Blood samples were obtained from six patients with sepsis caused by community-acquired pneumonia within 24 h after admission to the intensive care unit of Amsterdam UMC, location Academic Medical Center (AMC), University of Amsterdam, The Netherlands, and from four healthy subjects (Khan et al., 2020). Sepsis was defined on the basis of a “probable” or “definite” infection according to the Center for Disease Control and Prevention four-point scale (Garner et al., 1988) combined with at least one of general, inflammatory, hemodynamic, organ dysfunction, or tissue perfusion parameters derived from the International Sepsis Forum consensus definitions (Calandra et al., 2005), as previously described in detail (Klouwenberg et al., 2013). Monocytes were isolated using fluorescence-activated cell sorting (FACS) and stored in RNA protect cell reagent (Qiagen, Hilden, Germany). Total monocytes were identified as CD14⁺CD15⁻ cells in morphologically defined gates and sorted on FACS Canto II flow cytometer (BD Biosciences) with anti-CD14 antibody (Miltenyi Biotech), as previously described (Hoogendijk et al., 2017; Khan et al., 2020). The Medical Ethics Committee of the Academic Medical Center approved the study (IRB no. 10-056C), and written informed consent was obtained from all patients (or legal representative) and healthy controls.

Healthy volunteers for blood sampling were recruited in accordance with a study protocol that was reviewed by the Academic Medical Center Medical Ethical Committee (no. 2015_074). Prior to sample donation, all donors gave informed consent. Heparinized blood, from healthy donors or buffy coat, was diluted (1:1) with phosphate-buffered saline (PBS) and peripheral blood mononuclear cells (PBMCs) were isolated by density-gradient centrifugation (1,700 RPM for 30 min at 21°C; acceleration, 1; breaks, 0) using Ficoll-Paque PLUS (GE healthcare). PBMCs were washed twice in cold PBS containing 0.5% sterile endotoxin-free bovine serum albumin (Divbio Science Europe). CD14⁺ human monocytes were further purified with an anti-CD14–coated microbead-based purification step as per the manufacturer’s instructions (Miltenyi Biotec, Bergisch Gladbach, Germany). CD14⁺ monocytes were either directly used in cell stimulation experiments described below or differentiated into MDMs.

For MDMs, CD14⁺ monocytes cells were seeded in six-well plates at a density of 1 × 10⁶ cells per well in RPMI 1640 (Gibco, Amarillo, TX) with 10% sterile fetal calf serum (FCS), gentamicin (10 µg/ml; Lonza Bioscience, Durham, NC), 2 mM GlutaMAX (Thermo Fisher Scientific, Waltham, MA), 1 mM sodium pyruvate (Thermo Fisher Scientific, Waltham, MA), recombinant human granulocyte macrophage-colony stimulating factor (5 ng/ml; Prospec, Ness-Ziona, Israel) and cultured for 7 days. The cell medium was changed, and fresh medium was added on days 3 and 5. On day 7, the cells were washed and harvested using Tryple-select (Thermo Fisher Scientific, Waltham, MA) and replated to appropriate cell concentration 24 h prior to stimulation or used directly for RNA interference experiment.

For stimulation experiments of CD14⁺ monocytes, cells were seeded in a 48-well plate (677970; Greiner Bio-One, Frickenhausen, Germany) at a density of 5 × 10⁵ cells per well in 400 µl of RPMI 1640 (Gibco) with 10% sterile FCS, gentamicin (10 µg/ml; Lonza Bioscience, Durham, NC), 2 mM GlutaMAX (Thermo Fisher Scientific, Waltham, MA), and 1 mM sodium pyruvate (Thermo Fisher Scientific, Waltham, MA). Cells were subsequently stimulated with medium control, LPS (from Escherichia coli O111:B4; 100 ng/ml; In vivo gen, San Diego, CA), Pam3CSK4 (1 µg/ml; In vivo gen), flagellin (from P. aeruginosa; 1 µg/ml; FLA-PA Ultrapure, In vivo gen), and poly(I:C) High Molecular Weight (HMW) (10 µg/ml, In vivo gen) for 2 h or heat-killed bacteria [cell-bacteria ratio, 1:10; Streptococcus (S.) pneumoniae ATCC^® 6303 and Klebsiella (K.) pneumoniae ATCC43816] for 2, 6, and 24 h. Medium was collected, and the cells were stored in RNA protect cell reagent (Qiagen, Hilden, Germany) until further processing. In a separate experiment, primary monocytes (5 × 10⁵cells per well) were pretreated with the NF-κB inhibitor BAY11-7082 (Malladi et al., 2004) (10 µM; Tocris Bioscience, Bristol, UK) or vehicle control (dimethyl sulfoxide; Merck, Darmstadt, Germany) for 30 min and then incubated in medium, with or without LPS (100 ng/ml) for 2 h.

