The cell and gene therapy field has seen a staggering increase in products on the market, with an expectation for continued growth in the coming years. However, widespread development of commercially ...viable products addressing more therapeutic indications and larger patient populations is hindered by the complexity of these products and the consequent high costs for their manufacturing. Innovations in upstream, downstream, and analytical tools that account for such complexity is urgently needed to reduce costs, improve quality and yield, and ultimately make these therapies more affordable.
One barrier to cost-effective bioprocesses is the poor knowledge of the biology underlying product manufacturing. Poor analytical characterisation limits the generation of bespoke production systems and reliable in-process control strategies. To address this knowledge gap, we carried out an unprecedented multi-omics study to unravel the biology underpinning a scalable adeno-associated viral vector (AAV) process in transiently transfected HEK293 cell clones.
We conducted a time-course analysis to investigate transcriptomics, translation, proteomics, and metabolomics while comparing high and low producer HEK293 clones. Our integration of these multi-omics methods has revealed unique molecular kinetics and enabled an initial identification of differential pathways and biomarkers that are enriched in the high AAV yield production but not for the low producer clone. Further to these results, we are now applying machine learning and artificial intelligence tools to identify key AAV production biomarkers and subsequently engineer them to enhance AAV productivity.
Through harnessing the knowledge of the AAV production system biology in HEK293 cells, we are set out to generate novel production systems that may be fine-tuned by a rationally designed in-process control strategy. Productivity improvements for gene therapies, and particularly for AAVs, are key to reducing manufacturing costs and ultimately enabling patients’ access to advanced treatments for indications with large unmet therapeutic need.
•Tyrosineprotein kinase, GPR15LG, KAZALD1, ADH1B, and thirty metabolites have been identified as potential biomarkers for renal ischemia–reperfusion injury.•Different durations of renal ischemia lead ...to unique pathological processes and varying components in the venous blood.•During the duration of renal ischemia lasting 40 min, the primary pathological process involves protein digestion and absorption.•During the extended duration of renal ischemia lasting 60 min, the primary pathological mechanisms involve central carbon metabolism, the glucagon signaling pathway, and pyruvate metabolism.
Acute kidney injury (AKI) is frequently caused by renal ischemia–reperfusion injury (IRI). Identifying potential renal IRI disease biomarkers would be useful for evaluating AKI severity.
We used proteomics and metabolomics to investigate the differences in renal venous blood between ischemic and healthy kidneys in an animal model by identifying differentially expressed proteins (DEPs) and differentially expressed protein metabolites (DEMs).
Nine pairs of renal venous blood samples were collected before and at 20, 40, and 60 min post ischemia. The ischemia time of Group A, B and C was 20,40 and 60 min. The proteome and metabolome of renal venous blood were evaluated to establish the differences between renal venous blood before and after ischemia.
We identified 79 common DEPs in all samples of Group A, 80 in Group B, and 131 in Group C. Further common DEPs among all three groups were Tyrosineprotein kinase, GPR15LG, KAZALD1, ADH1B. We also identified 81, 64, and 83 common DEMs in each group respectively, in which 30 DEMs were further common to all groups. Bioinformatic analysis of the DEPs and DEMs was conducted.
This study demonstrated that different pathological processes occur during short- and long-term renal IRI. Tyrosine protein kinase, GPR15LG, Kazal-type serine peptidase inhibitor domain 1, and all-trans-retinol dehydrogenase are potential biomarkers of renal IRI.
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•The type of combined effect of PFOA and 4-HBP changed from additive to synergistic from 1.25 × 10−8 M to 4 × 10−7 M.•Critical role of mTORC1 in MCF-7 cell proliferation identified by ...multi-omics analysis.•Molecular docking validated the binding capacity of PFOA and 4-HBP to the estrogen receptor.
