Graphical abstract graphic file with name fx1.jpg [61]Open in a new tab Highlights * • MPXV infection primarily altered amino acid and hormone metabolism * • 5′-dihydroadenosine (MPXV) and uric acid (HIV-MPXV) are potential molecular markers * • MPXV infection disrupts steroid, cortisol, and cholesterol metabolic pathways * • MPXV lowers testosterone and progesterone levels, linked to innate immunity __________________________________________________________________ Virology; Metabolomics Introduction Monkeypox (mpox) is a zoonotic disease caused by MPXV, a member of the Orthopoxvirus genus of the Poxviridae family.[62]^1 MPXV is closely related to the notorious variola virus and causes a febrile rash similar to but milder than smallpox in humans. Since the World Health Organization declared the complete eradication of smallpox in 1980, MPXV has become the most significant poxvirus in terms of public health impact. Before the turn of the century, there has been an increasing burden of mpox generally among endemic regions; however, the dramatic increase pre-2022 is more difficult to constitute given the lack of surveillance post-1986 through to the early 2000s, and that was largely concentrated in DRC.[63]^2 However, in the 21st century the number of cases has increased dramatically and their geographical scope has expanded globally. In May 2022, mpox outbreaks occurred in several non-endemic areas outside of Africa. As of September 2023, 115 countries had been affected, with a total of more than 90,630 confirmed cases and approximately 161 deaths.[64]^3 The number of cases, epidemic areas, and infected populations in this epidemic were different from previous epidemics, raising concerns about possible changes in the transmission patterns of mpox, and may pose a greater global threat. The clinical spectrum of mpox ranges from mild to severe disease, even death, depending on the clade, the majority of patients have self-limiting disease. For some high-risk patients, such as young children and immunocompromised individuals, may experience complications, including secondary bacterial infections at the lesion site, bronchopneumonia, encephalitis, corneal infections, and sepsis. Additionally, scabs resulting from MPXV infection can leave behind erythema, pigmentation, or even long-lasting scars after healing. Patients with HIV , as a highly susceptible population, account for a significant proportion of Clade IIb MPXV infections in the current epidemic. However, their role in influencing the severity of mpox disease has not yet been thoroughly investigated. For the prevention and treatment of mpox, several smallpox vaccines, such as MVA-BN and LC16, are available and have demonstrated good preventive efficacy. However, these vaccines do not completely block MPXV infection and are associated with multiple side effects. While they may be useful for mpox prevention, further clinical data are needed to fully assess their safety.[65]^4 In terms of treatment, drugs such as tecovirimat (approved in the United States and Europe) and brincidofovir (approved in the United States) have shown some therapeutic effects. However, tecovirimat is currently approved only for smallpox treatment, and its use for mpox has not yet received full regulatory approval. Brincidofovir, on the other hand, is limited by gastrointestinal and hepatic toxicity, making its safety profile inferior to that of tecovirimat.[66]^5 Given these limitations, there is an urgent need to develop more widely accessible, safe, and effective drugs and vaccines specifically targeting MPXV. It is well known that viruses rely entirely on the cellular energy and metabolic resources of the host to provide energy for the different stages of viral infection, such as entry, proliferation and exit for a new round of infection.[67]^6 Viral infections strongly remodel host cell metabolism,[68]^7 which is thought to be associated with changes in organ function and immune response.[69]^8 For example, viral infections switch from oxidative phosphorylation to aerobic glycolysis, with glycolytic lactic acid playing an important role in immune escape in hepatitis B virus.[70]^9 The immunomodulatory metabolite succinate has been recognized as an innate immune signal that enhances interleukin (IL) −1 β production.[71]^10 Vaccinia virus (VACV) promotes infection by activating levels of tricarboxylic acid (TCA) cycle intermediates through specific viral factors during infection.[72]^11 Since VACV prefers glutamine over glucose for efficient replication, asparagine is a critical limiting metabolite for VACV protein synthesis during glutamine deprivation.[73]^12 HIV infection of macrophages leads to metabolic dysregulation and mitochondrial damage, resulting in lipid accumulation. Simultaneously, HIV infection reduces energy dependence and metabolic activity, enabling the evasion of immune recognition and promoting the survival of a small population of infected cells.[74]^13^,[75]^14 Urine originates from the peripheral circulation and is the final product of metabolism. It can be obtained through non-invasive techniques, which is crucial for the diagnosis of various diseases. In addition, the concentrations of metabolites in urine are generally higher than those in plasma, and the risk of infection for the researchers is lower, providing a richer substrate for analysis.[76]^15^,[77]^16 Urine is currently being studied extensively using magnetic resonance imaging (MRI) to analyze the composition of urine and the metabolic effects of various compounds in urine.[78]^17^,[79]^18 Several studies using urine samples from patients with COVID-19 have shown important metabolic changes due to SARS-CoV-2 infection.[80]^19^,[81]^20 Another study used urine metabolites to analyze the metabolic correlation of influenza vaccine response, and to screen for prognostic and diagnostic markers before and after vaccination.[82]^21 A recent study identified the metabolic characteristics of HIV/TB co-infection using a non-targeted urine metabolomics method.