The liver is a common secondary metastasis site of many malignant tumours, such as the colorectum, pancreas, stomach, breast, prostate, and lung cancer. The clinical management of liver metastases is challenging because of their strong heterogeneity, rapid progression, and poor prognosis. Now, exosomes, small membrane vesicles that are 40–160 nm in size, are released by tumour cells, namely, tumour‐derived exosomes (TDEs), and are being increasingly studied because they can retain the original characteristics of tumour cells. Cell–cell communication via TDEs is pivotal for liver pre‐metastatic niche (PMN) formation and liver metastasis; thus, TDEs can provide a theoretical basis to intensively study the potential mechanisms of liver metastasis and new insights into the diagnosis and treatment of liver metastasis. Here, we systematically review current research progress about the roles and possible regulatory mechanisms of TDE cargos in liver metastasis, focusing on the functions of TDEs in liver PMN formation. In addition, we discuss the clinical utility of TDEs in liver metastasis, including TDEs as potential biomarkers, and therapeutic approaches for future research reference in this field. Exosomes are released by a variety of tumours, including colorectal, pancreatic, gastric, liver, breast, lung, and prostate cancers. Tumour‐derived exosomes (TDEs) reach the liver through the blood and lymphatic system to promote the formation of liver pre‐metastatic niche, thus promoting liver metastasis. Specifically, TDEs induce the liver pre‐metastatic niche formation by promoting the release of inflammatory molecules, promoting epithelial–mesenchymal transition, inducing vascular remodelling, macrophage polarization and mediating immunosuppression. Exocarta.org). Despite the extensive research on exosomes, the underlying mechanism by which exosomes selectively package their parental cell‐derived cargos remain unclear, and there are limited studies on the liver metastasis of tumours, an area that needs to be further explored.Exosomes contain complex and diverse contents Figure , includiThe examination of the biological functions of exosomes has largely relied on the ‘purity’ of exosome samples. Therefore, in the past decade, based on the properties of an exosome, numerous exosome isolation technologies have emerged.3TDEs are exosomes released by tumour cells widely present in the tumour tissues and various body fluids of patients with tumours.As early as 1889, Step Paget proposed the ‘seed and soil’ theory, which indicates that tumour metastasis is not random but determined by seeds and soil that provide favourable conditions for tumour cell growth.3.1Chronic inflammation affects cancer metastasis into or within the liver, creating a microenvironment for tumour cells to metastasize quickly.In addition to PDAC, the most common target organ for colorectal cancer (CRC) metastasis is the liver, with up to 30%–50% of patients developing liver metastasis during their disease course, and approximately 50% of patients have synchronous liver metastases at initial diagnosis.3.2EMT is a key process in the initiation of tumour metastasis.TDEs can not only modify normal liver cells or cancer cells to acquire a mesenchymal phenotype but also induce the formation of cancer‐associated fibroblasts (CAFs)3.3Increasing vascular permeability and promoting angiogenesis are also crucial in PMN formation.in vivo and inducing liver PMN formation.GC cell‐derived EVs containing AMIGO2 significantly enhanced the adhesion of LSECs to cancer cells and promoted the initial step of liver metastasis.Conversely, TDEs can also suppress angiogenesis in liver metastasis. In a study by Jiang et al., CRC‐derived exosomes containing ANGPTL1 proteins attenuated liver metastasis and impeded vascular leakiness in a liver PMN by regulating KCs secretion pattern and decreasing its MMP9 levels by inhibiting the Janus kinase (JAK2)‐STAT3 signalling pathway; however, whether exosomal ANGPTL1 affected vascular permeability by directly or indirectly affecting endothelial cells remained unclear.3.4Macrophages are an indispensable part of the liver microenvironment, and it contains resident KCs and infiltrating monocytes, which play an important role in regulating liver inflammation, HSC activation, and fibrosis.In CRC, exosomes carrying CCL2 prime the formation of fibrotic and suppressive liver PMN by activating macrophage (KCs) recruitment and shifting the M1/M2 paradigm to the M2 phenotype. Furthermore, the authors found that Dahuang Zhechong Pill can inhibit liver metastasis in CRC by reducing macrophage infiltration and TGF‐β1 gene expression, suppressing the CCL2‐mediated polarization of M2 macrophages.Although most studies have shown that TDEs can polarize macrophages into the M2 phenotype, Shao et al. showed that TDEs could potentially orchestrate M1‐phenotype macrophage polarization. They reported that CRC‐derived exosomes carrying miR‐21 were specifically colocalized with KCs. These KCs were polarized to the IL‐6‐secreting M1 phenotype via the toll‐like receptor (TLR)7 pathway, which created a permissive inflammatory PMN in the mouse liver, thereby promoting the survival and colonization of CRC cells in the liver and eventually led to liver‐specific metastasis. Moreover, miR‐21 levels in exosomes from the plasma samples of patients with CRC with liver metastasis were significantly increased, and plasma IL‐6 levels were also observed significantly higher in CRC patients with liver metastasis.3.5TDEs also modulate immune responses, an important step in liver PMN establishment. TDEs could promote the recruitment of immune‐suppressing cells, which skewed the immune repertoire toward an immunosuppressive kind. In PDAC, TDEs promoted bone marrow‐derived macrophage recruitment in liver PMN.+ T cells, which resulted in impaired cytotoxic function and tumour immune escape.+ T activation, thereby selectively facilitating the formation of an immunosuppressive PMN for CRC liver metastases (CRLM). Of note, the researchers have also observed that hypoxia could not only promote exosome secretion but also increase the expression of exosomal miR‐135a‐5p.+ T cells,On the contrary, TDEs triggered the formation of an immunosuppressive PMN of the liver by affecting immune cell functions. PDAC‐derived exosomal TGF‐β1 inhibited the functions of NK cells by downregulating the activating receptors (NKG2D and CD107a), decreasing cytokine release (TNF‐α and INF‐γ) and glucose uptake ability, and inducing the phosphorylation of Smad2/3 in NK cells. Furthermore, NK cells pretreated with PDAC‐derived exosomes showed the attenuation of NK cell cytotoxicity against pancreatic cancer stem cells, which allowed metastatic tumour cells to escape from NK cell immune surveillance in the liver PMN.44.1Many previous studies have shown that exosomes have great potential as novel biomarkers in liquid biopsy, which have the following significant advantages4.1.1In patients with CRC, the levels of miR‐21 in plasma exosomes were significantly higher than those of healthy individuals, and elevated exosomal miR‐21 expression was significantly associated with liver metastasis and TNM stage, suggesting that exosomal miR‐21 may be a promising non‐invasive diagnostic biomarker for patients with CRLM.In patients with rectum cancer, plasma exosomal miR‐141‐3p and miR‐375 were highly expressed in patients with liver metastasis than those without liver metastasis and could be reliable biomarkers for diagnosing liver metastasis.4.1.2As exosomal miRNA is an ideal candidate for non‐invasive liquid biopsy and early detection, there is a growing interest in exosomal proteins as a therapeutic tool for liver metastasis. Chen et al. reported that higher levels of exosomal PD‐L1 were associated with more significant liver metastasis lesions. Furthermore, in patients with CRLM following hepatectomy, those with higher level postoperative exosomal PD‐L1 have a high rate of early recurrence, and preoperative exosomal PD‐L1 was negatively correlated with recurrence‐free survival (RFS) and OS, which implied that exosomal PD‐L1 was a useful biomarker for the prediction of poor prognosis and early recurrence in patients with CRLM following hepatectomy.In addition to miRNAs and proteins, a recent study also showed that exosomal mRNA emerges as a promising biomarker candidate for liver metastasis. Lee et al. reported that CXCL10 RNA was highly expressed in plasma exosomes from patients with CRC with liver metastasis. Furthermore, high expression of CXCL10 was related to a poor prognosis, suggesting that CRC‐derived exosomal CXCL10 may be a novel biomarker for patients with CRLM, but more work is needed to verify its suitability as a biomarker for liver metastasis.4.2To date, available conventional therapies for cancer are not effective for the treatment of liver metastasis.The ablation of exosomal‐specific active cargos provides an effective treatment option for inhibiting liver metastasis. For instance, breast cancer‐secreted exosomal miR‐4443 induced liver metastasis by downregulating the expression of tissue inhibitors of metalloproteinase 2 (TIMP2) and upregulating the expression of matrix metalloproteinases. Interestingly, when the researcher armed breast cancer‐secreted exosomes with miR‐4443 inhibitors, the liver metastasis was inhibited, which suggested that directly targeting exosomal miR‐4443 can be a valuable therapeutic modality for liver metastasis.Apart from emerging as potential drug targets, over the last few years, many studies have shown that exosomes are ideal drug delivery vehicles in cancer therapy because of their biological properties such as nanoscale size, excellent biocompatibility, high stability, low immunogenicity, low toxicity, biological barrier permeability, targeted delivery, and easy modification.ExoASO‐STAT6 is a novel engineered exosome that is designed to deliver an antisense oligonucleotide (ASO) to selectively silence the STAT6 signalling pathway in TAMs. In many preclinical tumour models, ExoASO‐STAT6 treatment resulted in an effective conversation TAMs to a pro‐inflammatory M1 phenotype, accompanied by the generation of a CD8 T cell‐mediated adaptive immune response. After intravenous administration of ExoASO‐STAT6, maximal biodistribution and STAT6‐silencing activity were observed in the liver compared with other tissues, and KCs cells displayed significantly higher ASO signals than other cell subsets. Moreover, in the HCC model, ExoASO‐STAT6 showed significant antitumoral efficacy, which suggested that tumours located in the liver, including primary liver cancer or liver metastasis of other tumour types, can benefit from treatment with ExoASO‐STAT6.5In the present review, we elaborate on the roles and underlying mechanisms of TDEs in liver PMN formation. Exosomes derived from different tumour cells drive liver PMN formation and consequently increase liver metastatic burden by promoting EMT and the release of inflammatory factors, inducing vascular remodelling and macrophage polarization, and mediating immunosuppression. Moreover, TDEs are expected to become diagnostic and prognostic biomarkers and can be useful as novel targets and drug delivery vehicles for cancer therapy and the inhibition of liver metastasis.Even though the vital role of TDEs in tumour metastasis of liver has been gradually shown and its application prospect is positive, it is worth noting that several challenges are yet to be addressed. The separation methods of TDEs are complicated, and no optimal separation method can ensure the purity, concentration, and activity of TDEs, which greatly limits the clinical application of TDEs. Moreover, the detection system of TDEs is not mature, which is difficult to meet the detection of large clinical samples. Therefore, more convenient, high‐throughput, and highly sensitive methods are required to optimize the extraction and detection of TDEs in the future. The function, exact delivery mechanism, and contribution rate of TDEs in the liver PMN have not been fully elucidated. Furthermore, the contents of TDEs are complex and diverse, and it is not clear whether there is a potential link among TDE contents that can promote liver metastasis. Presently, most studies on the mechanism of TDEs in liver metastasis are focused on the premetastatic stage, and the research on the metastasis stage is very limited. Interestingly, tumour metastasis is a continuous multi‐step process, and TDEs can also play crucial roles in metastasis after tumour colonization in the liver, which is worthy of further studies in the future. TDEs have great potential as biomarkers for the diagnosis and prognostic assessment of liver metastases. However, previous studies usually used small samples and had poor repeatability, which needs to be further validated in large multicenter studies to improve the reliability of liquid biopsy. Finally, targeting exosomes associated with liver metastasis is a new way to develop novel and effective anti‐tumour therapeutic agents and has the potential to become an integral part of personalized medicine. Nevertheless, little information is currently available on the involvement of exosomes in the treatment of liver metastatic disease, and establishing exosomes with satisfactory cancer‐targeting ability is highly challenging as well as large‐scale production and storage. Therefore, solving the above issues will help elucidate the underlying mechanisms of liver metastases and provide more powerful guidance for the diagnosis and treatment of liver metastasis in the future.Hua Bai and Jie Wang designed this study. Sini Li, Yan Qu, and Lihui Liu drafted the manuscript and completed the figures. Chao Wang and Li Yuan collected the references and completed the tables. All authors approved the final version of the manuscript for submission.The authors declare no conflicts of interest.


