Simulation and Reconstruction of Metabolite-Metabolite Association Networks Using a Metabolic Dynamic Model and Correlation Based Algorithms

Enhancing Gene Co-Expression Network Inference for the Malaria Parasite Plasmodium falciparum

BACKGROUND

Malaria results in more than 550,000 deaths each year due to drug resistance in the most lethal Plasmodium (P.) species P. falciparum. A full P. falciparum genome was published in 2002, yet 44.6% of its genes have unknown functions. Improving the functional annotation of genes is important for identifying drug targets and understanding the evolution of drug resistance.

RESULTS

Genes function by interacting with one another. So, analyzing gene co-expression networks can enhance functional annotations and prioritize genes for wet lab validation. Earlier efforts to build gene co-expression networks in P. falciparum have been limited to a single network inference method or gaining biological understanding for only a single gene and its interacting partners. Here, we explore multiple inference methods and aim to systematically predict functional annotations for all P. falciparum genes. We evaluate each inferred network based on how well it predicts existing gene-Gene Ontology (GO) term annotations using network clustering and leave-one-out crossvalidation. We assess overlaps of the different networks' edges (gene co-expression relationships), as well as predicted functional knowledge. The networks' edges are overall complementary: 47-85% of all edges are unique to each network. In terms of the accuracy of predicting gene functional annotations, all networks yielded relatively high precision (as high as 87% for the network inferred using mutual information), but the highest recall reached was below 15%. All networks having low recall means that none of them capture a large amount of all existing gene-GO term annotations. In fact, their annotation predictions are highly complementary, with the largest pairwise overlap of only 27%. We provide ranked lists of inferred gene-gene interactions and predicted gene-GO term annotations for future use and wet lab validation by the malaria community.

CONCLUSIONS

The different networks seem to capture different aspects of the P. falciparum biology in terms of both inferred interactions and predicted gene functional annotations. Thus, relying on a single network inference method should be avoided when possible.

SUPPLEMENTARY DATA

Attached.

Discrimination of Lipogenic or Glucogenic Diet Effects in Early-Lactation Dairy Cows Using Plasma Metabolite Abundances and Ratios in Combination with Machine Learning

During early lactation, dairy cows have a negative energy balance since their energy demands exceed their energy intake: in this study, we aimed to investigate the association between diet and plasma metabolomics profiles and how these relate to energy unbalance of course in the early-lactation stage. Holstein-Friesian cows were randomly assigned to a glucogenic (n = 15) or lipogenic (n = 15) diet in early lactation. Blood was collected in week 2 and week 4 after calving. Plasma metabolite profiles were detected using liquid chromatography-mass spectrometry (LC-MS), and a total of 39 metabolites were identified. Two plasma metabolomic profiles were available every week for each cow. Metabolite abundance and metabolite ratios were used for the analysis using the XGboost algorithm to discriminate between diet treatment and lactation week. Using metabolite ratios resulted in better discrimination performance compared with the metabolite abundances in assigning cows to a lipogenic diet or a glucogenic diet. The quality of the discrimination of performance of lipogenic diet and glucogenic diet effects improved from 0.606 to 0.753 and from 0.696 to 0.842 in week 2 and week 4 (as measured by area under the curve, AUC), when the metabolite abundance ratios were used instead of abundances. The top discriminating ratios for diet were the ratio of arginine to tyrosine and the ratio of aspartic acid to valine in week 2 and week 4, respectively. For cows fed the lipogenic diet, choline and the ratio of creatinine to tryptophan were top features to discriminate cows in week 2 vs. week 4. For cows fed the glucogenic diet, methionine and the ratio of 4-hydroxyproline to choline were top features to discriminate dietary effects in week 2 or week 4. This study shows the added value of using metabolite abundance ratios to discriminate between lipogenic and glucogenic diet and lactation weeks in early-lactation cows when using metabolomics data. The application of this research will help to accurately regulate the nutrition of lactating dairy cows and promote sustainable agricultural development.

