Novel tRNA-like transcripts from the NEAT1-MALAT1 genomic region critically influence human innate immunity and macrophage functions

doi:10.21203/rs.3.rs-1540419/v1

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Novel tRNA-like transcripts from the NEAT1-MALAT1 genomic region critically influence human innate immunity and macrophage functions

https://doi.org/10.21203/rs.3.rs-1540419/v1

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The evolutionary conserved NEAT1-MALAT1 gene cluster generates large noncoding transcripts remaining nuclear, while tRNA-like transcripts (mascRNA, menRNA) enzymatically generated from these precursors translocate to the cytosol. NEAT1-/- and MALAT1-/- mice display massive atherosclerosis and vascular inflammation.

Here, we identify the tRNA-like molecules as critical components of innate immunity. They appear as prototypes of a new class of noncoding RNAs distinct from others (miRNAs, siRNAs) by biosynthetic pathway and intracellular kinetics. CRISPR-generated human ΔmascRNA and ΔmenRNA monocytes/macrophages display defective innate immune sensing, loss of cytokine control, imbalance of growth/angiogenic factor expression impacting upon angiogenesis, and altered cell-cell interaction systems. Antiviral response, foam cell formation/oxLDL uptake, and M1/M2 polarization are defective in ΔmascRNA/ΔmenRNA macrophages, defining the tRNA-like molecules’ first described biological functions.

menRNA and mascRNA represent novel components of innate immunity arising from the noncoding genome. Their NEAT1-MALAT1 region of origin appears as archetype of a functionally highly integrated RNA processing system.

Immunology

innate immunity

immunogenetics

noncoding genome

tRNA biology

evolutionary genetics

The evolutionary conserved NEAT1-MALAT1 cluster encounters high interest in both cardiovascular medicine and oncology. In the cardiovascular field, we observed suppression of lncRNA NEAT1 in circulating immune cells of post-myocardial infarction (MI) patients ¹. Mice lacking lncRNAs NEAT1 ¹ or MALAT1 ^2-4 displayed immune disturbances affecting monocyte-macrophage and T cell differentiation and rendering the immune system highly vulnerable to system highly vulnerable to stress stimuli, thereby promoting the development of atherosclerosis. Uncontrolled inflammation is also a key driver of multiple other diseases ^1,2,4-6.

Here, we report the first biological functions of two novel tRNA-type transcripts from the NEAT1-MALAT1 cluster (Fig. 1) and describe deep impact upon innate immunity and macrophage functions. While we previously investigated mice deficient in the entire NEAT1 or MALAT1 locus, we now aimed to selectively disrupt only tRNA-like transcripts ‘menRNA’ arising from NEAT1 ^7,8, or ‘mascRNA’ arising from MALAT1. To date, no biological function of neither one of the tRNA-like transcripts has been reported. Both lncRNAs give rise to transcripts of vastly different size (NEAT1: 23kb MENb, 3.7kb MENe, 59nt ‘menRNA’; MALAT1: 8.3 kb primary, 58nt ‘mascRNA’), and traditional knockout methods are unable to selectively inactivate one of the small transcripts only. Through CRISPR-Cas9 editing ⁹, we therefore developed human monocyte-macrophage cell lines with short deletions in the respective tRNA-encoding sequences to disrupt normal menRNA or mascRNA formation, respectively. These editing procedures occur outside of the primary transcript sequences required for regular formation of the triple-helix structures at their 3’-ends which support stabilization of the respective lncRNAs (Fig. 1AB). The CRISPR-Cas9-based editing of menRNA or mascRNA selectively prevented only the normal transcript folding and formation of mature menRNA or mascRNA, respectively. Unlike monocytes and macrophages in NEAT1^-/- mice ¹, CRISPR-Cas9-generated DmenRNA cells retained MEN-b and MEN-e expression. Similarly, DmascRNA monocytes preserved expression of the long MALAT1 precursor while mascRNA, normally highly enriched in this cell type, became ablated.

Beyond prior work documenting immune function of the NEAT1-MALAT1 cluster, the current study identifies menRNA and mascRNA as novel elements of innate immunity impacting upon cytokine regulation, immune cell-endothelium interactions, angiogenesis, and monocyte-macrophage differentiation and functions. These molecules are prototypes of a new class of RNAs distinct from other small transcripts (miRNAs, siRNAs) by biosynthetic pathway and intracellular kinetics. From an evolutionary perspective, the NEAT1-MALAT1 genomic region appears as archetype of a functionally highly integrated RNA processing system.

Targeted deletion of tRNA-like transcripts from the NEAT1-MALAT1 cluster

While previously investigated mice were deficient in the entire NEAT1 or MALAT1 locus ^1,2,4, we here aimed to selectively disrupt only the novel 59-nt tRNA-like transcript ‘menRNA’ with so far unknown biological functions (Fig. 1A). Through CRISPR-Cas9 editing, we developed human THP-1 monocyte-macrophage cell lines with deletions of different extent all of which prevent, however, normal transcript folding and formation of menRNA or mascRNA, respectively. Unlike monocytes/macrophages in NEAT1^-/- mice ¹, the CRISPR/Cas9-generated DmenRNA cells retained apparently normal MEN-b and MEN-e expression. Similarly, DmascRNA monocytes/macrophages preserved expression of the MALAT1 precursor (Fig. 1B) whereas mascRNA, normally highly enriched in this cell type, became ablated (Suppl. Fig. S1-S3). As a consequence of its rapid turnover, the cellular steady-state level of menRNA is very low. For this reason, the silencing efficacy of ASOs or siRNAs with regard to menRNA abundance in the cells could not be ascertained neither by RT-PCR nor Northern blot analyses (Suppl. Fig. S1C and D). Very low menRNA abundance in immune cells is in sharp contrast to the enrichment of mascRNA in monocytes first described by our group ³. We found high efficacy of antisense oligonucleotides (ASOs) targeting the mascRNA sequence, regarding reduction of the mascRNA level in monocytes. Importantly, however, neither ASOs nor siRNAs are capable to selectively react with and thereby ablate only the ’mature’ menRNA or mascRNA, respectively. Instead, they will also react with the 23kb NEAT1 and 8.3kb MALAT1 long precursor transcripts and mediate their premature decay, making unequivocal distinction between biological effects of menRNA/mascRNA ablation from those of NEAT1/MALAT1 reduction ^1,2,4 impossible. In the peculiar case of the high-turnover menRNA, the CRISPR-Cas9 deletion clone as employed here is obviously a highly specific system for selective menRNA ablation and subsequent functional assignment of cellular/biological functions to menRNA. Finally, any attempt to generate germ-line menRNA or mascRNA deficient animal models would be technically demanding and likely also display developmental/ embryonic anomalies of disturbances in somatic cells other than immune cells/monocytes. One would instead need mice with a monocyte-specific, adult-age inducible, selective knockout of the menRNA or mascRNA sequence only to resolve these issues. Beyond these technical challenges, there exists no murine homologue to human IL-8 which was, however, the most strongly deregulated cytokine in circulating immune cells of post-MI patients ¹. These ambiguities are avoided by CRISPR-mediated deletion in one specific cell type/line of human origin. We examined whether absence of menRNA in DmenRNA monocytes affects, by some cytosolic-nuclear feedback mechanism, the cellular expression level of NEAT1. Under the experimental conditions employed here, however, there was no change as detectable by RNA-sequencing (RNA-seq) and qRT-PCR.

