Phenomics (Cham, Switzerland)最新文献

Women's health is important for society. Despite the known biological and sex-related factors influencing the risk of diseases among women, the network of the full spectrum of diseases in women is underexplored. This study aimed to systematically examine the women-specific temporal pattern (trajectory) of the disease network, including the role of baseline physical examination indexes, and blood and urine biomarkers. In the UK Biobank study, 502,650 participants entered the cohort from 2006 to 2010, and were followed up until 2019 to identify disease incidence via linkage to the patient registers. For those diseases with increased risk among women, conditional logistic regression models were used to estimate odds ratios (ORs), and the binomial test of direction was further used to build disease trajectories. Among 301 diseases, 82 diseases in women had ORs > 1.2 and p < 0.00017 when compared to men, involving mainly diseases in the endocrine, skeletal and digestive systems. Diseases with the highest ORs included breast diseases, osteoporosis, hyperthyroidism, and deformity of the toes. The biomarker and disease trajectories suggested estradiol as a risk predictor for breast cancer, while a high percentage of reticulocyte, body mass index and waist circumference were associated with an increased risk of upper-limb neuropathy. In addition, the risk of cholelithiasis was increased in women diagnosed with dyspepsia and diaphragmatic hernia. In conclusion, women are at an increased risk of endocrine, skeletal and digestive diseases. The biomarker and disease trajectories in women suggested key pathways to a range of adverse outcomes downstream, which may shed light on promising targets for early detection and prevention of these diseases.

Supplementary information: The online version contains supplementary material available at 10.1007/s43657-022-00054-1.

Currently, drug resistance of anti-cancer therapy has become the main cause of low survival rate and poor prognosis. Full understanding of drug resistance mechanisms is an urgent request for further development of anti-cancer therapy and improvement of prognosis. Here we present our N-glycoproteomics study of putative N-glycoprotein biomarkers of drug resistance in doxorubicin resistance breast cancer cell line michigan cancer foundation-7 (MCF-7/ADR) relative to parental michigan cancer foundation-7 (MCF-7) cells. Intact N-glycopeptides (IDs) from MCF-7/ADR and MCF-7 cells were enriched with zwitterionic hydrophilic interaction liquid chromatography (ZIC-HILIC), labeled with stable isotopic diethylation (SIDE), and analyzed with C18-RPLC-MS/MS (HCD with stepped normalized collision energies); these IDs were identified with database search engine GPSeeker, and the differentially expressed intact N-glycopeptides (DEGPs) were quantified with GPSeekerQuan. With target-decoy searches and control of spectrum-level FDR ≤ 1%, 322 intact N-glycopeptides were identified; these intact N-glycopeptides come from the combination of 249 unique peptide backbones (corresponding to 234 intact N-glycoproteins) and 90 monosaccharide compositions (corresponding to 248 putative N-glycosites). The sequence structures of 165 IDs were confirmed with structure-diagnostic fragment ions. With the criteria of observation at least twice among the three technical replicates, ≥ 1.5-fold change and p value < 0.05, 20 DEGPs were quantified, where five of them were up-regulated and 15 of them were down-regulated; the corresponding intact N-glycoproteins as putative markers of drug resistance were discussed.

Supplementary information: The online version contains supplementary material available at 10.1007/s43657-021-00029-8.

Lung nodule classification based on low-dose computed tomography (LDCT) images has attracted major attention thanks to the reduced radiation dose and its potential for early diagnosis of lung cancer from LDCT-based lung cancer screening. However, LDCT images suffer from severe noise, largely influencing the performance of lung nodule classification. Current methods combining denoising and classification tasks typically require the corresponding normal-dose CT (NDCT) images as the supervision for the denoising task, which is impractical in the context of clinical diagnosis using LDCT. To jointly train these two tasks in a unified framework without the NDCT images, this paper introduces a novel self-supervised method, termed strided Noise2Neighbors or SN2N, for blind medical image denoising and lung nodule classification, where the supervision is generated from noisy input images. More specifically, the proposed SN2N can construct the supervision information from its neighbors for LDCT denoising, which does not need NDCT images anymore. The proposed SN2N method enables joint training of LDCT denoising and lung nodule classification tasks by using self-supervised loss for denoising and cross-entropy loss for classification. Extensively experimental results on the Mayo LDCT dataset demonstrate that our SN2N achieves competitive performance compared with the supervised learning methods that have paired NDCT images as supervision. Moreover, our results on the LIDC-IDRI dataset show that the joint training of LDCT denoising and lung nodule classification significantly improves the performance of LDCT-based lung nodule classification.