Poxviridae family, that replicates in the cytoplasm and must encode for its own RNA processing machinery including the capping machinery. Here, we present crystal structures of its 2′-O-RNA methyltransferase (MTase) VP39 in complex with the pan-MTase inhibitor sinefungin and a series of inhibitors that were discovered based on it. A comparison of this 2′-O-RNA MTase with enzymes from unrelated single-stranded RNA viruses (SARS-CoV-2 and Zika) reveals a conserved sinefungin binding mode, implicating that a single inhibitor could be used against unrelated viral families. Indeed, several of our inhibitors such as TO507 also inhibit the coronaviral nsp14 MTase.Monkeypox is a disease with pandemic potential. It is caused by the monkeypox virus (MPXV), a double-stranded DNA virus from the  Monkeypox virus is a pathogen with pandemic potential, encoding for its own RNA capping machinery. Here, the authors present crystal structures of its 2′-O-RNA methyltransferase VP39 in complex with sub-micromolar inhibitors and reveal similarities to SARS-CoV−2 nsp14 methyltransferase. Poxviridae family, clinically resembling the eradicated smallpox1. Until recently, its occurrence was limited to central and western Africa where its natural reservoir consists of rodents and primates;2 however, it can also infect humans, and the mortality rate is estimated to be 3–6%. This rate is lower than deadly smallpox, but much higher than COVID-19 caused by SARS-CoV-2. Monkeypox has become increasingly spread across the globe with the number of cases growing exponentially reaching a somewhat alarming 54,900 cases, and it has been reported in some 100 countries3. It is no wonder that the prospective of another viral pandemic has alarmed public health authorities as well as the general public.Monkeypox is a disease caused by the monkeypox virus (MPXV), an Orthopox virus belonging to the 4 because the human enzymes are located in the nucleus. Besides the DNA-dependent DNA polymerase and the DNA-dependent RNA polymerase, it also encodes for the RNA capping machinery. In an early stage of cellular infection, the RNA cap is important for initiation of the translation of the viral RNA (vRNA)6. This cap plays an important role in innate immunity, as uncapped RNA is recognized by the innate immunity IFIT and RIG-I sensors8. The cap is also important for stability of the mRNA, thus the poxviruses encode for the decapping enzymes to prevent accumulation of dsRNA later during infection which would induce the innate antiviral response9. Indeed, protection against innate immunity is of the uttermost importance to the MPXV, and it also encodes poxin, an enzyme that blocks the cGAS-STING pathway triggered by the presence of dsDNA in cytoplasm10.MPXV is a dsDNA virus; however, it replicates in cytoplasm, implying that it must encode for DNA and RNA replication machineryPoxviridae family, cap-0 is synthesized in cytoplasm by the heterodimeric capping enzyme that has RNA 5′ triphosphatase (RTPase), guanylyltransferase (GTase) and (guanine-N7)-methyltransferase (MTase) activity11. The addition of another methyl group to the 2′-O position of the adjacent ribose converts cap-0 to cap-1. This step is also important in preventing an innate-immune response12 and is catalyzed by the MPXV 2′-O MTase VP3913.Chemically, the mRNA cap is a structure where an N7-methylated guanosine is linked via a triphosphate to the 5’ end of the RNA yielding m7GpppRNA, also referred to as cap-0. In the fully matured cap (cap-1), the first nucleotide is also methylated at the 2′-O position of the ribose moiety. In the Here, we present the structure of the MPXV VP39 in complex with the pan-MTase inhibitor sinefungin. The structure reveals the mechanism of VP39 inhibition and comparison of this structure to the 2′-O-MTases from unrelated viruses SARS-CoV-2 and Zika has important implications in the design of pan-antivirals based on MTase inhibitors.E. coli. The recombinant protein was supplemented with sinefungin and screening of the crystallization conditions was performed. Initial crystals were of poor quality but diffraction quality crystals were obtained upon a few rounds of seeding. The crystals diffracted to 2 Å resolution and belonged to the orthorhombic P212121 space group. The structure was solved by molecular replacement and refined to Rwork = 20.81% and Rfree = 23.24% and good geometry  to verify the enzymatic activity of our recombinant VP39 enzyme. Both substrates were accepted; however, the guanine base was clearly preferred in the penultimate position  and an order of magnitude better than sinefungin (IC50 = 2.3 µM).We identified several compounds that inhibited the VP39 MTase significantly better than sinefungin Fig. . Among tTo confirm our hypothesis that the inhibitors use the discovered water-filled cavity Fig.  we perfo50 = 128 nM while all the inhibitors with bulky substituents had a lower IC50 except for the only substituent that is able to form a hydrogen bond, the TO500 that possesses a benzimidazole heterocycle. These results suggest that for optimal inhibitor binding it is probably important to disrupt the water network, but it is not important to replace it with other hydrogen bonds.The crystallographic analysis revealed that in each case the moiety attached to the 7-deaza position is located within the water cavity. In the case of TO1119 that bears the large iodine atom at the 7-deaza position of the nucleobase only the water molecules near the iodine bound to the 7-deaza position were rearranged Fig. . However19. Indeed, some compounds such as TO507 were already identified as nanomolar inhibitors of SARS-CoV-2 nsp14 and docking studies using the previously published structure of nsp14 (pdb entry 7R2V20) revealed that the large aromatic substituent at the 7-deaza position is located within this nsp14 cavity 25; yet, none of the FDA approved drugs target a viral MTase. Most current antivirals are inhibitors of polymerases, integrases or proteases. In fact, only a handful of inhibitors of viral MTases, mostly targeting flaviviruses27, were known prior to the COVID-19 pandemic. However, recently many inhibitors of SARS-CoV-2 MTases were reported by us and others33.RNA MTases are present in multiple unrelated viral families, including both RNA and DNA viruses34 or the complex water network adjacent to the adenine base in the case of MPXV VP39. Nonetheless, inhibitors of viral MTases are urgently needed to further elucidate the role of these enzymes during viral infection.It remains to be established if MPXV MTases are viable antiviral targets; however, the high conservation of VP39 across orthopoxviruses suggests an important role in the viral lifecycle. In addition, the fact that the same compounds can be sub-micromolar inhibitors of both MPXV VP39 and SARS-CoV-2 Fig.  and the Escherichia coli and commercially synthesized (Azenta). Subsequently it was cloned into a pSUMO (with 8xHis-SUMO tag) using restriction cloning (BamHI and NotI sites). The recombinant protein was purified according to our established protocols for viral methyltransferases35. Briefly, E. coli BL21(DE3) cells were transformed with the VP39 expression plasmid and incubated in LB medium supplemented with ZnSO4 (10 µM) and ampicillin (100 µg/ml) and cultured at 37 °C. When the cells reached optical density 0.6 the production of VP39 was induced by the addition of IPTG  (250 µM) and the temperature was lowered to 20 °C for 15 h. The cells were pelleted by centrifugation and resuspended in lysis buffer  and lysed by sonication. The clarified lysate was subjected to affinity chromatography on Ni2+ agarose resin (Macherey-Nagel) and washed extensively with lysis buffer. The recombinant protein was eluted with lysis buffer supplemented with 300 mM imidazole pH = 8.0. The 8xHis-SUMO tag was cleaved by Ulp1 protease during dialysis against the lysis buffer. Subsequently the Ulp1 and His8x-SUMO tag were removed by a second round of affinity chromatography. Unbound fraction was concentrated and loaded on HiLoad 16/600 Superdex 75 gel filtration column (Cytivia) in size exclusion buffer: 10 mM Tris pH 7.4, 150 mM NaCl, 2 mM β-mercaptoethanol and 5% glycerol. The purified VP39 was concentrated to a set of different concentrations from 7 to 45 mg/ml and directly used for crystallization trials. Coronaviral nsp16 and nsp10 were expressed and purified as before14 as detailed in Supplementary Information.The gene for the monkeypox (USA-May22 strain) viral MTase VP39 was codon-optimized for expression in 19. All other compounds were synthesized using standard methods of organic synthesis as detailed in the Supplementary Methods. The purity of each compound was verified using an NMR 1H and 13C NMR spectra  was plated using the sitting drops method. 