Defective innate immune sensing by DmenRNA and DmascRNA cells

RNA-seq identified profound alterations in the baseline transcriptomes of DmenRNA and DmascRNA monocytes (Fig. 2AB, Suppl. Tables S1/S2). A prominent finding was gross dysbalance between multiple innate immune sensors in DmenRNA monocytes (Fig. 3AB, Table S1A), including cytosolic NOD-like receptors NOD2 ¹⁰ and CIITA ¹¹, NLR-class receptors NLRC3, NLRC4 and MEFV ^12-14, and membrane-bound Toll-like receptors (TLR1, TLR2, TLR7, TLR10). In the context of baseline induction of NOD2 and TLR2, it is notable that twist family bHLH transcription factor 2 (TWIST2) ¹⁵ was ~10-fold down in DmenRNA cells (Fig. 3GH), and loss of NLRC3 and TWIST2 expression in DmenRNA monocytes could not be rescued by LPS (Fig. 3E)(compare Fig. 6A. DmascRNA monocytes displayed induction of interferon (IFN)-induced transmembrane (IFITM) proteins ¹⁶(Fig. 3C, Table S2A).

Transcription, translation, and epigenome level anomalies in defective monocytes

Transcription and nuclear factors and epigenome modifiers (Fig. 3G-I, Tables S1B/S2B), as well as translation factors, ribosomal proteins and nucleic acid modifiers (Fig. 3K-M, Tables S1C/S2C) were deregulated in defective cells. At the transcriptional level, in addition to TWIST2, transcription factor (TF) GATA2 was ~4-fold induced and GATA6 ~20-fold suppressed, and NFATC2 switched off in DmenRNA cells (Fig. 3G). Further TFs and epigenome modifiers turned into disequilibrium upon LPS challenge (Fig. 3H), among them histone deacetylases, lysine demethylases, and METTL methyltransferases ¹⁷. At the level of translation, three initiation and elongation-related factors (EIF3CL, EEF1A2, CTIF) were induced while initiation factor EIF1AY ¹⁸ and ribosomal protein RPS4Y1 were switched off in DmenRNA cells (Fig. 3KL). Nucleic acid-modifying enzymes deregulated include DEAD-box helicase DDX3Y (shut off), and NOP2/Sun methyltransferase NSUN7 ^19-21 which is switched on in DmenRNA cells (Fig. 3K, Table S1C). At the level of tRNAs, genomically or mitochondrially encoded tRNAs ^20,21 and tRNA methyltransferases turned into imbalance. In DmascRNA cells (Fig. 3M), cytosolic nucleotidase NT5C1A ²² dephosphorylating 5' and 2'(3')-phosphates of deoxyribonucleotides with broad substrate specificity, is suppressed. Furthermore, RNA-seq identified deregulation of long noncoding RNAs (lncRNAs) and antisense (AS) RNAs in DmenRNA and DmascRNA monocytes (Tables S1D/2D). Thus, DmenRNA cells showed ~7-fold downregulation of a recently discovered novel transcript designated ’MARCKS cis-regulating lncRNA promoter of cytokines and inflammation’ or ‘regulator of cytokines and inflammation’ (ROCKI) (Fig. 4DE, Table S1D/S2D). This transcript of particular interest was identified by Zhang et al. ²³ by genome-wide scan of macrophages for pairs of cis-acting lncRNAs and protein-coding genes involved in innate immunity.

Excessive inflammatory cytokine production by DmenRNA and DmascRNA cells

We found massively elevated basal expression and secretion of IL-8, TNF, and IL1B in DmenRNA monocytes (Fig. 4AB). DmascRNA monocytes displayed exaggerated LPS induction of IL1A, IL1B, IL10, and ISG15 (Fig. 4C). Conversely, DmenRNA but not DmascRNA macrophages displayed blunted NOS2 expression and impaired ROS production upon LPS or H2O2 challenge (Fig. 7C). There were anomalies of further interleukin (IL)/receptor and TNF cytokine/receptor systems (Fig. 4D-F, Tables S1E/S2E). Receptor IL4R was ~7-fold and M2 macrophage polarization regulator IL4I1 ^24,25 ~11-fold down in DmenRNA monocytes (Fig. 4DE). Even more pronounced was disequilibrium within the IL18 system. Receptor IL18R1 and IL18 receptor accessory protein (IL18RAP) were switched off. IL2 receptor IL2R-b (IL2RB) was ~52-fold and IL2R-g (IL2Rc) ~13-fold suppressed. IL1 receptor antagonist (IL1RN) was ~12-fold and IL1 receptor associated kinase 2 (IRAK2) ~8-fold down in DmenRNA cells. Among ligands, IL16 remained ~18-fold suppressed even after LPS stimulation.

‘Regulator of cytokines and inflammation’ (ROCKI) was ~7-fold and SLFN5, a transcriptional co-repressor of interferon (IFN) responses and antiviral restriction factor ^26-29, ~10-fold suppressed in DmenRNA monocytes (Fig. 4DE) while another member (SLFN12L) of the SLFN family of IFN-induced genes was ~26-fold up. Several ADAM metallopeptidases (Tables S1E/S2E) as well as cluster of differentiation (CD) and other leukocyte marker proteins ^30-39 (Tables S1F/S2F) were likewise deregulated and some of them switched off in DmenRNA (Fig. 6HI) or DmascRNA cells (Fig. 6K).

