WHOLE TRANSCRIPTION ANALYSIS IDENTIFIED THE REGULATION OF HYPOXIA-INDUCIBLE FACTORS IN MONOCYTES WITH IMMUNE SUPPRESSION: IMPLICATIONS FOR CLINICAL OUTCOMES
Sepsis, which is characterized by an aberrant immune response to infection, can quickly escalate to produce life-threatening organ dysfunction ( ). The recent COVID-19 pandemic notably increased the incidence of sepsis, placing substantial strain on healthcare systems globally. There was an alarming 49 million new sepsis cases annually worldwide, resulting in approximately 11 million deaths, which accounted for about 20% of global mortality ( ). The immune response triggered by sepsis is biphasic, initially presenting as an overactive inflammatory stage causing extensive tissue and organ damage, followed by an immunosuppressive phase marked by reduced immune functionality and potential tissue hypoxia ( – ). Immunosuppression has a prognostic connotation of sepsis, yet the precise mechanisms driving its development remain largely unexplored and enigmatic ( ). The pathogenesis of sepsis involves intricate interactions among immune cells, particularly monocytes, which proliferate substantially during infection ( ). Initially pivotal in infection control through phagocytosis and cytokine secretion ( ), monocytes exhibit dysregulation as sepsis advances, enhancing vulnerability to secondary infections ( ). A marked decline in inflammatory response and antigen presentation serves as a significant hallmark of immune suppression in sepsis, with monocytes playing a central role in these processes ( ). Extensive research has established a correlation between the diminished functional capacity of monocytes and adverse sepsis outcomes in animals and patients ( , ). Despite these insights, there remains a gap in targeted studies on monocyte subgroups and a dearth of detailed mechanisms explaining these phenomena. The role of hypoxia-inducible factors (HIFs) in the pathophysiology of sepsis is pivotal ( – ), particularly for supporting cellular survival and tissue oxygenation through promoting angiogenesis, which is vital for restoring blood supply to hypoxic tissues ( ). HIF also plays a role in erythropoiesis and metabolic adaptation, which are crucial during periods of reduced oxygen availability in sepsis. However, the specific functions and effects of HIF vary across different immune cells and species, leading to ongoing debate about its precise role in sepsis and complicating its potential as a therapeutic target ( ). Importantly, HIF is considered a key factor with immunosuppressive properties ( ), yet a thorough exploration of the underlying mechanisms in sepsis, particularly concerning monocyte subgroups, is still lacking. The present study identified distinct trends among hypoxia, inflammation, and antigen presentation signaling genes, which correlated with varying clinical outcomes. Septic survivors displayed a marked proinflammatory and antigen-presenting profile in classical monocytes (CMs), whereas nonsurvivors had elevated HIF-1α levels. During the recovery phase of sepsis, monocytes still exhibited high HIF-1α expression. Mechanistically, innovative findings revealed HIF’s inhibitory effects on CM proportions, inflammatory responses, and antigen presentation in CMs. These results indicate that HIF pathways could be a potential target in the progression of sepsis. 

