Transcriptomic Evidence of Acquired Cannabis Hypersensitivity in Cannabinoid Hyperemesis Syndrome

Transcriptomic Evidence of Acquired Cannabis Hypersensitivity in Cannabinoid Hyperemesis Syndrome | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Transcriptomic Evidence of Acquired Cannabis Hypersensitivity in Cannabinoid Hyperemesis Syndrome Andrew Meltzer, Ziva Cooper, Ryan Heidish, Aditya Loganathan, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8116530/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 9 You are reading this latest preprint version Abstract Background Cannabinoid hyperemesis syndrome (CHS) is a paradoxical and increasingly prevalent disorder characterized by recurrent vomiting in people with chronic cannabis use. Despite its growing clinical impact, the underlying mechanisms remain poorly understood. Methods A genome-wide RNA sequencing was used to characterize transcriptomic differences and identify potential pathways involved in CHS pathogenesis. In this pilot study, whole blood RNA sequencing was performed on 7 patients with CHS and 7 matched controls. Differentially expressed genes (DEGs) were identified, annotated and analyzed by automated and manual analysis. RNA sequences were further analyzed by digital isotyping for HLA Class I and II allele usage. Results CHS was associated with marked activation of the adaptive immune system, including upregulation of B-cell related immunoglobin transcripts and altered expression of T cell, monocyte, and neutrophil-related transcripts. DEGs also suggested increased matrix degradation, and reduced adhesion and protease inhibitor transcripts, consistent with impaired gut barrier function. Digital HLA isotyping revealed increased MHC Class I expression, Class II allele restriction, and down-regulation of IgE receptor transcripts, a known response to elevated IgE levels in allergic hypersensitivity. Conclusions Together, these findings suggest that CHS may represent an acquired, gut-restricted, immune-mediated hypersensitivity response to cannabis. This transcriptomic analysis provides new mechanistic insights into CHS and lays groundwork for future studies to identify biomarkers, clarify immune triggers, and develop targeted therapies. Cannabinoid Hyperemesis Syndrome Cannabis Cannabis allergy Hypersensitivity Transcriptomics Blood biomarkers Immune activation HLA genotype Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Key Points Central Problem: Cannabinoid hyperemesis syndrome (CHS) has become increasingly prevalent concurrent with both legalization and increasing potency of cannabis. Prior studies suggested there may be inherited changes in the endogenous cannabinoid system (ECS) that triggers a paradoxical hyperemesis in susceptible patients. Key Results: There were not notable differences in the RNA levels of the major ECS components in CHS patients. However, there were striking differences in the RNA expression levels of B cell immunoglobin genes, as well as higher expression of HLA Class I genes, and potential restriction of the HLA Class II allele usage in the CHS patients. The pattern of changes is consistent with an acquired, gut restricted hypersensitivity to cannabis that may be more likely in subjects with specific HLA alleles. Implications: A ‘gut hypersensitivity’ theory suggests that further studies should include analysis of the HLA allele usage and acquired immunity pathways in CHS patients. Hypersensitivity reactions to cannabis could be treated via a range of therapeutics developed for other gut hypersensitivities, including antihistamines, IgE-directed therapies, and induced tolerance to specific allergens. Background Cannabinoid Hyperemesis Syndrome (CHS) is emerging as a critical public health issue, with the incidence of CHS growing by 150% annually over the past ten years and continuing to rise nationally due to the widespread legalization of cannabis alongside an increasing potency of cannabis products. As additional jurisdictions legalize cannabis use 1 , there has been a twentyfold increase over the past 3 decades to about 17.7 million Americans that are daily or near-daily (DND) cannabis users 2 . This rise has coincided with a dramatic increase in the tetrahydrocannabinol (THC) content of marijuana, from less than 1% in the 1970s to 4% in 1995 and 16% in 2022 3 , Characterized by episodes of unremitting nausea, cyclic vomiting, and severe abdominal pain, CHS has become the most common cannabis-related cause of emergency department (ED) visits in the United States. The syndrome, with characteristic scream vomiting (scromiting) due to the intensity of symptoms, is frequently associated with compulsive hot showers, which patients use to temporarily relieve their discomfort 4 . In the absence of biomarkers, and having numerous possible etiologies, the diagnosis of CHS has been difficult, with an average range of 3–6 years before accurate diagnosis 5 . CHS is paradoxical because cannabis is historically recognized for its anti-emetic properties, particularly for chemotherapy-induced nausea. Yet, in some chronic DND cannabis users, the drug becomes highly pro-emetic. Symptoms of CHS are minimally responsive to conventional anti-emetics such as ondansetron, but may respond to antipsychotics like haloperidol or olanzapine 6 . The most effective strategy for eliminating CHS and reducing repeat ED visits is cannabis abstinence. Once affected, patients often remain in a sensitized state for months to years, even after cessation of cannabis use. A prevailing theory posits that CHS results from pharmacological overstimulation of the endocannabinoid system (ECS), especially through alterations in CB1 and CB2 receptor signaling 7 . The ECS is a complex network that regulates homeostasis throughout the body, including the gastrointestinal tract. Changes in the activity or expression of ECS enzymes and receptors have been proposed as one mechanism underlying CHS 8 . To better characterize the population affected by CHS, our group recently conducted a survey of 1,052 participants with self-identified CHS 9 . Participants were typically young (mean age 32.1 years), initiated DND cannabis use at a mean age of 19.3 years, and reported high daily consumption (≥ 3 times daily, 82.2%). Rates of healthcare utilization were high, with 84.9% reporting at least one ED visit and 44.2% at least one hospitalization for CHS. Importantly, individuals who initiated DND cannabis use before age 18 were more likely to require hospitalization. Despite the growing prevalence and burden of CHS, its underlying mechanisms remain poorly understood, and there are no established biomarkers to confirm diagnosis. The present study addresses this gap by applying genome-wide RNA sequencing to while blood from CHS patients and matched controls. Our goals are to generate new hypotheses regarding CHS pathophysiology, identify non-invasive blood-based biomarkers, and lay the groundwork for future preventive and therapeutic strategies. Methods Patients. CHS patients were enrolled in the Emergency Department (ED) and provided written informed consent under IRB Protocol #NCR 213728. Patients were diagnosed with CHS according to ROME IV criteria supplemented by supportive clinical features and exclusion of other causes. Control subjects matched for sex were enrolled under a related IRB protocol #NCR213645 with explicit opt-in consent for future use of samples. It was not appropriate or possible to involve patients or the public in the design, or conduct, or reporting, or dissemination plans of our research, although a large survey was conducted to understand CHS patients and their healthcare usage 9 . RNA purification. Whole blood (3 ml) was collected into Tempus Blood RNA Tubes according to the manufacturer’s instructions and then frozen at − 80°C. High-quality mRNA was isolated from whole blood using the Invitrogen Tempus Spin RNA Isolation kit with an on-column DNase treatment to remove any contaminating genomic DNA. Final elution of RNA was performed using DEPC-treated water at 70°C, yielding RNA that was stored at − 80°C until further use. RNA analysis and sequencing. Purified RNA was quantified using Agilent TapeStation analysis. A standardized amount of RNA from each sample was submitted to the GW Genomics Core Facility for sequencing, employing the Illumina Stranded Total RNA Preparation and Ligation with Ribo-Zero Plus, followed by a NextSeq 2000 P1 XLEAP-SBS 300 Cycle sequencing run. RNA and sequencing quality metrics are summarized in Table 1 . RNAseq analysis. Paired-end FASTQ files were uploaded to the Galaxy platform and processed using standard settings. Low quality reads were removed with Trimmomatic, and sequence quality was measured using FastQC. Alignment was performed against the HG38 genome using HISAT2. Transcript counts were obtained with FeatureCount, and differential expression analysis was performed with DESeq2. Gene classification was conducted via automated ontology/pathway analysis using NIH DAVID 10 , supplemented by manual curation through PubMed and GeneCard searches. Blood microbiome ddPCR . To quantify total bacterial 16S and fungal 28S DNA fragments in blood, DNA was isolated from Tempus-preserved samples with minor modifications to published methods 11 . Briefly, microbial lysis was achieved by treating samples with a lytic chemical solution and subjecting them to multiple freeze/thaw cycles, followed by nucleic acid precipitation using phenol/chloroform/isoamyl alcohol. Purified DNA (50 ng) was analyzed by ddPCR using primers specific for bacterial 16S (Gram-negative and Gram-positive) and fungal 23S rRNA, as previously described 12 . HLA isotyping. Seq2HLA, a Python/R-scripted tool, was used to generate probable human leukocyte antigen (HLA) genotypes from RNA-Seq datasets. FASTQ files were aligned using Bowtie2 to a custom index of all known HLA alleles. Class I and II HLA expression (RPKM) was quantified, and HLA haplotypes were inferred to 4-digit resolution, accompanied by a confidence p-value for each call. Analyses were performed on the Galaxy platform using default parameters. ddPCR confirmation of host immune activation markers. Host immune RNA levels were quantified using the Bio-Rad Digital Droplet PCR (ddPCR) System. Extracted RNA was reverse transcribed into cDNA using gene-specific primers and RNAse H + reverse transcriptase (iScript, BioRad). cDNA was partitioned into droplets, PCR amplification was performed on a Bio-Rad C100 thermocycler, and probe fluorescence intensity was measured using the Bio-Rad QX200 Reader. Results Patient Characteristics In this pilot study, seven CHS patients were enrolled for in-depth RNAseq analysis of whole blood RNA. Their demographic and clinical laboratory information is summarized in Table 1 . Control subjects were recruited from the clinical/research staff with an effort to match gender, because six of the seven CHS patients were female. Notable features among CHS patients included a predominance of females (6/7), self-reported Black race (7/7), a borderline elevated white blood cell count (10.4 K/µL, where normal is 4–11 K/µL), an elevated neutrophil count (8.14 K/µL, where normal is 1.8–7.7), and an elevated neutrophil/lymphocyte ratio (5.7, where normal is 1-3.5). Control subjects were more racially diverse (4 White, 1 Black, 1 Asian, 1 Hispanic), and complete blood counts (CBCs) were not performed (normal reference ranges shown). Co-morbidities among the CHS cohort included a hemorrhagic ovarian cyst (1), esophageal perforation with pneumomediastinum (1), and chronic gastritis with hypertension (1). All seven CHS patients received haloperidol (Haldol) in the ED for CHS management. Table 1 Demographic and Laboratory Values of CHS Patients CHS Patients Controls Average S.E.M. (or nml range) N 7 7 Age (yrs) 28.60 3.2 42.71 Sex (%Male) 14.20 14.20 Race (%White) 0 57.10 Temp (C) 36.81 0.05 37 Systolic BP (mm Hg) 148.43 16.21 120–130 Haldol Rx (%) 100.00 0 White Blood Cell Ct (K/uL) 10.37 1.27 4.0–11.0 Red Blood Cell Ct (M/ul) 4.73 0.34 4.1–5.9 Hemoglobin (g/dL) 13.00 0.77 12.1–17.2 Hematocrit (% RBC) 38.26 1.95 36.1–50.3 Mean Corp Vol (fL) 81.90 2.67 80–100 Mean Corp Hemo (pg) 27.79 1.05 27–34 Mean Corp Hem Conc (g/dL) 33.93 0.81 32–36 RBC Dist Width-CV (%) 13.81 1.07 11.5–14.5 Platelet count (K/uL) 329.57 32.17 150–450 Mean Platelet Vol (fL) 10.24 0.22 7.5–12.0 Neut Ct (K/uL) 8.14 1.23 1.8–7.7 Lymph Ct (K/uL) 1.50 0.14 1–4.8 Neut/Lympho Ratio (NLR) 5.70 1.00 1.0–3.0 Mono Ct (K/uL) 0.66 0.12 0.2–0.8 Eos Ct (K/uL) 0.02 0.01 0.0–0.4 Baso # (K/uL) 0.02 0.01 0.0–0.1 Immature Gran/Bands (%) 0.04 0.01 0.0-0.05 RNA Yield (ug/3 ml) 17.84 5.26 11.47 RNA Integrity (RIN) 8.37 