Our new record indicates warm tropical SSTs (27–32 °C) at the upper end of the modern reef growth window. We suggest that the observed temperatures potentially exceeded the optimal calcification temperatures of corals. In combination with a low aragonite supersaturation in the ocean, this could have reduced coral growth rates and ultimately lowered the aggradation potential of the reef system. These sub-optimal growth rates could have made the coral reefs more susceptible to other stressors, such as relative sea-level rise and/or changes in currents leading to reef drowning. Given that these changes affected coral reefs that were likely adapted to high temperature/low aragonite saturation conditions suggests that reefs that have adapted to non-ideal conditions may still be susceptible to future climate changes due to the interaction of multiple stressors associated with climate change.Evidence shows that in the modern ocean, coral reefs are disappearing, and these losses are tied to climate change. However, research also shows that coral reefs can adapt rapidly to changing conditions leading some researchers to suggest that some reef systems will survive future climate change through adaptation. It is known that there were changes in the area covered by coral reefs in the past. Therefore, it is important to investigate the long-term response of coral reefs to environmental changes and high sea-surface temperatures (SSTs). However, because of diagenetic issues with SST proxies in neritic, metastable carbonate-rich environments, there is an incomplete and sometimes even incorrect understanding of how changes in SSTs affect carbonate reef systems. A good example is the Queensland Plateau offshore northeast Australia next to the threatened Great Barrier Reef. In the Late Miocene, between 11 and 7 Ma, a partial drowning caused the reef area on the Queensland Plateau to decline by ~ 50% leading to a Late Miocene change in platform geometry from a reef rimmed platform to a carbonate ramp. This reef decline was interpreted to be the result of SSTs at the lower limit of the modern reef growth window (20–18 °C). This article presents a new Late Miocene—ased SST record from the Coral Sea based on the TEX Corals provide key ecosystem services and are the highest economic value environment due to a combination of benefits for tourism, fishing, and coastal protection, among other benefits3. However, many of these reef systems are impacted by modern climate change1. This is true of the Australian Great Barrier Reef (GBR), which has shown repeated bleaching events over the last 10 years4. Yet at the same time, work from the Persian Gulf shows that corals can adapt to maximum monthly average SSTs around 34 °C in less than 6 ka7. Other studies have suggested coral reef adaptions to other aspects of future climate change, including acidification and changing sea levels10. This has led to a debate about the future of coral reefs between those who argue that adaptation will allow at least some reef systems to survive8 and those that argue that multiple stressors will lead to complete coral reef loss11. Resolving this debate is difficult as coral reef systems normally develop and decline on geological time scales beyond direct human observation. Therefore, it is important to understand what caused coral reef loss in the past, especially during periods where SSTs were higher than today. However, our understanding of how climate changes influenced reef systems in the past is poor.According to the Intergovernmental Panel on Climate Change, coral reefs are considered to be one of the most susceptible environments to future climate change12. This has led to a distorted view of the causes of coral loss in the past and our poor understanding of how changes in multiple environmental stressors affect coral reef systems and carbonate platforms over geological time scales.A key challenge is that many quantitative geochemical proxies do not work on and in the vicinity of carbonate platforms, especially on multi-million-year time scales. This is because of issues with carbonate diagenesis, low total organic matter content, and a lack of long drill cores from reefs. Therefore, even though the timing of the loss of coral reefs and degradation of carbonate platforms is known during periods of the past, tying these changes to climatic change has been difficult13. The modern reefs are relics of an Early to Middle Miocene (20–11 Ma) reef rimmed platform which covered ~ 64,000 km214. In the Late Miocene, between 11 and 7 Ma, a partial drowning resulted in a decline of the reef area by 50%15. This change was accompanied by a shift in platform geometry from a reef-rimmed platform to a carbonate ramp during the Late Miocene16.The Queensland Plateau forms an isolated plateau offshore the GBR in the Coral Sea. Today it hosts several small pinnacle reefs and larger reef complexes, which together occupy 10–15% of its surface17. However, despite this, cool surface temperatures have been suggested as a reason for this dramatic reef decline, combined with other stressors such as changes in sea level and erosion19. This interpretation is based on a δ18OGlobigerinoides ruber SST-reconstruction from ODP Site 811 on the Queensland Plateau 16. This shift in the skeletal assemblage was interpreted to reflect a transition from tropical to non-tropical, warm-temperate climate conditions18, partly supported by the oxygen isotope record18.The reduction in coral reef platform area in the late Miocene was accompanied by a transition in carbonate platform morphology and biota on the analyzed southwestern margin of the Queensland Plateau15 have discussed the possibility that changes in nutrients influenced the facies transition on the Queensland Plateau. Based on paleontological data it was thought that an increased nutrient availability would have contributed to the Late Miocene facies transition15. Others in contrast argued that an influence of nutrients would be unlikely, partly based on the fact that large benthic foraminifera assemblages in mesotrophic environments typically would be rich in large imperforate forms while the Late Miocene carbonates from the Queensland Plateau are characterized by perforated forms16. However, it has been shown that perforate large benthic foraminifera are common in Miocene equatorial carbonates with foramol facies44. These carbonates were deposited under tropical temperatures where nutrient levels were in the high oligotrophy to mesotrophy range44. There is now a growing number of studies demonstrating that carbonate platforms dominated by foramol and other heterozoan skeletal grain associations were thriving under tropical temperature conditions during the Miocene47. All of these studies emphasized that carbonate grain associations on their own are insufficient to allow an unequivocal interpretation of paleoclimatic conditions. It has been stressed that other data, e.g. from climate proxy studies, are required to support an environmental interpretation48. Our presented new temperature proxy data presented here shows that environmental factors other than temperature are responsible for the observed carbonate facies change on the Queensland Plateau during the Miocene. A more detailed assessment of the paleontological and facies evidence for sea surface temperature conditions is given in the supplemental "Additionally, a number of researchers86H SST data suggests a new driver is needed to explain the loss of corals. We find it is unlikely that cooler SSTs were responsible for the loss of these reefs, as suggested in the classical study of Isern et al.15. Our TEX86H SST data fall within the upper end of the modern “reef growth window” during the time of the coral reef drowning, which defines the environmental conditions that are optimal for the development of reefs41. SSTs between 25° and 29 °C generally are considered to be ideal for reef development41, while winter temperatures of 18 °C define the lower limit20 , tropical ocean temperatures likely were similar to, or higher then SSTs during the late Miocene42. Research showed an increase in subsidence on the Marion Plateau just south of the Queensland Plateau between 11 and 10 Ma, concurrent with the loss of the coral reefs. This was matched by high late Neogene subsidence rates seen on the NW shelf of Australia62. Therefore, there is some evidence for increased subsidence rates over large parts of the northern Australian plate around 11 Ma. However, the subsidence rates were insufficient to outpace the aggradation potential of modern coral reefs42. Therefore, the combined effects of high subsidence rates, the lower aragonite supersaturation during the Late Miocene, and the higher SSTs we found in this study could have put the coral reefs at risk. This would have meant that the coral reefs were not able to keep up with the change to higher subsidence rates at around 10 Ma. Therefore, this event could have caused the coral reefs, which had adapted to high SSTs by reducing extension rates to drown.Therefore, or study suggests that additional changes must have triggered the collapse of an adapted coral reef system. There are two main candidates for this: increased subsidence or changes in ocean circulation. Increased subsidence could have contributed to the drowning of the coral reefs in the Coral Sea63, which could have aggravated the Late Miocene coral reef demise in the Coral Sea19. There is evidence of shifting wind and current patterns during the late Miocene64. It was hypothesized that cooler late Miocene SSTs at the Queensland Plateau could indicate an increased influence of southern sourced waters and intensification of the sub-thermocline water mixing15. This does not fit our SST record, and we do not see any evidence for long-term permanent SST cooling during 11 to 7 Ma when the coral reefs drowned . Samples were screened for nannofossils and their presence was recorded based on a distinction between few (F: one to five specimens per slide), rare (R: one specimen every other field view), common (C: one to five specimens per field view), abundant (A: more than 10 specimens per field view), and dominant (D: taxon occurs ubiquitously with only rare occurrence of other taxa present). The maximum potential depth error of the recorded nannofossil datums results from sample spacing between the unequivocal record of a taxon and its absence in the next sample above (for top occurrences: T), or below (for base occurrences: B). In the age-depth plot . Elemental sulfur was removed by the addition of activated copper turnings. Excess solvent was evaporated by a Büchi solvent evaporator to a final volume of 2 ml and samples were then transferred into a 4 ml vial, where the total extract (TE) was taken to dryness under a gentle stream of nitrogen. TEs were fractionated into aliphatic, aromatic, and polar fractions by silica gel-column chromatography  using solvents with increasing polarity in an LC-TECH automated SPE system. NSO (polar) compounds were eluted with 14 ml DCM/ MeOH . The polar fraction was reconstituted in hexane/isopropanol  and re-chromatographed over aminopropyl-substituted silica gel . The alcohol fraction containing the GDGTs was eluted with 5 ml of hexane/isopropanol  and after drying was re-dissolved in hexane/isopropanol  to a final concentration of 6 mg/ml for injection into the HPLC/MS system.73. The HPLC instrument was equipped with two Waters BEH HILIC silica columns  and a guard column maintained at 30 °C. Target compounds were eluted with a flow rate of 0.2 ml/min starting isocratically with 82% eluent A (n-hexane) and 18% eluent B ) for 25 min. Thereafter, a linear gradient was set to 65% eluent A and 35% eluent B in 25 min, followed by a linear gradient to 0% eluent A and 100% eluent B in 30 min. Re-equilibration of the column afforded 82% eluent A and 18% eluent B for 20 min. Detection of isoprenoid GDGTs was achieved using a Micromass ZQ single quadrupole mass spectrometer (MS) equipped with an atmospheric pressure chemical ionization (APCI) interface operated in positive ion mode. MS settings were: source temperature, 150 °C; vaporizer temperature, 500 °C; corona, 2 µA; cone voltage, 40 V; extractor voltage, 3 V; radio frequency (RF) lens, 0.1 V; desolvation gas, N2 at 8 L/min. Detection of archaeal core lipids was achieved by single ion recording of their protonated molecular ions [M + H]+  and compounds were quantified by integration of peak areas using MassLynx© software. Calculation of TEX86 followed74 and TEX86H followed34. Reproducibility upon duplicate measurements showed a relative standard error of < 2% and samples analyzed using both methods showed a relative standard error of < 3%.GDGTs were measured on an Alliance 2695 HPLC system  coupled to a Micromass ZQ single quadrupole mass spectrometer following the analytical protocol ofSupplementary Information 1.Supplementary Information 2.