Transcriptome data analysis (GSE54514) The research utilized RNA-seq data from the GEO dataset GSE54514 ( ). This dataset includes whole blood from both healthy individuals and patients diagnosed with sepsis, encompassing survivors and nonsurvivors of bacterial infections with the blood and lungs as sites of infection. Strict inclusion criteria were applied to minimize interindividual variability. Samples were collected at various intervals postdiagnosis. Data were obtained in MINiML format, incorporating complete information across platforms and samples. The normalization of raw data was achieved via log2 transformation. For microarray data, the “normalize quantiles” function in the preprocessCore R package (v3.4.1) was applied. Probe-to-gene mapping was refined by excluding probes with multiple gene associations and calculating the average expression for genes represented by multiple probes. This streamlined approach provided a robust framework for RNA-seq data analysis in sepsis, ensuring data integrity and reliability ( , ). Differential expression Analysis differential expression was ascertained using the limma R package. Rigorous criteria were set for identifying genes as differentially expressed, which included an adjusted P -value <0.05 coupled with an absolute Log2(Fold Change) > 1. This approach ensures that we robustly identify genes with significant alterations in expression levels between conditions ( ). To visualize these differentially expressed genes, heatmaps were generated using the pheatmap R package. Functional enrichment analysis Following the differential expression analysis, the identified genes were subjected to functional enrichment analysis using the ClusterProfiler R package (version 3.18.0), focusing on KEGG pathway enrichment. This process mapped genes to relevant biological pathways and processes, emphasizing changes in mRNA profiles related to sepsis. KEGG pathways were considered significantly enriched when P < 0.05 ( ). Single-cell data acquisition and preprocessing Single-cell RNA-sequencing (scRNAseq) data of sepsis patients (GSE167363) were acquired from the GEO database ( https://www.ncbi.nlm.nih.gov/geo/ ), encompassing human peripheral blood mononuclear cells (PBMCs) from survivors (n = 3) and nonsurvivors (n = 2) of gram-negative sepsis. All patients included in the study met stringent inclusion criteria. The publicly available dataset used in this study had previously obtained all necessary ethical approvals ( ). Patient classification (S0, S6, N0, N6) was based on survival outcomes and diagnostic times ( ). The Seurat package ( ) was utilized to generate the object and filter out cells with poor quality. Standard preprocessing included the calculation of gene percentages, cell counts, and mitochondrial gene counts. Genes detected in fewer than three cells were excluded, and cells with fewer than 200 genes were disregarded. Retention of genes was stringent, with a threshold set to include genes detected. Cells with gene counts falling outside the range of 200 to 4,000, as well as those with mitochondrial content exceeding 10%, were filtered out. After filtration, the remaining cells were conserved for downstream analysis. Normalization for library size was achieved by scaling UMI counts with a scale factor of 10,000. Log transformation of the data ensued, and variables including “percent.mt,” “nCount_RNA,” and “nFeature_RNA” were regressed for correction of variation using the ScaleData function in Seurat (v3.0.2). The corrected normalized data metrics were applied to the standard analysis as described in the Seurat R package. The top 2000 variable genes were extracted for principal component analysis (PCA). The top 20 principal components were kept for Uniform Manifold Approximation and Projection (UMAP) visualization and clustering. Cell clustering was performed by using the FindClusters function (resolution = 0.5) implemented in the Seurat R package. Pathway analysis Pathway activity analysis was conducted by first computing the mean gene expression for each specified group utilizing the AverageExpression function. This calculation facilitated the comparative assessment of gene activity across groups. Following this, pathway activity scores were determined using Gene Set Variation Analysis (GSVA), with gene sets sourced from the Molecular Signatures Database (MSigDB) ( , ), specifically from the hallmark gene set collections. The pathway activity scores obtained were then visualized using the pheatmap package in R. This visualization approach effectively represented the functional status of the various groups in the study, as indicated by the pathway activity scores. The general workflow of the study design is shown in Figure S1, http://links.lww.com/SHK/C112 . Transcriptome data analysis (GSE46955) The research utilized RNA-seq data from the GEO dataset GSE46955 ( ). This dataset includes monocytes from 6 healthy controls and 16 septic patients, with 8 patients in the acute phase and 8 in the recovery phase of sepsis. The specific analysis steps are similar to GSE54514. Patients and controls Twenty adult sepsis patients were recruited from the Second Xiangya Hospital, Central South University in China. Sepsis was diagnosed by established clinical criteria, originating from blood or lung infections, with samples collected within 24 h of diagnosis. Ten healthy age-matched volunteers served as controls. The baseline characteristics of sepsis patients and healthy volunteers are shown in Table S1, http://links.lww.com/SHK/C122 . Exclusion criteria included the following: (a) preexisting chronic inflammatory diseases; (b) immunosuppressive therapy; (c) history of malignancy; (d) severe liver or renal dysfunction; (e) pregnancy or lactation; and (f) refusal to provide informed consent. All participants signed informed consent for blood sample donation. The study was approved by the Institutional Medical Ethics Review Board of the Second Xiangya Hospital and registered in the Chinese Clinical Trial Registry (no. ChiCTR2000033592). Magnetic-activated cell sorting PBMCs were harvested from heparinized blood using density gradient separation. CD14 + monocytes were efficiently isolated from these PBMCs using a Magnetic-Activated Cell Sorting (MACS) kit (Miltenyi Biotec, Germany), achieving purities exceeding 94%, as confirmed by flow cytometry. Cell culture The human monocytic leukemia-derived cell line THP-1 was obtained from the American Type Culture Collection (ACC 16, ATCC, Manassas, VA). PBMCs or magnetically separated CD14 + monocytes were isolated using density gradient separation from heparinized blood collected preoperatively. THP-1 was grown in suspension in RPMI medium 1640 (Gibco, USA) supplemented with 10% fetal bovine serum (FBS) (Sigma-Aldrich, Berlin, Germany) in 75 cm 2 culture flasks in a humidified atmosphere with 5% CO 2 in air at 37°C in a cell incubator. Details of the reagents used are listed in Table S2, http://links.lww.com/SHK/C123 . In vitro experiments THP-1 cells were initially cultured, followed by exposure to the hypoxia-mimetic agent dimethyloxalylglycine (DMOG) (Cayman, USA) for 12 h, or subjected to hypoxic conditions at 1% O 2 for equivalent durations. Subsequently, cells were stimulated with LPS (Merck KGaA, Darmstadt, Germany) for 12 h to induce an inflammatory response. Finally, cells were reoxygenated for 12 h posthypoxia to simulate the reversion to normoxic conditions; PBMCs or CD14 + monocytes were first exposed to DMOG treatment, followed by a co-culture with LPS for 12 h. Western blotting Proteins quantified via BCA assay were subjected to SDS-PAGE, followed by transfer to a nitrocellulose membrane. After blocking and antibody incubation, protein detection was performed using appropriate chemiluminescent reagents and captured using an Amersham Imager 600. ELISA IL-1β levels were quantified using a human ELISA kit from R&D Systems as described by the manufacturer. RT-PCR Total RNA was extracted from cells using Trizol (Ambion, USA), according to the manufacturer’s instructions. cDNA was synthesized from total RNA using the TaqMan High-Capacity cDNA Reverse Transcription Kit (Thermo Scientific, USA). Gene expression levels were analyzed using the Real-Time PCR system (Applied Biosystems). The sequences of primers and probes are shown in Table S3, http://links.lww.com/SHK/C124 . Flow cytometry PBMCs were labeled with fluorescently conjugated monoclonal antibodies (mAbs) for 30 min at 4°C, followed by thorough washing with FACS buffer (1% FBS in PBS). Flow cytometry analysis was promptly conducted, and the acquired data were analyzed using FlowJo Software (BD Biosciences). To identify and exclude dead cells, a fluorescent dye was utilized in the staining process, and subsequent flow cytometric analysis was performed on the treated cells. Statistics In this study, continuous variables that followed a normal distribution were presented as mean ± SEM, and categorical variables were shown as n (%). To assess the behavior of individual genes over time within groups, linear mixed models (LMMs) were employed. For the analysis of two independent groups, an independent Student’s t test was used. In the case of paired data, a paired t test was utilized. When comparing more than two groups, an ordinary one-way ANOVA was performed, and post hoc comparisons were made using Tukey’s method. For nonparametric data, comparisons between two groups were conducted using the Wilcoxon test, whereas the Kruskal-Wallis test was applied for comparisons involving three or more groups. All statistical analyses were carried out using GraphPad Prism version 8. The levels of statistical significance were set as follows: * P < 0.05; ** P < 0.01; *** P < 0.001; **** P < 0.0001. 

