We found that Tempus™ tubes maintained a slightly higher RNA quantity and integrity relative to PAXgene® tubes at suboptimal tropical conditions. Both PAXgene® and Tempus™ tubes gave similar RNA purity (A260/A280). Additionally, Tempus™ tubes preferentially maintained the stability of mRNA transcripts for two reference genes tested, Succinate dehydrogenase complex, subunit A (SDHA) and TATA-box-binding protein (TBP), even when RNA quality decreased with storage length and temperature. Both tube types preserved the rRNA transcript 18S ribosomal RNA (18S) equally. Our results suggest that Tempus™ blood RNA collection tubes are preferable to PAXgene® for whole-blood collection in suboptimal tropical conditions for RNA-based studies in resource-limited settings.Whole-blood-derived transcriptional profiling is widely used in biomarker discovery, immunological research, and therapeutic development. Traditional molecular and high-throughput transcriptomic platforms, including molecular assays with quantitative PCR (qPCR) and RNA-sequencing (RNA-seq), are dependent upon high-quality and intact RNA. However, collecting high-quality RNA from field studies in remote tropical locations can be challenging due to resource restrictions and logistics of post-collection processing. The current study tested the relative performance of the two most widely used whole-blood RNA collection systems, PAXgene Gene expression profiles from whole-blood-derived RNA have proven useful as a molecular signature reflecting physiological and pathological changes in the body . Immune ® and Tempus™ [® and Tempus™ in specific suboptimal conditions [Commercially available blood collection systems with additives that stabilize RNA have been developed to address RNA stability, resulting in significantly enhanced quantity and quality of RNA extracted from whole blood ,16. The  Tempus™ ,18. Thes Tempus™ . Those s Tempus™ . However Tempus™ . Severalnditions ,20. Ther® or Tempus™ systems.In this study, we compared the effects of extended storage times and high temperatures simulating suboptimal tropical conditions before freezing of whole-blood RNA stabilized in either the PAXgene® QIAGEN/BD; Tempus™ Applied Biosystems) or magnetic-bead-based  systems. RNA concentration (ng/μL) and purity (A260/A280 and A260/A230) were evaluated by spectroscopic quantification using NanoPhotometer® N60 . RNA integrity number (RIN) was determined using an Agilent 2100 Bioanalyzer (Agilent Technologies). Total RNA yield (ng) was normalized to whole-blood volume collected in each blood RNA collection system . All RNA samples were extracted fresh post-collection for the comparison of RNA isolation protocols.The quantity, quality, and purity of RNA extracted were evaluated using two common RNA isolation systems : spin-cop = 0.875, RIN: p = 0.124, A260/A280: p = 0.101, A260/A230: p = 0.318, MagMAX™ vs. columns, unpaired t-test) , suggesting that the PAXgene® tubes gave higher RNA yields than Tempus™ when extracted in optimal laboratory conditions (™) RNA purification system compatible with PAXgene® and Tempus™ systems were used for the rest of the extractions.We found the RNA isolation protocol did not affect the amount of total RNA extracted, the RNA quality, or the purity of RNA  . Additionditions . Given t® and Tempus™ tubes. To simulate suboptimal tropical conditions, matched whole-blood samples were collected in either PAXgene® or Tempus™ tubes and stored at different temperatures  for different lengths of time  before final storage at −80 °C for later extraction. These samples were compared to matched samples immediately frozen at −80 °C for later extraction (D1/Control) or unmatched samples collected and processed post collection immediately in optimal laboratory conditions (D0/Fresh). The tube type had no effect on RNA quantity  when extracted in Fresh  or Control  conditions . Tempus™ tubes had a significant effect on the RNA yield (p = 1.15 × 10−6), and temperature variation in tube type significantly impacted RNA yield ; p = 0.0004 (30 °C); p = 0.033 (35 °C); p = 0.023 (40 °C), ™ tubes gave higher RNA yields when measured with Agilent Bioanalyzer ; p = 9.3 × 10−9, 2R = 0.518 (Tempus™)); To investigate these data further, we applied three multiple linear regression models to evaluate the effects of explanatory variables  on normalized RNA concentrations, A260/A280 ratios, and RIN values . Model 1p = 0.480, (Fresh); p = 0.111, (Control), paired t-test). However, at tropical storage and temperature conditions, the tube type (p = 0.011) and the storage temperature on tube type (p = 0.003) had significant effects on A260/A280 ratios as per the multiple linear regression model (p = 0.007), 35 °C (p = 0.011) and 40 °C (p = 4.89 × 10−5), ™ blood tubes compared to PAXgene® tubes in suboptimal tropical conditions. These data also showed that RNA yields significantly decreased with increasing temperature in both PAXgene® and Tempus™ tubes.When considering RNA purity, all extracted RNA samples had A260/A280 ratios >2 regardless of tube type, indicating a high purity under all test conditions  and control conditions  compared to Tempus™. The electropherograms showed comparable results for different conditions applied on PAXgene® and Tempus™ systems  when RNA was extracted post collection immediately or when samples were maintained at the optimal storage conditions as recommended by the manufacturers.We determined if high RNA quality was preserved using PAXgeneion tube A. PAXgen systems C. RibosoR2 = 0.797) of the variation in RIN values. RIN values decreased significantly over time (p = 0.045), decreasing by 0.862 per day . RIN values also decreased significantly with the increasing of temperature (p < 0.0001), decreasing by 0.921  for each degree Celsius (°C) increase in temperature. These data demonstrated that RNA integrity significantly decreased with the length of storage time and temperature.A multiple linear regression model was built to explore the effects of suboptimal tropical conditions on RNA integrity to determine the effect of tube type, storage temperature, and storage time on RNA integrity  , Model 3™ than PAXgene® tubes at 30 °C (p = 0.0001) and 35 °C (p = 0.001). However, no significant differences in RIN at 25 °C (p = 0.787) or 40 °C (p = 0.399) was found for tube types  under certain suboptimal tropical conditions compared to PAXgene® tubes.The impact of storage time on RNA integrity  was also analysed at each temperature for each tube type . RIN valbe types A. In agrC p = 0.39 was fou® and Tempus™ tubes using RT-qPCR. We tested the relative mRNA abundance of two human reference genes, Succinate dehydrogenase complex, subunit A (SDHA) and TATA-box-binding protein (TBP), and one rRNA transcript, 18S ribosomal RNA (18S). The RNA concentration of all samples was normalized at pre-cDNA synthesis . Hence, an increasing cycle threshold (Ct) value indicated a decreasing relative transcript quality rather than abundance [In order to validate our RNA quality measurements, we quantified mRNA and rRNA extracted from PAXgenebundance . We testp = ns, paired t-test: D1/Control, 100–200 bp) or with larger amplicons , 25.48 (SDHA), 27.39 (TBP)) and in Tempus™ tubes (21.33 (18S), 25.59 (SDHA), 27.59 (TBP)) were largely consistent. In addition, we found no statistically significant difference between matched fresh and control samples between PAXgene® and Tempus™ tubes  collected in PAXgene® tubes were significantly impacted by storage time and temperature compared to Tempus™ tubes  as the difference between samples collected under suboptimal tropical conditions and the mean of the control samples. Strong statistically significant correlations were found between ∆Ct and RIN for all tested genes  was indicative of the presence of PCR inhibitors [® or Tempus™ tubes when the samples were diluted below 60 ng/uL  ,24, coulhibitors . There w60 ng/uL . These d™ tubes maintained a higher RNA quantity and integrity comparatively to PAXgene® tubes when RNA is stored in suboptimal tropical conditions. Furthermore, Tempus™ tubes maintained stability of mRNA in conditions where RNA samples were heavily degraded as indicated by RIN. Taken together, this study establishes that the Tempus™ blood RNA collection system resulted in a better quality of RNA and enhanced stability of mRNA when whole-blood samples are stored under suboptimal tropical conditions.In summary, our data showed that Tempus® and Tempus™ blood RNA stabilization tubes for preserving RNA quantity, purity, quality, and gene transcript stability at suboptimal tropical conditions.Gene expression profiling with molecular techniques such as RT-qPCR and next-generation