N-acetyltransferase 10 (NAT10) is an N4‐acetylcytidine (ac4C) writer that catalyzes RNA acetylation at cytidine N4 position on tRNAs, rRNAs and mRNAs. Recently, NAT10 and the associated ac4C have been reported to increase the stability of HIV-1 transcripts. Here, we show that NAT10 catalyzes ac4C addition to the polyadenylated nuclear RNA (PAN), a long non-coding RNA encoded by the oncogenic DNA virus Kaposi’s sarcoma-associated herpesvirus (KSHV), triggering viral lytic reactivation from latency. Mutagenesis of ac4C sites in PAN RNA in the context of KSHV infection abolishes PAN ac4C modifications, downregulates the expression of viral lytic genes and reduces virion production. NAT10 knockdown or mutagenesis erases ac4C modifications of PAN RNA and increases its instability, and prevents KSHV reactivation. Furthermore, PAN ac4C modification promotes NAT10 recruitment of IFN-γ-inducible protein-16 (IFI16) mRNA, resulting in its ac4C acetylation, mRNA stability and translation, and eventual inflammasome activation. These results reveal a novel mechanism of viral and host ac4C modifications and the associated complexes as a critical switch of KSHV replication and antiviral immunity. N-acetyltransferase 10 (NAT10) is an N4‐acetylcytidine (ac4C) writer, which catalyzes RNA acetylation at cytidine N4 position on RNAs. Here, the authors show that NAT10 catalyzes ac4C addition to a long non-coding RNA encoded by the oncogenic DNA virus Kaposi’s sarcoma-associated herpesvirus (KSHV), triggering viral lytic reactivation from latency, which promotes NAT10 recruitment of IFI16 mRNA, resulting in inflammasome activation. N6-methyladenosine (m6A) is the most abundant and has been the most extensively explored1. N4-acetylcytidine (ac4C) has been identified as a novel mRNA modification catalyzed by N-acetyltransferase 10 (NAT10)3. ac4C, which refers to a modification occurring at the fourth position of cytidine (C) bases in RNA, influences the stability and translational efficiency of mRNAs and hence has been implicated in development and diseases4. Importantly, NAT10 is the only known human enzyme with both acetyltransferase and RNA binding activities2. In 2020, the first report on the role of ac4C in viral infection revealed that multiple cytidines on HIV-1 RNAs are acetylated to ac4C by cellular NAT10, while silent mutagenesis of these ac4C sites led to inhibited HIV-1 replication by reducing viral RNA stability5. Recently, ac4C on RNAs of enterovirus 71 and influenza A virus were identified7. However, whether ac4C modification occurs during infection of DNA viruses such as Kaposi’s sarcoma-associated herpesvirus (KSHV) remains unknown.The studies of RNA modifications in the virology field have blossomed in recent years. Among more than 150 RNA modifications with distinct properties and functions, 9. Like other herpesviruses, KSHV displays two distinct phases in the life cycle, including a latent phase and a productive lytic replication phase10. In most infected individuals, KSHV is predominantly latent, while the viral genome persists as a circular episome within the nucleus, with the expression of only a limited number of viral genes, including latency-associated nuclear antigen , viral cyclin , viral FLICE inhibitory protein , kaposin (K12), and viral pre-microRNAs (pre-miRNAs)12. Upon intracellular or extracellular stimulation, KSHV latently infected cells can be reactivated into lytic replication, leading to the transcriptional activation in a cascade manner of three classes of lytic genes, characterized as immediate early (IE), early (E), and late (L) genes, and the production of viral progeny13. Both latent and lytic replication phases are crucial for the long-term persistence of KSHV in the host14. Therefore, understanding the mechanisms regulating the expression of viral genes in latency and lytic reactivation can provide insights into the KSHV life cycle and KSHV-associated pathogenesis.KSHV is causally associated with several malignancies, including Kaposi’s sarcoma (KS), primary effusion lymphoma (PEL), and multicentric Castleman’s disease (MCD)6A on transcripts of genes implicated in cellular transformation and viral latency15. We therefore hypothesized that the ac4C machinery might also modulate the expression of viral genes and KSHV lytic reactivation. Here, we found that NAT10-dependent ac4C modifications participated in the regulation of expression of viral genes and KSHV lytic replication and that this was involved with the RNA helicase domain and acetyltransferase domain of NAT10. Genome-wide analysis of KSHV transcripts identified NAT10-specific ac4C sites by acRIP-seq and NAT10-binding sites by RIP-seq. In particular, the PAN RNA, a KSHV-encoded long non-coding RNA (lncRNA), accumulated ac4C modifications at an exceedingly high level during viral reactivation. Genetic analysis of PAN ac4C sites showed that these modifications were critical for PAN RNA stability, the expression of viral late genes and virus production.Recent studies have revealed that during viral latency, KSHV alters m17. IFI16 was reported to interact with the KSHV genome, triggering the formation of a functional inflammasome, i.e., caspase-1 activation and IL-1β cytokine maturation19. The KSHV genome was epigenetically silenced by IFI16-mediated H3K9me2/me3 deposition during de novo infection and latency20. Here we also observed that IFI16 mRNA was acetylated following KSHV reactivation. This process relied on NAT10-dependent PAN ac4C modification. Acetylated PAN assisted NAT10 to better recognize IFI16 mRNA, resulting in enhanced IFI16 translation efficiency and subsequent inflammasome activation.Upon sensing of viral DNA in the nucleus, IFN-γ-inducible protein-16 (IFI16) translocates to the cytoplasm to activate the NF-κB pathway and induce the transcription of type I interferon (IFN)4C deposition on KSHV PAN RNA to promote its stability and KSHV reactivation, which assists NAT10 recruitment of IFI16 mRNA for innate inflammasome induction.Therefore, in this work, we reveal a novel mechanism by which a DNA virus hijacks a cellular protein to facilitate virus replication but, at the same time, triggers host innate immunity. We demonstrate that the acetyltransferase NAT10 catalyzes ac4C modification regulates the expression of KSHV genes and viral lytic replication, we employed the iSLK-KSHV system21. Treatment with doxycycline and sodium butyrate can trigger KSHV reactivation in iSLK-KSHV cells21. We utilized the CRISPR-Cas9 strategy to generate a heterozygous NAT10 cell line (NAT10+/−)  NAT10 or NAT10 mutants with mutations in K290A or G641E in NAT10+/− iSLK-KSHV cells  and more evenly on lncRNAs based on acRIP-seq results  in iSLK-KSHV cells transduced with Flag-tagged WT NAT10 or a control Flag-tagged red fluorescent protein (RFP) after doxycycline and sodium butyrate induction to identify NAT10-binding viral transcripts. In agreement with the mapped acRNA Fig. . As expeRNA Fig. . We used+/+ iSLK-KSHV and NAT10+/− iSLK-KSHV cells  followed by quantitative reverse transcription PCR (RT-qPCR) and successfully detected PAN, ORF-K12 and ORF73 but not non-acetylated viral transcript vIL-6 in NAT10 overexpressed (OE) iSLK-KSHV cells Fig. . Meanwhiion Fig.  while PAlls Fig. . Further8.1 Fig. .4C modifications in viral and cellular transcripts but also revealed the regulation of PAN RNA by NAT10. To define the mechanism regulating PAN RNA expression by NAT10 and the ac4C modification, we examined the PAN RNA and the expression of other viral genes in iSLK-KSHV cells overexpressing WT NAT10 or its mutant of the RNA helicase domain or the acetyltransferase domain. WT but not the mutants significantly increased PAN expression as well as other viral genes  reduction of ac4C followed by sequencing  PAN RNA into KSHV-negative iSLK-Puro cells enhanced the interaction between IFI16 mRNA and NAT10  but no reader has been found so far32, while NAT10 has been identified as the only known acetyltransferase in human RNAs, which catalyzes ac4C deposition on different RNA substrates with acetyl-CoA serving as the acetyl donor33. In the case of cellular mRNAs, Arango et al. utilized transcriptome-wide approaches to investigate ac4C localization and function in mRNA and discovered that ac4C catalyzed by NAT10 within target mRNAs conferred enhanced stability and translation efficiency4. However, a recent study challenged the presence of ac4C in eukaryotic mRNA with a similar strategy involving reduction with sodium cyanoborohydride (NaCNBH3) followed by sequencing (termed ac4C-seq)34. To identify the reason for this inconsistency, Arango et al. employed sodium borohydride (NaBH4) reduction of ac4C followed by sequencing (termed RedaC:T-seq) and reanalyzed the ac4C-seq results. The authors found that sequencing depth was the major factor behind the inconsistency. Importantly, C:T mismatches previously detected by acRIP-seq were encompassed within the RedaC:T-seq peaks, thus supporting the accuracy and consistency of acRIP-seq and RedaC:T-seq35. We have also orthogonally validated the presence of one ac4C site in the PAN RNA region with NaBH4 treatment, which was consistent with the acRIP-seq result. However, other ac4C sites were not revealed, possibly because of the sequencing