200 nl protein solution and 200 nl reservoir solution were mixed in several commercial crystallization screens using a Mosquito robot (SPT Labtech). Only low-quality crystals were generated in the initial experiments, which were then used as crystallization seeds in further experiments. The initial crystals were crushed with a glass rod and diluted 10 000x to produce seeds. The seeding screen was prepared using Dragonfly (SPT Labtech) by gradually decreasing the PEG concentration of the original hit  PEG 3350). The use of Angstrom Additive Screen TM (Molecular Dimensions) improved the quality of the seed. Finally, thin plate shaped crystals grew within 24 h at 18 °C in 200 mM lithium citrate and 14.5% (w/v) PEG 3350 and were cryoprotected in a 20% (v/v) glycerol-enriched well solution and snap-frozen in liquid nitrogen.36. The datasets were collected at the temperature of 100 K using the wavelength of 0.9184 Å. The crystals diffracted in the range of 2–3 Å resolution and belonged to the P212121 space group. The data was integrated and scaled using XDS37. The structure of the MPXV VP39/sinefungin complex was solved by molecular replacement using the structure of the vaccinia virus VP39/SAH complex as a search model (pdb entry 1VP3)38. The structures with other inhibitors were solved in a similar fashion except that the structure of MPXV VP39 was used as a search model. The initial models were obtained with Phaser39 from the Phenix package40. The models were further improved using automatic model refinement with Phenix.refine41 from the Phenix package40 and manual model building with Coot42. Statistics for data collection and processing, structure solution and refinement are summarized in Table The crystallographic datasets were collected from a single crystal on the BL14.1 beamline at the BESSY II electron storage ring operated by the Helmholtz-Zentrum Berlin43). The reaction was incubated at 37 °C for 8 h. Next, the RNA was purified using RNA Clean & Concentrator-5 from Zymo Research. DNA was removed by DNase I treatment performed directly on the column.For the 2′-O-MTase, an m7GpppG-capped RNA or an m7GpppA-capped RNA were used. These RNAs were 35 nucleotides long and they were prepared by in vitro transcription using a double-stranded DNA template (5′-CAGTAATACGACTCACTATAGGGGAAGCGGGCATGCGGCCAGCCATAGCCGATCA-3′) and the TranscriptAid T7 high-yield transcription kit (Thermo Scientific). The reaction was performed in a 50-μl mixture containing 1× TranscriptAid reaction buffer, 7.5 mM nucleoside triphosphates [NTPs], 1 μg template DNA, and 1× TranscriptAid enzyme mix and 6 mM cap analog m7GpppG (Jena Bioscience) or m7GpppA . The reaction was initiated by adding 500 nM VP39, mixed, spun, and incubated at 30 °C for 120 min. The reactions were stopped by adding 30 μl of 7.5% formic acid and analyzed on an Echo mass spectrometry system coupled with a Sciex 6500 triple-quadrupole mass spectrometer operating with an electrospray ionization source. The rate of MTase activity was measured as the amount of the product of the reaction SAH. The spectrometer was run in the multiple-reaction-monitoring (MRM) mode with the interface heated to 350 °C. The declustering potential was 20 V, the entrance potential 10 V, and the collision energy 28 eV. Ten nanoliters was injected in the mobile phase . The characteristic product ion of SAH, m/z 385.1  >  134.1, was used for quantification.The reaction was performed in a total volume of 15 μl and all the compounds were diluted in DEPC water to prevent contamination with RNases. The reaction mixture contained 4 μM SAM (S-Adenosyl-L-methionine), 4 μM m7GpppG-capped RNA or m7GpppA-capped RNA in the MTase reaction buffer  was calculated by variable slope model using Graph Pad 8.3.4 software using the equation: Y = Min + (Max-Min)/(1 + 10^((LogIC50-X)*HillSlope)), where Max, Min are the upper and lower asymptotes.For measurements of inhibition of the 2´-O-MTase reaction the protocol was modified for 1536-well plates. The total volume was 4 µl per well and the reaction mixture contained 4 μM SAM and 4 μM m7GpppG-capped RNA in the MTase buffer. RNase inhibitor  was added to the reaction in the concentration of 1 U/μl to prevent potential RNase degradation. The reaction was initiated by adding 200 nM VP39, mixed, spun, and incubated at 30 °C for 45 min. The reactions were stopped by adding 2 μl of 15% formic acid and analyzed as stated above. For IC2, 3 mM DTT).For measurements of inhibition of the SARS-CoV-2 2′-O-MTase nsp16, a similar protocol was used. The 2´-O-MTase reaction mixture contained 500 nM SARS-CoV-2 nsp16, 8 μM SARS-CoV-2 nsp10 (the activating subunit of nsp16), 4 μM SAM, 4 μM m7GpppA-capped RNA and 1 U/μl of RNase inhibitor in the reaction buffer (5 mM Tris pH 8, 1 mM MgClFurther information on research design is available in the Supplementary InformationPeer Review FileReporting Summary


Circular RNAs (circRNAs) are a unique class of single-stranded RNA molecules that are covalently closed and lack 5′ and 3′ ends. They were first discovered in eukaryotic cells >30 years ago, but technical limitations hindered the characterization of circRNAs until the 2010s. Recent advances in high-throughput RNA sequencing (RNA-seq), coupled with circRNA enrichment using RNase R treatment and new bioinformatic tools, have led to the discovery of a large number of circRNAs in various eukaryotic organisms, including mammals, insects, nematodes, plants, and yeast. Most circRNAs are generated through back-splicing of pre-mRNAs. Several studies showed that the expression levels of some circRNAs are significantly higher than that of their corresponding linear RNAs , indicatSome studies showed that most circRNAs are non-functional Xu and, while sBased on large datasets of high-throughput RNA-seq, thousands of circRNAs were identified in different types of cells. Most circRNAs are expressed at lower levels compared with their linear RNA isoforms from the same gene , and onlFos) are upregulated in ciRS-7 knockout mice and thus affect brain function. Other circ-RNAs, such as circHIPK3 and circBIRC6, were also reported to regulate miRNA function as miRNA sponges  could be translated in an in vitro translation system (rabbit reticulocyte lysate). Twenty years later, 6-methyladenosine modification in circRNAs also drove their translation in a cap-independent fashion (Since circRNAs lack the 5′-cap and 3′-poly(A) tail, they were considered non-coding RNAs. However, cap-independent translation, an alternative method that can initiate translation without a 5′-cap and 3′-poly(A) tail, is involved in the translation of some circRNAs . This de fashion . Moreove fashion , suggestRecent studies have demonstrated that circRNAs play important roles in multiple diseases, such as cancer, cardiovascular disease, and neurodegenerative diseases , and thus are ideal for vaccines, protein replacement therapeutic drugs, and other applications. However, mRNA stability and tissue-specific expression remain significant challenges. CircRNAs can overcome these issues, being more stable and translated in a cap-independent manner. For example, the and mice . Moreoveand mice . Therefo expression system was designed to produce circRNA-based aptamers, which are highly stable and achieve remarkably high expression levels in cells . Thus, cin vivo and the therapeutic potential of circRNAs have sparked a growing interest in strategies for RNA circularization in vitro. Various approaches have been developed for generating circRNAs in vitro, including chemical, enzymatic, and ribozyme strategies, which are summarized below  are RNA molecules that can catalyze specific biochemical reactions, acting similarly to protein enzymes. Several different types of ribozymes are used for circRNA production in vitro , leadingequences . In addiin vitro circRNA production.In general, a reliable strategy for circ-RNA production should be highly efficient, standardized , and easy to scale up. Based on previous results, a self-splicing ribozyme strategy is a universal approach for While circRNAs have shown promise in non-human primate studies for infectious disease vaccines, clinical studies for circRNA drugs are lacking. CircRNA vaccines show less side effects and higher efficacies than mRNA vaccines, representing an ideal platform for RNA-based vaccines. However, a reliable and cost-effective manufacturing system for circRNAs has to be established to support future clinical research. For therapeutic purposes, circRNA drugs will be given at higher frequency and dosage than vaccines. Therefore, both circRNA molecules and delivery systems need to be carefully designed and optimized for specific indications.Developing efficient and safe delivery systems will be critical for realizing the full potential of circRNA-based therapeutics. Although the current lipid nanoparticle system can deliver circRNAs into cells, its efficiency is limited and it primarily targets the liver, which hampers the potential of circRNA drugs. Thus, future development efforts could be particularly put into creating more efficient and safer delivery systems.