Disturbed growth pattern and endothelium interactions of DmenRNA monocytes

At the cellular level, DmenRNA monocytes displayed an anomalous growth pattern with spontaneous cell cluster formation in liquid culture (Fig. 5A), and defective endothelial adhesion under multiple flow conditions (Fig. 5B). The LPS-dependent induction of cell adhesion molecules (ICAM1, VCAM1) was defective in DmenRNA monocytes (Fig. 5C), contrasting with exacerbated induction in DmascRNA monocytes. DmenRNA monocyte-conditioned medium altered expression of multiple gene in HAEC monolayers (Fig. 5D), whereas DmascRNA monocyte medium had no such effect. This observation of transcriptome changes in endothelial cells under influence of the DmenRNA monocyte secretome prompted further studies regarding indirect influence of the anomalous monocyte clones upon endothelial cell behaviour, mediated via their secretomes, or via interaction between the monocytes and endothelial cells in co-cultures of both cell types in matrigel (Fig. 5HI). RNA-seq identified profound deregulation of further cell adhesion molecules in DmenRNA monocytes (Fig. 5EF, Table S1G), including adhesion G protein-coupled receptors ⁴⁰, intercellular and neural cell adhesion molecules, integrins, vascular cell adhesion molecule, melanoma cell adhesion molecule MCAM (CD146), and endothelial cell-specific adhesion molecule (ESAM). DmascRNA cells also displayed anomalous cell adhesion molecule expression (Fig. 5G, Table S2G), however distinct from DmenRNA monocytes. Neuronal growth regulator NEGR1 is ~28-fold suppressed in DmenRNA but unaltered in DmascRNA monocytes, whereas cytoskeleton regulating NCKAP1 was ~14-fold increased in DmascRNA while unaltered in DmenRNA cells. lncRNA SENCR, involved in maintenance of endothelial cell homeostasis, is ~12-fold down in DmenRNA monocytes (Table S2D), suggesting menRNA loss may lead to SENCR downregulation and cellular dysfunction in endothelial cells, too.

Impact of DmenRNA and DmascRNA monocytes upon angiogenesis

A quantitative effect of DmenRNA and DmascRNA monocyte-conditioned media upon HAEC-based tube formation was observed in matrigel assays. Supernatant from each of the defective cell clones significantly reduced the tube number (Fig. 5H). Beyond this quantitative effect of secreted factors from the cells, direct co-culture of DmenRNA as well as of DmascRNA monocytes with the HAECs in the matrigel assay lead to profound alterations of HAEC cell morphology (Fig. 5I). In co-cultures with either of the defective monocyte clones, there was a massive increase in the size of HAECs at branch side nodes where ≥3 endothelial cells came into contact. When the defective monocytes clones were stained red before addition to the HAEC-matrigel mixture, there was significant accumulation of red cells at branch side nodes where HAEC hypertrophy occurred (Fig. 5I). Consistent with these observations, multiple growth and angiogenesis-associated factors and chemokines ^41,42 were in disequilibrium in DmenRNA (Fig. 5KL, Table S1H) and DmascRNA monocytes (Fig. 5M, Table S12H).

Response of DmenRNA and DmascRNA macrophages to human-pathogenic viruses

PMA-based in vitro differentiation of DmenRNA and DmascRNA monocytes into adherent macrophages rendered the cells susceptible to transduction by recombinant adenovirus expressing GFP (Suppl. Fig. S8A). Similarly, DmenRNA and DmascRNA macrophages became transducible with human coxsackievirus B3 (CVB3) and displayed an immune response to the internalized CVB3 ssRNA genome, in the absence of active CVB3 virus replication (Suppl. Fig. S8B). In DmenRNA macrophages, without LPS stimulation or adenovirus transduction, IL8 expression was >100-fold induced compared to controls (Fig. 6A). Adenovirus resulted in moderate IL8 decrease, while control cells retained extremely low IL8 expression upon transduction. Several other genes had significantly higher baseline expression in DmenRNA macrophages, either without response to virus (TNF, SLFN12L, NOD2, TLR2, Il1B), or with significant further induction upon exposure (RIG-like receptors RLR1 and RLR2, ISG 15, TGFB2). Most conspicuous was a complete shutdown of genes suppressed in DmenRNA monocytes (Fig. 3A-E, Fig. 4EF) already before their transformation into macrophages: NOD-like innate immune genes NLRC3 and MEFV, IL2 receptor subunits b and g, transcription factor TWIST2, and NFATC2. None of these genes fully silenced in macrophages could be ’rescued’ by adenovirus exposure (Fig. 6A), similar to findings in LPS-stimulated monocytes (Fig. 3A-E, Fig. 4EF). Fig. 6B shows the response of DmenRNA macrophages to CVB3. Regarding IL8, their response to CVB3 with further IL8 induction beyond their already very high baseline level, was opposite to their anti-adenovirus reaction. ISG15 and Il1B responded similarly to CVB3 and RR5, while IRF7 and IRF9 expression were downregulated by CVB3 only. Inducible NO synthase (NOS2) expression as well as ROS production upon LPS or H2O2 challenge was significantly reduced in DmenRNA macrophages (Fig. 6C). Fig. 6D summarizes key defects of the antiviral response.

menRNA deletion critically disturbs scavenger receptor expression and oxLDL uptake

DmenRNA macrophages display loss of oxLDL uptake (Fig. 6E), consistent with their loss of scavenger receptors (Fig. 6F) ⁴³. DmenRNA cells showed further anomalies regarding receptors involved in phagocytosis: Fcγ receptors (FcγRs) ^44,45 and complement receptors (CRs) (Fig. 6HI).

Defective monocyte-macrophage transition and polarization of DmenRNA and DmascRNA cells

DmenRNA monocytes are unable to normally differentiate into M0 macrophages upon PMA exposure (Fig. 7A). This is consistent with disturbances of CD molecule expression in these cells, including CD11b (ITGAM), CD11c (ITGAX), and CD93 ^46-48.

Beyond monocyte-macrophage transition, there was defective M1/M2 polarization of DmenRNA and DmascRNA cells. Rather simple expression profiles allowed distinction between DmascRNA and DmenRNA macrophages and controls (Fig. 7BC). A “M2-like” pattern CD163^hi CD200R^hi CD206^hi TGFB3^hi TLR10^hiwas observed in DmascRNA cells ⁴⁹. A profile involving IL1B, CD93, TGFB2, TLR7, CSF1 and its receptor CSF1R ^50,51 unequivocally characterizes DmenRNA monocytes-macrophages and is preserved through polarization. Upon prolonged culture, the morphological aspect of M2-polarized DmenRNA cell cultures was clearly distinct from control and DmascRNA cells (Fig. 7E).