[83]^22 The use of urine samples to characterize changes in metabolism following viral infection is receiving increasing attention. In our study, we used LC-MS non-targeted and targeted metabolomics techniques to characterize urinary metabolites in patients with MPXV and HIV. Urine samples from 23 patients with MPXV , 32 patients with HIV, 26 patients with HIV-MPXV co-infected, and 40 healthy individuals were analyzed. It was found that the metabolites changed significantly during viral infection. Steroid hormone biosynthesis, cholesterol metabolism, and cortisol synthesis and secretion pathways were significantly downregulated using an untargeted approach, and then we confirmed our findings using a targeted approach. Further valuable information on metabolic changes was obtained by comparing the MPXV group with other groups. Results Stability assessment and multivariate statistical analysis of metabolomic profiling from patients with monkeypox and HIV Since June 2023, an outbreak of mpox disease has been reported in China. We analyzed 49 confirmed cases of MPXV infection during this period ([84]Table 1; [85]Table S1). All patients were adult males under 45 years of age, and 86% had a history of sexually transmitted infections, such as syphilis (TP) and HIV. Skin eruptions were the most common prodromal symptom among the majority of patients. Individuals sought medical attention and were hospitalized after presenting with obvious clinical symptoms, such as a rash. Among patients with mpox, 53.06% were co-infected with HIV. These individuals exhibited more severe clinical symptoms compared to patients with HIV-negative mpox, including higher rates of fever, lymphadenopathy, and proctitis, with 73.08% requiring antiviral therapy. HIV-positive cases were collected from outpatient visits of patients who had been HIV-positive for many years and were on antiretroviral therapy with controlled viral loads. To investigate changes in circulating metabolites associated with MPXV infection, untargeted metabolomic analysis and targeted metabolomics validation were performed on samples from four groups. ([86]Figure 1A). All urine samples were processed and analyzed by UPLC-MS for metabolomics following the standardized protocol. The results indicated that the response intensity and retention time of the QCs overlapped and the typical metabolite spectral peaks of urine samples obtained by LC-MS were well separated from each other ([87]Figure 1B), indicating that this method has strong robustness, good stability, and small changes caused by instrument errors and the chromatographic and MS conditions were suitable for the measurement of the samples in this study. The PCA result indicated that the instrument was stable and the repeatability of the collected data was better ([88]Figure 1C). It was noteworthy that the metabolites of the HIV-MPXV group were most coincident with the HIV or MPXV group, while the health control group were partially coincident with the other patient group, and they could be well distinguished, which was consistent with the clinical phenotype ([89]Figure S1). The score plots of orthogonal partial least squares discrimination analysis (OPLS-DA) indicated significant separation occurred in different comparison groups ([90]Figures 1D–1H).To evaluate the quality of the model without the risk of fitting, 200 response permutation tests (RPT) were performed on the OPLS-DA model ([91]Figure S2). All pairwise comparison results showed a red dotted line sloping upward, and the intercept between Q2 and the vertical axis was less than 0, indicating that the model was meaningful. Table 1. Clinical presentation of confirmed MPXV cases MPXV cases (n = 49) PLwoH (n = 23) PLWH (n = 26) Age, years (Median) 32(18–45) 35(18–45) 30(25–40) Sex, male [n (%)] 49(100%) 23(100%) 26(100%) Symptoms, no. (%) fever 36(73.47%) 15(65.22%) 21(80.77%) lymphadenopathy 7(14.29%) 2(8.7%) 5(19.23%) rash 49(100%) 23(100%) 26(100%) Comorbidity, no. (%) HIV 26(53.06%) 0 26(100%) Treponema pallidum 16(32.65%) 2(8.7%) 14(53.85%) others[92]^a 2(4.08%) 2(8.7%) 0 Complication, no. (%) proctitis/colitis 9(18.37%) 2(8.7%) 7(26.92%) lymphadenitis 1(0.02%) 1(4.34%) 0 Penile edema 8(16.33%) 5(21.74%) 3(11.54%) others[93]^b 9(18.37%) 4(17.39%) 5(19.23%) Treatment, no. (%) Antihistamine 42(85.71%) 19(82.61%) 23(88.46%) Antibacterial 22(44.9%) 8(34.78%) 14(53.85%) Painkiller 15(30.61%) 2(8.7%) 13(50%) liver protection 6(12.24%) 1(4.34%) 5(19.23%) Hormonal drugs 3(6.12%) 3(13.04%) 0 Antiviral 19(38.78%) 0 19(73.08%) [94]Open in a new tab See also [95]Table S1. ^a Includes HBV and Clonorchis sinensis and Hookworm disease. ^b Anal fistula, anusitis, and perianal abscess. Figure 1. [96]Figure 1 [97]Open in a new tab Stability assessment and multivariate statistical analysis of metabolomic data from patients with MPXV and HIV (A) Schematic diagram of the study design. Four groups-healthy control (n = 40), patients with MPXV+HIV (n = 26), patients with MPXV (n = 23), and patients with HIV (n = 32) were included in this study. (B) Stability of analytical methods based on the chromatograms overlap of QC samples. (C) PCA analysis depicts the overall distribution of all samples and the stability of the whole analysis process. Each point represents an individual QC sample or a real tested sample, where red dots represent QC, green dots represent the real tested sample. (D–H) The score plots of OPLS-DA pairwise comparisons of differential metabolites. The abscissa Score represents the predicted principal component score of the first principal component, and the ordinate orthoScore represents the orthogonal principal component score. Each point generation represents a sample, different colors represent different sample groups, and ellipses represent 95% confidence intervals (See also [98]Figures S1 and [99]S2). Monkeypox infection caused downregulation of a large number of metabolites Based on the OPLS-DA results, a total of 19671 metabolites were identified ([100]Figure S3A), of which 369 metabolites were upregulated and 7880 metabolites were downregulated due to MPXV infection. HIV-MPXV infection caused 246 metabolites up-regulated and 8463 metabolites down-regulated. MS/MS identification of metabolites was achieved by matching with fragment ions and other information of each metabolite in the database, and a total of 559 differential metabolites were identified. The quantity of differentially expressed metabolites between two sample groups could be visualized by a bar chart, a large amount of metabolites were significantly downregulated when infected with MPXV or HIV-MPXV group compared with health group, 190 were significantly upregulated for HIV-MPXV group compared with HIV group, while only 9 metabolites were significantly upregulated and 5 were