A network analysis of cofactor-protein interactions for analyzing associations between human nutrition and diseases

Human Nutrition Research in the Data Era: Results of 11 Reports on the Effects of a Multiple-Micronutrient-Intervention Study

Large datasets have been used in molecular and genetic research for decades, but only a few studies have included nutrition and lifestyle factors. Our team conducted an n-of-1 intervention with 12 vitamins and five minerals in 9- to 13-year-old Brazilian children and teens with poor healthy-eating indices. A unique feature of the experimental design was the inclusion of a replication arm. Twenty-six types of data were acquired including clinical measures, whole-genome mapping, whole-exome sequencing, and proteomic and a variety of metabolomic measurements over two years. A goal of this study was to use these diverse data sets to discover previously undetected physiological effects associated with a poor diet that include a more complete micronutrient composition. We summarize the key findings of 11 reports from this study that (i) found that LDL and total cholesterol and fasting glucose decreased in the population after the intervention but with inter-individual variation; (ii) associated a polygenic risk score that predicted baseline vitamin B12 levels; (iii) identified metabotypes linking diet intake, genetic makeup, and metabolic physiology; (iv) found multiple biomarkers for nutrient and food groups; and (v) discovered metabolites and proteins that are associated with DNA damage. This summary also highlights the limitations and lessons in analyzing diverse omic data.

Fetal spina bifida associates with dysregulation in nutrient-sensitive placental gene networks: Findings from a matched case-control study

To improve outcomes in fetuses with spina bifida (SB), better understanding is needed of the molecular drivers of SB and its comorbidities. Pregnant people carrying a fetus with isolated SB (cases; n = 12) or a fetus with no congenital anomalies (controls; n = 21) were recruited at Mount Sinai Hospital, Toronto, Ontario, Canada. Clinical data and placental samples were collected. Placental transcriptome was sequenced (Clariom D microarray) and a nutrient-focused gene expression analysis pipeline was applied to determine whether fetal SB associates with placental dysfunction. Of the 391 differentially expressed genes (DEGs) in cases, 11% (n = 42) had at least one nutrient cofactor, including B vitamins (n = 7 genes), iron/heme (n = 6), and zinc (n = 11). Cases had dysregulation in genes not previously known to associate with SB, and in placental genes that have known links to SB but have not been previously identified in the placenta. Cases also had downregulated nutrient transport and upregulated branching angiogenesis and immune/inflammatory processes. Five nutrient-dependent transcription regulators, collectively predicted to target 46% of DEGs in cases, were identified and were most commonly dependent on B vitamins (n = 3) and zinc (n = 2). Placental gene expression changes were most acute in cases with poor growth. Placentae from fetuses with SB have dysregulation in several gene networks, including those that are sensitive to multiple micronutrients beyond the well-known folic acid. An improved understanding of placental phenotype in fetuses with SB may help identify novel mechanisms associated with comorbidities in fetuses with SB, and reveal new targets to improve fetal outcomes in this population.

Integrated Multi-Omics Analysis and Validation in Yeast Model of Amyotrophic Lateral Sclerosis

Transcriptomics is a complex process that involves raw data extraction, normalization, differential gene expression, and analysis. The Gene Expression Omnibus (GEO) database at the National Center for Biotechnology Information (NCBI) is a repository of experimental datasets. Amyotrophic lateral sclerosis (ALS) datasets are deposited by various scientists and research investigators to expand the horizon of scientific knowledge. R-statistical tools are the most common ways for conducting these kinds of studies. The first step is the identification of appropriate datasets. Since the raw data is available in a variety of formats, a large array of software is used for extraction and analysis. Normalization is conducted for the datasets using NetworkAnalyst. Differential analysis is further conducted on the normalized data to identify significantly enriched genes. The significant genes are then grouped into pathways. The results were validated using yeast model of ALS in which the yeast is transformed with ALS plasmids encoding genes associated with ALS. The resulting GFP-tagged protein aggregates are imaged using fluorescence microscopy and subsequently validated using filter retardation assay and quantified using ImageJ software. Functional role of different genes is studied using metabolite treatment and knockout studies.