sequencing requires high-quality intact RNA. It is well-established that the pre-analytical variables in blood sample collection and processing have profound effects on RNA quality that may consequently introduce substantial technical bias for molecular analysis ,25. Pre-® blood RNA tubes effectively stabilize RNA for up to three days at room temperature, five days at 2–8 °C, and up to 11 years at −20 °C or −70 °C, whilst Tempus™ blood RNA tubes stabilize RNA for up to five days at room temperature and at least a week at 4 °C or −80 °C for long-term storage [According to the respective manufacturers, PAXgene storage ,27. Dual® and Tempus™. These conditions were selected to simulate conditions in field sites in tropical or subtropical regions for a 10-day field trip, representing a challenging situation for preserving RNA. The performances of commercially available kits with columns (spin-column-based) and MagMAX™ (magnetic-beads-based) protocols were used to extract RNA from blood collected in PAXgene® and Tempus™ tubes. The total RNA yield, RNA integrity (RIN), and purity were used as performance measures.This study determined the impact of warm tropical temperatures  and prolonged storage times  on total RNA yield, purity, quality, and transcript stability of the two most widely used commercially available blood RNA stabilizing systems, PAXgene™ extraction methods had no significant differences in normalized total RNA yields, RIN, and A260/280 ratios in PAXgene® and Tempus™ tubes. However, total RNA obtained from Tempus™ tubes was significantly lower than PAXgene® tubes. We observed comparable average OD 260/280 ratios and RIN > 7 for column and MagMAX™ extraction methods for blood collected in PAXgene® and Tempus™ tubes. One limitation of experiment A was that only two biological subjects were evaluated for Tempus™ column vs. MagMAX™ extractions due to the unavailability of Tempus™ blood collection tubes of the same batch. An A260/A280 ratio between 1.8 to 2.2 indicates highly purified RNA with minimum DNA contamination [® or Tempus™ systems. Higher RIN value indicates better RNA integrity, and RIN values above seven are considered ideal for high-throughput downstream applications [™ RNA extractions were used as the method of RNA purification for the rest of the study.In experiment A, both columns vs. MagMAXications ,30. Howeications ,31. MagM® and Tempus™ tubes gave similar RNA yields and purity (A260/A280) when the samples were extracted fresh or post freezing, whilst RIN values were significantly higher in blood samples extracted from PAXgene® than Tempus™. The reason for this difference is not yet known. Overall RIN values were much lower in control samples compared to freshly extracted RNA. A previous study investigating the impact of storage duration  and storage temperatures  of whole blood collected in heparin tubes on the qualities of DNA and RNA showed that RNA integrity declined dramatically when the samples were frozen [Both PAXgenee frozen . Freezine frozen .™ tubes result in a higher RNA yield than PAXgene®, while we demonstrated that the tube type and temperature significantly affect RNA yields. However, similar RNA purities were obtained from both tube types. A similar study by Duale et al., comparing PAXgene® vs. Tempus™ tubes stored for 0, 2, 5, and 7 days at RT (~22 °C) and then stored at −80 °C until extraction, showed that RNA yields collected in the Tempus™ tubes were consistently higher than PAXgene® tubes. However, RNA quality  was similar in both systems [™ tubes compared to PAXgene® tubes [® extraction kits [™ extraction kits [The linear regression model on normalized RNA concentrations indicated that the Tempus systems . Consiste® tubes ,35,36,37e® tubes . Historiion kits ,17,34,35ion kits ,22,39.™ tubes had improved RINs compared to PAXgene® at suboptimal tropical conditions. RNA integrity was influenced mainly by increased storage temperatures at higher temperatures. It has been well-documented that RNA molecules are sensitive to physical degradation due to high temperatures [™ and PAXgene® blood RNA stabilizing systems have not been previously studied. Our data suggested that good quality RNA (average RIN >5) can be obtained in both tube types when samples are kept at 25 °C room temperature for up to 5 days of storage duration. Overall, we demonstrated that satisfactory amounts of good-quality RNA can be achieved using blood RNA stabilizing systems in warm tropical temperatures (25–30 °C) and at storage times up to a week. When the storage temperatures are above 30 °C, the RNA quality drops significantly and may not be adequate in downstream applications. RT-qPCR data further demonstrated that PAXgene® tubes do not preserve mRNA with the same efficiency as Tempus™ tubes, but both tubes equally preserved rRNA from degradation in suboptimal tropical conditions.Our results indicated a gradual decrease of RNA quality in terms of RIN values over storage duration and increased temperatures in both tube types. However, RNA extracted from Tempuseratures . However™ blood RNA collection system resulted in higher-quality RNA and maintained more consistent stability of mRNA when whole-blood samples were stored under suboptimal tropical conditions as compared with the PAXgene® system.In most low-resource settings, microscopy and serological assays such as ELISAs remain the standard methods for diagnosis of tropical infections, especially in low-income and middle-income countries, despite limited sensitivity and specificity. More sensitive molecular methods have potential to inform disease, diagnosis, and treatment and to facilitate field-based intervention and biobanking studies . This report provides important information to facilitate such studies. In particular, our data show that the Tempus™ (3 mL) Blood RNA tubes  or PAXgene® (2.5 mL) Blood RNA tubes  according to manufacturer’s instructions. Briefly, whole blood was collected directly into each tube by standard venepuncture and immediately shaken vigorously for 10 s to ensure that the stabilizing reagent made uniform contact with the sample as per the manufacturer’s instructions.Whole blood was collected from healthy adult volunteer donors into Tempus™. All blood samples stored in PAXgene® tubes were thawed for 2 h at room temperature, whereas Tempus™ tubes were thawed for 30 min on ice prior RNA isolation.Experiment A compared RNA yields, purity, and integrity from MagMAX™ extractions using spin columns or magnetic beads . All sam® tubes was extracted according to manufacturer’s instructions using PAXgene® Blood RNA Kit , which included DNase I treatment. Total RNA was eluted in 40 μL elution buffer. According to the manufacturer’s instructions, total RNA from blood collected in Tempus™ tubes was extracted using the Tempus™ Spin RNA Isolation Kit . RNA was eluted in 90 μL of elution solution. DNase treatment was an optional step in the Tempus™ column extraction system and therefore not included, as genomic DNA contamination using this procedure is minimal (less than 0.05% by weight) according to the manufacturer’s specifications.Total RNA from whole blood collected in PAXgene® Blood RNA Tubes using MagMAX™ for Stabilized Blood Tubes RNA Isolation Kit  according to the manufacturer’s protocol, including a TURBO™ DNase and protease step. RNA was isolated using MagMAX™ for Stabilized Blood Tubes RNA Isolation Kit, compatible with Tempus™ Blood RNA tubes  following the manufacturer’s protocol with TURBO™ DNase treatment. All extracted RNA samples were stored at −80 °C. The technical characteristics of each extraction method are summarised in Total RNA from whole blood was extracted from PAXgene® N60 . RNA integrity was measured using the Agilent 2100 Bioanalyzer (Agilent Technologies) and the Eukaryote Total RNA Nano assay, complementing RNA 6000 NanoChip kit , following the manufacturer’s instructions. The RNA integrity number (RIN) was calculated by the Agilent 2100 Expert software . The RIN ranges from 1 to 10; an RIN of fully intact RNA is 10, and an RIN of completely degraded RNA is 1.RNA concentration (ng/μL), A260/A280, and A260/230 ratios to indicate RNA purity were measured by spectroscopic quantification using NanoPhotometer™ First-Strand Synthesis System™ . All samples were primed with 37.5 ng of random hexamers and 10 mM dNTPs at 65 °C for 5 min and then 4 °C for 1 min. Reverse transcription was then performed using the SuperScript IV™ reverse-transcriptase (SSIV) for 10 min at 23 °C, 10 min at 50 °C, and 10 min at 85 °C. SSIV concentration was assessed at 20 U (20 units/μL RNA) reactions compared with 5 U reactions (All reverse transcription (RT) reactions