Genetic heterogeneity of the NEAT1-MALAT1 region in humans

Since multiple data suggests inflammation control functions of the NEAT1-MALAT1 cluster, we investigated the extent of variability of this region in humans by screening for sequence variants in cohorts from the population-wide SHIP study. The SHIP database encompasses clinical, biochemical, and molecular genetic data from 7.500 individuals from Central Europe (Suppl. Fig. S5 and S6AB). Full sequence data for the specific target regions of the current study, i.e. mascRNA and menRNA, are currently unavailable for any patient cohorts, although several SNP are known to occur mascRNA and menRNA (Suppl. Fig. S4). One MALAT1 SNP with low minor allele frequency (MAF=0.01) located in the MALAT1 promotor was associated (p=0.0062) with systemic low level inflammation (CRP>3.0 mg/L) (Suppl. Fig. S7A).

NEAT1 and MALAT1 are involved in two fields of medicine (cardiovascular and malignant diseases) linked to some degree by common immune-related mechanisms ⁵. A unified concept to explain the connections of this genomic region to apparently diverse diseases may be derived from recent observations regarding immunoregulatory functions of transcripts from this cluster, encompassing the current study assigning novel immune functions to both tRNA-like transcripts. Despite differences in their specific regulatory properties, it appears that the fundamental principle of ’employing’ this peculiar type of small ncRNAs was evolutionarily advantageous.

Prior studies of the NEAT1-MALAT1 cluster: We reported suppression of lncRNA NEAT1 in circulating immune cells of post-MI patients. In mice lacking lncRNAs NEAT1 ¹ or MALAT1 ^2-4 we observed immune disturbances rendering the immune system unstable and highly vulnerable to immune stress. MALAT1^-/+ ApoE^-/-mice suffered accelerated atherosclerosis despite normal diet compared to ApoE^-/- mice. NEAT1^-/- mice showed anomalous T cell and monocyte-macrophage differentiation, and systemic inflammation. NEAT1 promotes inflammasome activation in macrophages, regulates M2 polarization ⁵², and influences Th17/CD4⁺ T cell differentiation. NEAT1 knockdown induces a tolerogenic phenotype in dendritic cells by inhibiting NLRP3 inflammasome activation. Further NEAT1 or MALAT1 related anomalies were reported in non-immune cell types: cardiomyocytes, endothelial cells, and smooth muscle cells where an HDAC9-MALAT1-BRG1 complex mediates dysfunction ⁵³. Clinically, NEAT1 correlated with increased exacerbation risk, severity, and inflammation in asthma ⁵⁴, and with worse disease condition and poor recurrence-free survival in acute ischemic stroke. NEAT1 is elevated in peripheral blood cells of Parkinson disease patients ⁵⁵ and abnormally expressed in a wide variety of human cancers ⁵⁶.

Functional dissection of the NEAT1-menRNA system: A subset of lncRNAs, termed architectural RNAs (arcRNAs), function in formation and maintenance of phase-separated membraneless organelles. In the crowded intracellular environment these are important forms of compartmentalization. Thus, NEAT1 is a well-characterized arcRNA acting as an essential scaffold of paraspeckle nuclear bodies. In contrast, no cellular or biological functions of the cytosolic menRNA or mascRNA were known so far. Synopsis of prior genetic studies of MALAT1 and NEAT1 with the present one delineate the NEAT1-MALAT1 region as a highly integrated RNA processing circuitry critically contributing to immune homeostasis. Its components MEN-b, MEN-e, menRNA, MALAT1, TALAM1, and mascRNA are obviously set for balanced interaction with each other, and genetic ablation of any element leads to major dysfunction.

Critical defects of innate immune sensing: One key finding in DmenRNA cells was deregulation of membrane-bound (TLR2) and cytosolic immune sensors (Fig. 3). NOD2 and CIITA were massively induced while NLR-class receptors NLRC3 and MEFV were shut off in DmenRNA cells and could be rescued neither by LPS nor virus exposure. TWIST2, a critical regulator of cytokines in human monocyte-derived macrophages ¹⁵, NFATC2 which translocates to the nucleus upon T cell receptor stimulation, and IL2 receptor subunits b and g, were likewise shut down and not rescuable. Further, there was massive downregulation of lncRNA ROCKI, apparently resulting from mere deletion of the small ‘menRNA’ sequence from an otherwise intact ~23 kb NEAT1. An important recent study ²³ discovered that ROCKI is a master regulator of inflammatory responses. Beyond the transcription level, DmenRNA and DmascRNA cells displayed major changes of their epigenomes (Fig. 3).

Loss of inflammatory cytokine control: DmenRNA and DmascRNA cells display anomalous antiviral responses (Fig. 5). Even in absence of infectious agents or other immune challenges, however, DmenRNA cells display massive inductions of IL8 and TNF (Fig. 4). Regarding IL8, clinical observational studies suggest a critical role of IL8 in cardiovascular diseases ^57,58 and stroke ⁵⁹. IL8 is mechanistically involved in the innate immune response by TLR4 signaling, induces neutrophil extracellular traps (NETs) via activation NFkB signaling ⁶⁰, mediates hyper-signaling in aortic aneurysms, and is involved in endothelial adhesion ⁶¹. With regard to IL8, our prior transcriptome analysis of circulating immune cells from post-MI patients ¹ is of interest. IL8 was the most decisively upregulated gene post-MI, contrary to an extremely low level in healthy subjects, although all patients were receiving state-of-the-art post-MI pharmacological treatment. In that study, the long primary lncRNA transcript NEAT1 was significantly suppressed in post-MI PBMCs. The new data from DmenRNA cells are consistent with the assumption that reduction of the menRNA level in PBMCs (consecutive to or independent of the observed NEAT1 reduction) directly contributes to IL8 induction. Further, the marked IL1B induction in post-MI PBMCs would parallel the high baseline IL1B expression in DmenRNA cells. It would have been most interesting to directly measure menRNA levels in the post-MI PBMCs. Due to the complex secondary and tertiary structure of mature menRNA this would have required, however, Northern blot analyses for which there was insufficient RNA available in that former post-MI study. For future translational studies, our approach to study menRNA and mascRNA directly suggests new avenue since their highly dynamic levels may be more closely related to clinical parameters and clinical course than those of their nuclear precursors.