Integrated multi-omics analysis of Alzheimer's disease shows molecular signatures associated with disease progression and potential therapeutic targets

Alzheimer's disease (AD) is a progressive neurodegenerative disease characterized by the formation of amyloid plaques implicated in neuronal death. Genetics, age, and sex are the risk factors attributed to AD. Though omics studies have helped to identify pathways associated with AD, an integrated systems analysis with the available data could help to understand mechanisms, potential biomarkers, and therapeutic targets. Analysis of transcriptomic data sets from the GEO database, and proteomic and metabolomic data sets from literature was performed to identify deregulated pathways and commonality analysis identified overlapping pathways among the data sets. The deregulated pathways included those of neurotransmitter synapses, oxidative stress, inflammation, vitamins, complement, and coagulation pathways. Cell type analysis of GEO data sets showed microglia, endothelial, myeloid, and lymphoid cells are affected. Microglia are associated with inflammation and pruning of synapses with implications for memory and cognition. Analysis of the protein-cofactor network of B2, B6, and pantothenate shows metabolic pathways modulated by these vitamins which overlap with the deregulated pathways from the multi-omics analysis. Overall, the integrated analysis identified the molecular signature associated with AD. Treatment with anti-oxidants, B2, B6, and pantothenate in genetically susceptible individuals in the pre-symptomatic stage might help in better management of the disease.

Identification of novel nutrient-sensitive gene regulatory networks in amniocytes from fetuses with spina bifida

Neural tube defects (NTDs) remain among the most common congenital anomalies. Contributing risk factors include genetics and nutrient deficiencies, however, a comprehensive assessment of nutrient-gene interactions in NTDs is lacking. We applied a nutrient-focused gene expression analysis pipeline to identify nutrient-sensitive gene regulatory networks in amniocyte gene expression data (GSE4182) from fetuses with NTDs (cases; n = 3) and fetuses with no congenital anomalies (controls; n = 5). Differentially expressed genes (DEGs) were screened for having nutrient cofactors. Nutrient-dependent transcriptional regulators (TRs) that regulated DEGs, and nutrient-sensitive miRNAs with a previous link to NTDs, were identified. Of the 880 DEGs in cases, 10% had at least one nutrient cofactor. DEG regulatory network analysis revealed that 39% and 52% of DEGs in cases were regulated by 22 nutrient-sensitive miRNAs and 10 nutrient-dependent TRs, respectively. Zinc- and B vitamin-dependent gene regulatory networks (Zinc: 10 TRs targeting 50.6% of DEGs; B vitamins: 4 TRs targeting 37.7% of DEGs, 9 miRNAs targeting 17.6% of DEGs) were dysregulated in cases. We identified novel, nutrient-sensitive gene regulatory networks not previously linked to NTDs, which may indicate new targets to explore for NTD prevention or to optimise fetal development.

Vitamin B6, B12 and folate modulate deregulated pathways and protein aggregation in yeast model of Huntington disease

Huntington's disease (HD) is an incurable and progressive neurodegenerative disease affecting the basal ganglia of the brain. HD is caused due to expansion of the polyglutamine tract in the protein Huntingtin resulting in aggregates. The increased PolyQ length results in aggregation of protein Huntingtin leading to neuronal cell death. Vitamin B6, B12 and folate are deficient in many neurodegenerative diseases. We performed an integrated analysis of transcriptomic, metabolomic and cofactor-protein network of vitamin B6, B12 and folate was performed. Our results show considerable overlap of pathways modulated by Vitamin B6, B12 and folate with those obtained from transcriptomic and metabolomic data of HD patients and model systems. Further, in yeast model of HD we showed treatment of B6, B12 or folate either alone or in combination showed impaired aggregate formation. Transcriptomic analysis of yeast model treated with B6, B12 and folate showed upregulation of pathways like ubiquitin mediated proteolysis, autophagy, peroxisome, fatty acid, lipid and nitrogen metabolism. Metabolomic analysis of yeast model shows deregulation of pathways like aminoacyl-tRNA biosynthesis, metabolism of various amino acids, nitrogen metabolism and glutathione metabolism. Integrated transcriptomic and metabolomic analysis of yeast model showed concordance in the pathways obtained. Knockout of Peroxisomal (PXP1 and PEX7) and Autophagy (ATG5) genes in yeast increased aggregates which is mitigated by vitamin B6, B12 and folate treatment. Taken together our results show a role for Vitamin B6, B12 and folate mediated modulation of pathways important for preventing protein aggregation with potential implications for HD.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-023-03525-y.

