Multiomics and digital monitoring during lifestyle changes reveal independent dimensions of human biology and health

Marabita, Francesco; Tod, James; Karhu, Anu; Virtanen, Heidi; Kettunen, K; Stenlund, Hans; Boulund, Fredrik; Hellström, Cecilia; Neiman, Maja; Mills, Robert; Perheentupa, Teemu; Laivuori, Hannele; Helkkula, Pyry; Byrne, Myles; Jokinen, Ilkka; Honko, Harri; Kallonen, Antti; Ermes, Miikka; Similä, Heidi; Lindholm, Mikko; Widén, Elisabeth; Ripatti, Samuli; Perälä-Heape, Maritta; Engstrand, Lars; Nilsson, Peter; Moritz, Thomas; Miettinen, Timo; Sallinen, Riitta; Kallioniemi, Olli

doi:10.1016/j.cels.2021.11.001

Cited by 13 publications

(4 citation statements)

References 60 publications

(62 reference statements)

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“…Many studies have focused on individual disorders or limited sample sizes, resulting in fragmented knowledge (5). Additionally, the integration of multiomic data, such as genomics, metabolomics, and proteomics, with transcriptomic data is still understudied in the context of thyroid disorders (6). Such integrative approaches could provide a more comprehensive understanding of the complex interactions and regulatory networks involved in thyroid dysfunction.…”

Section: Introductionmentioning

confidence: 99%

Utilizing Bioinformatics Approaches to Conduct a Comparative Analysis of the Thyroid Transcriptome in Thyroid Disorders

De Oliveira Andrade,

Matos de Oliveira,

Vinhaes Bittencourt

et al. 2024

Rev.ACE

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Introduction: This study aims to identify common gene expression patterns and dysregulated pathways in various thyroid disorders by leveraging publicly available transcriptomic datasets. The integration of other omics data, when possible, will allow us to uncover potential molecular drivers and biomarkers associated with specific thyroid dysfunctions. However, there are still gaps in the analysis of the transcriptomes of the various thyroid disorders. Objective: To conduct a comparative analysis of the thyroid transcriptome in thyroid disorders using bioinformatics approaches. Methods: We retrieved publicly available gene expression datasets related to the thyroid from the European Nucleotide Archive. Data preprocessing involved conducting quality control, trimming reads, and aligning them to a reference genome. Differential expression analysis was performed using bioinformatics packages, and finally, a functional enrichment analysis was conducted to gain insights into the biological processes. Network analysis was conducted to explore interactions and regulatory relationships among differentially expressed genes (DEGs). Results: Our analysis included a total of 18 gene expression datasets, of which 15 were selected based on inclusion criteria and quality assessment. Numerous genes exhibiting differential expression (P < 0.01) were discerned, and their significance was systematically ranked. Functional enrichment analysis revealed numerous biological processes associated with the differentially expressed genes, providing insights into the molecular mechanisms of thyroid disorders. Network analysis using Cytoscape software revealed potential interactions among differentially expressed genes and identified key hub genes and potential therapeutic targets. Conclusion: This study demonstrates an accessible methodology for conducting a comparative analysis of the thyroid transcriptome in different disorders without the need for thyroid tissue samples. The integration of bioinformatics approaches provides a comprehensive understanding of the molecular mechanisms underlying thyroid diseases.

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Section: Introductionmentioning

confidence: 99%

Utilizing Bioinformatics Approaches to Conduct a Comparative Analysis of the Thyroid Transcriptome in Thyroid Disorders

De Oliveira Andrade,

Matos de Oliveira,

Vinhaes Bittencourt

et al. 2024

Rev.ACE

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“…Recent advances in high-throughput sequencing technologies and computational methods have enabled the generation of large and complex -omics datasets, providing an unprecedented opportunity to integrate simultaneous information from multiple molecular levels to investigate the complexity of biological systems ( T et al, 2019 ; Park et al, 2022 ; Argelaguet et al, 2020 ; Tanabe et al, 2021 ; Clark et al, 2021 ). The integration of various -omics data, including genomics, transcriptomics, proteomics, metabolomics, and epigenomics, can help to understand the intricate interplay between different biological molecules and pathways, enabling the identification of key regulators and mechanisms of disease ( Hoadley et al, 2014 ; Meng et al, 2016 ; Marabita et al, 2022 ). In metabolomics, a multiplatform strategy combines many analytical tools to study the entire metabolic phenotype.…”

Section: Introductionmentioning

confidence: 99%

“…In metabolomics, a multiplatform strategy combines many analytical tools to study the entire metabolic phenotype. Combining data from multiple sources could result in a better comprehension of the underlying biological mechanisms driving complex diseases including cancer, obesity, and cardiovascular disease ( Hoadley et al, 2014 ; Meng et al, 2016 ; Marabita et al, 2022 ; Park et al, 2022 ). Although much effort has been made in recent years to integrate information from different -omics technologies into a single analysis, it is still usual to use a multiplatform strategy individually ( T et al, 2019 ; Argelaguet et al, 2020 ; Tanabe et al, 2021 ; Zhang et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Identifying subgroups of childhood obesity by using multiplatform metabotyping

Chamoso-Sanchez,

Rabadán Pérez,

Argente

et al. 2023

Front. Mol. Biosci.

