A distinct adipose tissue gene expression response to caloric restriction predicts 6-mo weight maintenance in obese subjects

BACKGROUND: Weight loss has been shown to reduce risk factors associated with cardiovascular disease and diabetes; however, successful maintenance of weight loss continues to pose a challenge. OBJECTIVE: The present study was designed to assess whether changes in subcutaneous adipose tissue (scAT) gene expression during a low-calorie diet (LCD) could be used to differentiate and predict subjects who experience successful short-term weight maintenance from subjects who experience weight regain. DESIGN: Forty white women followed a dietary protocol consisting of an 8-wk LCD phase followed by a 6-mo weight-maintenance phase. Participants were classified as weight maintainers (WMs; 0-10% weight regain) and weight regainers (WRs; 50-100% weight regain) by considering changes in body weight during the 2 phases. Anthropometric measurements, bioclinical variables, and scAT gene expression were studied in all individuals before and after the LCD. Energy intake was estimated by using 3-d dietary records. RESULTS: No differences in body weight and fasting insulin were observed between WMs and WRs at baseline or after the LCD period. The LCD resulted in significant decreases in body weight and in several plasma variables in both groups. WMs experienced a significant reduction in insulin secretion in response to an oral-glucose-tolerance test after the LCD; in contrast, no changes in insulin secretion were observed in WRs after the LCD. An ANOVA of scAT gene expression showed that genes regulating fatty acid metabolism, citric acid cycle, oxidative phosphorylation, and apoptosis were regulated differently by the LCD in WM and WR subjects. CONCLUSION: This study suggests that LCD-induced changes in insulin secretion and scAT gene expression may have the potential to predict successful short-term weight maintenanc

Adipose Gene Expression Prior to Weight Loss Can Differentiate and Weakly Predict Dietary Responders

BACKGROUND: The ability to identify obese individuals who will successfully lose weight in response to dietary intervention will revolutionize disease management. Therefore, we asked whether it is possible to identify subjects who will lose weight during dietary intervention using only a single gene expression snapshot. METHODOLOGY/PRINCIPAL FINDINGS: The present study involved 54 female subjects from the Nutrient-Gene Interactions in Human Obesity-Implications for Dietary Guidelines (NUGENOB) trial to determine whether subcutaneous adipose tissue gene expression could be used to predict weight loss prior to the 10-week consumption of a low-fat hypocaloric diet. Using several statistical tests revealed that the gene expression profiles of responders (8-12 kgs weight loss) could always be differentiated from non-responders (<4 kgs weight loss). We also assessed whether this differentiation was sufficient for prediction. Using a bottom-up (i.e. black-box) approach, standard class prediction algorithms were able to predict dietary responders with up to 61.1%+/-8.1% accuracy. Using a top-down approach (i.e. using differentially expressed genes to build a classifier) improved prediction accuracy to 80.9%+/-2.2%. CONCLUSION: Adipose gene expression profiling prior to the consumption of a low-fat diet is able to differentiate responders from non-responders as well as serve as a weak predictor of subjects destined to lose weight. While the degree of prediction accuracy currently achieved with a gene expression snapshot is perhaps insufficient for clinical use, this work reveals that the comprehensive molecular signature of adipose tissue paves the way for the future of personalized nutrition

Author Correction: Multi-ancestry genome-wide association analyses improve resolution of genes and pathways influencing lung function and chronic obstructive pulmonary disease risk

Correction to: Nature Geneticshttps://doi.org/10.1038/s41588-023-01314-0, published online 13 March 2023. In the version of the article initially published, the sample sizes in the main text and Supplementary Tables 1 and 2 were incorrect. In the abstract, the last paragraph of the Introduction, the first paragraph of the Results, the top box in Figure 1a and the Supplementary Information, the total sample size has been corrected from 580,869 to 588,452 participants and the size of the European cohort from 468,062 to 475,645. Some of the effect sizes in Supplementary Table 14 (columns W, Z, AC, AF) had the wrong sign. There was also an error in Supplementary Table 3 where the sample size instead of the variant count was shown for EXCEED. The errors do not affect the conclusions of the study. Additionally, two acknowledgments for use of INTERVAL pQTL and Lung eQTL consortium data were omitted from the Supplementary Information. These errors have been corrected in the Supplementary Information and HTML and PDF versions of the article

Multi-ancestry genome-wide association analyses improve resolution of genes and pathways influencing lung function and chronic obstructive pulmonary disease risk

Lung-function impairment underlies chronic obstructive pulmonary disease (COPD) and predicts mortality. In the largest multi-ancestry genome-wide association meta-analysis of lung function to date, comprising 580,869 participants, we identified 1,020 independent association signals implicating 559 genes supported by ≥2 criteria from a systematic variant-to-gene mapping framework. These genes were enriched in 29 pathways. Individual variants showed heterogeneity across ancestries, age and smoking groups, and collectively as a genetic risk score showed strong association with COPD across ancestry groups. We undertook phenome-wide association studies for selected associated variants as well as trait and pathway-specific genetic risk scores to infer possible consequences of intervening in pathways underlying lung function. We highlight new putative causal variants, genes, proteins and pathways, including those targeted by existing drugs. These findings bring us closer to understanding the mechanisms underlying lung function and COPD, and should inform functional genomics experiments and potentially future COPD therapies

Combinaison de sources de données pour l'amélioration de la prédiction en apprentissage : une application à la prédiction de la perte de poids chez l'obèse à partir de données transcriptomiques et cliniques

