Transcriptomic signatures of severe acute mountain sickness during rapid ascent to 4,300 m

Abstract

Introduction: Acute mountain sickness (AMS) is a common altitude illness that occurs when individuals rapidly ascend to altitudes ≥2,500 m without proper acclimatization. Genetic and genomic factors can contribute to the development of AMS or predispose individuals to susceptibility. This study aimed to investigate differential gene regulation and biological pathways to diagnose AMS from high-altitude (HA; 4,300 m) blood samples and predict AMS-susceptible (AMS+) and AMS-resistant (AMS─) individuals from sea-level (SL; 50 m) blood samples.

Methods: Two independent cohorts were used to ensure the robustness of the findings. Blood samples were collected from participants at SL and HA. RNA sequencing was employed to profile gene expression. Differential expression analysis and pathway enrichment were performed to uncover transcriptomic signatures associated with AMS. Biomarker panels were developed for diagnostic and predictive purposes.

Results: At HA, hemoglobin-related genes (HBA1, HBA2, and HBB) and phosphodiesterase 5A (PDE5A) emerged as key differentiators between AMS+ and AMS- individuals. The cAMP response element-binding protein (CREB) pathway exhibited contrasting regulatory patterns at SL and HA, reflecting potential adaptation mechanisms to hypoxic conditions. Diagnostic and predictive biomarker panels were proposed based on the identified transcriptomic signatures, demonstrating strong potential for distinguishing AMS+ from AMS- individuals.

Discussion: The findings highlight the importance of hemoglobin-related genes and the CREB pathway in AMS susceptibility and adaptation to hypoxia. The differential regulation of these pathways provides novel insights into the biological mechanisms underlying AMS. The proposed biomarker panels offer promising avenues for the early diagnosis and prediction of AMS risk, which could enhance preventive and therapeutic strategies.

Keywords: NGS - next generation sequencing; acute mountain sickness; biomarker; high altitude; machine learning.

FIGURE 1

(A) The individuals in this study consisted of two groups; those susceptible to acute mountain sickness (AMS+) and those resistant to AMS (AMS-) with samples collected both at sea level (SL) and high altitude (HA) such that there were four groups of blood samples (AMS + _HA, AMS─_HA, AMS + _SL, and AMS─_SL). (B) Differential genomic analyses were performed between these four groups of samples to identify biomarkers for various purposes; diagnosis, prediction, pathogenesis, and protection from AMS.

FIGURE 2

(A) Volcano plot shows the differentially expressed genes (DEGs) (pv < 0.05 and |Log2 Fold change| > 0.7) between AMS+_HA and AMS─_HA in the Pikes Peak and Chamber cohorts, with the x-axis representing the log fold change and the y-axis representing the −log10 p-value. The number of up- and downregulated DEGs is indicated on the side of the graph. (B) The heatmap shows the pathways that are commonly enriched (FDR < 0.05) and have a pathway activity score z that is not equal to zero. The red/blue color stands for the up-/downregulated pathways, respectively.

FIGURE 3

(A) Volcano plot shows the differentially expressed genes (DEGs) (pv < 0.05 and |Log2 Fold change | > 0.7) between AMS + _SL and and AMS─_SL in the Pikes Peak and Chamber cohorts, with the x-axis representing the log fold change and the y-axis representing the −log10 p-value. The number of up- and downregulated DEGs is indicated on the side of the graph. (B) The heatmap displays the pathways that are commonly enriched (FDR <0.05) and have a nonzero pathway activity. The red/blue color stands for the up-/downregulated pathways.

FIGURE 4

Box plots of HBA1, PDE5A, HBB in four categories: AMS + _HA, AMS─_HA, AMS + _SL, and AMS─_SL. The t-test p-values between any two categories are listed on the top of the bars, with three significance levels. Three biomarkers were significantly increased in AMS individuals in response to altitude changes, compared to the non-AMS individuals. The blue dot indicates the mean and middle bar is the median.

FIGURE 5

CREB signaling is downregulated in AMS + _HA compared to AMS- _HA individuals but upregulated when individuals were downregulated at sea level (AMS + _SL vs. AMS- _SL). Green means downregulation and red mean upregulation. Blue network means inhibition while orange network means activation.

FIGURE 6

(A) The forward trajectory of the AUC for different gene combinations based on the 23 common DEGs in Table 2. (B) The ROC curve of the best 5-gene diagnostic biomarker panel.

FIGURE 7

General mechanism of Nitric oxide pathway and CREB signaling. Nitric oxide binds to guanylyl cyclase and results in 3′–5′–cyclic guanosine monophosphate (cGMP) from guanosine 5′-triphosphate (GTP). Increased PDE5 will help degrade cGMP to GMP and thus lead to less cGMP-dependent protein kinase (PKG) and inhibit CREB signaling. The inflammation factors, such as S100B also regulates the NO pathway (Created with BioRender.com).