With the discovery of evidence that many endocrine-disrupting chemicals (EDCs) in the environment influence human health, their toxic effects and mechanisms have become a hot topic of research. However, investigations into their endocrine-disrupting toxicity under combined binary exposure, especially the molecular mechanism of combined effects, have rarely been documented. In this study, two typical EDCs, perfluorooctanoic acid (PFOA) and 4-hydroxybenzophenone (4-HBP), were selected to examine their combined effects and molecular mechanism on MCF-7 cell proliferation at environmentally relevant exposure concentrations. We have successfully established a model to evaluate the binary combined toxic effects of endocrine disruptors, presenting combined effects in a simple and direct way. Results indicated that the combined effect changed from additive to synergistic from 1.25 × 10−8 M to 4 × 10−7 M. Metabolomics analyses suggested that exposure to PFOA and 4-HBP caused significant alterations in purine metabolism, arginine, and proline metabolism and had superimposed influences on metabolism. Enhanced combined effects were observed in glycine, serine, and threonine metabolic pathways compared to exposure to PFOS and 4-HBP alone. Additionally, the differentially expressed genes (DEGs) are primarily involved in Biological Processes, especially protein targeting the endoplasmic reticulum, and significantly impact the oxidative phosphorylation and thermogenesis-related KEGG pathway. By integrating metabolome and transcriptome analyses, PFOA and 4-HBP regulate purine metabolism, the TCA cycle, and endoplasmic reticulum protein synthesis in MCF-7 cells via mTORC1, which provides genetic material, protein, and energy for cell proliferation. Furthermore, molecular docking confirmed the ability of PFOA and 4-HBP to stably bind the estrogen receptor, indicating that they have different binding pockets. Collectively, these findings will offer new insights into understanding the mechanisms by which EDCs produce combined toxicity.
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•Total, insoluble, and soluble dietary fiber were extracted from high barley.•All three dietary fibers (HDF, HIDF, HSDF) significantly improved obesity in mice.•Intestinal microbiota ...exhibited distinct responses to HDF, HIDF, and HSDF.•PPAR signaling was a key pathway for the anti-obesity effects.•The potential targets of HDF, HIDF, and HSDF were Abcc3, Dapk1, and Pck1.
The impact of different forms of dietary fiber (total, insoluble or soluble) derived from the same source on health remains incompletely understood. In this study, the effects of total, insoluble, and soluble dietary fiber extracted from highland barley (HDF, HIDF, and HSDF) on combating obesity were evaluated and compared. A high-fat diet (HFD) was used to induce obesity in a murine model, followed by gavage administration of HDF, HIDF, or HSDF, and a comprehensive multi-omics approach was utilized to assess and compare the effects of these dietary fibers on obesity-related parameters. The results showed that all three dietary fibers significantly reduced body weight, modified blood lipid profiles, and ameliorated tissue damage in HFD-fed mice. Additionally, 16S rRNA sequencing analysis of mice feces showed that three types of dietary fiber exerted varying degrees of impact on the composition and abundance of gut microbiota while simultaneously promoting the biosynthesis of short-chain fatty acids. Specifically, HDF supplementation remarkably enhanced the abundance of Coprococcus, while HIDF and HSDF supplementation elevated the levels of Akkermansia and Allobaculum, respectively. Transcriptomic and proteomic results suggested the PPAR signaling pathway as a central regulatory mechanism influenced by these fibers. HDF and HIDF were particularly effective in modulating biological processes related to triglyceride and fatty acid metabolism, identifying Abcc3 and Dapk1 as potential targets. Conversely, HSDF primarily affected processes related to membrane lipids, ceramides, and phospholipids metabolism, with Pck1 identified as a potential target. Collectively, HDF, HIDF, and HSDF demonstrated distinct mechanisms in exerting exceptional anti-obesity properties. These insights may inform the development of personalized dietary interventions for obesity.
