A network-based approach for predicting key enzymes explaining metabolite abundance alterations in a disease phenotype

Detection of genomic regions associated malformations in newborn piglets: a machine-learning approach

Background

A significant proportion of perinatal losses in pigs occurs due to congenital malformations. The purpose of this study is the identification of genomic loci associated with fetal malformations in piglets.

Methods

The malformations were divided into two groups: associated with limb defects (piglet splay leg) and associated with other congenital anomalies found in newborn piglets. 148 Landrace and 170 Large White piglets were selected for the study. A genome-wide association study based on the gradient boosting machine algorithm was performed to identify markers associated with congenital anomalies and piglet splay leg.

Results

Forty-nine SNPs (23 SNPs in Landrace pigs and 26 SNPs in Large White) were associated with congenital anomalies, 22 of which were localized in genes. A total of 156 SNPs (28 SNPs in Landrace; 128 in Large White) were identified for piglet splay leg, of which 79 SNPs were localized in genes. We have demonstrated that the gradient boosting machine algorithm can identify SNPs and their combinations associated with significant selection indicators of studied malformations and productive characteristics.

Data availability

Genotyping and phenotyping data are available at http://www.compubioverne.group/data-and-software/.

Computational Biology and Machine Learning Approaches to Understand Mechanistic Microbiome-Host Interactions

The microbiome, by virtue of its interactions with the host, is implicated in various host functions including its influence on nutrition and homeostasis. Many chronic diseases such as diabetes, cancer, inflammatory bowel diseases are characterized by a disruption of microbial communities in at least one biological niche/organ system. Various molecular mechanisms between microbial and host components such as proteins, RNAs, metabolites have recently been identified, thus filling many gaps in our understanding of how the microbiome modulates host processes. Concurrently, high-throughput technologies have enabled the profiling of heterogeneous datasets capturing community level changes in the microbiome as well as the host responses. However, due to limitations in parallel sampling and analytical procedures, big gaps still exist in terms of how the microbiome mechanistically influences host functions at a system and community level. In the past decade, computational biology and machine learning methodologies have been developed with the aim of filling the existing gaps. Due to the agnostic nature of the tools, they have been applied in diverse disease contexts to analyze and infer the interactions between the microbiome and host molecular components. Some of these approaches allow the identification and analysis of affected downstream host processes. Most of the tools statistically or mechanistically integrate different types of -omic and meta -omic datasets followed by functional/biological interpretation. In this review, we provide an overview of the landscape of computational approaches for investigating mechanistic interactions between individual microbes/microbiome and the host and the opportunities for basic and clinical research. These could include but are not limited to the development of activity- and mechanism-based biomarkers, uncovering mechanisms for therapeutic interventions and generating integrated signatures to stratify patients.

Activiation of the nitric oxide cycle by citrulline and arginine restores susceptibility of resistant brown planthoppers to the insecticide imidacloprid

Pest management, which is critical for global crop productivity, is hampered by rapidly evolving insecticide resistance in insect pests. The ability to manage the development of insecticide resistance is thus vital. Nitric oxide (NO) is a ubiquitous signaling molecule with important functions in a variety of biological processes. Here we show that imidacloprid-resistant brown planthoppers (BPH) are deficient in citrulline and arginine, both of which are involved in NO production, but exogenous citrulline and arginine render resistant BPH vulnerable to imidacloprid. BPH insecticide resistance results from low NO production; exogenous arginine and citrulline augment the NO signaling in BPH, leading to downregulation of CYP6AY1 and CYP6ER1, the cytochrome P450 s that contribute to imidacloprid detoxification, thereby restoring susceptibility. Two amino acids that can be used to restore susceptibility in insecticide-resistant insects are identified, establishing a novel metabolome-based approach for killing insecticide-resistant pests and providing a useful template for managing insecticide resistance.

1H-NMR based metabolomic profiling of cord blood in gestational hypothyroidism

Background

Gestational hypothyroidism (GHT) is a common pregnancy-related thyroid disfunction. The adverse outcomes by GHT has been increasingly recognized, leading to more public awareness of the disease. However, comprehensive understanding of the prognosis of GHT has not yet achieved. Metabolomics is a powerful tool in evaluation of disease outcomes, and cord blood represents an excellent candidate for the investigation of gestational outcomes.

