blood biomarkers of pathophysiological relevance: white blood cells (WBC) and plasma derived from six VWA1-patients were investigated by proteomics. Four proteins, BET1, HNRNPDL, NEFM and PHGDH, known to be involved in neurological diseases and dysregulated in WBC were further validated by muscle-immunostainings unravelling HNRNPDL as a protein showing differences between VWA1-patients, healthy controls and patients suffering from neurogenic muscular atrophy and BICD2-related neuromyopathy. Immunostaining studies of PHGDH indicate its involvement in apoptotic processes via co-localisation with caspase-3. NEFM showed an increase in cells within the ECM in biopsies of all patients studied. Plasma proteomics unravelled dysregulation of 15 proteins serving as biomarker candidates among which a profound proportion of increased ones (6/11) are mostly related to antioxidative processes and have even partially been described as blood biomarkers for other entities of neuromuscular disorders before. CRP elevated in plasma also showed an increase in the extracellular space of VWA1-mutant muscle. Results of our combined studies for the first time describe pathophysiologically relevant biomarkers for VWA1-related neuromyopathy and suggest that VWA1-patient derived blood might hold the potential to study disease processes of clinical relevance, an important aspect for further preclinical studies.

Exploring the Hypocholesterolemic Potential of a Fucus vesiculosus Extract: Omic Insights into Molecular Mechanisms at the Intestinal Level.

High blood cholesterol levels are a major risk factor for cardiovascular diseases. A purified aqueous extract of Fucus vesiculosus, rich in phlorotannins and peptides, has been described for its potential to inhibit cholesterol biosynthesis and intestinal absorption. In this work, the effect of this extract on intestinal cells' metabolites and proteins was analysed to gain a deeper understanding of its mode of action on lipids' metabolism, particularly concerning the absorption and transport of exogenous cholesterol. Caco-2 cells, differentiated into enterocytes, were exposed to the extract, and analysed by untargeted metabolomics and proteomics. The results of the metabolomic analysis showed statistically significant differences in glutathione content of cells exposed to the extract compared to control cells, along with an increased expression of fatty acid amides in exposed cells. A proteomic analysis showed an increased expression in cells exposed to the extract compared to control cells of FAB1 and NPC1, proteins known to be involved in lipid metabolism and transport. To the extent of our knowledge, this study is the first use of untargeted metabolomics and a proteomic analysis to investigate the effects of F. vesiculosus on differentiated Caco-2 cells, offering insights into the molecular mechanism of the extract's compounds on intestinal cells.

Characterization of the Mitochondrial Proteome in the Ctenophore Mnemiopsis leidyi Using MitoPredictor.

Mitochondrial proteomes have been experimentally characterized for only a handful of animal species. However, the increasing availability of genomic and transcriptomic data allows one to infer mitochondrial proteins using computational tools. MitoPredictor is a novel random forest classifier, which utilizes orthology search, mitochondrial targeting signal (MTS) identification, and protein domain content to infer mitochondrial proteins in animals. MitoPredictor's output also includes an easy-to-use R Shiny applet for the visualization and analysis of the results. In this article, we provide a guide for predicting and analyzing the mitochondrial proteome of the ctenophore Mnemiopsis leidyi using MitoPredictor.

Data-Independent Acquisition-Based Quantitative Proteomic Analysis Reveals Potential Salivary Biomarkers of Primary Sjögren's Syndrome.