The research utilized RNA-seq data from the GEO dataset GSE54514 ( ). This dataset includes whole blood from both healthy individuals and patients diagnosed with sepsis, encompassing survivors and nonsurvivors of bacterial infections with the blood and lungs as sites of infection. Strict inclusion criteria were applied to minimize interindividual variability. Samples were collected at various intervals postdiagnosis. Data were obtained in MINiML format, incorporating complete information across platforms and samples. The normalization of raw data was achieved via log2 transformation. For microarray data, the “normalize quantiles” function in the preprocessCore R package (v3.4.1) was applied. Probe-to-gene mapping was refined by excluding probes with multiple gene associations and calculating the average expression for genes represented by multiple probes. This streamlined approach provided a robust framework for RNA-seq data analysis in sepsis, ensuring data integrity and reliability ( , ). 

Analysis differential expression was ascertained using the limma R package. Rigorous criteria were set for identifying genes as differentially expressed, which included an adjusted P -value <0.05 coupled with an absolute Log2(Fold Change) > 1. This approach ensures that we robustly identify genes with significant alterations in expression levels between conditions ( ). To visualize these differentially expressed genes, heatmaps were generated using the pheatmap R package. 

Following the differential expression analysis, the identified genes were subjected to functional enrichment analysis using the ClusterProfiler R package (version 3.18.0), focusing on KEGG pathway enrichment. This process mapped genes to relevant biological pathways and processes, emphasizing changes in mRNA profiles related to sepsis. KEGG pathways were considered significantly enriched when P < 0.05 ( ). 

Single-cell RNA-sequencing (scRNAseq) data of sepsis patients (GSE167363) were acquired from the GEO database ( https://www.ncbi.nlm.nih.gov/geo/ ), encompassing human peripheral blood mononuclear cells (PBMCs) from survivors (n = 3) and nonsurvivors (n = 2) of gram-negative sepsis. All patients included in the study met stringent inclusion criteria. The publicly available dataset used in this study had previously obtained all necessary ethical approvals ( ). Patient classification (S0, S6, N0, N6) was based on survival outcomes and diagnostic times ( ). The Seurat package ( ) was utilized to generate the object and filter out cells with poor quality. Standard preprocessing included the calculation of gene percentages, cell counts, and mitochondrial gene counts. Genes detected in fewer than three cells were excluded, and cells with fewer than 200 genes were disregarded. Retention of genes was stringent, with a threshold set to include genes detected. Cells with gene counts falling outside the range of 200 to 4,000, as well as those with mitochondrial content exceeding 10%, were filtered out. After filtration, the remaining cells were conserved for downstream analysis. Normalization for library size was achieved by scaling UMI counts with a scale factor of 10,000. Log transformation of the data ensued, and variables including “percent.mt,” “nCount_RNA,” and “nFeature_RNA” were regressed for correction of variation using the ScaleData function in Seurat (v3.0.2). The corrected normalized data metrics were applied to the standard analysis as described in the Seurat R package. The top 2000 variable genes were extracted for principal component analysis (PCA). The top 20 principal components were kept for Uniform Manifold Approximation and Projection (UMAP) visualization and clustering. Cell clustering was performed by using the FindClusters function (resolution = 0.5) implemented in the Seurat R package. 

Pathway activity analysis was conducted by first computing the mean gene expression for each specified group utilizing the AverageExpression function. This calculation facilitated the comparative assessment of gene activity across groups. Following this, pathway activity scores were determined using Gene Set Variation Analysis (GSVA), with gene sets sourced from the Molecular Signatures Database (MSigDB) ( , ), specifically from the hallmark gene set collections. The pathway activity scores obtained were then visualized using the pheatmap package in R. This visualization approach effectively represented the functional status of the various groups in the study, as indicated by the pathway activity scores. The general workflow of the study design is shown in Figure S1, http://links.lww.com/SHK/C112 . 

The research utilized RNA-seq data from the GEO dataset GSE46955 ( ). This dataset includes monocytes from 6 healthy controls and 16 septic patients, with 8 patients in the acute phase and 8 in the recovery phase of sepsis. The specific analysis steps are similar to GSE54514. 

Twenty adult sepsis patients were recruited from the Second Xiangya Hospital, Central South University in China. Sepsis was diagnosed by established clinical criteria, originating from blood or lung infections, with samples collected within 24 h of diagnosis. Ten healthy age-matched volunteers served as controls. The baseline characteristics of sepsis patients and healthy volunteers are shown in Table S1, http://links.lww.com/SHK/C122 . Exclusion criteria included the following: (a) preexisting chronic inflammatory diseases; (b) immunosuppressive therapy; (c) history of malignancy; (d) severe liver or renal dysfunction; (e) pregnancy or lactation; and (f) refusal to provide informed consent. All participants signed informed consent for blood sample donation. The study was approved by the Institutional Medical Ethics Review Board of the Second Xiangya Hospital and registered in the Chinese Clinical Trial Registry (no. ChiCTR2000033592). 