0.11 8.27 Total Paired Reads 17106641 707816 16703231 RNAseq analysis of Whole Blood RNA RNA purification yielded somewhat, though not significantly, higher total RNA in CHS patients compared to controls (CON = 11.5 µg/3 mL blood, CHS = 17.8 µg/3 mL, p = 0.26), a difference largely attributed to a single CHS patient with an unusually high RNA yield. Both groups demonstrated high and comparable RNA quality (RIN scale 1–10: CON = 8.27, CHS = 8.37, p = 0.46) as assessed by Agilent TapeStation. Illumina RNAseq produced similar numbers of paired end reads for CHS patients (17.1 million) and controls (16.7 million, p = 0.77). Aligned read counts were also comparable at about 50% of total reads between groups. DeSeq2 analysis identified 220 transcripts upregulated and 140 transcripts downregulated (fold change > 4, p < 0.05 uncorrected; see Supplementary Data 1 for volcano plot). All transcript data, including raw read counts, normalized read counts (nRC), fold changes, and p-values for the 360 differentially expressed genes (DEG) are provided in Supplementary Data 2. Transcripts were annotated by both automated and manual approaches and subsequently grouped into relevant classifications, such as upregulated (Table 2 ) and downregulated transcripts (Table 3 ). Hierarchical clustering of the 360 DEGs revealed that upregulated transcripts exhibit a unique pattern in each CHS patient, as opposed to a uniform pattern across the cohort (Fig. 1 ). Examination of the most elevated transcripts in individual patients (“hot” clusters) showed each included some Ig-related transcripts (e.g., HLA-E and IGKV2D-29), indicating that while each patient mounts a distinct immune response, there is a shared elevation of immune activation markers. The larger number of increased versus decreased transcripts reflects individual variation among CHS patients, primarily within the adaptive and innate immunity gene families. Cannabinoid Pathway Surprisingly, none of the top DEGs were related to the ECS. Thus, to examine the potential role of cannabis-induced changes in the endocannabinoid system, the major gene transcripts associated with the ECS were examined. A prior small-scale study had identified some potential DNA variants in ECS-related genes associated with CHS 13 . The expression levels of key ECS-associated transcripts as well as those previously linked to CHS 13 , encompassing CB receptors (CNR1, CNR2), TRPVs (1–6), transporters/metabolizers (5), and signaling (3) were compared between groups (Supplementary Data 3). Even with a reduced threshold of 2-fold change, none of these transcripts were significantly altered. A nearly twofold increase in dopamine receptor D2 (DRD2) was observed, but the absolute expression level was low (nRC = 0.16, p = 0.76), limiting confidence in this result. Cell Type Analysis Even on cursory examination, noticeable changes were observed in transcripts associated with specific cell types—especially B cells, T cells, monocytes, and neutrophils. This was objectively assessed using a two-pronged approach employing a blood cell transcript database (Blood Atlas) derived from single-cell and sorted cell type RNAseq studies 14 . First, DEGs were mapped to known cell types by comparing their expression levels. Of the 360 DEGs, 63 could be mapped to the Blood Atlas. The average gene expression among the mapped upregulated versus downregulated transcripts is shown for their expression in the major cell types (Fig. 2 A). Upregulated transcripts were most highly expressed in classical monocytes, neutrophils, and myeloid dendritic cells (DC). Conversely, downregulated transcripts were expressed mainly in non-classical monocytes, neutrophils, and basophils. Thus, while the DEGs were associated with particular cell types, they were also expressed across various immune cell populations. As a second approach, pre-curated lists of cell type-enriched transcripts, unrelated to the DEGs, were averaged for each group to assess relative abundance changes. Average expression levels of curated cell-type markers showed that CHS patients had increased levels of markers for classical/intermediate monocytes and neutrophils, but decreased markers for eosinophils, basophils, T cells, gdT-cells, and MAIT cells (Fig. 2 B). Notably, RNA markers of B cells were not different between groups. Because these markers are independent of the DEGs, the data suggest potential shifts in immune cell populations, although changes in activation state could also account for the observed transcript fluctuations. B Cell-Related transcripts Further manual analysis showed that among the increased transcripts, 28 transcripts were clearly related to B cell immunoglobulins, exhibiting 4–8-fold increases in CHS patients (Table 2 ). B-cell activation has also been described in irritable bowel syndrome (IBS) 15 . However, Netrin G1 (NTNG1), another B-cell–related transcript, was reduced in CHS patients. T Cell-Related transcripts Eight downregulated transcripts were related to T cell receptors (TRAJs 8, 15, 30, 50; TRAV8-1; TRBC2; TRBJ1-4; TRDV2), with decreases of 4–6-fold in CHS (Table 3 ). Additional T cell–specific changes were explored in the cell type analysis above. Innate Immune Cell-Related Transcripts A subset of upregulated DEGs corresponded to markers of innate immune activation previously identified in studies of bacterial, viral, and biofilm infections in abdominal pain, pneumonia, or COVID-19 patients 16 – 18 . Notable increases included DEFA1, ALPL, and IFI27, as well as neutrophil-related (DAAM2, VNN1) and monocyte/macrophage markers (SIGLEC11, VSIG4). Red Blood Cell-Related Changes CHS patients had marked increases in transcripts for hemoglobins HbA, HbB, HbD, and HbTheta with 5-22-fold elevations over controls (Table 2 ). Hb transcript changes are noted in spaceflight and bedrest 19 , as well as in high-altitude climbers 20 . These changes may reflect physiological stress from emesis rather than intrinsic RBC abnormalities, as CHS patients have been reported to exhibit electrolyte disorders, hypertension, chronic lung disease, and anemia 21 . Although racial differences and sickle cell carrier status could be relevant, the transcript elevations were consistent across patients, with no clinical or RNAseq evidence for sickle cell disease, although two were likely carriers 22 . Allergic/Immune changes Three transcripts (HLA-B, HLA-E, HCP5) suggested a possible immune genotype associated with CHS. HLA-B genotypes and RNA levels have previously been linked to inflammatory disorders such as IBS 23 and Crohn’s Disease 24 . A five-fold decrease in mRNA for MS4A2 (FCER1B, the beta subunit of the high-affinity IgE receptor) was also observed. This may suggest involvement of IgE-mediated mechanisms in CHS, because elevated IgE can induce a compensatory decrease in MS4A2/FCER1B expression 25 . The FCER1A IgE receptor had a 4-fold higher base mean expression (164.2) relative to FCER1B and showed a 3-fold down regulation in the CHS patients. This could indicate that an IgE-mediated mechanism, such as allergic reaction is involved in CHS because it is known that elevated IgE causes a compensatory decrease in the MS4A2/FCER1B IgE receptor RNA and protein. HLA RNA Expression Levels The observed upregulation of HLA-B and HLA-E, and downregulation of IgE receptor, in CHS suggested a role for acquired immunity, and prompted detailed HLA expression and isotype analysis. RNAseq reads were realigned to a curated database using Seq2HLA. The QC metrics were consistent with the HISAT2 HG38 alignment. Overall read numbers did not differ significantly between groups (CHS = 17.2M, CON = 16.8M, p = 0.75). However, reads aligning to the HLA region were significantly higher in CHS (CHS = 25,568, CON = 16,417, p = 0.007; Fig. 3 ). Differences in expression level were confined to HLA Class I: HLA-A (RPKMs CHS = 290.1, CON = 179.2, p = 0.046), HLA-B (CHS = 555.4, CON = 421.1, p = 0.026), and non-classical HLA-E (CHS = 340.8, CON = 256.2, p = 0.004) and HLA-H (CHS = 66.7, CON = 42.7, p = 0.012). Other HLA-I and all HLA-II alleles were not significantly different in expression level of their respective RNA transcripts (all p > 0.2). HLA Digital Isotyping Seq2HLA analysis identified isotype-specific alleles with high confidence (p < 0.05) for almost all isotypes. All CHS patients (7/7) carried at least one HLA-B allele previously associated with allergic hypersensitivity. Notably, one CHS patient had the HLA-B27 allele, seen in 95% of ankylosing spondylitis cases, but only 5% of the general population (Supplementary Data 4). HLA-A and HLA-C alleles showed no systematic differences in isotypes (Supplementary Data 4). HLA Class II digital isotype analysis revealed three main findings (Fig. 4 ): (1) CHS patients were more likely to be homozygous for DQB1 (CHS 4/7 vs CON 2/7); (2) the DRB1*03 allele was more common in CHS (CHS 5/7, CON 2/7); and (3) nearly all CHS patients carried DPA1*02 (CHS 6/7 vs CON 0/7), an allele overrepresented compared to the expected frequency in Blacks (85.7% observed vs 23% expected, chi-square p = 0.007). Most DPA1*02 carriers were homozygous to four digits, while the one patient with DPA1*01:03 (used by all controls) also had the high-risk HLA-B27 allele. DPA1 allele calls had high read depth and confidence. A caveat is that from RNAseq isotyping it is difficult to differentiate between true homozygosity versus preferential allele usage in blood cells. Although limited by small sample size, these findings suggest that people with specific inherited HLA alleles may be prone to CHS, and warrant further study of HLA usage in CHS. Innate immune activation transcripts CHS patients exhibited significant upregulation of innate immunity transcripts—particularly those related to neutrophil and monocyte activation. DEFA1 (neutrophil defensin A1) is a known biomarker of host response to bacterial infection 16 , 17 . Transcripts for INHBB, LTF, MMP9, and IL1R2 are neutrophil-associated and likely signal neutrophil activation. In the case of IFI27, it is a well-known signal of viral infection 18 , 26 – 28 . To validate the RNAseq findings, a panel of infection-related RNA biomarkers was analyzed by ddPCR. The ddPCR panel, previously validated in infection studies 16 – 18 , confirmed increased RNA levels of DEFA1 (~ 2-fold), which is responsive to bacterial infection, and IFI27 (~ 3-fold), which is responsive to viral infection. This is consistent with the RNAseq data, though the ddPCR changes were of smaller magnitude than the RNAseq changes. Increases in IL8RB and ALPL, both responsive to bacterial infections, were also observed, but not RSAD2, which is responsive to viral infection, and none of the differences reached statistical significance (Supplementary Data 5). When compared to established thresholds for intra-abdominal infections, only one CHS patient had an abnormal DEFA1 level (16.1% vs 10% threshold), and two patients had elevated scores for a biofilm-type infection (ALPL + IL8RB = 19.8% and 19.6%, vs 18% threshold). No patients had positive viral scores, and none of the Controls had abnormal results. Thus, although infection biomarkers were modestly elevated in some cases, most CHS patients lacked a host immune response profile indicative of infection. However, it is likely that secondary infections resulting from hyperemesis in some patients are a contributing factor to the innate immune signal seen in the CHS patients. Circulating Bacterial/Fungal DNA Using methods developed for bacteremia in sepsis, total DNA was isolated from Tempus-preserved blood and analyzed by ddPCR for bacterial (Gram + and Gram– 16S) and fungal (23S) DNA. In contrast to septic patients in prior studies, CHS patients showed no detectable increase in circulating pathogen DNA, indicating the absence of overt bacteremia despite the immune activation signature (not shown). Eicosanoid-related changes A notable switch was observed in ALOX15 (down 10x) and ALOX15B (up 10x) expression in CHS patients. While these are separate genes, their opposing regulation may reflect coordinated activity with potential implications for the ECS, as both are involved in arachidonic acid metabolism and can influence cannabinoid signaling. Changes in ALOX15 and ALOX15B may also relate to reticulocyte maturation, but no evidence of increased reticulocyte counts was found in CHS patients. Adhesion/Tight Junction changes Numerous DEGs were related to adhesion and tight junctions, processes integral to both immune cell communication and gut barrier integrity. A 4.4-fold decrease in ARPIN, a key tight junction component, was observed (Table 3 ). Reduced ARPIN is a hallmark of acute inflammation and has been