depth issue. A previous study identified ac4C residues in HIV-1 transcripts, and the loss of NAT10 expression resulted in a decline in HIV-1 gene expression through the stabilization of viral RNA transcripts5. Ac4C was subsequently characterized in the enterovirus 71 genome and found to promote viral replication6. Multiple ac4C sites were also detected within RNA regions of influenza A virus7. However, there is currently no report on the role of ac4C machinery in the transcriptome of any DNA viruses.Recently, ac4C modification in KSHV PAN RNA inhibited viral replication. Consistent with the findings in RNA viruses, depletion of the NAT10-dependent ac4C modification in the PAN RNA impeded viral RNA stability, resulting in reduced expression of numerous viral genes at both RNA and protein levels.By genetic knockdown of NAT10 and mutagenesis of NAT10 residues required for RNA helicase or acetyltransferase function, we revealed that loss of ac37. As an early lytic gene, PAN RNA is not expressed or is expressed at a low level during viral latency39. Following reactivation into the lytic phase, the PAN promoter is directly targeted by the immediate early transcription factor RTA40, resulting in an increased expression level of greater than 1000-fold, reaching as high as 1–5 × 105 copies per cell41. In fact, as the most abundant viral transcript during lytic reactivation, PAN RNA is required for the expression of a subset of KSHV lytic genes, including K8.1, vIL-6, ORF6, ORF18 and ORF29, as well as the production of infectious virions42. Therefore, the dysregulation of  other KSHV lytic gene expression  may alter the structure of that region, it is unlikely to alter the interaction with ORF2625 and any unknown cellular proteins , MTA responsive element (MRE) and other regions in PAN RNA that mediate its functions are unlikely to be affected.PAN RNA is a long non-coding polyadenylated transcript that facilitates viral lytic replication and suppresses the expression of host genes involved in the antiviral responseson Figs.  is likel4C sites on ORF73 and K12 transcripts, whose ac4C peaks were less enriched than that of the PAN RNA. Different from PAN RNA, ORF73 and K12 are mainly expressed during KSHV latency. ORF73 is unchanged following lytic induction with 12-O-tetradecanoylphorbol-13-acetate (TPA)43 but is dramatically upregulated following tetracycline induction of RTA44. In contrast, ORF-K12 is significantly increased following TPA treatment or RTA induction44. Our findings implied that NAT10-mediated ac4C modifications on ORF73 and ORF-K12 genes may affect their expression levels; however, whether these modifications affect KSHV latent or lytic replication is still unknown. Given that the most abundant intracellular targets of NAT10 are 18S rRNA and tRNA46, KSHV reactivation might also be regulated by the abnormal tRNA and rRNA in the NAT10+/− cells. The presence of ac4C on tRNAs or rRNAs might increase the fidelity during translation and improve translation efficiency46, which should be further explored in the future.Besides PAN RNA, our acRIP-seq data also showed several NAT10-dependent ac19. Other than KSHV, IFI16 directly senses influenza A virus (IAV) RNA to enhance IFN-I production and inhibit IAV replication47. However, the mechanism by which IAV infection affects the IFI16 mRNA level remains unclear. Here we found that IFI16 mRNA was acetylated by NAT10 in the presence of KSHV PAN RNA. During KSHV reactivation, PAN RNA is highly expressed during KSHV early lytic replication and mediates the expression of other viral genes and viral production36. As a nuclear lncRNA, PAN RNA exerts its effects via interactions on chromatin48. Our results show that PAN RNA assists the interaction between IFI16 RNA and NAT10 during KSHV reactivation, leading to the ac4C modification of IFI16 RNA, which increases IFI16 mRNA and protein levels and inflammasome activation. As far as we know, this is the first description of such IFI16 mRNA modification by viral products and activation of inflammasome. It would be interesting to examine whether a similar mechanism is also present in other viral infections.IFI16 and inflammasome activation were well studied in KSHV de novo infection23. We analyzed the potential base pairing between PAN RNA and IFI16 RNA but failed to identify such an interaction. We speculated that NAT10 might have a low binding affinity for IFI16 RNA. However, the interaction of IFI16 RNA with PAN RNA might have helped recruit NAT10 to the IFI16 RNA, while the underlying sensing mechanism remains unknown. Thus, while KSHV utilizes NAT10 to acetylate PAN RNA to promote replication, the interaction of the IFI16 RNA with the extremely abundant PAN RNA might lead to IFI16 RNA acetylation, stabilization and enhanced translation, resulting in inflammasome activation. Therefore, our results provide insights into an alternative mechanism underlying innate inflammasome activation during KSHV replication and possibly de novo infection.The box C/D snoRNA U13 is required for human 18S rRNA acetylation through base-pairing between U13 and the 3′ end of 18S rRNA49, but inflammation has been shown to promote viral lytic replication50, indicating that KSHV might hijack the inflammasome to promote lytic replication. Here we described an interplay scenario between KSHV replication and host response. Both pathogens and hosts evolve a mechanism to counter one another and reach a balance. It should be noted that N4-acetylcytidine, as a nucleoside metabolite itself, induces the activation of the inflammasome in microglia or THP-1 cells52. Therefore, the reduced inflammasome activation in NAT10+/− cells might be a consequence of reduced ac4C release from cells, which should be further investigated.KSHV de novo infection and lytic replication induce host inflammation4C by NAT10 and that the ac4C modification in PAN RNA is critical for KSHV lytic replication. Knockdown of NAT10 or mutation of the ac4C site in PAN RNA led to decreased RNA stability and reduced expression of viral lytic genes. Cellular IFI16 mRNA was modified with ac4C, which relied on NAT10-dependent PAN ac4C modification. PAN RNA assisted in NAT10 recruitment to IFI16 mRNA, resulting in IFI16 ac4C modification and increased expression of mRNA and protein, and activation of innate inflammasome . HEK293T was purchased from ATCC (CRL-11268) and transfected with the Lipofectamine 2000 Reagent . All of the cell lines were negative for mycoplasma contamination.iSLK-KSHV cells were cultured in high-glucose DMEM supplemented with 10% fetal bovine serum (FBS), 1 μg/mL puromycin , 250 μg/mL G418 , and 1.2 mg/mL hygromycin B , while iSLK-Puro cells as the KSHV-negative control were maintained under the same conditions as iSLK-KSHV cells except for the absence of hygromycin B supplementThe plasmid with Flag-tag (pCDH-Flag-RFP), the NAT10 overexpression plasmid (pCDH-Flag-NAT10) and the NAT10 mutant plasmids (pCDH-Flag-NAT10 mut 1 and pCDH-Flag-NAT10 mut 2) were constructed based on the lentiviral expression vector (pCDH-CMV-MCS-EF1-RFP) . The sequence of NAT10 open reading frame (ORF) was referenced from pENTER-NAT10  with a series of Flag- and His-tag connected to the C-terminus of NAT10 ORF. The lysine at position 290 and the glycine at position 641 of the amino acids encoded by NAT10 were mutated to alanine (K290A) and glutamic acid (G641E), respectively. The ORF26 overexpression plasmid (pCDH-ORF26-HA) was constructed by Sangong Biotech Co., Ltd. All plasmids were prepared using the Vazyme FastPure Plasmid Mini Kit.+/− iSLK-KSHV cells were obtained by lentivirus transduction, which were produced by the lentiviral expression vector LentiCRISPR-NAT10 that encoded Cas9 and a single guide RNA (sgRNA): 5’-TGAGTTCATGGTCCGTAGG-3’. Two days after transduction, the cells were sorted with BD FACS ARIA II SORP and seeded into 96-well plates. iSLK-KSHV cells obtained by LentiCRISPRv2 transduction were used as a control. To verify the mutation efficacy, genomic DNA was extracted from a monoclonal cell line using a genomic DNA extraction kit. The genomic region flanking the Cas9 target site of each NAT10+/− cell line was amplified by PCR and cloned into the Not I/BamH I site of pHAGE. Bacterial cell clones of pHAGE genomic region plasmids isolated from each CRISPR knockdown clone were subjected to Sanger sequencing. The lentiviral expression vector, pCDH-Flag-NAT10 or pCDH-Flag-RFP, was used to generate iSLK-KSHV cells expressing Flag-NAT10 or Flag-RFP as a control.NAT10TM ac4C immunoprecipitation kit and then was purified and recovered by Zymo RNA clean and concentrator-25 kit. Next, RNA mixed with ac4C antibody and washed protein G-magnetic beads  in precipitation buffer were incubated together at 4 °C for 4–6 h. After magnetic separation, the supernatant was removed and the magnetic beads were washed twice with precipitation buffer. The supernatant was discarded and RNA purification product, 5 × precipitation buffer and RNase inhibitor were added to incubate overnight at 4 °C. On the second day, magnetic beads were washed twice with low-salt precipitation buffer and high-salt buffer orderly. The ac4C-enriched RNA was eluted from the beads in the RLT buffer supplied in the RNeasy Mini Kit . The RNA-seq library was