Male factors-caused decline in total fertility has raised significant concern worldwide. LncRNAs have been identified to play various roles in biological systems, including spermatogenesis. This study aimed to explore the role of lncRNA5251 in mouse spermatogenesis.The expression of lncRNA5251 was modulated in mouse testes in vivo or spermatogonial stem cells (C18-4 cells) in vitro by shRNA.The sperm motility in two generations mice after modulation of lncRNA5251 (muF0 and muF1) was decreased significantly after overexpression of lncRNA5251. GO enrichment analysis found that knockdown lncRNA5251 increased the expression of genes related to cell junctions, and genes important for spermatogenesis in mouse testes. Meanwhile, overexpressing lncRNA5251 decreased the gene and/or protein expression of important genes for spermatogenesis and immune pathways in mouse testes. In vitro, knockdown lncRNA5251 increased the expression of genes for cell junction, and the protein levels of some cell junction proteins such as CX37, OCLN, JAM1, VCAM1 and CADM2 in C18-4 cells. LncRNA5251 is involved in spermatogenesis by modulation of cell junctions.This will provide a theoretical basis for improving male reproductive ability via lncRNA.The online version contains supplementary material available at 10.1186/s13062-023-00381-x. At present, the decline of male fertility has caused widespread concern. Reasons for reduced reproductive ability and even infertility include obesity, diabetes, environmental chemicals and genetic factors –4. Many LncRNA is a type of RNA with a length more than 200 nt and no coding capacity, which is usually transcribed by RNA polymerase II/III . It is r3, H2S)-decreased mouse male fertility can be heritable, and many lncRNAs were altered in mouse sperm. However, we did not know the functions of these changed lncRNAs in mouse sperm. The aim of this study was designed to explore the functions of the altered mouse sperm lncRNAs and the underlying mechanisms, which will provide a theoretical basis for improving male reproductive ability at molecular level, and provide a scientific guidance for improving male reproductive ability.Our previous investigation uncovered that air pollutants  environment under a light: dark cycle of 12:12 h, at a temperature of 23 ℃ and humidity of 50–70%; they had free access to food (chow diet) and water  were mixed and then injected into the testes. For overexpression, virus (9 µl with titer greater than 6 × 108/ml) for 5NC, or lncRNA5251 (OV) individually were mixed and then injected into the testes. Then the mice were raised regularly for five weeks till nine weeks of age  with RNAi-mate for the transfection of C18-4, while for overexpression, shRNAs (titer > 3 × 108/ml) with RNAi-mate for the transfection of C18-4. The transfection medium was changed after 12 h. Stable transfected cells were cultured in a similar manner to the non-transfected cells in their respective media.-4 cells , 28. TheEvaluation of spermatozoa motility using a computer-assisted sperm analysis system. Spermatozoa motility was assessed using a computer-assisted sperm assay (CASA) method according to World Health Organization guidelines and reported in our early studies [ studies , 29–31.Morphological observations of spermatozoa.The extracted murine caudal epididymis were placed in RPMI medium, finely chopped, and then Eosin Y (1%) was added for staining as described previously [eviously , 21, 31.Assessment of acrosome integrity. The procedure of analysis of acrosome integrity was reported in our recent articles [articles , 21, 31.RNA Isolation and RNA-seq analyses. The RNA-seq analysis procedure has been published in our recent article [ article .Histopathological analysis. Testicular tissues were fixed in 10% neutral buffered formalin, paraffin embedded, cut into 5 μm sections and subsequently stained with hematoxylin and eosin (H&E) for histopathological analysis [analysis , 21, 31.Western blotting. Western blotting analysis of proteins was carried out as previously reported [reported , 21, 31.Detection of protein levels and location in testis using immunofluorescence staining. The methodology for immunofluorescence staining of testicular samples is reported in our recent publications [ications , 21, 31.Immunofluorescence staining with frozen sections for C18-4 cells. The protocol for immunofluorescence staining analysis of C18-4 cells was reported in our recent article [ article .Statistical analysis. Data were analyzed using SPSS statistical software  with one-way analysis of variance (ANOVA) followed by LSD multiple comparison tests or T-test. The data were shown as the mean ± SEM. Statistical significance was based on p < 0.05.3 + H2S [3 and H2S, the number of known up-regulated lncRNAs in the mouse sperm of F0, F1, and F2 generations showed a downward trend in Fig. 3 + H2S) vs. control group  and male fertility were decreased by the treatment of NH3 + H2S , 22. AftIn order to explore the effect of lncRNA5251 on the reproduction of male mice, lncRNA5251 was overexpressed (5251OV) and inhibited (5251KD) in the testis of F0 mice by shRNA (muF0). The sperm concentration in muF0 generation mice was increased by 5251KD and decreased by 5251OV, although it was not significant . The results showed that the expression of germ cell marker DDX4 was significantly reduced in 5251OV group, sperm protein maker PGK2 was significantly increased in muF0 5251KD group, while the meiosis marker SYCP3 was not change significantly Fig. . GO enriModification of the expression of lncRNA5251 altered the spermatogenesis in muF1 mouse testis. The IHF data showed that the expression levels of germ cell marker DDX4, meiosis marker SYCP3, and transit protein TP1 were significantly reduced in 5251OV group F1 mouse testis samples  and overexpressed (C18-5251OV) by shRNA. The gene expression after modification of the expression of lncRNA5251 was determined by RNA-seq analysis. The heatmap showed that 321 genes were up-regulated while 288 genes were down-regulated in the C18-5251KD group (compared to NC), however, 17 genes were up-regulated and 25 genes were down-regulated in the C18-5251OV group . And lncRNA5251 was interesting because it was increased in the three generational mouse sperm. And we explored the effects and underlying mechanisms of lncRNA5251 on sperm quality (concentration and motility) and spermatogenesis by knocking down or overexpressing the expression of lncRNA5251 through shRNA. Interestingly, the results from in vivo experiments showed that the sperm quality (concentration and motility) of F0 and F1 mice was decreased after overexpression of lncRNA5251 which suggested that it is involved in spermatogenesis.LncRNA lacks an open reading frame and was regarded as a by-product in the transcription process in the early time, so it has not received much attention. With the advancement of sequencing technology, lncRNA has truly entered people’s field of vision. This non-protein coding RNA, once regarded as “junk”, plays important roles in many biological processes such as embryonic development, cell differentiation and tumor metastasis , 15, 34.rational , 22. In Spermatogenesis is a complex process including mitotic cell division, meiosis and the process of spermiogenesis . RNA-seqIn the process of spermatogenesis, germ cells migrate from the base to the lumen through BTB, completing the process of sperm formation under various adjustments . And BTBIn summary, the in vivo and in vitro results together prove that lncRNA5251 regulates spermatogenesis via cell-cell junctions. This work provides a theoretical basis for improving male reproductive ability through lncRNA to help the infertile patients to be parenthood.Below is the link to the electronic supplementary material.Supplementary Material 1Supplementary Material 2Supplementary Material 3Supplementary Material 4Supplementary Material 5Supplementary Material 6Supplementary Material 7


E. coli function across different Gram-negative species and can actuate using phage RNA polymerase, suggesting that RNA systems acting at the level of transcription are portable. Second, we explore an RNA design strategy that uses arrays of tandem and transcriptionally fused RNA regulators to precisely alter regulator concentration from 1 to 8 copies. This provides a simple means to predictably tune output gain across species and does not require access to large regulatory part libraries. Finally, we show RNA arrays can be used to achieve tunable cascading and multiplexing circuits across species, analogous to the motifs used in artificial neural networks.A lack of composable and tunable gene regulators has hindered efforts to engineer non-model bacteria and consortia. Toward addressing this, we explore the broad-host potential of small transcription activating RNA (STAR) and propose a design strategy to achieve tunable gene control. First, we demonstrate that STARs optimized for  A lack of composable and tunable gene regulators has hindered efforts to engineer nonmodel bacteria and consortia. Here the authors deliver a tunable gene regulatory platform based on RNA arrays to control gene expression across different microbes. Motivated by this, there has been a significant emphasis placed on creating libraries of DNA, RNA, and protein-based regulatory systems that enable the precise and advanced programming of gene expression4. As access to better genetic parts has been achieved, this has led to a dramatic acceleration in our ability to precisely control the expression of individual genes6, optimize the expression of multi-gene pathways8, and compose larger genetic circuits able to implement signal processing10 and feedback control12. In bacteria, robust genetic programming frameworks exist for model microbes, most notably domesticated laboratory strains of Escherichia coli. However, our ability to program other diverse bacteria and non-model species is currently lacking. This is problematic because it fundamentally limits our ability to utilize diverse chassis that are naturally adapted for different environments 13, have distinct metabolic capabilities14, or offer valuable phenotypes 15. This also hinders efforts to program native microbial consortia and microbiomes that are predominantly composed of non-model and currently genetically intractable species. Thus, there is strong motivation to create genetic engineering frameworks for the programming of non-model species.Controlling gene expression is at the heart of how cells control function and phenotype18, and enact regulation through interactions with host-cell machinery that is ubiquitous. While there have been a handful of demonstrations of the portability of synthetic RNA switches23, we currently lack systematic investigation. Additionally, it remains hard to tune input-output relations . For example, while RNA engineering can be applied to alter gain30, this requires a detailed understanding of the system and is often arduous. A more convenient approach is to tune the expression of the RNA components of a given switch through the use of promoter strength libraries31. However, this can often be unpredictable due to non-linear transfer functions of RNA switches and requires access to large promoter libraries that are often lacking in microbes beyond E. coli. Thus, while RNA offers an intriguing solution for engineering diverse bacterial species, there is a need to investigate portability and create simple strategies to tune these genetic parts across different hosts.RNA regulators offer the intriguing potential of broad-host genetic control elements that could be functionally portable across diverse microbes. This is because most RNA switches rely upon the formation of simple structures  that depend upon universal RNA interactions. Additionally, most RNA switches use RNA motifs  that have relatively conserved sequence-function relationsIn the present study, we verify the portability of a synthetic RNA switch called small transcription activating RNA (STAR) across different Gram-negative species and cellular contexts. We then establish a design