identified as differentially methylated in heavy cannabis users 29 , 30 . Other notable changes included decreased transcripts for AJAP1, EPHA2, PARVA, and protocadherins (PCDH18, PCDHGB), as well as increased IL-4, which may compromise intestinal barrier function 31 . Upregulation of metalloproteases (MMP8, MMP9) and proteases (ADAMTS2, hepsin, HTRA1) suggests increased extracellular matrix turnover and potential intestinal barrier disruption, paralleling findings in IBS 32 , 33 . The striking increase in ADAMTS2 (> 100-fold) is particularly interesting because increases in the close family member, ADAMTS1, has been associated with hyperemesis of pregnancy 34 . Neurotransmission-related Several transcripts implicated in brain-gut neurotransmission were altered in CHS. Dopamine beta-hydroxylase (DBH) was decreased 5-fold (Table 2 ), consistent with changes seen in a post-operative nausea/vomiting (PONV) model 35 and associated with migraine in GWAS. Chronic cannabis use is known to impact DBH activity 36 . Conversely, monoamine oxidase A (MAOA), which degrades serotonin/5-HT, is increased 5-fold in the CHS patients. Serotonin is a major regulator of the brain-gut axis and mediates both allergic and visceral hypersensitivity responses 37 , 38 . Discussion Pharmacological Hyperactivation of the ECS Analysis of currently known transcripts within the endocannabinoid system (ECS) revealed no striking changes in cannabinoid receptors or in the downstream signaling and metabolic pathways of endogenous cannabinoids. It remains possible that important alterations in ECS signaling are not reflected at the mRNA level, warranting further investigation using alternative approaches. Cannabis-Induced Epigenetic Changes in Gene/Protein Expression Previous evidence for altered methylation of genes such as ARPIN is strengthened by the present findings of reduced RNA expression for these transcripts in CHS. While it is possible this is coincidental, it is highly plausible that long-term, high-dose exposure to cannabinoids induces substantial epigenetic changes—including DNA methylation and histone modification—which impact gene and protein expression in CHS. Cannabis-induced Loss of Gut Barrier Function Theory The observed increase in proteolytic enzyme transcripts with concomitant reductions in protease inhibitors and key adhesion proteins, particularly ARPIN, may reflect a progressive loss of gut barrier function if these changes are paralleled in the gastrointestinal tract. Notably, similar imbalances between proteolytic and anti-proteolytic pathways have been proposed as contributing to the ‘soft collagen’ theory of IBS, where genetic variants in connective tissue genes combined with infection or inflammation drive pronounced gastrointestinal inflammation 39 . In CHS, a comparable imbalance may disrupt the GI barrier, causing leakage of bacteria and bacterial products, thus triggering pain and emesis. Communication between the gut epithelial barrier and immune cells is essential to maintaining barrier integrity 40 . Loss of mucosal integrity would permit the gut microbiome and its soluble products to interact with immune cells in the submucosa. In particular, gut enterochromaffin cells are key drivers of visceral pain and anxiety 41 . Histamine released from mast cells in response to IgE activates these enterochromaffin cells 42 , which are the primary source of serotonin (5-hydroxytryptamine, 5-HT)—a major neurotransmitter within the GI tract that regulates peristalsis and can trigger hyperemesis 43 , 44 . Notably, changes in transcripts such as NECAB2 45 and GPER1 46,47 have been linked to visceral hypersensitivity and pain. Mucosal Allergy to Cannabis Theory A third theory, building on the epigenetic and barrier hypotheses, posits that CHS represents an acquired, gut-restricted allergic response to one or more cannabis components (Fig. 5 ). Multiple lines of evidence support this: (1) B cell activation; (2) T cell suppression; (3) neutrophil activation; (4) HLA restriction and increased expression; (5) IgE receptor down-regulation; (6) lipoxygenase imbalance (ALOX15/15B); and (7) changes in interleukins and their receptors. It is conceivable that pairing cannabis byproducts in the gut with a concurrent viral or bacterial infection may sensitize patients to cannabis antigens, similar to the development of a-gal syndrome following tick exposure. Certain cannabis antigens, such as the non-specific lipid transfer protein (nsLTP), are known to provoke allergic and even anaphylactic reactions in susceptible individuals 48 . Reports of “cannabis allergy” have increased in parallel with CHS 49 . Thus, CHS may represent a gut-restricted allergic reaction to cannabis. In this model, a primary acquired immune response—likely involving co-exposure to cannabis and classical pathogenic antigens—drives a hypersensitivity reaction to cannabis components, activating gut-innervating sensory neurons and resulting in hyperemesis syndrome 50 . The restriction of the reaction to the gut could be due to some necessary processing of the cannabis allergen by gut enzymes. This allergy model is consistent with the partial efficacy of antihistamines combined with droperidol in treating CHS 6 , 51 , although antihistamines alone are typically only modestly effective for hypersensitivity reactions. Gastrointestinal disorders and food allergies often show HLA associations. For example, in celiac disease, unknown environmental triggers elicit adverse reactions to gluten [48], with both celiac disease and IBS strongly associated with HLA-DQ2/DQ8 [49–51]. HLA-B52 and HLA-DR15 have been linked to early-onset ulcerative colitis in Japanese children [52]. In animal models of HLA-B27 spondylarthritis, compromised gut barriers allow microbial antigens to translocate and trigger immune responses [53]. GWAS studies in food allergy consistently highlight genes involved in barrier and immune function—including HLA and IL-4 [54, 55]. Epigenome-wide association studies also repeatedly identify IL-4 among the top candidates for food allergy [56]. Food protein-induced enterocolitis syndrome (FPIES), a non-IgE food allergy, can affect up to 1% of children and 0.22% of adults, presenting with severe vomiting episodes reminiscent of CHS [57]. Surprisingly, there are very limited studies measuring immune changes in CHS, which have described elevated white blood cell counts with associated neutrophilia (75.8%) and mild hypokalemia (57.9%). Lipase is typically not elevated, and C-reactive protein remains less than 50 mmol/L in most cases (98.2%) [58]. Implications for Potential Therapies If the “gut allergy” hypothesis is correct, symptom relief might be achievable with standard allergy therapies. Foremost, elimination of the allergen is critical, but antihistamines and/or corticosteroids could be beneficial. While expensive, and not appropriate for acute use, monoclonal antibodies such as omalizumab (Xolair), a humanized anti-IgE antibody approved for food allergies, might offer preventative options when exposure cannot be avoided [59, 60]. Such drugs, given subcutaneously, are unlikely to impact acute episodes, but could prevent progression to the acute phase or speed recovery. Furthermore, identifying the relevant allergen and employing desensitization regimens may help reduce hypersensitivity. It is conceivable that selective breeding or genetic engineering could produce cannabis strains lacking the offending allergens. Conclusions • There were not notable differences in the RNA levels of the major ECS components in CHS patients. • However, there were striking differences in the RNA expression levels of B cell immunoglobin genes, as well as higher expression of HLA Class I genes, and potential restriction of the HLA Class II allele usage in the CHS patients. • There was evidence of host immune activation of neutrophil biomarkers of infection, but this could be easily explained by complications secondary to the hyperemesis. • The pattern of changes is consistent with an acquired, gut restricted hypersensitivity to cannabis that may be more likely in subjects with specific HLA alleles. Limitations This pilot study is limited by its small sample size and demographic differences between CHS patients and controls, which may confound transcriptomic and HLA findings. The absence of control groups with emesis unrelated to cannabis use, or chronic cannabis users without CHS, restricts our ability to isolate CHS-specific effects from those of emesis or cannabis exposure alone. Additionally, as our data are derived from whole blood rather than gut tissue, the extent to which these biomarkers reflect local gut processes is uncertain. Larger, more diverse cohorts and inclusion of additional control groups will be essential to validate and expand upon these findings. Future Directions Future studies should investigate more deeply into the potential allergic mechanisms underlying CHS. While total IgE may be nonspecific, assays for cannabis-specific IgE could be highly informative for diagnosis. Expanding HLA genotyping in larger, more diverse CHS cohorts will help determine the extent of genetic susceptibility and HLA restriction. The inclusion of additional control groups—such as chronic cannabis users without emesis and patients with emesis unrelated to cannabis—will be essential to distinguish the specific contributions of cannabinoids and emesis to the CHS transcriptomic profile. Finally, investigating familial aggregation, sibling or twin concordance, and the heritability of CHS could provide critical insight into the genetic and environmental interplay underlying disease susceptibility, particularly in relation to HLA genotype and immune sensitization. Abbreviations CBC complete blood count CHS cannabis hyperemesis syndrome ddPCR droplet digital PCR DEGs differentially expressed genes DND daily or near daily ECS endocannabinoid system ED emergency department ELISA enzyme-linked immunosorbent assay IBS irritable bowel syndrome HLA human leucocyte antigen MHC major histocompatibility complex nRC normalized read count RIN RNA integrity number RNAseq RNA sequencing rRNA ribosomal RNA RPKM reads per kilobase of exon per million mapped total reads WBC white blood cell Declarations Acknowledgements/Funding The authors are very grateful to the patients and their families who kindly agreed to participate in this research study despite their difficult health situations. The authors are grateful to The Ulvi and Reykhan Kasimov Family and The St. Laurent Institute for generous financial support that made these studies possible. Other support was obtained from the NIH S10 OD021622 to TM. Ethics approval and consent to participate The current study was approved by The George Washington University Institutional Review Board. CHS patients provided written informed consent under IRB Protocol #NCR 213728. Control subjects were enrolled under a related IRB protocol #NCR213645 with explicit opt-in consent for future use of samples. Consent for publication Not applicable. Availability of data and materials The full RNAseq dataset, as normalized and raw read counts, is available at NCBI Gene Expression Omnibus (GEO) Accession GSE303922. The raw sequence data (fastq) files contain personally identifiable data and therefore can be shared only with appropriate protections for the subjects. The digital PCR RNA biomarker panel for host immune activation can be obtained from True Bearing Diagnostics, Inc. by purchase or license agreement. Competing Interests TM has an equity interest in True Bearing Diagnostics, Inc., a diagnostics company developing RNA biomarkers for various diseases, including internal infections, although this project is not sponsored by True Bearing. TM and AM are seeking intellectual property protection for technology related to the current studies. This does not alter our adherence to Journal policies on sharing data and materials. The other authors declare there are no competing interests. Author contributions AM, TM, ZC, JM, and SE conceived and designed the studies. RH, AL, TL, TB, EP, and IL, identified and consented patients, collected clinical and laboratory data, and contributed clinical expertise on the conduct and analysis of the studies. JP conducted RNA isolations, RNA sequencing, ddPCR, and bioinformatic analysis. TM, JA, and KJ conducted the statistical, annotation, and bioinformatic analyses. JP conducted ddPCR for microbial DNA in whole blood. 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Ca2+-binding protein NECAB2 facilitates inflammatory pain hypersensitivity. J Clin Invest. 2018;128(9):3757–68. 10.1172/jci120913 . Jouffre B, Acramel A, Jacquot Y, et al. GPER involvement in inflammatory pain. Steroids. 