downregulated for HIV-MPXV group compared with MPXV group ([101]Figure S3B). This indicated that metabolites from patients with MPXV were mainly downregulated compared with healthy controls, and the contribution effect of MPXV or HIV virus to patients with HIV-MPXV coinfection was different. In addition, HIV-MPXV coinfection caused a greater number of differential metabolites compared to single MPXV infection. The expression patterns of differentially expressed metabolites could also be seen from the hierarchical clustering analysis ([102]Figure S3C), MPXV and HIV-MPXV groups had similar expression patterns, most of the differential metabolites showed downregulation. Metabolic alteration patterns resulting from MPXV and HIV infections exhibit notable distinctions, with MPXV playing a major role in co-infection The difference in the expression level of metabolites between the two sample groups could be visualized using a volcano plot. Based on the p-value, the top five metabolites were marked on the volcano plot of each pairwise comparison for differentially expressed metabolites. Compared with the health group, MPXV or HIV-MPXV group showed the significant differences were mainly concentrated in some amino acid metabolites such as 4-guanidinobutanoic acid, N-acetyl-D-phenylalanine, urocanic acid, (S)-2-acetolactate, which are mainly responsible for arginine and proline metabolism, D-amino acid metabolism, histidine metabolism and valine, leucine and isoleucine biosynthesis ([103]Figures 2A and 2C). During HIV infection, the major differential metabolites are not related to amino acid metabolism ([104]Figure 2E). Figure 2. [105]Figure 2 [106]Open in a new tab The difference in the expression level of metabolites between the two sample groups (A, C, E, G, and I) Volcano plot of each pairwise comparison for differentially expressed metabolites. The horizontal axis represents the logarithmic value of Log[2], which represents the multiple difference in quantitative values of a certain metabolite between two samples. The y-axis represents the logarithmic value of - log[10] for p value, and each point in the graph represents a metabolite. The size of the dots represents the size of the VIP value, with red dots representing an increase in difference, blue dots representing a decrease in difference, and gray dots representing metabolites that do not meet the difference screening criteria. The top 5 metabolite names with the smallest p value were displayed. (B, D, F, H, and J) Up- and down-regulation of the top ten differential metabolites were listed by butterfly diagram based on log[2]FC value with p < 0.05. The horizontal axis represents the logarithmic value of Log[2], and the y axis represents the name of the metabolite. (K) Venn Diagram was used to count the number of common and unique differential metabolites in different comparison groups. Different colored regions represent differential metabolites in different groups, while overlapping regions represent differential metabolites shared by multiple groups (See also [107]Figure S3; [108]Table S2). To have a more accurate understanding of the changes in metabolites in patients caused by MPXV or HIV infection, the top ten upregulated and downregulated differentially expressed metabolites were obtained by the fold change (FC) value based on the condition of significant differential metabolites. 4-Hydroxyphenylpyruvic acid, N-acetyl-L-aspartic acid, with the higher FC value as the important metabolites in the alanine pathway, showed significant downregulation in both MPXV and HIV-MPXV groups compared with the healthy group. Various metabolites related to amino acid metabolism had a larger FC and were significantly downregulated after MPXV infection ([109]Figures 2B and 2D). HIV-MPXV coinfection not only caused significant differences in amino acid metabolites, but also significantly affected the synthesis of steroid hormones, such as the significant difference in the amount of tetrahydrocortisone, androsterone glucuronide, and cortolone ([110]Figure 2D). HIV infection mainly affected the metabolites with significant downregulation related to glycerophospholipid metabolism, such as triethanolamine ([111]Figures 2F; [112]Table S2). All these results suggest that metabolite accumulation shows significant differences during MPXV or HIV infection, and HIV-MPXV coinfection caused more significant differential metabolites, which may have an impact on disease severity. Were the differences in metabolites caused by HIV-MPXV coinfection due to the effect of HIV or MPXV? To clarify this issue, we performed pairwise comparison analysis of metabolites between HIV-MPXV coinfection and HIV or MPXV single infection. The difference in metabolite amount caused by HIV-MPXV vs. MPXV group was less than the difference in metabolite amount caused by HIV-MPXV vs. HIV group ([113]Figure S3B). Then, we used a Venn diagram to analyze the common and unique differential metabolites in different comparison groups ([114]Figure 2K). The common differential metabolite between the HIV-MPXV vs. MPXV group and the HIV-MPXV vs. HIV group was tryptophanol, which may serve as a specific metabolite for HIV-MPXV coinfection, and was significantly decreased by approximately 2-fold. Compared with the MPXV group, the HIV-MPXV group mainly caused changes in N-acetyl-L-glutamate-5-semialdehyde (arginine biosynthesis), 15-deoxy-d-12,14-PGJ2 (arachidonic acid metabolism) and ursodeoxycholic acid (secondary bile acid biosynthesis) due to HIV factors, especially arachidonic acid metabolism, HIV plays a synergistic effect ([115]Figures 2G and 2H). Compared with the HIV group, the HIV-MPXV group significantly affected the accumulation of multiple metabolites and affected multiple metabolic pathways, so MPXV had a greater contribution to the HIV-MPXV coinfection effect ([116]Figures 2I and 2J). The above evidence suggests that HIV-MPXV coinfection could cause unique differential metabolites in patients, and MPXV contributes more to the effects of coinfection. HIV infection had a cumulative effect on the degree of metabolic dysfunction in patients with MPXV. Potential metabolite markers for identifying MPXV and HIV-MPXV infection from patients To test whether the identified discriminative metabolites could be used to identify MPXV and HIV-MPXV infection in the study population, we used a random forest-based Monte-Carlo cross validation machine learning feature selection method to select representative markers. The ROC analyses of the top twenty metabolites, the values of AUC obtained from the ROC curves, since ROC curves with an AUC ≤0.8 are not considered, in this way, ROC curves with AUCs values higher than 0.8 were selected. 