Identifying the essential nodes in network pharmacology based on multilayer network combined with random walk algorithm

Compared with the general complex network, the multilayer network is more suitable for the description of reality. It can be used as a tool of network pharmacology to analyze the mechanism of drug action from an overall perspective. Combined with random walk algorithm, it measures the importance of nodes from the entire network rather than a single layer. Here a four-layer network was constructed based on the data about the action process of prescriptions, consisting of ingredients, target proteins, metabolic pathways and diseases. The random walk algorithm was used to calculate the betweenness centrality of the protein layer nodes to get the rank of their importance. According to above method, we screened out the top 10% proteins that play a key role in treatment. Prescriptions Xiaochaihu Decoction was taken as example to prove our method. The selected proteins were measured with the ones that have been validated to be associated with the treated diseases. The results showed that its accuracy was no less than the topology-based method of single-layer network. The applicability of our method was proved by another prescription Yupingfeng Decoction. Our study demonstrated that multilayer network combined with random walk algorithm was an effective method for pre-screening vital target proteins related to prescriptions.

Genetic assimilation and accommodation: Models and mechanisms

Genetic assimilation and genetic accommodation are mechanisms by which novel phenotypes are produced and become established in a population. Novel characters may be fixed and canalized so they are insensitive to environmental variation, or can be plastic and adaptively responsive to environmental variation. In this review we explore the various theories that have been proposed to explain the developmental origin and evolution of novel phenotypes and the mechanisms by which canalization and phenotypic plasticity evolve. These theories and models range from conceptual to mathematical and have taken different views of how genes and environment contribute to the development and evolution of the properties of phenotypes. We will argue that a deeper and more nuanced understanding of genetic accommodation requires a recognition that phenotypes are not static entities but are dynamic system properties with no fixed deterministic relationship between genotype and phenotype. We suggest a mechanistic systems-view of development that allows one to incorporate both genes and environment in a common model, and that enables both quantitative analysis and visualization of the evolution of canalization and phenotypic plasticity.

Systems analysis of avascular necrosis of femoral head using integrative data analysis and literature mining delineates pathways associated with disease

Avascular necrosis of femoral head (AVNFH) is a debilitating disease, which affects the middle aged population. Though the disease is managed using bisphosphonate, it eventually leads to total hip replacement due to collapse of femoral head. Studies regarding the association of single nucleotide polymorphisms with AVNFH, transcriptomics, proteomics, metabolomics, biophysical, ultrastructural and histopathology have been carried out. Functional validation of SNPs was carried out using literature. An integrated systems analysis using the available datasets might help to gain further insights into the disease process. We have carried out an analysis of transcriptomic data from GEO-database, SNPs associated with AVNFH, proteomic and metabolomic data collected from literature. Based on deficiency of vitamins in AVNFH, an enzyme-cofactor network was generated. The datasets are analyzed using ClueGO and the genes are binned into pathways. Metabolomic datasets are analyzed using MetaboAnalyst. Centrality analysis using CytoNCA on the data sets showed cystathionine beta synthase and methylmalonyl-CoA-mutase to be common to 3 out of 4 datasets. Further, the genes common to at least two data sets were analyzed using DisGeNET, which showed their involvement with various diseases, most of which were risk factors associated with AVNFH. Our analysis shows elevated homocysteine, hypoxia, coagulation, Osteoclast differentiation and endochondral ossification as the major pathways associated with disease which correlated with histopathology, IHC, MRI, Micro-Raman spectroscopy etc. The analysis shows AVNFH to be a multi-systemic disease and provides molecular signatures that are characteristic to the disease process.