2023;200(109311). 10.1016/j.steroids.2023.109311 . Xu S, Wang X, Zhao J, et al. GPER-mediated, oestrogen-dependent visceral hypersensitivity in stressed rats is associated with mast cell tryptase and histamine expression. Fundam Clin Pharmacol. 2020;34(4):433–43. 10.1111/fcp.12537 . Ebo DG, Toscano A, Rihs HP, et al. IgE-Mediated Cannabis Allergy and Cross-Reactivity Syndromes: A Roadmap for Correct Diagnosis and Management. Curr Allergy Asthma Rep. 2024;24(8):407–14. 10.1007/s11882-024-01159-5 . Decuyper I, Ryckebosch H, Van Gasse AL, et al. Cannabis Allergy: What do We Know Anno 2015. Arch Immunol Ther Exp (Warsz). 2015;63(5):327–32. 10.1007/s00005-015-0352-z . Wallrapp A, Chiu IM. Neuroimmune Interactions in the Intestine. Annu Rev Immunol. 2024;42(1):489–519. 10.1146/annurev-immunol-101921-042929 . Richards JR, Gordon BK, Danielson AR, et al. Pharmacologic Treatment of Cannabinoid Hyperemesis Syndrome. Syst Rev Pharmacotherapy. 2017;37(6):725–34. 10.1002/phar.1931 . Tables Tables 2 and 3 are available in the Supplementary Files section. Additional Declarations Competing interest reported. Conflict of Interest Disclosure: TM has an equity interest in True Bearing Diagnostics, Inc., a diagnostics company developing RNA biomarkers for various diseases, including internal infections, although this project is not sponsored by True Bearing. TM and AM are seeking intellectual property protection for technology related to the current studies. This does not alter our adherence to Journal policies on sharing data and materials. The other authors declare there are no competing interests. 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16:18:20","extension":"png","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":62696,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8116530/v1/eb187f31c6d46bb42859a98a.png"},{"id":97367722,"identity":"117b3bb7-01b2-44d4-af5c-9c42d1a85be8","added_by":"auto","created_at":"2025-12-03 16:20:27","extension":"png","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":72009,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8116530/v1/2e1c005538bf246c417ceaf9.png"},{"id":97260728,"identity":"7b9d9edd-8f52-42ef-b404-6071dc5d438d","added_by":"auto","created_at":"2025-12-02 13:58:56","extension":"xml","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":141454,"visible":true,"origin":"","legend":"","description":"","filename":"2d0de59c0ca440869ba35cf9c47236ee1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8116530/v1/3b39d429c0b4a734687f4c0e.xml"},{"id":97260727,"identity":"d61e4003-1add-408c-ac11-b153b95adac7","added_by":"auto","created_at":"2025-12-02 13:58:56","extension":"html","order_by":23,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":157025,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8116530/v1/3ca1ffc909327f04323ae5aa.html"},{"id":97367797,"identity":"564c241d-bc91-4c04-aa72-4bf1ad1b55f9","added_by":"auto","created_at":"2025-12-03 16:20:48","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":257145,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHierarchical clustering of differentially expressed genes in whole blood of CHS patients.\u003c/strong\u003e\u0026nbsp; DeSeq2 analysis identified 240 upregulated and 120 downregulated transcripts. Hierarchical clustering was used to identify patterns of covariance between transcripts across the seven CHS patients and seven controls (X-axis). Changes in transcript levels are normalized per transcript and are color-coded on a log\u003csub\u003e2\u003c/sub\u003e scale (dark red = 3log\u003csub\u003e2\u003c/sub\u003e = 8x increase; dark blue = –3log\u003csub\u003e2\u003c/sub\u003e = 8x decrease). Selected transcripts are highlighted.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8116530/v1/d2cf0ba2ae5543f5b3f52aef.png"},{"id":97260698,"identity":"ce855b95-9995-4e14-9e7a-54be5e26a9ae","added_by":"auto","created_at":"2025-12-02 13:58:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":203258,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCell type analysis in RNAseq of CHS patients. Panel A) \u003c/strong\u003e\u003cem\u003eDEG expression across major blood cell types:\u003c/em\u003e Upregulated (red) and downregulated (blue) DEGs were mapped against the Blood Atlas, with normalized read counts (nRC) used to indicate DEG transcript abundance by cell type. \u003cstrong\u003ePanel B)\u003c/strong\u003e Cell-specific markers in CHS RNAseq data: Pre-curated, cell type-enriched RNA transcripts were analyzed for expression across CHS and control groups. The average expression in CHS patients was expressed as a percent of control levels. \u003cem\u003eAsterisk indicates p \u0026lt; 0.05 between groups.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8116530/v1/36aa888241d4e213f3d46963.png"},{"id":97260696,"identity":"431bc677-72ab-4cae-8ba3-3cbd4513cee9","added_by":"auto","created_at":"2025-12-02 13:58:55","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":73594,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHLA-Locus Specific RNA Expression in CHS Patients.\u003c/strong\u003e RNAseq paired-end reads were realigned to an HLA-specific library using Bowtie2 and Seq2HLA. Average expression of MHC Class I isotypes is shown as RPKM. Error bars show SEM; * indicates p \u0026lt; 0.05 between groups.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8116530/v1/b191e4825261eba2f6b4acc5.png"},{"id":97367755,"identity":"2d2e9141-9470-4172-b33d-17966cb9b75a","added_by":"auto","created_at":"2025-12-03 16:20:35","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":402659,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMHC Class II digital isotyping from RNAseq.\u003c/strong\u003e RNAseq data was realigned and counted against an HLA-specific database as in Figure 4. The aligned reads were used to calculate the major MHC Class II isotypes for each CHS patient or Control (blue shade). Notable trends in the isotypes are highlighted in red. The DRA locus could not be reliably genotyped and the results are not shown.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8116530/v1/210e470b8b8111a0dc2126b3.png"},{"id":97260702,"identity":"927473ff-31f8-4eca-b1fe-17b68d705ba3","added_by":"auto","created_at":"2025-12-02 13:58:55","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":374932,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraphic summary of proposed model for cannabis-induced gut hypersensitivity.\u003c/strong\u003e Our preliminary genome-wide transcriptomic analysis in CHS suggests a multifactorial etiology. Heavy use of high-potency cannabis appears to induce stable changes in gene expression via epigenetic modifications such as DNA methylation, affecting both junctional/adhesive and proteolytic/anti-protease pathways. The result may be a reduction in gut barrier integrity, permitting leakage of microbiome products into the submucosa. The mucosal immune response may then be dysregulated, producing a hypersensitivity reaction to cannabis components, especially in HLA-restricted subjects.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8116530/v1/82089dc9690661b70a3d7f40.png"},{"id":97664772,"identity":"c4781345-c03b-4d5c-b6dc-f65c3453589f","added_by":"auto","created_at":"2025-12-08 09:14:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2544609,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8116530/v1/cca964bd-afba-4c27-821d-3730c5af5e08.pdf"},{"id":97260695,"identity":"4767bb4d-9b74-49fc-b2d1-8c977ed2c565","added_by":"auto","created_at":"2025-12-02 13:58:55","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":289366,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryData1.Volcano.png","url":"https://assets-eu.researchsquare.com/files/rs-8116530/v1/c1562752ac3f836c18e1c0e9.png"},{"id":97367599,"identity":"7f1c7284-4365-462e-9fcf-19b4d1b42668","added_by":"auto","created_at":"2025-12-03 16:19:42","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":52266,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryData2DEGsmall.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8116530/v1/e491ac3a7a2bccc7d5702d56.xlsx"},{"id":97368085,"identity":"09e206d0-33e5-4bd5-8dd1-6a66d16baaff","added_by":"auto","created_at":"2025-12-03 16:21:33","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":99942,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryData3ECSPathway.docx","url":"https://assets-eu.researchsquare.com/files/rs-8116530/v1/9f3713156dcad8512145b78e.docx"},{"id":97367408,"identity":"e5ddb12d-265f-426a-aa2b-c049a463d90f","added_by":"auto","created_at":"2025-12-03 16:18:24","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":23744,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryData4CompleteHLAresults.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8116530/v1/297f6695fb2007b18481cfca.xlsx"},{"id":97367443,"identity":"3e7f22f8-de77-4075-8741-44508389d163","added_by":"auto","created_at":"2025-12-03 16:18:35","extension":"png","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":151203,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryData5.ddPCR.png","url":"https://assets-eu.researchsquare.com/files/rs-8116530/v1/64c6e50c7aa59a1641ada63d.png"},{"id":97260706,"identity":"1cc60489-a20b-4982-a2c6-c9fe8d3c0aa4","added_by":"auto","created_at":"2025-12-02 13:58:55","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":356301,"visible":true,"origin":"","legend":"","description":"","filename":"Table2and3.docx","url":"https://assets-eu.researchsquare.com/files/rs-8116530/v1/e2eebab3ef0f9b3f8636fdef.docx"}],"financialInterests":"Competing interest reported. Conflict of Interest Disclosure:\nTM has an equity interest in True Bearing Diagnostics, Inc., a diagnostics company developing RNA biomarkers for various diseases, including internal infections, although this project is not sponsored by True Bearing. TM and AM are seeking intellectual property protection for technology related to the current studies. This does not alter our adherence to Journal policies on sharing data and materials. The other authors declare there are no competing interests.","formattedTitle":"Transcriptomic Evidence of Acquired Cannabis Hypersensitivity in Cannabinoid Hyperemesis Syndrome","fulltext":[{"header":"Key Points","content":"\u003cp\u003e\u003cstrong\u003eCentral Problem: \u0026nbsp;\u003c/strong\u003eCannabinoid hyperemesis syndrome (CHS) has become increasingly prevalent concurrent with both legalization and increasing potency of cannabis. \u0026nbsp;Prior studies suggested there may be inherited changes in the endogenous cannabinoid system (ECS) that triggers a paradoxical hyperemesis in susceptible patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKey Results:\u0026nbsp;\u003c/strong\u003eThere were not notable differences in the RNA levels of the major ECS components in CHS patients. \u0026nbsp; However, there were striking differences in the RNA expression levels of B cell immunoglobin genes, as well as higher expression of HLA Class I genes, and potential restriction of the HLA Class II allele usage in the CHS patients. \u0026nbsp;The pattern of changes is consistent with an acquired, gut restricted hypersensitivity to cannabis that may be more likely in subjects with specific HLA alleles.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImplications:\u0026nbsp;\u003c/strong\u003eA ‘gut hypersensitivity’ theory suggests that further studies should include analysis of the HLA allele usage and acquired immunity pathways in CHS patients. \u0026nbsp; Hypersensitivity reactions to cannabis could be treated via a range of therapeutics developed for other gut hypersensitivities, including antihistamines, IgE-directed therapies, and induced tolerance to specific allergens.