Imbalance of angiogenic factors and chemokine systems: Excessive inflammatory cytokine production appears as one downstream consequence of defective immune sensing. Imbalance of angiogenic factors, chemokines, and cell-cell adhesion molecules may be considered as further sequelae of dysfunction at the sensor level. It is not unexpected that the anomalies at this level should have far-reaching impact upon the cells’ biological behavior. Thus, irrespective of differences between DmenRNA and DmascRNA cells, both significantly decreased tube formation in matrigel assay and displayed profound deregulation of cell-cell adhesion molecules (Fig. 5). In the case of DmenRNA cells, the latter was immediately apparent by their anomalous growth in clusters, and their defective endothelial flow adhesion to HAECs. While de-repression of multiple growth/angiogenetic factors appears directly linked to anomalous endothelial cell growth and morphology in the monocyte-endothelial cell co-cultures, this imbalance of growth/angiogenetic factors apparently does not support effective tube formation, but instead hinders it.

Defective foam cell formation and macrophage polarization: This occurs in the context of large-scale disturbance of the cells’ immune sensors from NOD-like and Toll-like (Fig. 3) to phagocytosis-related receptors (Fig. 6). Overall, the cells are unable to adequately respond to immune challenges including LPS, viruses, oxLDL, and polarization cytokines (Fig. 7). Defective interaction with endothelium and angiogenesis-promoting matrix may be added to their anomalous relationship with the environment (Fig. 5).

Loss of nucleus-to-cytosol supply of menRNA or mascRNA: As both menRNA and mascRNA are continuously exported from nucleus to cytosol under normal conditions (Fig. 8), changes of the translational/ribosomal apparatus are likely to be linked to the complete loss of nucleus-to-cytosol supply of these tRNA-like molecules in the defective cells. Importantly, mascRNA displays very high steady-state levels within immune cells, while other cell types are essentially devoid of it, although they do express precursor MALAT1 at normal levels ^2,3. While the exact molecular function of the high mascRNA levels in immune cells (Suppl. Fig. S1E) remains undefined in humans and other species carrying MALAT1-like genomic loci ⁶², key importance for immune homeostasis was highly likely by inference and is definitely confirmed by the present data. In this context, a recent study by Lu et al. ⁶² is of interest showing that mascRNA binds directly to multi-tRNA synthetase complex component glutaminyl-tRNA synthetase, and promotes global protein translation and cell proliferation by positively regulating QARS protein levels.

In the case of menRNA, on the other hand, steady-state levels in immune cells are very low. This may be entirely due, however, to the known CCACCA-tagging of menRNA for rapid degradation ⁶³. menRNA’s primary nucleus-to-cytosol supply rate may be similar to that of mascRNA, which just remains stable as it is not CCACCA-tagged. DmenRNA cells display major disequilibrium at the tRNA/translational level with induction of initiation and elongation factors EIF3CL, EEF1A2 and CTIF, whereas translation initiation factor EIF1AY ¹⁸ was switched off. Further, there was deregulation of tRNAs, tRNA methyltransferases (TRMT61, TRMT2B), and RNA methyltransferase NSUN7.

Pioneering recent studies discovered hitherto unknown immune functions of tRNAs and their enzymatic processing products and may contribute to explain our current findings regarding the impact of menRNA and mascRNA ablation. tRNA products encompass tRNA-derived small RNAs (tsRNAs), tRNA-derived stress-induced RNAs (tiRNAs), and tRNA-derived fragments (tRFs). Pereira et al. ⁴¹ found that m(5)U54 tRNA hypomodification, from lack of methyltransferase TRMT2A, drives tsRNAs generation. As similar tRNA methyltransferases are deregulated in DmenRNA and DmascRNA cells, this may change tsRNA generation therein.

Yue et al. ⁴² found SLFN2 protection of tRNAs from stress-induced cleavage to be essential for T cell-mediated immunity. SLFN2 binds tRNAs and protects them from cleavage by the ribonuclease angiogenin (~12-fold induced in DmascRNA cells) ⁴¹. SLFN2 deficient T cells display accumulation of stress-induced tiRNAs which inhibit translation and promote stress-granule formation. They found IL2R-b and IL2R-g fail to be translationally up-regulated after T cell receptor stimulation, while we observed ~52-fold IL2R-b and ~13-fold IL2R-g suppression in DmenRNA monocytes upon LPS stimulation. In these cells, SLFN family member SLFN12L was ~31-fold induced whereas SLNF5 was ~10-fold suppressed compared to controls. Yue et al. showed ROS trigger an oxidative stress response leading to translation repression which is countered by SLFN2. In DmenRNA monocytes we found LPS-stimulated ROS production as well as NOS2 expression are blunted, suggesting disturbance of a regulatory circuit encompassing menRNA, SLFN family members ^26,64, and NO and ROS biosynthetic enzymes in these defective cells.

Genetic variability of the NEAT1-MALAT1 genomic region in humans: Within a cohort of 7.492 individuals we found one rare (MAF 0.0113) MALAT1 promotor SNP associated with low-level chronic inflammation. The current pilot study in a population wide-cohort was not designed to identify clinical phenotype associations with mascRNA or menRNA sequence variants which are known to occur, however, in humans. For five of the known menRNA SNPs MAFs are very low ranging from 0.0044 to 0.0002. Given the distinct functional sensitivity of the tRNA-like sequences to point mutations, it seems worthwhile to obtain full sequences in patient cohorts suffering from atherosclerosis and other disorders associated with chronic low-level inflammation.

Beyond prior work in mice documenting immune function of the NEAT1-MALAT1 region, the current study identifies menRNA and mascRNA as novel components of innate immunity with deep impact upon cytokine regulation, immune cell - endothelium interactions, angiogenesis, and macrophage formation and functions. These tRNA-like transcripts are prototypes of a new class of ncRNAs distinct from other small transcripts (miRNAs, siRNAs) by biosynthetic pathway and intracellular kinetics, suggesting a novel link for the apparent relevance of the NEAT1-MALAT1 cluster in cardiovascular and neoplastic diseases. The NEAT1-MALAT1 region has emerged as a highly integrated RNA processing circuitry. Its components MEN-b, MEN-e, menRNA, MALAT1, TALAM1, and mascRNA are set for well-balanced interactions with each other, and ablation of any element leads to major cellular and systemic dysfunction. For future translational studies, our approach to study menRNA and mascRNA directly suggests new avenue since their highly dynamic levels may be more closely related to clinical parameters and clinical course than those of their nuclear precursors. They may also have value as therapeutic targets for pharmacological intervention since they are more easily accessible than their extremely complex nuclear-located precursor molecules.