prepared by NEBNext® UltraTM II RNA Library Prep Kit for Illumina® . PCR amplification was performed to enrich the library fragments, and the DNA purification magnetic beads were used to purify and screen the fragments. Beads purification library fragments were obtained by Bioptic Qsep100 Analyzer for quality inspection in the library. Both the input samples without IP and the ac4C IP samples were subjected to 150-bp, paired-end sequencing on an Illumina NovaSeq 6000 sequencer. The sequencing depth is 10G base.acRIP-seq was mainly completed by Guangzhou Epibiotek Co., Ltd. Briefly, total RNA was separated and purified by the phenol-chloroform method, and rRNAs were removed by QIAseq FastSelect-rRNA HMR Kits ; the concentration of total RNA was measured by Qubit RNA HS assay kit . Then, 150 μg of total RNA was taken for DNA digestion and fragmented with Epi4C antibody  or anti-rabbit IgG antibody was pre-incubated with 50 μL Protein A/G magnetic beads in IP buffer  at room temperature for 0.5 h. Then, 300 μg of RNA fragments were added to the pre-incubated beads and incubated at 4 °C for 3 h. The bound ac4C-modified RNA was eluted for analysis by RT-qPCR as described below. Equal amounts of RNA fragments (not subjected to immunoprecipitation) were used as the input controls.Total RNA was isolated from cells using TRIzol reagent (Invitrogen) and then fragmented into ~100 nucleotides using RNA fragmentation reagent (Invitrogen) and incubated at 95 °C. A total of 10 μg anti-acTM Stranded mRNA Library Prep Kit for Illumina®  according to the manufacturer’s instruction, which eliminated duplication bias in PCR and sequencing steps by a unique molecular identifier (UMI) of eight random bases to label the pre-amplified cDNA molecules. The library products corresponding to 200–500 bps were enriched, quantified and sequenced on a Novaseq 6000 sequencer (Illumina) with PE150 model.NAT10 RIP-seq assay on iSLK-KSHV cells was performed by SeqHealth Co., Ltd. . The cells were treated with lysis buffer, and 10% cell lysis was used as an “input”, while 80% cell lysis was used in immunoprecipitation reactions with anti-NAT10 antibody and referred to as “IP”. The remaining 10% cell lysis was incubated with rabbit IgG  as a negative control and named as “IgG”. The RNA from input and IP groups were extracted using TRIzol reagent (Invitrogen). The stranded RNA sequencing library was constructed by KC-Digital7 cells per sample). Then, the RNA-protein IP complexes were washed 6 times and incubated with proteinase K to isolate immunoprecipitated protein-bound RNA. Finally, the released RNA was purified by phenol-chloroform RNA extraction for qPCR analysis.RIP assay was performed using Magna RIP Kit  according to the manufacturer’s instructions. Briefly, 5 μg anti-DDDDK , anti-HA  or anti-Mouse IgG  incubated with 50 μL magnetic beads were added into cell lysates (~10https://www.ncbi.nlm.nih.gov/nuccore/NC_009333.1) with the modified maximum mismatch penalties (=2). ac4C peaks were identified using exomePeak R package (v2.13.2) under parameters: PEAK_CUTOFF_PVALUE = 0.05, PEAK_CUTOFF_FDR = NA, FRAGMENT_LENGTH = 200, while the differential ac4C peak were indicated with parameters: PEAK_CUTOFF_PVALUE = 0.05, PEAK_CUTOFF_FDR = NA, FRAGMENT_LENGTH = 200. Gene ontology (GO) analysis and Kyoto encyclopedia of genes and genomes (KEGG) analysis were performed using clusterprofile R package (v3.6.0), and ac4C-RNA-related genomic features were visualized using Guitar R package (v1.16.0). ac4C peaks that with P-value < 0.05 were chosen for the de novo motif analysis using homer (v4.10.4) under parameters: -len 12 -rna. Raw sequencing data was initially filtered by Trimmomatic (version 0.36). Low-quality reads were discarded and the reads contaminated with adapter sequences were trimmed. Clean Reads were further treated with in-house scripts to eliminate duplication bias introduced in library preparation and sequencing. In brief, clean reads were firstly clustered according to the UMI sequences, in which reads with the same UMI sequence were grouped into the same cluster. Reads in the same cluster were compared to each other by pairwise alignment, and then reads with sequence identity over 95% were extracted to a new sub-cluster. After all sub-clusters were generated, multiple sequence alignment was performed to obtain one consensus sequence for each sub-cluster. At last, any errors and biases introduced by PCR amplification or sequencing were eliminated.acRIP-seq data processing Cutadapt (v2.5) was used to trim adapters and filter for sequences. The remaining reads were aligned to the human Ensemble genome (GRCh38) by Hisat2 aligner (v2.1.0) with default parameters, which were removed for subsequent alignment to the KSHV genome  with default parameters. RSeQC (version 2.6) and exomePeak (version 3.8) were used for reads distribution analysis and peak calling, respectively. Peaks were annotated by bedtools (version 2.25.0), while the deepTools (version 2.4.1) was used for peak distribution analysis. Using the Fisher test, the differential binding peaks were identified by a Python script. Sequence motifs enriched in peak regions were identified using Homer (version 4.10). GO analysis and KEGG enrichment analysis for annotated genes were implemented by KOBAS software (version 2.1.1) with a corrected 4  was added to 2 μg RNA in nuclease-free H2O to a final concentration of 100 mM. NaBH4-treated RNA samples were incubated at 37 °C for 1 h, and NaBH4 was then quenched with 1 M HCl (15 μL) and neutralized by the addition of 1 M Tris-HCl (pH 8.0) buffer (15 μL).Total RNA was purified from iSLK-KSHV cells induced for 72 h using TRIzol reagent (Invitrogen). Then, 1 M NaBH+/− cells and the control cells were seeded in a 6-well plate at a density of 1 × 105 cells/well, and 1 μg/mL doxycycline was further added to stimulate KSHV lytic reactivation for 72 h. Above induced cells and their supernatant were collected. Then, 1 μL DNase I was added to the supernatant and bathed in 37 °C water for 1 h to digest linear DNA molecules. Then, KSHV progeny virus DNA in the supernatant was extracted with a micro-sample genomic DNA extraction kit and determined by absolute quantification. To establish a standard curve of Ct value versus genome copy number, pcDNA3.1-ORF26 plasmid (KSHV minor capsid gene ORF26) was serially diluted to the range of 4 × 102 to 4 × 107 molecules/µL. Relative viral protein and mRNA levels were detected by Western blot and RT-qPCR, respectively.iSLK-KSHV NAT104 cells/well. On the second day, when the cell confluence reached about 40%, 1 µg/mL doxycycline was added to the culture medium for 72 h. Then, 5 µg/mL Actinomycin D was added to the supernatant to inhibit mRNA transcription. Cells were harvested after Actinomycin D treatment for 0, 0.5, 1 and 1.5 h, and total cell RNA was extracted with TRIzol reagent. Viral RNA levels were assessed by RT-qPCR and analyzed using the 2−ΔΔCt method. The residual RNAs were normalized to 0 h.Cells were seeded in a 12-well plate at a density of 5 × 10TM Real-Time PCR System (Applied Biosystems). The results were analyzed using the 2−ΔΔCt method and standardized with GAPDH. All specific RT-qPCR primers were listed in Supplementary Table Total RNA was extracted using TRIzol reagent (Invitrogen) and then reverse transcribed into cDNA using HiScript III RT SuperMix . RT-qPCR analysis was performed with ChamQ SYBR qPCR Master Mix  on StepOnePlus4C  were from Abcam. Anti-pro Caspase-1+p10+p12  was also obtained from Abcam. IL-1β (3A6) mouse mAb  and cleaved Caspase-1 (Asp296) (E2G2I) rabbit mAb  were from Cell Signaling Technology. The monoclonal rabbit anti-vIL-6 antibody (1:500) was kindly provided by Dr. Robert Yarchoan from the Center for Cancer Research, National Cancer Institute 54, and polyclonal rabbit anti-vIRF1 antibody (1:300) was from Dr. Gary Hayward from Viral Oncology Program, The Johns Hopkins School of Medicine 58.Cells were lysed with RIPA lysis buffer  to obtain protein samples. Then, the samples were separated with SDS-PAGE and transferred to the PVDF membrane. The blots were visualized by ECL after incubating with the primary antibodies and secondary antibodies. Anti-GAPDH , anti-β-actin  and anti-Tubulin  were purchased from Santa Cruz Biotechnology, while anti-Flag was purchased from MBL . Antibodies against NAT10 , LANA , IFI16  and ac59. The kanamycin sequence carrying PAN homologous arms was amplified by PCR and transformed into GS1783 by electroporation to complete the first recombination. Then, under the induction of arabinose, the kanamycin sequence was cut off for the second recombination. PCR primers for mutagenesis are listed in Supplementary Table PAN mutant plasmid was constructed with a two-step Red mutagenesis systemBam HI. After purification, PAN was in vitro transcribed with CTP or ac4CTP (synthesized based on a published protocol60) and purified according to the manufacturer’s instructions . RNA integrity was assessed by 1% agarose after purification.T7 RNA polymerase promoter and PAN sequence were constructed into pHAGE plasmid. Then the plasmid was linearized with The cell cycle was analyzed by 50 µg/mL propidium iodide (PI) staining. Apoptotic cells were detected by Annexin V-FITC Apoptosis Detection Kit  according to the manufacturer’s instructions. Flow cytometry was performed by BD FACSCalibur  and analyzed by ModFit LT 5.0 (Verity Software House) and FlowJo V10.8.0 (BD Biosciences) for cell cycle analysis and apoptosis assay, respectively, while the gating strategy was defined as compared to non-stained cells.t-test was used for two groups comparisons. One- or two-way ANOVA was used for multiple comparison test as appropriate. P < 0.05 was considered statistically significant. All the experiments were repeated no less than three biological times. The data shown represent mean ± SEM from three independent experiments at least.GraphPad Prism 8 was used to calculate the standard deviation of the data. An unpaired two-tailed Further information on research design is available in the Supplementary InformationReporting SummaryDescription of Additional Supplementary FilesSupplementary Data 1Source Data