\u003c/p\u003e"},{"header":"Background","content":"\u003cp\u003eCannabinoid Hyperemesis Syndrome (CHS) is emerging as a critical public health issue, with the incidence of CHS growing by 150% annually over the past ten years and continuing to rise nationally due to the widespread legalization of cannabis alongside an increasing potency of cannabis products. As additional jurisdictions legalize cannabis use \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e, there has been a twentyfold increase over the past 3 decades to about 17.7\u0026nbsp;million Americans that are daily or near-daily (DND) cannabis users\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. This rise has coincided with a dramatic increase in the tetrahydrocannabinol (THC) content of marijuana, from less than 1% in the 1970s to 4% in 1995 and 16% in 2022 \u003csup\u003e3\u003c/sup\u003e, Characterized by episodes of unremitting nausea, cyclic vomiting, and severe abdominal pain, CHS has become the most common cannabis-related cause of emergency department (ED) visits in the United States. The syndrome, with characteristic scream vomiting (scromiting) due to the intensity of symptoms, is frequently associated with compulsive hot showers, which patients use to temporarily relieve their discomfort \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. In the absence of biomarkers, and having numerous possible etiologies, the diagnosis of CHS has been difficult, with an average range of 3\u0026ndash;6 years before accurate diagnosis \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eCHS is paradoxical because cannabis is historically recognized for its anti-emetic properties, particularly for chemotherapy-induced nausea. Yet, in some chronic DND cannabis users, the drug becomes highly pro-emetic. Symptoms of CHS are minimally responsive to conventional anti-emetics such as ondansetron, but may respond to antipsychotics like haloperidol or olanzapine \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. The most effective strategy for eliminating CHS and reducing repeat ED visits is cannabis abstinence. Once affected, patients often remain in a sensitized state for months to years, even after cessation of cannabis use.\u003c/p\u003e\u003cp\u003eA prevailing theory posits that CHS results from pharmacological overstimulation of the endocannabinoid system (ECS), especially through alterations in CB1 and CB2 receptor signaling \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. The ECS is a complex network that regulates homeostasis throughout the body, including the gastrointestinal tract. Changes in the activity or expression of ECS enzymes and receptors have been proposed as one mechanism underlying CHS \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTo better characterize the population affected by CHS, our group recently conducted a survey of 1,052 participants with self-identified CHS \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Participants were typically young (mean age 32.1 years), initiated DND cannabis use at a mean age of 19.3 years, and reported high daily consumption (\u0026ge;\u0026thinsp;3 times daily, 82.2%). Rates of healthcare utilization were high, with 84.9% reporting at least one ED visit and 44.2% at least one hospitalization for CHS. Importantly, individuals who initiated DND cannabis use before age 18 were more likely to require hospitalization.\u003c/p\u003e\u003cp\u003eDespite the growing prevalence and burden of CHS, its underlying mechanisms remain poorly understood, and there are no established biomarkers to confirm diagnosis. The present study addresses this gap by applying genome-wide RNA sequencing to while blood from CHS patients and matched controls. Our goals are to generate new hypotheses regarding CHS pathophysiology, identify non-invasive blood-based biomarkers, and lay the groundwork for future preventive and therapeutic strategies.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003ePatients.\u003c/b\u003e CHS patients were enrolled in the Emergency Department (ED) and provided written informed consent under IRB Protocol #NCR 213728. Patients were diagnosed with CHS according to ROME IV criteria supplemented by supportive clinical features and exclusion of other causes. Control subjects matched for sex were enrolled under a related IRB protocol #NCR213645 with explicit opt-in consent for future use of samples. It was not appropriate or possible to involve patients or the public in the design, or conduct, or reporting, or dissemination plans of our research, although a large survey was conducted to understand CHS patients and their healthcare usage \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eRNA purification.\u003c/b\u003e Whole blood (3 ml) was collected into Tempus Blood RNA Tubes according to the manufacturer\u0026rsquo;s instructions and then frozen at \u0026minus;\u0026thinsp;80\u0026deg;C. High-quality mRNA was isolated from whole blood using the Invitrogen Tempus Spin RNA Isolation kit with an on-column DNase treatment to remove any contaminating genomic DNA. Final elution of RNA was performed using DEPC-treated water at 70\u0026deg;C, yielding RNA that was stored at \u0026minus;\u0026thinsp;80\u0026deg;C until further use.\u003c/p\u003e\u003cp\u003e\u003cb\u003eRNA analysis and sequencing.\u003c/b\u003e Purified RNA was quantified using Agilent TapeStation analysis. A standardized amount of RNA from each sample was submitted to the GW Genomics Core Facility for sequencing, employing the Illumina Stranded Total RNA Preparation and Ligation with Ribo-Zero Plus, followed by a NextSeq 2000 P1 XLEAP-SBS 300 Cycle sequencing run. RNA and sequencing quality metrics are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eRNAseq analysis.\u003c/b\u003e Paired-end FASTQ files were uploaded to the Galaxy platform and processed using standard settings. Low quality reads were removed with Trimmomatic, and sequence quality was measured using FastQC. Alignment was performed against the HG38 genome using HISAT2. Transcript counts were obtained with FeatureCount, and differential expression analysis was performed with DESeq2. Gene classification was conducted via automated ontology/pathway analysis using NIH DAVID \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, supplemented by manual curation through PubMed and GeneCard searches.\u003c/p\u003e\u003cp\u003e\u003cb\u003eBlood microbiome ddPCR\u003c/b\u003e. To quantify total bacterial 16S and fungal 28S DNA fragments in blood, DNA was isolated from Tempus-preserved samples with minor modifications to published methods \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Briefly, microbial lysis was achieved by treating samples with a lytic chemical solution and subjecting them to multiple freeze/thaw cycles, followed by nucleic acid precipitation using phenol/chloroform/isoamyl alcohol. Purified DNA (50 ng) was analyzed by ddPCR using primers specific for bacterial 16S (Gram-negative and Gram-positive) and fungal 23S rRNA, as previously described \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eHLA isotyping.\u003c/b\u003e Seq2HLA, a Python/R-scripted tool, was used to generate probable human leukocyte antigen (HLA) genotypes from RNA-Seq datasets. FASTQ files were aligned using Bowtie2 to a custom index of all known HLA alleles. Class I and II HLA expression (RPKM) was quantified, and HLA haplotypes were inferred to 4-digit resolution, accompanied by a confidence p-value for each call. Analyses were performed on the Galaxy platform using default parameters.\u003c/p\u003e\u003cp\u003e\u003cb\u003eddPCR confirmation of host immune activation markers.\u003c/b\u003e Host immune RNA levels were quantified using the Bio-Rad Digital Droplet PCR (ddPCR) System. Extracted RNA was reverse transcribed into cDNA using gene-specific primers and RNAse H\u0026thinsp;+\u0026thinsp;reverse transcriptase (iScript, BioRad). cDNA was partitioned into droplets, PCR amplification was performed on a Bio-Rad C100 thermocycler, and probe fluorescence intensity was measured using the Bio-Rad QX200 Reader.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003ePatient Characteristics\u003c/h2\u003e\u003cp\u003eIn this pilot study, seven CHS patients were enrolled for in-depth RNAseq analysis of whole blood RNA. Their demographic and clinical laboratory information is summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Control subjects were recruited from the clinical/research staff with an effort to match gender, because six of the seven CHS patients were female. Notable features among CHS patients included a predominance of females (6/7), self-reported Black race (7/7), a borderline elevated white blood cell count (10.4 K/\u0026micro;L, where normal is 4\u0026ndash;11 K/\u0026micro;L), an elevated neutrophil count (8.14 K/\u0026micro;L, where normal is 1.8\u0026ndash;7.7), and an elevated neutrophil/lymphocyte ratio (5.7, where normal is 1-3.5). Control subjects were more racially diverse (4 White, 1 Black, 1 Asian, 1 Hispanic), and complete blood counts (CBCs) were not performed (normal reference ranges shown). Co-morbidities among the CHS cohort included a hemorrhagic ovarian cyst (1), esophageal perforation with pneumomediastinum (1), and chronic gastritis with hypertension (1). All seven CHS patients received haloperidol (Haldol) in the ED for CHS management.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDemographic and Laboratory Values of CHS Patients\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eCHS Patients\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eControls\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAverage\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eS.E.M.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(or nml range)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eN\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge (yrs)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e42.71\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSex (%Male)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14.20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRace (%White)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e57.10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTemp (C)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e37\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSystolic BP (mm Hg)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e148.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e120\u0026ndash;130\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHaldol Rx (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e100.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWhite Blood Cell Ct (K/uL)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.0\u0026ndash;11.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRed Blood Cell Ct (M/ul)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.1\u0026ndash;5.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHemoglobin (g/dL)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.1\u0026ndash;17.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHematocrit (% RBC)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e38.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e36.1\u0026ndash;50.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMean Corp Vol (fL)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e81.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e80\u0026ndash;100\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMean Corp Hemo (pg)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e27.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27\u0026ndash;34\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMean Corp Hem Conc (g/dL)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32\u0026ndash;36\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRBC Dist Width-CV (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.5\u0026ndash;14.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePlatelet count (K/uL)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e329.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e150\u0026ndash;450\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMean Platelet Vol (fL)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.5\u0026ndash;12.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNeut Ct (K/uL)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.8\u0026ndash;7.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLymph Ct (K/uL)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u0026ndash;4.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNeut/Lympho Ratio (NLR)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.0\u0026ndash;3.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMono Ct (K/uL)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.2\u0026ndash;0.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEos Ct (K/uL)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0\u0026ndash;0.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBaso # (K/uL)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0\u0026ndash;0.