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Article

Hemophagocytic Lymphohistiocytosis Gene Variants in Severe COVID-19 Cytokine Storm Syndrome

by
Abhishek Kamath
1,
Mingce Zhang
2,
Devin M. Absher
3,
Lesley E. Jackson
4,
Walter Winn Chatham
5 and
Randy Q. Cron
1,2,4,*
1
Heersink School of Medicine, University of Alabama at Birmingham (UAB), Birmingham, AL 35233, USA
2
Division of Rheumatology, Children’s of Alabama, Birmingham, AL 35233, USA
3
Kaiser Permanente Bernard J. Tyson School of Medicine, Oakland, CA 94612, USA
4
Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham (UAB), Birmingham, AL 35233, USA
5
Division of Rheumatology, University of Nevada, Las Vegas (UNLV), Las Vegas, NV 89102, USA
*
Author to whom correspondence should be addressed.
Viruses 2025, 17(8), 1093; https://doi.org/10.3390/v17081093
Submission received: 25 June 2025 / Revised: 22 July 2025 / Accepted: 4 August 2025 / Published: 8 August 2025
(This article belongs to the Section Coronaviruses)
Browse Figure
Versions Notes

Abstract

Severe COVID-19 infection resulting in hospitalization shares features with cytokine storm syndromes (CSSs) such as hemophagocytic lymphohistiocytosis (HLH). Various published criteria were explored to define CSS among patients (n = 32) enrolled in a COVID-19 clinical trial. None of the patients met HLH-04 or HScore criteria, but the ferritin to erythrocyte sedimentation rate (ferritin–ESR) ratio and the COVID-19 cytokine storm score (CSs) identified 84% and 81% of patients, respectively. As 30–40% of patients in published secondary HLH cohorts possess rare heterozygous mutations in familial HLH (fHLH) genes, whole genome sequencing was undertaken to explore immunologic gene mutation associations among 20 patients enrolled in the trial. Rare mutations in fHLH genes were identified in 6 patients (30%), and 4 patients (20%) possessed rare mutations in DOCK8 (a novel CSS gene). Foamy viral transduction of the 3 DOCK8 missense mutations into NK-92 natural killer (NK) cells diminished NK cell cytolytic function, a feature of HLH. This severe COVID-19 cohort, like others, shares CSS features but is best identified by the ferritin–ESR ratio. Rare heterozygous CSS gene (fHLH genes and DOCK8) mutations were frequently (45%) identified in this severe COVID-19 cohort, and DOCK8 missense mutations may contribute to CSS via diminished lymphocyte cytolytic activity.

1. Introduction

Cytokine storm syndromes are hyperinflammatory febrile states associated with rare genetic defects (e.g., perforin deficiency), infections (e.g., Epstein–Barr virus), rheumatic diseases (e.g., Still disease), and hematopoetic malignancies (e.g., T-cell lymphoma) [1]. CSS can result in multi-organ system failure and is frequently fatal [2]. The cytokine storm syndrome (CSS) associated with severe SARS-CoV-2 infection (COVID-19) is somewhat unique but shares features with other CSS, such as hemophagocytic lymphohistiocytosis (HLH) and macrophage activation syndrome (MAS) [3]. Different CSSs are classifiable by various published criteria [4]. These include HLH-04 and HScore criteria among others [5,6]. Neither the HLH-04 nor the HScore criteria perform well at identifying COVID-19 CSS, and during the COVID-19 pandemic, various CSS criteria were introduced to identify severe SARS-CoV-2 infection [7]. Approximately 10–15% percent of patients infected with SARS-CoV-2 early during the pandemic developed severe COVID-19, requiring hospitalization [8]. A clinical trial to treat severe SARS-CoV-2 infection with the interleukin-1 inhibitor, anakinra (beneficial for secondary HLH, sHLH) [9], was recently published [8]. Entry criteria included several features of CSS, including elevated serum C-reactive protein, ferritin, liver enzymes, and D-dimers, as well as diminished levels of lymphocytes and platelets [8]. Of 235 hospitalized severe COVID-19 patients screened, only 53 met the stringent CSS-based entry criteria, making them the sickest of the sick [8]. Herein, various published HLH and MAS criteria, as well as COVID-19-specific CSS criteria, were analyzed to explore their utility in identifying CSS in the setting of COVID-19. As previously noted [7], the HLH-04 and HScore criteria performed poorly, but the ferritin to erythrocyte sedimentation rate (ESR) ratio (ferritin–ESR) [10] and the COVID-19 cytokine storm score (CSs) [11] identified over 80% of those enrolled in this severe COVID-19 clinical trial [8]. Thus, the subset of COVID-19 patients in this trial shared features of CSS [12].
Why some individuals with various infections, including SARS-CoV-2, develop severe disease manifestations including features of CSS is unclear. However, fatal cases of another severe respiratory infection, H1N1 influenza, have been associated with heterozygous mutations in genes linked with familial HLH (fHLH) [13]. Indeed, mutations in several fHLH genes involved in perforin-mediated cytolysis by cytotoxic T-cells and natural killer (NK) cells have been identified in individuals with severe COVID-19 [14,15,16,17,18]. Mutations in novel and related sHLH-associated genes, DOCK2 and DOCK8, which are important for lymphocyte cytolytic activity [19,20], have also been linked to severe COVID-19 [21] and post-COVID-19 multi-system inflammatory syndrome in children (MIS-C) [22], respectively. Moreover, poor lymphocyte cytolytic activity, in general, has also been associated with severe COVID-19 [23]. In exploring immune gene mutations by whole genome sequencing (WGS) among 20 of the severe COVID-19 patients enrolled in the anakinra clinical trial, several fHLH genes and DOCK8 mutations were identified in the cohort. Functional testing of the DOCK8 mutations, the most frequently mutated gene (n = 4, 20%), was performed. Missense mutations were explored in NK cells, demonstrating decreased cytolytic function. Thus, mutations in fHLH and DOCK8 genes may serve as risk alleles for developing severe COVID-19 with features of CSS upon infection with SARS-CoV-2.

2. Materials and Methods

2.1. Study Participants

The double-blind, randomized, placebo-controlled trial of anakinra for severe COVID-19 was previously reported [8] and was registered with ClinicalTrials.gov, NCT04362111, where the full list of eligibility criteria is provided. In brief, inclusion criteria were as follows: 1. 18 years old or older; 2. molecular diagnosis of SARS-CoV-2 infection and COVID-19 pneumonia; 3. radiographic imaging consistent with COVID-19 pneumonia; 4. room air oxygen saturation <93%; 5. hyperferritinemia (>700 ng/mL); 6. any 3 of the following: a. elevated D-dimer (>500 ng/mL), b. thrombocytopenia (<130,000/mm3), c. leukopenia (WBC < 3500/mm3) or lymphopenia (<1000/mm3), d. elevated AST or ALT (>2X ULN), e. elevated LDH (>2X ULN), or f. CRP >100 mg/L. The University of Alabama at Birmingham (UAB) Institutional Review Board approved the trial (IRB-300005216), which was conducted in accordance with Good Clinical Practice guidelines. The study was funded by the UAB School of Medicine COVID-19 Research Initiative (to R.Q.C. and W.W.C.), and Swedish Orphan Biovitrum (Sobi) provided the anakinra and placebo but played no role in data analysis, data interpretation, or manuscript preparation.

2.2. Data Collection and Statistical Analysis

Clinical and laboratory variables were collected on a UAB secure server, and for purposes of CSS criteria fulfillment, only data obtained within 72 h of hospitalization were analyzed. An Excel spreadsheet was used to record and calculate the various HLH/MAS/CSS criteria for each of 32 patients enrolled in the clinical trial. The following criteria were calculated as previously described [7]: HLH-04 [24], HScore [5], sJIA MAS score [25], MS score [26], ferritin–ESR ratio [10], COVID-19-associated hyperinflammatory syndrome (cHIS criteria) [27], COVID-Cytokine Storm (COVID-CS) [28], and CSs [11]. Continuous variables are reported as medians with interquartile ranges, and categorical variables are summarized as frequencies and percentages. Comparisons of nonparametric laboratory values were made using the Kruskal–Wallis test with a significance p value of 0.05. Demographics, comorbidities, and other treatments are previously reported [8].