were conducted using the SuperScript IVeactions  as previeactions  using 5 ® SuperMix , which facilitates excellent reaction efficiencies [™ [2 dilutions of pooled cDNA from 1 × 106 unstimulated PBMCs. The PCR cycling program included an enzyme activation step at 95 °C for 2 min and then 40 cycles of annealing and extension at 95 °C for 15 s and 60 °C for 30 s, respectively. The cycle threshold (Ct) value was set to 0.3 ΔRN, and a pooled cDNA positive control was included across all plates to ensure reproducibility. qPCR was performed using the QuantStudio 5 Real-Time PCR system running QuantStudio Design and Analysis Software .qPCR was run with 5 μL total reaction volume using SsoAdvanced SYBRciencies . All reancies [™  . Unpaired t-tests were used to compare the data from spin-column-based and magnetic-bead-based MagMAX™ RNA extractions to determine any significant difference between RNA isolation systems for total RNA yield, A260/A280 ratios, and RNA integrity. Statistical significance was defined using p-values < 0.05. Normal distribution of data and normality of residuals were evaluated using the Shapiro–Wilks test.Statistical analyses were performed using R statistical software  for RNA extracted on fresh and control conditions. Whole-blood samples processed post collection immediately at optimal laboratory conditions without −80 °C storage are referred to as D0/Fresh. Samples frozen immediately at −80 °C for later extraction are referred to as D1/Control.The average Ct values for each replicates/triplicate and targets with Ct-values > 35 or undefined were considered beyond the limit of detection (LOD) and removed from the analysis . Paired ® and Tempus™ tube types, we performed a two-way analysis of variance with multiple comparisons.Multiple linear regression models were fit to investigate the overall relationship of independent variables at experimental conditions  on normalized RNA yields, A260/A280 ratios, RIN values, or Ct values. To further explore the significant interaction between temperatures and tube type, separate analyses were performed at each temperature. To compare the changes of Ct values on control and test conditions on PAXgene™ tubes is the preferred system of choice for gene expression and molecular studies in rural and remote resource-limited settings where electricity and storage facilities are compromised. Our findings are especially relevant to research on RNA biology, which could help future directions of diagnosis, treatment, and interventions against diseases that are prevalent in tropical countries, including neglected tropical diseases.We conclude that collection of whole blood samples in Tempus


The goal of this Special Issue on “ABC Transporters in Human Diseases”, for which I was invited as a Guest Editor, was to provide an overview of the state-of-the-art research, understandings, and advances made in recent years on human diseases implicating ATP-binding cassette (ABC) transporters. Mammalian ABC transporters form a protein superfamily composed of 49 members , most ofABCC11 and ABCA13 identified in patients can be associated with breast cancer, even if the data were not found to have strong statistical relevance [ABCA genes in a small cohort of 105 patients with breast cancer [In the field of multidrug resistance (MDR) and cancer, findings of Low et al. suggested that ABCC1 (MRP1) AND ABCC4 (MRP4) are expressed in breast cancer cell lines and that these transporters are implicated in cell proliferation and migration, respectively . Hlaváč elevance . Using pt cancer . Lower et cancer . The rolt cancer . Sasithat cancer .Addressing the topic of breast cancer resistance protein (BCRP/AGCG2), another ABC transporter also implicated in MDR and gout pathogenesis, Toyoda et al. reported two new genetic variations (M131I and R236X) identified in family members affected by hyperuricemia and gout and responsible for urate transport defects of the transporter . On thisChai et al. described that amyloid-beta peptides accumulating in the brain of patients with Alzheimer’s disease (AD) are substrates of ABCB1/MDR1/P-gp , settinghttps://www.rcsb.org/, accessed on 4 April 2022), mostly owing to the recent explosion of cryogenic electron microscopy (cryo-EM), which now allows a resolution below 4 Å, almost making X-ray crystallography outdated [biochemical and structural cryo-EM studies with molecular dynamics simulations and other biophysical methods” [Beyond using cell models and sequence alignments, a striking aspect in the recent body of literature on ABC transporters, including most of the contributions gathered in this Special Issue, is the use of 3D structural analyses to unravel the molecular mechanisms regulating the folding and the function of ABC transporters. Indeed, these approaches are very useful for understanding the cell biology of ABC transporters in normal and pathological conditions, as well as to study the potential direct interactions of small molecules with these transporters. Such approaches are now largely facilitated by the exponentially growing numbers of published 3D structures of ABC transporters in several conformational states (PDB structures available at outdated ,25. As sFinally, I would like to focus on canalicular ABC transporters specifically expressed at the canalicular membrane of hepatocytes, in which I am particularly interested in the frame of our research projects. These transporters include ABCB4, ABCB11, and ABCG5/G8 implicated in the secretion of phosphatidylcholine, bile acids, and cholesterol into bile, respectively . Regardi


Headspace solid-phase microextraction (HS-SPME) was optimized to study the volatile fractions of sourdoughs. The parameters selected were 60 °C, 15 min of equilibrium, and 30 min of extraction. It was found that the addition of Cava lees resulted in higher concentrations of volatile compounds , with the highest values being reached with the 2% Cava lees. Moreover, Cava lees contributed to aroma due to the compounds usually found in sparkling wine, such as 1-butanol, octanoic acid, benzaldehyde and ethyl hexanoate. The volatile fraction is of great importance for the organoleptic quality and consumer acceptance of bread. The use of sourdough improves the sensory profile of bread, as well as the addition of new ingredients to the fermentation. Cava lees are a sparkling wine by-product formed of dead microorganisms, tartaric acid, and other inorganic compounds, rich in antioxidant compounds as well as β-glucans and mannoproteins. The aim of this study was to evaluate the effect of different concentrations of Cava lees (0–2%  Sourdough is the result of fermenting a mixture of flour and water, and it is traditionally used during bread making as a leavening agent, influencing bread quality ,2. This The volatile profile is very significant for the organoleptic quality and consumer acceptance of bread. More than 500 volatile compounds have been reported in bread , while sMoreover, sourdough bread flavor strongly depends on the fermenting microbiota that produces a range of secondary metabolites, as well as on the enzymatic and autoxidation of flour lipids, and the Maillard reaction ,2,3. In Saccharomyces cerevisiae), tartaric acid, and other adsorbed compounds [Lees are a residue formed during the ageing process of Cava (Spanish sparkling wine) and consist, mostly, of dead microorganisms  between 50 °C and 60 °C  and 60 ± 1.0 (esters)). Since 60 °C was the temperature at which the number of compounds extracted was higher, it was the selected temperature for the HS-SPME in sourdough.To evaluate the effect of the heating temperature, four temperatures were selected: 20 °C, 50 °C, 60 °C, and 75 °C. The impact of heating temperature on the extraction of the volatile compounds from sourdough is shown in For the optimization of the HS-SPME method, three periods of time were assessed for the equilibration of the samples: 10 min, 15 min and 30 min b. It canFour periods of time were tested for extraction: 20 min, 30 min, 40 min and 50 min. The TN and TA of the volatile compounds extracted depending on extraction time are shown in As shown in The effect of different percentages of Cava lees on the volatile profile were assessed following the optimized HS-SPME method . The volatile compounds of Cava lees were also analyzed by HS-SPME. During the sourdough fermentation, volatile compounds such as alcohols, acids, aldehydes, ketones, and esters were formed .p < 0.05). As a general rule, with higher amounts of Cava lees added to the sourdough formulation, there was a greater production of volatile compounds, especially alcohols and esters (p < 0.05). In fact, Cava