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eImmature Gran/Bands (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0-0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRNA Yield (ug/3 ml)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.47\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRNA Integrity (RIN)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.27\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal Paired Reads\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17106641\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e707816\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16703231\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eRNAseq analysis of Whole Blood RNA\u003c/h3\u003e\n\u003cp\u003eRNA purification yielded somewhat, though not significantly, higher total RNA in CHS patients compared to controls (CON\u0026thinsp;=\u0026thinsp;11.5 \u0026micro;g/3 mL blood, CHS\u0026thinsp;=\u0026thinsp;17.8 \u0026micro;g/3 mL, p\u0026thinsp;=\u0026thinsp;0.26), a difference largely attributed to a single CHS patient with an unusually high RNA yield. Both groups demonstrated high and comparable RNA quality (RIN scale 1\u0026ndash;10: CON\u0026thinsp;=\u0026thinsp;8.27, CHS\u0026thinsp;=\u0026thinsp;8.37, p\u0026thinsp;=\u0026thinsp;0.46) as assessed by Agilent TapeStation. Illumina RNAseq produced similar numbers of paired end reads for CHS patients (17.1\u0026nbsp;million) and controls (16.7\u0026nbsp;million, p\u0026thinsp;=\u0026thinsp;0.77). Aligned read counts were also comparable at about 50% of total reads between groups.\u003c/p\u003e\u003cp\u003eDeSeq2 analysis identified 220 transcripts upregulated and 140 transcripts downregulated (fold change\u0026thinsp;\u0026gt;\u0026thinsp;4, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 uncorrected; see Supplementary Data 1 for volcano plot). All transcript data, including raw read counts, normalized read counts (nRC), fold changes, and p-values for the 360 differentially expressed genes (DEG) are provided in Supplementary Data 2. Transcripts were annotated by both automated and manual approaches and subsequently grouped into relevant classifications, such as upregulated (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) and downregulated transcripts (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHierarchical clustering of the 360 DEGs revealed that upregulated transcripts exhibit a unique pattern in each CHS patient, as opposed to a uniform pattern across the cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Examination of the most elevated transcripts in individual patients (\u0026ldquo;hot\u0026rdquo; clusters) showed each included some Ig-related transcripts (e.g., HLA-E and IGKV2D-29), indicating that while each patient mounts a distinct immune response, there is a shared elevation of immune activation markers. The larger number of increased versus decreased transcripts reflects individual variation among CHS patients, primarily within the adaptive and innate immunity gene families.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eCannabinoid Pathway\u003c/h3\u003e\n\u003cp\u003eSurprisingly, none of the top DEGs were related to the ECS. Thus, to examine the potential role of cannabis-induced changes in the endocannabinoid system, the major gene transcripts associated with the ECS were examined. A prior small-scale study had identified some potential DNA variants in ECS-related genes associated with CHS \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. The expression levels of key ECS-associated transcripts as well as those previously linked to CHS \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, encompassing CB receptors (CNR1, CNR2), TRPVs (1\u0026ndash;6), transporters/metabolizers (5), and signaling (3) were compared between groups (Supplementary Data 3). Even with a reduced threshold of 2-fold change, none of these transcripts were significantly altered. A nearly twofold increase in dopamine receptor D2 (DRD2) was observed, but the absolute expression level was low (nRC\u0026thinsp;=\u0026thinsp;0.16, p\u0026thinsp;=\u0026thinsp;0.76), limiting confidence in this result.\u003c/p\u003e\n\u003ch3\u003eCell Type Analysis\u003c/h3\u003e\n\u003cp\u003eEven on cursory examination, noticeable changes were observed in transcripts associated with specific cell types\u0026mdash;especially B cells, T cells, monocytes, and neutrophils. This was objectively assessed using a two-pronged approach employing a blood cell transcript database (Blood Atlas) derived from single-cell and sorted cell type RNAseq studies \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. First, DEGs were mapped to known cell types by comparing their expression levels.\u003c/p\u003e\u003cp\u003eOf the 360 DEGs, 63 could be mapped to the Blood Atlas. The average gene expression among the mapped upregulated versus downregulated transcripts is shown for their expression in the major cell types (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Upregulated transcripts were most highly expressed in classical monocytes, neutrophils, and myeloid dendritic cells (DC). Conversely, downregulated transcripts were expressed mainly in non-classical monocytes, neutrophils, and basophils. Thus, while the DEGs were associated with particular cell types, they were also expressed across various immune cell populations.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAs a second approach, pre-curated lists of cell type-enriched transcripts, unrelated to the DEGs, were averaged for each group to assess relative abundance changes. Average expression levels of curated cell-type markers showed that CHS patients had increased levels of markers for classical/intermediate monocytes and neutrophils, but decreased markers for eosinophils, basophils, T cells, gdT-cells, and MAIT cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Notably, RNA markers of B cells were not different between groups. Because these markers are independent of the DEGs, the data suggest potential shifts in immune cell populations, although changes in activation state could also account for the observed transcript fluctuations.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eB Cell-Related transcripts\u003c/h2\u003e\u003cp\u003eFurther manual analysis showed that among the increased transcripts, 28 transcripts were clearly related to B cell immunoglobulins, exhibiting 4\u0026ndash;8-fold increases in CHS patients (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). B-cell activation has also been described in irritable bowel syndrome (IBS) \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. However, Netrin G1 (NTNG1), another B-cell\u0026ndash;related transcript, was reduced in CHS patients.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eT Cell-Related transcripts\u003c/h3\u003e\n\u003cp\u003eEight downregulated transcripts were related to T cell receptors (TRAJs 8, 15, 30, 50; TRAV8-1; TRBC2; TRBJ1-4; TRDV2), with decreases of 4\u0026ndash;6-fold in CHS (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Additional T cell\u0026ndash;specific changes were explored in the cell type analysis above.\u003c/p\u003e\n\u003ch3\u003eInnate Immune Cell-Related Transcripts\u003c/h3\u003e\n\u003cp\u003eA subset of upregulated DEGs corresponded to markers of innate immune activation previously identified in studies of bacterial, viral, and biofilm infections in abdominal pain, pneumonia, or COVID-19 patients \u003csup\u003e\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Notable increases included DEFA1, ALPL, and IFI27, as well as neutrophil-related (DAAM2, VNN1) and monocyte/macrophage markers (SIGLEC11, VSIG4).\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eRed Blood Cell-Related Changes\u003c/h2\u003e\u003cp\u003eCHS patients had marked increases in transcripts for hemoglobins HbA, HbB, HbD, and HbTheta with 5-22-fold elevations over controls (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Hb transcript changes are noted in spaceflight and bedrest \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, as well as in high-altitude climbers \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. These changes may reflect physiological stress from emesis rather than intrinsic RBC abnormalities, as CHS patients have been reported to exhibit electrolyte disorders, hypertension, chronic lung disease, and anemia \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Although racial differences and sickle cell carrier status could be relevant, the transcript elevations were consistent across patients, with no clinical or RNAseq evidence for sickle cell disease, although two were likely carriers \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eAllergic/Immune changes\u003c/h2\u003e\u003cp\u003eThree transcripts (HLA-B, HLA-E, HCP5) suggested a possible immune genotype associated with CHS. HLA-B genotypes and RNA levels have previously been linked to inflammatory disorders such as IBS \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e and Crohn\u0026rsquo;s Disease \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. A five-fold decrease in mRNA for MS4A2 (FCER1B, the beta subunit of the high-affinity IgE receptor) was also observed. This may suggest involvement of IgE-mediated mechanisms in CHS, because elevated IgE can induce a compensatory decrease in MS4A2/FCER1B expression \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. The FCER1A IgE receptor had a 4-fold higher base mean expression (164.2) relative to FCER1B and showed a 3-fold down regulation in the CHS patients. This could indicate that an IgE-mediated mechanism, such as allergic reaction is involved in CHS because it is known that elevated IgE causes a compensatory decrease in the MS4A2/FCER1B IgE receptor RNA and protein.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eHLA RNA Expression Levels\u003c/h2\u003e\u003cp\u003eThe observed upregulation of HLA-B and HLA-E, and downregulation of IgE receptor, in CHS suggested a role for acquired immunity, and prompted detailed HLA expression and isotype analysis. RNAseq reads were realigned to a curated database using Seq2HLA. The QC metrics were consistent with the HISAT2 HG38 alignment. Overall read numbers did not differ significantly between groups (CHS\u0026thinsp;=\u0026thinsp;17.2M, CON\u0026thinsp;=\u0026thinsp;16.8M, p\u0026thinsp;=\u0026thinsp;0.75). However, reads aligning to the HLA region were significantly higher in CHS (CHS\u0026thinsp;=\u0026thinsp;25,568, CON\u0026thinsp;=\u0026thinsp;16,417, p\u0026thinsp;=\u0026thinsp;0.007; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Differences in expression level were confined to HLA Class I: HLA-A (RPKMs CHS\u0026thinsp;=\u0026thinsp;290.1, CON\u0026thinsp;=\u0026thinsp;179.2, p\u0026thinsp;=\u0026thinsp;0.046), HLA-B (CHS\u0026thinsp;=\u0026thinsp;555.4, CON\u0026thinsp;=\u0026thinsp;421.1, p\u0026thinsp;=\u0026thinsp;0.026), and non-classical HLA-E (CHS\u0026thinsp;=\u0026thinsp;340.8, CON\u0026thinsp;=\u0026thinsp;256.2, p\u0026thinsp;=\u0026thinsp;0.004) and HLA-H (CHS\u0026thinsp;=\u0026thinsp;66.7, CON\u0026thinsp;=\u0026thinsp;42.7, p\u0026thinsp;=\u0026thinsp;0.012). Other HLA-I and all HLA-II alleles were not significantly different in expression level of their respective RNA transcripts (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.2).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eHLA Digital Isotyping\u003c/h2\u003e\u003cp\u003eSeq2HLA analysis identified isotype-specific alleles with high confidence (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) for almost all isotypes. All CHS patients (7/7) carried at least one HLA-B allele previously associated with allergic hypersensitivity. Notably, one CHS patient had the HLA-B27 allele, seen in 95% of ankylosing spondylitis cases, but only 5% of the general population (Supplementary Data 4). HLA-A and HLA-C alleles showed no systematic differences in isotypes (Supplementary Data 4).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eHLA Class II digital isotype analysis revealed three main findings (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e): (1) CHS patients were more likely to be homozygous for DQB1 (CHS 4/7 vs CON 2/7); (2) the DRB1*03 allele was more common in CHS (CHS 5/7, CON 2/7); and (3) nearly all CHS patients carried DPA1*02 (CHS 6/7 vs CON 0/7), an allele overrepresented compared to the expected frequency in Blacks (85.7% observed vs 23% expected, chi-square p\u0026thinsp;=\u0026thinsp;0.007). Most DPA1*02 carriers were homozygous to four digits, while the one patient with DPA1*01:03 (used by all controls) also had the high-risk HLA-B27 allele. DPA1 allele calls had high read depth and confidence. A caveat is that from RNAseq isotyping it is difficult to differentiate between true homozygosity versus preferential allele usage in blood cells. Although limited by small sample size, these findings suggest that people with specific inherited HLA alleles may be prone to CHS, and warrant further study of HLA usage in CHS.