Prolonging healthy aging: Longevity vitamins and proteins

It is proposed that proteins/enzymes be classified into two classes according to their essentiality for immediate survival/reproduction and their function in long-term health: that is, survival proteins versus longevity proteins. As proposed by the triage theory, a modest deficiency of one of the nutrients/cofactors triggers a built-in rationing mechanism that favors the proteins needed for immediate survival and reproduction (survival proteins) while sacrificing those needed to protect against future damage (longevity proteins). Impairment of the function of longevity proteins results in an insidious acceleration of the risk of diseases associated with aging. I also propose that nutrients required for the function of longevity proteins constitute a class of vitamins that are here named "longevity vitamins." I suggest that many such nutrients play a dual role for both survival and longevity. The evidence for classifying taurine as a conditional vitamin, and the following 10 compounds as putative longevity vitamins, is reviewed: the fungal antioxidant ergothioneine; the bacterial metabolites pyrroloquinoline quinone (PQQ) and queuine; and the plant antioxidant carotenoids lutein, zeaxanthin, lycopene, α- and β-carotene, β-cryptoxanthin, and the marine carotenoid astaxanthin. Because nutrient deficiencies are highly prevalent in the United States (and elsewhere), appropriate supplementation and/or an improved diet could reduce much of the consequent risk of chronic disease and premature aging.

Clinical and Vitamin Response to a Short-Term Multi-Micronutrient Intervention in Brazilian Children and Teens: From Population Data to Interindividual Responses

SCOPE

Micronutrients are in small amounts in foods, act in concert, and require variable amounts of time to see changes in health and risk for disease. These first principles are incorporated into an intervention study designed to develop new experimental strategies for setting target recommendations for food bioactives for populations and individuals.

METHODS AND RESULTS

A 6-week multivitamin/mineral intervention is conducted in 9-13 year olds. Participants (136) are (i) their own control (n-of-1); (ii) monitored for compliance; (iii) measured for 36 circulating vitamin forms, 30 clinical, anthropometric, and food intake parameters at baseline, post intervention, and following a 6-week washout; and (iv) had their ancestry accounted for as modifier of vitamin baseline or response. The same intervention is repeated the following year (135 participants). Most vitamins respond positively and many clinical parameters change in directions consistent with improved metabolic health to the intervention. Baseline levels of any metabolite predict its own response to the intervention. Elastic net penalized regression models are identified, and significantly predict response to intervention on the basis of multiple vitamin/clinical baseline measures.

CONCLUSIONS

The study design, computational methods, and results are a step toward developing recommendations for optimizing vitamin levels and health parameters for individuals.

A computationally driven analysis of the polyphenol-protein interactome

Polyphenol-rich foods are part of many nutritional interventions aimed at improving health and preventing cardiometabolic diseases (CMDs). Polyphenols have oxidative, inflammatory, and/or metabolic effects. Research into the chemistry and biology of polyphenol bioactives is prolific but knowledge of their molecular interactions with proteins is limited. We mined public data to (i) identify proteins that interact with or metabolize polyphenols, (ii) mapped these proteins to pathways and networks, and (iii) annotated functions enriched within the resulting polyphenol-protein interactome. A total of 1,395 polyphenols and their metabolites were retrieved (using Phenol-Explorer and Dictionary of Natural Products) of which 369 polyphenols interacted with 5,699 unique proteins in 11,987 interactions as annotated in STITCH, Pathway Commons, and BindingDB. Pathway enrichment analysis using the KEGG repository identified a broad coverage of significant pathways of low specificity to particular polyphenol (sub)classes. When compared to drugs or micronutrients, polyphenols have pleiotropic effects across many biological processes related to metabolism and CMDs. These systems-wide effects were also found in the protein interactome of the polyphenol-rich citrus fruits, used as a case study. In sum, these findings provide a knowledgebase for identifying polyphenol classes (and polyphenol-rich foods) that individually or in combination influence metabolism.

Ancestors' dietary patterns and environments could drive positive selection in genes involved in micronutrient metabolism-the case of cofactor transporters

Background

During evolution, humans colonized different ecological niches and adopted a variety of subsistence strategies that gave rise to diverse selective pressures acting across the genome. Environmentally induced selection of vitamin, mineral, or other cofactor transporters could influence micronutrient-requiring molecular reactions and contribute to inter-individual variability in response to foods and nutritional interventions.

Methods

A comprehensive list of genes coding for transporters of cofactors or their precursors was built using data mining procedures from the HGDP dataset and then explored to detect evidence of positive genetic selection. This dataset was chosen since it comprises several genetically diverse worldwide populations whom ancestries have evolved in different environments and thus lived following various nutritional habits and lifestyles.

Results

We identified 312 cofactor transporter (CT) genes involved in between-cell or sub-cellular compartment distribution of 28 cofactors derived from dietary intake. Twenty-four SNPs distributed across 14 CT genes separated populations into continental and intra-continental groups such as African hunter-gatherers and farmers, and between Native American sub-populations. Notably, four SNPs were located in SLC24A3 with one being a known eQTL of the NCKX3 protein.