For human studies approval was granted by the institutional ethics review board and the regulatory authorities. Investigation of human tissues was conform to the principles in the Declaration of Helsinki. Informed consent was given prior to inclusion of people in the study.

SHIP population study and cohorts

SHIP samples were genotyped using the Affymetrix Genome-Wide Human SNP Array 6.0. Genotyping of samples within SHIP-TREND was obtained in two batches using Illumina Infinium HumanOmni2.5 BeadChip and the Illumina Infinium Global Screening Array, respectively. Genotypes were determined using Birdseed 2 for SHIP and the GenomeStudio 2.0 Genotyping Module (GenCall algorithm) for SHIP-TREND. Standard genotype quality control was performed excluding arrays < 92% sample call rate for SHIP and <94% sample call rate for SHIP-TREND and duplicates (based on estimated IBD), mismatches between reported and genotyped sex, genetic PCA outliers, and arrays with extreme heterozygosity. Variants with call rate < 0.95 and a Hardy-Weinberg equilibrium p-value < 0.0001 were removed before imputation. Pre-phasing and Imputation of genotypes was performed with the Eagle and Minimac3 software to HRC reference v1.1 panel on Michigan Imputation Server v1.0.1 (https://imputationserver.sph.umich.edu/). Further details regarding the SHIP cohorts are provided in the Supplement.

CRISPR-Cas9 experiments

THP-1 cells were grown in RPMI medium containing FCS (10% [v/v]) and penicillin/streptomycin (50 I.U./50 μg/ml). Parts of the menRNA and mascRNA regions were deleted from THP-1 cells using an adaptation of the CRISPR/Cas9 protocol described in Gundry et al. 2016. Protospacer sequences for each target gene were identified using the CRISPRscan scoring algorithm [www.crisprscan.org (Moreno-Mateos et al.)]. Extensive target searches employing http://www.crisprscan.com/, http://crispor.tefor.net/, and https://cctop.cos.uni-heidelberg.de identified no off-targets of potential relevance. DNA templates for single guide RNAs (sgRNAs) were generated by PCR (KAPA HiFi HotStart ReadyMix PCR Kit) using the pX458 plasmid containing the sgRNA scaffold sequence and using the following primers:

G1_human_mascRNA	taatacgactcactataGGTTGGCACTCCTGGTTTCCgttttagagctagaaatagc
G5_human_mascRNA	taatacgactcactataGGACGGGGTTCAAATCCCTGgttttagagctagaaatagc

G9_human_menRNA	taatacgactcactataGGGGCACGTCCAGCACGGCTgttttagagctagaaatagc
G10_human_menRNA	taatacgactcactataGGTCCAGCACGGCTGGGCCGgttttagagctagaaatagc

universal reverse

AGCACCGACTCGGTGCCACT

PCR products were used to generate sgRNAs by in vitro transcription using HiScribe T7 High Yield RNA Synthesis Kit. 0.5µg of purified sgRNA was incubated with Cas9 protein (1 μg; PNA Bio) for 15-20 min at room temperature. 2x10⁵ THP-1 cells were electroporated with the sgRNA/Cas9 complex using the Neon Transfection System at 1400 V, 20 ms, and one pulse. For the small RNA-deficient cell lines, two sgRNAs were selected at either end of the target sequence to delete the region in between. Deletion of mascRNA and menRNA was confirmed by PCR with the following primer pairs:

CRISPR_hu-masc_fw	CGTATTGTTTTCTCAGGTTTTGC
CRISPR_hu-masc_rev	ACCTCCCCAAAACTCCAAGA

CRISPR_hu-men_fw	TCTGTGAAAGAGTGAGCAGGA
CRISPR_hu-masc_rev	CCCAATGCTACCCCTCTAGG

Single-cell clones were generated by single-cell plating of the parental cell line. Gene deletions in the single-cell clones were confirmed by sequencing and proved to be stable over >25 passages so far.

Cell culture studies

Human monocyte cultures

Human THP-1 cells were cultured in RPMI 1640 medium with 10% fetal bovine serum, 2 mM L-glutamine, 100 U/mL penicillin and 100 μg/mL streptomycin. For gene expression analysis of wildtype (WT) and CRISPR-Cas9 targeted THP-1 cells under immune challenge, cells were stimulated with 100 ng/ml LPS, 10 ng/ml LPS 1 μg/ml, 1 ng/ml LPS, or 1 μg/ml Concanavalin A (Con A). After 24 h, RNA was isolated by TRIzol/Chloroform.

THP-1 monocyte adhesion to flow-primed human aortic endothelial cells

For analysis of WT and CRISPR-Cas9 targeted THP-1 monocytes adhesion to flow-primed endothelial cells, primary human aortic endothelial cells (HAECs) were cultured in Endothelial Growth Medium-2 with 10% fetal bovine serum, 100 U/mL penicillin and 100 μg/mL streptomycin and seeded to confluence in µ-Slide y-shaped ibiTreat chambers . Endothelial cells were exposed to unidirectional flow (20 dyn/cm²) for 48 h using yellow/green perfusion sets prior to the experiment. THP-1 monocytes were labelled with 1,1'-dioctadecyl-3,3,3',3'-tetramethylindocarbocyanine (DiI) for 15 min at 37°C, respectively, and after three times wahsing 1 × 10⁶ labelled cells were added to the flow reservoir for 30 min. After flow-termination, non-adherent cells were gently washed out with PBS, then cells were fixed with 4% paraformaldehyde and the number of cells adhering to both the straight and branched channel regions was assessed by fluorescence phase-contrast microscope quantified using ImageJ Software.

Tube formation angiogenesis assay

(a) Conditioned media transfer experiment: Tube formation by HAECs treated with DmenRNA and DmascRNA monocyte-conditioned media was assayed on reduced growth factor basement membrane extract (BME) in 96-well tissue cultured-treated clear-bottom plates (15 × 10³ cells per well). Briefly, 60 µl of ice-cold BME was added per well and incubated at 37°C and 5% CO₂for 30 min to allow gel formation. HAECs were plated on top of the gelled BME at a density of 1.5 x 10⁴cells/well in 50 µl culture medium followed by transfer of 50 µl of monocyte-conditioned media of both DmenRNA and DmascRNA and incubated further for 24 h. After tube formation was observed, endothelial cells were stained by adding 50 µl of 6 µM Calcein AM solution per well for 15 min at 37°C. Tubular network of the cells was captured using a fluorescent inverted microscope and quantified by ImageJ Software.