lees were characterized by esters (489.38 ± 60.34 mg/kg).Overall, the control and 0.5% Cava lees samples showed no significant differences in the concentration of volatile compounds reported, except for esters . The dominant alcohol quantified was 1-hexanol in sourdoughs formulated with and without Cava lees. Additionally, 1-butanol was the dominant alcohol in sourdoughs with 2% Cava lees (249.65 ± 29.86 mg/kg).Most of the alcohols identified in the sourdough samples increased their concentration with the addition of Cava lees, reaching the highest values at 2% Cava lees  was the most prevalent acid in the control sourdough, its concentration decreased with the addition of Cava lees, and was not detected in the 2% Cava lees sourdough. Pentanoic and heptanoic acid followed the same trend, being identified in the control sourdough but not in the sourdoughs with Cava lees. The opposite occurred to acetic acid, with a lower concentration in the control sourdough (66.08 ± 9.99 mg/kg) that increased with lees, reaching values of 246.10 ± 14.64 mg/kg with the 2% Cava lees. w/w). The ketones quantified in this study were acetoin and 2-undecanone, representing 1% of the total volatile fractions in all samples  and amino acid metabolism  ,31, and Lactiplantibacillus plantarum (formerly Lactobacillus plantarum), can produce 1-hexanol via pathways other than the reduction of hexanal and that it can facilitate the production of hexanal, resulting in more substrate to transform into the corresponding alcohol. The dominant alcohol quantified was 1-hexanol in the control sourdoughs and in the sourdoughs formulated with Cava lees . 1-Hexan1-Butanol was the dominant alcohol in sourdoughs with 2% Cava lees (249.65 ± 29.86 mg/kg). It can be observed that the mentioned compound increases its concentration by the addition of Cava lees. This higher alcohol has been reported in wine fermentation ,40 and ip < 0.05), which may be due to higher survival rates among the microorganisms fermenting the sourdough. In fact, it has recently been reported that Cava lees have a growth-promoting effect on different species of LAB in vitro and in sourdough [In summary, most of the alcohols identified in the sourdough samples increased their concentration with the addition of Cava lees . In addiourdough ,20. p < 0.05) (Acids are produced during fermentation throughout the catabolism of long-chain fatty acids . The tot < 0.05) . In geneMoreover, there are acids that were not detected in the control sourdoughs that increased in concentration with the addition of lees, as was the case of decanoic acid . DecanoiAcetobacter cerevisiae) [Nevertheless, short-chain fatty acids (SCFAs) (butyric and pentanoic acids) decreased in concentration with the addition of lees. In fact, pentanoic acid was not detected in the sourdoughs with Cava lees, even though both SCFAs are volatiles of fermentation origin that have been reported in wheat sourdough ,7. IndeeAldehydes are formed by unsaturated fatty acid decarboxylation as well as lipid oxidation ,5,7,31. w/w) were added to the sourdough formulation. Moreover, benzaldehyde was only identified in sourdoughs with 1% and 2% Cava lees. It is an aldehyde commonly found in sparkling wine [Nonanal, an aldehyde that has also been reported in sparkling wine , showed ing wine ,32 and iing wine . Neverthing wine ,38.Regarding ketones, acetoin was the main one in all samples . AcetoinAs for 2-undecanone, this ketone has only been reported once in gluten-free hemp-enriched sourdough bread , but it Esters were the most prevalent volatiles in all types of sourdough, especially in samples with Cava lees . As a geSome of the esters reported in sourdough with Cava lees were not found in the control (ethyl laurate and decyl acetate). These compounds have previously been reported in wine and sparkling wine, being dependent of the yeast strain as well as the grapes used ,50,51,52Overall, all sourdoughs shared 14 volatile compounds . MoreoveLastly, a PCA was performed with the aim to observe the differentiation between the produced sourdoughs . After tFor the sourdough formulation, a commercial wheat flour was used  with the following composition (g/100 g): carbohydrates 72.0, fat 1.5, fibre 2.0, protein 11.5, and moisture 15.0.Sourdoughs were prepared by mixing 100 g of flour and 100 mL of sterile distilled water, without the inoculation of starter culture bacteria or yeasts, and incubated at room temperature for 24 h, following the method described by Martín-Garcia et al. 2022) . Briefly22 . BrieThe optimization of extraction of volatile compounds was performed using headspace solid-phase microextraction (HS-SPME) and it was carried out using a 2 cm long divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS) fiber supplied by Supelco . To that end, a control sourdough was produced, and samples of 5 g were prepared. Before extraction, the fiber was conditioned according to the manufacturer’s recommendations. After equilibration at a specified temperature  for a specified time , the fiber was exposed to the sample headspace for a specified time . A total of 48 runs were analyzed in triplicate for the optimization procedure based on a multilevel factorial design. Once the HS-SPME method was optimized, it was applied to the assessment of the different sourdoughs produced with and without lees. An internal standard , 100 µg/mL) was added (100 µL) for semi-quantification. Chromatographic analysis was carried out in a 6890N Network GC system  coupled to an MS Agilent technologies 5973 Network selective detector . Helium was used as a carrier gas. Separations were accomplished in a DB Wax USN 125-7031 column (30 m × 0.25 mm × 0.25 µm) . A splitless injector suitable for SPME was used. After extraction, the fiber was removed from the headspace vial and inserted directly into the injection port of the GC. The SPME fiber was thermally desorbed for 2.5 min at 260 °C.The initial temperature was 40 °C for 5 min, and this was subsequently increased at 4 °C/min using the splitless injection mode for 5 min up to 250 °C. GC-MS detection was performed in complete scanning mode (SCAN) in the 40–350 amu mass range with two scans per second. Electron impact mass spectra were recorded at an ionization voltage of 70 eV and an ion source of 280 °C. The volatile concentrations reported were calculated by dividing the peak area of the compounds of interest by the peak area of the internal standard . The relative response factor was considered to be 1. Identification was performed by comparison of the mass spectra with the mass spectra library database Wiley 6.0., and retention times with those of pure standards when they were available.p-value of ≤0.05. Principal component analysis (PCA) was also performed to determine the differences between the sourdoughs.All assays were performed in triplicate and in a randomized run order. The statistical analysis was performed using the Prism 9 version 9.1.2 (225)  statistical package. The results are reported as the means ± standard error (SE) for parametric data. A three-factor analysis was conducted on the optimization results. A one-way ANOVA and comparison of the means were conducted using Tukey’s test with a confidence interval of 95%. Significant results were identified with a After the optimization of the HS-SPME parameters, it was found that the best temperature of extraction was 60 °C, with 15 min of equilibrium and 30 min of extraction for wheat sourdough. Then, when applied to the studied sourdoughs, it was found that acids and esters were the most prevalent compounds quantified, followed by alcohols, aldehydes, and finally ketones. Regarding particular compounds, butyl butyrate, ethyl octanoate, ethyl decanoate, octanoic acid, and 1-hexanol were the most prevalent volatiles quantified.In general, the addition of Cava lees caused an increase in the concentration of the volatile compounds typically found in sourdough, such as 1-hexanol, acetic acid, hexanal, and ethyl decanoate. Additionally, compounds usually reported in sparkling wines were also identified in sourdough samples formulated with Cava lees, such as 1-butanol, octanoic acid, benzaldehyde, and ethyl hexanoate. Therefore, it can be concluded that Cava lees not only promote the production of sourdough volatile compounds, but they also provide volatiles frequently found in sparkling wines, which supports the fact that lees can retain volatile compounds on their surface. Moreover, the ability of Cava lees to retain odorous volatile compounds could be of great interest for the food and aroma industries that could revalorize and use such by-products, contributing to a circular economy.


