Combined transcriptomics and proteomics unveil the impact of vitamin C in modulating specific protein abundance in the mouse liver.

BACKGROUND: Vitamin C (ascorbate) is a water-soluble antioxidant and an important cofactor for various biosynthetic and regulatory enzymes. Mice can synthesize vitamin C thanks to the key enzyme gulonolactone oxidase (Gulo) unlike humans. In the current investigation, we used Gulo-/- mice, which cannot synthesize their own ascorbate to determine the impact of this vitamin on both the transcriptomics and proteomics profiles in the whole liver. The study included Gulo-/- mouse groups treated with either sub-optimal or optimal ascorbate concentrations in drinking water. Liver tissues of females and males were collected at the age of four months and divided for transcriptomics and proteomics analysis. Immunoblotting, quantitative RT-PCR, and polysome profiling experiments were also conducted to complement our combined omics studies. RESULTS: Principal component analyses revealed distinctive differences in the mRNA and protein profiles as a function of sex between all the mouse cohorts. Despite such sexual dimorphism, Spearman analyses of transcriptomics data from females and males revealed correlations of hepatic ascorbate levels with transcripts encoding a wide array of biological processes involved in glucose and lipid metabolisms as well as in the acute-phase immune response. Moreover, integration of the proteomics data showed that ascorbate modulates the abundance of various enzymes involved in lipid, xenobiotic, organic acid, acetyl-CoA, and steroid metabolism mainly at the transcriptional level, especially in females. However, several proteins of the mitochondrial complex III significantly correlated with ascorbate concentrations in both males and females unlike their corresponding transcripts. Finally, poly(ribo)some profiling did not reveal significant enrichment difference for these mitochondrial complex III mRNAs between Gulo-/- mice treated with sub-optimal and optimal ascorbate levels. CONCLUSIONS: Thus, the abundance of several subunits of the mitochondrial complex III are regulated by ascorbate at the post-transcriptional levels. Our extensive omics analyses provide a novel resource of altered gene expression patterns at the transcriptional and post-transcriptional levels under ascorbate deficiency.

Proteomic Profiling of Samples Derived from a Murine Model Following Cryptococcus neoformans Infection.

Proteomic profiling provides in-depth information about the regulation of diverse biological processes, activation of and communication across signaling networks, and alterations to protein production, modifications, and interactions. For infectious disease research, mass spectrometry-based proteomics enables detection of host defenses against infection and mechanisms used by the pathogen to evade such responses. In this chapter, we outline protein extraction from organs, tissues, and fluids collected following intranasal inoculation of a murine model with the human fungal pathogen Cryptococcus neoformans. We describe sample preparation, followed by purification, processing on the mass spectrometer, and a robust bioinformatics analysis. The information gleaned from proteomic profiling of fungal infections supports the detection of novel biomarkers for diagnostic and prognostic purposes.

SOS genes are rapidly induced while translesion synthesis polymerase activity is temporally regulated.

The DNA damage inducible SOS response in bacteria serves to increase survival of the species at the cost of mutagenesis. The SOS response first initiates error-free repair followed by error-prone repair. Here, we have employed a multi-omics approach to elucidate the temporal coordination of the SOS response. Escherichia coli was grown in batch cultivation in bioreactors to ensure highly controlled conditions, and a low dose of the antibiotic ciprofloxacin was used to activate the SOS response while avoiding extensive cell death. Our results show that expression of genes involved in error-free and error-prone repair were both induced shortly after DNA damage, thus, challenging the established perception that the expression of error-prone repair genes is delayed. By combining transcriptomics and a sub-proteomics approach termed signalomics, we found that the temporal segregation of error-free and error-prone repair is primarily regulated after transcription, supporting the current literature. Furthermore, the heterology index (i.e., the binding affinity of LexA to the SOS box) was correlated to the maximum increase in gene expression and not to the time of induction of SOS genes. Finally, quantification of metabolites revealed increasing pyrimidine pools as a late feature of the SOS response. Our results elucidate how the SOS response is coordinated, showing a rapid transcriptional response and temporal regulation of mutagenesis on the protein and metabolite levels.

Multi-Omics Data Integration Reveals Key Variables Contributing to Subgingival Microbiome Dysbiosis-Induced Inflammatory Response in a Hyperglycemic Microenvironment.