5′-dehydroadenosine (AUC-ROC = 0.963), urocanic acid (AUC-ROC = 1), and pseudouridine (AUC-ROC = 0.8426) were the best classifiers for MPXV infection, HIV-MPXV infection, and HIV infection, respectively ([117]Figure 3A; [118]Table S3). In addition, the boxplot also indicated that the overall distribution of these three metabolites was significantly different in this studied sample ([119]Figure 3B). Considering that urinary metabolites are influenced by multiple factors, a biomarker panel consisting of multiple metabolites rather than a single biomarker may be more helpful in diagnosing MPXV infection. We also used three potential urinary biomarkers for the diagnosis of MPXV ([120]Figures 3C and 3D). The ROC curves of the MPXV or HIV-MPXV group were not better, but surprisingly, the ROC performance of the three metabolites was better in the HIV group. In conclusion, 5′-dihydroadenosine and uronic acid can be considered as potential biomarkers of MPXV infection and HIV-MPXV coinfection, respectively. The panel consisting of pseudouridine, beta-alanine, and N-amidino-L-glutamate may serve as potential biomarkers for HIV diagnosis. Figure 3. [121]Figure 3 [122]Open in a new tab Potential metabolite markers for diagnosing patients with MPXV and patients with mpox with immunodeficiency by using a random forest-based machine learning algorithm and receiver operating curve (A and C) Random forest-based receiver operating characteristic curve analyses of the diagnostic performances using one or three metabolites, respectively. The horizontal axis represents the false positive rate, and the lower the indicator, the lower the misjudgment rate. The vertical axis represents true positives, and the higher the indicator, the higher the diagnostic accuracy, so the closer it is to the upper left corner of the ROC curve, the better its diagnostic performance will be. (B) Statistics on the overall distribution of metabolites in a set of data. The horizontal axis represents different groups, and the vertical axis represents the range of metabolite quantification values, ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001, ∗∗∗∗p < 0.0001 (Welch’s t-test). (D) Statistics on the overall distribution of three metabolites in a set of data. These metabolites include N-Acetyl-D-phenylalanine, 5′-Dehydroadenosine, 4-Hydroxyphenylpyruvic acid for MPXV VS Health, urocanic acid, 2-Hydroxyestrone, progesterone for HIV-MPXV VS Health and pseudouridine, beta-Alanine, N-Amidino-L-glutamate for HIV VS Health (See also [123]Table S3). MPXV infection triggers disruption in the biosynthesis of steroid hormones To characterize the significant pathways and biological functions involved in the identified metabolite signatures upon MPXV and HIV infection, bioinformatics analysis was performed using MetaboAnalyst and Benjaminiand and Hochberg. As shown, steroid hormone biosynthesis, cholesterol metabolism, cortisol synthesis and secretion, prostate cancer were disrupted when MPXV infection occurred regardless of whether infected with HIV or not ([124]Figures 4A and 4B; [125]Table S4). Of course, some metabolites in significant differences, such as arginine and proline, both of which are crucial components in the urea cycle, presented a remarkable decrease or increase. The effects on the TCA cycle, pyruvate metabolism, and glycolysis combined to indicate a disturbance in energy metabolism. At the same time, the key metabolite of the phenylalanine pathway, phenylacetylglucosamine, also showed a significant disturbance in the MPXV and HIV-MPXV groups ([126]Table S2). ABC transporters, beta-alanine metabolism was significantly affected in HIV infection ([127]Figure 4C). There was an increase in the disruption of the alanine pathway and urea cycle after patients with HIV were infected with MPXV ([128]Figure 4D). In patients infected with MPXV and then re-infected with HIV, arginine biosynthesis was mainly affected ([129]Figure 4E). Figure 4. [130]Figure 4 [131]Open in a new tab KEGG pathway analysis of differential metabolites for each pairwise comparison KEGG annotations and enrichment of differentially expressed metabolites of each pairwise comparison (A) MPXV vs. Health, (B) HIV-MPXV vs. Health, (C)HIV vs. Health, (D) HIV-MPXV vs. HIV, (E) HIV-MPXV vs. MPXV. The impact values are enriched in different metabolic pathways, while the vertical axis represents the enriched pathways. The dot size represents the number of corresponding metabolites on the pathway. Color is related to p value; the redder the color, the smaller the p value, the bluer the color, and the larger the p value. (F) Network diagram of the top five differential metabolic pathways shared by HIV-MPXV vs. Health and Health vs. MPXV (p < 0.05 and impact >0.2) (See also [132]Table S4). Steroid hormone biosynthesis, cholesterol metabolism, and cortisol synthesis and secretion were ranked as the top three metabolic pathways affected in patients with MPXV according to the analysis ([133]Figure 4F). 3 key metabolites, namely cortisol, pregnenolone, and progesterone, involved in the three pathways were observed with significant changes in the MPXV group compared with the healthy group. Furthermore, there were a total of 20 metabolites with significant differences among the three pathways between the two groups. The most enriched differential metabolite was steroid hormone biosynthesis, with the lowest p value. In the MPXV group, 18 metabolites related to steroid hormone synthesis were significantly downregulated, suggesting the dysregulation of the steroid hormone synthesis pathway during the progression of MPXV. Targeted validation confirmed the dysregulation of hormone cycles during the progression of mpox To further quantify the changes in steroid hormone biosynthesis metabolites between the four groups, we performed urine targeted metabolomics analysis. By constructing the standard curve of 43 steroid substances ([134]Table S5), the content of 43 substances in the actual samples can be obtained. In this study ([135]Table S6). The first 10 metabolites were labeled according to their FC values, and the dynamic distribution map of