Conclusions

These findings could support the importance of considering individual’s genetic makeup along with their metabolic profile when tailoring personalized dietary interventions for optimizing health.

Electronic supplementary material

The online version of this article (10.1186/s12263-017-0579-x) contains supplementary material, which is available to authorized users.

Systems biology of personalized nutrition

Personalized nutrition is fast becoming a reality due to a number of technological, scientific, and societal developments that complement and extend current public health nutrition recommendations. Personalized nutrition tailors dietary recommendations to specific biological requirements on the basis of a person's health status and goals. The biology underpinning these recommendations is complex, and thus any recommendations must account for multiple biological processes and subprocesses occurring in various tissues and must be formed with an appreciation for how these processes interact with dietary nutrients and environmental factors. Therefore, a systems biology-based approach that considers the most relevant interacting biological mechanisms is necessary to formulate the best recommendations to help people meet their wellness goals. Here, the concept of "systems flexibility" is introduced to personalized nutrition biology. Systems flexibility allows the real-time evaluation of metabolism and other processes that maintain homeostasis following an environmental challenge, thereby enabling the formulation of personalized recommendations. Examples in the area of macro- and micronutrients are reviewed. Genetic variations and performance goals are integrated into this systems approach to provide a strategy for a balanced evaluation and an introduction to personalized nutrition. Finally, modeling approaches that combine personalized diagnosis and nutritional intervention into practice are reviewed.

Propelling the paradigm shift from reductionism to systems nutrition

The complex physiology of living organisms represents a challenge for mechanistic understanding of the action of dietary bioactives in the human body and of their possible role in health and disease. Animal, cell, and microbial models have been extensively used to address questions that could not be pursued experimentally in humans, posing an additional level of complexity in translation of the results to healthy and diseased metabolism. The past few decades have witnessed a surge in development of increasingly sensitive molecular techniques and bioinformatic tools for storing, managing, and analyzing increasingly large datasets. Application of such powerful means to molecular nutrition research led to a major leap in study designs and experimental approaches yielding experimental data connecting dietary components to human health. Scientific journals bear major responsibilities in the advancement of science. As primary actors of dissemination to the scientific community, journals can impose rigid criteria for publishing only sound, reliable, and reproducible data. Journal policies are meant to guide potential authors to adopt the most updated standardization guidelines and shared best practices. Such policies evolve in parallel with the evolution of novel approaches and emerging challenges and therefore require constant updating. We highlight in this manuscript the major scientific issues that led to formulating new, updated journal policies for Genes & Nutrition, a journal which targets the growing field of nutritional systems biology interfacing personalized nutrition and preventive medicine, with the ultimate goal of promoting health and preventing or treating disease. We focus here on relevant issues requiring standardization in nutrition research. We also introduce new sections on human genetic variation and nutritional bioinformatics which follow the evolution of nutritional science into the twenty-first century.

SSR marker-assisted screening of commercial tomato genotypes under salt stress

Salt stress was applied to tomato commercial genotypes to study adverse effects on their phenotypic traits. Three were saline tolerant (San Miguel, Romelia and Llanero), two were mildly tolerant (Perfect peel HF1 and Heinz 1350) whereas the remaining were sensitive. Genotyping cultivars using 19 polymorphic SSRs out of 25 tested produced a total of 70 alleles with an average of 3.68 alleles per locus and PIC values ranging from 0.22 (SSR 26, 92, 66 and TG35) to 0.82 (SSR 356). Principal component analysis (PCA) showed two contrasting panels discriminating tolerant and sensitive groups and one panel with scattered genotypes. STRUCTURE analysis clustered genotypes within three groups in accordance with their salt stress behavior. The success of tomato salt-tolerance breeding programs can be enhanced through molecular characterization of diversity within commercial cultivars that adapt differently to stress conditions. To this end, we combined phenotypes and SSR marker-genotypes to seek sources of salt tolerance that might be tomato species-specific. We integrated and represented genotype-phenotype associations from multiple loci into a multi-layer network representation. It is a systemic view linking discriminating genotypes to salt stress phenotypes, which may guide strategies for the introgression of valuable traits in target tomato varieties to overcome salinity.