Alternative splicing (AS) in the tumor biological process has provided a novel perspective on carcinogenesis. However, the clinical significance of individual AS patterns of adrenocortical carcinoma (ACC) has been underestimated, and in-depth investigations are lacking. We selected 76 ACC samples from the Cancer Genome Atlas (TCGA) SpliceSeq and SpliceAid2 databases, and 39 ACC samples from Fudan University Shanghai Cancer Center (FUSCC). Prognosis-related AS events (PASEs) and survival analysis were evaluated based on prediction models constructed by machine-learning algorithm. In total, 23,984 AS events and 3,614 PASEs were detected in the patients with ACC. The predicted risk score of each patient suggested that eight PASEs groups were significantly correlated with the clinical outcomes of these patients (p < 0.001). Prognostic models produced AUC values of 0.907 in all PASEs' groups. Eight splicing factors (SFs), including BAG2, CXorf56, DExD-Box Helicase 21 (DDX21), HSPB1, MBNL3, MSI1, RBMXL2, and SEC31B, were identified in regulatory networks of ACC. DDX21 was identified and validated as a novel clinical promoter and therapeutic target in 115 patients with ACC from TCGA and FUSCC cohorts. In conclusion, the strict standards used in this study ensured the systematic discovery of profiles of AS events using genome-wide cohorts. Our findings contribute to a comprehensive understanding of the landscape and underlying mechanism of AS, providing valuable insights into the potential usages of DDX21 for predicting prognosis for patients with ACC.

Supplementary information: The online version contains supplementary material available at 10.1007/s43657-021-00026-x.

Mathematical calculation usually requires sustained attention to manipulate numbers in the mind, while listening to light music has a relaxing effect on the brain. The differences in the corresponding brain functional network topologies underlying these behaviors remain rarely known. Here, we systematically examined the brain dynamics of four behaviors (resting with eyes closed and eyes open, tasks of music listening and mental calculation) using 64-channel electroencephalogram (EEG) recordings and graph theory analysis. We developed static and dynamic minimum spanning tree (MST) analysis method and demonstrated that the brain network topology under mental calculation is a more line-like structure with less tree hierarchy and leaf fraction; however, the hub regions, which are mainly located in the frontal, temporal and parietal regions, grow more stable over time. In contrast, music-listening drives the brain to exhibit a highly rich network of star structure, and the hub regions are mainly located in the posterior regions. We then adopted the dynamic dissimilarity of different MSTs over time based on the graph Laplacian and revealed low dissimilarity during mental calculation. These results suggest that the human brain functional connectivity of individuals has unique dynamic diversity and flexibility under various behaviors.

Supplementary information: The online version contains supplementary material available at 10.1007/s43657-021-00027-w.

Cardiovascular diseases (CVDs) are a large group of diseases and have become the leading cause of morbidity and mortality worldwide. Although considerable progresses have been made in the diagnosis, treatment and prognosis of CVD, communication barriers between clinicians and researchers still exist because the phenotypes of CVD are complex and diverse in clinical practice and lack of unity. Therefore, it is particularly important to establish a standardized and unified terminology to describe CVD. In recent years, there have been several studies, such as the Human Phenotype Ontology, attempting to provide a standardized description of the disease phenotypes. In the present article, we outline recent advances in the classification of the major types of CVD to retrospectively review the current progresses of phenotypic studies in the cardiovascular field and provide a reference for future cardiovascular research.

β cells are defined by the ability to produce and secret insulin. Recent studies have evaluated that human pancreatic β cells are heterogeneous and demonstrated the transcript alterations of β cell subpopulation in diabetes. Single-cell RNA sequence (scRNA-seq) analysis helps us to refine the cell types signatures and understand the role of the β cells during metabolic challenges and diseases. Here, we construct the pseudotime trajectory of β cells from publicly available scRNA-seq data in health and type 2 diabetes (T2D) based on highly dispersed and highly expressed genes using Monocle2. We identified three major states including 1) Normal branch, 2) Obesity-like branch and 3) T2D-like branch based on biomarker genes and genes that give rise to bifurcation in the trajectory. β cell function-maintain-related genes, insulin expression-related genes, and T2D-related genes enriched in three branches, respectively. Continuous pseudotime spectrum might suggest that β cells transition among different states. The application of pseudotime analysis is conducted to clarify the different cell states, providing novel insights into the pathology of β cells in T2D.