Human spermatogenesis is a qualitatively and quantitatively different process than that observed for most other mammals. In contrast with most other mammals, human spermatogenesis is characterized by reduced quantitative production and more abnormal sperm morphology. Until recently, direct evaluation and observations of human sperm production has been limited and the majority of scientific knowledge regarding spermatogenesis was derived from rodent models of study. Unique opportunities to observe human spermatogenesis have occurred as a consequence of the treatment of severe male infertility. These patients have sperm production so limited that no sperm reach the ejaculate so their fertility treatment involves surgical sperm retrieval from the testis, coupled with use of those sperm with advanced assisted reproductive techniques. Treatment of men with severe male infertility has enhanced identification of new genetic abnormalities that may cause this condition, since they now seek medical care. Three key novel concepts have resulted: (a) spermatogenesis is spatially heterogeneous in the human male, especially when sperm production is compromised, (b) genetic abnormalities are common in men with severe male infertility, particularly in men with diffuse maturation arrest and (c) rodent studies may not be an ideal model for understanding human male infertility. Scientific understanding of human spermatogenesis has been enhanced by these clinical observations. With the advent of assisted reproductive technologies, the ability to treat severe forms of male infertility has been significantly enhanced. Spermatozoa that would have had limited chances of oocyte fertilization can now routinely be used for fertilization and subsequent pregnancy. In addition, the ability to make direct observations from use of single male gametes in assisted reproduction further increases our ability to understand the role of a male factor in human fertility.The treatment of severe male factor infertility, where sperm production is so limited that no viable sperm are present in the ejaculate requires surgical sperm retrieval. This surgical intervention has also given us opportunities to directly investigate the internal function of the testis through clinical observation. The most effective form of sperm retrieval for non-obstructive azoospermia is microdissection testicular sperm extraction (microTESE) , 2 whereEvaluation of men with severe male infertility now occurs routinely as part of their treatment process. The routine genetic testing for potential abnormalities such as Y chromosome microdeletions  prior toSpermatogenesis in the rodent has been carefully characterized since the 1800s  and qualIn distinction, human spermatogenesis has been typically characterized as being chaotic, with no reliable staging within the seminiferous tubule. Although attempts to define spermatogenic cycles in the human have been proposed by Nikkanen , and a cThe treatment of men with non-obstructive azoospermia is complex since, by definition, these men have spermatogenesis so impaired that no sperm are observed in the ejaculate. As a result, surgery coupled with assisted reproduction is needed for fertility. Testicular histology is uniformly abnormal in these men. Interestingly, even men with predominant Sertoli cell-only pattern will have focal areas of sperm production identifiable with mTESE . Since sLarge studies examining men with a normal karyotype have shown that only 52% of men will have normal spermatogenesis on testis biopsy, with the remainder showing globally decreased sperm production or late defects in spermatogenic development . Taken tDefined genetic abnormalities causative of non-obstructive azoospermia are detected in approximately 20% of men with severely impaired sperm production, and include microdeletions of the AZFa, AZFb or AZFc regions of the Y chromosome as well as karyotypic abnormalities. Although Klinefelter syndrome is the more common karyotypic abnormality associated with non-obstructive azoospermia, autosomal translocations can also be found in men with this condition. Another 5% of men have a history of chemotherapy or radiation associated with their azoospermia, and about 10% of men have a history of cryptorchidism and prior orchiopexy. That leaves another 65% of men for whom we can either characterize their etiology based on histologic appearance (Sertoli cell-only and/or maturation arrest) or simply refer to them as being idiopathic. Since histologic characterization does not reflect an etiology, the majority of men with non-obstructive azoospermia could have a genetic cause for their severely impaired sperm production.The recognition that infertility could be hereditary is not a new concept. In 1981, well before advanced genetic techniques identified specific genetic abnormalities, Cantu et al. recognized the presence of maturation arrest in 3 of 13 brothers from a consanguineous marriage, suggesting the possibility of an autosomal recessive genetic defect that could be causal for disordered spermatogenesis . In a liMore recent evaluation of a population of 96 men from Northern Africa who were negative for karyotypic or Y microdeletion defects found 23% of these men had highly deleterious variants identified using a panel of only 151 genes. Six of the 16 variants identified in these 22 men had novel genes associated with their infertility . As discDespite having a uniform, identifiable genetic abnormality, men may still result in a variable spermatogenic pattern within the testis. Deletions of AZFc are uniformly associated with impaired spermatogenesis; about 40% of these men are azoospermic and the remainder have severe oligospermia or even cryptozoospermia. However, within the testis, we commonly observe heterogeneity between different seminiferous tubules. So, although the AZFc deletion is the same in every cell of the body, individual tubules may have Sertoli cell-only, maturation arrest or hypospermatogenesis. Typically, each tubule will have the same pattern of spermatogenesis within the tubule, but an adjacent seminiferous tubule will often have a different histologic pattern. The explanation for such variation between tubules remains elusive, even when the genetic defect is uniform within the testis.Unfortunately, genetic variants that cause spermatogenic failure may have unique or varied roles when evaluated in different ethnic groups or countries. For example, when Iberian investigators looked for 6 variants found in an Asian population, they observed that 3 variants were associated with spermatogenic failure in both additive and dominant models, with an associated negative predictive value for sperm retrieval for one of the variants. Of note, some of the variants are associated with lincRNAs, non-coding RNAs longer than 200 bp that are transcribed autonomously and do not overlap coding genes. It is widely accept that these lincRNAs control the expression of nearly genes in a tissue-specific manner. Of note, the testis represents the most enriched tissue in lincRNAs in humans .The search for specific causal variants in populations with male infertility has been slow and tedious with only rare mutations identified using