the metabolite content difference was drawn. The steroid hormone biosynthesis metabolites were indeed dysregulated when MPXV infection or HIV-MPXV coinfection in contrast to healthy people, and most metabolites were consistently downregulated ([136]Figures 5A and 5B). Notably, after HIV infection, the vast majority of steroid hormones were upregulated in the top ten dynamically distributed metabolites with FC values, representing a different dysregulation from MPXV infection ([137]Figure 5C). In patients with HIV-MPXV coinfected , the contribution of HIV or MPXV factors to steroid dysfunction was different ([138]Figures 5D and 5E). A total of 7 steroid hormones were significantly downregulated in the MPXV or HIV-MPXV coinfection group ([139]Figure S4; [140]Table S7). To verify the accuracy of the targeted metabolomics results, we used a competitive ELISA method to measure the concentrations of testosterone and progesterone in the urine. The results showed that the levels of testosterone and progesterone in the MPXV group and the HIV-MPXV coinfected group were significantly lower than those in the healthy group, which was consistent with the results of metabolomic validation ([141]Figures 5F and 5G). The hormone metabolic cycle is a complex and very important pathway that affects all aspects of the human body. The levels of cholesterol, progesterone, 17a-hydroxyprogesterone, cortisone, androsterone, and testosterone were significantly downregulated after MPXV infection by targeted metabolomics analysis. These hormones are involved in cholesterol metabolism, cortisol synthesis and secretion, and steroid hormone biosynthesis, three metabolic pathways that are interconnected and work together to maintain hormone metabolism balance ([142]Figure 6). In summary, mpox induces a widespread downregulation of steroid hormone levels, particularly the sex hormones testosterone and progesterone, suggesting a dysregulation of the hormonal cycle during the progression of mpox. Figure 5. [143]Figure 5 [144]Open in a new tab Dynamic distribution map and bar chart of differences in metabolite content related to steroid hormone biosynthesis (A–E) Dynamic distribution map of each pairwise comparison. The horizontal axis represents the cumulative quantity of matter, and the vertical axis represents log[2]FC . Each point represents a substance, green represents the top 10 substances that have been downgraded, and red represents the top 10 substances that have been upgraded. (F and G) The differences in targeted metabolite content of the four groups. The horizontal axis represents the group, the vertical axis represents the content (See also [145]Tables S5 and [146]S6). Figure 6. [147]Figure 6 [148]Open in a new tab An overview of partial metabolites closely related to the steroid hormone biosynthesis, cholesterol metabolism, and cortisol synthesis and secretion Boxplots show levels of these metabolites in the MPXV samples and Health samples. Metabolomics pathway analysis identified the steroid hormone biosynthesis, cholesterol metabolism, and cortisol synthesis and secretion as the major three pathways affected in patients with MPXV. ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001, ∗∗∗∗p < 0.0001 (Welch’s t-test) (See also [149]Figure S4; [150]Table S7). Discussion This study systematically provides a comprehensive view of the metabolic characterization of urine from patients with MPXV and HIV-MPXV coinfected, while also recruiting urine from patients with HIV and healthy individuals as cohort controls. Untargeted metabolomic profiling of urine identified different patterns of metabolite changes in the MPXV group, the HIV group, and the HIV-MPXV group. More than half of the individuals acquiring MPXV infection were HIV-MPXV co-infected. A comprehensive analysis of metabolites from patients with mpox would help us to develop a comprehensive strategy for better management of this disease. The datasets from this study are a resource characterizing significant changes of 198 metabolites in MPXV urine and 206 metabolites in HIV-MPXV urine. And we found 160 overlapping significant metabolites changes in both groups of patients. It indicated that a serious disease progression has been caused by MPXV infection. We identified the metabolites N-acetyl-L-glutamate-5-semialdehyde and N-acetyl-L-aspartic acid as upstream metabolites in arginine biosynthesis, which were significantly downregulated in patients with MPXV infection. The reduction of arginine could disrupt the host-virus relationship and exert antiviral effects.[151]^23^,[152]^24 Some metabolites, including 4-hydroxyphenylpyruvic acid, N-acetyl-D-phenylalanine, urocanic acid, and N-acetyl-L-aspartic acid, were also significantly reduced compared to normal levels, suggesting that the dysregulated synthesis of essential amino acids may be critical for severity and recovery during the course of MPXV disease. 5′-Dihydroadenosine, an intermediate in oxidoreductase reactions involved in purine and pyrimidine metabolism, was significantly reduced in the urine of MPXV-infected patients. Furthermore, ROC curve analysis suggests that 5′-dihydroadenosine could serve as a potential biomarker for mpox. According to a recent study, 5′-dihydroadenosine strongly interacts with the I4L receptor (Ribonucleotide reductase large subunit R1) and alters the C-alpha backbone of I4L,[153]^25 suggesting that 5′-dehydroadenosine plays an important role in MPXV infection. Urocanic acid (UCA), a product of histidine metabolism, has been implicated in various diseases and physiological processes. For instance, reduced levels of UCA in urine have been associated with atopic asthma in children,[154]^26 while elevated levels in feces may serve as a marker in rats[155]^27 In our study, UCA levels were significantly lower in HIV-MPXV co-infected patients compared to the healthy group. ROC curve analysis revealed an AUC value of 1, suggesting that UCA could be a potential biomarker for HIV-MPXV co-infection, warranting further investigation in future studies. Arachidonic acid (AA) metabolism is critical for the initiation and resolution of inflammation,[156]^28 and its deficiency is associated with viral susceptibility.