Phenomics (Cham, Switzerland)最新文献

Previous observational and genomic-wide association studies (GWAS) suggested the association between several phenotypic factors and keratinocyte carcinoma, including lifestyle and dietary, photodamage-related conditions and socioeconomic status. The causal effect of these phenotypic factors in keratinocytes carcinoma etiology remains unclear. In this study, we utilized two-sample mendelian randomization analysis from multiple large-scale GWAS datasets of keratinocytes carcinoma including more than 300,000 patients. Genetic instrumental variables (IVs) were constructed by identifying single nucleotide polymorphisms (SNPs) that associate with corresponding factors. The inverse variance weighted (IVW) method and four robust MR approaches, including weighted median estimator, MR-Egger regression, simple mode and weighted mode were implemented for causal inferences and assess the sensitivity across findings. In this analysis, ease of skin tanning was identified as casual protective factor of keratinocyte carcinoma (Basal cell carcinoma: IVW OR = 0.718, 95% CI 0.654-0.788, p < 0.001; Cutaneous squamous cell carcinoma: IVW OR = 0.601, 95% CI 0.516-0.701, p < 0.001). Other phenotypic factors, such as coffee intake, alcohol consumption, smoking and socioeconomic status, indicated insignificant effects on keratinocyte carcinoma risk in the analysis, and therefore, our study does not support their roles in modifying keratinocytes carcinoma risks. Our extensive analysis provides strong evidence of the causative protective effect of ease of skin tanning in keratinocyte carcinoma. The findings suggest that individuals who are less prone to tanning may need to pay greater attention to sun protection in their daily activities to reduce the potential risk of keratinocyte cancers.

Supplementary information: The online version contains supplementary material available at 10.1007/s43657-024-00174-w.

Genotype and phenotype are both the themes of modern biology. Despite the elegant protein coding rules recognized decades ago in genotype, little is known on how traits are coded in a phenotype space (P). Mathematically, P can be partitioned into a subspace determined by genetic factors (P ^G) and a subspace affected by non-genetic factors (P ^NG). Evolutionary theory predicts P ^G is composed of limited dimensions while P ^NG may have infinite dimensions, which suggests a dimension decomposition method, termed as uncorrelation-based high-dimensional dependence (UBHDD), to separate them. We applied UBHDD to a yeast phenotype space comprising ~ 400 traits in ~ 1000 individuals. The obtained tentative P ^G matches the actual genetic components of the yeast traits, explains the broad-sense heritability, and facilitates the mapping of quantitative trait loci, suggesting the tentative P ^G be the yeast genetic subspace. A limited number of latent dimensions in the P ^G were found to be recurrently used for coding the diverse yeast traits, while dimensions in the P ^NG tend to be trait specific and increase constantly with trait sampling. A similar separation success was achieved when applying UBHDD to the UK Biobank human brain phenotype space that comprises ~ 700 traits in ~ 26,000 individuals. The obtained P ^G helped elucidate the genetic versus non-genetic origins of the left-right asymmetry of human brain, and reveal several hundred novel genetic correlations between brain regions and dozens of mental traits/diseases. In sum, by developing a dimension decomposition method we show that phenotypic traits are coded by a limited number of genetically determined common dimensions and unlimited trait-specific dimensions shaped by non-genetic factors, a rule fundamental to the emerging field of phenomics.

Supplementary information: The online version contains supplementary material available at 10.1007/s43657-024-00195-5.

Type-2 diabetes mellitus (T2DM) is a global epidemic with significant societal costs. The gut microbiota, including its metabolites, plays a pivotal role in maintaining health, while gut dysbiosis is implicated in several metabolic disorders, including T2DM. Although data exists on the relationship between the gut bacteriome and metabolic disorders, further attention is needed for the mycobiome and virome. Recent advancements have begun to shed light on these connections, offering potential avenues for preventive measures. However, more comprehensive investigations are required to untangle the interrelations between different microbial kingdoms and their role in T2DM development or mitigation. This review presents a simplified overview of the alterations in the gut bacteriome in T2DM and delves into the current understanding of the mycobiome and virome's role in T2DM, along with their interactions with the cohabiting bacteriome. Subsequently, it explores into the age-related dynamics of the gut microbiome and the changes observed in the microbiome composition with the onset of T2DM. Further, we explore the basic workflow utilized in gut microbiome studies. Lastly, we discuss potential therapeutic interventions in gut microbiome research, which could contribute to the amelioration of the condition, serve as preventive measures, or pave the way towards personalized medicine.

Molecular characterization of a biological sample, e.g., with omics approaches, is fundamental for the development and implementation of personalized and precision medicine approaches. In this context, quality assessment is one of the most critical aspects. Accurate performance and interpretation of omics techniques is based on consensus, harmonization, and standardization of protocols, procedures, data analysis and reference values and materials. EATRIS, the European Infrastructure for Translational Medicine (www.EATRIS.eu), brings together resources and services to support researchers in developing their biomedical discoveries into novel translational tools and interventions for better health outcomes. Here we describe the efforts within the Horizon 2020 EATRIS-Plus project and activities of member facilities of EATRIS towards quality assessment of pre-clinical sample processing, clinical omics data generation, multi-omics data integration, and dissemination of the resources in a Multi-Omics Toolbox, which is the principal deliverable of the EATRIS-Plus project for the consolidation of EATRIS towards translational medicine.