large study groups of well-characterized patients with severe male infertility. The genetic variants have also been identified as having a broad series of potential roles in spermatogenesis, including roles in genome integrity  as well Rodents have remarkably high spermatogenic efficiency, and uniform patterns of histology, both of which are very different from human spermatogenesis. It is likely that a toxic effect on human fertility can occur without detection during screening in a rodent model. One example where rodent models have not been helpful is in the detection of the adverse effects of selective serotonin antagonists on male fertility potential. Whereas testosterone levels drop by 200 ng/dL and 50% of a cohort of normal men will have abnormal sperm DNA integrity produced within weeks of taking the SSRI , paroxetAnother area where rodent models were limited in their ability to identify and/or quantify the role of specific genetic defects in spermatogenesis was for genes on the Y chromosome. In part, this relates to the observation that mammalian Y chromosomes can be highly divergent, but also that Y-gene targeting is made more difficult by the highly repetitive nature of the Y, also limiting genetic sequencing difficult with classical approaches . From a Certainly, there continue to be roles for murine models of spermatogenesis, to detect or confirm a putative genetic cause of male infertility. At a minimum, such data help to support statistical evaluations of an association between genetic variants and impaired spermatogenesis. However, there are substantial limitations to using a rodent model to predict human spermatogenesis.Human spermatogenesis is unique in mammalian models of testicular function. Not only should we avoid assuming that an observation in a rodent model will predict human testicular function, but continued work to evaluate human spermatogenesis directly will be required to understand male fertility.The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author.This work is based on the sole authors observations, writing, editing and review of final submission.The article was funded in part by NICHD P50HD096723, as well as The Reeve Foundation fund of Weill Cornell Medicine.The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Coronavirus were heat‐inactivated, a procedure shown to preserve antigenicity of the major Coronavirus antigens, specifically the spike protein antigenicity, as previously described (Edouard et al., We have recently applied paleo serological methods that we have previously developed (Oumarou Hama et al., These very preliminary experimental results of a few samples support the hypothesis that a Coronavirus was responsible for the ‘Russian flu’, as derived from a review of the medical historical texts recently published (Brüssow and Brüssow, The authors have no conflicts of interest to declare.


Caulerpa J.V. Lamouroux, 1809 are known as widespread and persistent marine invaders. They grow rapidly, particularly in disturbed areas where they can opportunistically monopolize substratum and compete with native species, thus reducing biodiversity. Caulerpa chemnitzia increased in abundance and overgrew corals on the reef since 2012, ultimately raising fears that a phase-shift from coral to algae might be imminent. However, from 2019 onwards algae populations strongly contracted and while not having returned to baseline level, there is currently low risk of corals being displaced. Visual censuses were conducted on a yearly basis since 2004 using sample quadrats (0.5 x 0.5m) every 5 m along a 50-m-long transects at a depth of 6–15 m at 5 permanent subtidal ecological monitoring sites around Darwin. In addition, 10 m photo-transects were taken using a graduated meter-long measuring stick in the centre of the frame in 2012, 2014, 2016, 2017, 2018 and 2021 at a depth of 15m at Wellington reef. The authors hypothesize that this species could have expanded its distribution over Wellington Reef because of its known morphological plasticity due to a response to change in the environment, in this case high temperature and low nutrients. As ENSO events are predicted to increase in intensity and frequency due to the impact of climate change it is important to develop and implement a functional alert system. Early Detection Rapid Response (EDRR) protocols are recommended to avoid climate driven Non-Indigenous Species (NIS) entering the GMR or for native species becoming invasive due to warming-related phase shifts.Coral reefs are rare in the Galapagos and there is concern that, like in many areas around the world, they may be degrading due to increasing anthropogenic pressure, which can cause changes and reorganizations of structure and function with associated phase shifts. Algae of the genus  However2 , 7Caulerpa J.V. Lamouroux, 1809 are known as widespread and persistent marine invaders [Coral reefs are rare in the Galapagos  and therinvaders , 12. Theinvaders . Their ainvaders .Caulerpa species have been reported in different islands of the archipelago  [Caulerpa present in different locations around the archipelago, Caulerpa chemnitzia (Esper) Lamouroux, previously recorded as Caulerpa peltata [Caulerpa racemosa also recorded as Caulerpa racemosa var. clarifera and uvifera (Forsskål) J. Agardh, [Galapagos algae have been studied since the 1831 Beagle expedition by several subsequent scientific expeditions –20. Sponpeltata) , 21, 22.peltata) –25 has r peltata , 27 and  Agardh, –31Caulerpa chemnitzia [C. chemnitzia on Darwin Island’s Wellington Reef by documenting the abundance of algae on the reef over the past 15 years and discussing possible scenarios for the observed population outbreak and future impacts on the biodiversity of this important reef system in scenarios involving introduced species and climate change.emnitzia , 32, 33 The Galapagos Islands are in the Eastern Tropical Pacific (ETP) 1000km off the coast of Ecuador. The islands of Darwin and Wolf are the most northern islands in the Galapagos archipelago and are situated within the Galapagos Marine Reserve (GMR) , formingThere has been an ongoing subtidal ecological monitoring effort since 1995, with the currently employed standard method for reef communities introduced in 2004 . Five peC. chemnitzia for the photo-transects, 30 regions of interest (ROI) were plotted within each quadrat photograph. ROI sizes were chosen to be equivalent of ~50 cm in pixels  making accurate data comparison possible (one logger from 15-3-2016 to the 28-4-2017 was placed at 15 m). An issue with taking temperature data from loggers located at Wolf is that they will likely not exactly replicate the temperature fluctuations and levels from Darwin’s Wellington Reef, from where biological monitoring data for this study were obtained. But only a limited number of loggers had been placed at Darwin over the years and then at varying depths. To verify that temperature data from Wolf would be valid when analysing compared .In order to verify the accuracy of the logger data values and their trends, data were compared to the NOAA Twice-Weekly Global 50km Satellite Coral Reef Watch (CRW) Sea Surface Temperature (SST) data , 45 betwTemperatures at Darwin