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eInnate immune activation transcripts\u003c/h2\u003e\u003cp\u003eCHS patients exhibited significant upregulation of innate immunity transcripts\u0026mdash;particularly those related to neutrophil and monocyte activation. DEFA1 (neutrophil defensin A1) is a known biomarker of host response to bacterial infection \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Transcripts for INHBB, LTF, MMP9, and IL1R2 are neutrophil-associated and likely signal neutrophil activation. In the case of IFI27, it is a well-known signal of viral infection \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTo validate the RNAseq findings, a panel of infection-related RNA biomarkers was analyzed by ddPCR. The ddPCR panel, previously validated in infection studies \u003csup\u003e\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, confirmed increased RNA levels of DEFA1 (~\u0026thinsp;2-fold), which is responsive to bacterial infection, and IFI27 (~\u0026thinsp;3-fold), which is responsive to viral infection. This is consistent with the RNAseq data, though the ddPCR changes were of smaller magnitude than the RNAseq changes. Increases in IL8RB and ALPL, both responsive to bacterial infections, were also observed, but not RSAD2, which is responsive to viral infection, and none of the differences reached statistical significance (Supplementary Data 5). When compared to established thresholds for intra-abdominal infections, only one CHS patient had an abnormal DEFA1 level (16.1% vs 10% threshold), and two patients had elevated scores for a biofilm-type infection (ALPL\u0026thinsp;+\u0026thinsp;IL8RB\u0026thinsp;=\u0026thinsp;19.8% and 19.6%, vs 18% threshold). No patients had positive viral scores, and none of the Controls had abnormal results. Thus, although infection biomarkers were modestly elevated in some cases, most CHS patients lacked a host immune response profile indicative of infection. However, it is likely that secondary infections resulting from hyperemesis in some patients are a contributing factor to the innate immune signal seen in the CHS patients.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eCirculating Bacterial/Fungal DNA\u003c/h2\u003e\u003cp\u003eUsing methods developed for bacteremia in sepsis, total DNA was isolated from Tempus-preserved blood and analyzed by ddPCR for bacterial (Gram\u0026thinsp;+\u0026thinsp;and Gram\u0026ndash; 16S) and fungal (23S) DNA. In contrast to septic patients in prior studies, CHS patients showed no detectable increase in circulating pathogen DNA, indicating the absence of overt bacteremia despite the immune activation signature (not shown).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eEicosanoid-related changes\u003c/h2\u003e\u003cp\u003eA notable switch was observed in ALOX15 (down 10x) and ALOX15B (up 10x) expression in CHS patients. While these are separate genes, their opposing regulation may reflect coordinated activity with potential implications for the ECS, as both are involved in arachidonic acid metabolism and can influence cannabinoid signaling. Changes in ALOX15 and ALOX15B may also relate to reticulocyte maturation, but no evidence of increased reticulocyte counts was found in CHS patients.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eAdhesion/Tight Junction changes\u003c/h2\u003e\u003cp\u003eNumerous DEGs were related to adhesion and tight junctions, processes integral to both immune cell communication and gut barrier integrity. A 4.4-fold decrease in ARPIN, a key tight junction component, was observed (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Reduced ARPIN is a hallmark of acute inflammation and has been identified as differentially methylated in heavy cannabis users \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Other notable changes included decreased transcripts for AJAP1, EPHA2, PARVA, and protocadherins (PCDH18, PCDHGB), as well as increased IL-4, which may compromise intestinal barrier function \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Upregulation of metalloproteases (MMP8, MMP9) and proteases (ADAMTS2, hepsin, HTRA1) suggests increased extracellular matrix turnover and potential intestinal barrier disruption, paralleling findings in IBS \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. The striking increase in ADAMTS2 (\u0026gt;\u0026thinsp;100-fold) is particularly interesting because increases in the close family member, ADAMTS1, has been associated with hyperemesis of pregnancy \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eNeurotransmission-related\u003c/h2\u003e\u003cp\u003eSeveral transcripts implicated in brain-gut neurotransmission were altered in CHS. Dopamine beta-hydroxylase (DBH) was decreased 5-fold (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), consistent with changes seen in a post-operative nausea/vomiting (PONV) model \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e and associated with migraine in GWAS. Chronic cannabis use is known to impact DBH activity \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Conversely, monoamine oxidase A (MAOA), which degrades serotonin/5-HT, is increased 5-fold in the CHS patients. Serotonin is a major regulator of the brain-gut axis and mediates both allergic and visceral hypersensitivity responses \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003ePharmacological Hyperactivation of the ECS\u003c/h2\u003e\u003cp\u003eAnalysis of currently known transcripts within the endocannabinoid system (ECS) revealed no striking changes in cannabinoid receptors or in the downstream signaling and metabolic pathways of endogenous cannabinoids. It remains possible that important alterations in ECS signaling are not reflected at the mRNA level, warranting further investigation using alternative approaches.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003eCannabis-Induced Epigenetic Changes in Gene/Protein Expression\u003c/h2\u003e\u003cp\u003ePrevious evidence for altered methylation of genes such as ARPIN is strengthened by the present findings of reduced RNA expression for these transcripts in CHS. While it is possible this is coincidental, it is highly plausible that long-term, high-dose exposure to cannabinoids induces substantial epigenetic changes\u0026mdash;including DNA methylation and histone modification\u0026mdash;which impact gene and protein expression in CHS.\u003c/p\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003eCannabis-induced Loss of Gut Barrier Function Theory\u003c/h2\u003e\u003cp\u003eThe observed increase in proteolytic enzyme transcripts with concomitant reductions in protease inhibitors and key adhesion proteins, particularly ARPIN, may reflect a progressive loss of gut barrier function if these changes are paralleled in the gastrointestinal tract. Notably, similar imbalances between proteolytic and anti-proteolytic pathways have been proposed as contributing to the \u0026lsquo;soft collagen\u0026rsquo; theory of IBS, where genetic variants in connective tissue genes combined with infection or inflammation drive pronounced gastrointestinal inflammation \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. In CHS, a comparable imbalance may disrupt the GI barrier, causing leakage of bacteria and bacterial products, thus triggering pain and emesis. Communication between the gut epithelial barrier and immune cells is essential to maintaining barrier integrity \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eLoss of mucosal integrity would permit the gut microbiome and its soluble products to interact with immune cells in the submucosa. In particular, gut enterochromaffin cells are key drivers of visceral pain and anxiety \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Histamine released from mast cells in response to IgE activates these enterochromaffin cells \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e, which are the primary source of serotonin (5-hydroxytryptamine, 5-HT)\u0026mdash;a major neurotransmitter within the GI tract that regulates peristalsis and can trigger hyperemesis \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Notably, changes in transcripts such as NECAB2 \u003csup\u003e45\u003c/sup\u003e and GPER1 \u003csup\u003e46,47\u003c/sup\u003e have been linked to visceral hypersensitivity and pain.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003eMucosal Allergy to Cannabis Theory\u003c/h2\u003e\u003cp\u003eA third theory, building on the epigenetic and barrier hypotheses, posits that CHS represents an acquired, gut-restricted allergic response to one or more cannabis components (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Multiple lines of evidence support this: (1) B cell activation; (2) T cell suppression; (3) neutrophil activation; (4) HLA restriction and increased expression; (5) IgE receptor down-regulation; (6) lipoxygenase imbalance (ALOX15/15B); and (7) changes in interleukins and their receptors. It is conceivable that pairing cannabis byproducts in the gut with a concurrent viral or bacterial infection may sensitize patients to cannabis antigens, similar to the development of a-gal syndrome following tick exposure. Certain cannabis antigens, such as the non-specific lipid transfer protein (nsLTP), are known to provoke allergic and even anaphylactic reactions in susceptible individuals \u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. Reports of \u0026ldquo;cannabis allergy\u0026rdquo; have increased in parallel with CHS \u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. Thus, CHS may represent a gut-restricted allergic reaction to cannabis. In this model, a primary acquired immune response\u0026mdash;likely involving co-exposure to cannabis and classical pathogenic antigens\u0026mdash;drives a hypersensitivity reaction to cannabis components, activating gut-innervating sensory neurons and resulting in hyperemesis syndrome \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. The restriction of the reaction to the gut could be due to some necessary processing of the cannabis allergen by gut enzymes. This allergy model is consistent with the partial efficacy of antihistamines combined with droperidol in treating CHS \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e, although antihistamines alone are typically only modestly effective for hypersensitivity reactions.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eGastrointestinal disorders and food allergies often show HLA associations. For example, in celiac disease, unknown environmental triggers elicit adverse reactions to gluten [48], with both celiac disease and IBS strongly associated with HLA-DQ2/DQ8 [49\u0026ndash;51]. HLA-B52 and HLA-DR15 have been linked to early-onset ulcerative colitis in Japanese children [52]. In animal models of HLA-B27 spondylarthritis, compromised gut barriers allow microbial antigens to translocate and trigger immune responses [53]. GWAS studies in food allergy consistently highlight genes involved in barrier and immune function\u0026mdash;including HLA and IL-4 [54, 55]. Epigenome-wide association studies also repeatedly identify IL-4 among the top candidates for food allergy [56]. Food protein-induced enterocolitis syndrome (FPIES), a non-IgE food allergy, can affect up to 1% of children and 0.22% of adults, presenting with severe vomiting episodes reminiscent of CHS [57]. Surprisingly, there are very limited studies measuring immune changes in CHS, which have described elevated white blood cell counts with associated neutrophilia (75.8%) and mild hypokalemia (57.9%). Lipase is typically not elevated, and C-reactive protein remains less than 50 mmol/L in most cases (98.2%) [58].\u003c/p\u003e\u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\u003ch2\u003eImplications for Potential Therapies\u003c/h2\u003e\u003cp\u003eIf the \u0026ldquo;gut allergy\u0026rdquo; hypothesis is correct, symptom relief might be achievable with standard allergy therapies. Foremost, elimination of the allergen is critical, but antihistamines and/or corticosteroids could be beneficial. While expensive, and not appropriate for acute use, monoclonal antibodies such as omalizumab (Xolair), a humanized anti-IgE antibody approved for food allergies, might offer preventative options when exposure cannot be avoided [59, 60]. Such drugs, given subcutaneously, are unlikely to impact acute episodes, but could prevent progression to the acute phase or speed recovery. Furthermore, identifying the relevant allergen and employing desensitization regimens may help reduce hypersensitivity. It is conceivable that selective breeding or genetic engineering could produce cannabis strains lacking the offending allergens.