Metabolic Connectome and Its Role in the Prediction, Diagnosis, and Treatment of Complex Diseases

The interconnectivity of advanced biological systems is essential for their proper functioning. In modern connectomics, biological entities such as proteins, genes, RNA, DNA, and metabolites are often represented as nodes, while the physical, biochemical, or functional interactions between them are represented as edges. Among these entities, metabolites are particularly significant as they exhibit a closer relationship to an organism's phenotype compared to genes or proteins. Moreover, the metabolome has the ability to amplify small proteomic and transcriptomic changes, even those from minor genomic changes. Metabolic networks, which consist of complex systems comprising hundreds of metabolites and their interactions, play a critical role in biological research by mediating energy conversion and chemical reactions within cells. This review provides an introduction to common metabolic network models and their construction methods. It also explores the diverse applications of metabolic networks in elucidating disease mechanisms, predicting and diagnosing diseases, and facilitating drug development. Additionally, it discusses potential future directions for research in metabolic networks. Ultimately, this review serves as a valuable reference for researchers interested in metabolic network modeling, analysis, and their applications.

Phomactinine, the First Nitrogen-Bearing Phomactin, Produced by Biatriospora sp. CBMAI 1333

Metabolomics analyses and improvement of growth conditions were applied toward diversification of phomactin terpenoids by the fungus Biatriospora sp. CBMAI 1333. Visualization of molecular networking results on Gephi assisted the observation of phomactin diversification and guided the isolation of new phomactin variants by applying a modified version of chemometrics based on a fractional factorial design. Consequentially, the first nitrogen-bearing phomactin, phomactinine (1), with a new rearranged carbon skeleton, was isolated and identified. The strategy combining metabolomics and chemometrics can be extended to include bioassay potency, structure novelty, and metabolic diversification connected or not to genomic analyses.

Systemic immune activation profiles in streptococcal necrotizing soft tissue infections: A prospective multicenter study

OBJECTIVE

Early stages with streptococcal necrotizing soft tissue infections (NSTIs) are often difficult to discern from cellulitis. Increased insight into inflammatory responses in streptococcal disease may guide correct interventions and discovery of novel diagnostic targets.

METHODS

Plasma levels of 37 mediators, leucocytes and CRP from 102 patients with β-hemolytic streptococcal NSTI derived from a prospective Scandinavian multicentre study were compared to those of 23 cases of streptococcal cellulitis. Hierarchical cluster analyses were also performed.

RESULTS

Differences in mediator levels between NSTI and cellulitis cases were revealed, in particular for IL-1β, TNFα and CXCL8 (AUC >0.90). Across streptococcal NSTI etiologies, eight biomarkers separated cases with septic shock from those without, and four mediators predicted a severe outcome.

CONCLUSION

Several inflammatory mediators and wider profiles were identified as potential biomarkers of NSTI. Associations of biomarker levels to type of infection and outcomes may be utilized to improve patient care and outcomes.

Exploration of Blood Metabolite Signatures of Colorectal Cancer and Polyposis through Integrated Statistical and Network Analysis

Colorectal cancer (CRC), one of the most prevalent and deadly cancers worldwide, generally evolves from adenomatous polyps. The understanding of the molecular mechanisms underlying this pathological evolution is crucial for diagnostic and prognostic purposes. Integrative systems biology approaches offer an optimal point of view to analyze CRC and patients with polyposis. The present study analyzed the association networks constructed from a publicly available array of 113 serum metabolites measured on a cohort of 234 subjects from three groups (66 CRC patients, 76 patients with polyposis, and 92 healthy controls), which concentrations were obtained via targeted liquid chromatography-tandem mass spectrometry. In terms of architecture, topology, and connectivity, the metabolite-metabolite association network of CRC patients appears to be completely different with respect to patients with polyposis and healthy controls. The most relevant nodes in the CRC network are those related to energy metabolism. Interestingly, phenylalanine, tyrosine, and tryptophan metabolism are found to be involved in both CRC and polyposis. Our results demonstrate that the characterization of metabolite-metabolite association networks is a promising and powerful tool to investigate molecular aspects of CRC.

Analysis of host-pathogen gene association networks reveals patient-specific response to streptococcal and polymicrobial necrotising soft tissue infections

BACKGROUND

Necrotising soft tissue infections (NSTIs) are rapidly progressing bacterial infections usually caused by either several pathogens in unison (polymicrobial infections) or Streptococcus pyogenes (mono-microbial infection). These infections are rare and are associated with high mortality rates. However, the underlying pathogenic mechanisms in this heterogeneous group remain elusive.

METHODS

In this study, we built interactomes at both the population and individual levels consisting of host-pathogen interactions inferred from dual RNA-Seq gene transcriptomic profiles of the biopsies from NSTI patients.