(b) Monocyte-HAEC co-cultures in matrigel: Endothelial tube formation in reduced growth factor matrigel was performed similarly as described above. Here, direct DiI-labeled co-cultured DmenRNA and DmascRNA monocytes at a cell density of 5 x 10³ per well together with HAECs (1.5 x 10⁴cells/well) were added on the BME gel and cultured for 24 h at 37°C and 5% CO² to form tubular networks.

Reactive oxygen species assay

Intracellular ROS production in WT and CRISPR-Cas9 targeted THP-1 macrophages was determined as previously described using 2',7'-dichlorodihydrofluorescein diacetate (H2DCFDA). Upon cleavage of the acetate groups by intracellular esterases, the cell-permeant H₂DCFDA is retained within the cells and easily oxidized to the highly fluorescent 2′,7′-dichlorofluorescin (DCF) in response to ROS production/oxidative stress. Control and CRISPR/Cas9-targeted THP-1 cells were cultured in black 96 wells (overnight treatment with 0,1 µM PMA) before LPS addition for 24h. After washing with HBSS, cells were incubated with 5 μM H2DCFDA (Molecular Probes) in HBSS for 1 h at 37 °C. Cells were washed again and ROS production was induced by 400 μM H₂O₂. Fluorescence intensity was quantified every 10 min (excitation 485 nm, emission 535 nm) at 37°C using a fluorescence plate reader (Tecan).

Cytokine measurements

Conditioned cell culture media of wildtype (WT) and CRISPR-Cas9 targeted THP-1 cells were tested for IFNγ, TNF, IL6, MCP1, IL10, and IL12p70 by use of Mouse Inflammation Cytometric Bead Assay (CBA) (BD Biosciences, Heidelberg, Germany) on a FACS CantoII flow cytometer (BD Biosciences) according to the manufacturer’s protocol.

Cell proliferation studies

The proliferation assay of WT and CRISPR-Cas9 targeted THP-1 cells was conducted as follows: 5 x10⁴ cells were seeded into clear 96 well plates, one plate for each time point. Proliferation was determined using WST1 reagent (Sigma-Aldrich) according to the manufacturer’s instructions. Absorbance at 450 nm was measured 2 h after addition of WST1 and incubation at 37°C on a Tecan plate reader. Absorbance of WT cells was set to one at each time point.

Foam cell formation and oxLDL uptake

To induce foam cell formation, we seeded 5x10⁵ THP-1 monocytes per well containing glass cover slips. Monocyte differentiation into macrophages was induced by adding 100 nM PMA to each well on days 0 and 1 and incubation for 48h hours. Thereafter the thus generated macrophages were washed with PBS, then incubated with 50µg/ml human ox-LDL for 24h in serum-free medium to induce foam cell formation. After ox-LDL incubation, Oil Red O staining (ORO) of the cells was performed as follows: wash with PBS, fix cells with 4% PFA/PBS for 15 min at RT, wash 3 times with PBS, rinse with 60% isopropanol for 15 s to facilitate the staining of neutral lipids, stain cells with filtered ORO working solution for 15 min at RT and in dark. ORO stock solution was prepared by dissolving 0.5 g ORO powder in 80 ml isopropanol (100%), mixed at 56 °C overnight, adjusted to 100 ml, mixed under gentle stirring, and filtered after pre-warming to 60°C. Working solution was prepared by diluting ORO stock solution with ddH2O 3:2 = factor 1.5. For staining, cells were rinsed with water and then hematoxylin used as a counterstain (cell nuclei) for 10 s. Destaining by washing with 60% isopropanol for 15 s, then PBS 3 times. Glass cover slips were taken out and mounted on slides for polarization microscopy and microphotography.

Monocyte-macrophage transition and macrophage polarization experiments

Monocyte-M0 macrophage differentiation and subsequent M1/M2 macrophage polarization studies employing FACS and TaqMan were conducted as follows: First, M0 macrophages were generated by incubation of THP-1 monocyte clones for seven days, with PMA at a concentration of 100 ng/ml. Thereafter, the cells were further incubated for another seven days, either with IFN-g at 20 ng/ml plus LPS at 100 ng/ml to induce M1 polarization, or with IL-4 at 20 ng/ml plus IL-13 at 20 ng/ml to induce M2 polarization. The ’M0’ TaqMan-based expression profiles and FACS data in Fig. 7 were obtained on day 7 of culture to characterize monocyte - M0 macrophage transition. The ’M1’ and ’M2’ expression profiles displayed there were obtained in day 14 of culture.

FACS analyses of the macrophage clones

After macrophage polarization, cells were washed once with ice-cold PBS and scrapped off using a mini scraper. Subsequently, macrophages were again washed with PBS + 5% FBS and incubated with 50 µl Fcγ-receptor block (BD Biosciences) in PBS for 10 min at RT to block unspecific binding. Cells were centrifuged at 300 x g for 5 min at 4°C. Then cells pellets were resuspended with 50 µl of FACS Buffer (PBS + 0,5%FBS + 0,05% NaN3) and stained with APC mouse anti-human CD14 (BioLegend), PE mouse anti-human CD11b (BD Biosciences) and Live/Dead Fixable Aqua Dead Cell Stain (Invitrogen) for 30 min at 4 °C in the dark. Cells were resuspended in 450 µl of FACS Buffer and analyzed with Attune™ NxT Flow Cytometer (Thermo Fisher Scientific). Statistical analysis was performed using GraphPad Prism 9 software. Experimental data were analyzed by using one-way analysis of variance (ANOVA) with Dunnett´s post hoc test for multiple comparisons. The distribution of variables was assessed by Kolmogorov–Smirnov tests of normality.