2.3. Whole Genome Sequencing (WGS) and Analysis

2.3.1. Sequencing and Analysis Process

Whole genome sequencing (WGS) was conducted among the first 20 patients enrolled for whom samples for WGS were available using the HudsonAlpha Clinical Services Laboratory (CSL) and HudsonAlpha-Discovery. The CSL is a CAP/CLIA genomics lab that currently handles samples and, together with HudsonAlpha Discovery, generates sequencing data for all the translational genomics research projects. Sequencing libraries were constructed using genomic DNA isolated from frozen buffy coat. Blood samples were collected by health care personnel, and buffy coats were prepared by biobank personnel at the clinical site, before being provided to the investigative team. Because COVID-19 is transmitted by respiratory spread and not by blood, DNA preparation by the team does not constitute an excessive biohazard beyond the handling of any other blood or blood-derived sample, and DNA preparation was handled by existing protocols to ensure safety and compliance with all relevant requirements. Genome sequencing was completed to an average of 30X coverage using Illumina NovaSeq sequencers generating 150 bp paired-end reads. Resulting sequence reads were aligned to the reference genome (GRCh38) using DRAGEN software (v07.011.352.3.2.8b). Detection of single-nucleotide variants (SNVs) and small insertion-deletion variants (indels) was performed using GATK (v3.8), in accordance with community best practices and our typical workflow.

2.3.2. Analyzing Variants to Search for Highly Penetrant Genetic Factors

Variation in each proband was analyzed using Codicem, a software platform built at HudsonAlpha to facilitate interpretation of variants in translational research projects and CSL’s clinical genomic testing. Codicem performs variant annotation and filtration with commonly used features (e.g., frequencies in gnomAD, gene body effects, conservation scores, impact predictions, genic intolerance scores, etc.). Codicem also provides automated American College of Medical Genetics and Genomics evidence code predictions for all variants and flags genes that are associated with disease in databases like Online Mendelian Inheritance in Man (OMIM). These data are presented to a variant analyst in an interactive form along with features like quality control metrics, links to gene-specific publications, information from clinical databases like ClinVar, previous in-house interpretations, and other features useful for variant curation and assessment.
Structural variants (SV) were called using four software packages, including Manta [29], Delly [30], ERDS [31], and CNVnator [32]. Mobster [33], Retroseq [34], and MELT [35] are also used to identify mobile element insertions (MEIs), including Alu, L1, and SVA events. While difficult to detect, MEIs have been identified in a number of rare disease cases [36], and the pipeline has led to the identification of three medically relevant variants (unpublished). A customized, automated pipeline then integrates the output from these callers into a single unified and annotated set of SVs suitable for filtration and curation. While some SVs can be analyzed within Codicem, many require customized tools. Towards that end, a mix of both off-the-shelf and in-house tools are used. For example, for all SVs that are of interest, the Integrative Genomics Viewer (IGV, [37]), an automated pipeline to generate images that show read-depth and SNV allelic balance metrics in the context of segmental duplications, mobile elements, gnomAD variant frequencies, and genes, similar in some ways to that seen in Cooper et al. [38] is used. Further, all SVs of interest were queried against SV databases like dbVar and against SVs found in all individuals that were previously sequenced at HudsonAlpha (over 3000). Rare and uncommon immune related gene mutations identified in the cohort are reported in Table 1.

2.4. DOCK8 Mutant Gene Preparations, Foamy Virus (FV) Preparation, and NK-92 Cells Infection

Wild-type (WT) human DOCK8 cDNA was previously generated [22]. Modification of the WT plasmid cDNA by site-directed mutagenesis to match the precise COVID-19 patient-derived mutant DOCK8 sequences was as previously described [20]. The 3 unique patient-derived mutant DOCK8 missense cDNA were confirmed by Sanger DNA sequence analysis. A recombinant foamy virus (FV) expression system for efficient infection of large cDNA, such as DOCK8, has been previously described [20]. Recombinant FV preparation and infection of human NK-92 NK cells were as described [20,22]. DOCK8-FV infected NK-92 cells were sorted based on enhanced green fluorescent protein (EGFP) co-expression by flow cytometry (FCM) prior to experimentation [20].

2.5. NK-92 Cell Degranulation and Cytotoxicity Assays

For degranulation assays, WT and patient-derived DOCK8-FV-infected NK-92 cells and K562 erythroleukemia target cells were mixed at a 2:1 effector to target cell ratio and incubated in 5% CO2 at 37 °C. Simultaneously, effectors (NK-92 cells in isolation) were set up as a background degranulation control. After 1.5 h, the cells were harvested, stained with fluorescein-conjugated anti-CD56 (NK cell marker, Pacific Blue, Biolegend, San Diego, CA, USA) and anti-CD107a/LAMP1 (Allophycocyanin (APC), Biolegend) antibodies. The CD56+ NK cells were then analyzed for cell surface expression of CD107a by FCM as previously described [39] using FlowJo 10.2 software (Ashland, OR, USA).
For cytotoxicity assays, WT and patient-derived DOCK8-FV-infected NK-92 cells and K562 erythroleukemia target cells were mixed at a 5:1 effector to target cell ratio and incubated in 5% CO2 at 37 °C for 4 h. Simultaneously, K562 target cells in the absence of NK-92 effector cells were set up as a background fluorescence control. The cells were harvested, stained with fluorescein-conjugated anti-CD56 and live/dead fixable cell dead reagent (Invitrogen, Waltham, MA, USA), and analyzed for cell death by FCM as previously described [39].

3. Results

3.1. Cytokine Storm Syndrome Criteria Met by Patients

Early during the pandemic (L-strain), 32 hospitalized adults with severe COVID-19 were endrolled in a clinical trial to test the potential benefit of IL-1 blockade with anakinra [8]. The patients were selected to meet criteria reflecting severe illness with features of CSS (e.g., hyperferritinemia, coagulopathy, liver dysfunction, cytopenias, elevated C-reactive protein). As severe COVID-19 was a relatively unique CSS, various COVID-19-specific criteria were quickly established [40]. Clincal features and laboratory values obtained during the first 3 days of hospital admission were used to assess the value of the established CSS criteria, and the newly developed COVID-19 CSS criteria (Table 2).
As NK cell activity and biopsy specimens to explore hemophagocytosis were not collected, modified HLH-2004 and HScore were employed. Nonetheless, none of the 32 clinical trial participants reached the threshold scores for either the modified HLH-04 [6] nor the modified HScore [5] criteria (Table 1). The 3 criteria developed for detection of MAS (a form of sHLH/CSS) in the setting of sJIA/Still disease were also examined. A modified MS score was also employed as data as to the presence of arthritis was not available. The original 2016 sJIA/MAS classification criteria [25] detected only 19% of participants, the modified MS score [26] identified 37% of participants, and the ferritin (ng/mL) to ESR (mm/h) ratio [10] with a threshold of 21.5 (for distinguishing sJIA MAS from disease flare) identified 41% of patients (Table 3). None of the established criteria performed well at identifying CSS among this cohort.
In contrast to the ferritin to ESR ratio with a threshold of 21.5, the threshold of 11.3 distinguishes sJIA MAS/CSS from systemic infection [10]. This simple and easy to obtain ratio identified 84% of severe COVID-19 patients in this trial (Table 3). Similarly, the CSs quick score [11] specific for COVID-19 CSS identified 81% of trial participants (Table 3). In contrast, another COVID-19 CSS criteria (the Caricchio score) [28] only identified one patient among the 32 (3%) (Table 3). Finally, the cHIS hyper-inflammatory score [27] detected all but 2 trial participants (94%) with a very sensitive threshold of only 2 criteria (Table 3). While there was no statistically significant correlation between the scores of the various diagnostic/classification criteria and mortality, all study participants who died had a minimum of 4 of the cHIS Score criteria (Table 2), consistent with the COVID-19 mortality association originally identified with 2 or more criteria [27]. Lastly, 7 of the 8 COVID-19 patient who died met the ferritin to ESR ratio of 11.3 threshold (Table 2). Thus, the ferritin to ESR ratio of 11.3 was both sensitive at identifying CSS among severely ill COVID-19 patients (84%), and it identified 88% of those who died in the study.

