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Integrative FourD omics approach profiles the target network of the carbon storage regulatory system.

ABSTRACT: Multi-target regulators represent a largely untapped area for metabolic engineering and anti-bacterial development. These regulators are complex to characterize because they often act at multiple levels, affecting proteins, transcripts and metabolites. Therefore, single omics experiments cannot profile their underlying targets and mechanisms. In this work, we used an Integrative FourD omics approach (INFO) that consists of collecting and analyzing systems data throughout multiple time points, using multiple genetic backgrounds, and multiple omics approaches (transcriptomics, proteomics and high throughput sequencing crosslinking immunoprecipitation) to evaluate simultaneous changes in gene expression after imposing an environmental stress that accentuates the regulatory features of a network. Using this approach, we profiled the targets and potential regulatory mechanisms of a global regulatory system, the well-studied carbon storage regulatory (Csr) system of Escherichia coli, which is widespread among bacteria. Using 126 sets of proteomics and transcriptomics data, we identified 136 potential direct CsrA targets, including 50 novel ones, categorized their behaviors into distinct regulatory patterns, and performed in vivo fluorescence-based follow up experiments. The results of this work validate 17 novel mRNAs as authentic direct CsrA targets and demonstrate a generalizable strategy to integrate multiple lines of omics data to identify a core pool of regulator targets.

SUBMITTER: Sowa SW

PROVIDER: S-EPMC5389547 | biostudies-literature | 2017 Feb

REPOSITORIES: biostudies-literature

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Integrative FourD omics approach profiles the target network of the carbon storage regulatory system.

Sowa Steven W SW Gelderman Grant G Leistra Abigail N AN Buvanendiran Aishwarya A Lipp Sarah S Pitaktong Areen A Vakulskas Christopher A CA Romeo Tony T Baldea Michael M Contreras Lydia M LM

Nucleic acids research 20170201 4

Multi-target regulators represent a largely untapped area for metabolic engineering and anti-bacterial development. These regulators are complex to characterize because they often act at multiple levels, affecting proteins, transcripts and metabolites. Therefore, single omics experiments cannot profile their underlying targets and mechanisms. In this work, we used an Integrative FourD omics approach (INFO) that consists of collecting and analyzing systems data throughout multiple time points, us ...[more]

PMID: 28126921

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Project description:Recent technological advances and international efforts, such as The Cancer Genome Atlas (TCGA), have made available several pan-cancer datasets encompassing multiple omics layers with detailed clinical information in large collection of samples. The need has thus arisen for the development of computational methods aimed at improving cancer subtyping and biomarker identification from multi-modal data. Here we apply the Integrative Network Fusion (INF) pipeline, which combines multiple omics layers exploiting Similarity Network Fusion (SNF) within a machine learning predictive framework. INF includes a feature ranking scheme (rSNF) on SNF-integrated features, used by a classifier over juxtaposed multi-omics features (juXT). In particular, we show instances of INF implementing Random Forest (RF) and linear Support Vector Machine (LSVM) as the classifier, and two baseline RF and LSVM models are also trained on juXT. A compact RF model, called rSNFi, trained on the intersection of top-ranked biomarkers from the two approaches juXT and rSNF is finally derived. All the classifiers are run in a 10x5-fold cross-validation schema to warrant reproducibility, following the guidelines for an unbiased Data Analysis Plan by the US FDA-led initiatives MAQC/SEQC. INF is demonstrated on four classification tasks on three multi-modal TCGA oncogenomics datasets. Gene expression, protein expression and copy number variants are used to predict estrogen receptor status (BRCA-ER, N = 381) and breast invasive carcinoma subtypes (BRCA-subtypes, N = 305), while gene expression, miRNA expression and methylation data is used as predictor layers for acute myeloid leukemia and renal clear cell carcinoma survival (AML-OS, N = 157; KIRC-OS, N = 181). In test, INF achieved similar Matthews Correlation Coefficient (MCC) values and 97% to 83% smaller feature sizes (FS), compared with juXT for BRCA-ER (MCC: 0.83 vs. 0.80; FS: 56 vs. 1801) and BRCA-subtypes (0.84 vs. 0.80; 302 vs. 1801), improving KIRC-OS performance (0.38 vs. 0.31; 111 vs. 2319). INF predictions are generally more accurate in test than one-dimensional omics models, with smaller signatures too, where transcriptomics consistently play the leading role. Overall, the INF framework effectively integrates multiple data levels in oncogenomics classification tasks, improving over the performance of single layers alone and naive juxtaposition, and provides compact signature sizes.