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Introduction: Obesity results from an interplay between genetic predisposition and environmental factors such as diet, physical activity, culture, and socioeconomic status. Personalized treatments for obesity would be optimal, thus necessitating the identification of individual characteristics to improve the effectiveness of therapies. For example, genetic impairment of the leptin-melanocortin pathway can result in rare cases of severe early-onset obesity. Metabolomics has the potential to distinguish between a healthy and obese status; however, differentiating subsets of individuals within the obesity spectrum remains challenging. Factor analysis can integrate patient features from diverse sources, allowing an accurate subclassification of individuals.Methods: This study presents a workflow to identify metabotypes, particularly when routine clinical studies fail in patient categorization. 110 children with obesity (BMI > +2 SDS) genotyped for nine genes involved in the leptin-melanocortin pathway (CPE, MC3R, MC4R, MRAP2, NCOA1, PCSK1, POMC, SH2B1, and SIM1) and two glutamate receptor genes (GRM7 and GRIK1) were studied; 55 harboring heterozygous rare sequence variants and 55 with no variants. Anthropometric and routine clinical laboratory data were collected, and serum samples processed for untargeted metabolomic analysis using GC-q-MS and CE-TOF-MS and reversed-phase U(H)PLC-QTOF-MS/MS in positive and negative ionization modes. Following signal processing and multialignment, multivariate and univariate statistical analyses were applied to evaluate the genetic trait association with metabolomics data and clinical and routine laboratory features.Results and Discussion: Neither the presence of a heterozygous rare sequence variant nor clinical/routine laboratory features determined subgroups in the metabolomics data. To identify metabolomic subtypes, we applied Factor Analysis, by constructing a composite matrix from the five analytical platforms. Six factors were discovered and three different metabotypes. Subtle but neat differences in the circulating lipids, as well as in insulin sensitivity could be established, which opens the possibility to personalize the treatment according to the patients categorization into such obesity subtypes. Metabotyping in clinical contexts poses challenges due to the influence of various uncontrolled variables on metabolic phenotypes. However, this strategy reveals the potential to identify subsets of patients with similar clinical diagnoses but different metabolic conditions. This approach underscores the broader applicability of Factor Analysis in metabotyping across diverse clinical scenarios.

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“…An analysis of the latest work in the literature has shown that modifiable factors positively affect our well-being and health. The best effect can be achieved by combining positive actions in the field of diet, supplementation, moderate but systematic physical activity, and adding the right amount of sleep per day [7,8].…”

Section: Introductionmentioning

confidence: 99%

The Influence of FTO, FABP2, LEP, LEPR, and MC4R Genes on Obesity Parameters in Physically Active Caucasian Men

Maculewicz

Leońska-Duniec

Mastalerz

et al. 2022

IJERPH

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Obesity is a complex multifactorial abnormality that has a well-confirmed genetic basis. However, the problem still lies in identifying the polymorphisms linked to body mass and composition. Therefore, this study aimed to analyze associations between FTO (rs9939609), FABP2 (rs1799883), and LEP (rs2167270), LEPR (rs1137101), and MC4R (rs17782313) polymorphisms and obesity-related parameters. Unrelated Caucasian males (n = 165) were recruited. All participants had similar physical activity levels. The participants were divided into two groups depending on their body mass index (BMI) and fat mass index (FMI). All samples were genotyped using real-time polymerase chain reaction (real-time PCR). When tested individually, only one statistically significant result was found. The FTO A/T polymorphism was significantly associated with FMI (p = 0.01). The chance of having increased FMI was >2-fold higher for the FTO A allele carriers (p < 0.01). Gene–gene interaction analyses showed the additional influence of all investigated genes on BMI and FMI. In summary, it was demonstrated that harboring the FTO A allele might be a risk factor for elevated fat mass. Additionally, this study confirmed that all five polymorphisms are involved in the development of common obesity in the studied population and the genetic risk of obesity is linked to the accumulation of numerous variants.

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Multiomics and digital monitoring during lifestyle changes reveal independent dimensions of human biology and health

Cited by 13 publications

References 60 publications

Utilizing Bioinformatics Approaches to Conduct a Comparative Analysis of the Thyroid Transcriptome in Thyroid Disorders

Utilizing Bioinformatics Approaches to Conduct a Comparative Analysis of the Thyroid Transcriptome in Thyroid Disorders

Identifying subgroups of childhood obesity by using multiplatform metabotyping

The Influence of FTO, FABP2, LEP, LEPR, and MC4R Genes on Obesity Parameters in Physically Active Caucasian Men

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