strategy to tune STAR output gain. This strategy that we call regulatory RNA arrays is inspired by naturally occurring CRISPR arrays that convert a single transcript into separate and independently acting RNA species. Using this approach, we show we can precisely alter regulator concentration from 1 to 8 copies, which in turn, results in corresponding changes in output gain. This approach works across different species and can achieve tunable gene control without the need for auxiliary part libraries. Finally, we show RNA arrays can be used across species to achieve modular signaling cascades with minimal signal loss, and to process a single input into multiple outputs achieving a tunable weighting comparable to a neural network motif.29. This switch is composed of a target RNA placed upstream of a gene to be controlled, which by default folds into a terminator hairpin turning the downstream gene off. This switch can be activated by transcribing a STAR, which prevents terminator formation and turns on the transcription of the gene. As this switch functions at the level of transcription—converting an RNA input into an RNA output—it is only dependent upon RNA polymerase (RNAP) and its ability to initiate and terminate transcription. Changing RNAP by implementing the switch in different hosts or using non-native RNAP has the potential to introduce a disturbance of the input-output relationship; however, we hypothesize that this disturbance will be minimal given the ubiquitous nature of RNA terminator motifs  and a construct encoding four tandem STARs separated by PlmJ (x4). For these experiments, we focused on STAR variant 50 which is from a previously described orthogonal library29. We posited that effective insulation would result in efficient separation of tandem STARs, allowing each to act independently and increase the level of activation proportionally to STAR copy. To allow for the efficient cloning of RNA arrays, we applied a modular cloning approach based on Golden Gate cloning , which structure prediction suggested would fold more robustly  of different inducible systems is often non-linear, we converted the inducer concentrations into a relative promoter activity term. To do this, we measured the transcription output of each inducible promoter system using a GFP reporter under the same experimental conditions to create a series of standard curves . This linear relation between array copy and output was observed across a range of different induction levels  or 4 (x4) copies was used as the connecting layer. From this, we observed that both cascades were functional and that the cascade with the x4 RNA array achieved significant amplification of the output signal ; however, using a x4 RNA array reduces signal attenuation resulting in an increase in the overall output signal Fig. . Given tn and m, were set to either 1 or 2 copies  system47. UBER combines a minimal set of broad-host parts and feedback control loops for T7 RNAP to implement an orthogonal transcriptional system that is portable across diverse species.A main feature of our study was to demonstrate that STARs have the potential to provide a broad-host tool for gene activation in diverse cellular contexts. This is important because while a versatile set of tools are available for model species like 48 and CRISPR-Cas regulators10. While we have focused on activating RNA in this work, we anticipate the same strategy can be applied to create RNA repressors arrays composed of transcriptional attenuators40 or STAR sequesters that repress transcription49. Additionally, regulatory RNA arrays provide a modular approach to tune the weights of transcriptional outputs within RNA network motifs. This characteristic makes them an ideal platform to construct complex circuits including ANN. Compared to protein-based transcription factors that have achieved weight tuning through the creation and screening of promoter mutant libraries50, regulatory RNA arrays could provide a more direct and predictable means to achieve tunability, which facilitates the optimization of weights in ANN through backpropagation. In the future, we envisage RNA arrays can be used to create RNA-based ANN in cells that can perform complex computations and make precise decisions that are useful for biomedical applications, for example, in the context of classification of healthy/diseased states and programmed drug secretion51.Our RNA design motif provides a simple approach for tuning output gain that we anticipate will advance RNA synthetic circuitry and applications. For example, regulatory RNA arrays can be used to amplify signals in RNA cascades to counteract signal attenuation and allow for the creation of longer cascades or single-copy circuits which have been achieved using protein transcription factors53. In particular, our results will make it possible to expand RNA synthetic biology to many new exciting applications in diverse, non-model cellular systems.In summary, our study contributes a route toward the systematic design of RNA circuitry, adding to other elegant approaches that are uniquely tailored to RNAThe information of all plasmids used in this study can be found in Supplementary Table E. coli cells, plasmids were transformed into chemically competent E. coli (TG1 strain). E. coli cells were then plated on LB-agar plates containing combinations of 100 μg/mL carbenicillin (Sigma-Aldrich), 34 μg/mL chloramphenicol (Sigma-Aldrich), 50 μg/mL spectinomycin (Sigma-Aldrich), 100 μg/mL kanamycin (Sigma-Aldrich) depending on the plasmids used, and incubated overnight at 37 °C. For experiments using S. oneidensis, P. fluorescens, and P. putida, plasmids were transferred into electrocompetent cells. These cells were prepared by washing overnight-cultured cells 3 times with 10% glycerol. Plasmids were electroporated into these cells using a MicroPulser Electroporator (Bio-rad) with 1.2 kV, 1.25 kV, or 2.5 kV pulse. The cells were then recovered at 30 °C in LB without antibiotics for 2 hours and plated on LB plates with 50 μg/mL kanamycin and incubated overnight at 30 °C. For experiments using P. stutzeri, the overnight-cultured cells were washed 3 times with 300 mM sucrose and then plasmids were electroporated using a 2.5 kV pulse. The cells were recovered at 30 °C in LB for 2 hours and plated on LB plates with 50 μg/mL kanamycin and incubated overnight at 30 °C. For experiments using V. natriegens, plasmids were electroporated into electrocompetent cells made from V. natriegens cells mixed in electroporation buffer  and pulsed at 0.7 kV. Electroporated cells were recovered at 37 °C for 1 hour, plated onto LB3-agar  with 200 μg/mL kanamycin, and incubated overnight at 37 °C. Relevant conditions are also included in Supplementary Table For experiments using V. natriegens) containing corresponding antibiotic in a 2 mL 96-well block (Costar), and grown overnight (~18 hours) at 37 °C for E. coli and V. natriegens or 30 °C for S. oneidensis, P. fluorescens, P. putida, and P. stutzeri. All cultures were grown at 1000 rpm in a VorTemp 56 bench top shaker (Labnet). For experiments without inducible promoters, 4 μL of each overnight culture were added to 196 μL (1:50 dilution) of LB (LB3 for V. natriegens) containing the corresponding antibiotic in a newly prepared 96-well block. The new block was incubated at 1000 rpm for 8 hours at 37 °C (E. coli and V. natriegens), 6 hours at 30 °C (S. oneidensis and P. fluorescens), and 12 hours at 30 °C (P. putida and P. stutzeri). For experiments with arabinose-inducible promoters, 4 μL of each overnight culture were added to 192 μL of LB containing corresponding antibiotics in a newly prepared 96-well block. After 4 hours of incubation, 4 μL of 0.5%, 1.5%, 5%, and 15% arabinose solution was added to reach a final concentration of 0.01%, 0.03%, 0.1%, and 0.3% respectively, and grown for another 4 hours before the measurement. For experiments with cumate-inducible plasmids, 4 μL of overnight culture were added to 196 μL of LB containing corresponding antibiotics and 10 μM, 30 μM, 60 μM, or 100 μM cumate, and grown for 8 hours before the measurement. Relevant conditions are also included in Supplementary Table For experimental measurements, four colonies were used to inoculate separate liquid cultures of 200 μL of LB ) in a 96-well plate. The GFP was measured with 485/520 mm as excitation/emission wavelength. The RFP was measured with 560/630 mm as excitation/emission wavelength. Optical density at 600 nm [OD] was also measured.E. coli transformed with combinations of empty control plasmids pJEC101, pJEC102, pJEC103, or S. oneidensis, P. fluorescens, P. putida, P. stutzeri, and V. natriegens without plasmids, referred to here as blank cells. Blank cells were used to determine autofluorescence levels. OD and FL values for each colony were first corrected by subtracting the mean value of the media blank from the respective value of the experimental conditions. The ratio of the corrected FL to the corrected OD (FL/OD) was then calculated for each well. Autofluorescence was removed through the subtraction of FL/OD of the blank cells.Each 96-well block included two sets of controls; a media blank and The online version of Nucleic Acids Package (NUPACK) version 4.0.0.27 was used to predict the secondary structure of RNA species. The insulator sequence and an example of regulatory RNA array sequence used in NUPACK simulation are shown in Supplementary Table p < .05 (*), p < .01 (**), p < .001 (***). The Pearson correlation coefficient was calculated between the copy number of the RNA array and fluorescence/OD to evaluate their linear correlation. One-way and two-way ANOVA with post-hoc Tukey test were used and the significance between groups are marked by asterisks indicating p < .05 (*), p < .01 (**), p < .001 (***).The mean and standard deviation were calculated from the biological replicates of each experiment. Two-tailed t-tests assuming unequal variance were used to calculate p-values. The statistical significance was marked with All models (reported in Supplementary Note Further information on research design is available in the Supplementary InformationReporting SummaryPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Source Data


CCDC88A with educational attainment and a progenitor apaQTL in EP300 with schizophrenia, suggesting genetically mediated post-transcriptional regulation during brain development lead to differences in brain function.The function of some genetic variants associated with brain-relevant traits has been explained through colocalization with expression quantitative trait loci (eQTL) conducted in bulk post-mortem adult brain tissue. However, many brain-trait associated loci have unknown cellular or molecular function. These genetic variants may exert context-specific function on different molecular phenotypes including post-transcriptional changes. Here, we identified genetic regulation of RNA-editing and alternative polyadenylation (APA), within a cell-type-specific population of human neural progenitors and neurons. More RNA-editing and isoforms utilizing longer polyadenylation sequences were observed in neurons, likely due to higher expression of genes encoding the proteins mediating these post-transcriptional events. We also detected hundreds of cell-type-specific editing quantitative trait loci (edQTLs) and alternative polyadenylation QTLs (apaQTLs). We found colocalizations of a neuron edQTL in  The vast majority of brain-relevant trait GWAS loci have been detected within non-coding genomic regions, and do not change protein coding sequence, implying that they may impact traits through gene regulation9. Expression quantitative trait loci (eQTL) analysis, which statistically tests the effects of genetic variants on gene expression, have been widely studied in adult brain bulk tissue to interpret the function of brain-relevant trait GWAS loci15. These studies aggregate steady-state expression across all potential isoforms regardless of post-transcriptional expression modulations. While adult post-mortem bulk tissue eQTLs have identified gene regulatory