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003e\u0026bull; There were not notable differences in the RNA levels of the major ECS components in CHS patients.\u003c/p\u003e\n\u003cp\u003e\u0026bull; However, there were striking differences in the RNA expression levels of B cell immunoglobin genes, as well as higher expression of HLA Class I genes, and potential restriction of the HLA Class II allele usage in the CHS patients.\u003c/p\u003e\n\u003cp\u003e\u0026bull; There was evidence of host immune activation of neutrophil biomarkers of infection, but this could be easily explained by complications secondary to the hyperemesis.\u003c/p\u003e\n\u003cp\u003e\u0026bull; The pattern of changes is consistent with an acquired, gut restricted hypersensitivity to cannabis that may be more likely in subjects with specific HLA alleles.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis pilot study is limited by its small sample size and demographic differences between CHS patients and controls, which may confound transcriptomic and HLA findings. The absence of control groups with emesis unrelated to cannabis use, or chronic cannabis users without CHS, restricts our ability to isolate CHS-specific effects from those of emesis or cannabis exposure alone. Additionally, as our data are derived from whole blood rather than gut tissue, the extent to which these biomarkers reflect local gut processes is uncertain. Larger, more diverse cohorts and inclusion of additional control groups will be essential to validate and expand upon these findings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFuture Directions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFuture studies should investigate more deeply into the potential allergic mechanisms underlying CHS. While total IgE may be nonspecific, assays for cannabis-specific IgE could be highly informative for diagnosis. Expanding HLA genotyping in larger, more diverse CHS cohorts will help determine the extent of genetic susceptibility and HLA restriction. The inclusion of additional control groups\u0026mdash;such as chronic cannabis users without emesis and patients with emesis unrelated to cannabis\u0026mdash;will be essential to distinguish the specific contributions of cannabinoids and emesis to the CHS transcriptomic profile. Finally, investigating familial aggregation, sibling or twin concordance, and the heritability of CHS could provide critical insight into the genetic and environmental interplay underlying disease susceptibility, particularly in relation to HLA genotype and immune sensitization.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCBC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ecomplete blood count\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCHS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ecannabis hyperemesis syndrome\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eddPCR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003edroplet digital PCR\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDEGs\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003edifferentially expressed genes\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDND\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003edaily or near daily\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eECS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eendocannabinoid system\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eED\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eemergency department\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eELISA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eenzyme-linked immunosorbent assay\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIBS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eirritable bowel syndrome\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHLA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ehuman leucocyte antigen\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMHC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003emajor histocompatibility complex\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003enRC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003enormalized read count\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eRIN\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eRNA integrity number\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eRNAseq\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eRNA sequencing\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003erRNA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eribosomal RNA\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eRPKM\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ereads per kilobase of exon per million mapped total reads\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eWBC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ewhite blood cell\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements/Funding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are very grateful to the patients and their families who kindly agreed to participate in this research study despite their difficult health situations. \u0026nbsp;The authors are grateful to The Ulvi and Reykhan Kasimov Family and The St. Laurent Institute for generous financial support that made these studies possible. \u0026nbsp; Other support was obtained from the NIH S10 OD021622 to TM.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe current study was approved by The George Washington University Institutional Review Board. CHS patients provided written informed consent under IRB Protocol #NCR 213728. Control subjects were enrolled under a related IRB protocol #NCR213645 with explicit opt-in consent for future use of samples.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe full RNAseq dataset, as normalized and raw read counts, is available at NCBI Gene Expression Omnibus (GEO) Accession GSE303922. \u0026nbsp;The raw sequence data (fastq) files contain personally identifiable data and therefore can be shared only with appropriate protections for the subjects. The digital PCR RNA biomarker panel for host immune activation can be obtained from True Bearing Diagnostics, Inc. by purchase or license agreement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTM has an equity interest in True Bearing Diagnostics, Inc., a diagnostics company developing RNA biomarkers for various diseases, including internal infections, although this project is not sponsored by True Bearing. \u0026nbsp; TM and AM are seeking intellectual property protection for technology related to the current studies. This does not alter our adherence to Journal policies on sharing data and materials. \u0026nbsp;The other authors declare there are no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAM, TM, ZC, JM, and SE conceived and designed the studies. \u0026nbsp;RH, AL, TL, TB, EP, and IL, identified and consented patients, collected clinical and laboratory data, and contributed clinical expertise on the conduct and analysis of the studies. \u0026nbsp;JP conducted RNA isolations, RNA sequencing, ddPCR, and bioinformatic analysis. TM, JA, and KJ conducted the statistical, annotation, and bioinformatic analyses. JP conducted ddPCR for microbial DNA in whole blood. EP and TM composed graphic illustrations. \u0026nbsp;TM and AM wrote the manuscript with input from all the authors.\u003cstrong\u003e\u003cbr\u003e\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCostales B, Lu Y, Young-Wolff KC, et al. Prevalence and trends of suspected cannabinoid hyperemesis syndrome over an 11-year period in Northern California: An electronic health record study. Drug Alcohol Depend. 2024;263(112418). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.drugalcdep.2024.112418\u003c/span\u003e\u003cspan address=\"10.1016/j.drugalcdep.2024.112418\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCaulkins JP. Changes in self-reported cannabis use in the United States from 1979 to 2022. 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Syst Rev Pharmacotherapy. 2017;37(6):725\u0026ndash;34. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/phar.1931\u003c/span\u003e\u003cspan address=\"10.1002/phar.1931\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 2 and 3 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-cannabis-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jcan","sideBox":"Learn more about [Journal of Cannabis Research](https://jcannabisresearch.biomedcentral.com/)","snPcode":"42238","submissionUrl":"https://submission.springernature.com/new-submission/42238/3","title":"Journal of Cannabis Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Cannabinoid Hyperemesis Syndrome, Cannabis, Cannabis allergy, Hypersensitivity, Transcriptomics, Blood biomarkers, Immune activation, HLA genotype","lastPublishedDoi":"10.21203/rs.3.rs-8116530/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8116530/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eCannabinoid hyperemesis syndrome (CHS) is a paradoxical and increasingly prevalent disorder characterized by recurrent vomiting in people with chronic cannabis use. Despite its growing clinical impact, the underlying mechanisms remain poorly understood.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA genome-wide RNA sequencing was used to characterize transcriptomic differences and identify potential pathways involved in CHS pathogenesis. In this pilot study, whole blood RNA sequencing was performed on 7 patients with CHS and 7 matched controls. Differentially expressed genes (DEGs) were identified, annotated and analyzed by automated and manual analysis. RNA sequences were further analyzed by digital isotyping for HLA Class I and II allele usage.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eCHS was associated with marked activation of the adaptive immune system, including upregulation of B-cell related immunoglobin transcripts and altered expression of T cell, monocyte, and neutrophil-related transcripts. DEGs also suggested increased matrix degradation, and reduced adhesion and protease inhibitor transcripts, consistent with impaired gut barrier function. Digital HLA isotyping revealed increased MHC Class I expression, Class II allele restriction, and down-regulation of IgE receptor transcripts, a known response to elevated IgE levels in allergic hypersensitivity.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eTogether, these findings suggest that CHS may represent an acquired, gut-restricted, immune-mediated hypersensitivity response to cannabis. This transcriptomic analysis provides new mechanistic insights into CHS and lays groundwork for future studies to identify biomarkers, clarify immune triggers, and develop targeted therapies.\u003c/p\u003e","manuscriptTitle":"Transcriptomic Evidence of Acquired Cannabis Hypersensitivity in Cannabinoid Hyperemesis Syndrome","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-02 13:58:50","doi":"10.21203/rs.3.rs-8116530/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-15T19:22:31+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-15T17:21:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"267738749088527264035763421813842703348","date":"2026-04-28T20:34:28+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-04T02:49:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"338784026575928582026331135950486696083","date":"2026-03-01T00:57:13+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-26T10:52:17+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-17T03:02:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-17T03:01:18+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Cannabis Research","date":"2025-11-14T15:27:02+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-cannabis-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jcan","sideBox":"Learn more about [Journal of Cannabis Research](https://jcannabisresearch.biomedcentral.com/)","snPcode":"42238","submissionUrl":"https://submission.springernature.com/new-submission/42238/3","title":"Journal of Cannabis Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ef693be6-09f7-4e5d-adf0-c6acaeb092a9","owner":[],"postedDate":"December 2nd, 2025","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-15T19:22:31+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-15T17:21:45+00:00","index":41,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-15T19:38:49+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-02 13:58:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8116530","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8116530","identity":"rs-8116530","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}