3.2. Whole Genome Sequencing (WGS) of Severe COVID-19 Trial Patients

WGS was conducted among the first 20 patients enrolled for whom samples for WGS were available. These were screened for rare and uncommon immune-related gene variants, including known fHLH genes and the newly identified CSS gene, DOCK8 [20]. Sixty percent of severe COVID-19 patients had heterozygous mutations in primary immunodeficiency genes for disorders that are largely recessive but rarely can have dominant phenotypes (Table 1, gray highlights). Fifteen percent of the cohort had heterozygous mutations in genes associated with autoinflammatory disorders but did not have underlying features of disease (Table 1, blue highlights). Likely, none of the autoimmune nor autoinflammatory mutations were disease contributory as heterozygotes.
Interestingly, 30% percent of DNA sequenced severe COVID-19 trial participants possessed novel, rare, or uncommon variants/mutations in known fHLH genes (Table 1, green highlights). Three of these 6 patients possessed 2 or 3 fHLH gene variants. In addition, 4 individuals (20% of those sequenced) had DOCK8 gene variants/mutations (Table 1, pink highlights). Three of these rare or uncommon DOCK8 variants/mutations were missense (amino acid changing), and one was intronically located. Various missense mutations in DOCK8 from CSS patients have previously been shown to act as partial dominant-negative mutations disrupting NK cell cytotoxicity similar to fHLH gene mutations [20,22].

3.3. Functional Testing of DOCK8 Missense Mutations

As the 3 missense DOCK8 mutations identified in this severe COVID-19 cohort had not previously been functionally tested, cDNA for the individual DOCK8 mutations were generated for use in FV transduction of the human NK cell line, NK-92 (see Section 2). FV transduction of NK-92 cells allowed for co-expression of WT or severe COVID-19 patient-derived DOCK8 missense mutation along with EGFP for cell sorting. EGFP+ FV transduced NK-92 cells were incubated with K562 target cells and tested for degranulation (CD107a), a process disrupted in fHLH perforin pathway disruption. Each of the 3 DOCK8 missense mutations independently partially disrupted (~50%) degranulation in comparison to WT DOCK8 control (Figure 1A,B). In addition, each of the 3 DOCK8 missense mutations independently partially disrupted NK cell cytotoxicity (~20%) as detected by FCM (Figure 1C,D). Since NK-92 cells already express WT DOCK8, these 3 DOCK8 missense mutations appear to act as partial dominant-negative mutations.
Since 45% of the clinical trial participants who underwent WGS possessed mutations in fHLH and/or DOCK8 genes that may have contributed to CSS via partially disrupted NK cell cytolysis, the association of these mutations was compared with the various CSS (general and COVID-19 specific) CSS criteria. Comparing patients with fHLH or DOCK8 gene mutations to those without these mutations was generally non-revealing (no obvious correlation) for most criteria explored (Table 4). Nonetheless, the ferritin to ESR threshold of 11.3 identified all (9/9) severe COVID-19 patients with a DOCK8 and/or fHLH gene mutation but also all of those without one of these mutations. Thus, the ferritin to ESR ratio may help identify severe COVID-19 patients with shared CSS pathophysiology (decreased lymphocyte cytolytic activity) to fHLH and sHLH [41,42]. This has implications for therapeutic approaches for severe COVID-19 [43,44,45].

4. Discussion

COVID-19 has killed over 7 million people worldwide, including over 1 million in the United States, many of whom suffered a CSS [1]. CSSs are frequently triggered by infections, yet SARS-CoV-2 (the etiology of COVID-19) severe infections invoke a relatively unique CSS that is poorly identified by pre-existing CSS criteria [3,7]. The urgency of the COVID-19 pandemic, and the lack of utility of prior CSS criteria [5,24], triggered multiple attempts to define the COVID-19 CSS [11,27,28]. Unfortunately, both the broad and COVID-19-specific CSS criteria often perform poorly [7,46].
The clinical trial of anakinra for severe COVID-19 with features of CSS studied herein [8] confirmed the poor performance of prior CSS/HLH criteria in identifying CSS among hospitalized severe COVID-19. However, the ferritin–ESR ratio with a threshold of 11.3 [10] and the cytokine storm score (CSs) [11] both identified over 80% of the severe COVID-19 patients in this clinical trial. Being so sensitive, the ferritin–ESR ratio identified severe COVID-19 patients regardless of DOCK8 or fHLH mutation (Table 4), likely reflecting other risk factors for severe disease in those without known CSS gene mutations. The small number of patients studied is a limitation of this study, and the value of the ferritin–ESR ratio in identifying COVID-19 CSS should be validated in larger cohorts. Nevertheless, increased criteria met of the COVID-19-associated hyperinflammatory syndrome (cHIS) correlated with mortality as previously reported [27]. Common to most all CSS criteria, COVID-19 included, is an elevated serum ferritin (typically >500–700 ng/mL), and as it is a simple, inexpensive, timely, and widely available laboratory, it can be used as a valuable sensitive tool for helping to identify febrile hospitalized individuals with probable CSS [47,48].
The ferritin–ESR ratio has previously been used to screen for MAS in the setting of Still disease [10,49]. In distinguishing between Still disease MAS and hospitalized febrile controls, the ferritin–ESR ratio demonstrated 78% specificity at a sensitivity of 95% [10]. Simplistically explained, in the setting of MAS, the ferritin rises with inflammation, and the ESR drops when coagulopathy ensues from diminished fibrinogen [10]. Severe COVID-19 also shares hyper-inflammation, but the relative rise in ferritin is typically less than seen in MAS [3]. However, with SARS-CoV-2 infection, the extent of thrombosis is considerably higher than in other MASs, as demonstrated by the frequently noted elevations of D-dimer during this infection [50]. There is therefore an attendant relative decrease in plasma fibrinogen, which would otherwise be elevated and promote red blood cell (RBC) clumping via neutralization of the positive RBC membrane charge that otherwise repels erythrocyte self-aggregation. Absent this impact of elevated intact fibrinogen on the speed of RBC sedimentation, the elevation of plasma proteins during the acute stages of the viral infection increases plasma viscosity, resulting in ESR levels that are lower than one might expect for a highly inflammatory state [51]. Thus, the ESR –ferritin ratio is a sensitive tool for identifying severe COVID-19 infection.
In addition to hyperferritinemia being common to many forms of CSS, heterozygous missense mutations in fHLH genes are seen in ~30–40% of many published CSS/sHLH cohorts [9,42,52]. The contribution of the individual fHLH gene mutations to a threshold model of CSS disease [53], however, remains unclear. Functionally testing the specific fHLH gene mutations in vitro or ex vivo may lead credence to causality [13,39,42,54,55], but even this approach raises challenges [56].
Recently, a novel CSS gene, DOCK8, has been proposed to be associated with CSS/sHLH [20]. DOCK8 missense mutations have been shown to diminish lymphocyte cytolytic activity in CSS patients with infection triggered CSS, autoimmune triggered CSS, as well as the post-COVID-19 CSS, MIS-C [20,22]. It is quite remarkable that 20% of the severe COVID-19 patients sequenced from this clinical trial [8] possessed DOCK8 mutations (Table 1), and 10% (4/39) of MIS-C children who underwent DNA sequencing had DOCK8 mutations [22]. Functionally, all 7 of these DOCK8 missense mutations acted in a partial dominant-negative fashion (Figure 1) [22]. It is possible the DOCK8 intronic mutation (Table 1, patient 10) results in diminished DOCK8 activity, similar to an earlier reported intronic mutation in the fHLH gene, UNC13D [55]. Previously, a homozygous DOCK8 defect [57] and a heterozygous intronic splice site DOCK8 mutation that disrupted mRNA splicing [20] were reported in children with CSS.
DOCK8, and the related protein, DOCK2, possess GTPase activity important for actin cytoskeleton function, including trafficking of perforin-containing cytolytic granules to the immunologic synapse, and DOCK2 and DOCK8 proteins are also required for optimal synapse formation to allow for effective perforin-mediated lysis of target cells [20,58,59,60,61]. Defects in perforin-mediated NK cell and CD8 T cell cytolysis in the setting of infection result in prolonged engagement of the lytic lymphocyte and its infected target cell (antigen presenting cell) [62,63]. This prolonged engagement yields excess pro-inflammatory cytokines responsible for the multi-organ system failure in CSS [39,62,63]. Presumably, disruptive DOCK8 mutations contribute to CSS in severe COVID-19 as well [12,41,44,64]. Along these lines a DOCK8 mutation was reported among a cohort of 8 fatal cases of COVID-19 with CSS [65]. Thus, DOCK8 may be a risk allele for COVID-19 CSS development.
Whether or not DOCK8 is a risk allele for COVID-19 infection cannot be ascertained from this current study and is a limitation, as non-severe COVID-19 patients did not undergo WGS. It seems unlikely, however, that DOCK8 gene mutations are as frequent as in the severe COVID-19 cohort in this study. It is striking that 20% of the severe COVID-19 patients possessed rare or uncommon variants with functional impacts on NK cell function. Based on the frequency of the most common of these variants (0.27%), one would expect less than one in 350 individuals to possess a similar DOCK8 mutation. Nonetheless, the small sample size of 20 severe COVID-19 patients who underwent CSS is a study limitation, and future studies are needed to replicate the role of DOCK8 in severe COVID-19.
Like DOCK8, DOCK2 mutations have also recently been reported in association with CSS [19,66], and DOCK2 has been implicated in severe COVID-19 pneumonia [21,67]. Homozygous and hemizygous DOCK11 deficiency has also been newly associated with hyper-inflammatory states [68,69], but whether DOCK11 mutations contribute to CSS or severe COVID-19 is currently unknown. For now, further exploration of the association and role of fHLH genes and DOCK8 in severe COVID-19 CSS is worthy of exploration.