Enteritis posed a significant health challenge to golden pompano (Trachinotus ovatus) populations. In this research, a comprehensive multi-omics strategy was implemented to elucidate the pathogenesis ...of enteritis by comparing both healthy and affected golden pompano. Histologically, enteritis was characterized by villi adhesion and increased clustering after inflammation. Analysis of the intestinal microbiota revealed a significant increase (P < 0.05) in the abundance of specific bacterial strains, including Photobacterium and Salinivibrio, in diseased fish compared to the healthy group. Metabolomic analysis identified 5479 altered metabolites, with significant impacts on terpenoid and polyketide metabolism, as well as lipid metabolism (P < 0.05). Additionally, the concentrations of several compounds such as calcitetrol, vitamin D2, arachidonic acid, and linoleic acid were significantly reduced in the intestines of diseased fish post-enteritis (P < 0.05), with the detection of harmful substances such as Efonidipine. In transcriptomic profiling, enteritis induced 68 upregulated and 73 downregulated genes, predominantly affecting steroid hormone receptor activity (P < 0.05). KEGG pathway enrichment analysis highlighted upregulation of SQLE and CYP51 in steroidogenesis, while the HSV-1 associated MHC1 gene exhibited significant downregulation. Integration of multi-omics results suggested a potential pathogenic mechanism: enteritis may have resulted from concurrent infection of harmful bacteria, specifically Photobacterium and Salinivibrio, along with HSV-1. Efonidipine production within the intestinal tract may have blocked certain calcium ion channels, leading to downregulation of MHC1 gene expression and reduced extracellular immune recognition. Upregulation of SQLE and CYP51 genes stimulated steroid hormone synthesis within cells, which, upon binding to G protein-coupled receptors, influenced calcium ion transport, inhibited immune activation reactions, and further reduced intracellular synthesis of anti-inflammatory substances like arachidonic acid. Ultimately, this cascade led to inflammation progression, weakened intestinal peristalsis, and villi adhesion. This study utilized multi-level omics detection to investigate the pathological symptoms of enteritis and proposed a plausible pathogenic mechanism, providing innovative insights into enteritis verification and treatment in offshore cage culture of golden pompano.
•Enteritis in golden pompano was associate with the intestinal microbiota in harmful bacteria, namely Photobacterium and Salinivibrio.•Efonidipine production lead to downregulation of MHC1 and reduced extracellular immune recognition.•The upregulation of SQLE and CYP51 leading to increased steroid hormone synthesis cause the intestinal inflammation.
Multi‐omics studies promise the improved characterization of biological processes across molecular layers. However, methods for the unsupervised integration of the resulting heterogeneous data sets ...are lacking. We present Multi‐Omics Factor Analysis (MOFA), a computational method for discovering the principal sources of variation in multi‐omics data sets. MOFA infers a set of (hidden) factors that capture biological and technical sources of variability. It disentangles axes of heterogeneity that are shared across multiple modalities and those specific to individual data modalities. The learnt factors enable a variety of downstream analyses, including identification of sample subgroups, data imputation and the detection of outlier samples. We applied MOFA to a cohort of 200 patient samples of chronic lymphocytic leukaemia, profiled for somatic mutations, RNA expression, DNA methylation and ex vivo drug responses. MOFA identified major dimensions of disease heterogeneity, including immunoglobulin heavy‐chain variable region status, trisomy of chromosome 12 and previously underappreciated drivers, such as response to oxidative stress. In a second application, we used MOFA to analyse single‐cell multi‐omics data, identifying coordinated transcriptional and epigenetic changes along cell differentiation.
Synopsis
Multi‐Omics Factor Analysis (MOFA) is a computational framework for unsupervised discovery of the principal axes of biological and technical variation when multiple omics assays are applied to the same samples. MOFA is a broadly applicable approach for multi‐omics data integration.
The inferred latent factors represent the underlying principal axes of heterogeneity across the samples. Factors can be shared by multiple data modalities or can be data‐type specific.
The model flexibly handles missing values and different data types.
In an application to Chronic Lymphocytic Leukaemia, MOFA discovers a low dimensional space spanned by known clinical markers and underappreciated axes of variation such as oxidative stress.
In an application to multi‐omics profiles from single‐cells, MOFA recovers differentiation trajectories and identifies coordinated variation between the transcriptome and the epigenome.
Multi‐Omics Factor Analysis (MOFA) is a computational framework for unsupervised discovery of the principal axes of biological and technical variation when multiple omics assays are applied to the same samples. MOFA is a broadly applicable approach for multi‐omics data integration.
COVID‐19 is characterized by dysregulated immune responses, metabolic dysfunction and adverse effects on the function of multiple organs. To understand host responses to COVID‐19 pathophysiology, we ...combined transcriptomics, proteomics, and metabolomics to identify molecular markers in peripheral blood and plasma samples of 66 COVID‐19‐infected patients experiencing a range of disease severities and 17 healthy controls. A large number of expressed genes, proteins, metabolites, and extracellular RNAs (exRNAs) exhibit strong associations with various clinical parameters. Multiple sets of tissue‐specific proteins and exRNAs varied significantly in both mild and severe patients suggesting a potential impact on tissue function. Chronic activation of neutrophils, IFN‐I signaling, and a high level of inflammatory cytokines were observed in patients with severe disease progression. In contrast, COVID‐19‐infected patients experiencing milder disease symptoms showed robust T‐cell responses. Finally, we identified genes, proteins, and exRNAs as potential biomarkers that might assist in predicting the prognosis of SARS‐CoV‐2 infection. These data refine our understanding of the pathophysiology and clinical progress of COVID‐19.