Methods

In the present study, we performed 1H-NMR based metabolomics on cord blood of 18 pregnant women with GHT and 18 non hypothyroidism (NHT) control.

Results

The metabolomic profile of GHT was separated with the NHT control. A total of 8 metabolites with altered abundances were observed, among which Creatinine and O-Phosphocholine were elevated and the others were downregulated in GHT. Spearman rank correlation suggested that the eight differential metabolites were correlated with the GHT related thyroid hormones. Pathway analysis of the differential metabolites indicated that two metabolic pathways were significantly altered in GHT (adjusted P<0.05), including tyrosine metabolism, phenylalanine, tyrosine and tryptophan biosynthesis. Enrichment analysis of the differential metabolites against disease-associated metabolite sets suggested that GHT was associated with disease risks of non-insulin dependent diabetes mellitus, isovaleric acidemia, and methylmalonic aciduria.

Conclusions

The results of this study revealed GHT associated metabolic changes in cord blood, providing insights into the metabolic intermediates between GHT and its related disease risks.

Systems Biology Approaches to Redox Metabolism in Stress and Disease States

SIGNIFICANCE

All cellular metabolic processes are tied to the cellular redox environment. Therefore, maintaining redox homeostasis is critically important for normal cell function. Indeed, redox stress contributes to the pathobiology of many human diseases. The cellular redox response system is composed of numerous interconnected components, including free radicals, redox couples, protein thiols, enzymes, metabolites, and transcription factors. Moreover, interactions between and among these factors are regulated in time and space. Owing to their complexity, systems biology approaches to the characterization of the cellular redox response system may provide insights into novel homeostatic mechanisms and methods of therapeutic reprogramming. Recent Advances: The emergence and development of systems biology has brought forth a set of innovative technologies that provide new avenues for studying redox metabolism. This article will review these systems biology approaches and their potential application to the study of redox metabolism in stress and disease states.

CRITICAL ISSUES

Clarifying the scope of biological intermediaries affected by dysregulated redox metabolism requires methods that are suitable for analyzing big datasets as classical methods that do not account for multiple interactions are unlikely to portray the totality of perturbed metabolic systems.

FUTURE DIRECTIONS

Given the diverse redox microenvironments within cells, it will be important to improve the spatial resolution of omic approaches. Futures studies on the integration of multiple systems-based methods and heterogeneous omics data for redox metabolism are required to accelerate the development of the field of redox systems biology. Antioxid. Redox Signal. 29, 953-972.

Degradation Network Reconstruction Guided by Metagenomic Data

Network reconstruction procedures based on meta-"omics" data are an invaluable tool for inferring total and active set of reactions mediated by different members in a microbial community. Within them, network-based methods for automatic analysis of catabolic capacities in metagenomes are currently limited. Here, we describe the complete workflow, scripts, and commands allowing the automatic reconstruction of biodegradation networks using as an input meta-sequences generated by direct DNA sequencing.

Metaproteomics and metabolomics analyses of chronically petroleum-polluted sites reveal the importance of general anaerobic processes uncoupled with degradation

Crude oil is one of the most important natural assets for humankind, yet it is a major environmental pollutant, notably in marine environments. One of the largest crude oil polluted areas in the word is the semi-enclosed Mediterranean Sea, in which the metabolic potential of indigenous microbial populations towards the large-scale chronic pollution is yet to be defined, particularly in anaerobic and micro-aerophilic sites. Here, we provide an insight into the microbial metabolism in sediments from three chronically polluted marine sites along the coastline of Italy: the Priolo oil terminal/refinery site (near Siracuse, Sicily), harbour of Messina (Sicily) and shipwreck of MT Haven (near Genoa). Using shotgun metaproteomics and community metabolomics approaches, the presence of 651 microbial proteins and 4776 metabolite mass features have been detected in these three environments, revealing a high metabolic heterogeneity between the investigated sites. The proteomes displayed the prevalence of anaerobic metabolisms that were not directly related with petroleum biodegradation, indicating that in the absence of oxygen, biodegradation is significantly suppressed. This suppression was also suggested by examining the metabolome patterns. The proteome analysis further highlighted the metabolic coupling between methylotrophs and sulphate reducers in oxygen-depleted petroleum-polluted sediments.