5. Conclusions

Severe COVID-19 presents as a relatively unique CSS, but it is identifiable by a ferritin to ESR ratio >11.3, or by the Cytokine Storm score (CSs). In this severe COVID-19 cohort, 45% (9/20) of those undergoing WGS possessed heterozygous mutations in fHLH and/or DOCK8 genes. The 3 unique DOCK8 missense mutations identified in this cohort, similar to fHLH gene mutations, functioned to partially disrupt NK cell cytolytic function. These data suggest that fHLH and DOCK8 gene mutations are risk alleles for development of severe COVID-19 CSS.

6. Patents

“Long noncoding RNA risk factor for COVID-19”, US Application No. 63/220,253, University of Alabama at Birmingham, was filed in March 2021.

Author Contributions

Conceptualization, R.Q.C. and W.W.C.; methodology, M.Z., D.M.A., R.Q.C. and W.W.C.; validation, L.E.J., M.Z. and D.M.A.; formal analysis, M.Z., L.E.J., A.K., D.M.A., R.Q.C. and W.W.C.; investigation, L.E.J., M.Z. and D.M.A.; resources, R.Q.C. and W.W.C.; data curation, L.E.J., A.K., D.M.A., M.Z. and W.W.C.; writing—original draft preparation, R.Q.C.; writing—review and editing, L.E.J., A.K., M.Z., D.M.A., R.Q.C. and W.W.C.; visualization, M.Z. and A.K.; supervision, R.Q.C. and W.W.C.; project administration, R.Q.C. and W.W.C.; funding acquisition, R.Q.C. and W.W.C. All authors have read and agreed to the published version of the manuscript.

Funding

This clinical trial and genetic studies were funded by a grant from the University of Alabama at Birmingham Heersink School of Medicine and Precision Medicine Institute (to W.W.C and R.Q.C.) with additional support from Swedish Orphan Biovitrum (Sobi), Inc. Sobi provided placebo and anakinra therapy, as well as additional financial support, but had no role in study design, data collection and analysis, nor writing/editing of the manuscript. Laboratory support was from grants awarded to R.Q.C. by the Kaul Pediatric Research Institute, the Rheumatology Research Foundation, and by the Histiocytosis Association.

Institutional Review Board Statement

The clinical trial was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of the University of Alabama at Birmingham (protocol code IRB-300005684 and originally approved 28 July 2020). The genetic analyses were covered under long-standing IRB protocol code IRB-300001443-016.

Informed Consent Statement

Written informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data underlying this article cannot be shared publicly for the privacy of individuals that participated in the clinical trial. De-identified data will be shared on reasonable request to the corresponding author.

Acknowledgments

The authors thank the UAB COVID-19 research infrastructure and administration, as well as the generous participation of the patients and families to advance science/care.

Conflicts of Interest

R.Q.C. reports speaker fees, consulting fees, and grant support from Sobi, consulting fees from Sironax, CareerPhysician, Neurogene, AS2 Biotherapeutics, and Novartis, speaker fees from Lilly, and clinical trial side-effect adjudication committee work support from AbbVie and Pfizer. W.W.C. receives consulting fees and grant support from Sobi. The remaining authors declare no conflicts of interest.

