Phenomics (Cham, Switzerland)最新文献

Recently, an increasing number of young never-smokers are diagnosed with lung cancer. The aim of this study is to investigate the genetic predisposition of lung cancer in these patients and discover candidate pathogenic variants for lung adenocarcinoma in young never-smokers. Peripheral blood was collected from 123 never-smoking east-Asian patients diagnosed with lung adenocarcinoma before the age of 40. Whole-exome sequencing (WES) was conducted on genomic DNA extracted from peripheral blood cells. As a result, 3,481 single nucleotide variants were identified. By bioinformatical tools and the published gene list associated with genetic predisposition of cancer, pathogenic variants were detected in ten germline genes: ATR, FANCD2, FANCE, GATA2, HFE, MSH2, PDGFRA, PMS2, SDHB, and WAS. Patients with pathogenic variants were more likely to occur in females (9/10, 90.0%) and have stage IV lung adenocarcinoma (4/10, 40%). Furthermore, germline mutations in 17 genes (ASB18, B3GALT5, CLEC4F, COL6A6, CYP4B1, C6orf132, EXO1, GATA4, HCK, KCP, NPHP4, PIGX, PPIL2, PPP1R3G, RRBP1, SALL4, and TTC28), which occurred in at least two patients, displayed potentially pathogenic effects. Gene ontology analysis further showed that these genes with germline mutations were mainly located in nucleoplasm and associated with DNA repair-related biological processes. The study provides spectrum of pathogenic variants and functional explanation for genetic predisposition of lung adenocarcinoma in young never-smokers, which sheds a light on prevention and early diagnosis of lung cancer.

Supplementary information: The online version contains supplementary material available at 10.1007/s43657-022-00062-1.

HDAC6 is involved in several biological processes related to aging-associated diseases. However, it was unknown whether HDAC6 could directly regulate lifespan and healthspan. We found that HDAC6 knockdown induced transcriptome changes to attenuate the aging changes in the Drosophila head, particularly on the inflammation and innate immunity-related genes. Whole-body knockdown of HDAC6 extended lifespan in the fly, furthermore brain-specific knockdown of HDAC6 extended both lifespan and healthspan in the fly. Our results established HDAC6 as a lifespan regulator and provided a potential anti-aging target.

Supplementary information: The online version contains supplementary material available at 10.1007/s43657-022-00045-2.

The aim of this cross-sectional survey was to analyze the semen parameters of volunteers from the Human Sperm Bank of Fudan University, as well as the related factors influencing these parameters. From January 2019 to December 2020, semen parameters from a total of 5214 men were included in this survey. The Kruskal-Wallis test was used to detect differences associated with several independent variables. A total of 5214 volunteers were included. The volunteers were registered in 33 provinces, autonomous regions, municipalities (including Macau and Taiwan) and 294 prefecture-level cities. The average age of volunteers was 27.40 years. Overall, 76.50% of the volunteers had a college education or higher. Volunteers with BMI values of 18.5-23.9 kg/m² accounted for 60.70% of participants. Semen parameters were significantly different according to season, education level, duration of abstinence, age group and BMI. The basic male fertility phenotypes (semen parameters) showed new trends in the study period, and accurate long-term tracking of male semen parameters will help researchers to better understand the changes in male fertility phenotypes (semen).

The msh homeobox 1 (Msx1) and msh homeobox 2 (Msx2) coordinate in myoblast differentiation and also contribute to muscle defects if altered during development. Deciphering the downstream signaling networks of Msx1 and Msx2 in myoblast differentiation will help us to understand the molecular events that contribute to muscle defects. Here, the proteomics characteristics in Msx1- and Msx2-mediated myoblast differentiation was evaluated  using isobaric tags for the relative and absolute quantification labeling technique (iTRAQ). The downstream regulatory proteins of Msx1- and Msx2-mediated differentiation were identified. Bioinformatics analysis revealed that these proteins were primarily associated with xenobiotic metabolism by cytochrome P450, fatty acid degradation, glycolysis/gluconeogenesis, arginine and proline metabolism, and apoptosis. In addition, our data show Acta1 was probably a core of the downstream regulatory networks of Msx1 and Msx2 in myoblast differentiation.

Supplementary information: The online version contains supplementary material available at 10.1007/s43657-022-00049-y.

Cancer metastasis is the major cause of cancer-related deaths and accounts for poor therapeutic outcomes. A metastatic cascade is a series of complicated biological processes. N⁶-methyladenosine (m⁶A) is the most abundant and conserved epitranscriptomic modification in eukaryotic cells, which has great impacts on RNA production and metabolism, including RNA splicing, processing, degradation and translation. Accumulating evidence demonstrates that m⁶A plays a critical role in regulating cancer metastasis. However, there is a lack of studies that review the recent advances of m⁶A in cancer metastasis. Here, we systematically retrieved the functions and mechanisms of how the m⁶A axis regulates metastasis, and especially summarized the organ-specific liver, lung and brain metastasis mediated by m⁶A in various cancers. Moreover, we discussed the potential application of m⁶A modification in cancer diagnosis and therapy, as well as the present limitations and future perspectives of m⁶A in cancer metastasis. This review provides a comprehensive knowledge on the m⁶A-mediated regulation of gene expression, which is helpful to extensively understand the complexity of cancer metastasis from a new epitranscriptomic point of view and shed light on the developing novel strategies to anti-metastasis based on m⁶A alteration.