mechanisms for a subset of brain-trait associated loci, many do not colocalize, leaving their gene-regulatory function unknown. This has recently been termed the “missing regulation” problem in the field of functional genomics16. One potential solution could be that brain-relevant trait GWAS loci may function as eQTLs detected only under certain conditions including developmental stage, cell-type, or stimuli17. Consistent with this, eQTL studies performed in fetal brain bulk data19, and using cell-type-specific approaches during human neurogenesis and adulthood20 identified novel genetic loci associated with brain-relevant traits that were not detected in bulk post-mortem adult brain tissue. Although context-specificity has been taken into account in these studies, there are still brain-trait associated loci that are not explained even by context specific eQTLs. While greater power and additional contexts will likely yield additional gene regulatory effects of brain-trait associated loci, an alternative approach is to study genetic variants influencing brain-relevant traits via alteration of other gene regulatory phenotypes beyond overall expression including post-transcriptional regulation23.Genome-wide association studies (GWAS) have detected many genetic loci associated with risk for neuropsychiatric disorders and inter-individual variation in brain structure and other brain related traits24 and specific cell-types26. Many other post-transcriptional events exist, including RNA-editing and alternative polyadenylation, and their genetic regulation are in general poorly studied, especially during the process of neurogenesis. RNA-editing is nucleotide changes in RNA sequence relative to those encoded by the DNA sequence. RNA editing can alter protein product encoded by mRNAs28, location of transcripts30, splicing32, transcript stability34, and microRNA binding sequences36. In humans, adenosine-to-inosine (A-to-I) changes are the most common type of RNA-editing38. A-to-I editing events largely overlap with Alu repeats in the human genome and are generated when ADAR enzymes bind to double-stranded RNA hairpins generated by inverted repeat Alus (IRAlus)40. A-to-I RNA-editing occurs in the human brain42, and has been shown to impact neurotransmission and neurodevelopment45. RNA-editing dysregulation has been also associated with neurological disorders including schizophrenia47, autism spectrum disorder49, major depression51, epilepsy52, and Alzheimer’s disease53. Genetic regulation of RNA-editing was observed across adult tissues by performing editing quantitative trait loci (edQTL) analysis, where 1.4% of edit sites (within 228 genes) were significantly regulated by at least one genetic variant46. As a potential mechanism for edQTLs, genetic variants can perturb RNA secondary structure, which can lead to differential RNA-editing in nearby regions40 . Though adult brain bulk tissues are most commonly used to study edQTLs54, recent studies have shown that RNA-editing increases from development to adulthood in the human brain55, and edQTLs that were not observed in adult brain bulk tissue were detected in bulk fetal brain tissue55. Despite the discovery of temporal-specific edQTLs, the cell-type specificity of RNA-editing events and their genetic regulation are as yet unknown during human cortical development and may have been masked in previous bulk tissue studies due to heterogeneity in cell-type composition.The most commonly studied post-transcriptional modifications, alternative splicing, has been found to be a major contributor to brain-trait variation using both bulk tissue57. This modification can influence a variety of cellular processes including gene expression, RNA stability, localization, translation rate, and inclusion of microRNA target sequences57. Dysregulated APA has been found in a variety of brain-relevant diseases including Amyotrophic lateral sclerosis58, Parkinson’s disease59 and Huntington’s disease60. Furthermore, the disruption of several APA regulators have been reported in Oculopharyngeal muscular dystrophy61 and multiple neuropsychiatric disorders62. Previous research described the dynamics of alternative polyadenylation (APA) sites during neuronal differentiation and detected that longer 3’UTR isoforms are abundant in neurons compared to progenitors65. Alternative polyadenylation quantitative trait loci  analysis can be applied to assess the genetic alteration of alternative polyadenylation site usage69. Mechanistically, genetic alteration of polyadenylation sites, polyadenylation signal motifs, or motifs of RNA binding proteins (RBPs) regulating APA can impact polyadenylation site usage66. apaQTLs performed in human adult brain bulk tissue have identified many genetic loci associated with alternative polyadenylation site usage69. However, the temporal and cell-type-specific regulation of apaQTLs in the human brain has also as yet remained unexplored.Alternative polyadenylation (APA) is another post-transcriptional modification occurring at the 3’UTR or introns of a given gene in which the poly(A) tail of transcribed mRNA is added in different genomic locationsin vitro cell-type-specific system model of human neurogenesis, we systematically evaluated genetic regulation of RNA-editing and APA events within progenitors and neurons across ~80 different donors. We discovered that RNA-editing was more prevalent in neurons compared to progenitors, likely due to higher expression of the genes encoding ADAR2 and ADAR3 proteins. Alternative polyadenylation also showed differences across cell types, where longer 3’ UTR isoforms were observed in neurons as compared to progenitors. We found that common genetic variation was associated with both editing and alternative polyadenylation in a cell-type-specific manner. Hundreds of cell-type-specific edQTLs and apaQTLs were identified that were not previously discovered using fetal bulk brain data. We also observed that these post-transcriptional QTLs, as well as sQTLs showed largely independent regulatory mechanisms as compared to eQTLs. Furthermore, they provided additional interpretation of brain-relevant GWAS loci in addition to eQTLs, suggesting that studying post-transcriptional QTLs is required for a comprehensive understanding of genetic regulation of molecular phenotypes impacting brain development.In this study using an in vitro cell-type-specific system recapitulating human neocortical differentiation in which we previously generated cell-type specific expression, splicing, and chromatin accessibility QTLs70. This QTL dataset includes progenitors (Ndonor = 84) and their differentiated, labeled, and sorted progeny, neurons (Ndonor = 74)26. We identified RNA-editing events by observing sequence variants in multiple RNA-sequencing reads (supported by at least 10 RNA-sequencing reads in at least 85% of donors per cell-type) using REDItools software71  and editing sites which were not found in previous datasets (novel) showed highly similar, and expected, motif enrichment in neurons  only in progenitors, but increased ADAR2 and ADAR3 expression was specifically positively correlated with global editing levels in neurons , which is calculated as the global measurement of A-to-G changes within Alu elementsgenitors . As a po neurons . Consist neurons . Higher ain data  and C. W neurons . Overall79. We found that cell-type-specific RNA-edit sites present during neurogenesis that were observed in our model system overlapped with disease-related edit sites found in schizophrenia, glioblastoma, Fragile X, and autism. We observed a greater overlap of disease associated editing events with neurons as compared to progenitors, but these overlaps in both progenitors and neurons occurred more than expected by chance . These findings suggest a developmental and cellular origin of RNA-editing dysregulation in neuropsychiatric disorders  and all nuclei (DAPI) with 31 wells measured per donor on average. We implemented an image analysis pipeline to quantify the percentage of cells labeled with TUJ1 as a measure of that donor's neurogenic potential, observing strong differences across donors  divided by the total read coverage at the edit site, within +/−100 kb from edit sites. We included only variants and edit sites located in the same gene (excluding intergenic variants) because we hypothesized that alterations in mRNA secondary structure alter editing. We controlled population structure and global editing principal components (PCs) as technical confounders. We controlled for one global PC of gene editing in progenitors and the major known technical confounder (FACS sorting) in neurons see , which wpression . We implpression . We idenpression . Primarypression . We alsopression . To invepression . Only sipression .We observed high cell-type specificity of genetic effects on editing, finding that 86% and 97% of edSites in progenitors and neurons were not detected in other cell types. We also found that 97% and 99% of edSites in progenitor and neurons were not detected in bulk fetal brain tissue, showing additional genetic discovery is enabled using a cell-type specific approach .83  site usage by applying the QAPA method, which allows identification and quantification of 3’UTR isoforms mapped to annotated polyA sites by using RNA-seq data83 see . After f83 see . We obse83 see . We alsontiation . We next neurons . CALM1 p69. As an example, we detected an apaQTL for RPL22L1 gene in both progenitor and neuron cells, and the T allele which was the AAUAAA motif matching allele was associated with increase in short 3’UTR and decrease in long 3’UTR (1 statistics. We estimated the proportion of progenitor and neuron primary aSNP-APA pairs that were non-null associations (π1) in neuron and progenitor apaQTLs as 36.3% and 47.4%, respectively, showing high cell-type specificity of alternative polyadenylation. We next evaluated the overlap of cell-type-specific apaQTLs with fetal brain bulk data, and observed high cell-type specificity of alternative polyadenylation  analysis by evaluating the association of each 3’UTR isoform with the genetic variants within +/− 25 kb window of 3’UTR start and end sites see . We idenng 3’UTR . To evalnylation , again s26. We observed that both primary edQTLs and apaQTLs were more often near transcription termination sites (TTS) as compared to primary eQTLs which were more often near transcription start sites (TSS) in both cell-types  together with those identified in this manuscript (editing and polyadenylation)ll-types -B. Unlikll-types . Functio70  in progenitor/neuron eQTLs were 12% and 2%; the proportion of progenitor and neuron primary apaSNP-aGene pairs that were non-null associations (π1) in progenitor and neuron eQTLs were 50% and 34.6%, and the proportion of progenitor and neuron primary sSNP-sGene pairs that were non-null associations (π1) in progenitor and neuron eQTLs were 46.5% and 40.6%  in progenitors and neurons, respectively. 