PBMCs were harvested from heparinized blood using density gradient separation. CD14 + monocytes were efficiently isolated from these PBMCs using a Magnetic-Activated Cell Sorting (MACS) kit (Miltenyi Biotec, Germany), achieving purities exceeding 94%, as confirmed by flow cytometry. 

The human monocytic leukemia-derived cell line THP-1 was obtained from the American Type Culture Collection (ACC 16, ATCC, Manassas, VA). PBMCs or magnetically separated CD14 + monocytes were isolated using density gradient separation from heparinized blood collected preoperatively. THP-1 was grown in suspension in RPMI medium 1640 (Gibco, USA) supplemented with 10% fetal bovine serum (FBS) (Sigma-Aldrich, Berlin, Germany) in 75 cm 2 culture flasks in a humidified atmosphere with 5% CO 2 in air at 37°C in a cell incubator. Details of the reagents used are listed in Table S2, http://links.lww.com/SHK/C123 . 

experiments THP-1 cells were initially cultured, followed by exposure to the hypoxia-mimetic agent dimethyloxalylglycine (DMOG) (Cayman, USA) for 12 h, or subjected to hypoxic conditions at 1% O 2 for equivalent durations. Subsequently, cells were stimulated with LPS (Merck KGaA, Darmstadt, Germany) for 12 h to induce an inflammatory response. Finally, cells were reoxygenated for 12 h posthypoxia to simulate the reversion to normoxic conditions; PBMCs or CD14 + monocytes were first exposed to DMOG treatment, followed by a co-culture with LPS for 12 h. 

Proteins quantified via BCA assay were subjected to SDS-PAGE, followed by transfer to a nitrocellulose membrane. After blocking and antibody incubation, protein detection was performed using appropriate chemiluminescent reagents and captured using an Amersham Imager 600. 

IL-1β levels were quantified using a human ELISA kit from R&D Systems as described by the manufacturer. 

Total RNA was extracted from cells using Trizol (Ambion, USA), according to the manufacturer’s instructions. cDNA was synthesized from total RNA using the TaqMan High-Capacity cDNA Reverse Transcription Kit (Thermo Scientific, USA). Gene expression levels were analyzed using the Real-Time PCR system (Applied Biosystems). The sequences of primers and probes are shown in Table S3, http://links.lww.com/SHK/C124 . 

PBMCs were labeled with fluorescently conjugated monoclonal antibodies (mAbs) for 30 min at 4°C, followed by thorough washing with FACS buffer (1% FBS in PBS). Flow cytometry analysis was promptly conducted, and the acquired data were analyzed using FlowJo Software (BD Biosciences). To identify and exclude dead cells, a fluorescent dye was utilized in the staining process, and subsequent flow cytometric analysis was performed on the treated cells. 

In this study, continuous variables that followed a normal distribution were presented as mean ± SEM, and categorical variables were shown as n (%). To assess the behavior of individual genes over time within groups, linear mixed models (LMMs) were employed. For the analysis of two independent groups, an independent Student’s t test was used. In the case of paired data, a paired t test was utilized. When comparing more than two groups, an ordinary one-way ANOVA was performed, and post hoc comparisons were made using Tukey’s method. For nonparametric data, comparisons between two groups were conducted using the Wilcoxon test, whereas the Kruskal-Wallis test was applied for comparisons involving three or more groups. All statistical analyses were carried out using GraphPad Prism version 8. The levels of statistical significance were set as follows: * P < 0.05; ** P < 0.01; *** P < 0.001; **** P < 0.0001. 