Gastroenteropancreatic neuroendocrine tumors (GEP-NETs) account for 80% of gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs). GEP-NETs are well-differentiated tumors, highly heterogeneous in biology and origin, and are often diagnosed at the metastatic stage. Diagnosis is commonly through clinical symptoms, histopathology, and PET-CT imaging, while molecular markers for metastasis and the primary site are unknown. Here, we report the identification of multi-gene signatures for hepatic metastasis and primary sites through analyses on RNA-SEQ datasets of pancreatic and small intestinal NETs tissue samples. Relevant gene features, identified from the normalized RNA-SEQ data using the mRMRe algorithm, were used to develop seven Machine Learning models . Two multi-gene random forest (RF) models classified primary and metastatic samples with 100% accuracy in training and test cohorts and >90% accuracy in an independent validation cohort. Similarly, three multi-gene RF models identified the pancreas or small intestine as the primary site with 100% accuracy in training and test cohorts, and >95% accuracy in an independent cohort. Multi-label models for concurrent prediction of hepatic metastasis and primary site returned >98.42% and >87.42% accuracies on training and test cohorts, respectively. A robust molecular signature to predict liver metastasis or the primary site for GEP-NETs is reported for the first time and could complement the clinical management of GEP-NETs. Neuroendocrine tumors constitute a well-differentiated group within the highly heterogeneous tumors originating from the diffuse neuroendocrine system, comprised of cells with neuronal and endocrine functions . GEP-NETLeveraging RNA-SEQ-generated large-scale gene expression data has proven effective in identifying biomarkers for cancer detection, metastasis prediction, cancer sub-type classification, and prognosis ,8. For in = 214) used in this study are listed in https://github.com/rvalieris/parallel-fastq-dump, accessed on 5 April 2022], and read quality was checked with FASTQC , Random Forest [RF], Classification and Regression Tree [CART], Support Vector Machine [SVM], k-Nearest Neighbor [k-NN], Extreme Gradient Boosting [XGB], and Gradient Boosting Machine [GBM] were selected based on their utility for gene expression-based classifications ,26. All Differential expression analysis for the primary versus liver metastasis samples was carried out using the DESeq2 package .R statistical programming language (v4.0.2) was used for all calculations and statistical analysis. All the graphs were generated with the R package ggplot2 .signed network and bicor selected as network cor functions. The TOM matrix was calculated from the adjacency matrix and converted into the dissimilarity matrix by subtracting the TOM matrix from 1 [Tutorial RScript provided with the R package WGCNA (Ver 1.7.0)  was usedx from 1 . Dissimin = 182) comprised of primary tumors  and liver metastases  samples had an average of 32 million reads. RNA-SEQ datasets were processed as described in The RNA-SEQ datasets  was randomly divided into training (70%) and test (30%) sets based on the tumor type (primary versus liver metastasis). The mRMRe algorithm identified nine gene features important for hepatic metastasis classification  and liver metastasis (n = 7) samples .Further, two aforementioned RF models were tested on an independent dataset (GSE118014), encompassing primary ( samples . Both RF samples . Notablyn = 30) and siNETs (n = 26) from GSM2626909 were randomly split into training (70%) and test (30%) sets using the Caret package , we focused our approach on utilizing liver metastasis samples. This strategy emulates cancers of unknown primary (CUP), aiming to uncover gene features pertinent to the primary tumor site by examining metastatic sites. Liver metastasis samples from pNETs  were generated . We traiWe further determined whether a single model could predict hepatic metastasis and the primary site. We constructed a single multi-label ensemble Random Forest model using the 21 mRMRe-derived features, consisting of 9 hepatic metastasis prediction gene features and 12 primary site prediction gene features. This model achieved 100% accuracy with the training dataset, comparable to the individual prediction models for hepatic metastasis or primary site. However, the multi-label model outperformed the individual models with the test dataset. Specifically, the multi-label model achieved 96.36% accuracy in predicting hepatic metastases, whereas the individual RF model achieved 92.45% accuracy . Similarn = 24,123) from 182 samples (GSM2626909). Following standard WGCNA network construction guidelines, we initially screened for outlier samples through hierarchical clustering  is a widely used method for identifying important modules containing highly correlated genes associated with the clinical attributes of interest ,33,34. Wustering . Subsequustering  to calcuustering A.p-value < 1 × 10−9) .To identify modules significantly associated with the tumor class (primary versus liver metastasis) and primary site (pNETs versus siNETs), we conducted module–trait relationship analyses. These analyses involved assessing correlations between the module eigengenes and the tumor class or site of origin. Notably, we identified a total of seven modules that exhibited significant associations: three positively correlated modules  and four negatively correlated modules  with the tumor class (correlation coefficient |0.4| or higher and  × 10−9) B. Cruciap-value < 2 × 10−8) (In the context of the primary site (pNETs versus siNETs), we identified nine modules  that were significantly associated with the primary site (correlation coefficient |0.4| or higher and  × 10−8) B. Two of × 10−8) .p-value > 1.38 × 10−4) . Additionally, all six features displayed a significant association with the primary site (GSE > 0.25 and  × 10−4) . In general, metastasis is a dynamic process of dissemination of tumor cells to a target site. This complex process involves molecular reprogramming, the tumor microenvironment, and interactions favoring the target site ,36. The The genes exhibiting higher expression in primary tumors are functionally linked to metastatic progression. For instance, SFRP2 plays a role in the Wnt signaling pathway, and its expression is regulated by methylation, influencing cell differentiation and growth . SFRP2 iThree genes, SFRP2, NKX2-3, and LMO3, identified in this study as important features associated with liver metastasis of NETs, belong to the metastatic gene signature derived for pNETs . SimilarPrimary sites cannot be confirmed in about 20% of NET cases . Such CUhttps://www.proteinatlas.org/, accessed on 22 December 2022). GABBR2 is a part of the GABAb receptor signaling. GABAb receptors are expressed throughout the small intestine and are involved in the secretion of the inhibitory neuron. In pancreatic Beta cells, the expression of GABBR2 is regulated while GABBR1 is constitutively expressed [SYT16 is a calcium-independent synaptogamin involved in membrane trafficking . SYT16 hxpressed . This maConversely, DPP6 has approximately 25-fold higher expression in the pancreatic alpha and beta cells than in the proximal tissue . SGPP1 iWith the availability of high-quality RNA-SEQ datasets of cancer tissues in the public domain, machine learning is poised to dramatically transform diagnosis and therapeutic management, predictably translating into better prognosis. Machine learning algorithms pick up subtle changes in the expression data and are suitable for developing a multi-gene model to distinguish the classifiers confidently. In this communication, we used GEP-NETs as a test system to evaluate the applicability of machine learning to predict either hepatic metastasis or the primary site. We conclude that the gene features extracted from the NET-tissue RNA-SEQ profiles can differentiate the classes under investigation with very high accuracy. Further, our study also clearly demonstrates that concise gene signatures perform better. In the future, this stratagem may complement the clinical management of cancer patients.This study did not investigate how conventional pathology including immunohistochemistry performs in comparison with gene expression algorithms in correctly predicting the primary site of metastases.