5.8% and 5.6% of apaSNPs significantly associated with at least one APA, were also significantly associated with the expression of the gene harboring the 3’UTR (aGene) in progenitors and neurons, respectively. Given the difference in statistical power between different datasets, we also applied πnd 40.6% . Taken t91 for variant-edit-gene and variant-APA site-gene triplets. While we did not detect any variant-edit-gene triplets supporting the causal forward model; we detected 335 and 49 variant-APA site-gene triplets. As an example, we found that an apaQTL in progenitors mediated expression of CEP250 gene  and APA sites (aGenes) in this study and eGenes and genes for which splice sites (sGenes) were found to be significantly regulated in our previous study26. We observed that sGenes, edGenes and aGenes showed lower pLOUEF scores than eGenes in both cell types. Lower pLOUEF scores indicate genes that are generally protected from rare damaging variation, suggesting that they are important for diseases. These findings indicate that the genes affected by editing and APA are likely to be more disease relevant  within the CCDC88A gene locus93   . We did x 10−13) . Variantx 10−13) 88, suggePA sites . This obWe also evaluated how many additional brain-relevant GWAS loci’s function could be explained via our cell-type-specific system in addition to adult brain eQTLs. We found that our cell-type-specific QTL approach allowed discovering the function of 0.6-4.5% of GWAS loci which could not be explained by adult brain eQTLs previously . FurtherADAR2 and ADAR3 in neurons. (2) Consistent with previous findings of 3’UTR isoforms lengthening during differentiation65, the majority of longer 3’UTR isoforms of the genes were upregulated in neurons. (3) Both edQTLs and apaQTLs were strongly cell-type-specific. (4) Both edQTLs and apaQTLs were enriched within genomic regions and regulatory elements which were different from eQTLs, suggesting independent genetic regulatory mechanisms. (5) We found that a few edQTLs and apaQTLs were colocalized with brain-relevant trait GWAS loci in progenitor and neuron cells, increasing the known gene regulatory mechanisms underlying complex brain traits.In this study, we identified the impact of genetic variants on cell-type-specific RNA-editing and alternative polyadenylation. We found that: (1) RNA-editing was more frequently observed in neurons compared to progenitor cells. This increase in RNA editing may be mediated by higher expression of 55. However, the mechanism causing the developmental increase of RNA-editing has remained unexplored. Our cell-type-specific design using approximately 80 different donors provided sufficient power to observe the difference in RNA-editing between cell types of the prenatal brain. RNA-editing was six times more frequently observed in neurons compared to progenitor cells during cortical development. This observation was consistent with ADAR2 and ADAR3 upregulation, but slight ADAR1 downregulation in neurons. We observed that higher ADAR1 expression was associated with increased editing levels in progenitors, but not in neurons. Also, we noted that higher ADAR2 and ADAR3 expression were associated with higher editing levels, specifically in neurons. Though increased ADAR2 was also previously found to be associated with increased editing in the adult brain, ADAR3 has been previously thought to inhibit RNA-editing using expression measured in the adult brain98 . However, here we show that ADAR3 is positively correlated with editing in neurons suggesting that ADAR3 can act as an activator of RNA-editing specifically during early development, but it may later decrease editing levels in adulthood. Future studies measuring RNA-editing levels following modulation of ADAR3 in immature and mature neurons will provide insights into addressing this controversy. Increased editing levels in neurons suggests that RNA-editing has functionality in neurons including neuronal differentiation, maturation and activity. For instance, we found that higher editing rate within the CEP104 gene was positively correlated with higher number of neurons generated during differentiation, while the same edit site was not discovered in progenitor cells  derived from voluntary terminated pregnancy according to the IRB regulations at UCLA through the Gene and Cell Therapy Core. The tissue pieces corresponding to the cortex were visually selected for generation of phNPCs. In the Geschwind lab at UCLA, we dissociated these tissues and formed neurospheres by using them as we have previously described106. We then plated the neurospheres on the plates coated with laminin/fibronectin and polyornithine, and after an average of 2.5 +/− 1.8 standard deviation passages, they were cryopreserved to transfer to Stein lab at UNC-Chapel Hill26.We established phNPCs culture including neural progenitor cells and neuronal progeny differentiated from these progenitors by following the experimental workflow described in our previous work70 for three weeks, and then prepared RNA-seq libraries. To differentiate progenitor cells into neurons, we first cultured the cells in the media without growth factors for 5 weeks and then transduced cells with AAV2-hSyn1-eGFP virus ) that carries a reporter gene expressed specifically in neurons26. After viral transduction, we differentiated the cells for 3 weeks longer, and isolated EGFP-labeled neurons using FACS sorting machines BD FACS Aria II or Sony SH800S. We kept the neurons within the Qiazol solution to prepare RNA-seq libraries.phNPCs from 89 unique donors were further randomly grouped into 8-9 donors for 12 rounds, and each round was thawed every 3 weeks. Each round was processed by mostly the same person and on the same day of the week, where changes in these technical variable changes were documented. We cultured progenitor cells in proliferation media as previously described26. We obtained 150 bp paired end reads with a mean read depth of 99.8M ± 29.8 SD read pairs per library.We prepared RNA-seq libraries and sequenced them as described in our previous work26, if the same library was sequenced on multiple flow cells, we merged .fastq files, trimmed the adapters for all libraries using Cutadapt/1.15 software107, and performed quality control with FastQC software. We aligned the RNA-seq reads into GRCh38 release92 reference genome including sequence of AAV2-hSyn1-eGFP plasmid using STAR/2.6.0a aligner program108.To process the RNA-sequencing data, as described in our previous work109, as described previously26. We retained the RNA-seq libraries with [CHIPMIX] < 0.04 and [FREEMIX] < 0.04, and assigned correct donor IDs for 8 libraries where there was a sample swap. Additionally, one library was missing cDNA concentration and removed, also libraries with eRIN score lower than 7 were not included. Following quality control, we obtained 84 and 74 unique donors for progenitors and neurons, respectively. We applied the same workflow to process RNA-seq data derived from fetal bulk brain samples and retained 235 unique donors as previously described26.To assess consistency of genotypes detected by genotyping array and RNA-seq, we performed VerifyBamID analysis (v.1.1.3)110 with the parameters –hwe 1 x 10−6 –geno 0.05 –mind 0.01 –maf 0.01 as described previously70. We utilized the TOPMed imputation server for imputation with the TOPMed reference panel (Version R2 on GRC38)111, after processing the genotype data via imputation preparation pipeline by using the algorithm perl HRC-1000G-check-bim_v3.pl -b <bim file> -f <frequency file> -r 1000GP_Phase3_combined.legend.gz -g -p ALL (https://www.well.ox.ac.uk/~wrayner/tools/). For downstream analyses, we retained the variants with following criteria: minor allele frequency (MAF) > 0.01, imputation quality score R2 > 0.3 and Hardy-Weinberg equilibrium at p > 1 x 10−6.We performed genotyping by utilizing HumanOmni2.5Exome or Illumina HumanOmni2.5 platforms followed by filtering via PLINK v.1.90b3 software112 and remapped RNA-seq reads mapped via STAR/2.6.0a aligner108. Following remapping, we discarded duplicated reads and extracted uniquely mapped reads as input for Reditools software v2.071. Using Reditools software, we identified and quantified RNA-editing sites by applying the following parameters: -S -s 2 -q 20 -bp 25 -ss 5 -mrl 50 -C -T 2 –os 5 where -S was used for including only edit sites in the output, -s was for strand inference, -q was for minimum read quality score, -bp was for minimum base quality score, -ss was for splicing span, -mrl was for minimum read length, -C was for strand correction, -T was for strand confidence and –os was for omopolymeric-span. We used Homo sapiens gene ensembl v.92 as the reference genome. We discarded multi-allelic sites and the sites overlapped with genomic variants from our genotype data with imputation R2 greater than 0.3 and common SNPs in dbSNP (v153) unless they were listed in the REDIportal database74. For downstream analysis, we retained RNA-editing sites supported by at least 85% of donors with 10 RNA-seq counts  per cell-type and fetal bulk tissue. We defined RNA-editing rate as the ratio of the number of read counts supporting the edited allele to the sum of the number of the read counts supporting both edited and unedited alleles.To detect RNA-editing events by using RNA-sequencing data, we first aimed to reduce mapping bias using the WASP method72 which overlapped with per edit site. We calculated the abundance of each unedited allele of each edit site per donor by getting the ratio of number of reads supporting the unedited allele to total coverage at that site  per RNA-sequencing sample bam file per cell-type and fetal bulk tissue  via the RNAEditingIndexer algorithm113.For each edit site, we extracted RNA-sequence within +/− 4 bp window of the edit sites. Providing these sequences to the EDLogo software, we quantified and visualized local sequence motifsTo assess the significance of overlap between disease-relevant edit sites  and edit sites detected in each cell type, we applied GeneOverlap R package.65. We initially built 3’UTR libraries including potential alternative polyadenylation sites for each gene by using biomart ensembl gene metadata table (human version 92), GTF file Homo_sapiens.GRCh38.92 as gene prediction table, and PolyASite database (hg38) and GENCODE poly(A) sites track (hg38) as poly(A) site annotations followed by extraction of 3’UTR sequences by integrating these annotations with reference genome fasta file (GRCh38 release92). After trimming the sequencing adaptors from RNA-seq libraries via Cutadapt/4.1107, we quantified 3’UTR isoforms via salmon/1.9.0114 based on the 3’UTR library generated by QAPA by correcting for GC and sequence-specific biases. To infer poly(A) usage (PAU) value for a given 3’UTR isoform of a gene, we divided the expression of this 3’UTR isoform by the sum of the expression of all other 3’UTR isoforms detected for that gene via qapa quant function65. For the downstream analyses, we applied the following steps: (1) we retained the 3’UTR isoforms supported by 10 counts at least 10% of the donors for each cell-type. (2) For the genes in which we detected only two 3’UTR isoforms, we randomly selected one of the isoforms to prevent statistical bias since these two isoforms were complementary to each other. (3) We normalized the PAU ratios for the remaining 3’UTR isoforms via quantile normalization.To detect and quantify alternative polyadenylation by using RNA-seq data, we followed the pipeline described by QAPA methodremoveBatchEffect function of limma R package115. Followed by batch correction for neurons, we combined two cell-type-specific data, and performed a paired differential gene expression via limma package by using the design matrix model.matrix(~Cell-type + as.factor(DonorID) + RIN, dataset). We identified differentially expressed 3’UTR isoforms between cell-types as adjusted p-values < 0.05 after multiple test correction via Benjamini-Hochberg method116. We assessed the isoform lengthening or shortening based on if the differentially expressed 3’UTR had the longest or the shortest 3’UTR isoform among all potential 3’UTR isoforms for a gene.Prior to the differential alternative 3’UTR expression analysis, we first corrected quantile normalized neuron gene expression data for the batch effect caused by the usage of different machines for FACS sorting via 70. At 8 weeks of differentiation, we fixed neuron cells in 4% PFA, and permeabilized them by using 0.4% Triton in PBST solution and performed blocking within 10% goat serum dissolved in PBST. After we incubated primary antibodies for TUJ1  overnight in 3% goat serum dissolved in PBST solution at 4°C, we washed the cells three times with PBST. We applied fluorophore-conjugated secondary antibodies  at room temperature for an hour, and applied DAPI staining for 10 minutes.We used the same experimental procedure to generate neurons from phNPCs as described previously117 for segmentation by implementing a nuclear segmentation method in which we set the nucleus diameter as 9 microns. We subtracted the cell outlines from the generated cell masks resulting in a final nuclear mask. To count TUJ1+ cells, we applied CellProfiler