Increasing HIF gene expression and compromised inflammatory responses and antigen presentation in nonsurviving septic patients In the GSE54514 dataset analysis using whole blood samples, distinct molecular profiles were observed, particularly in the expression of HIF-1α, HLA-DRs, and inflammatory genes. Compared to healthy individuals, septic patients exhibited upregulated HIF-1α and inflammatory pathways, along with downregulated antigen presentation pathways (Fig. , A and B) (Fig. S2, http://links.lww.com/SHK/C113 ). Notably, HIF-1α expression was significantly elevated on day 5 postdiagnosis in nonsurviving septic patients (Fig. C). An LMM analysis further quantified these variations, identifying significant effects of time and group for NF-κB, and a distinct group effect for HLA-DRB between survivors and nonsurvivors (Fig. , D and E). The receiver operating characteristic (ROC) curves underscored the diagnostic potential of decreased NF-κB and HLA-DR levels in predicting poor survival in sepsis, with notable areas under the curve (Fig. F). These findings highlight the variable expression of HIF, antigen presentation, and inflammatory genes across different survival outcomes in sepsis patients. Reduced monocyte proportion and sustained high HIF-1α expression in nonsurviving septic patients To investigate the relationship between immune cell composition and immune responses in sepsis, a comprehensive analysis was performed on the cellular composition of PBMCs and hypoxic gene expression in septic patients, utilizing the GSE167363 dataset from the GEO database. Initially, gene enrichment analysis in septic patients revealed that monocytes exhibited relatively stronger enrichment of signals in inflammation and hypoxia pathways (Fig. S3, http://links.lww.com/SHK/C114 ). To further analyze the expression of hypoxia and inflammation genes in immune cells across different patient outcomes, patients were divided into four groups based on survival outcomes and diagnostic timelines. NS0 and NS6 represent nonsurvivors at 0 and 6 h postdiagnosis, respectively, whereas S0 and S6 correspond to survivors at the same respective time points. The analysis indicated a trend of higher monocyte proportion in the immune cell populations of survivor groups (S0, S6) compared to nonsurvivors (Fig. A). Next, HIF-1α gene expression in monocytes in the surviving and nonsurviving groups was examined. In the survivor cohort (S0 to S6), a progressive decrease in HIF-1α expression was observed, contrasting with a persistent elevation of HIF-1α expression in the nonsurvivor group (N0 to N6) (Fig. B). Additionally, monocytes from survivors exhibited elevated levels of both NF-κB and HLA-DR (Fig. S4, A and B, http://links.lww.com/SHK/C115 ). These findings revealed variations in both the monocyte proportion and the gene expression levels of HIF-1α, NF-κB, and HLA-DR in monocytes from septic patients across different diagnostic times and survival outcomes, indicating a possible association with their prognoses. Classical monocytes in septic nonsurvivors exhibit high HIF-1α expression and compromised proinflammatory response features To explore the functional roles of monocyte subgroups in sepsis, an analysis was conducted using the GSE167363 dataset, categorizing patients into septic survivor and nonsurvivor groups. Extensive labeling of immune cell populations was performed (Fig. A). The analysis indicated that nonsurvivors experienced a more pronounced hypoxic state along with a subdued inflammatory response (Fig. B). In contrast, septic survivors showed a significantly higher proportion of CMs (Fig. C). CMs in survivors displayed a high gene enrichment of proinflammatory response compared to nonsurvivors, indicating the reduced inflammatory response in nonsurvivors (Fig. D). To explore the relationship between HIF-1α expression variability in monocyte subgroups and patient survival, a comparative analysis was conducted, revealing significant enrichment of hypoxia-related genes (Fig. D) and elevated HIF-1α expression in the CMs of nonsurvivors (Fig. E). High HIF-1α expression and low proinflammatory response in CMs of septic nonsurvivors indicate a potential HIF-mediated functional remodeling of monocytes. Monocytes display sustained high HIF-1α expression in postsepsis recovery The initial analysis of the GSE46955 dataset assessed monocytes’ function in sepsis. Gene expression in monocytes from healthy individuals was compared to those from septic recovery patients, revealing 171 genes downregulated and 367 genes upregulated in the sepsis group (Fig. A). The volcano plot indicated heightened HIF-1α expression in monocytes during the recovery stage of sepsis (Fig. B). In further analysis of gene expression trends, an observed upregulation of HIF-1α was noted in sepsis (Fig. C), with HIF-1α remaining highly expressed even after sepsis recovery (Fig. C). ROC curves highlighted the diagnostic potential of high HIF-1α levels in predicting the active phase and recovery phase of sepsis, with significant areas under the curve noted (Fig. D). The persistently elevated gene expression of HIF-1α, even during the recovery phase of sepsis, suggests that HIF-1α may be a key molecule influencing the entire course of sepsis. HIF further exacerbates immune suppression in monocytes of septic patients HLA-DR low monocyte has been proven to be a crucial subgroup for immune suppression for sepsis development ( ). To elucidate the immunosuppressive phenotype of monocytes in sepsis, we examined monocytes from age- and sex-matched healthy individuals and septic patients for HLA-DR and inflammatory cytokines. Notably, there was a downregulation in the proportion of HLA-DR high and an upregulation of the HLA-DR low monocyte subgroup (Fig. A), as well as a decrease in the expression of HLA-DR in both monocytes and CMs in septic patients (Fig. , B and C) (Fig. S5, A and B, http://links.lww.com/SHK/C116 ). PBMCs from septic patients were co-cultured with increasing concentrations of LPS. The results revealed an increased proportion of CMs and MFI of HLA-DR in CMs (Fig. , D and E). To assess the impact of HIF stabilization on monocyte subgroups, DMOG co-culture resulted in a significant reduction in the proportion of CMs and HLA-DR expression in CMs (Fig. , F and G), alongside a reduced HLA-DR high subpopulation and an increased HLA-DR low subpopulation (Fig. H). Further exploration into DMOG’s impact on inflammatory cytokine expression, both intracellular and extracellular, revealed a decrease in the secretion of inflammatory cytokines from CD14 + monocytes (Fig. I), and a reduction in intracellular inflammatory cytokines in the HLA-DR low monocyte subpopulation (Fig. J). To further explore different immune features in septic patients, the expression of inflammatory cytokines within HLA-DR low monocyte subgroups was found to be lower in nonsurviving septic patients compared to survivors (Fig. K), suggesting that low inflammation and antigen-presenting capabilities may be typical in nonsurviving sepsis patients. These results indicate that HIF signaling may inhibit HLA-DR and inflammatory features, thereby associating with the occurrence of immune suppression in septic monocytes. HIF induces immune suppression by inhibiting the p-IKK signal in human monocyte To clarify the mechanism of HIF-1α in immune suppression in human monocytes, the THP-1 cell line was first utilized for in vitro analysis. A marked upregulation of the HIF-1α gene was found following LPS stimulation (Fig. S6A, http://links.lww.com/SHK/C117 ), and DMOG further sustained the expression of the HIF-1α protein (Fig. S6B, http://links.lww.com/SHK/C117 ). The effect of DMOG on LPS-primed immune responses was next examined, and suppression of HLA-DR MFI in THP-1 cells was noticed (Fig. , A and B). Additionally, DMOG and hypoxic conditions (1% O 2 ) exerted an inhibitory impact on LPS-induced cytokine expression, which was reversed by reoxygenation (Fig. S7, http://links.lww.com/SHK/C118 , and Fig. , C and D). IKK-NF-κB phosphorylation is a pivotal process for the expression of IL-1β and IL-6 ( ), and it is integral to the regulation of antigen presentation capabilities ( , ). Probing into the mechanisms involved, it was found that both DMOG and hypoxia inhibited the phosphorylation of proteins in the IKK-NF-κB pathway (Fig. E, Fig. S8, http://links.lww.com/SHK/C119 ), an effect reversed by reoxygenation (Fig. S9, http://links.lww.com/SHK/C120 ). In contrast, hypoxia had no impact on the expression of proteins of NLRP3, caspase-1, and cleaved-caspase-1 (Fig. S10, A and B, http://links.lww.com/SHK/C121 ), suggesting that inflammatory suppression in these contexts is independent of the assembly of inflammasomes. Furthermore, the IKK inhibitor BMS-345541 was used to further suppress the immune response in THP-1 monocytes. Interestingly, upon administration of BMS-345541, hypoxia failed to further inhibit the release of inflammatory cytokines and HLA-DR expression (Fig. , F and G), highlighting that the effects of hypoxia are mediated through the inhibition of the IKK signaling pathway. These findings further corroborate the crucial role of HIF in dampening monocyte immune responses by suppressing the IKK pathway, thereby reducing inflammatory cytokine production and antigen presentation. 

