Using Composite Phenotypes to Reveal Hidden Physiological Heterogeneity in High-Altitude Acclimatization in a Chinese Han Longitudinal Cohort

Hepcidin mediates the disorder of iron homeostasis and mitochondrial function in mice under hypobaric hypoxia exposure

The human body to prolonged high-altitude environments exposure can cause the decreased hepcidin and the disorder of iron homeostasis. Few animal studies have examined the molecular basis of iron metabolism disorder and the impact of hepcidin on the complex phenotype of high-altitude environments. In this study, we display reduced hepcidin level and impaired iron homeostasis upon hypobaric hypoxia, accompanied by mitochondrial dysfunction, abnormal blood cells and metabolic disorder. Importantly, mice overexpressing hepcidin show resistance to hypoxia-induced mitochondrial injury in liver and improve the body homeostasis. The comprehensive characterization of the metabolic pathways analysis demonstrates the porphyrin metabolism modulated by hepcidin may play a significant role in improving blood cell abnormalities upon high altitude hypoxia. In addition, hepcidin transcription is inhibited by histone modification and interruption of SMAD signaling pathway in hepatocytes in response to hypoxic stress to decrease hepcidin level. Our data highlight the key role of hepcidin in regulating the complex phenotype of high-altitude environments by altered metabolic and mitochondrial function. These results provide a theoretical reference to understanding the role of hepcidin in maintaining physiological function in high altitude, thus assisting in the development of strategies to better prevent and alleviate imbalance of energy homeostasis and adverse effects.

Role of immune cell homeostasis in research and treatment response in hepatocellular carcinoma

Introduction Recently, immune cells within the tumor microenvironment (TME) have become crucial in regulating cancer progression and treatment responses. The dynamic interactions between tumors and immune cells are emerging as a promising strategy to activate the host's immune system against various cancers. The development and progression of hepatocellular carcinoma (HCC) involve complex biological processes, with the role of the TME and tumor phenotypes still not fully understood. Therefore, it is essential to investigate the importance of immune cell homeostasis in HCC. Additionally, understanding the molecular mechanisms and biological functions underlying tumor-immune cell interactions is increasingly recognized as vital for improving therapeutic outcomes in clinical settings. Methods A total of 790 HCC samples were selected from public databases and real-world independent clinical cohorts. Machine learning methods, focusing on immune-related indicators, were applied to these samples. The Boruta algorithm was employed to develop an ICI score, which was used to assess patient prognosis and predict responses to immunotherapy. Additionally, a new immune subtype analysis of HCC was performed. Cellular-level experiments confirmed the interaction between TME-related factors and the tumor microenvironment in HCC. To further validate the predictive power of the ICI score, a clinical cohort study was conducted at an independent clinical center. Results By evaluating immune gene expression levels, immune cell abundance, Immunescore, and Stromalscore, we initially identified three distinct immune subtypes of HCC, each showing significant differences in survival rates and heterogeneity. Subsequently, DEGs from 1022 immune subtypes were used to classify HCC samples into three immune genotypes, each characterized by distinct prognosis and tumor immune microenvironment (TIME) profiles. Furthermore, we developed the ICI score, a novel immunophenotyping method for HCC, which revealed significant variations based on gender, stage, progression, and DNA mutation profiles (p < 0.05). The ICI score also effectively predicted responses to immunotherapies, particularly through the chemokine signaling, focal adhesion, and JAK/STAT signaling pathways. Conclusion This research demonstrated that TME and immunophenotyping clusters can enhance prognostic accuracy for HCC patients. The independent prognostic indicators identified underscore the connection between tumor phenotype and the immune environment in HCC.

Assessing and mitigating batch effects in large-scale omics studies

Batch effects in omics data are notoriously common technical variations unrelated to study objectives, and may result in misleading outcomes if uncorrected, or hinder biomedical discovery if over-corrected. Assessing and mitigating batch effects is crucial for ensuring the reliability and reproducibility of omics data and minimizing the impact of technical variations on biological interpretation. In this review, we highlight the profound negative impact of batch effects and the urgent need to address this challenging problem in large-scale omics studies. We summarize potential sources of batch effects, current progress in evaluating and correcting them, and consortium efforts aiming to tackle them.