Real-time quantitative RT-PCR (RT-qPCR) was performed to evaluate RNA expression. Total RNA was extracted using Nucleospin RNA isolation kits (Macherey-Nagel, Germany), according to the manufacturer’s protocol, and reverse-transcribed using Moloney murine leukemia virus reverse transcriptase with random hexamer primers or oligo(dT) primer (Promega, Madison, WI). RT-qPCR analysis was performed using the SensiFast Sybr green mix (Bioline, London, UK) on a LightCycler system (LC480, Roche Applied Science, Penzberg, Germany). LinRegPCR software was used to analyze the data (Untergasser et al., 2021). Primers used are shown in Supplementary Table 1 .

Human TNF, IL-6, and IL-10 protein levels were measured by enzyme-linked immunosorbent assay (ELISA) (all DuoSet R&D systems, Minneapolis, MN) according to the manufacturer’s instructions.

RNA interference was performed using the Neon Transfection System according to the manufacturer’s instruction (Thermo Fisher Scientific, Waltham, MA). MDMs were washed with PBS twice and resuspended in buffer R (Thermo Fisher Scientific, Waltham, MA) with either short interfering (si) SMARTpool RNAs against JHDM1D-AS1 (R-187292-00, Dharmacon, Lafayette, CO) or siNon-Target as a control (D-001206-13, Lafayette, CO). The cells were subsequently subjected to 1,500 V for 20 ms to achieve transfection of siRNA. Transfected cells were seeded in 24-well plates in RPMI 1640 with 10% FCS (Gibco, Amarillo, TX) and 2 mM L-glutamine (Lonza Bioscience, Durham, NC), without antibiotics. After 48 h, viable cells were harvested, washed, and replated at a cell density of 2.0 to 5 × 10⁵cells per well in a 48-well plate in RPMI 1640 (Gibco, Amarillo, TX) medium containing 10% FCS, penicillin (100 U/ml), and 2 mM L-glutamine. Seventy-two hours after transfection, fresh medium was added, and cells were subsequently treated with LPS (100 ng/ml) or medium control for 2 and 6 h. Silencing of JHDM1D-AS1 expression was confirmed by RT-qPCR.

For retroviral transfection of PLAT A cells (Cell Biolabs Inc., San Diego, CA), 1.2 × 10⁶ cells per condition were plated in Advanced TC six-well plates (Greiner Bio‐One, Kremsmünster, Austria). The cells were incubated overnight at 37°C in the presence of transfection complexes containing 2 μg of PMX-puro retroviral vector (Cell Biolabs Inc., San Diago, CA) with or without full-length human JHDM1D-AS1 (synthesized by GenScript Biotech, Leiden), 0.4 μg of pCL‐Ampho (Novus Biologicals), and Lipofectamine 2000 (Thermo Fisher) supplemented Iscove's Modified Dulbecco's Medium (IMDM) medium (Thermo Fisher) containing 10% FCS. After overnight incubation, the supernatants of transfected cells were replaced with 1.5 ml of complete RPMI. Forty‐eight hours after transfection, viral supernatants were harvested, filtered over a 0.45-μm filter, and used to transduce THP1-MD2-CD14 cells (In vivo gen, San Diego, CA). PLAT A cells were washed with PBS and resuspended for FACS analysis to determine the transfection efficiency. For the transduction, 1 × 10⁶ THP1-MD2-CD14 cells in 1 ml of complete RPMI were seeded in a regular six-well plate. Virus-containing supernatant was mixed with polybrene (Sigma, Saint Louis, MO) before adding 1 ml to each well (final polybrene concentration of 8 µg/ml). The cells were centrifuged for 2 h at 1,000g at 32°C and incubated overnight at 37°C. The next day, supernatant was replaced with 2 ml of fresh complete RPMI and 48 h after transduction, antibiotic selection was performed throughout a period of at least 10 days by adding puromycin (1 μg/ml). The transduction of JHDM1D-AS1 in THP1-MD2-CD14 cells was confirmed with RT-qPCR.