Island’s Wellington Reef mostly followed the same trend as at Wolf’s anchorage point, albeit at a higher temperature due to different depths between the loggers 16 m at Darwin, 20 m at Wolf . The maiCaulerpa sp. and hermatypic coral data, particularly since the site is located at a similar depth to the Wolf loggers.The yearly average NOAA SST data  mostly fC. chemnitzia in DA02 and DA03 continued to increase as well. 2017 showed a strong increase across the sites for C. chemnitzia. The highest recorded coverage across the sites was in 2018, with the exception of the Darwin Anchorage Points (DA01 and DA02) which saw a reduction and complete absence respectively. During this year the algae was most abundant at the Arch with 47.16% of all quadrats covered as well as colonising the Hidden Reef (DA04) for the first recorded time. By March 2019 the cover at DA03 had declined sharply by 40.7%, whilst at Darwin Anchorage North (DA01) it increased slightly, and it reappeared at the South site (DA02). At the close of 2021, Caulerpa levels across the sites had decreased significantly, apart from Darwin’s Arch where it rose again to 32.16%.During 2007, coverage of C. chemnitzia was 1.85%  across DCaulerpa chemnitzia in Darwin according to the Subtidal Ecological Monitoring records was at Wellington Reef in 2007 with a percentage cover of just 1.85%. Up to this point only sites DA01 to DA03 had been monitored yearly since 2000, therefore it is possible there was some C. chemnitzia present at the reef before the first record. After the initial detection, percentage cover of the algae at the reef remained below this level, even disappearing from the transect in 2010, before increasing to 9.01% in 2012. Proceeding this there was a small dip in cover in 2013, but this was overshadowed by a continual sharp increase which extended to the year 2017, at which point the percentage cover plateaued around 38%. Unfortunately, there is no valid data available for the 2019–2020 period due to a changed transect location, which lead to a recorded cover of just 1.23% suggesting the algal spread is far from uniform across the reef, and COVID-19 complications. In 2021 the site was able to be monitored 3 separate times, producing an average cover of 23.2%. The second location to record the presence of Caulerpa chemnitzia in Darwin was the Southern Anchorage Point (DA02) in 2009, with only two of the possible 81 quadrant points falling on the algae and only in one quadrant. It then proceeded to disappear from recorded transects at the site until 2013 when it covered 6.17% of the transect, pointing to very low coverage at the site in the intermediate period. Its absence was once again noted in 2014, but it then rose to 17.35% cover by 2017, similar to the steep increase seen at Wellington Reef around the same time. From then on it has steadily decreased to 3.46%, barring an anomalous empty year in 2018. The Northern Anchorage Point (DA01) follows a steadier pattern than its sister site (DA02), with the first records of C. chemnitzia appearing in 2016, followed by its largest increase in 2017 of 5.56%, accompanying the general trend. It has since risen and fallen, with the current cover observed at 3.55%. The Hidden Reef site (DA04) only showed signs of C. chemnitzia cover for the first time in 2018 with 6.17%, although it had not been monitored in the previous three years , Clipperton Atoll (France), Gorgona National Natural Park (Colombia) and Galapagos Marine Reserve (Ecuador) [C. chemnitzia propagules, mainly vegetative fragments could have been transported by the Panama Current from any of the above-mentioned coasts or MPAs.This alga increased in abundance and overgrew corals on the reef since 2012, ultimately raising fears that a phase-shift from coral to algae , 48–51 mf Island . Since tEcuador) , It remaC. chemnitzia has been classed as a cryptogenic species in the GMR until more information is available [Furthermore, the Galapagos Archipelago has received vessels from around the world since its discovery in 1535. This long history of marine traffic has facilitated introduction of alien species  and as tvailable .Caulerpa chemitzia has been registered at seven GMR locations including Wolf Island with the highest abundance at Wellington Reef . In other sites, this species existed in small patches that did not form extensive mats covering the substrate. Sites were located in protected bays at shallower depths (1–6 m) than at Wellington Reef that lies between 12–18 m and is exposed to strong currents especially from June to December. It remains unclear why C. chemnitzia reached such high abundances on this exposed reef system in the far north region of the archipelago, however, there are several factors surrounding this system that can be explored to better understand this species expansion, including temperature and changes in upwelling and nutrient levels.Porites lobata, Pavona clavus and Pocillopora spp [Wellington Reef is characterized by large colonies of corals mainly pora spp . The reepora spp . The autpora spp , 36, 55 Warming-related phase shifts in marine systems can cause successive stages of geographic extension or contraction of species ranges . The GalA surprising number of marine species have invaded marine protected areas, including Galapagos  and may S1 FigData is at 50 km resolution from 2001 to 2019 and at 5 km resolution from 2020 onwards.(DOCX)Click here for additional data file.S1 TableSites listed with a tick were monitored at least once during the year, those marked with a P are occasions on which the divers could not do a full monitoring of the site and only registered presence of species rather than percentage cover. The site marked with an “In” represents an inconsistent transect which was placed in a different location to normal and whose data is incompatible with the rest.(DOCX)Click here for additional data file.S1 File(DOCX)Click here for additional data file.


The emergence of zoonotic viruses in the last decades culminating with COVID-19 and challenges posed by the resistance of RNA viruses to antiviral drugs requires the development of new antiviral drugs.n vitro and in vivo antiviral activity towards RNA viruses and analyses their distribution, molecular weights, solubility and modes of action.This review identifies natural products isolated from Asian and Pacific medicinal plants with iAll data in this review was compiled from Google Scholar, PubMed, Science Direct, Web of Science, ChemSpider, PubChem and library search from 1961 to 2022.O-tetradecanoylphorbol 13-acetate with IC50 below 0.01 µg/mL and selectivity index (S.I.) above 100 have the potential to be used for the development of anti-RNA virus leads.Out of about 350 molecules identified, 43 phenolics, 31 alkaloids, and 28 terpenes were very strongly active against at least one type of RNA virus. These natural products are mainly planar and amphiphilic, with a molecular mass between 200 and 400 g/mol and target viral genome replication. Hydroxytyrosol, silvestrol, lycorine, tylophorine and 12-The medicinal plants of Asia and the Pacific are a rich source of natural products with the potential to be developed as lead for the treatment of RNA viral infections.