Les maladies complexes comme l'obésité sont des maladies multifactorielles. Peu de travaux existent pour essayer de prédire les effets des différents traitements et ainsi mieux adapter les traitements aux patients. L'utilisation de modèles prédictifs pour mieux guider le choix des traitements de l'obésité reste un champ de recherche peu exploré malgré le fort impact qu'elle pourrait avoir vu la prévalence de cette maladie. Dans d'autres domaines de la médecine, comme la cancérologie par exemple, de telles méthodes sont déjà utilisées pour l'aide au diagnostic se basant notamment sur des données issues de puces à ADN. Cette technologie s'avère adaptée et son utilisation a donné lieu à des résultats intéressants pour dépister les maladies ou aider les médecins dans leur choix thérapeutique. Cependant si celle‐ci s'avère suffisante pour prédire d'une manière satisfaisante dans le domaine du cancer, en revanche elle s'avère d'un apport limité dans le cadre d'une application aux données de l'obésité. Cela suggère l'utilisation d'autres données patients pour améliorer les performances en prédiction. Les travaux de recherche présentés dans ce mémoire abordent les problèmes de la prédiction de la perte de poids suite à un régime ou une chirurgie bariatrique. Nous avons analysé le problème de la prédiction de la perte de poids à partir des données transcriptomique dans le cadre de deux projets européens et aussi à partir des données biocliniques dans le cadre de la chirurgie de l'obésité. Nous avons ensuite proposé trois concepts de combinaisons de modèles : combinaison de données, combinaison de méthodes et combinaison avec abstention. Nous avons analysé empiriquement ces trois approches et les expérimentations ont montré une amélioration des résultats pour les données de l'obésité même si ceux‐ci restent bien en deça de ce qu'on observe avec les données cancersno dat

Combinaison de sources de données pour l'amélioration de la prédiction en apprentissage (une application à la prédiction de la perte de poids chez l'obèse à partir de données transcriptomiques et cliniques)

Les maladies complexes comme l'obésité sont des maladies multifactorielles. Peu de travaux existent pour essayer de prédire les effets des différents traitements et ainsi mieux adapter les traitements aux patients. L'utilisation de modèles prédictifs pour mieux guider le choix des traitements de l'obésité reste un champ de recherche peu exploré malgré le fort impact qu'elle pourrait avoir vu la prévalence de cette maladie. Dans d'autres domaines de la médecine, comme la cancérologie par exemple, de telles méthodes sont déjà utilisées pour l'aide au diagnostic se basant notamment sur des données issues de puces à ADN. Cette technologie s'avère adaptée et son utilisation a donné lieu à des résultats intéressants pour dépister les maladies ou aider les médecins dans leur choix thérapeutique. Cependant si celle ci s'avère suffisante pour prédire d'une manière satisfaisante dans le domaine du cancer, en revanche elle s'avère d'un apport limité dans le cadre d'une application aux données de l'obésité. Cela suggère l'utilisation d'autres données patients pour améliorer les performances en prédiction. Les travaux de recherche présentés dans ce mémoire abordent les problèmes de la prédiction de la perte de poids suite à un régime ou une chirurgie bariatrique. Nous avons analysé le problème de la prédiction de la perte de poids à partir des données transcriptomique dans le cadre de deux projets européens et aussi à partir des données biocliniques dans le cadre de la chirurgie de l obésité. Nous avons ensuite proposé trois concepts de combinaisons de modèles : combinaison de données, combinaison de méthodes et combinaison avec abstention. Nous avons analysé empiriquement ces trois approches et les expérimentations ont montré une amélioration des résultats pour les données de l'obésité même si ceux ci restent bien en deça de ce qu'on observe avec les données cancersPARIS-BIUP (751062107) / SudocPARIS-BIUSJ-Physique recherche (751052113) / SudocSudocFranceF

Semantic Assembly and Annotation of Draft RNAseq Transcripts without a Reference Genome

<div><p>Transcriptomes are one of the first sources of high-throughput genomic data that have benefitted from the introduction of Next-Gen Sequencing. As sequencing technology becomes more accessible, transcriptome sequencing is applicable to multiple organisms for which genome sequences are unavailable. Currently all methods for <i>de novo</i> assembly are based on the concept of matching the nucleotide context overlapping between short fragments-reads. However, even short reads may still contain biologically relevant information which can be used as hints in guiding the assembly process. We propose a computational workflow for the reconstruction and functional annotation of expressed gene transcripts that does not require a reference genome sequence and can be tolerant to low coverage, high error rates and other issues that often lead to poor results of <i>de novo</i> assembly in studies of non-model organisms. We start with either raw sequences or the output of a context-based <i>de novo</i> transcriptome assembly. Instead of mapping reads to a reference genome or creating a completely unsupervised clustering of reads, we assemble the unknown transcriptome using nearest homologs from a public database as seeds. We consider even distant relations, indirectly linking protein-coding fragments to entire gene families in multiple distantly related genomes. The intended application of the proposed method is an additional step of semantic (based on relations between protein-coding fragments) scaffolding following traditional (i.e. based on sequence overlap) <i>de novo</i> assembly. The method we developed was effective in analysis of the jellyfish <i>Cyanea capillata</i> transcriptome and may be applicable in other studies of gene expression in species lacking a high quality reference genome sequence. Our algorithms are implemented in C and designed for parallel computation using a high-performance computer. The software is available free of charge via an open source license.</p></div