Project description:Bacteria constantly face stress conditions and therefore mount specific responses to ensure adaptation and survival. Stress responses were believed to be predominantly regulated at the transcriptional level. In the phototrophic bacterium Rhodobacter sphaeroides the response to singlet oxygen is initiated by alternative sigma factors. Further adaptive mechanisms include post-transcriptional and post-translational events, which have to be considered to gain a deeper understanding of how sophisticated regulation networks operate. To address this issue, we integrated three layers of regulation: (1) total mRNA levels at different time-points revealed dynamics of the transcriptome, (2) mRNAs in polysome fractions reported on translational regulation (translatome), and (3) SILAC-based mass spectrometry was used to quantify protein abundances (proteome). The singlet oxygen stress response exhibited highly dynamic features regarding short-term effects and late adaptation, which could in part be assigned to the sigma factors RpoE and RpoH2 generating distinct expression kinetics of corresponding regulons. The occurrence of polar expression patterns of genes within stress-inducible operons pointed to an alternative of dynamic fine-tuning upon stress. In addition to transcriptional activation, we observed significant induction of genes at the post-transcriptional level (translatome), which identified new putative regulators and assigned genes of quorum sensing to the singlet oxygen stress response. Intriguingly, the SILAC approach explored the stress-dependent decline of photosynthetic proteins, but also identified 19 new open reading frames, which were partly validated by RNA-seq. We propose that comparative approaches as presented here will help to create multi-layered expression maps on the system level ("expressome"). Finally, intense mass spectrometry combined with RNA-seq might be the future tool of choice to re-annotate genomes in various organisms and will help to understand how they adapt to alternating conditions.

Project description:BackgroundMultiple pathophysiological processes have been described in Alzheimer's disease (AD). Their inter-individual variations, complex interrelations, and relevance for clinical manifestation and disease progression remain poorly understood. We hypothesize that specific molecular patterns indicating both known and yet unidentified pathway alterations are associated with distinct aspects of AD pathology.MethodsWe performed multi-level cerebrospinal fluid (CSF) omics in a well-characterized cohort of older adults with normal cognition, mild cognitive impairment, and mild dementia. Proteomics, metabolomics, lipidomics, one-carbon metabolism, and neuroinflammation related molecules were analyzed at single-omic level with correlation and regression approaches. Multi-omics factor analysis was used to integrate all biological levels. Identified analytes were used to construct best predictive models of the presence of AD pathology and of cognitive decline with multifactorial regression analysis. Pathway enrichment analysis identified pathway alterations in AD.ResultsMulti-omics integration identified five major dimensions of heterogeneity explaining the variance within the cohort and differentially associated with AD. Further analysis exposed multiple interactions between single 'omics modalities and distinct multi-omics molecular signatures differentially related to amyloid pathology, neuronal injury, and tau hyperphosphorylation. Enrichment pathway analysis revealed overrepresentation of the hemostasis, immune response, and extracellular matrix signaling pathways in association with AD. Finally, combinations of four molecules improved prediction of both AD (protein 14-3-3 zeta/delta, clusterin, interleukin-15, and transgelin-2) and cognitive decline (protein 14-3-3 zeta/delta, clusterin, cholesteryl ester 27:1 16:0 and monocyte chemoattractant protein-1).ConclusionsApplying an integrative multi-omics approach we report novel molecular and pathways alterations associated with AD pathology. These findings are relevant for the development of personalized diagnosis and treatment approaches in AD.

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Integrative FourD omics approach profiles the target network of the carbon storage regulatory system.

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Integrative FourD omics approach profiles the target network of the carbon storage regulatory system.

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