References

  1. Cron, R.Q.; Goyal, G.; Chatham, W.W. Cytokine Storm Syndrome. Annu. Rev. Med. 2023, 74, 321–337. [Google Scholar] [CrossRef]
  2. Crayne, C.; Cron, R.Q. Pediatric macrophage activation syndrome, recognizing the tip of the Iceberg. Eur. J. Rheumatol. 2019, 7, S13–S20. [Google Scholar] [CrossRef]
  3. Cron, R.Q. No perfect therapy for the imperfect COVID-19 cytokine storm. Lancet Rheumatol. 2022, 4, e308–e310. [Google Scholar] [CrossRef]
  4. Henderson, L.A.; Cron, R.Q. Macrophage Activation Syndrome and Secondary Hemophagocytic Lymphohistiocytosis in Childhood Inflammatory Disorders: Diagnosis and Management. Paediatr. Drugs 2020, 22, 29–44. [Google Scholar] [CrossRef]
  5. Fardet, L.; Galicier, L.; Lambotte, O.; Marzac, C.; Aumont, C.; Chahwan, D.; Coppo, P.; Hejblum, G. Development and validation of a score for the diagnosis of reactive hemophagocytic syndrome (HScore). Arthritis Rheumatol. 2014, 66, 2613–2620. [Google Scholar] [CrossRef]
  6. Henter, J.I.; Sieni, E.; Eriksson, J.; Bergsten, E.; Hed Myrberg, I.; Canna, S.W.; Coniglio, M.L.; Cron, R.Q.; Kernan, K.F.; Kumar, A.R.; et al. Diagnostic guidelines for familial hemophagocytic lymphohistiocytosis revisited. Blood 2024, 144, 2308–2318. [Google Scholar] [CrossRef]
  7. Reiff, D.D.; Cron, R.Q. Performance of Cytokine Storm Syndrome Scoring Systems in Pediatric COVID-19 and Multisystem Inflammatory Syndrome in Children. ACR Open Rheumatol. 2021, 3, 820–826. [Google Scholar] [CrossRef]
  8. Jackson, L.E.; Khullar, N.; Beukelman, T.; Chapleau, C.; Kamath, A.; Cron, R.Q.; Chatham, W.W. Prediction of Survival by IL-6 in a Randomized Placebo-Controlled Trial of Anakinra in COVID-19 Cytokine Storm. Viruses 2023, 15, 2036. [Google Scholar] [CrossRef]
  9. Eloseily, E.M.; Weiser, P.; Crayne, C.B.; Haines, H.; Mannion, M.L.; Stoll, M.L.; Beukelman, T.; Atkinson, T.P.; Cron, R.Q. Benefit of Anakinra in Treating Pediatric Secondary Hemophagocytic Lymphohistiocytosis. Arthritis Rheumatol. 2020, 72, 326–334. [Google Scholar] [CrossRef]
  10. Eloseily, E.M.A.; Minoia, F.; Crayne, C.B.; Beukelman, T.; Ravelli, A.; Cron, R.Q. Ferritin to Erythrocyte Sedimentation Rate Ratio: Simple Measure to Identify Macrophage Activation Syndrome in Systemic Juvenile Idiopathic Arthritis. ACR Open Rheumatol. 2019, 1, 345–349. [Google Scholar] [CrossRef]
  11. Cappanera, S.; Palumbo, M.; Kwan, S.H.; Priante, G.; Martella, L.A.; Saraca, L.M.; Sicari, F.; Vernelli, C.; Di Giuli, C.; Andreani, P.; et al. When Does the Cytokine Storm Begin in COVID-19 Patients? A Quick Score to Recognize It. J. Clin. Med. 2021, 10, 297. [Google Scholar] [CrossRef]
  12. Henderson, L.A.; Canna, S.W.; Schulert, G.S.; Volpi, S.; Lee, P.Y.; Kernan, K.F.; Caricchio, R.; Mahmud, S.; Hazen, M.M.; Halyabar, O.; et al. On the alert for cytokine storm: Immunopathology in COVID-19. Arthritis Rheumatol. 2020, 72, 1059–1063. [Google Scholar] [CrossRef]
  13. Schulert, G.S.; Zhang, M.; Fall, N.; Husami, A.; Kissell, D.; Hanosh, A.; Zhang, K.; Davis, K.; Jentzen, J.M.; Napolitano, L.; et al. Whole-Exome Sequencing Reveals Mutations in Genes Linked to Hemophagocytic Lymphohistiocytosis and Macrophage Activation Syndrome in Fatal Cases of H1N1 Influenza. J. Infect. Dis. 2016, 213, 1180–1188. [Google Scholar] [CrossRef]
  14. Cabrera-Marante, O.; Rodriguez de Frias, E.; Pleguezuelo, D.E.; Allende, L.M.; Serrano, A.; Laguna-Goya, R.; Mancebo, M.E.; Talayero, P.; Alvarez-Vallina, L.; Morales, P.; et al. Perforin gene variant A91V in young patients with severe COVID-19. Haematologica 2020, 105, 2844–2846. [Google Scholar] [CrossRef]
  15. Chen, G.; Li, L.; Wang, R.; Liu, B.; Cao, Z.; Zhao, N.; Tan, Y.; He, X.; Zhao, J.; Lu, C. Integrative network analysis identifies pivotal host genes and pathways for SARS-CoV-2 infection. Genes Dis. 2025, 12, 101206. [Google Scholar] [CrossRef]
  16. Luo, H.; Liu, D.; Liu, W.; Wang, G.; Chen, L.; Cao, Y.; Wei, J.; Xiao, M.; Liu, X.; Huang, G.; et al. Germline variants in UNC13D and AP3B1 are enriched in COVID-19 patients experiencing severe cytokine storms. Eur. J. Hum. Genet. 2021, 29, 1312–1315. [Google Scholar] [CrossRef]
  17. Schulert, G.S.; Blum, S.A.; Cron, R.Q. Host genetics of pediatric SARS-CoV-2 COVID-19 and multisystem inflammatory syndrome in children. Curr. Opin. Pediatr. 2021, 33, 549–555. [Google Scholar] [CrossRef]
  18. Zanchettin, A.C.; Barbosa, L.V.; Dutra, A.A.; Pra, D.M.M.; Pereira, M.R.C.; Stocco, R.B.; Martins, A.P.C.; Vaz de Paula, C.B.; Nagashima, S.; de Noronha, L.; et al. Role of Genetic Polymorphism Present in Macrophage Activation Syndrome Pathway in Post Mortem Biopsies of Patients with COVID-19. Viruses 2022, 14, 1699. [Google Scholar] [CrossRef]
  19. Reiff, D.D.; Zhang, M.; Cron, R.Q. DOCK2 Mutation and Recurrent Hemophagocytic Lymphohistiocytosis. Life 2023, 13, 434. [Google Scholar] [CrossRef]
  20. Zhang, M.; Cron, R.R.; Chu, N.; Nguyen, J.; Gordon, S.M.; Eloseily, E.M.; Atkinson, T.P.; Weiser, P.; Walter, M.R.; Kreiger, P.A.; et al. Role of DOCK8 in cytokine storm syndromes. J. Allergy Clin. Immunol. 2025, 155, 1015–1026.e5. [Google Scholar] [CrossRef]
  21. Namkoong, H.; Edahiro, R.; Takano, T.; Nishihara, H.; Shirai, Y.; Sonehara, K.; Tanaka, H.; Azekawa, S.; Mikami, Y.; Lee, H.; et al. DOCK2 is involved in the host genetics and biology of severe COVID-19. Nature 2022, 609, 754–760. [Google Scholar] [CrossRef]
  22. Vagrecha, A.; Zhang, M.; Acharya, S.; Lozinsky, S.; Singer, A.; Levine, C.; Al-Ghafry, M.; Fein Levy, C.; Cron, R.Q. Hemophagocytic Lymphohistiocytosis Gene Variants in Multisystem Inflammatory Syndrome in Children. Biology 2022, 11, 417. [Google Scholar] [CrossRef]
  23. Vigon, L.; Fuertes, D.; Garcia-Perez, J.; Torres, M.; Rodriguez-Mora, S.; Mateos, E.; Corona, M.; Saez-Marin, A.J.; Malo, R.; Navarro, C.; et al. Impaired Cytotoxic Response in PBMCs From Patients With COVID-19 Admitted to the ICU: Biomarkers to Predict Disease Severity. Front. Immunol. 2021, 12, 665329. [Google Scholar] [CrossRef]
  24. Henter, J.I.; Horne, A.; Arico, M.; Egeler, R.M.; Filipovich, A.H.; Imashuku, S.; Ladisch, S.; McClain, K.; Webb, D.; Winiarski, J.; et al. HLH-2004: Diagnostic and therapeutic guidelines for hemophagocytic lymphohistiocytosis. Pediatr. Blood Cancer 2007, 48, 124–131. [Google Scholar] [CrossRef]
  25. Ravelli, A.; Minoia, F.; Davi, S.; Horne, A.; Bovis, F.; Pistorio, A.; Arico, M.; Avcin, T.; Behrens, E.M.; De Benedetti, F.; et al. 2016 Classification Criteria for Macrophage Activation Syndrome Complicating Systemic Juvenile Idiopathic Arthritis: A European League Against Rheumatism/American College of Rheumatology/Paediatric Rheumatology International Trials Organisation Collaborative Initiative. Ann. Rheum. Dis. 2016, 75, 481–489. [Google Scholar] [CrossRef]
  26. Minoia, F.; Bovis, F.; Davi, S.; Horne, A.; Fischbach, M.; Frosch, M.; Huber, A.; Jelusic, M.; Sawhney, S.; McCurdy, D.K.; et al. Development and initial validation of the MS score for diagnosis of macrophage activation syndrome in systemic juvenile idiopathic arthritis. Ann. Rheum. Dis. 2019, 78, 1357–1362. [Google Scholar] [CrossRef]
  27. Webb, B.J.; Peltan, I.D.; Jensen, P.; Hoda, D.; Hunter, B.; Silver, A.; Starr, N.; Buckel, W.; Grisel, N.; Hummel, E.; et al. Clinical criteria for COVID-19-associated hyperinflammatory syndrome: A cohort study. Lancet Rheumatol. 2020, 2, e754–e763. [Google Scholar] [CrossRef]
  28. Caricchio, R.; Gallucci, M.; Dass, C.; Zhang, X.; Gallucci, S.; Fleece, D.; Bromberg, M.; Criner, G.J.; Temple University, C.-R.G. Preliminary predictive criteria for COVID-19 cytokine storm. Ann. Rheum. Dis. 2021, 80, 88–95. [Google Scholar] [CrossRef]
  29. Chen, X.; Schulz-Trieglaff, O.; Shaw, R.; Barnes, B.; Schlesinger, F.; Kallberg, M.; Cox, A.J.; Kruglyak, S.; Saunders, C.T. Manta: Rapid detection of structural variants and indels for germline and cancer sequencing applications. Bioinformatics 2016, 32, 1220–1222. [Google Scholar] [CrossRef]
  30. Rausch, T.; Zichner, T.; Schlattl, A.; Stutz, A.M.; Benes, V.; Korbel, J.O. DELLY: Structural variant discovery by integrated paired-end and split-read analysis. Bioinformatics 2012, 28, i333–i339. [Google Scholar] [CrossRef]
  31. Zhu, M.; Need, A.C.; Han, Y.; Ge, D.; Maia, J.M.; Zhu, Q.; Heinzen, E.L.; Cirulli, E.T.; Pelak, K.; He, M.; et al. Using ERDS to infer copy-number variants in high-coverage genomes. Am. J. Hum. Genet. 2012, 91, 408–421. [Google Scholar] [CrossRef]
  32. Abyzov, A.; Urban, A.E.; Snyder, M.; Gerstein, M. CNVnator: An approach to discover, genotype, and characterize typical and atypical CNVs from family and population genome sequencing. Genome Res. 2011, 21, 974–984. [Google Scholar] [CrossRef]