THP1-MD2-CD14 cells overexpressing JHDM1D-AS1 transcripts or control cells (empty vector) in RPMI 1640 containing 10% FCS, penicillin (100 U/ml), and 2 mM L-glutamine were seeded at a density of 5 × 10⁵cells per well in a 48-well plate and stimulated with LPS (100 ng/ml) or medium control for 2 and 6 h.

Total RNA was isolated from the following: (i) JHDM1D-AS1–deficient MDMs (n = 4) and control cells (n = 4), exposed to LPS (100 ng/ml) or not (medium control) for 6 h; and (ii) THP1-MD2-CD14 cells expressing ectopic JHDM1D-AS1 (n = 4) or empty vector control (n = 4), treated with LPS (100 ng/ml) or medium control for 2 and 6 h. We used RNeasy mini kits (Qiagen, Hilden, Germany) according to the manufacturer’s instructions. RNA quality was assessed by bioanalysis (Agilent), with all samples having RNA integrity numbers > 7. Total RNA concentrations were determined by Qubit^® 2.0 Fluorometer (Life Technologies, Carlsbad, CA, USA). RNA sequencing libraries were prepared from 300 ng of total RNA using KAPA RNA HyperPrep with RiboErase (Roche) library kits and sequenced using the Illumina HiSeq4000 instrument (Illumina) to generate single reads (50 bp). Quality was assessed using FastQC [v0.11.5; Trimmomatic version 0.36 (Bolger et al., 2014) was used to trim Illumina adapters and poor-quality bases (trimmomatic parameters: leading = 3, trailing = 3, sliding window = 4:15, minimum length = 40)]. The remaining high-quality reads were used to align against the Genome Consortium human genome build 38 (GRCh38). Mapping was performed by HISAT2 version 2.1.0 (Kim et al., 2015) with parameters as default. Count data of the siRNA knockdown experiments were generated by means of the HTSeq method (Anders et al., 2015), whereas featureCounts (Liao et al., 2014) was used for overexpression experiments. Counts were subsequently analyzed using DESeq2 (Love et al., 2014) in the R statistical computing environment (R Core Team 2014. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria). Canonical signaling pathways were inferred using Ingenuity Pathway Analysis (QIAGEN bioinformatics), specifying human species and ingenuity database as reference. All other parameters were default. Statistically significant differences were defined by Benjamini–Hochberg adjusted probabilities < 0.05. Monocyte transcriptomes from healthy donors and patients with sepsis (Khan et al., 2020) were analyzed for proportions of cell subsets by means of a transcriptome deconvolution method, Absolute Immune Signal (ABIS), by applying the online Shiny application (https://github.com/giannimonaco/ABIS) (Monaco et al., 2019). Sequence libraries are publicly available through the National Center for Biotechnology Information (NCBI) gene expression omnibus (GEO) with accession identifier GSE201958.

Expression patterns of JHDM1D-AS1 were examined in an additional cohort of all-cause patients with sepsis (n = 156) relative to healthy subjects (n = 82) publicly available in the GEO NCBI database under accession number GSE134347 (Scicluna et al., 2020).