Condensation by phase separation has recently emerged as a mechanism underlying many nuclear compartments essential for cellular functions. Nuclear condensates enrich nucleic acids and proteins, localize to specific genomic regions, and often promote gene expression. How diverse properties of nuclear condensates are shaped by gene organization and activity is poorly understood. Here, we develop a physics-based model to interrogate how spatially-varying transcription activity impacts condensate properties and dynamics. Our model predicts that spatial clustering of active genes can enable precise localization and de novo nucleation of condensates. Strong clustering and high activity results in aspherical condensate morphologies. Condensates can flow towards distant gene clusters and competition between multiple clusters lead to stretched morphologies and activity-dependent repositioning. Overall, our model predicts and recapitulates morphological and dynamical features of diverse nuclear condensates and offers a unified mechanistic framework to study the interplay between non-equilibrium processes, spatially-varying transcription, and multicomponent condensates in cell biology. Through a physics-based model framework, the authors propose a central role for the nonequilibrium processes underling gene activity in shaping morphology, dynamics, and regulation of diverse nuclear condensates. Condensates concentrate multiple yet specific biomolecules through a network of multivalent and dynamic interactions5. Further, condensates exhibit a wide variety of physical and material properties and are actively regulated across the cell cycle6. In the crowded cellular environment, distinct condensates are coupled to non-equilibrium processes such as ATP-dependent chemical fluxes and mechanical remodeling that modulate their emergent properties8. This is particularly evident in the nucleus, where condensates interact with and are regulated by the genome, a large polymeric assembly of proteins, DNA, and RNA. The genome is intrinsically multi-scale and exhibits many layers of organization and regulation - from nanoscale nucleosomal clutches and microloops, to larger micron-scale compartments domains of active and inactive genes, and nucleus-scale territories for individual chromosomes12. Across these scales, genome organization is both modulated by and directly modulates active nuclear condensates. Examples include condensates that broadly promote gene expression such as the nucleolus, Histone locus body, nuclear speckles, and transcription-associated condensates, many first observed over a century ago21. While many studies have emerged in the past decade to probe spatial gene organization12 and condensates3 individually, our understanding of how spatial gene organization and ATP-dependent transcription can affect nuclear condensate behavior and morphology remains nascent. More generally, how this interplay between active processes, multicomponent interactions, and heterogeneous environments dictate condensate properties is poorly understood.The cellular milieu is organized into dozens of membraneless compartments or biomolecular condensates, many of which form through phase separation25, which in turn promote active transcription at specific genomic loci. Examples include: (1) Histone locus bodies (HLBs) enriched in transcriptional and regulatory proteins as well as multiple genic, enhancer, and small nuclear RNAs that form around the histone gene cluster26 (2) Transcription-associated condensates which concentrate the transcriptional apparatus as well as noncoding and mRNAs, and preferentially localize to regulatory DNA elements called super-enhancers28 (3) Nuclear speckles which are enriched in the splicing apparatus as well as poly-adenylated mRNAs30 and interfacially localize particular subsets of active genes32. Many nuclear condensates assemble in a manner dependent on active transcription35 and exhibit stereotypic localization in the nucleus40. Further, emerging evidence indicates that low and specific levels of non-coding RNA may contribute to the formation of particular genomic or nuclear compartments40. How specific gene compartments modulate localization or nucleation of condensates through RNA transcription is not well understood.Interactions between RNA and proteins are central in driving the condensation of nuclear bodies42, aspherical shapes39, and layered organization of molecules45 that have been documented over many decades. Yet, how these morphologies arise remain poorly understood. Nuclear condensates also exhibit unusual dynamics including bursts of directed motion49, and inappropriate or aberrant morphologies of nuclear condensates often reflect pathological cell states51. Overall, nuclear condensates are highly variable transcription-dependent compartments with diverse morphologies, dynamics, and localization. Despite their central role in gene regulation, we do not have a unified mechanistic framework to study the emergent properties of nuclear condensates, in large part due to a lack of physically-grounded models of the underlying biology.Unlike simple uniform liquids, nuclear condensates exhibit a wide gamut of transcription-dependent morphologies such as vacuolesIn this paper, we build a physically motivated in silico model to explore how actively transcribed gene compartments or clusters modulate condensate properties, and therefore transcription Fig. . We trea37 and are proximate to sites of transcription on the genome. The genome itself is organized into spatially clustered hubs of active and inactive genes, referred to as A and B compartments, arising through structural and sequence-based interactions amongst the polymeric DNA scaffold, nuclear proteins, and RNA23. Actively transcribed genes, in turn, modulate both genomic compartments and properties of nuclear condensates in an RNA-dependent manner52. How spatial clustering of active RNA synthesis due to structures such as chromatin compartments affects nuclear condensates is poorly understood.Many nuclear condensates enrich molecules that catalyze gene expression27. In this model, genomic compartmentalization into active hubs is effectively described by a spatially clustered region of gene activity , rDNA repeats (nucleoli), and histone-gene repeats (HLBs). Consistent with emerging experimental studies, our model provides a mechanistic framework that implicates RNA and compartments of actively transcribed genes as key players in regulating nuclear condensate organization 57. To explore whether genomic compartments can drive assembly of nuclear condensates de novo, we ran simulations where no condensates are initially present . Aspherical condensates localized to the edge of active sites are observed in our models, mimicking the interfacial localization of actively transcribed genic regions around nuclear speckles64. Additional factors likely play important roles to drive in vivo organization and morphology, including interactions between transcriptional and splicing proteins as well as post-translational modifications65, RNA-dependent changes in interfacial tension, and forces from the chromatin network66.At high transcription rates, we find that vacuoles spontaneously break symmetry to form aspherical condensates that partially overlap the site of active transcription Fig. . Physica66, limited by the chromatin or cytoskeletal network. However, many condensates also exhibit bursts of super-diffusive or directed motion48, particularly upon signaling or stress49. A mechanistic basis for this directed motion remains lacking.Condensates are present in crowded and heterogeneous environments in the cell and typically move randomly, often sub-diffusively66 in cells and occurs over minutes-long time-scales. Upon simulating the resultant dynamics, we find that condensates that are initially located far away from the active site flow towards it and upregulate transcription  contribute to long-range condensate motion - driven by favorable RNA-protein interactions. Consistent with this hypothesis, decreasing the strength of RNA-protein interactions  in Fig. 49. While our model neglects thermal diffusion, active nuclear condensates that are otherwise diffusing randomly can exhibit directional motion when a nearby gene compartment becomes active. This provides a mechanistic framework to explain why different nuclear bodies exhibit super-diffusive or partly ballistic motion in their cellular trajectories48 and may provide a mechanism for how nuclear bodies quickly and dynamically allocate machinery upon signaling or stress.We next explored whether RNA gradients may be significant enough to drive condensate flow in cells. Based on typical rates of RNA diffusion and transcription in cells , which can vary significantlymentclass2pt{minim59, provides mechanisms by which nuclear bodies exhibit directed or super-diffusive motion48, and shows that compartmentalized gene activity may be sufficient to locally nucleate a condensate70.Given the diverse morphology and dynamics we observed, we sought to derive a phase space of possible outcomes with varying compartment activity and relative initial position of condensate Fig. . We foun52 that often interact across large distances. While our primary focus has been to dissect the interplay between nuclear condensates and a single active gene compartment, we next sought to explore how multiple active compartments modulate condensates. To this end, we ran simulations with two active compartments whose properties  we changed while holding other parameters constant. Note that we held the total protein levels constant and limiting for all cases. We first considered two nearby compartments - one active  affect nuclear condensates that form by phase separation and the role of active transcription (RNA synthesis) in modulating emergent properties of condensate dynamics, morphology, and organization.Condensates are biomolecular assemblies that compartmentalize and organize active cellular processes. In the nucleus, condensates interact with the genome and often regulate active gene expression through concentrating specific molecules or pathways74, histone-gene repeats57, or rDNA repeats39 and explain how multiple condensates may nucleate in a transcription-dependent manner70. Although we focused on condensates that activate transcription in this study, similarly motivated models will contribute to better understanding of the emerging nexus between inactive heterochromatin and RNA-dependent nuclear condensates77 as well as interactions between different types of condensates.First, we develop a coarse-grained model of the underlying process with three main features 1) Description of nuclear condensate phase behavior in terms of