  33. Thung, D.T.; de Ligt, J.; Vissers, L.E.; Steehouwer, M.; Kroon, M.; de Vries, P.; Slagboom, E.P.; Ye, K.; Veltman, J.A.; Hehir-Kwa, J.Y. Mobster: Accurate detection of mobile element insertions in next generation sequencing data. Genome Biol. 2014, 15, 488. [Google Scholar] [CrossRef]
  34. Keane, T.M.; Wong, K.; Adams, D.J. RetroSeq: Transposable element discovery from next-generation sequencing data. Bioinformatics 2013, 29, 389–390. [Google Scholar] [CrossRef]
  35. Gardner, E.J.; Lam, V.K.; Harris, D.N.; Chuang, N.T.; Scott, E.C.; Pittard, W.S.; Mills, R.E.; Genomes Project, C.; Devine, S.E. The Mobile Element Locator Tool (MELT): Population-scale mobile element discovery and biology. Genome Res. 2017, 27, 1916–1929. [Google Scholar] [CrossRef]
  36. Torene, R.I.; Galens, K.; Liu, S.; Arvai, K.; Borroto, C.; Scuffins, J.; Zhang, Z.; Friedman, B.; Sroka, H.; Heeley, J.; et al. Mobile element insertion detection in 89,874 clinical exomes. Genet. Med. 2020, 22, 974–978. [Google Scholar] [CrossRef]
  37. Robinson, J.T.; Thorvaldsdottir, H.; Wenger, A.M.; Zehir, A.; Mesirov, J.P. Variant Review with the Integrative Genomics Viewer. Cancer Res. 2017, 77, e31–e34. [Google Scholar] [CrossRef]
  38. Cooper, G.M.; Zerr, T.; Kidd, J.M.; Eichler, E.E.; Nickerson, D.A. Systematic assessment of copy number variant detection via genome-wide SNP genotyping. Nat. Genet. 2008, 40, 1199–1203. [Google Scholar] [CrossRef]
  39. Zhang, M.; Bracaglia, C.; Prencipe, G.; Bemrich-Stolz, C.J.; Beukelman, T.; Dimmitt, R.A.; Chatham, W.W.; Zhang, K.; Li, H.; Walter, M.R.; et al. A heterozygous RAB27A mutation associated with delayed cytolytic granule polarization and hemophagocytic lymphohistiocytosis. J. Immunol. 2016, 196, 2492–2503. [Google Scholar] [CrossRef]
  40. Cron, R.Q.; Schulert, G.S.; Tattersall, R.S. Defining the scourge of COVID-19 hyperinflammatory syndrome. Lancet Rheumatol. 2020, 2, e727–e729. [Google Scholar] [CrossRef]
  41. Canna, S.W.; Cron, R.Q. Highways to hell: Mechanism-based management of cytokine storm syndromes. J. Allergy Clin. Immunol. 2020, 146, 949–959. [Google Scholar] [CrossRef]
  42. Zhang, M.; Behrens, E.M.; Atkinson, T.P.; Shakoory, B.; Grom, A.A.; Cron, R.Q. Genetic defects in cytolysis in macrophage activation syndrome. Curr. Rheumatol. Rep. 2014, 16, 439–446. [Google Scholar] [CrossRef]
  43. Cron, R.Q. COVID-19 cytokine storm: Targeting the appropriate cytokine. Lancet Rheumatol. 2021, 3, e236–e237. [Google Scholar] [CrossRef]
  44. Cron, R.Q.; Caricchio, R.; Chatham, W.W. Calming the cytokine storm in COVID-19. Nat. Med. 2021, 27, 1674–1675. [Google Scholar] [CrossRef]
  45. Kelmenson, D.A.; Cron, R.Q. Who, what, and when-effective therapy for severe COVID-19. Lancet Rheumatol. 2022, 4, e2–e3. [Google Scholar] [CrossRef]
  46. Alam, F.; Becetti, K.; Alamlih, L.; Cackamvalli, P.; Veettil, S.; Awadh, B.; Ibrahim, M.; Al Emadi, S. Rate of secondary HLH and performance of H-score in patients with severe COVID-19. Qatar Med. J. 2022, 2022, 11. [Google Scholar] [CrossRef]
  47. Carol, H.A.; Mayer, A.S.; Zhang, M.S.; Dang, V.; Varghese, J.; Martinez, Z.; Schneider, C.; Baker, J.E.; Tsoukas, P.; Behrens, E.M.; et al. Hyperferritinemia Screening to Aid Identification and Differentiation of Patients with Hyperinflammatory Disorders. J. Clin. Immunol. 2024, 45, 4. [Google Scholar] [CrossRef]
  48. Harrowell, I.; Cron, R.Q.; Ramanan, A.V. Hospitalised with fever: Worthy of serum ferritin. Rheumatology 2025. [Google Scholar] [CrossRef]
  49. Kim, J.W.; Jung, J.Y.; Suh, C.H.; Kim, H.A. Systemic immune-inflammation index combined with ferritin can serve as a reliable assessment score for adult-onset Still’s disease. Clin. Rheumatol. 2021, 40, 661–668. [Google Scholar] [CrossRef]
  50. van de Veerdonk, F.L. COVID-19 Pneumonia and Cytokine Storm Syndrome. Adv. Exp. Med. Biol. 2024, 1448, 307–319. [Google Scholar] [CrossRef]
  51. Crayne, C.B.; Albeituni, S.; Nichols, K.E.; Cron, R.Q. The Immunology of Macrophage Activation Syndrome. Front. Immunol. 2019, 10, 119. [Google Scholar] [CrossRef]
  52. Miao, Y.; Zhu, H.Y.; Qiao, C.; Xia, Y.; Kong, Y.; Zou, Y.X.; Miao, Y.Q.; Chen, X.; Cao, L.; Wu, W.; et al. Pathogenic Gene Mutations or Variants Identified by Targeted Gene Sequencing in Adults With Hemophagocytic Lymphohistiocytosis. Front. Immunol. 2019, 10, 395. [Google Scholar] [CrossRef]
  53. Strippoli, R.; Caiello, I.; De Benedetti, F. Reaching the threshold: A multilayer pathogenesis of macrophage activation syndrome. J. Rheumatol. 2013, 40, 761–767. [Google Scholar] [CrossRef]
  54. Reiff, D.D.; Zhang, M.; Smitherman, E.A.; Mannion, M.L.; Stoll, M.L.; Weiser, P.; Cron, R.Q. A Rare STXBP2 Mutation in Severe COVID-19 and Secondary Cytokine Storm Syndrome. Life 2022, 12, 149. [Google Scholar] [CrossRef]
  55. Schulert, G.S.; Zhang, M.; Husami, A.; Fall, N.; Brunner, H.; Zhang, K.; Cron, R.Q.; Grom, A.A. Brief Report: Novel UNC13D Intronic Variant Disrupting an NF-kappaB Enhancer in a Patient With Recurrent Macrophage Activation Syndrome and Systemic Juvenile Idiopathic Arthritis. Arthritis Rheumatol. 2018, 70, 963–970. [Google Scholar] [CrossRef]
  56. Cron, R.Q. Genetic challenges in the hole puncher. Blood 2025, 145, 2934–2936. [Google Scholar] [CrossRef]
  57. Chinn, I.K.; Eckstein, O.S.; Peckham-Gregory, E.C.; Goldberg, B.R.; Forbes, L.R.; Nicholas, S.K.; Mace, E.M.; Vogel, T.P.; Abhyankar, H.A.; Diaz, M.I.; et al. Genetic and mechanistic diversity in pediatric hemophagocytic lymphohistiocytosis. Blood 2018, 132, 89–100. [Google Scholar] [CrossRef]
  58. Kearney, C.J.; Vervoort, S.J.; Ramsbottom, K.M.; Freeman, A.J.; Michie, J.; Peake, J.; Casanova, J.L.; Picard, C.; Tangye, S.G.; Ma, C.S.; et al. DOCK8 Drives Src-Dependent NK Cell Effector Function. J. Immunol. 2017, 199, 2118–2127. [Google Scholar] [CrossRef]
  59. Mizesko, M.C.; Banerjee, P.P.; Monaco-Shawver, L.; Mace, E.M.; Bernal, W.E.; Sawalle-Belohradsky, J.; Belohradsky, B.H.; Heinz, V.; Freeman, A.F.; Sullivan, K.E.; et al. Defective actin accumulation impairs human natural killer cell function in patients with dedicator of cytokinesis 8 deficiency. J. Allergy Clin. Immunol. 2013, 131, 840–848. [Google Scholar] [CrossRef]
  60. Sakai, Y.; Tanaka, Y.; Yanagihara, T.; Watanabe, M.; Duan, X.; Terasawa, M.; Nishikimi, A.; Sanematsu, F.; Fukui, Y. The Rac activator DOCK2 regulates natural killer cell-mediated cytotoxicity in mice through the lytic synapse formation. Blood 2013, 122, 386–393. [Google Scholar] [CrossRef]
  61. Zhang, Q.; Dove, C.G.; Hor, J.L.; Murdock, H.M.; Strauss-Albee, D.M.; Garcia, J.A.; Mandl, J.N.; Grodick, R.A.; Jing, H.; Chandler-Brown, D.B.; et al. DOCK8 regulates lymphocyte shape integrity for skin antiviral immunity. J. Exp. Med. 2014, 211, 2549–2566. [Google Scholar] [CrossRef]
  62. Anft, M.; Netter, P.; Urlaub, D.; Prager, I.; Schaffner, S.; Watzl, C. NK cell detachment from target cells is regulated by successful cytotoxicity and influences cytokine production. Cell Mol. Immunol. 2020, 17, 347–355. [Google Scholar] [CrossRef]
  63. Jenkins, M.R.; Rudd-Schmidt, J.A.; Lopez, J.A.; Ramsbottom, K.M.; Mannering, S.I.; Andrews, D.M.; Voskoboinik, I.; Trapani, J.A. Failed CTL/NK cell killing and cytokine hypersecretion are directly linked through prolonged synapse time. J. Exp. Med. 2015, 212, 307–317. [Google Scholar] [CrossRef]
  64. Cron, R.Q. Coronavirus is the trigger but the immune response is deadly. Lancet Rheumatol. 2020, 2, e370–e371. [Google Scholar] [CrossRef]
  65. Canny, S.P.; Stanaway, I.B.; Holton, S.E.; Mitchem, M.; O’Rourke, A.R.; Pribitzer, S.; Baxter, S.K.; Wurfel, M.M.; Malhotra, U.; Buckner, J.H.; et al. Proteomic Analyses in COVID-19-Associated Secondary Hemophagocytic Lymphohistiocytosis. Crit. Care Explor. 2025, 7, e1203. [Google Scholar] [CrossRef]
  66. Aytekin, E.S.; Cagdas, D.; Tan, C.; Cavdarli, B.; Bilgic, I.; Tezcan, I. Hematopoietic stem cell transplantation complicated with EBV associated hemophagocytic lymphohistiocytosis in a patient with DOCK2 deficiency. Turk. J. Pediatr. 2021, 63, 1072–1077. [Google Scholar] [CrossRef]
  67. Biglari, S.; Youssefian, L.; Tabatabaiefar, M.A.; Saeidian, A.H.; Abtahi-Naeini, B.; Khorram, E.; Sherkat, R.; Moghaddam, A.S.; Mohaghegh, F.; Rahimi, M.; et al. DOCK2 Deficiency and GATA2 Haploinsufficiency Can Underlie Critical Coronavirus Disease 2019 (COVID-19) Pneumonia. J. Clin. Immunol. 2025, 45, 85. [Google Scholar] [CrossRef]
  68. Elsayed, A.; von Hardenberg, S.; Atschekzei, F.; Siek, P.; Witte, T.; Sogkas, G.; Ringshausen, F.C. A novel hemizygous nonsense variant in DOCK11 causes systemic inflammation and immunodeficiency. Clin. Immunol. 2025, 276, 110504. [Google Scholar] [CrossRef]