Supplementary information: The online version contains supplementary material is available at 10.1007/s43657-021-00024-z.

Gene polymorphism of acetaldehyde dehydrogenase 2 (ALDH2), a key enzyme for alcohol metabolism in humans, can affect catalytic activity. The ALDH2 Glu504Lys mutant allele has a high-frequency distribution in East Asian populations and has been demonstrated to be associated with an increased risk of cardiovascular disease, stroke, and tumors. Available evidence suggests that the evolution of the ALDH2 gene has been influenced by multiple factors. Random mutations produce Glu504Lys, and genetic drift alters the frequency of this allele; additionally, environmental factors such as hepatitis B virus infection and high-elevation hypoxia affect its frequency through selective effects, ultimately resulting in a high frequency of this allele in East Asian populations. Here, the origin, selection, and spread of the ALDH2 Glu504Lys allele are discussed, and an outlook for further research is proposed to realize a precision medical strategy based on the genetic and environmental variations in ALDH2.

The complement system is activated during the development of nonalcoholic fatty liver disease (NAFLD). We aimed to evaluate the causal relationship between serum C3 and C4 levels and NAFLD. After exclusion criteria, a total of 1600 Chinese Han men from the Fangchenggang Area Male Health and Examination Survey cohort were enrolled in cross-sectional analysis, while 572 participants were included in the longitudinal analysis (average follow-up of 4 years). We performed a bidirectional Mendelian randomization (MR) analysis using two C3-related, eight C4-related and three NAFLD-related gene loci as instrumental variables to evaluate the causal associations between C3, C4, and NAFLD risk in cross-sectional analysis. Per SD increase in C3 levels was significantly associated with higher risk of NAFLD (OR = 1.65, 95% CI 1.40, 1.94) in cross-sectional analysis while C4 was not (OR = 1.04, 95% CI 0.89, 1.21). Longitudinal analysis produced similar results (HR_C3 = 1.20, 95% CI 1.02, 1.42; HR_C4 = 1.10, 95% CI 0.94, 1.28). In MR analysis, there were no causal relationships for genetically determined C3 levels and NAFLD risk using unweighted or weighted GRS_C3 (β_{E_unweighted} = -0.019, 95% CI -0.019, -0.019, p = 0.202; β_{E_weighted} = -0.019, 95% CI -0.019, -0.019, p = 0.322). Conversely, serum C3 levels were significantly effected by the genetically determined NAFLD (β_{E_unweighted} = 0.020, 95% CI 0.020, 0.020, p = 0.004; β_{E_weighted} = 0.021, 95% CI 0.020, 0.021, p = 0.004). Neither the direction from C4 to NAFLD nor the one from NAFLD to C4 showed significant association. Our results support that the change in serum C3 levels but not C4 levels might be caused by NAFLD in Chinese Han men.

Supplementary information: The online version contains supplementary material available at 10.1007/s43657-021-00023-0.

Deciphering the relationship between human proteins (genes) and phenotypes is one of the fundamental tasks in phenomics research. The Human Phenotype Ontology (HPO) builds upon a standardized logical vocabulary to describe the abnormal phenotypes encountered in human diseases and paves the way towards the computational analysis of their genetic causes. To date, many computational methods have been proposed to predict the HPO annotations of proteins. In this paper, we conduct a comprehensive review of the existing approaches to predicting HPO annotations of novel proteins, identifying missing HPO annotations, and prioritizing candidate proteins with respect to a certain HPO term. For each topic, we first give the formalized description of the problem, and then systematically revisit the published literatures highlighting their advantages and disadvantages, followed by the discussion on the challenges and promising future directions. In addition, we point out several potential topics to be worthy of exploration including the selection of negative HPO annotations and detecting HPO misannotations. We believe that this review will provide insight to the researchers in the field of computational phenotype analyses in terms of comprehending and developing novel prediction algorithms.