RESULTS

NSTI type-specific responses in the host were uncovered. The S. pyogenes mono-microbial subnetwork was enriched with host genes annotated with involved in cytokine production and regulation of response to stress. The polymicrobial network consisted of several significant associations between different species (S. pyogenes, Porphyromonas asaccharolytica and Escherichia coli) and host genes. The host genes associated with S. pyogenes in this subnetwork were characterised by cellular response to cytokines. We further found several virulence factors including hyaluronan synthase, Sic1, Isp, SagF, SagG, ScfAB-operon, Fba and genes upstream and downstream of EndoS along with bacterial housekeeping genes interacting with the human stress and immune response in various subnetworks between host and pathogen.

CONCLUSIONS

At the population level, we found aetiology-dependent responses showing the potential modes of entry and immune evasion strategies employed by S. pyogenes, congruent with general cellular processes such as differentiation and proliferation. After stratifying the patients based on the subject-specific networks to study the patient-specific response, we observed different patient groups with different collagens, cytoskeleton and actin monomers in association with virulence factors, immunogenic proteins and housekeeping genes which we utilised to postulate differing modes of entry and immune evasion for different bacteria in relationship to the patients' phenotype.

Altered small-world property of a dynamic metabolic network in murine left hippocampus after exposure to acute stress

The acute stress response is a natural and fundamental reaction that balances the physiological conditions of the brain. To maintain homeostasis in the brain, the response is based on changes over time in hormones and neurotransmitters, which are related to resilience and can adapt successfully to acute stress. This increases the need for dynamic analysis over time, and new approaches to examine the relationship between metabolites have emerged. This study investigates whether the constructed metabolic network is a realistic or a random network and is affected by acute stress. While the metabolic network in the control group met the criteria for small-worldness at all time points, the metabolic network in the stress group did not at some time points, and the small-worldness had resilience after the fifth time point. The backbone metabolic network only met the criteria for small-worldness in the control group. Additionally, creatine had lower local efficiency in the stress group than the control group, and for the backbone metabolic network, creatine and glutamate were lower and higher in the stress group than the control group, respectively. These findings provide evidence of metabolic imbalance that may be a pre-stage of alterations to brain structure due to acute stress.

Mycorrhiza-Induced Alterations in Metabolome of Medicago lupulina Leaves during Symbiosis Development

The present study is aimed at disclosing metabolic profile alterations in the leaves of the Medicago lupulina MlS-1 line that result from high-efficiency arbuscular mycorrhiza (AM) symbiosis formed with Rhizophagus irregularis under condition of a low phosphorus level in the substrate. A highly effective AM symbiosis was established in the period from the stooling to the shoot branching initiation stage (the efficiency in stem height exceeded 200%). Mycorrhization led to a more intensive accumulation of phosphates (glycerophosphoglycerol and inorganic phosphate) in M. lupulina leaves. Metabolic spectra were detected with GS-MS analysis. The application of complex mathematical analyses made it possible to identify the clustering of various groups of 320 metabolites and thus demonstrate the central importance of the carbohydrate and carboxylate-amino acid clusters. The results obtained indicate a delay in the metabolic development of mycorrhized plants. Thus, AM not only accelerates the transition between plant developmental stages but delays biochemical “maturation” mainly in the form of a lag of sugar accumulation in comparison with non-mycorrhized plants. Several methods of statistical modeling proved that, at least with respect to determining the metabolic status of host-plant leaves, stages of phenological development have priority over calendar age.

Discriminatory plasma biomarkers predict specific clinical phenotypes of necrotizing soft-tissue infections

BACKGROUNDNecrotizing soft-tissue infections (NSTIs) are rapidly progressing infections frequently complicated by septic shock and associated with high mortality. Early diagnosis is critical for patient outcome, but challenging due to vague initial symptoms. Here, we identified predictive biomarkers for NSTI clinical phenotypes and outcomes using a prospective multicenter NSTI patient cohort.METHODSLuminex multiplex assays were used to assess 36 soluble factors in plasma from NSTI patients with positive microbiological cultures (n = 251 and n = 60 in the discovery and validation cohorts, respectively). Control groups for comparative analyses included surgical controls (n = 20), non-NSTI controls (i.e., suspected NSTI with no necrosis detected upon exploratory surgery, n = 20), and sepsis patients (n = 24).RESULTSThrombomodulin was identified as a unique biomarker for detection of NSTI (AUC, 0.95). A distinct profile discriminating mono- (type II) versus polymicrobial (type I) NSTI types was identified based on differential expression of IL-2, IL-10, IL-22, CXCL10, Fas-ligand, and MMP9 (AUC >0.7). While each NSTI type displayed a distinct array of biomarkers predicting septic shock, granulocyte CSF (G-CSF), S100A8, and IL-6 were shared by both types (AUC >0.78). Finally, differential connectivity analysis revealed distinctive networks associated with specific clinical phenotypes.CONCLUSIONSThis study identifies predictive biomarkers for NSTI clinical phenotypes of potential value for diagnostic, prognostic, and therapeutic approaches in NSTIs.TRIAL REGISTRATIONClinicalTrials.gov NCT01790698.FUNDINGCenter for Innovative Medicine (CIMED); Region Stockholm; Swedish Research Council; European Union; Vinnova; Innovation Fund Denmark; Research Council of Norway; Netherlands Organisation for Health Research and Development; DLR Federal Ministry of Education and Research; and Swedish Children's Cancer Foundation.