Context-specific metabolic network reconstruction of a naphthalene-degrading bacterial community guided by metaproteomic data

MOTIVATION

With the advent of meta-'omics' data, the use of metabolic networks for the functional analysis of microbial communities became possible. However, while network-based methods are widely developed for single organisms, their application to bacterial communities is currently limited.

RESULTS

Herein, we provide a novel, context-specific reconstruction procedure based on metaproteomic and taxonomic data. Without previous knowledge of a high-quality, genome-scale metabolic networks for each different member in a bacterial community, we propose a meta-network approach, where the expression levels and taxonomic assignments of proteins are used as the most relevant clues for inferring an active set of reactions. Our approach was applied to draft the context-specific metabolic networks of two different naphthalene-enriched communities derived from an anthropogenically influenced, polyaromatic hydrocarbon contaminated soil, with (CN2) or without (CN1) bio-stimulation. We were able to capture the overall functional differences between the two conditions at the metabolic level and predict an important activity for the fluorobenzoate degradation pathway in CN1 and for geraniol metabolism in CN2. Experimental validation was conducted, and good agreement with our computational predictions was observed. We also hypothesize different pathway organizations at the organismal level, which is relevant to disentangle the role of each member in the communities. The approach presented here can be easily transferred to the analysis of genomic, transcriptomic and metabolomic data.

Integrating gene and protein expression data with genome-scale metabolic networks to infer functional pathways

Background

The study of cellular metabolism in the context of high-throughput -omics data has allowed us to decipher novel mechanisms of importance in biotechnology and health. To continue with this progress, it is essential to efficiently integrate experimental data into metabolic modeling.

Results

We present here an in-silico framework to infer relevant metabolic pathways for a particular phenotype under study based on its gene/protein expression data. This framework is based on the Carbon Flux Path (CFP) approach, a mixed-integer linear program that expands classical path finding techniques by considering additional biophysical constraints. In particular, the objective function of the CFP approach is amended to account for gene/protein expression data and influence obtained paths. This approach is termed integrative Carbon Flux Path (iCFP). We show that gene/protein expression data also influences the stoichiometric balancing of CFPs, which provides a more accurate picture of active metabolic pathways. This is illustrated in both a theoretical and real scenario. Finally, we apply this approach to find novel pathways relevant in the regulation of acetate overflow metabolism in Escherichia coli. As a result, several targets which could be relevant for better understanding of the phenomenon leading to impaired acetate overflow are proposed.

Conclusions

A novel mathematical framework that determines functional pathways based on gene/protein expression data is presented and validated. We show that our approach is able to provide new insights into complex biological scenarios such as acetate overflow in Escherichia coli.

Refining carbon flux paths using atomic trace data

MOTIVATION

Pathway analysis tools are a powerful strategy to analyze 'omics' data in the field of systems biology. From a metabolic perspective, several pathway definitions can be found in the literature, each one appropriate for a particular study. Recently, a novel pathway concept termed carbon flux paths (CFPs) was introduced and benchmarked against existing approaches, showing a clear advantage for finding linear pathways from a given source to target metabolite. CFPs are simple paths in a metabolite-metabolite graph that satisfy typical constraints in stoichiometric models: mass balancing and thermodynamics (irreversibility). In addition, CFPs guarantee carbon exchange in each of their intermediate steps, but not between the source and the target metabolites and consequently false positive solutions may arise. These pathways often lack biological interest, particularly when studying biosynthetic or degradation routes of a metabolite. To overcome this issue, we amend the formulation in CFP, so as to account for atomic fate information. This approach is termed atomic CFP (aCFP).

RESULTS

By means of a side-by-side comparison in a medium scale metabolic network in Escherichia Coli, we show that aCFP provides more biologically relevant pathways than CFP, because canonical pathways are more easily recovered, which reflects the benefits of removing false positives. In addition, we demonstrate that aCFP can be successfully applied to genome-scale metabolic networks. As the quality of genome-scale atomic reconstruction is improved, methods such as the one presented here will undoubtedly be of value to interpret 'omics' data.