Multi-omics data integration using ratio-based quantitative profiling with Quartet reference materials

Characterization and integration of the genome, epigenome, transcriptome, proteome and metabolome of different datasets is difficult owing to a lack of ground truth. Here we develop and characterize suites of publicly available multi-omics reference materials of matched DNA, RNA, protein and metabolites derived from immortalized cell lines from a family quartet of parents and monozygotic twin daughters. These references provide built-in truth defined by relationships among the family members and the information flow from DNA to RNA to protein. We demonstrate how using a ratio-based profiling approach that scales the absolute feature values of a study sample relative to those of a concurrently measured common reference sample produces reproducible and comparable data suitable for integration across batches, labs, platforms and omics types. Our study identifies reference-free 'absolute' feature quantification as the root cause of irreproducibility in multi-omics measurement and data integration and establishes the advantages of ratio-based multi-omics profiling with common reference materials.

Recent advances in predicting acute mountain sickness: from multidimensional cohort studies to cutting-edge model applications

High-altitude illnesses, encompassing a spectrum of health threats including Acute Mountain Sickness (AMS), pose significant challenges to individuals exposed to high altitude environments, necessitating effective prophylaxis and immediate management. Given the variability in individual responses to these conditions, accurate prediction of high-altitude illnesses onset is of paramount importance. This review systematically consolidates recent advancements in research on predicting AMS by evaluating existing cohort data, predictive models, and methodologies, while also delving into the application of emerging technologies. Through a thorough analysis of scholarly literature, we discuss traditional prediction methods anchored in physiological parameters (e.g., heart rate, respiratory frequency, blood pressure) and biochemical markers, as well as the integration and utility of novel technologies such as biosensors, genetic testing, and artificial intelligence within high-altitude prediction research. While conventional pre-diction techniques have been extensively used, they are often constrained by limitations in accuracy, reliability, and multifactorial influences. The advent of these innovative technologies holds promise for more precise individual risk assessments and personalized preventive and therapeutic strategies across various forms of AMS. Future research endeavors must pivot decisively towards the meticulous identification and stringent validation of innovative predictive biomarkers and models. This strategic re-direction should catalyze intensified interdisciplinary cooperation to significantly deepen our mechanistic insights into the pathogenesis of AMS while refining existing prediction methodologies. These groundbreaking advancements harbor the potential to fundamentally transform preventive and therapeutic frameworks for high-altitude illnesses, ultimately securing augmented safety standards and wellbeing for individuals operating at elevated altitudes with far-reaching global implications.

Aldehyde dehydrogenase 2 serves as a key cardiometabolic adaptation regulator in response to plateau hypoxia in mice

High-altitude heart disease (HAHD) is a complex pathophysiological condition related to systemic hypobaric hypoxia in response to transitioning to high altitude. Hypoxia can cause myocardial metabolic dysregulation, leading to an increased risk of heart failure and sudden cardiac death. Aldehyde dehydrogenase 2 (ALDH2) could regulate myocardial energy metabolism and plays a protective role in various cardiovascular diseases. This study aims to determine the effects of plateau hypoxia (PH) on cardiac metabolism and function, investigate the associated role of ALDH2, and explore potential therapeutic targets. We discovered that PH significantly reduced survival rate and cardiac function. These effects were exacerbated by ALDH2 deficiency. PH also caused a shift in the myocardial fuel source from fatty acids to glucose; ALDH2 deficiency impaired this adaptive metabolic shift. Untargeted/targeted metabolomics and transmission electron microscopy revealed that ALDH2 deficiency promoted myocardial fatty-acid deposition, leading to enhanced fatty-acid transport, lipotoxicity and mitochondrial dysfunction. Furthermore, results showed that ALDH2 attenuated PH-induced impairment of adaptive metabolic programs through 4-HNE/CPT1 signaling, and the CPT1 inhibitor etomoxir significantly ameliorated ALDH2 deficiency-induced cardiac impairment and improved survival in PH mice. Together, our data reveal ALDH2 acts as a key cardiometabolic adaptation regulator in response to PH. CPT1 inhibitor, etomoxir, may attenuate ALDH2 deficiency-induced effects and improved cardiac function in response to PH.

Phenomic Studies on Diseases: Potential and Challenges

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.

