Phenomics (Cham, Switzerland)最新文献

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.

This study aimed to explore the value of deep learning (DL)-assisted quantitative susceptibility mapping (QSM) in glioma grading and molecular subtyping. Forty-two patients with gliomas, who underwent preoperative T2 fluid-attenuated inversion recovery (T2 FLAIR), contrast-enhanced T1-weighted imaging (T1WI + C), and QSM scanning at 3.0T magnetic resonance imaging (MRI) were included in this study. Histopathology and immunohistochemistry staining were used to determine glioma grades, and isocitrate dehydrogenase (IDH) 1 and alpha thalassemia/mental retardation syndrome X-linked gene (ATRX) subtypes. Tumor segmentation was performed manually using Insight Toolkit-SNAP program (www.itksnap.org). An inception convolutional neural network (CNN) with a subsequent linear layer was employed as the training encoder to capture multi-scale features from MRI slices. Fivefold cross-validation was utilized as the training strategy (seven samples for each fold), and the ratio of sample size of the training, validation, and test dataset was 4:1:1. The performance was evaluated by the accuracy and area under the curve (AUC). With the inception CNN, single modal of QSM showed better performance in differentiating glioblastomas (GBM) and other grade gliomas (OGG, grade II-III), and predicting IDH1 mutation and ATRX loss (accuracy: 0.80, 0.77, 0.60) than either T2 FLAIR (0.69, 0.57, 0.54) or T1WI + C (0.74, 0.57, 0.46). When combining three modalities, compared with any single modality, the best AUC/accuracy/F1-scores were reached in grading gliomas (OGG and GBM: 0.91/0.89/0.87, low-grade and high-grade gliomas: 0.83/0.86/0.81), predicting IDH1 mutation (0.88/0.89/0.85), and predicting ATRX loss (0.78/0.71/0.67). As a supplement to conventional MRI, DL-assisted QSM is a promising molecular imaging method to evaluate glioma grades, IDH1 mutation, and ATRX loss.

Supplementary information: The online version contains supplementary material available at 10.1007/s43657-022-00087-6.

Human meridian (Jingluo) system was hypothesized by traditional Chinese medicine (TCM) for thousands of years, suggesting 12 normal meridian channels going through respective organs, carrying fluid and energy, and laying thermal effects. Some treatments based on meridians have been proved effective. However, existence of meridians has never been confirmed, let alone the lack of measurement for meridian phenotypes. Thermal effect is one of the major phenotypes of meridian metabolism. Infrared photograph was employed to display the picture of meridians since 1970. Unfortunately, no satisfactory results have been obtained. It is possible that only when a certain meridian is activated will there be thermal effect for successful infrared photograph. In this study, 13 types of tea were selected out of the herbs to activate the hypothesized 12 meridians for imagery taking. Forty-two volunteers took part in the experiment lasted for 13 days. Different tea was tested in different day. Infrared imageries of the human bodies were taken immediately after each tea was drunk. The highest temperatures of the fingers, palms, and above the organs were derived from the imageries and analyzed. The temperatures of the organs and fingers possibly connected by 12 hypothesized meridians rose together significantly following the meridian hypothesis. Infrared imageries showed quite clear shapes of the organs activated by different kinds of tea, e.g., heart and kidneys by yellow tea, etc. Some high temperature lines also matched the hypothetic meridians. Our work displayed the probable imageries of all the 12 hypothetic meridians for the first time, and proved with data that different foods may activate different organs following the meridian hypothesis, shedding light on a possible new method of targeted drug designs. Measurements of meridian phenotypes can be developed based on this method of activation.

Supplementary information: The online version contains supplementary material available at 10.1007/s43657-022-00090-x.

An altered proteome in lymph nodes often suggests abnormal signaling pathways that may be associated with diverse lymphatic disorders. Current clinical biomarkers for histological classification of lymphomas have encountered many discrepancies, particularly for borderline cases. Therefore, we launched a comprehensive proteomic study aimed to establish a proteomic landscape of patients with various lymphatic disorders and identify proteomic variations associated with different disease subgroups. In this study, 109 fresh-frozen lymph node tissues from patients with various lymphatic disorders (with a focus on Non-Hodgkin's Lymphoma) were analyzed by data-independent acquisition mass spectrometry. A quantitative proteomic landscape was comprehensively characterized, leading to the identification of featured protein profiles for each subgroup. Potential correlations between clinical outcomes and expression profiles of signature proteins were also probed. Two representative signature proteins, phospholipid-binding proteins Annexin A6 (ANXA6) and Phospholipase C Gamma 2 (PLCG2), were successfully validated via immunohistochemistry. We also evaluated the capability of acquired proteomic signatures to segregate multiple lymphatic abnormalities and identified several core signature proteins, such as Sialic Acid Binding Ig Like Lectin 1 (SIGLEC1) and GTPase of immunity-associated protein 5 (GIMAP5). In summary, the established lympho-specific data resource provides a comprehensive map of protein expression in lymph nodes during multiple disease states, thus extending the existing human tissue proteome atlas. Our findings will be of great value in exploring protein expression and regulation underlying lymphatic malignancies, while also providing novel protein candidates to classify various lymphomas for more precise medical practice.

Supplementary information: The online version contains supplementary material available at 10.1007/s43657-022-00075-w.