In the GSE54514 dataset analysis using whole blood samples, distinct molecular profiles were observed, particularly in the expression of HIF-1α, HLA-DRs, and inflammatory genes. Compared to healthy individuals, septic patients exhibited upregulated HIF-1α and inflammatory pathways, along with downregulated antigen presentation pathways (Fig. , A and B) (Fig. S2, http://links.lww.com/SHK/C113 ). Notably, HIF-1α expression was significantly elevated on day 5 postdiagnosis in nonsurviving septic patients (Fig. C). An LMM analysis further quantified these variations, identifying significant effects of time and group for NF-κB, and a distinct group effect for HLA-DRB between survivors and nonsurvivors (Fig. , D and E). The receiver operating characteristic (ROC) curves underscored the diagnostic potential of decreased NF-κB and HLA-DR levels in predicting poor survival in sepsis, with notable areas under the curve (Fig. F). These findings highlight the variable expression of HIF, antigen presentation, and inflammatory genes across different survival outcomes in sepsis patients. 

To investigate the relationship between immune cell composition and immune responses in sepsis, a comprehensive analysis was performed on the cellular composition of PBMCs and hypoxic gene expression in septic patients, utilizing the GSE167363 dataset from the GEO database. Initially, gene enrichment analysis in septic patients revealed that monocytes exhibited relatively stronger enrichment of signals in inflammation and hypoxia pathways (Fig. S3, http://links.lww.com/SHK/C114 ). To further analyze the expression of hypoxia and inflammation genes in immune cells across different patient outcomes, patients were divided into four groups based on survival outcomes and diagnostic timelines. NS0 and NS6 represent nonsurvivors at 0 and 6 h postdiagnosis, respectively, whereas S0 and S6 correspond to survivors at the same respective time points. The analysis indicated a trend of higher monocyte proportion in the immune cell populations of survivor groups (S0, S6) compared to nonsurvivors (Fig. A). Next, HIF-1α gene expression in monocytes in the surviving and nonsurviving groups was examined. In the survivor cohort (S0 to S6), a progressive decrease in HIF-1α expression was observed, contrasting with a persistent elevation of HIF-1α expression in the nonsurvivor group (N0 to N6) (Fig. B). Additionally, monocytes from survivors exhibited elevated levels of both NF-κB and HLA-DR (Fig. S4, A and B, http://links.lww.com/SHK/C115 ). These findings revealed variations in both the monocyte proportion and the gene expression levels of HIF-1α, NF-κB, and HLA-DR in monocytes from septic patients across different diagnostic times and survival outcomes, indicating a possible association with their prognoses. 

To explore the functional roles of monocyte subgroups in sepsis, an analysis was conducted using the GSE167363 dataset, categorizing patients into septic survivor and nonsurvivor groups. Extensive labeling of immune cell populations was performed (Fig. A). The analysis indicated that nonsurvivors experienced a more pronounced hypoxic state along with a subdued inflammatory response (Fig. B). In contrast, septic survivors showed a significantly higher proportion of CMs (Fig. C). CMs in survivors displayed a high gene enrichment of proinflammatory response compared to nonsurvivors, indicating the reduced inflammatory response in nonsurvivors (Fig. D). To explore the relationship between HIF-1α expression variability in monocyte subgroups and patient survival, a comparative analysis was conducted, revealing significant enrichment of hypoxia-related genes (Fig. D) and elevated HIF-1α expression in the CMs of nonsurvivors (Fig. E). High HIF-1α expression and low proinflammatory response in CMs of septic nonsurvivors indicate a potential HIF-mediated functional remodeling of monocytes. 

The initial analysis of the GSE46955 dataset assessed monocytes’ function in sepsis. Gene expression in monocytes from healthy individuals was compared to those from septic recovery patients, revealing 171 genes downregulated and 367 genes upregulated in the sepsis group (Fig. A). The volcano plot indicated heightened HIF-1α expression in monocytes during the recovery stage of sepsis (Fig. B). In further analysis of gene expression trends, an observed upregulation of HIF-1α was noted in sepsis (Fig. C), with HIF-1α remaining highly expressed even after sepsis recovery (Fig. C). ROC curves highlighted the diagnostic potential of high HIF-1α levels in predicting the active phase and recovery phase of sepsis, with significant areas under the curve noted (Fig. D). The persistently elevated gene expression of HIF-1α, even during the recovery phase of sepsis, suggests that HIF-1α may be a key molecule influencing the entire course of sepsis. 