Systems biology for crop improvement

In recent years, generation of large-scale data from genome, transcriptome, proteome, metabolome, epigenome, and others, has become routine in several plant species. Most of these datasets in different crop species, however, were studied independently and as a result, full insight could not be gained on the molecular basis of complex traits and biological networks. A systems biology approach involving integration of multiple omics data, modeling, and prediction of the cellular functions is required to understand the flow of biological information that underlies complex traits. In this context, systems biology with multiomics data integration is crucial and allows a holistic understanding of the dynamic system with the different levels of biological organization interacting with external environment for a phenotypic expression. Here, we present recent progress made in the area of various omics studies-integrative and systems biology approaches with a special focus on application to crop improvement. We have also discussed the challenges and opportunities in multiomics data integration, modeling, and understanding of the biology of complex traits underpinning yield and stress tolerance in major cereals and legumes.

Exploration of Blood Lipoprotein and Lipid Fraction Profiles in Healthy Subjects through Integrated Univariate, Multivariate, and Network Analysis Reveals Association of Lipase Activity and Cholesterol Esterification with Sex and Age

In this study, we investigated blood lipoprotein and lipid fraction profiles, quantified using nuclear magnetic resonance, in a cohort of 844 healthy blood donors, integrating standard univariate and multivariate analysis with predictive modeling and network analysis. We observed a strong association of lipoprotein and lipid main fraction profiles with sex and age. Our results suggest an age-dependent remodulation of lipase lipoprotein activity in men and a change in the mechanisms controlling the ratio between esterified and non-esterified cholesterol in both men and women.

Reconstruction and analysis of correlation networks based on GC-MS metabolomics data for hypercholesterolemia

BACKGROUND AND AIMS

Hypercholesterolemia is characterized by the elevation of plasma total cholesterol level, especially low-density lipoprotein (LDL) cholesterol. This disease is usually caused by a mutation in genes such as LDL receptor, apolipoprotein B, or proprotein convertase subtilisin/kexin type 9. However, a considerable number of patients with hypercholesterolemia do not have any mutation in these candidate genes. In this study, we examined the difference in the metabolic level between patients with hypercholesterolemia and healthy subjects, and screened the potential biomarkers for this disease.

METHODS

Analysis of plasma metabolomics in hypercholesterolemia patients and healthy controls was performed by gas chromatography-mass spectrometry and metabolic correlation networks were constructed using Gephi-0.9.2.

RESULTS

First, metabolic profile analysis confirmed the distinct metabolic footprints between the patients and the healthy ones. The potential biomarkers screened by orthogonal partial least-squares discrimination analysis included l-lactic acid, cholesterol, phosphoric acid, d-glucose, urea, and d-allose (Variable importance in the projection > 1). Second, arginine and methionine metabolism were significantly perturbed in hypercholesterolemia patients. Finally, we identified that l-lactic acid, l-lysine, l-glutamine, and l-cysteine had high scores of centrality parameters in the metabolic correlation network.

CONCLUSION

Plasma l-lactic acid could be used as a sensitive biomarker for hypercholesterolemia. In addition, arginine biosynthesis and cysteine and methionine metabolism were profoundly altered in patients with hypercholesterolemia.