SYNOPSIS
Proteomics, metabolomics and RNAseq data map immune responses in COVID‐19 patients with different disease severity, revealing molecular makers associated with disease progression and alterations of tissue‐specific proteins.
A multi‐omics profiling of the host response to SARS‐CoV2 infection in 66 clinically diagnosed and laboratory confirmed COVID‐19 patients and 17 uninfected controls.
Significant correlations between multi‐omics data and key clinical parameters.
Alteration of tissue‐specific proteins and exRNAs.
Enhanced activation of immune responses is associated with COVID‐19 pathogenesis.
Biomarkers to predict COVID‐19 clinical outcomes pending clinical validation as prospective marker.
Proteomics, metabolomics and RNAseq data map immune responses in COVID‐19 patients with different disease severity, revealing molecular makers associated with disease progression and alterations of tissue‐specific proteins.
Multi-omics data integration is one of the major challenges in the era of precision medicine. Considerable work has been done with the advent of high-throughput studies, which have enabled the data ...access for downstream analyses. To improve the clinical outcome prediction, a gamut of software tools has been developed. This review outlines the progress done in the field of multi-omics integration and comprehensive tools developed so far in this field. Further, we discuss the integration methods to predict patient survival at the end of the review.
Background: The Guangzhou Nutrition and Health Study (GNHS) aims to assess the determinants of metabolic disease in nutritional aspects, as well as other environmental and genetic factors, and ...explore possible biomarkers and mechanisms with multi-omics integration.Methods: The population-based sample of adults in Guangzhou, China (baseline: 40-83 years old; n = 5118) was followed up about every 3 years. All will be tracked via on-site follow-up and health information systems. We assessed detailed information on lifestyle factors, physical activities, dietary assessments, psychological health, cognitive function, body measurements, and muscle function. Instrument tests included dual-energy X-ray absorptiometry scanning, carotid artery and liver ultrasonography evaluations, vascular endothelial function evaluation, upper-abdomen and brain magnetic resonance imaging, and 14-d real-time continuous glucose monitoring tests. We also measured multi-omics, including host genome-wide genotyping, serum metabolome and proteome, gut microbiome (16S rRNA sequencing, metagenome, and internal transcribed spacer 2 sequencing), and fecal metabolome and proteome.Results: The baseline surveys were conducted from 2008 to 2015. Now, we have completed 3 waves. The 3rd and 4th follow-ups have started but have yet to end. A total of 5118 participants aged 40-83 took part in the study. The median age at baseline was approximately 59.0 years and the proportion of female participants was about 69.4%. Among all the participants, 3628 (71%) completed at least one on-site follow-up with a median duration of 9.48 years.Conclusion: The cohort will provide data that have been influential in establishing the role of nutrition in metabolic diseases with multi-omics.
Humans heavily rely on dozens of domesticated plant species that have been further improved through intensive breeding. To evaluate how breeding changed the tomato fruit metabolome, we have generated ...and analyzed a dataset encompassing genomes, transcriptomes, and metabolomes from hundreds of tomato genotypes. The combined results illustrate how breeding globally altered fruit metabolite content. Selection for alleles of genes associated with larger fruits altered metabolite profiles as a consequence of linkage with nearby genes. Selection of five major loci reduced the accumulation of anti-nutritional steroidal glycoalkaloids in ripened fruits, rendering the fruit more edible. Breeding for pink tomatoes modified the content of over 100 metabolites. The introgression of resistance genes from wild relatives in cultivars also resulted in major and unexpected metabolic changes. The study reveals a multi-omics view of the metabolic breeding history of tomato, as well as provides insights into metabolome-assisted breeding and plant biology.
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•Multi-omic analysis of tomato fruits revealed new metabolic genes and pathways•Selection of fruit mass gene-altered metabolites altered due to nearby hitchhiking genes•Domestication acted on five major loci that reduced anti-nutritional compounds•Pink tomato breeding modified hundreds of metabolites, leading to unexpected changes
Multi-omic analysis reveals how the appearance/taste-oriented breeding process modulates the metabolic makeup of tomato.
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