RNA/protein interactions and concentrations 2) Spatial model of gene compartments and (3) Dynamical coupling between condensates and gene compartments through direct activation of transcription when proximate  of the free energy functional with respect to the concentration fields becomes negative i.e. det(J) < 0. This is used to define the interface of the condensate in Fig. In general, active nuclear condensates contain nuclear proteins and RNA species. These species can interact with each other via a plethora of interactions including, but not limited to - interactions mediated by disordered domains27 and described below. The total amount of protein is treated as a conserved quantity in the time scales of interested and the protein concentration field is assumed to undergo Model B dynamics:89The model used in this paper to study the dynamics of active nuclear condensates is similar to the one used in a prior studyThe dynamics of the protein concentration field is coupled to reaction diffusion dynamics of the RNA species:The first term on the right-hand sides captures the diffusion of the RNA species in the nucleus and the last term is a first order decay of the RNA species. The second term on the RHS, the RNA production term, captures the spatial clustering of genic hubs through a spatially varying Gaussian rate constant, which can be interpreted to be proportional to the local gene density. The RNA transcription rate is a product of this rate constant with the protein concentration, reflecting the increased rate of transcription that is expected when higher concentrations of catalyzing protein machinery is present.The free energy of interaction between the RNA and protein species is given by Eq. .Starting from a total RNA concentration of The above system of five equations need to be solved to get the five variables entclass1pt{minimahttps://github.com/npradeep96/RNA_localization_final. The code uses the finite volume solver Fipy, developed by the National Institute of Standards and Technology90 under the hood. All simulations in this paper were done in a 2D circular domain of radius 30 units, with a circular discrete mesh. The spatially discretized PDEs were solved for each incremental time step using the sweep function in Fipy, with adaptive time stepping to pick smaller or larger time steps depending on the how quickly or slowly the concentration fields change. A grid size of Δr = 0.2 and a typical time step size on the scale of Δt ~ 0.5 worked well for the simulations. Simulations were run for a duration of about 15,000 time steps, which was sufficient for the system to reach a steady state.The model partial differential equations were numerically simulated using a custom python code available at The coexistence concentrations for the protein based on the double well potential are For simulations varying the width of the gene-dense region RNA-producing region To calculate the radii of the vacuoles if Fig. 91. Depending on the type of RNA species expressed, median half-lives range from ~1 min (for nc and eRNAs) to 50 min (for lnc and mRNAs)68 and mammalian RNAs are transcribed across a wide range of rates, with some typical ranges for mRNAs between ~0.5–3 mRNAs/min69. Since its not clear a priori whether the transcription or degradation rates of RNA are limiting in vivo, we estimate an approximate range of gradient length-scales as 92.RNAs in the nucleus exhibit a range of diffusivities, with typically chromatin-associated mobilities around ~ρs, α, β, we equate the coefficient of the For Fig. We assume radial symmetry for the problem and solve the equations in a circular domain. There is symmetry about the center of the domain and no-flux boundary condition for the RNA at the edge of the domain.The protein concentration at the site of gene activity is assumed to be a constant value of The RNA transcription rate is assumed to be a constant value of To derive analytical expressions for the RNA concentration profile and gradient, we set up a transport model with the following assumptions. This approximates the RNA concentration around the site of gene activity when the condensate is far away from it :Model equations describing localized RNA production, diffusion and degradation in 2D radial coordinates:K0Solving these transport equations, we get the expression for the RNA concentration profile as a combination of the modified Bessel functions The largest value of the RNA gradient happens at Using the series expansion of the modified Bessel functions about We get the following scaling behaviors of From analyzing the above functions:We postulate that the peak flow velocity must be proportional to the largest value of the RNA concentration gradient in the domain i.e.The constant that relates peak flow velocity to the concentration gradient was fit with linear least squares using the data points in Fig. Further information on research design is available in the Supplementary InformationPeer review fileReporting Summary


Environmental pollution seriously affects human health. The concentration of negative air ions (NAIs), which were discovered at the end of the nineteenth century, is one of the factors used to evaluate air quality. Additionally, NAIs have been widely considered markers by scholars due to their unique biological function. The aim of this study was to summarize existing research and propose future research on the generation and temporal and spatial dynamic patterns of NAIs concentrations as well as the relationship between NAIs and human health. We identified 187 studies (published January 2013–January 2023) that met our inclusion criteria. Fourteen English studies evaluated the effects of NAIs on depression, the cardiovascular system, the respiratory system, reproduction and development, cognition, and sports muscle injury. Only two studies reported the associations of NAIs exposure with metabolic omics. NAIs concentrations vary temporally with solar radiation, air temperature, and relative humidity, while the temporal dynamic patterns of NAIs are affected by season, time, meteorological factors, air quality index, geographical location, forest vegetation, and other factors. Researchers have shown that exposure to NAIs may benefit our health by changing amino acid metabolism, which mainly manifests as increased anti-inflammation and reduced inflammation and antioxidation. Furthermore, exposure to NAIs promotes energy production, affects the expression of c-fos, and regulates 5-HT levels. There has been considerable interest in the potential effects of NAIs on human health and well-being, but the conclusions have been inconsistent and the mechanisms remain unclear. The use of omics to elucidate the biological mechanism of NAIs is relatively new and has some advantages. With the accelerating process of industrialization and urbanization, the problem of environmental pollution has gradually become more prominent, thereby seriously threatening the quality of human life and physical and mental health. Therefore, it is important to pay more attention to the living environment. Furthermore, since people spend more than 80% of their time indoors, previous studies have indicated that indoor air pollution has an equal or more significant effect on human health compared with ambient air pollution  is regarded as an essential indicator of urban air quality and the effects of forests on human health  and the outcomes of interest . In addition, we manually reviewed the reference lists of all retrieved articles to identify potentially eligible publications. Ultimately, 14 English-language studies met our inclusion criteria and were included in the analysis , relative humidity (RH), wind and radiation, forest vegetation, and environmental conditions, but the research conclusions are different due to the influence of study area, season, and forest type. Li A et al. , heart rate (HR), and heart rate variability (HRV) were monitored. The expression of c-fos in the PVN, LC, Na, and nucleus tractus solitarius (NTS) was detected by immunohistochemistry. The results showed that NAIs significantly reduced blood pressure and heart rate and increased the high-frequency power of the HRV spectrum. The expression of c-fos was significantly downregulated in the PVN and LC regions and upregulated in the NA and NTS regions. After vagotomy, the above physiological changes and neuronal activity were not observed. NAIs can regulate autonomic regulation by inhibiting the activity of PVN and LC neurons and activating NA neurons, and the vagal nerve may mediate these effects.Previous studies have suggested that NAIs can effectively inhibit the sympathetic nerve and activate the parasympathetic nerve. Additionally, the paraventricular nucleus of the hypothalamus (PVN), locus coeruleus (LC), and nucleus ambiguous (NA) plays a key role in the regulation of sympathetic nervous system activity  of volunteers increased by 4.4%, and fractional nitric oxide (FeNO) decreased by 14.7%. Therefore, it could be considered that using a negative ion purifier can significantly improve the function of the respiratory system. Liu S et al.  exposure had any effect on oxygen metabolism during exercise   NAIs for 18 days, 30 min, or 60 min a day  responding to exposure levels. The genes identified involve cancer-related pathways such as TP53, TGF-beta receptor signalling, p75, WNT-beta-catenin, and nonsense-mediated decay. MVN models revealed that hsa-miR-197-3p, hsa-miR-29a-3p, hsa-miR-15a-5p, hsa-miR-16-5p, and hsa-miR-92a-3p were significantly expressed in association with exposure.Omics is a powerful platform for screening internal exposure- and effect-related biological functions. Some epidemiological studies have adopted omics to investigate the relationship between environmental pollution and its effect on fundamental processes such as gene and microRNA expression, protein synthesis, enteric microorganisms, and metabolism  being not appropriately controlled.In summary, our narrative review described the generation and temporal and spatial dynamic patterns of NAIs concentration as well as the relationship between NAIs concentration and health effects. Much of the research in this arena is relatively old, and very little new research on this topic has been pursued in recent years. Our conclusion is consistent with those of recent comprehensive reviews and meta-analyses of NAIs and their effects on health. Exposure to NAIs may benefit our health by changing amino acid metabolism, which mainly reflects increased anti-inflammation and reduced inflammation and antioxidation, promotes energy production, affects the expression of c-fos, or regulates 5-HT levels. Multiomics studies may provide a new perspective for research on the biological effects of NAIs.