  69. Kumar, V.; Kumar, K.; Jerath, N.; Sibal, A. DOCK11 deficiency-related immune dysregulation leading to paediatric acute liver failure. BMJ Case Rep. 2025, 18, e263427. [Google Scholar] [CrossRef]
Viruses 17 01093 g001
Figure 1. DOCK8 missense mutations partially disrupt NK cell cytotoxicity. (A) Representative FCM plots (Y-axis: side scatter, X-axis: CD107a expression) of NK-92 cell degranulation following incubation without (top row) or with (bottom row) K562 target cells (from left to right, NK-92 cells with FV expression of DOCK8 WT, c.1249T>C, c.3784C>G, c.3815A>G). Percent expression and MFI (mean fluorescence intensity are noted). (B) Bar graph of percent CD107a expression means ± SEM (n = 4) for NK-92 cells with FV expression of DOCK8 WT, c.1249T>C, c.3784C>G, c.3815A>G. (C) Representative FCM plots (Y-axis: side scatter, X-axis: cell death reagent) of K562 (CD56 gated) cell death in the presence of NK-92 cells with FV expression of (left to right: DOCK8 WT, c.1249T>C, c.3784C>G, c.3815A>G). (D) Bar graph of K562 cell death percentage means ± SEM (n = 4) by NK-92 cells with FV expression of DOCK8 WT, c.1249T>C, c.3784C>G, c.3815A>G. * = p < 0.05, ** = p < 0.005, *** = p < 0.001, **** = p < 0.0001.
Figure 1. DOCK8 missense mutations partially disrupt NK cell cytotoxicity. (A) Representative FCM plots (Y-axis: side scatter, X-axis: CD107a expression) of NK-92 cell degranulation following incubation without (top row) or with (bottom row) K562 target cells (from left to right, NK-92 cells with FV expression of DOCK8 WT, c.1249T>C, c.3784C>G, c.3815A>G). Percent expression and MFI (mean fluorescence intensity are noted). (B) Bar graph of percent CD107a expression means ± SEM (n = 4) for NK-92 cells with FV expression of DOCK8 WT, c.1249T>C, c.3784C>G, c.3815A>G. (C) Representative FCM plots (Y-axis: side scatter, X-axis: cell death reagent) of K562 (CD56 gated) cell death in the presence of NK-92 cells with FV expression of (left to right: DOCK8 WT, c.1249T>C, c.3784C>G, c.3815A>G). (D) Bar graph of K562 cell death percentage means ± SEM (n = 4) by NK-92 cells with FV expression of DOCK8 WT, c.1249T>C, c.3784C>G, c.3815A>G. * = p < 0.05, ** = p < 0.005, *** = p < 0.001, **** = p < 0.0001.
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Table 1. Patient-derived immune-related gene variants identified by WGS.
Table 1. Patient-derived immune-related gene variants identified by WGS.
Pt.#Gene 1Gene DescriptionMutationFrequency (gnomAD)
3IFNGR1IFN-gamma receptor 1c.58G>A (p.Val20Ile)0.27%
LYSTChediak-Higashi (fHLH)c.3683A>G (p.Asn1228Ser)0.17%
NLRC4Enterocolitis autoinflammatoryc.787+102T>C (intron)0.38%
NLRP12Cold autoinflammatory 2c.1063 (p.Glu355Lys)0.16%
4DOCK8Hyper-IgE syndrome (NK function)c.3784C>G (p.Leu1262Val)0.09%
LACC1Oxidoreductase (systemic JIA)c.-239+12_-239+13ins (5’ UTR)Novel
NLRP4Pathogenic E. coli infectionsc.2119_2122deltCTC (p.Ser707Valfs)Novel
RAG1SCID (immunodeficiency)c.295G>A (p.Gly99Ser)0.09%
6C7Complement 7c.1561C>A (p.Arg521Ser)0.25%
CARMIL2Combined immunodeficiencyc.2489G>A (p.Arg830Gln)0.56%
TNFAIP3Behcet-like autoinflammatoryc.755A>G (p.Tyr252Cys)0.0007%
10DOCK8Hyper-IgE syndrome (NK function)c.53+199C>G (intron)0.21%
MRTFAImmunodeficiency 66 (TF)c.1693dupG (p.Ala565GlyfsTer67)Novel
11
12DOCK8Hyper-IgE syndrome (NK function)c.1249T>C (p.Phe417Leu)0.001%
IL17RAImmunodeficiency 51 (IL-17 rec. A)c.1361 (p.Pro454Arg)0.001%
ITKIL-2 induc. T cell kinase (agamma.)c.1510A>T (p.Thr504Ser)0.09%
LRBACommon variable immunodef. 8c.7708+337C>T (intron)0.12%
XIAPX-linked lymphoproliferative 2c.1408A>T (p.Thr470Ser)0.05%
13LYSTChediak-Higashi (fHLH)c.891T>G (p.ASn2971Lys)0.27%
STAT2Immunodeficiency 44c.2378A>T (p.His793Leu)Novel
STXBP2UNC18-2 (fHLH)n.1610G>C (non-coding exon)0.14%
14IFNGR1IFN-gamma receptor 1c.1325A>G (p.Glu442Gly)Novel
15LYSTChediak-Higashi (fHLH)c.1889C>T (p.Pro630Leu)Novel
LYSTChediak-Higashi (fHLH)c.3359G>T (p.Ser1120Ile)0.29%
NLRC4Enterocolitis autoinflammatoryc.262+1187G>A (intron)0.14%
RAB27AGriscelli 2 syndrome (fHLH)c.468-3C>T (3’ splice site)0.23%
STAT3Hyper-IgE syndrome (TF)c.550+250C>A (intron)1.7%
16DOCK8Hyper-IgE syndrome (NK function)c.3815A>G (p.Tyr1272Cys)0.27%
17
18STXBP2UNC18-2 (fHLH)c.1529+10C>T (intron)0.21%
20WASWiskott-Aldrich syndromec995T>C (p.Val332Ala)0.49%
21
22LRBACommon variable immunodef. 8c.5291+3024A>G (interior intron)0.019%
PIK3CDPI kinase catalyticc.*270C>T (3’ of stop codon)0.081%
23
24
25CASP10ALPS lymphoproliferative syndromec.*1043C>T (3’ coding exon)0.35%
STAT4SLE 11 (IL-23 signaling)c.719G>A (p.Arg240Gln)0.14%
UNC13DMUNC13-4 (fHLH)c.2542A>C (p.Ile848Leu)0.10%
UNC13DMUNC13-4 (fHLH)c.2983G>C (p.Ala995Pro)0.10%
26MRTFAImmunodeficiency 66 (TF)c.1603G>A (p.Glu535Lys)0.025%
27
1 fHLH gene: 30%; DOCK8 novel HLH gene: 20%; autoinflammatory: 15%; immunodeficiency:60%.
Table 2. Established and COVID-19-specific cytokine storm syndrome criteria met by the clinical trial participants.
Table 2. Established and COVID-19-specific cytokine storm syndrome criteria met by the clinical trial participants.
PatientPlacebo (P), Anakinra (A), or Withdraw (WD)* HLH-2004 Score
(≥5)		** HScore
(≥169)		2016 sJIA/MAS Score*** MS Score
(≥−2.1)		Ferritin–ESR
(>11.3)		CSs ScorecHIS Score
(≥2)		Caricchio Score
1A287No−2.219Pos2No
2A2107No−2.516Pos3No
3P283No−3.019.3Pos5No
4P383No−1.572.3Pos4No
5WD383Yes−2.59.1Pos3No
6P3157Yes4.516.1Neg5Yes
7A163No−2.415.7Pos3No
8A3118Yes1.540.4Pos5No
9WD383No−3.611.6Pos3No
10A163No−0.115.9Pos3No
11A397Yes−2.828.6Pos5No
12P263No−3.832.2Neg2No
13A2107No−2.618.31Pos3No
14P144No−2.112.8Pos3No
15P164No−3.811.6Pos4No
16P183No−3.016.2Neg1No
17A3117Yes−1.263.7Pos2No
18A283No−2.727Pos4No
19A183No1.15.7Neg1No
20P183No−4.218.8Pos3No
21P3118No−2.838.1Pos4No
22A164No−1.799.2Pos3No
23P1118No−0.2149.8Pos4No
24P283No−2.679.7Neg2No
25A2118No−1.5111.8Pos4No
26P264No−2.612.9Pos2No
27A183No−2.623.8Pos3No
28A283No−3.27.9Pos3No
29A283Yes−3.714.4Pos4No
30P264No−1.324Neg3No
31P383No−2.310Pos4No
32P264No−0.78.6Pos4No
* Modified HLH-2004 Criteria—hemophagocytosis and NK cell cytolytic data not collected; ** Modified HScore Criteria—hemophagocytosis data not collected; *** Modified MS Score Criteria—Arthritis status not collected. Abbreviations: HLH-2004 = hemophagocytic lymphohistiocytosis; HScore = Hemophagocytic Syndrome; 2016 sJIA Score = systemic juvenile idiopathic arthritis; MS Score = Macrophage activation syndrome/systemic juvenile idiopathic arthritis; CSs Score = COVID-19 CSS Quick Score; cHIS Score = COVID-19-associated hyperinflammatory syndrome. Red text indicates deceased patient following anakinra trial for COVID-19 induced cytokine storm.
Table 3. Summary and performance of various CSS criteria in identifying CSS among the COVID-19 cohort.
Table 3. Summary and performance of various CSS criteria in identifying CSS among the COVID-19 cohort.
Total Patients (n = 32)
2016 sJIA/MAS Score: Yes6/32 (19%)
MS Score, Median (IQR) −2.5 (−3.0 to −1.4) (37%)
Ferritin–ESR > 11.327/32 (84%)
Ferritin–ESR > 21.513/32 (41%)
Ferritin–ESR, Median (IQR)18.6 (12.8 to 36.6)
CSs Score: Pos26/32 (81%)
cHIS Score, Median (IQR)3 (3 to 4) (94%)
Caricchio Score1/32 (3.1%)
Abbreviations: sJIA = systemic juvenile idiopathic arthritis; MAS = macrophage activation syndrome; MS = MAS/sJIA; ESR = erythrocyte sedimentation rate; IQR = interquartile range; CSs = cytokine storm score; cHIS = COVID-19-associated hyperinflammatory syndrome.
Table 4. Comparison of CSS criteria percentages met by severe COVID-19 patients with or without DOCK8 and/or fHLH gene mutations.
Table 4. Comparison of CSS criteria percentages met by severe COVID-19 patients with or without DOCK8 and/or fHLH gene mutations.
Non-Mutant
(n = 11)		DOCK8
(n = 4)		fHLH
(n = 6)
2016 sJIA/MAS Score Yes 3/11 (27%)0/4 (0%)0/6 (0%)
MS Score, Median (IQR) −2.6 (−2.8 to −1.2)−2.3 (−3.6 to −0.5)−3 (−3.3 to −1.8)
Ferritin–ESR > 11.3 11/11 (100%)4/4 (100%)6/6 (100%)
Ferritin–ESR > 21.5 7/11 (64%)2/4 (50%)3/6 (50%)
Ferritin–ESR, Median (IQR) 28.6 (18.1 to 79.7)24.2 (16 to 62.3)23.1 (16.6 to 52.1)
CSs Score Pos 9/11 (82%)2/4 (50%)5/6 (83.3%)
cHIS Score, Median (IQR) 3 (2 to 4)2.5 (1.3 to 3.8)4 (2.8 to 4.3)
Caricchio Score 1/11 (9%)0/4 (0%)0/6 (0%)
Abbreviations: fHLH = familial hemophagocytic lymphohistiocytois sJIA = systemic juvenile idiopathic arthritis; MAS = macrophage activation syndrome; MS = MAS/sJIA; ESR = erythrocyte sedimentation rate; IQR = interquartile range; CSs = cytokine storm score; cHIS = COVID-19-associated hyperinflammatory syndrome.
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MDPI and ACS Style