HLA-DR low monocyte has been proven to be a crucial subgroup for immune suppression for sepsis development ( ). To elucidate the immunosuppressive phenotype of monocytes in sepsis, we examined monocytes from age- and sex-matched healthy individuals and septic patients for HLA-DR and inflammatory cytokines. Notably, there was a downregulation in the proportion of HLA-DR high and an upregulation of the HLA-DR low monocyte subgroup (Fig. A), as well as a decrease in the expression of HLA-DR in both monocytes and CMs in septic patients (Fig. , B and C) (Fig. S5, A and B, http://links.lww.com/SHK/C116 ). PBMCs from septic patients were co-cultured with increasing concentrations of LPS. The results revealed an increased proportion of CMs and MFI of HLA-DR in CMs (Fig. , D and E). To assess the impact of HIF stabilization on monocyte subgroups, DMOG co-culture resulted in a significant reduction in the proportion of CMs and HLA-DR expression in CMs (Fig. , F and G), alongside a reduced HLA-DR high subpopulation and an increased HLA-DR low subpopulation (Fig. H). Further exploration into DMOG’s impact on inflammatory cytokine expression, both intracellular and extracellular, revealed a decrease in the secretion of inflammatory cytokines from CD14 + monocytes (Fig. I), and a reduction in intracellular inflammatory cytokines in the HLA-DR low monocyte subpopulation (Fig. J). To further explore different immune features in septic patients, the expression of inflammatory cytokines within HLA-DR low monocyte subgroups was found to be lower in nonsurviving septic patients compared to survivors (Fig. K), suggesting that low inflammation and antigen-presenting capabilities may be typical in nonsurviving sepsis patients. These results indicate that HIF signaling may inhibit HLA-DR and inflammatory features, thereby associating with the occurrence of immune suppression in septic monocytes. 

To clarify the mechanism of HIF-1α in immune suppression in human monocytes, the THP-1 cell line was first utilized for in vitro analysis. A marked upregulation of the HIF-1α gene was found following LPS stimulation (Fig. S6A, http://links.lww.com/SHK/C117 ), and DMOG further sustained the expression of the HIF-1α protein (Fig. S6B, http://links.lww.com/SHK/C117 ). The effect of DMOG on LPS-primed immune responses was next examined, and suppression of HLA-DR MFI in THP-1 cells was noticed (Fig. , A and B). Additionally, DMOG and hypoxic conditions (1% O 2 ) exerted an inhibitory impact on LPS-induced cytokine expression, which was reversed by reoxygenation (Fig. S7, http://links.lww.com/SHK/C118 , and Fig. , C and D). IKK-NF-κB phosphorylation is a pivotal process for the expression of IL-1β and IL-6 ( ), and it is integral to the regulation of antigen presentation capabilities ( , ). Probing into the mechanisms involved, it was found that both DMOG and hypoxia inhibited the phosphorylation of proteins in the IKK-NF-κB pathway (Fig. E, Fig. S8, http://links.lww.com/SHK/C119 ), an effect reversed by reoxygenation (Fig. S9, http://links.lww.com/SHK/C120 ). In contrast, hypoxia had no impact on the expression of proteins of NLRP3, caspase-1, and cleaved-caspase-1 (Fig. S10, A and B, http://links.lww.com/SHK/C121 ), suggesting that inflammatory suppression in these contexts is independent of the assembly of inflammasomes. Furthermore, the IKK inhibitor BMS-345541 was used to further suppress the immune response in THP-1 monocytes. Interestingly, upon administration of BMS-345541, hypoxia failed to further inhibit the release of inflammatory cytokines and HLA-DR expression (Fig. , F and G), highlighting that the effects of hypoxia are mediated through the inhibition of the IKK signaling pathway. These findings further corroborate the crucial role of HIF in dampening monocyte immune responses by suppressing the IKK pathway, thereby reducing inflammatory cytokine production and antigen presentation. 