Machine Learning Applications for Mass Spectrometry-Based Metabolomics

The metabolome of an organism depends on environmental factors and intracellular regulation and provides information about the physiological conditions. Metabolomics helps to understand disease progression in clinical settings or estimate metabolite overproduction for metabolic engineering. The most popular analytical metabolomics platform is mass spectrometry (MS). However, MS metabolome data analysis is complicated, since metabolites interact nonlinearly, and the data structures themselves are complex. Machine learning methods have become immensely popular for statistical analysis due to the inherent nonlinear data representation and the ability to process large and heterogeneous data rapidly. In this review, we address recent developments in using machine learning for processing MS spectra and show how machine learning generates new biological insights. In particular, supervised machine learning has great potential in metabolomics research because of the ability to supply quantitative predictions. We review here commonly used tools, such as random forest, support vector machines, artificial neural networks, and genetic algorithms. During processing steps, the supervised machine learning methods help peak picking, normalization, and missing data imputation. For knowledge-driven analysis, machine learning contributes to biomarker detection, classification and regression, biochemical pathway identification, and carbon flux determination. Of important relevance is the combination of different omics data to identify the contributions of the various regulatory levels. Our overview of the recent publications also highlights that data quality determines analysis quality, but also adds to the challenge of choosing the right model for the data. Machine learning methods applied to MS-based metabolomics ease data analysis and can support clinical decisions, guide metabolic engineering, and stimulate fundamental biological discoveries.

Network-based strategies in metabolomics data analysis and interpretation: from molecular networking to biological interpretation

INTRODUCTION

Metabolomics has become a crucial part of systems biology; however, data analysis is still often undertaken in a reductionist way focusing on changes in individual metabolites. Whilst such approaches indeed provide relevant insights into the metabolic phenotype of an organism, the intricate nature of metabolic relationships may be better explored when considering the whole system.

AREAS COVERED

This review highlights multiple network strategies that can be applied for metabolomics data analysis from different perspectives including: association networks based on quantitative information, mass spectra similarity networks to assist metabolite annotation and biochemical networks for systematic data interpretation. We also highlight some relevant insights into metabolic organization obtained through the exploration of such approaches.

EXPERT OPINION

Network based analysis is an established method that allows the identification of non-intuitive metabolic relationships as well as the identification of unknown compounds in mass spectrometry. Additionally, the representation of data from metabolomics within the context of metabolic networks is intuitive and allows for the use of statistical analysis that can better summarize relevant metabolic changes from a systematic perspective.

On the Use of Correlation and MI as a Measure of Metabolite-Metabolite Association for Network Differential Connectivity Analysis

Metabolite differential connectivity analysis has been successful in investigating potential molecular mechanisms underlying different conditions in biological systems. Correlation and Mutual Information (MI) are two of the most common measures to quantify association and for building metabolite-metabolite association networks and to calculate differential connectivity. In this study, we investigated the performance of correlation and MI to identify significantly differentially connected metabolites. These association measures were compared on (i) 23 publicly available metabolomic data sets and 7 data sets from other fields, (ii) simulated data with known correlation structures, and (iii) data generated using a dynamic metabolic model to simulate real-life observed metabolite concentration profiles. In all cases, we found more differentially connected metabolites when using correlation indices as a measure for association than MI. We also observed that different MI estimation algorithms resulted in difference in performance when applied to data generated using a dynamic model. We concluded that there is no significant benefit in using MI as a replacement for standard Pearson's or Spearman's correlation when the application is to quantify and detect differentially connected metabolites.

Integrated Univariate, Multivariate, and Correlation-Based Network Analyses Reveal Metabolite-Specific Effects on Bacterial Growth and Biofilm Formation in Necrotizing Soft Tissue Infections

Necrotizing soft-tissue infections (NSTIs) have multiple causes, risk factors, anatomical locations, and pathogenic mechanisms. In patients with NSTI, circulating metabolites may serve as a substrate having impact on bacterial adaptation at the site of infection. Metabolic signatures associated with NSTI may reveal the potential to be useful as diagnostic and prognostic markers and novel targets for therapy. This study used untargeted metabolomics analyses of plasma from NSTI patients (n = 34) and healthy (noninfected) controls (n = 24) to identify the metabolic signatures and connectivity patterns among metabolites associated with NSTI. Metabolite-metabolite association networks were employed to compare the metabolic profiles of NSTI patients and noninfected surgical controls. Out of 97 metabolites detected, the abundance of 33 was significantly altered in NSTI patients. Analysis of metabolite-metabolite association networks showed a more densely connected network: specifically, 20 metabolites differentially connected between NSTI and controls. A selected set of significantly altered metabolites was tested in vitro to investigate potential influence on NSTI group A streptococcal strain growth and biofilm formation. Using chemically defined media supplemented with the selected metabolites, ornithine, ribose, urea, and glucuronic acid, revealed metabolite-specific effects on both bacterial growth and biofilm formation. This study identifies for the first time an NSTI-specific metabolic signature with implications for optimized diagnostics and therapies.