Despite recent advances in technology, clinical phenotyping of Parkinson's disease (PD) has remained relatively limited as current assessments are mainly based on empirical observation and subjective categorical judgment at the clinic. A lack of comprehensive, objective, and quantifiable clinical phenotyping data has hindered our capacity to diagnose, assess patients' conditions, discover pathogenesis, identify preclinical stages and clinical subtypes, and evaluate new therapies. Therefore, deep clinical phenotyping of PD patients is a necessary step towards understanding PD pathology and improving clinical care. In this review, we present a growing community consensus and perspective on how to clinically phenotype this disease, that is, to phenotype the entire course of disease progression by integrating capacity, performance, and perception approaches with state-of-the-art technology. We also explore the most studied aspects of PD deep clinical phenotypes, namely, bradykinesia, tremor, dyskinesia and motor fluctuation, gait impairment, speech impairment, and non-motor phenotypes.

Although many methods have been developed to explore the function of cells by clustering high-dimensional (HD) single-cell omics data, the inconspicuously differential expressions of biomarkers of proteins or genes across all cells disturb the cell cluster delineation and downstream analysis. Here, we introduce a hashing-based framework to improve the delineation of cell clusters, which is based on the hypothesis that one variable with no significant differences can be decomposed into more diversely latent variables to distinguish cells. By projecting the original data into a sparse HD space, fly and densefly hashing preprocessing retain the local structure of data, and improve the cluster delineation of existing clustering methods, such as PhenoGraph. Moreover, the analyses on mass cytometry dataset show that our hashing-based framework manages to unveil new hidden heterogeneities in cell clusters. The proposed framework promotes the utilization of cell biomarkers and enriches the biological findings by introducing more latent variables.

Supplementary information: The online version contains supplementary material available at 10.1007/s43657-022-00056-z.

During the last decade, there has been rapid adoption of ground and aerial platforms with multiple sensors for phenotyping various biotic and abiotic stresses throughout the developmental stages of the crop plant. High throughput phenotyping (HTP) involves the application of these tools to phenotype the plants and can vary from ground-based imaging to aerial phenotyping to remote sensing. Adoption of these HTP tools has tried to reduce the phenotyping bottleneck in breeding programs and help to increase the pace of genetic gain. More specifically, several root phenotyping tools are discussed to study the plant's hidden half and an area long neglected. However, the use of these HTP technologies produces big data sets that impede the inference from those datasets. Machine learning and deep learning provide an alternative opportunity for the extraction of useful information for making conclusions. These are interdisciplinary approaches for data analysis using probability, statistics, classification, regression, decision theory, data visualization, and neural networks to relate information extracted with the phenotypes obtained. These techniques use feature extraction, identification, classification, and prediction criteria to identify pertinent data for use in plant breeding and pathology activities. This review focuses on the recent findings where machine learning and deep learning approaches have been used for plant stress phenotyping with data being collected using various HTP platforms. We have provided a comprehensive overview of different machine learning and deep learning tools available with their potential advantages and pitfalls. Overall, this review provides an avenue for studying various HTP platforms with particular emphasis on using the machine learning and deep learning tools for drawing legitimate conclusions. Finally, we propose the conceptual challenges being faced and provide insights on future perspectives for managing those issues.

Understanding the way genes work amongst individuals and across generations to shape form and function is a common theme for many genetic studies. The recent advances in genetics, genome engineering and DNA sequencing reinforced the notion that genes are not the only players that determine a phenotype. Due to physiological or pathological fluctuations in gene expression, even genetically identical cells can behave and manifest different phenotypes under the same conditions. Here, we discuss mechanisms that can influence or even disrupt the axis between genotype and phenotype; the role of modifier genes, the general concept of genetic redundancy, genetic compensation, the recently described transcriptional adaptation, environmental stressors, and phenotypic plasticity. We furthermore highlight the usage of induced pluripotent stem cells (iPSCs), the generation of isogenic lines through genome engineering, and sequencing technologies can help extract new genetic and epigenetic mechanisms from what is hitherto considered 'noise'.

Increasing evidence has demonstrated that abnormal epigenetic modifications are strongly related to cancer initiation. Thus, sensitive and specific detection of epigenetic modifications could markedly improve biological investigations and cancer precision medicine. A rapid development of molecular imaging approaches for the diagnosis and prognosis of cancer has been observed during the past few years. Various biomarkers unique to epigenetic modifications and targeted imaging probes have been characterized and used to discriminate cancer from healthy tissues, as well as evaluate therapeutic responses. In this study, we summarize the latest studies associated with optical molecular imaging of epigenetic modification targets, such as those involving DNA methylation, histone modification, noncoding RNA regulation, and chromosome remodeling, and further review their clinical application on cancer diagnosis and treatment. Lastly, we further propose the future directions for precision imaging of epigenetic modification in cancer. Supported by promising clinical and preclinical studies associated with optical molecular imaging technology and epigenetic drugs, the central role of epigenetics in cancer should be increasingly recognized and accepted.