Phenomics (Cham, Switzerland)最新文献

Alternative splicing exists in most multi-exonic genes, and exploring these complex alternative splicing events and their resultant isoform expressions is essential. However, it has become conventional that RNA sequencing results have often been summarized into gene-level expression counts mainly due to the multiple ambiguous mapping of reads at highly similar regions. Transcript-level quantification and interpretation are often overlooked, and biological interpretations are often deduced based on combined transcript information at the gene level. Here, for the most variable tissue of alternative splicing, the brain, we estimate isoform expressions in 1,191 samples collected by the Genotype-Tissue Expression (GTEx) Consortium using a powerful method that we previously developed. We perform genome-wide association scans on the isoform ratios per gene and identify isoform-ratio quantitative trait loci (irQTL), which could not be detected by studying gene-level expressions alone. By analyzing the genetic architecture of the irQTL, we show that isoform ratios regulate educational attainment via multiple tissues including the frontal cortex (BA9), cortex, cervical spinal cord, and hippocampus. These tissues are also associated with different neuro-related traits, including Alzheimer's or dementia, mood swings, sleep duration, alcohol intake, intelligence, anxiety or depression, etc. Mendelian randomization (MR) analysis revealed 1,139 pairs of isoforms and neuro-related traits with plausible causal relationships, showing much stronger causal effects than on general diseases measured in the UK Biobank (UKB). Our results highlight essential transcript-level biomarkers in the human brain for neuro-related complex traits and diseases, which could be missed by merely investigating overall gene expressions.

Supplementary information: The online version contains supplementary material available at 10.1007/s43657-023-00100-6.

The human microbiome plays a crucial role in human health. In the past decade, advances in high-throughput sequencing technologies and analytical software have significantly improved our knowledge of the human microbiome. However, most studies concerning the human microbiome did not provide repeatable protocols to guide the sample collection, handling, and processing procedures, which impedes obtaining valid and timely microbial taxonomic and functional results. This protocol provides detailed operation methods of human microbial sample collection, DNA extraction, and library construction for both the amplicon sequencing-based measurements of the microbial samples from the human nasal cavity, oral cavity, and skin, as well as the shotgun metagenomic sequencing-based measurements of the human stool samples among adult participants. This study intends to develop practical procedure standards to improve the reproducibility of microbiota profiling of human samples.

Supplementary information: The online version contains supplementary material available at 10.1007/s43657-023-00097-y.

Dermatomyositis (DM) is a heterogeneous autoimmune disease associated with numerous myositis specific antibodies (MSAs) in which DM with anti-melanoma differentiation-associated gene 5-positive (MDA5 + DM) is a unique subtype of DM with higher risk of developing varying degrees of Interstitial lung disease (ILD). Glycosylation is a complex posttranslational modification of proteins associated with many autoimmune diseases. However, the association of total plasma N-glycome (TPNG) and DM, especially MDA5 + DM, is still unknown. TPNG of 94 DM patients and 168 controls were analyzed by mass spectrometry with in-house reliable quantitative method called Bionic Glycome method. Logistic regression with age and sex adjusted was used to reveal the aberrant glycosylation of DM and the association of TPNG and MDA5 + DM with or without rapidly progressive ILD (RPILD). The elastic net model was used to evaluate performance of glycans in distinguishing RPLID from non-RPILD, and survival analysis was analyzed with N-glycoslyation score by Kaplan-Meier survival analysis. It was found that the plasma protein N-glycome in DM showed higher fucosylation and bisection, lower sialylation (α2,3- not α2,6-linked) and galactosylation than controls. In MDA5 + DM, more severe disease condition was associated with decreased sialylation (specifically α2,3-sialylation with fucosylation) while accompanying elevated H6N5S3 and H5N4FSx, decreased galactosylation and increased fucosylation and the complexity of N-glycans. Moreover, glycosylation traits have better discrimination ability to distinguish RPILD from non-RPILD with AUC 0.922 than clinical features and is MDA5-independent. Survival advantage accrued to MDA5 + DM with lower N-glycosylation score (p = 3e-04). Our study reveals the aberrant glycosylation of DM for the first time and indicated that glycosylation is associated with disease severity caused by ILD in MDA5 + DM, which might be considered as the potential biomarker for early diagnosis of RPILD and survival evaluation of MDA5 + DM.

Supplementary information: The online version contains supplementary material available at 10.1007/s43657-023-00096-z.

The gut microbiota refers to the gross collection of microorganisms, estimated trillions of them, which reside within the gut and play crucial roles in the absorption and digestion of dietary nutrients. In the past decades, the new generation 'omics' (metagenomics, transcriptomics, proteomics, and metabolomics) technologies made it possible to precisely identify microbiota and metabolites and describe their variability between individuals, populations and even different time points within the same subjects. With massive efforts made, it is now generally accepted that the gut microbiota is a dynamically changing population, whose composition is influenced by the hosts' health conditions and lifestyles. Diet is one of the major contributors to shaping the gut microbiota. The components in the diets vary in different countries, religions, and populations. Some special diets have been adopted by people for hundreds of years aiming for better health, while the underlying mechanisms remain largely unknown. Recent studies based on volunteers or diet-treated animals demonstrated that diets can greatly and rapidly change the gut microbiota. The unique pattern of the nutrients from the diets and their metabolites produced by the gut microbiota has been linked with the occurrence of diseases, including obesity, diabetes, nonalcoholic fatty liver disease, cardiovascular disease, neural diseases, and more. This review will summarize the recent progress and current understanding of the effects of different dietary patterns on the composition of gut microbiota, bacterial metabolites, and their effects on the host's metabolism.