Human adenovirus and coxsackievirus B3 studies

THP-1 cells were cultured in RPMI 1640 medium (ATCC modification + 10 % fetal calf serum + 1 % P/S) at 37 °C, 5 % CO2. 5x10⁵ cells were seeded in 12-well culture plates and differentiation to macrophage-like cells was triggered by addition of 0.1 μM PMA o/n. Afterwards cells were transduced with coxsackievirus B3 at MOI 30. Detection of CVB3 genome, replicative intermediates, and plaque forming units (PFU) was conducted as described. In the adenovirus experiments, cells were instead transduced with a recombinant adenoviral virus expressing GFP (AdV5-GFP) or an “empty” adenovector expressing no transgene, at MOI 25, 12h post PMA addition. Virus structures were described previously.

RNA sequencing and data analysis

For the transcriptome mapping of control vs. CRISPR‐Cas9 generated DmenRNA or DmascRNA cells, four biological cell culture replicates were grown for each of the three clones. From each of these cultures separate total RNA isolations were conducted by TRIZOL/Chloroform method. Thereafter the individual RNA preps were pooled for each of the clones, and the three resulting RNA pools (control, DmenRNA, DmascRNA) were subsequently used for RNA-seq analyses as follows. RNA integrity was visualized using Agilent Bioanalyzer 2100. For NGS-library preparation we used Illumina TruSeq Stranded Total RNA Library Prep Human/Mouse/Rat (S45-S56) or NEBNext Ultr II Directional RNA Library Prep Kit Illumina (E7760S) in combination with the NEBNext rRNA Depletion Kit (Human/Mouse/Rat) (E6310L) (S145-S154). For all samples paired end (2x75bp) sequencing was carried out on an Illumina NextSeq platform using NextSeq 500/550 High Output Kit v2.5 (150 cycles). The resulting reads were mapped to the human genome (GRCh37 release 87/hg19) using STAR v2.7.5b with standard options. We then ran the htseq-count module from software package HTSeq with the stranded=reverse option reflecting the used library kit. As we used rRNA-depleted samples, all with rRNA-annotation were excluded from further analyses. To detect differentially expressed genes we used the R package DESeq v1.34.1. Next we normalized the raw gene specific read counts as Transcripts per Million base pairs (TPM) in order to perform a gene set enrichment analysis (GSEA) using the R package ssGSEA2.0 by mapping them against a selection of gene set collections from Molecular Signature Database (MsigDB). We thus generated Enrichment Scores (ES) for each sample and various gene sets. ES reflects how strongly the majority of genes from an individual gene set are expressed per regarded sample. This ES was then normalized to account for variations in gene set size. To correct for multiple testing the ssGSEA package uses Benjamini-Hochberg method, which calculates false discovery rates (FDR). For any combination of gene set and sample with a FDR value ≥ 0.05 we set the NES to zero. The gene set collections we used were: C2.CP: Canonical pathways; KEGG selection as a subset of CP; C2.CGP: chemical and genetic perturbations; C7: immunologic signature gene sets.

Quantitative RT-PCR

cDNA transcription and qPCR were conducted using standard methods. Reference gene was HPRT. Expression levels measured by qPCR were quantified as DDC_t values, determined by the C_t value of a candidate RNA minus the C_t of the reference gene. The TaqMan RT-PCR probes used are given in the Supplement.

Statistical analyses

Cell culture experiments: Statistical data analyses were done using IBM SPSS Statistics 24 or GraphPad software. Descriptive statistics include absolute and relative frequencies for categorial variables and mean and standard deviation, median, and range for quantitative measurements. For inter-group comparisons Student’s t-test or the χ² test was used for quantitative or categorical variables, respectively. P-values ≤ 0.05 are considered significant, and no Bonferroni adjustment has been performed.

Human molecular genetics: Genome-wide association analyses of healthy samples versus samples with inflammatory, metabolic, and/or cardiovascular conditions were performed via logistic regression analysis implemented in snptest v2.5.2. For sensitivity analysis samples were stratified by sex. Summary-level results were meta-analyzed with METAL (Willer CJ, Li Y, Abecasis GR. METAL: fast and efficient meta- analysis of genomewide association scans. Bioinformatics. 2010;26:2190–1) using the classical approach which utilizes the effect size estimates and standard errors. Only variants with an imputation quality > 30% and Hardy-Weinberg equilibrium p-value > 0.0001 were included in the meta-analysis. Variants with p-value < 5×10^-8 (the standard threshold) were considered to be genome-wide significant.

For further details of SHIP cohorts, methods and associated references, and supplementary figures and tables see Supplement.

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Acknowledgements

M.G., V.N., X.W., A.H. and W.P. conducted and analyzed the cell biological and molecular genetic studies. M.G., L.H.M., X.W., A.T., A.W.K. and W.P. designed, conducted and evaluated the CRISPR-Cas experiments. A.W.K., A.T. and S.W. supervised and analyzed the RNA-sequencing experiments and their statistical analysis. B.R., S.W. and A.W.K. conducted multivariate statistical analyses of the SNP data from the SHIP study cohorts. H.V., W.H., U.V., S.F. and M.D. are coordinators of the SHIP trials program. A.B., K.K. and X.W. conducted and evaluated the virological experiments. S.S., T.Z., C.S., and B.H. provided logistic and organizational support. T.H. and S.N. generated the NEAT1 and MALAT1 knockout mice and made critical intellectual input regarding the molecular pathology of the CRISPR-generated clones. F.S. and W.C.P. were critically involved in interpretation of the cytokine, cell-cell interaction and angiogenetic data. W.P. and A.H. initially designed and supervised the project, and drafted and finalized the manuscript based on input from the other authors. W.P. and T.Z. received grants B19-006_SE (FKZ 81X2100257 and =81X2710170 ’Transcriptome analysis of circulating immune cells to improve the assessment of prognosis and the response to novel anti-inflammatory treatments after myocardial infarction’) and B18-004_SE (FKZ 81X2100253, ’Functional role and therapeutic potential of the long noncoding RNA NEAT1-MALAT1 genomic region in cardiovascular inflammation control and atheropathogenesis’) from the German Center for Cardiovascular Research (DZHK). TZ is supported by the German Center of Cardiovascular Disease (FKZ 81Z0710102; Systems-medicine approaches in cardiovascular disease, partner site project). A.H. received a grant (FKZ 01ZX1906B ‘Systems-medicine of pneumonia-aggravated atherosclerosis’) from the German Federal Ministry of Education and Research (BMBF). A.H. is supported by the Berlin Institute of Health (BIH) Advanced Clinician Scientist program.

There is NO Competing Interest.

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Novel tRNA-like transcripts from the NEAT1-MALAT1 genomic region critically influence human innate immunity and macrophage functions

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