Subgingival microbiome dysbiosis promotes the development of periodontitis, an irreversible chronic inflammatory disease associated with metabolic diseases. However, studies regarding the effects of a hyperglycemic microenvironment on host-microbiome interactions and host inflammatory response during periodontitis are still scarce. Here, we investigated the impacts of a hyperglycemic microenvironment on the inflammatory response and transcriptome of a gingival coculture model stimulated with dysbiotic subgingival microbiomes. HGF-1 cells overlaid with U937 macrophage-like cells were stimulated with subgingival microbiomes collected from four healthy donors and four patients with periodontitis. Pro-inflammatory cytokines and matrix metalloproteinases were measured while the coculture RNA was submitted to a microarray analysis. Subgingival microbiomes were submitted to 16s rRNA gene sequencing. Data were analyzed using an advanced multi-omics bioinformatic data integration model. Our results show that the genes krt76, krt27, pnma5, mansc4, rab41, thoc6, tm6sf2, and znf506 as well as the pro-inflammatory cytokines IL-1ß, GM-CSF, FGF2, IL-10, the metalloproteinases MMP3 and MMP8, and bacteria from the ASV 105, ASV 211, ASV 299, Prevotella, Campylobacter and Fretibacterium genera are key intercorrelated variables contributing to periodontitis-induced inflammatory response in a hyperglycemic microenvironment. In conclusion, our multi-omics integration analysis unveiled the complex interrelationships involved in the regulation of periodontal inflammation in response to a hyperglycemic microenvironment.

Overview of methods for characterization and visualization of a protein-protein interaction network in a multi-omics integration context.

At the heart of the cellular machinery through the regulation of cellular functions, protein-protein interactions (PPIs) have a significant role. PPIs can be analyzed with network approaches. Construction of a PPI network requires prediction of the interactions. All PPIs form a network. Different biases such as lack of data, recurrence of information, and false interactions make the network unstable. Integrated strategies allow solving these different challenges. These approaches have shown encouraging results for the understanding of molecular mechanisms, drug action mechanisms, and identification of target genes. In order to give more importance to an interaction, it is evaluated by different confidence scores. These scores allow the filtration of the network and thus facilitate the representation of the network, essential steps to the identification and understanding of molecular mechanisms. In this review, we will discuss the main computational methods for predicting PPI, including ones confirming an interaction as well as the integration of PPIs into a network, and we will discuss visualization of these complex data.

The interaction of secreted phospholipase A2-IIA with the microbiota alters its lipidome and promotes inflammation.

Secreted phospholipase A2-IIA (sPLA2-IIA) hydrolyzes phospholipids to liberate lysophospholipids and fatty acids. Given its poor activity toward eukaryotic cell membranes, its role in the generation of proinflammatory lipid mediators is unclear. Conversely, sPLA2-IIA efficiently hydrolyzes bacterial membranes. Here, we show that sPLA2-IIA affects the immune system by acting on the intestinal microbial flora. Using mice overexpressing transgene-driven human sPLA2-IIA, we found that the intestinal microbiota was critical for both induction of an immune phenotype and promotion of inflammatory arthritis. The expression of sPLA2-IIA led to alterations of the intestinal microbiota composition, but housing in a more stringent pathogen-free facility revealed that its expression could affect the immune system in the absence of changes to the composition of this flora. In contrast, untargeted lipidomic analysis focusing on bacteria-derived lipid mediators revealed that sPLA2-IIA could profoundly alter the fecal lipidome. The data suggest that a singular protein, sPLA2-IIA, produces systemic effects on the immune system through its activity on the microbiota and its lipidome.

timeOmics: an R package for longitudinal multi-omics data integration.

MOTIVATION: Multi-omics data integration enables the global analysis of biological systems and discovery of new biological insights. Multi-omics experimental designs have been further extended with a longitudinal dimension to study dynamic relationships between molecules. However, methods that integrate longitudinal multi-omics data are still in their infancy. RESULTS: We introduce the R package timeOmics, a generic analytical framework for the integration of longitudinal multi-omics data. The framework includes pre-processing, modeling and clustering to identify molecular features strongly associated with time. We illustrate this framework in a case study to detect seasonal patterns of mRNA, metabolites, gut taxa and clinical variables in patients with diabetes mellitus from the integrative Human Microbiome Project. AVAILABILITYAND IMPLEMENTATION: timeOmics is available on Bioconductor and github.com/abodein/timeOmics. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Interpretation of network-based integration from multi-omics longitudinal data.