Kamath, A.; Zhang, M.; Absher, D.M.; Jackson, L.E.; Chatham, W.W.; Cron, R.Q. Hemophagocytic Lymphohistiocytosis Gene Variants in Severe COVID-19 Cytokine Storm Syndrome. Viruses 2025, 17, 1093. https://doi.org/10.3390/v17081093

AMA Style

Kamath A, Zhang M, Absher DM, Jackson LE, Chatham WW, Cron RQ. Hemophagocytic Lymphohistiocytosis Gene Variants in Severe COVID-19 Cytokine Storm Syndrome. Viruses. 2025; 17(8):1093. https://doi.org/10.3390/v17081093

Chicago/Turabian Style

Kamath, Abhishek, Mingce Zhang, Devin M. Absher, Lesley E. Jackson, Walter Winn Chatham, and Randy Q. Cron. 2025. "Hemophagocytic Lymphohistiocytosis Gene Variants in Severe COVID-19 Cytokine Storm Syndrome" Viruses 17, no. 8: 1093. https://doi.org/10.3390/v17081093

APA Style

Kamath, A., Zhang, M., Absher, D. M., Jackson, L. E., Chatham, W. W., & Cron, R. Q. (2025). Hemophagocytic Lymphohistiocytosis Gene Variants in Severe COVID-19 Cytokine Storm Syndrome. Viruses, 17(8), 1093. https://doi.org/10.3390/v17081093

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