[157]^29 AA can act as an endogenous antiviral compound, was downregulated in MPXV and HIV-MPXV urine, suggesting an imbalance between the pro-inflammatory metabolites and anti-inflammatory metabolites with MPXV infection. In addition, there was evidence suggesting that orthopoxviruses have an obligate requirement for arachidonic acid metabolites during replication,[158]^30 that is, the decrease in amino acid in our study was a result of host interaction with MPXV. Viral replication relies on host cells for nutrients and energy, and virus-infected host cells exhibit a marked increase in glutamine utilization and metabolism. Glutamine metabolism generates ATP and precursors for the synthesis of macromolecules for the assembly of progeny viruses.[159]^31 Other studies have shown that vaccinia virus prefers glutamine to glucose for efficient replication.[160]^12 On the contrary, the derivatives of glutamine were significantly reduced in patients with MPXV, which may be due to the effective treatment with clinical medication. Viral infection not only triggers an innate antiviral response, but also alters metabolic homeostasis in the host. In recent years, several studies have shown that the innate antiviral response is regulated by cellular metabolism, such as cholesterol depletion,[161]^32 bile acid synthesis,[162]^33 lactate accumulation,[163]^34 and lipid oxidation.[164]^35 Steroid hormone biosynthesis is significantly disrupted, and bile acid synthesis was apparently reduced in both patients with MPXV and HIV-MPXV in our study. We speculated that MPXV viruses hijack the host machinery, including steroid hormone biosynthesis, to promote the pathogenesis of MPXV. It has been reported that steroid hormones such as glucocorticoids affect bacterial, protozoan, and viral infections, sex hormones modulate immune responses, and are involved in sex-associated susceptibility to infectious agents.[165]^36 Glucocorticoids and sex hormones are regulated by the hypothalamic-pituitary-adrenal (HPA) axis and the hypothalamic-pituitary-gonadal (HPG) axis, respectively. Most of the metabolites involved in the synthesis of glucocorticoids and sex hormones were consistently downregulated, indicating that MPXV infection severely affected these two hormonal regulatory axes. Two studies reported that glucocorticoids exert their anti-inflammatory effects in part via macrophages and DCs.[166]^37^,[167]^38 The Ugolini laboratory analyzed the effect of glucocorticoids in host resistance to viral infection via group 1 innate lymphoid cells.[168]^39^,[169]^40 Pregnenolone, a glucocorticoid, has been shown to have anti-inflammatory properties through the regulation of lymphoid tissue.[170]^41 Sex hormones such as progesterone induce downstream antiviral genes and promote the innate antiviral response in the host.[171]^42 Sex hormones may also control the immune response through circadian rhythms. Many hormones, such as cortisol, are known to regulate T cell mediated inflammation.[172]^43 Testosterone inhibits inflammation by increasing anti-inflammatory cytokines and decreasing pro-inflammatory cytokines. However, testosterone deficiency has been associated with an increase in pro-inflammatory cytokines.[173]^44 There were experimental studies showing that pro-inflammatory cytokines, such as IL-6, tumor necrosis factor-alpha (TNF-a) and interleukin-1 beta (IL-1b) could inhibit testosterone secretion by modulating the hypothalamic-pituitary-gonadal axis.[174]^45 Another study suggested that low testosterone levels were associated with the severity of COVID-19.[175]^46 Progesterone can regulate the immune response of the whole body, especially in mucosal areas, and can affect the immune response and susceptibility to infection in different mucosal areas.[176]^47 Progesterone has been reported to have immunomodulatory and anti-inflammatory effects on viruses, including SARS-CoV-2.[177]^48 In our study, the levels of progesterone, testosterone and other sex hormones were significantly decreased, suggesting that MPXV may inhibit the secretion of these hormones by hijacking the hypothalamic-pituitary-gonadal axis, thereby reducing the host’s anti-inflammatory ability and causing a disease response in the host. In addition, the virus-induced disruption of sex hormone levels may also cause damage to the testes, testosterone secretion, or sperm production.[178]^49 Taken together, all of this evidence suggests that the function of the innate immune system has been compromised by MPXV and may have some impact on reproductive function. Another point to note is whether the significant changes in metabolites caused by MPXV infection will further induce some secondary metabolic sequelae, which requires further tracking and exploration in the future. In this study, the elucidation of the full spectrum of metabolic characterization will provide a basis for MPXV treatment, will be important in further investigations. We successfully recruited 23 patients with single MPXV infection, 32 patients with HIV infection, and 26 patients with HIV-MPXV co-infection. A comprehensive analysis of the patients' clinical symptoms and omics validation results revealed that MPXV infection induced metabolic disorders, particularly downregulating the steroid hormone synthesis pathway. In patients with HIV-MPXV co-infection, more severe consequences were observed, not only in the extent of metabolic disruption but also in certain clinical manifestations. Unfortunately, due to the relatively small cohort size, the targeted metabolomics validation results did not achieve statistical significance in p values. These findings can be further validated in future studies using larger MPXV cohorts or through cellular and animal-level experiments. Taken together, our metabolomic data presented here provide a comprehensive view of metabolite characterization from patients with MPXV, patients with HIV, and patients with HIV-MPXV and identify disorders in steroid hormone biosynthesis and cholesterol metabolism as potential pathological mechanisms of MPXV. Limitations of the study Our data demonstrated that MPXV infection caused a significant downregulation of hormone levels, but the mechanism of downregulation during the progression of mpox is still unclear. The conclusion that MPXV infection leads to the downregulation of steroid hormones also needs further clarification in cells or animals. In addition, the levels of various inflammatory factors in patients after MPXV infection need further verification. Resource availability Lead contact Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Linna Liu (liulinna7@126.com). Materials