Tumor-specific antigens or neoantigens are peptides that are expressed only in cancer cells and not in healthy cells. Some of these molecules can induce an immune response, and therefore, their use in immunotherapeutic strategies based on cancer vaccines has been extensively explored. Studies based on these approaches have been triggered by the current high-throughput DNA sequencing technologies. However, there is no universal nor straightforward bioinformatic protocol to discover neoantigens using DNA sequencing data. Thus, we propose a bioinformatic protocol to detect tumor-specific antigens associated with single nucleotide variants (SNVs) or "mutations" in tumoral tissues. For this purpose, we used publicly available data to build our model, including exome sequencing data from colorectal cancer and healthy cells obtained from a single case, as well as frequent human leukocyte antigen (HLA) class I alleles in a specific population. HLA data from Costa Rican Central Valley population was selected as an example. The strategy included three main steps: (1) pre-processing of sequencing data; (2) variant calling analysis to detect tumor-specific SNVs in comparison with healthy tissue; and (3) prediction and characterization of peptides (protein fragments, the tumor-specific antigens) derived from the variants, in the context of their affinity with frequent alleles of the selected population. In our model data, we found 28 non-silent SNVs, present in 17 genes in chromosome one. The protocol yielded 23 strong binders peptides derived from the SNVs for frequent HLA class I alleles for the Costa Rican population. Although the analyses were performed as an example to implement the pipeline, to our knowledge, this is the first study of an in silico cancer vaccine using DNA sequencing data in the context of the HLA alleles. It is concluded that the standardized protocol was not only able to identify neoantigens in a specific but also provides a complete pipeline for the eventual design of cancer vaccines using the best bioinformatic practices.

Supplementary information: The online version contains supplementary material available at 10.1007/s43657-022-00084-9.

High myopia has long been highly prevalent worldwide with a largely yet unexplained genetic contribution. To identify novel susceptibility genes for axial length (AL) in highly myopic eyes, a genome-wide association study (GWAS) was performed using the genomic dataset of 350 deep whole-genome sequencing data from highly myopic patients. Top single nucleotide polymorphisms (SNPs) were functionally annotated. Immunofluorescence staining, quantitative polymerase chain reaction, and western blot were performed using neural retina of form-deprived myopic mice. Enrichment analyses were further performed. We identified the four top SNPs and found that ADAM Metallopeptidase With Thrombospondin Type 1 Motif 16 (ADAMTS16) and Phosphatidylinositol Glycan Anchor Biosynthesis Class Z (PIGZ) had the potential of clinical significance. Animal experiments confirmed that PIGZ expression could be observed and showed higher expression level in form-deprived mice, especially in the ganglion cell layer. The messenger RNA (mRNA) levels of both ADAMTS16 and PIGZ were significantly higher in the neural retina of form-deprived eyes (p = 0.005 and 0.007 respectively), and both proteins showed significantly upregulated expression in the neural retina of deprived eyes (p = 0.004 and 0.042, respectively). Enrichment analysis revealed a significant role of cellular adhesion and signal transduction in AL, and also several AL-related pathways including circadian entrainment and inflammatory mediator regulation of transient receptor potential channels were proposed. In conclusion, the current study identified four novel SNPs associated with AL in highly myopic eyes and confirmed that the expression of ADAMTS16 and PIGZ was significantly upregulated in neural retina of deprived eyes. Enrichment analyses provided novel insight into the etiology of high myopia and opened avenues for future research interest.

Supplementary information: The online version contains supplementary material available at 10.1007/s43657-022-00082-x.

Quantification of brain oxygenation and metabolism, both of which are indicators of the level of brain activity, plays a vital role in understanding the cerebral perfusion and the pathophysiology of brain disorders. Magnetic resonance imaging (MRI), a widely used clinical imaging technique, which is very sensitive to magnetic susceptibility, has the possibility of substituting positron emission tomography (PET) in measuring oxygen metabolism. This review mainly focuses on the quantitative blood oxygenation level-dependent (qBOLD) method for the evaluation of oxygen extraction fraction (OEF) in the brain. Here, we review the theoretic basis of qBOLD, as well as existing acquisition and quantification methods. Some published clinical studies are also presented, and the pros and cons of qBOLD method are discussed as well.

Skin is a complex ecosystem colonized by millions of microorganisms, including bacteria, fungi, and viruses. Skin microbiota is believed to exert critical functions in maintaining host skin health. Profiling the structure of skin microbial community is the first step to overview the ecosystem. However, the community composition is highly individualized and extremely complex. To explore the fundamental factors driving the complexity of the ecosystem, namely the selection pressures, we review the present studies on skin microbiome from the perspectives of ecology. This review summarizes the following: (1) the composition of substances/nutrients in the cutaneous ecological environment that are derived from the host and the environment, highlighting their proposed function on skin microbiota; (2) the features of dominant skin commensals to occupy ecological niches, through self-adaptation and microbe-microbe interactions; (3) how skin microbes, by their structures or bioactive molecules, reshape host skin phenotypes, including skin immunity, maintenance of skin physiology such as pH and hydration, ultraviolet (UV) protection, odor production, and wound healing. This review aims to re-examine the host-microbe interactions from the ecological perspectives and hopefully to give new inspiration to this field.