To evaluate lncRNA expression in monocytes, we performed RNA sequencing in primary human blood monocytes (CD14⁺) obtained from patients with sepsis due to community-acquired pneumonia and healthy subjects (Khan et al., 2020). No differences were detected in monocyte subsets (classical, non-classical, or intermediate) by deconvolution of absolute immune signal ( Supplementary Figure 1A ). Relative to health, 245 lncRNAs were significantly altered in monocytes of patients with sepsis (adjusted p-value < 0.05; fold expression ≥ 1.2 or ≤ −1.2), representing 3.9% of all lncRNAs identified; 140 lncRNAs were elevated and 105 lncRNAs were reduced in monocytes of patients with sepsis ( Figure 1A ). A substantial proportion of altered lncRNAs belonged to the antisense RNA family ( Figure 1B ). Among these antisense RNAs, we identified the previously described JHDM1D-AS1 (Kondo et al., 2017; Yao et al., 2019; Shi et al., 2019; Liu et al., 2020), which is physically located on chromosome 7 (140,177,261–140,179,640 bp). Expression patterns of JHDM1D-AS1 antisense RNA were also significantly higher in whole-blood transcriptomes obtained from publicly available data of all-cause patients with sepsis (n = 156) relative to healthy donors (n = 82) ( Supplementary Figure 1B ) (Scicluna et al., 2020). Next, we used RT-qPCR to examine the inducibility of JHMD1D-AS1 in primary human monocytes obtained from healthy donors, stimulated with LPS (TLR4 agonist), Pam3CysSerLys4 lipopeptide (PAM3CSK4; TLR1/2 agonist), flagellin (TLR5 agonist), or polyinosinic:polycytidylic acid [poly (I:C); TLR3 agonist]. JHDM1D-AS1 expression was significantly induced by LPS, Pam3CSK4, and flagellin, but not poly(I:C) ( Figure 1C ), suggesting that MyD88 (adapter protein for all TLRs except TLR3) but not TRIF (TIR domain–containing adapter-inducing interferon-β; adaptor protein for TLR3) drives JHDM1D-AS1 expression. Moreover, expression of JHDM1D-AS1 was significantly induced in monocytes after stimulation with clinically relevant pathogens, S. pneumoniae (gram-positive) and K. pneumoniae (gram-negative), which peaked after 6 h ( Figure 1D ). Expression kinetics of JHDM1D-AS1 after stimulation with these bacteria closely followed mRNA expression and secreted protein kinetics of TNF and IL-6, but not IL-10 ( Supplementary Figures 2A–E ). The expression kinetics of JHDM1D-AS1 after exposure to pathogen lysates was corroborated by stimulations with LPS ( Supplementary Figures 3A–G ). These results indicate that JHDM1D-AS1 expression is induced during sepsis and by various TLR ligands in primary human monocytes.

TLR activation triggers MyD88-dependent signaling pathways that culminate in the activation of NF-κB, a critical transcription factor that controls expression of multiple immune response genes. To evaluate the impact of NF-κB on the inducibility of JHDM1D-AS1 expression, we pretreated (−30 min) monocytes from healthy donors with BAY11-7082, an irreversible inhibitor of the IkappaB kinase kinases at 10 μM (Malladi et al., 2004; Atianand et al., 2016), followed by LPS stimulation for 2 h. LPS-induced secretion of TNF and expression of JHDM1D-AS1 were significantly reduced by BAY11-7082 ( Figure 1E and Supplementary Figure 1C ), indicating that the inducibility of JHDM1D-AS1 expression depends, at least in part, on NF-kB activation.

We next set out to evaluate the regulatory potential of JHDM1D-AS1 on the inflammatory response. To address this aim, we used siRNA electroporation to introduce a pool of four JHDM1D-AS1–specific siRNA or control siRNA in primary human macrophages ( Figure 2A ). MDMs, in conformity with monocytes, also produced JHDM1D-AS1 in a temporal specific pattern after LPS stimulation ( Supplementary Figure 4A ). Silencing of JHM1D-AS1 by RNA interference resulted in ~80% knockdown of JHDM1D-AS1 expression, both basal and after stimulation with LPS for 6 h relative to control cells ( Figure 2B ).