availability This study did not generate new unique reagents. Data and code availability * • All metabolomic raw data used in this publication have been deposited on Mendeley Data: [179]https://www.doi.org/10.17632/4f3txzm3j4.1. * • No original code has been produced in this study. * • Any additional information required to reanalyze the data reported in this article is available from the [180]lead contact upon request. STAR★Methods Key resources table REAGENT or RESOURCE SOURCE IDENTIFIER Biological samples __________________________________________________________________ HIV patients Institute of Infectious Disease, Guangzhou Eighth People’s Hospital N/A HIV-MPXV patients Institute of Infectious Disease, Guangzhou Eighth People’s Hospital N/A MPXV patients Institute of Infectious Disease, Guangzhou Eighth People’s Hospital N/A Healthy Institute of Infectious Disease, Guangzhou Eighth People’s Hospital N/A __________________________________________________________________ Chemicals, peptides, and recombinant proteins __________________________________________________________________ Methanol Thermo Fisher Scientific Cat# A454-4 HPLC-grade acetonitrile Thermo Fisher Scientific Cat# A998-4 Ammonia formate Honeywell Fluka Cat#17843250G Formic acid DIMKA Cat#50144-50mL Acetic acid Sigma-Aldrich Cat#[181]AX0073 Isopropanol Merck Cat# [182]W292907 steroid hormone standard Merck N/A __________________________________________________________________ Deposited data __________________________________________________________________ Metabolomics Singapore-MIT Alliance in Research and Technology, Agilent [183]https://doi.org/10.17632/4f3txzm3j4.1 __________________________________________________________________ Software and algorithms __________________________________________________________________ Proteowizard v3.0.8789 N/A R XCMS v3.12.0 N/A proteowizard - MSConvert V3.0.8789 N/A R/pheatmap V1.0.12 N/A R/dendextend v1.15.2 N/A R/cor v4.0.3 N/A R/Wilcox.test/t.test v4.0.3 N/A R/ropls V1.22.0 N/A metaX R/Bioconductor N/A Analyst 1.6 AB Sciex N/A __________________________________________________________________ Other __________________________________________________________________ QTRAP 6500 + SCIEX N/A Q Exactive high resolution mass spectrometer Thermo Fisher Scientific N/A Waters ACQUITY UPLC BEH C18 column Waters N/A Waters UPLC I-Class Plus Waters N/A [184]Open in a new tab Experimental model and study participant details Ethics statement and biosafety All patients were enrolled from GuangZhou Eighth People′s Hospital, Guangzhou Medical University between 2023.6.7 and 2023.7.29. All methods were performed in accordance with relevant guidelines and regulations. This work was approved by the Ethics Committee on Research with Humans from the Institute of Guangzhou Eighth People’s Hospital, Guangzhou Medical University. All participants provided written informed consent, which was the review procedure of the ethics committee. Human participants According to the characteristics of this MPXV infection, all patients are male with a mean age of 32 years (range 18–45 years), and about half of the patients were co-infected with HIV. Therefore, all the patients were into three subgroups for urine metabolism group determination:23 cases in single MPXV infection, 32 cases in HIV infection and 26 cases in MPXV and HIV coinfection. At the same time, age- and sex-matched 40 healthy volunteers were recruited. Method details Sample processing and clinical data collection The mpox samples were collected from patients who exhibited obvious clinical symptoms such as rash and sought medical assistance and admitted to hospital during the onset of the disease. We recorded in detail the demographic characteristics, including age, gender, marital status, admission symptoms, the present history and epidemiological were recorded for each study patient. Diagnosis of MPXV confirmed by RT-PCR test, with rash, fever, headache, back pain, muscle pain, lymph node enlargement and other symptoms. All inpatient clinical data were obtained. The accuracy of the clinical data was confirmed by two clinicians caring for the patients. HIV urine samples were collected from outpatient patients who have been diagnosed for many years. The healthy subjects were recruited as volunteers and were aged between 25 and 45. They were male, healthy and had no underlying diseases and no infectious diseases for nearly one month. Human urine samples were collected from patients in 120 mL sterile urine specimen cups. Upon receipt (typically within 1 h of collection),thesamples were frozen in liquid nitrogen and stored at −80°C refrigerator. All four groups of samples were completed within three months. After collecting all samples, they were thawed and inactivated under biosafety level 3 laboratory containment conditions following the mpox Diagnosis and Treatment Guidelines (2022 Edition) promulgated by National Health Commission and then packaged and stored. Untargeted liquid chromatography with tandem mass spectrometry analysis 100 μL urine samples were extracted by adding 300 μL of pre-cooled methanol, acetonitrile and water (4:2:1, V/V/V). Following vortex extraction for 1 min and incubation at −20°C for 2 h, samples were centrifuged at 4°C for 15 min at 25000 ×g. Samples were moved out of centrifuge and 600 μL supernatant was transferred into a split new EP tube for vacuum freeze-drying. The metabolites were resuspended in 180 μL methanol and and water (1:1, V/V), then vortex until all metabolites were dissolved in the reconstituted solution and centrifuged at 4°C for 15 min at 25000 ×g, and the supernatants were transferred to autosampler vials for analysis. Quality control (QC) samples were prepared by pooling the same volume of each sample to evaluate the reproducibility of the analysis. A quality control (QC) sample was tested in every ten samples using both positive ion mode (BPC+) and negative ion mode (BPC-) BPC detection to validate the reliability of the system. Total ion BPC (base peak chromatograms) of the QC samples were overlayed and compared.The samples were performed on a system including Waters UPLC I-Class Plus(Waters, USA), coupled to a Q Exactive high resolution mass spectrometer (Thermo Fisher Scientific, USA) for separation and detection of metabolites. Extracts were separated on a Waters ACQUITY UPLC BEH C18 column (1.7 μm, 2.1 mm × 100 mm, Waters, USA). The