Fluorescence labeling and imaging provide an opportunity to observe the structure of biological tissues, playing a crucial role in the field of histopathology. However, when labeling and imaging biological tissues, there are still some challenges, e.g., time-consuming tissue preparation steps, expensive reagents, and signal bias due to photobleaching. To overcome these limitations, we present a deep-learning-based method for fluorescence translation of tissue sections, which is achieved by conditional generative adversarial network (cGAN). Experimental results from mouse kidney tissues demonstrate that the proposed method can predict the other types of fluorescence images from one raw fluorescence image, and implement the virtual multi-label fluorescent staining by merging the generated different fluorescence images as well. Moreover, this proposed method can also effectively reduce the time-consuming and laborious preparation in imaging processes, and further saves the cost and time.

Supplementary information: The online version contains supplementary material available at 10.1007/s43657-023-00094-1.

Immunophenotyping is proving crucial to understanding the role of the immune system in health and disease. High-throughput flow cytometry has been used extensively to reveal changes in immune cell composition and function at the single-cell level. Here, we describe six optimized 11-color flow cytometry panels for deep immunophenotyping of human whole blood. A total of 51 surface antibodies, which are readily available and validated, were selected to identify the key immune cell populations and evaluate their functional state in a single assay. The gating strategies for effective flow cytometry data analysis are included in the protocol. To ensure data reproducibility, we provide detailed procedures in three parts, including (1) instrument characterization and detector gain optimization, (2) antibody titration and sample staining, and (3) data acquisition and quality checks. This standardized approach has been applied to a variety of donors for a better understanding of the complexity of the human immune system.

Supplementary information: The online version contains supplementary material available at 10.1007/s43657-022-00092-9.

Intraductal papillomas (IDPs), including central papilloma and peripheral papilloma, are common in the female population. Due to the lack of specific clinical manifestations of IDPs, it is easy to misdiagnose or miss diagnose. The difficulty of differential diagnosis using imaging techniques also contributes to these conditions. Histopathology is the gold standard for the diagnosis of IDPs while the possibility of under sample exists in the percutaneous biopsy. There have been some debates about how to treat asymptomatic IDPs without atypia diagnosed on core needle biopsy (CNB), especially when the upgrade rate to carcinoma is considered. This article concludes that further surgery is recommended for IDPs without atypia diagnosed on CNB who have high-risk factors, while appropriate imaging follow-up may be suitable for those without risk factors.

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disorder with phenotypic and genetic heterogeneity. Recent studies have suggested an oligogenic basis of ALS, in which the co-occurrence of two or more genetic variants has additive or synergistic deleterious effects. To assess the contribution of possible oligogenic inheritance, we profiled a panel of 43 relevant genes in 57 sporadic ALS (sALS) patients and eight familial ALS (fALS) patients from five pedigrees in east China. We filtered rare variants using the combination of the Exome Aggregation Consortium, the 1000 Genomes and the HuaBiao Project. We analyzed patients with multiple rare variants in 43 known ALS causative genes and the genotype-phenotype correlation. Overall, we detected 30 rare variants in 16 different genes and found that 16 of the sALS patients and all the fALS patients examined harbored at least one variant in the investigated genes, among which two sALS and four fALS patients harbored two or more variants. Of note, the sALS patients with one or more variants in ALS genes had worse survival than the patients with no variants. Typically, in one fALS pedigree with three variants, the family member with three variants (Superoxide dismutase 1 (SOD1) p.V48A,  Optineurin (OPTN) p.A433V and TANK binding kinase 1 (TBK1) p.R573H) exhibited much more severe disease phenotype than the member carrying one variant (TBK1 p.R573H). Our findings suggest that rare variants could exert a negative prognostic effect, thereby supporting the oligogenic inheritance of ALS.

The rapid development of such research field as multi-omics and artificial intelligence (AI) has made it possible to acquire and analyze the multi-dimensional big data of human phenomes. Increasing evidence has indicated that phenomics can provide a revolutionary strategy and approach for discovering new risk factors, diagnostic biomarkers and precision therapies of diseases, which holds profound advantages over conventional approaches for realizing precision medicine: first, the big data of patients' phenomes can provide remarkably richer information than that of the genomes; second, phenomic studies on diseases may expose the correlations among cross-scale and multi-dimensional phenomic parameters as well as the mechanisms underlying the correlations; and third, phenomics-based studies are big data-driven studies, which can significantly enhance the possibility and efficiency for generating novel discoveries. However, phenomic studies on human diseases are still in early developmental stage, which are facing multiple major challenges and tasks: first, there is significant deficiency in analytical and modeling approaches for analyzing the multi-dimensional data of human phenomes; second, it is crucial to establish universal standards for acquirement and management of phenomic data of patients; third, new methods and devices for acquirement of phenomic data of patients under clinical settings should be developed; fourth, it is of significance to establish the regulatory and ethical guidelines for phenomic studies on diseases; and fifth, it is important to develop effective international cooperation. It is expected that phenomic studies on diseases would profoundly and comprehensively enhance our capacity in prevention, diagnosis and treatment of diseases.