Chlamydia trachomatis (CT), Neisseria gonorrhoeae (NG), and Ureaplasma urealyticum (UU). Although evaluations of DNA-based NAATs have already existed, the comparison of the two methods is scarce. Thus, we compared the limits of detection (LODs) of DNA-based and RNA-based NAATs on the same experimental conditions. Inactivated culture supernatants of CT, NG, and UU with determined pathogen DNA and RNA load were used to evaluate LODs of seven DNA kits and one RNA kit. The LODs of the seven DNA kits for CT, NG, and UU ranged between 38–1,480, 94–20,011, and 132–2,011 copies/mL, respectively. As for RNA kits, they could detect samples at RNA concentrations of 3,116, 2,509, and 2,896 copies/mL, respectively. The RNA concentrations of CT, NG, and UU were 40, 885, and 42 times that of corresponding pathogen DNA concentrations in the employed supernatants, so RNA kits could detect pathogen DNA concentrations as low as 78 copies/mL, 3 copies/mL, and 69 copies/mL, respectively, but the level of pathogen load that the RNA tests could detect was primarily dependent on the infectious phase and transcriptional level of RNA. Thus, a schematic of bacterial dynamics during the period of reproductive tract infections was provided, which suggests that in terms of the analytical sensitivity of pathogen detection, RNA tests are more suitable for detecting active infection and recovery phase, while DNA tests are more suitable for detection in the early stage of infection.Currently, DNA-based nucleic acid amplification tests (NAATs) and RNA-based NAATs are employed to detect reproductive tract infection (RTI) pathogens including CT, NG , and UU are common pathogens. Although evaluation of DNA-based tests has already existed, the comparison between DNA-based and RNA-based tests is rare. Therefore, this study compared the limits of detection of the two tests on the same experimental conditions. Results suggested that most DNA-based NAATs could detect CT, NG, and UU at DNA concentrations lower than 1,000 copies/mL, while RNA-based NAATs could detect bacteria at RNA concentrations around 3,000 copies/mL. Considering the copy number of RNA per bacterium is dynamic through the growth cycle, further comparison is combined with a schematic of bacterial dynamics. Results suggested that in terms of the analytical sensitivity of pathogen detection, RNA tests are more suitable for detecting active infection and recovery phase, while DNA tests are more suitable for detection in the early stage of infection.Reproductive tract infections have considerable effects on the health of humans.  Chlamydia trachomatis (CT), Neisseria gonorrhoeae (NG), and Ureaplasma urealyticum (UU) are the common pathogens. As estimated by the World Health Organization (WHO), CT and NG resulted in 129 million and 82 million infections, respectively, in 2020 (Reproductive tract infections (RTIs) and their complications have considerable effects on the health and well-being of men, women, adults, and newborns. Among RTIs, CT is an intracellular pathogen that can cause genital infection. Although the majority of infected patients show no clinical symptoms or abnormal physical exam findings , which may lead to the same long-term sequelae as CT . These DNA detection kits are designed to amplify stable and specific DNA sequences in the cryptic plasmid or chromosome , 19. ResCT, NG, and UU with determined DNA and RNA concentrations.In domestic, an RNA-based NAAT, simultaneous amplification testing method (SAT), relying on isothermal amplification of bacterial rRNA, has been developed by Shanghai Rendu Biotechnology Co., Ltd. Although several studies have claimed it to be more sensitive than DNA-based NAATs , 25, theCT, NG, and UU, inactivated culture supernatants of these three pathogens with determined concentrations were employed (CT (ATCC VR-571B), NG (ATCC 19424), and UU (ATCC 27816) were provided by Shanghai Rendu Biotechnology Co., Ltd.To evaluate the analytical sensitivity of DNA-based and RNA-based NAATs for employed . InactivCT 23S ribosomal DNA (rDNA), NG 16S rDNA, and UU 16S rDNA, respectively .For comparable quantification among these different pathogens, plasmids containing the barcode sequence and target sequences were constructed by Sangon Biotech (Shanghai) Co., Ltd and linearized to calculate the amplification efficiency of quantitative PCR (qPCR) for each target using primers and probes designed by Oligo 7 software  . The priFor DNA quantification, the pathogen DNA was extracted with the QIAamp DNA Mini Kit . Subsequently, qPCR and droplet digital PCR (ddPCR) were performed on the Applied Biosystems 7500 Real-time PCR System (USA) and Bio-Rad QX-200 system (USA), respectively, using primers and probes verified above . For RNACT, NG, and UU culture supernatants were diluted at 6.561 × 105, 2.187 × 105, 7.29 × 104, 2.43 × 104, 8.1 × 103, 2.7 × 103, 9 × 102, and 3 × 102 copies/mL according to the quantitative results of rDNA, respectively. Subsequently, 200 µL supernatants at each concentration level were employed to extract DNA using the Tianlong automatic nucleic acid extraction system . Samples at each concentration level were tested 20 times using seven China’s National Medical Products Administration (NMPA) approved DNA-based NAATs  Co., Ltd.)  ; Table 1CT, NG, and UU culture supernatants were diluted at 6.561 × 105, 2.187 × 105, 7.29 × 104, 2.43 × 104, 8.1 × 103, 2.7 × 103, 9 × 102, and 3 × 102 copies/mL according to the quantitative results of RNA, respectively. Subsequently, RNA was extracted using the Tianlong automatic nucleic acid extraction system  with matched extraction reagents and tested 20 times for each concentration level using an NMPA-approved RNA-based NAAT   ; Table 1MedCalc Statistical Software version 20.0.26  was used for probit regression analysis to estimate the LODs of seven DNA detection kits and one RNA detection kit.According to standard curves established by plasmids using self-designed primers and probes, the correlation coefficient was greater than 0.99, the amplification efficiency of qPCR was close to 100%, and the copy number ratio of quantitative target/barcode was close to 1 for each quantitative target (Table S3).CT, the original concentrations of CT 23S rDNA and CT 23S rRNA were 4.232 × 107 and 8.42 × 108 copies/mL, respectively. Considering there are two copies of 23S rDNA in the CT genome, the pathogen load of CT was 2.116 × 107 copies/mL, indicating that the concentration of 23S rRNA was around 40 times that of pathogen DNA in the employed supernatants. For NG, the original 16S rDNA and 16S rRNA concentrations were 9.56 × 108 and 2.116 × 1011 copies/mL, respectively. Considering there are four copies of 16S rDNA in the NG genome, the pathogen load of NG was 2.39 × 108 copies/mL, indicating that the concentration of 16S rRNA was around 885 times that of pathogen DNA in the employed supernatants. For UU, the original 16S rDNA and 16S rRNA concentrations were 5 × 106 and 1.05 × 108 copies/mL, respectively. Considering there are two copies of 16S rDNA gene in the UU genome, the pathogen load of UU was 2.5 × 106 copies/mL, indicating that the concentration of 16S rRNA was around 42 times that of pathogen DNA in the employed supernatants.For inactivated culture supernatants of CT, the LODs of Sansure, Tianlong, Liferiver, Acon, Bioperfects, Daan, and Fosun were 190, 38, 554, 295, 218, 216, and 1,480 copies/mL, respectively. For NG, the LODs of Sansure, Tianlong, Liferiver, Acon, Bioperfects, Daan, and Fosun were 111, 145, 94, 157, 11,985, 722, and 20,011 copies/mL, respectively. For UU, the LODs of Sansure, Tianlong, Liferiver, Acon, Bioperfects, Daan, and Fosun were 1,287, 132, 270, 244, 2,011, 287, and 650 copies/mL, respectively. The fivefold dilution effect of Tianlong, Liferiver, Acon, and Daan was considered when calculating the LODs. The results of the probit regression analysis are presented in Probit regression analysis of the positive rate of 160 samples was conducted to calculate the LODs of DNA-based NAATs . For CT,CT, NG, and UU were 3,116, 2,509, and 2,896 copies/mL, respectively. The results of the probit regression analysis are presented in At the same time, probit regression analysis was conducted on the positive rate of 160 samples to calculate the LODs of the Rendu RNA Detection Kit . The LODCT, NG, and UU are curable with antibiotics, the untimely diagnosis and treatment due to the possibility of coinfection with other pathogens and the culture difficulty is likely to result in severe complications such as PID, infertility, and adverse pregnant outcomes  diagnostic test  targeting at CT 23S rRNA was reported due to a single base mutation (C1515T) in 23S rRNA, and this newly discovered variant strain of CT was announced as Finnish new variant of CT (FI-nvCT)  . TherefoCT, NG, and UU at DNA concentrations lower than 1,000 copies/mL, while RNA-based NAATs could detect bacteria at RNA concentrations of around 3,000 copies/mL. When converting the RNA concentration level to pathogen DNA concentration level using the copy number ratios of RNA/DNA based on the employed supernatants, RNA detection kits exhibited satisfactory analytical sensitivity with LODs as low as 78 copies/mL, 3 copies/mL, and 69 copies/mL for CT, NG, and UU, respectively, but the level of pathogen load that the RNA tests could detect was primarily dependent on the infectious phase and transcriptional level of RNA. As is described in the schematic of bacterial dynamics, during the early stage of infection, the rRNA is at a low transcriptional level where RNA-based tests show inferior performance in analytical sensitivity compared with DNA-based tests, while in active infection, the rRNA maintains a high transcriptional level so that RNA-based NAATs can detect pathogens at much lower concentrations compared to DNA-based NAATs (In conclusion, most DNA-based NAATs could detect ed NAATs , 37–39ed NAATs , 23. The