Multi-omics integration is key to fully understand complex biological processes in an holistic manner. Furthermore, multi-omics combined with new longitudinal experimental design can unreveal dynamic relationships between omics layers and identify key players or interactions in system development or complex phenotypes. However, integration methods have to address various experimental designs and do not guarantee interpretable biological results. The new challenge of multi-omics integration is to solve interpretation and unlock the hidden knowledge within the multi-omics data. In this paper, we go beyond integration and propose a generic approach to face the interpretation problem. From multi-omics longitudinal data, this approach builds and explores hybrid multi-omics networks composed of both inferred and known relationships within and between omics layers. With smart node labelling and propagation analysis, this approach predicts regulation mechanisms and multi-omics functional modules. We applied the method on 3 case studies with various multi-omics designs and identified new multi-layer interactions involved in key biological functions that could not be revealed with single omics analysis. Moreover, we highlighted interplay in the kinetics that could help identify novel biological mechanisms. This method is available as an R package netOmics to readily suit any application.

Integration strategies of multi-omics data for machine learning analysis.

Increased availability of high-throughput technologies has generated an ever-growing number of omics data that seek to portray many different but complementary biological layers including genomics, epigenomics, transcriptomics, proteomics, and metabolomics. New insight from these data have been obtained by machine learning algorithms that have produced diagnostic and classification biomarkers. Most biomarkers obtained to date however only include one omic measurement at a time and thus do not take full advantage of recent multi-omics experiments that now capture the entire complexity of biological systems. Multi-omics data integration strategies are needed to combine the complementary knowledge brought by each omics layer. We have summarized the most recent data integration methods/ frameworks into five different integration strategies: early, mixed, intermediate, late and hierarchical. In this mini-review, we focus on challenges and existing multi-omics integration strategies by paying special attention to machine learning applications.

Adipose Tissue Transcriptome Is Related to Pollutant Exposure in Polar Bear Mother-Cub Pairs from Svalbard, Norway.

Being at the food chain apex, polar bears (Ursus maritimus) are highly contaminated with persistent organic pollutants (POPs). Females transfer POPs to their offspring through gestation and lactation; therefore, young cubs present higher POPs concentrations than their mothers. Recent studies suggest that POPs affect the lipid metabolism in female polar bears; however, the mechanisms and impact on their offspring remain unknown. Here, we hypothesized that exposure to POPs differentially alters genome-wide gene transcription in the adipose tissue from mother polar bears and their cubs, highlighting molecular differences in response between adults and young. Adipose tissue biopsies were collected from 13 adult female polar bears and their twin cubs in Svalbard, Norway, in April 2011, 2012, and 2013. Total RNA extracted from biopsies was subjected to next-generation RNA sequencing. Plasma concentrations of summed polychlorinated biphenyls, organochlorine pesticides, and polybrominated diphenyl ethers in mothers ranged from 897 to 13620 ng/g wet weight and were associated with altered adipose tissue gene expression in both mothers and cubs. In mothers, 2502 and 2586 genes in total were positively and negatively, respectively, correlated to POP exposure, whereas in cubs, 2585 positively and 1690 negatively genes. Between mothers and cubs, 743 positively and negatively genes overlapped between mothers and cubs suggesting partially shared molecular responses to ΣPOPs. ΣPOP-associated genes were involved in numerous metabolic pathways in mothers and cubs, indicating that POP exposure alters the energy metabolism, which, in turn, may be linked to metabolic dysfunction.

A Generic Multivariate Framework for the Integration of Microbiome Longitudinal Studies With Other Data Types.

Simultaneous profiling of biospecimens using different technological platforms enables the study of many data types, encompassing microbial communities, omics, and meta-omics as well as clinical or chemistry variables. Reduction in costs now enables longitudinal or time course studies on the same biological material or system. The overall aim of such studies is to investigate relationships between these longitudinal measures in a holistic manner to further decipher the link between molecular mechanisms and microbial community structures, or host-microbiota interactions. However, analytical frameworks enabling an integrated analysis between microbial communities and other types of biological, clinical, or phenotypic data are still in their infancy. The challenges include few time points that may be unevenly spaced and unmatched between different data types, a small number of unique individual biospecimens, and high individual variability. Those challenges are further exacerbated by the inherent characteristics of microbial communities-derived data (e.g., sparse, compositional). We propose a generic data-driven framework to integrate different types of longitudinal data measured on the same biological specimens with microbial community data and select key temporal features with strong associations within the same sample group. The framework ranges from filtering and modeling to integration using smoothing splines and multivariate dimension reduction methods to address some of the analytical challenges of microbiome-derived data. We illustrate our framework on different types of multi-omics case studies in bioreactor experiments as well as human studies.