column temperature was maintained at 45°C. The mobile phase consisted of 0.1% formic acid (A) and acetonitrile (B) in the positive mode, and consisted of 10 mM ammonium formate (A) and acetonitrile (B) in the negative mode. The separation was conducted through the following gradient: 0–1 min (2% B); 1–9 min (2–98% B); 9–12 min (98% B); 12–12.1 min (98–2% B); and 12.1–15 min (2% B). The flow rate was 0.35 mL min^−1 and the injection volume was 5 μL. The mass spectrometry parameters for positive/negative ionization modes were set as following: spray voltage 3.8/−3.2 kV; sheath gas flow rate 40 arbitrary units (AU); auxiliary gas flow rate 10 AU; auxiliary gas heater temperature 350°C; capillary temperature 320°C. The untargeted raw data (raw file) was imported into Compound Discoverer 3.3 (Thermo Fisher Scientific, USA) for data processing, including peak extraction, retention time correction within and between groups, additive ion pooling, missing value filling, background removal and metabolite identification. Metabolite identification was conducted against the BGI reference library (Shenzhen, China), mzCloud (Thermo Fisher Scientific), ChemSpider, the Kyoto Encyclopedia of Genes and Genomes (KEGG), the Human Metabolome Database (HMDB), and the Panomix Biomedical Tech Co., Ltd. (Shuzhou, China). Statistical analysis was performed on the resulting normalised peak intensities using metaX software.[185]^50 There are multiple steps in metaX processing, including data quality assessment, missing value imputation, data normalization, univariate and multivariate statistics.[186]^51^,[187]^52 The significant differential metabolites were screened as following: p value <0.05 and VIP value >1. Targeted liquid chromatography with tandem mass spectrometry verification of identified metabolites Targeted liquid chromatography (LC) with tandem mass spectrometry (MS) analysis was performed using an LC-ESI-MS/MS system (UPLC, ExionLC AD, [188]https://sciex.com.cn/; MS, QTRAP 6500+ System, [189]https://sciex.com/). 43 standards were purchased from Olchemlm Ltd. (Olomouc, Czech Republic). The stock solutions of standards were prepared at the concentration of 1 mg/mL in MeOH (Darmstadt, Germany). All stock solutions were stored at −20°C. The stock solutions were diluted with MeOH to working solutions before analysis. Then sample preparation and extraction were carried out, after the sample was thawed, the sample was vortexd for 10 s. 100 μL of the sample was transferred to a centrifuge tube, and mixed with 400 μL of methanol, vortexed for 10 min, stand on ice for 10 min and centrifuged at 12000 r/min for 5 min at 4°C. Took 400 μL of supernatant into a new centrifuge tube and concentrated it at 20°C until it was completely dry. Then the sample was redissolved with 100 μL methanol, vortex for 5 min, centrifuged at 12000 r/min for 3 min at 4°C. After centrifugation, transfer 80 μL of the supernatant for further LC-MS analysis. The UPLC analytical conditions were as follows, HPLC: column, Phenomenex Kinetex C18(1.7 μm, 100 mm × 2.1 mm i.d.); solvent system, 30% acetonitrile/water with 0.04% Acetic acid (A), 50% acetonitrile/isopropanol with 0.04% Acetic acid (B); The gradient was started at 5% B (0 min–1.0 min), increased to 90% B (1.0–10 min), maintained at 90% B (10–12.5 min), finaly ramped back to 5% B (12.6–15 min); flow rate, 0.35 mL/min; temperature, 40°C; injection volume: 5 μL The ESI-MS/MS conditions were as follows, AB 6500+ QTRAP LC-MS/MS System, equipped with an ESI Turbo Ion-Spray interface, operating in positive ion mode and controlled by Analyst 1.6 software (AB Sciex). The ESI source operation parameters were as follows: ion source, turbo spray; source temperature 550°C; ion spray voltage (IS) 5500 V(Positive); curtain gas (CUR) were set at 35.0 psi; DP and CE for individual MRM transitions was done with further DP and CE optimization. A specific set of MRM transitions were monitored for each period according to the neurotransmitters eluted within this period. Steroids contents were detected by MetWare ([190]http://www.metware.cn/) based on the AB Sciex QTRAP 6500 LC-MS/MS platform. The targeted raw data were processed through calibration curve of standards. The calibration curve was analyzed by instrument response and standard concentration. Orthogonal partial least-square (OPLS) discriminant analysis (DA) was used to maximise identification of differences in metabolic profiles between groups. The significantly differential metabolites were screened as the following criteria: FC ≥ 2 or ≤0.5 in two comparison groups. ELISA Considering the sample size, the steroid hormone content of the discovery cohort and the validation cohort were also measured by ELISA. Due to missing parts of the sampleswe selected 23 cases in single MPXV infection, 20 cases in HIV infection, 25 cases in MPXV and HIV coinfection and 20 cases in healthy volunteers from the total number of participants in each group for the experiment. Two substances, testosterone and progesterone, that were significantly lower in metabolomics, then two kits (Beyotime, PT872; Beyotime, PP773) were used to determine the relative levels of testosterone and progesterone according to the manufacturer’s instructions. Quantification and statistical analysis All data were analyzed using R (version 3.5.0) and differential metabolomic analysis was performed using the MSstats R package, which includes log[2] transformation, normalisation and p-value calculation on the Spectronaut and Skyline quantitative data. Data scaling (Scale) of metabolite quantitative values was performed by the Pheatmap package in R, while bidirectional clustering of samples and metabolites was performed to map clustering heatmaps. Differential metabolites were subjected to pathway analysis by MetaboAnalyst,[191]^53 which combines results from powerful pathway enrichment analysis with the pathway topology analysis. The identified metabolites in metabolomics were then mapped to the KEGG pathway for biological interpretation of higher-level systemic functions. The metabolites and corresponding pathways were visualized using KEGG Mapper tool. The statistically significant differences in metabolites between groups were annotated as follows: ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001, ∗∗∗∗p < 0.0001 (Welch’s t-test). Additional resources The work is not part of/involves a clinical trial. Acknowledgments