JHDM1D-AS1–deficient cells secreted significantly more TNF and IL-6 after LPS stimulation ( Figure 2C ). This observation was also confirmed in more biological replicates ( Supplementary Figures 4B,C ). To evaluate the influence of JHDM1D-AS1 on the LPS-inducible transcriptome, we performed RNA sequencing of JHDM1D-AS1–deficient and control cells, at basal conditions and after a 6-h exposure to LPS. Relative to control cells, basal levels of 2,582 RNA transcripts were significantly altered (adjusted p-value < 0.05; fold expression ≥ 1.2 or ≤ −1.2) in JHDM1D-AS1–deficient cells (1,263 upregulated and 1,319 downregulated) ( Figure 2D ). Ingenuity pathway analysis of upregulated genes revealed an association with various cell growth, cell cycle, DNA damage, and inflammatory response pathways, including mitotic roles of polo-like kinase, role of BRCA1 in DNA damage response, agranulocyte adhesion and aiapedesis (including CXCL2, CXCL3, CXCL8, and IL1RN), and TREM1 signaling (with TREM1, IL6, CCL2, and CCL3) ( Figure 2E ). Downregulated genes were associated with mainly transcription factor and metabolic pathways, including the aryl hydrocarbon receptor signaling (with ALDH1A1, ALDH2, ESR1, and GSTK1), STAT3 pathway (having SOCS1, TGFA, CXCR1, IL10RA, and IL1R1), and pentose phosphate pathway (including G6PD, H6PD, and PGD). These findings suggest that basal expression of JHDM1D-AS1 may influence cell growth and homeostasis. In line with these observations, JHDM1D-AS1 was shown by others to promote tumor cell growth and metastasis (Kondo et al., 2017; Yao et al., 2019). Comparing the transcriptomes of LPS-exposed JHDM1D-AS1–deficient cells to LPS-exposed control cells, unmasked 2,263 significantly altered transcripts (adjusted p-value < 0.05; fold expression ≥ 1.2 or ≤ −1.2) with 1,074 upregulated and 1,189 downregulated RNA transcripts ( Figure 2F ). Upregulated genes were associated with primarily cell mobility, cell cycle, cell development, and inflammatory pathways that included granulocyte adhesion and diapedesis, cell cycle control of chromosomal replication, differential regulation of cytokine production in macrophages, TREM1 signaling, and IL-10 signaling ( Figure 2G ). Among the upregulated genes were several well-known inflammatory mediators such as CXCL2, CXCL8, CCL3L1, IL1RN, TREM1, TNF, and IL6. Downregulated genes were associated with transcription factor and metabolic and oxidative stress response pathways, including NRF2-mediated oxidative stress responses. Considering the differences that we observed in basal conditions, we fit a linear model that also accounted for those baseline differences. In this way, we identified 29 significantly altered RNA transcripts in LPS-stimulated JHDM1D-AS1–deficient cells ( Supplementary Figure 4D ). Altogether, these findings suggest that JHDM1D-AS1 plays a dual role, both at a basal cell level and upon TLR-dependent cell activation. JHDM1D-AS1 deficiency at a basal level influenced gene expression pathways attuned to cell development and hemostasis, whereas LPS exposure resulted in an enhanced inflammatory response.

Next, we performed “gain-of-function” studies by overexpressing JHDM1D-AS1 using retroviral transduction of a JHDM1D-AS1 overexpression vector or a control vector in a THP1-MD2-CD14 pro-monocytic cell line. THP1-MD2-CD14 cells are a suitable model system because the expression of JHDM1D-AS1, both at baseline and upon LPS stimulation, is low compared with primary monocytes and macrophages ( Supplementary Figure 5A ). This approach led to a 150-fold increase of JHDM1D-AS1 expression in THP1-MD2-CD14 cells relative to controls ( Figure 3A ). After exposure to LPS for 2 or 6 h, THP1-MD2-CD14 cells overexpressing JHDM1D-AS1 secreted lower levels of TNF and IL-6 as compared with control cells ( Figure 3B ). RNA sequencing and differential expression analysis showed minimal basal differences between JHDM1D-AS1 overexpression and control cells ( Supplementary Figures 5B,C ). After 2 h of LPS exposure, 112 RNA transcripts were significantly altered (39 upregulated and 73 downregulated) in JHDM1D-AS1 overexpression cells compared with control cells ( Figure 3C ). Of note, inflammatory response genes that included S100A8, S100A9, CCL3L1, and IL1RN were significantly downregulated in JHDM1D-AS1 overexpression cells. We observed similar patterns after 6 h of LPS exposure, with 19 upregulated and 47 downregulated RNA transcripts in JHDM1D-AS1 overexpression cells relative to control cells ( Figure 3D ). Again, S100A8, S100A9, and IL1RN were significantly downregulated. Ingenuity pathway analysis revealed significant associations of downregulated genes in JHDM1D-AS1 overexpression cells stimulated with LPS for 2 h. We found enrichment of genes involved in cholesterol biosynthesis, antigen presentation, LXR/RXR activation, IL-17A signaling, geranylgeranyl diphosphate biosynthesis I, and tricarboxylic acid cycle ( Figure 3E ). Therefore, JHDM1D-AS1 overexpression in THP1-MD2-CD14 pro-monocytic cells resulted in a dampened inflammatory response after exposure to LPS.