Proteomic and clinical biomarkers for acute mountain sickness in a longitudinal cohort

Ascending to high-altitude by non-high-altitude natives is a well-suited model for studying acclimatization to extreme environments. Acute mountain sickness (AMS) is frequently experienced by visitors. The diagnosis of AMS mainly depends on a self-questionnaire, revealing the need for reliable biomarkers for AMS. Here, we profiled 22 AMS symptom phenotypes, 65 clinical indexes, and plasma proteomic profiles of AMS via a combination of proximity extension assay and multiple reaction monitoring of a longitudinal cohort of 53 individuals. We quantified 1069 proteins and validated 102 proteins. Via differential analysis, machine learning, and functional association analyses. We found and validated that RET played an important role in the pathogenesis of AMS. With high-accuracies (AUCs > 0.9) of XGBoost-based models, we prioritized ADAM15, PHGDH, and TRAF2 as protective, predictive, and diagnostic biomarkers, respectively. Our findings shed light on the precision medicine for AMS and the understanding of acclimatization to high-altitude environments.

Phenotype correlations reveal the relationships of physiological systems underlying human ageing

Ageing is characterized by degeneration and loss of function across multiple physiological systems. To study the mechanisms and consequences of ageing, several metrics have been proposed in a hierarchical model, including biological, phenotypic and functional ageing. In particular, phenotypic ageing and interconnected changes in multiple physiological systems occur in all ageing individuals over time. Recently, phenotypic age, a new ageing measure, was proposed to capture morbidity and mortality risk across diverse subpopulations in US cohort studies. Although phenotypic age has been widely used, it may overlook the complex relationships among phenotypic biomarkers. Considering the correlation structure of these phenotypic biomarkers, we proposed a composite phenotype analysis (CPA) strategy to analyse 71 biomarkers from 2074 individuals in the Rugao Longitudinal Ageing Study. CPA grouped these biomarkers into 18 composite phenotypes according to their internal correlation, and these composite phenotypes were mostly consistent with prior findings. In addition, compared with prior findings, this strategy exhibited some different yet important implications. For example, the indicators of kidney and cardiovascular functions were tightly connected, implying internal interactions. The composite phenotypes were further verified through associations with functional metrics of ageing, including disability, depression, cognitive function and frailty. Compared to age alone, these composite phenotypes had better predictive performances for functional metrics of ageing. In summary, CPA could reveal the hidden relationships of physiological systems and identify the links between physiological systems and functional ageing metrics, thereby providing novel insights into potential mechanisms underlying human ageing.

Malignant Tumor Purity Reveals the Driven and Prognostic Role of CD3E in Low-Grade Glioma Microenvironment

The tumor microenvironment (TME) contributes to the initiation and progression of many neoplasms. However, the impact of low-grade glioma (LGG) purity on carcinogenesis remains to be elucidated. We selected 509 LGG patients with available genomic and clinical information from the TCGA database. The percentage of tumor infiltrating immune cells and the tumor purity of LGG were evaluated using the ESTIMATE and CIBERSORT algorithms. Stromal-related genes were screened through Cox regression, and protein-protein interaction analyses and survival-related genes were selected in 487 LGG patients from GEO database. Hub genes involved in LGG purity were then identified and functionally annotated using bioinformatics analyses. Prognostic implications were validated in 100 patients from an Asian real-world cohort. Elevated tumor purity burden, immune scores, and stromal scores were significantly associated with poor outcomes and increased grade in LGG patients from the TCGA cohort. In addition, CD3E was selected with the most significant prognostic value (Hazard Ratio=1.552, P<0.001). Differentially expressed genes screened according to CD3E expression were mainly involved in stromal related activities. Additionally, significantly increased CD3E expression was found in 100 LGG samples from the validation cohort compared with adjacent normal brain tissues. High CD3E expression could serve as an independent prognostic indicator for survival of LGG patients and promotes malignant cellular biological behaviors of LGG. In conclusion, tumor purity has a considerable impact on the clinical, genomic, and biological status of LGG. CD3E, the gene for novel membrane immune biomarker deeply affecting tumor purity, may help to evaluate the prognosis and develop individual immunotherapy strategies for LGG patients. Evaluating the ratio of differential tumor purity and CD3E expression levels may provide novel insights into the complex structure of the LGG microenvironment and targeted drug development.