Supplementary information: The online version contains supplementary material available at 10.1007/s43657-024-00170-0.

The application of new image technology (NIT) in biomedical and clinical fields is developing at a top speed with the emergence of newly invented technics such as multiparametric, multimodal or molecule imaging. However, due to the fast-paced nature of this development, many theories and technologies have not been thoroughly summarized and reviewed. This lack of comprehensive reviews has hindered our ability to fully comprehend the utility and effectiveness of NIT in clinical settings. Furthermore, the rapid development has sometimes outpaced our ability to provide ideal applications. This review focuses on the comparison of eight types of novel and conventional imaging techniques, which aims to inform readers about the history and characteristic of new medical imaging, providing details such as the clinical functional data. This review first briefly introduces a large number of novel techniques which have been developed to help bridge the gap in medical imaging. Secondly, by systematically overviewings the development of novel imaging technologies, readers could understand the basic changing trends in clinical vision field. Lastly, this search provides strong evidence to instruct future development.

Supplementary information: The online version contains supplementary material available at 10.1007/s43657-024-00167-9.

Tongue analysis holds promise for disease detection and health monitoring, especially in traditional Chinese medicine. However, its subjectivity hinders clinical applications. Deep learning offers a path for automated tongue diagnosis, yet existing methods struggle to capture subtle details, and the lack of large datasets hampers the development of robust and generalizable models. To address these challenges, we introduce TonguExpert (https://www.biosino.org/TonguExpert), a free platform for archiving, analyzing, and extracting phenotypes from tongue images. Our deep learning framework integrates cutting-edge techniques for tongue segmentation and phenotype extraction. TonguExpert analyzes a massive dataset of 5992 tongue images from a Chinese population and extracts 773 phenotypes including five predicted labels and their probabilities, 355 global features (entire tongue, tongue body, and tongue coating) and 408 local features (fissures and tooth marks) in a unified process. Besides, 580 additional features for five tongue subregions are also available for future study. Notably, TonguExpert outperforms manual classification methods, achieving high accuracy (ROC-AUC 0.89-0.99 for color, 0.97 for fissures, 0.88 for tooth marks). Additionally, the model generalizes well to predict new phenotypes (e.g., greasy coating) using external datasets. This allows the model to learn from a broader spectrum of data, potentially improving its overall performance. We also release the largest publicly available dataset of tongue images and phenotypes, which is invaluable for advancing automated analysis and clinical applications of tongue diagnosis. In summary, this research advances automated tongue diagnosis, paving the way for wider clinical adoption and potentially expanding the applications in the future.

Supplementary information: The online version contains supplementary material available at 10.1007/s43657-024-00210-9.

Lynch syndrome (LS) is an inherited condition caused by germline mutations in genes involved in DNA mismatch repair (MMR), which could increase the risk of developing colorectal cancer and other types of cancers. Current understanding of MMR gene mutations cannot fully account for the genetic predisposition to LS-associated colon cancer. This study uncovered a novel germline mutation, EPCAM c.661 A > G, in members of a three-generation family using next-generation sequencing technique, which was related to a vertically transmitted risk for LS-associated colon cancer. Genetically, EPCAM c.661 A > G was proposed to modulate the transcriptional activity of MSH2 through the DNA methylation alteration, as well as influence the stability of EpCAM protein. Through spatial transcriptomic analysis, we discovered a "cold" tumor microenvironment feature and distinct cellular interactions among epithelial cell subpopulations. In conclusion, these findings highlight the importance of identifying and characterizing novel pathogenic mutations of MMR genes to better understand the genetic basis of LS and its association with colon cancer.

Supplementary information: The online version contains supplementary material available at 10.1007/s43657-024-00202-9.

Cervical cancer (CC) is the second most common cancer of female reproductive system. However, satisfactory prognostic model for CC remains to be established. In this study, we perform whole-exome sequencing on formalin-fixed and paraffin-embedded tumor specimens extracted from 67 recurrent and 28 matched non-recurrent CC patients. As a result, four core mutated genes (i.e., DCHS2, DNAH10, RYR1, and WDFY4) that are differentially presented in recurrent and non-recurrent CC patients are screened out to construct a recurrence-free related score (RRS) model capable of predicting CC prognosis in our cohort, which is further confirmed in TCGA CESC cohort. Moreover, combining tumor mutational burden (TMB) and RRS into an integrated RRS/TMB model enables better stratification of CC patients with distinct prognosis in both cohorts. Increased infiltration of multiple immune cell types, enriched interferon signaling pathway, and elevated cytolytic activity are evident in tumors from patients with a higher RRS and/or a higher TMB. In summary, this study establishes a novel mutation-based prognostic model for CC, the predictive value of which can be attributable to immunological mechanisms. This study will provide insight into the utilization of mutational analysis in guiding therapeutic strategies for CC patients.

Supplementary information: The online version contains supplementary material available at 10.1007/s43657-024-00158-w.