Objective As primary Sj?gren's syndrome (pSS) primarily affects the salivary glands, saliva can serve as an indicator of the glands' pathophysiology and the disease's status. This study aims to illustrate the salivary proteomic profiles of pSS patients and identify potential candidate biomarkers for diagnosis.Methods The discovery set contained 49 samples (24 from pSS and 25 from age- and gender-matched healthy controls [HCs]) and the validation set included 25 samples (12 from pSS and 13 from HCs). Totally 36 pSS patients and 38 HCs were centrally randomized into the discovery set or to the validation set at a 2:1 ratio. Unstimulated whole saliva samples from pSS patients and HCs were analyzed using a data-independent acquisition (DIA) strategy on a 2D LC?HRMS/MS platform to reveal differential proteins. The crucial proteins were verified using DIA analysis and annotated using gene ontology (GO) and International Pharmaceutical Abstracts (IPA) analysis. A prediction model for SS was established using random forests.Results A total of 1,963 proteins were discovered, and 136 proteins exhibited differential representation in pSS patients. The bioinformatic research indicated that these proteins were primarily linked to immunological functions, metabolism, and inflammation. A panel of 19 protein biomarkers was identified by ranking order based on P-value and random forest algorichm, and was validated as the predictive biomarkers exhibiting good performance with area under the curve (AUC) of 0.817 for discovery set and 0.882 for validation set.Conclusions The candidate protein panel discovered may aid in pSS diagnosis. Salivary proteomic analysis is a promising non-invasive method for prognostic evaluation and early and precise treatments for pSS patients. DIA offers the best time efficiency and data dependability and may be a suitable option for future research on the salivary proteome.

Overcoming the detrimental O-acylation in TMTpro labeling improves the proteome depth and quantification precision.

BACKGROUND: With the advent of proline-based reporter isobaric Tandem Mass Tag (TMTpro) reagents, the sample multiplexing capacity of tandem mass tags (TMTs) has been expanded, and up to 18 samples can be quantified in a multiplexed manner. Like classic TMT reagents, TMTpro reagents contain a tertiary amine group, which markedly enhances their reactivity toward hydroxyl groups and results in O-acylation of serine, threonine and tyrosine residues. This overlabeling significantly compromises proteome analysis in terms of depth and precision. In particular, the reactivity of hydroxyl-containing residues can be dramatically enhanced when coexisting with a histidine in the same peptides, leading to a severe systematic bias against the analysis of these peptides. Although some protocols using a reduced molar excess of TMT under alkaline conditions can alleviate overlabeling of histidine-free peptides to some extent, they have a limited effect on histidyl- and hydroxyl-containing peptides. RESULTS: Here, we report a novel TMTpro labeling method that overcomes detrimental overlabeling while providing high labeling efficiency of amines. Additionally, our method is cost-effective, as it requires only half the amount of TMTpro reagents recommended by the reagent manufacturer. In a deep-scale analysis of a yeast/human two-proteome model sample, we compared our method with a typical alkaline labeling method using a reduced molar excess of TMTpro. Even at a depth of over 10,000 proteins, our method detected 23.7% more unique peptides and 8.7% more protein groups compared to the alkaline labeling method. Moreover, our method significantly improved the quantitative precision due to the reduced variability in labeling and increased protein sequence coverage. This substantially enhanced the statistical power of our method for detecting differentially abundant proteins, providing an average of 13% more yeast proteins that reached statistical significance. SIGNIFCANCE: We presented a novel TMTpro labeling method that overcomes the detrimental O-acylation and thus significantly improves the depth and quantitative precision for proteome analysis.

A compendium of multi-omics data illuminating host responses to lethal human virus infections.

Human infections caused by viral pathogens trigger a complex gamut of host responses that limit disease, resolve infection, generate immunity, and contribute to severe disease or death. Here, we present experimental methods and multi-omics data capture approaches representing the global host response to infection generated from 45 individual experiments involving human viruses from the Orthomyxoviridae, Filoviridae, Flaviviridae, and Coronaviridae families. Analogous experimental designs were implemented across human or mouse host model systems, longitudinal samples were collected over defined time courses, and global multi-omics data (transcriptomics, proteomics, metabolomics, and lipidomics) were acquired by microarray, RNA sequencing, or mass spectrometry analyses. For comparison, we have included transcriptomics datasets from cells treated with type I and type II human interferon. Raw multi-omics data and metadata were deposited in public repositories, and we provide a central location linking the raw data with experimental metadata and ready-to-use, quality-controlled, statistically processed multi-omics datasets not previously available in any public repository. This compendium of infection-induced host response data for reuse will be useful for those endeavouring to understand viral disease pathophysiology and network biology.