The role of HIF-1α in sepsis has been a subject of increasing interest in sepsis due to its potential involvement in immune modulation and metabolic reprogramming. Our study revealed that in septic nonsurvivors, there was a marked increase in HIF-1α gene expression, accompanied by enhanced signs of immune suppression, as evidenced by diminished inflammatory responses and compromised antigen presentation in peripheral blood immune cells. Further in-depth analysis indicated a reduction in inflammation and a rise in HIF-1α expression in CMs of nonsurvivors, coupled with impaired antigen presentation in septic monocytes. Through in vitro monocyte studies, we shed light on the role of HIF-1α in dampening inflammation, diminishing the proportion of CMs, and impairing antigen presentation through inhibiting the IKK phosphorylation pathway. Based on these findings, we propose that HIF-1α may represent a critical immunosuppressive factor influencing the progression of sepsis (Fig. ). In secondary analysis of the published data, our study present several innovative findings. The original study (GSE54514) emphasized lymphocyte depletion and reduced antigen-presenting capacity in nonsurviving septic patients ( ). In contrast, our secondary analysis found increased HIF-1α gene expression and compromised inflammatory responses. Original findings (GSE167363) demonstrated immunosuppression and suggested that HIF-1α may shift metabolism from oxidative phosphorylation to glycolysis in monocytes within the nonsurvivor group ( ). Building on this, our analysis explores the immunosuppressive characteristics specifically displayed in CM subsets. Additionally, initial findings (GSE46955) showed that HIF-1α upregulates tissue remodeling and antimicrobial functions of monocytes ( ). Our secondary analysis suggests the potential role of HIF-1α throughout the entire course of sepsis, even after recovery. Building upon the original literature, our study enriched the transcriptomic analytical methods. By taking into account individual differences and the impact of time factors on gene expression, LMMs were employed in the secondary analysis of the GSE54514 dataset to evaluate individual genes over time within groups of sepsis survivors and nonsurvivors. In the analysis of the GSE167363 dataset, the UMAP plots specifically illustrated the distribution of CMs in monocytes and the expression patterns of HIF-1α genes in different cells among sepsis nonsurvivors and survivors. Additionally, GSVA was used to determine pathway activity scores related to inflammatory and cellular hypoxia response, enhancing our understanding of cellular heterogeneity. For the GSE46955 dataset, ROC curve analysis was conducted using the expression of HIF-1α genes to predict sepsis at different stages. HIF, discovered by Semenza and Wang in 1995 ( , ), has shown promise in clinical trials for treating chronic kidney disease ( , ) and renal cell carcinoma ( ). However, progress in HIF inhibition for sepsis treatment remains slow. Early severe inflammation in sepsis increases cellular oxygen consumption and bacterial replication, elevating HIF-1α levels ( , ). As sepsis progresses, organ damage and reduced oxygenation cause hypoxia, further increasing HIF-1α expression. Our assays demonstrated that inflammatory responses significantly enhance HIF protein expression, supporting previous studies linking inflammation to hypoxia ( , ). The specific role of HIF in sepsis remains controversial, as evidenced by its complex implications across various studies. Elevated HIF-1 mRNA levels in septic shock patients suggest severe hypoxia, yet these levels do not consistently correlate with clinical outcomes ( ). Furthermore, genetic variations in HIF-1 have proven to be unreliable indicators of 30-day mortality in severe sepsis ( ), and a clinical trial (NCT02163473) aimed at investigating HIF as a biomarker for septic shock is still inconclusive. On the other hand, animal studies indicate that inhibiting HIF signaling could potentially reduce organ damage in sepsis ( , ). The diverse experimental results, influenced by variations in HIF subtypes and differences between species, underscore the urgent need for continued research into targeting HIF. It is a recognized feature of sepsis that decreased HLA-DR expression on monocytes is indicative of immunosuppression ( , ). Although clinical studies have demonstrated the successful enhancement of HLA-DR expression in patients with severe sepsis ( ), the role of immune suppression in mediating sepsis progression remains limited, especially considering that an unbalanced inflammatory response and secondary infections contribute minimally to late mortality ( , ). Our in vitro studies have shown that HIF inhibits both monocyte inflammation and antigen presentation, reaffirming its well-documented immunosuppressive role ( ). Nevertheless, it is concerning that animal experiments have revealed that HIF-1α stabilizers failed to protect mice from the adverse effects of polymicrobial sepsis ( ), underscoring the potential risks of employing HIF stabilizers in sepsis treatment. The depletion of monocyte counts and poor immune status is also a characteristic phenotype of immune suppression, as evidenced by patients who initially present with low monocyte counts experiencing increased mortality, higher rates of bacteremia, and more severe organ dysfunction ( ). The functional state of monocytes, particularly CM subsets, plays a crucial role in determining sepsis outcomes, with higher CM proportions linked to better survival in septic shock ( ). Metabolic reprogramming ( ), especially involving HIF-1α, is increasingly recognized as essential for monocyte dysfunction during sepsis ( ). Our research innovatively discovers that sepsis survivors had significantly higher proportions of CMs, which uniquely displayed higher inflammatory features and a lower HIF gene expression. Additionally, we discovered that stabilizing HIF in vitro can suppress the proportion of CMs and inhibit monocyte inflammatory responses. Furthermore, contrary to the belief that HIF diminished expression during sepsis recovery ( ), our data indicate that HIF-1α remains elevated in monocytes from recovered septic patients compared to healthy controls. This prolonged elevation of HIF-1α suggests its potential as a therapeutic target not only during active phase sepsis but also throughout the recovery phase. The mechanisms of immune suppression in sepsis are partly obscured by species differences, as HLA-DR is not expressed in wild-type mice, limiting its study as a key target. Central to the inflammatory response, TLR4/NFκB also regulates HLA-DR ( ). Our research proves HIF’s crucial role in inhibiting IKK phosphorylation, providing a new explanation for HIF’s involvement in immune suppression. Additionally, our research has innovatively revealed that the HLA-DR low monocyte subgroup in septic nonsurvivors is associated with reduced cellular inflammation. These suggest that HIF’s suppression of inflammation may concurrently inhibit HLA-DR, contributing to immune suppression. In the present study, several limitations have been identified. First, the datasets adhered to different sepsis definitions: GSE167363 used the Sepsis-3 guidelines, whereas GSE54514 and GSE46955 followed the 2003 and 1992 criteria, respectively. Patients diagnosed according to the Sepsis-3 guidelines are considered to present with a more severe form of sepsis and exhibit higher mortality rates compared to those diagnosed using previous criteria ( ). Consequently, the analysis results of GSE167363 may reflect more severe cases of sepsis. This variation in sepsis definitions may affect the generalizability of our findings. Second, the limited number of subjects in the GSE167363 database, comprising only three survivors and two nonsurvivors of gram-negative sepsis, restricts the ability to achieve statistically significant differences in certain results. Third, the rapid degradation of HIF-1α proteins in nonhypoxic conditions poses a significant challenge in elucidating HIF-1α at the protein level in clinical specimens. Additionally, this study does not include animal research to further demonstrate the immunosuppressive effects of HIF signaling. In conclusion, the present study suggests that HIF-1α in monocytes may be associated with reduced immune inflammatory responses and antigen presentation, potentially serving as a marker for sepsis progression. Future research will continue to explore the clinical implications of HIF inhibitors and other therapeutic approaches, aiming to improve outcomes for sepsis patients. 