to masks generated by Cellpose. We excluded cells if they had nuclei smaller than 6 microns in diameter, which were likely dead cells. In the other image channels, objects with high intensity were considered debris and masked out of the images to aid in threshold and background intensity calculations. For each channel, we corrected images for illumination inhomogeneity, measured background intensity, and images intensities. We classified each cell for the TUJ1 marker if the average intensities were at least 1.5 standard deviations above the median of the background intensity.We performed imaging by using Nikon Eclipse Ti2 with pco.edge 4.2Q High QE sCMOS camera via 10x objective. Prior to segmentation, we used ImageJ to isolate the DAPI channel, transformed them to grayscale and divided images into 4 crops at 2862 by 2862 pixels. We applied Cellpose software26. Since the donors showing the sufficient read counts might be different across editing sites, for each editing site, we used 85% of the donors that every one of them supported editing site with at least 10 counts in each cell type .We tested association of editing rate with genetic variants within +/− 100 kb window of editing site and located in the same gene harboring edit site to perform editing QTL (edQTL) analysis. We included the editing sites if at least 85% of samples had at least 10 read counts  to support the editing site for each cell type. We retained only the variants if at least two heterozygous donors and at least two homozygous minor allele donors, or no homozygous minor allele donors were present as a filtering strategy we previously used−7 for PC1 of global editing and FACS sorter relationship). After each run, we calculated the number of edit sites significantly associated with at least one genetic variant (edSite) at a 5% false discovery rate. Since we found that including 1 PC of global editing maximized the edSite discovery in both progenitor and neurons:We performed cell-type-specific edQTL mapping analysis by using a generalized linear model with binomial distribution that controls for population stratification and unmeasured technical variation. To control for population stratification, we calculated MDS of global genotype and used the first three MDS components as covariates. We controlled the unmeasured technical variation which affects RNA-editing via an optimization strategy. For each cell-type, we utilized principal component analysis (PCA) for unmeasured technical variation, and computed global editing PCs via prcomp function from stats R package by using edit rate values per edit site. During the optimization strategy, we re-performed edQTL analysis by sequentially adding the global editing PCs, first 3 MDSs of global genotype for each cell-type. We detected FACS sorter as a major technical factor impacting editing rate in neurons, and controlled for it for neuron edQTL analysis  analysis, we tested association of quantile normalized PAU values per 3’UTR isoform with the genetic variants within the +/−25 kb window of isoform start and end sites. We retained the genetic variants if they were carried by at least two heterozygous donors and without any homozygous minor allele donors, or if they were carried by at least two minor allele homozygous donors identical to edQTL analysis.120. We controlled the population stratification by using the first three MDS components of global genotype as covariates. We generated the identity by state (IBS) kinship matrix by implementing emmax-kin -v -h -d algorithm by using variants from the non-imputed genotype data, and excluded the variants on the same chromosome via MLMe method121. Similar to edQTL analysis, we performed an optimization strategy to identify the number of PCs of global expression of 3’UTR isoforms, which was computed via prcomp function of stats R package by using quantile normalized PAU values per edit site separately for each cell type. After sequentially adding PCs of global expression of 3’UTR isoforms as covariates to re-run apaQTL analysis, we identified 9 PCs and 6 PCs of global 3’UTR isoform expression in progenitor and neurons showed the highest number of APA site significant associated with at least one genetic variant at 5% FDR. As a result, we applied the following models per cell-type:We performed apaQTL mapping analysis by controlling for population stratification and cryptic relatedness via a linear mixed effects regression model by using EMMAX softwareThe optimized model we used for progenitors was:The optimized model we used for neurons was:65 and we defined an error term We conducted same pipeline to discover apaQTLs in fetal bulk data and the optimized model we used for fetal bulk was:π1 statistics89 by using the qvalue R package122. Similarly, for edQTL/apaQTL to eQTL comparison, we estimated the proportion of progenitor and neuron primary SNP-edGene pairs, gene including edit site or SNP-aGene pairs, gene including APA site pairs that are non-null associations in progenitor and neuron eQTL data (π1) by using the corresponding p-values to SNP-Gene pairs detected in both datasets.For cell-to-cell comparison, the proportion of progenitor and neuron primary SNP-edit site pairs or APA site pairs that are non-null associations in neuron and progenitor edQTLs or apaQTLs data was estimated by utilizing the corresponding p-values to SNP-edit site pairs or APA via 94.To predict IRAlu RNA hairpin secondary structure, we extracted RNA-sequences between two Alu repeats in opposite directions, and generated two sequences corresponding to different alleles of a given genetic variant. These sequences were provided to viennaRNA RNAfold software, and secondary structures were predicted and visualized via graphical output from the software22, for each edSite (edit site significantly associated with at least one genetic variant), we extracted the RNA sequences including genetic variants within +/− 800 bp window of the edSites. We included only sequences within gene start and end coordinates within this window; therefore, some sequences were shorter than 1601 bp. We matched the alleles for variants within this genomic window if the LD r2 between them was greater than 0.8. We first converted sequences files to bpseq via contrafold software123. Next, providing these bpseq file formats for bpRNA software124, we predicted RNA secondary structures including bulge, hairpin loop, interior loop, multiloop, stem within these RNA-sequences. To assess enrichment of significant edQTLs within RNA-secondary structures, for each structure, we randomly selected non-significant eQTLs in equal number of significant edQTLs in each structure category, which were matching minor allele frequency (MAF) and distance from edit sites with 50% standard error of both features for 1,000 times. We computed the enrichment p-value for each RNA structure as a number of observations in which the overlap of nonsignificant edQTLs with the RNA structure was higher than the overlap of significant edQTLs with the RNA structure divided by 1,000.As described in previous study26, and cell-type-specific primary edQTLs and apaQTLs discovered in the current study for comparison. For distance from TSS/TTS, we calculated the distance between genetic variant and TSS/TTS of genes for which expression was tested in eQTLs, the distance between genetic variant and TSS/TTS of genes where alternative splicing tested was located in sQTLs, the distance between genetic variant and TSS/TTS of genes where edit site tested was located in edQTLs, and the distance between genetic variant and TSS/TTS of genes where APA site tested was located in apaQTLs considering the expression of the gene in either forward or reverse strand. For distance from splice sites, we computed the distance of genetic variants from either intron start and end sites for all potential alternative splicing events for a given gene, and used the shortest distance for comparison. To compare enrichment of primary QTLs within chromatin accessibility sites, we assessed the overlap of genetic variants within chromatin accessibility regions which were differentially accessible in progenitors/neuron70 for progenitor and neuron QTLs, respectively. To compare enrichment of primary QTLs within RBP binding sites, we utilized CLIPdb data, and assessed the overlap of genetic variants with this dataset88. Pairwise comparisons were performed via chi-square test.We extracted cell-type-specific primary eQTLs and sQTL from our previous study125. Summary statistics from GWAS for schizophrenia (SCZ)3, educational attainment (EA)93, major depression disorder (MDD)126, cortical thickness and surface area from UKBB6, and the ENIGMA project5, neuroticism127, IQ4, cognitive performance (CP)93, bipolar disorder (BP)1, attention-deficit/hyperactivity disorder (ADHD)128, Parkinson’s disease (PD)129 and Alzheimer's disease (AD)130 were used. Index GWAS SNPs were defined as two LD-independent genome-wide significant GWAS signals  with pairwise LD r2 < 0.2 calculated by using European population of 1000 Genomes (phase 3). For comparison of colocalizations with eQTLs and sQTLs26, we used LD r2 < 0.5 to detect index GWAS variants. To perform colocalization analysis, first, we detected two variants (one from index variants of GWAS and one from index variants of the QTL study) which had pairwise LD r2 greater than 0.8 based on either European population or our study). Then, we re-performed the edQTL/apaQTL analysis by conditioning on GWAS index variant, and if the association between edit rate/APA usage and the QTL index variant was no longer significant, we considered these two loci as co-localized.We applied LD-thresholded colocalization analysis to find edQTLs and apaQTLs colocalized with the traits for each cell type separatelySupplement 1Supplement 2Supplement 3Supplement 4Supplement 5Supplement 6Supplement 7Figure S1. Validation of RNA-editing sites discovered in RNA-seq data via ATAC-seq data.(A) The number of donors supporting that genomic DNA includes unedited alleles are shown on the x-axis. The percentage of unedited allele per edit sites is shown on the right y-axis (red) and the total number of ATAC-seq supporting unedited allele is given on the left y-axis (blue).(B) Example ATAC-seq reads for RNA-editing sites chr19:18365401:A>G, chr6:163422494:A>G and chr2:55406089:A>G.Figure S2. ALU editing index is correlated with ADAR gene expression and developmental time.(A) Correlation of AEI values with VST normalized ADAR1, ADAR2 and ADAR3 expressions in progenitors (purple) and neurons (green). Correlation coefficients (r) and p-values are shown per relationship.(B) Alu editing index across different gestational weeks in fetal bulk brain data.ADAR1-3 gene expressions per cell-type. Correlation efficient (r) and p-values are reported.(C) Correlation of technical confounders with Figure S3. Comparison of cell-type-specific molecular QTLs(A) Overlap of primary eQTLs, sQTLs, edQTLs and apaQTLs with chromatin accessibility regions are RBP binding sites per cell type. Chi-square test p-values (p) are reported for each pairwise comparison.CEP250 gene mediated its expression. Genomic tracks for progenitor eQTLs and apaQTL are shown, and tracks are colored based on relative LD r2 for the variant rs2236160, which is the nearest variant to APA site.(B) APA site (chr20:35511626-35519280) within the CEP250 gene expression via APA site. Lower cartoon illustrates the relationship between variant (X) within the APA site, mediator APA site (M) and CEP250 gene.(C) Posterior probability of forward, independence and reactive models for mediation of Figure S4. Comparison of GWAS colocalizations across cell-type-specific molecular QTLs(A) Number of brain-relevant trait-loci pairs are found in eQTLs, sQTLs, edQTLs and apaQTLs, and shared pairs with eQTLs are indicated by black color, and pairs which were detected by using QTLs other than that eQTLs are indicated by blue color.(B) Number of brain-relevant trait-loci pairs are found in cell-type-specific eQTLs, sQTLs, edQTLs and apaQTLs in addition to loci explained by adult brain eQTLs.