Collectively, this work offers a resolved atlas linking spatiotemporal variation in editing levels to genetic regulatory effects throughout distinct stages of brain maturation.Posttranscriptional RNA modifications by adenosine-to-inosine (A-to-I) editing are abundant in the brain, yet elucidating functional sites remains challenging. To bridge this gap, we investigate spatiotemporal and genetically regulated A-to-I editing sites across prenatal and postnatal stages of human brain development. More than 10,000 spatiotemporally regulated A-to-I sites were identified that occur predominately in 3′ UTRs and introns, as well as 37 sites that recode amino acids in protein coding regions with precise changes in editing levels across development. Hyper-edited transcripts are also enriched in the aging brain and stabilize RNA secondary structures. These features are conserved in murine and non-human primate models of neurodevelopment. Finally, thousands of  This resource article illuminates precise regulation of A-to-I editing throughout human brain development. These findings provide an atlas of spatiotemporally and genetically regulated A-to-I sites and their putative functional effects during prenatal and postnatal periods. Additional avenues to dissect the role of RNA editing in neurodevelopment are provided.  These “recoding” sites are required for typical neurodevelopment because they tightly regulate of Ca2+ permeability,11 enhance recovery rates from desensitization,12 and remodel actin cytoskeleton at excitatory synapses,14 among other functions.16 Importantly, many, if not most, physiologically crucial A-to-I sites illustrate a precise increase in editing levels throughout the various stages of neurodevelopment. Yet, as the catalog of A-to-I editing sites continues to expand, both the function and temporal profiles for most sites remain uncharted.Adenosine-to-inosine (A-to-I) editing is a major contributor to the global diversity of RNA sequences in the human brain18 or across many neighboring adenosines in an extended region (“hyper-editing”)21 and is catalyzed by family of adenosine deaminases acting on RNA (ADAR) enzymes. In mice, the spatiotemporal, cellular, and functional properties of essential A-to-I recoding sites in Gria2 (Q/R and R/G sites), Grik1 and Grik2 (Q/R), Gabra3 (I/M), and Cyfip2 (K/E), among others, have been extensively studied.25 These sites increase in editing levels across cortical developmental. Yet, the landscape of A-to-I editing is vastly under-represented in murine models since RNA editing is enriched in primate-specific Alu elements. In humans, our current knowledge base of temporally regulated A-to-I editing sites in the brain is subject to 742 selective editing sites that increase in editing levels across 9 fetal and 24 postnatal cortical samples, the majority in non-coding regions and show specific editing level changes in experimental models of brain disease.26 To build upon these efforts, a large-scale systematic approach to uncover spatiotemporally regulated A-to-I editing sites across large collections of deeply sequenced prenatal and postnatal tissues is warranted. Such an approach will be central for fully elucidating the landscape of temporally regulated A-to-I editing sites in the human brain.A-to-I editing occurs at single isolated adenosines (“selective editing”)31 and it has not yet been dissected across fetal and postnatal developmental periods.Based on previous work, we reason that A-to-I sites that display discrete spatiotemporal differences in editing levels may signal a potentially critical role in supporting the functional diversity of brain development. Moreover, elucidating the genetic regulation of RNA editing at these sites may also offer unique insight into their role in both health and disease,state-of-the-art paired whole-genome and bulk-tissue RNA sequencing (RNA-seq) data from the dorsolateral prefrontal cortex (DLPFC) of 176 donors spanning 6 post-conception weeks (PCWs) to young adulthood (20 years).32 We cross-validated these findings in two independent developmental brain datasets of the cerebrum (n = 55) and cerebellum (n = 59).33 By combining these data with complementary RNA-seq data from in vitro models of corticogenesis (n = 151),35 hundreds of postmortem cortical tissues from advanced stages of normal aging (n = 261),36 and nonhuman primate (n = 96)37 and murine models of cortical development (n = 18),38 we provide comprehensive analysis and validation of the spatiotemporal regulation of A-to-I editing in the brain. Furthermore, we elucidate RNA editing quantitative trait loci (edQTLs), which act during prenatal and/or postnatal development. Collectively, this work provides a catalog of spatiotemporal and genetically regulated A-to-I editing sites throughout the widely different stages of human brain development.The current study is anchored around Alu elements. Thus, to quantify global selective editing levels we computed an Alu editing index (AEI)39 per donor, defined as the total number of A-to-G mismatches over the total coverage of adenosines in Alu elements , with a major shift between the mid-fetal period and infancy (in vitro models of corticogenesis: (1) human embryonic stem cell , covering stages of pluripotency through upper layer generation (day 0–77); and (2) human induced pluripotent stem cells (hiPSCs) (n = 127), covering early differentiating cells through mature differentiated neurons (day 2–77). We observed a significant increase in the AEI across all datasets, including the cerebrum , cerebellum  , and hiPSC in vitro models   , hESCs ( = 1.68) . A meta- = 1.68) .ADAR2 expression across all datasets (pooled Cohen’s d = 1.26) relative to a general prenatal bias for ADAR1 (pooled Cohen’s d = −1.36) , suggesting that these changes are not fully driven by underlying cell-type composition  , suggester donor . Followiposition .Alu editing during advanced stages of aging. The AEI was computed for 261 additional cortical samples from older adults (61–108 years)  (−121).Given the substantial change in AEI between prenatal and postnatal periods, we next aimed to resolve any changes in 8 years) . While e8 years) , we comp 10−225) , peakingde novo callers (REDItools240 and JACUSA241) to uncover high-quality A-to-I sites not already cataloged in editing databases together with a supervised approach27 applied to three large lists of known sites43 to ensure ample collection of bona fide A-to-I editing sites across development. In the DLPFC, ~60,951 editing sites were detected per sample  (−13)  ; (3) werstnatal) ; (4) sitstnatal) ; (5) sitl) (−13) . Notablyl) (−13) .−112, ~10% higher, Next, we evaluated the overlap of high-quality editing sites detected for each sample relative to all other samples. A convergence of sites consistently detected either during prenatal and/or postnatal periods was uncovered, with the largest shift in convergence occurring during the late fetal transition . Based oAlu elements   and cerebellar (n = 59) development. Across the cerebrum and cerebellum, ~22,400 and ~39,209 sites were detected per sample, respectively, and the majority were A-to-G events  and mapped to ebellum) . The cerebellum) . Ensuingebellum) ; (2) a cebellum) ; (3) andr = 0.82, p = 5.7 × 10−58)  . Differe× 10−58) . Adjustied sites .−32, Mann-Whitney U test)  , suggest U test) .sites = 7,155) and cerebellum  samples. Similarly, in the cerebrum and cerebellum, PC1 was positively correlated with developmental age , most sites were postnatally biased and significantly increased in editing levels (FDR < 5%)  and mapped to 3′ UTRs . Again, adjusting for ADAR expression had the largest effect on the number of temporally regulated sites ; the vast majority (~99%) were postnatally biased . All together, these recoding sites mapped to 31 unique genes enriched for ionotropic glutamate receptor activity (FDR = 1.46 × 10−8), ligand-gated ion channel activity (FDR = 4.95 × 10−8), AMPA glutamate receptor activity (FDR = 2.27 × 10−7), and neuronal projection (FDR = 9.38 × 10−5). Ranking these sites by temporal effect sizes confirmed several functional recoding sites known to increase editing levels through development, including those mapping to a collection of excitatory, inhibitory, and G-coupled protein receptors . However, we also highlight many recoding sites with undefined roles in neuronal development: p.R61G site in cyclin I (CCNI), peaking postnatally in the cortex but not in the cerebellum; p.K95R site in insulin-like growth factor binding protein 7 (IGFBP7), peaking postnatally in the cerebellum but prenatally in the cortex; p.I64M and p.S75G sites in signal recognition particle 9 (SRP9) peaking postnatally across all regions; a postnatal biased p.R580G site in SON DNA and RNA binding protein (RBP) , among others   (2 = 0.41) and ADAR2 expression (R2 = 0.21) (−11). Local sequence motifs were consistent with the expected distribution of guanosines 1 bp (±) the target adenosine  . To mini = 3.06) . These r = 0.21)  and remadenosine .2+ signaling, protein kinase activity, and ion channel regulator activity (KCNIP4)  .To complement these results, minimum free energy (MFE) and degree of RNA double-strandedness was calculated for each hyper-edited region . Investigating the natural unedited primary sequences for each region revealed an elevated MFE and reduced double-strandedness specifically for regions and transcripts that significantly accumulate hyper-editing sites during postnatal development . Thus, g−19) yet accounted for only a small fraction of the total number of sites (<1%)  . Second,es (<1%) ; notably−12, Cohen’s d = 2.82) and cerebellum development   (−9) (2 = 0.58) (−90)  , and val = 2.43) . Next, w= 0.007) . Notably07) (−9) –S15F. In07) (−9) , we vali = 0.58) . Finally8) (−90) .−17) and in normalized hyper-editing signal (p = 7.1 × 10−10) (−12), normalized RNA hyper-editing signal (p = 1.8 × 10−7), and conservation of common local sequence motif for all hyper-editing sites  bulk tissue RNA-seq of four cortical regions of 26 rhesus macaques (60 post-conception days to 11 years); (2) whole-cortex RNA-sequencing of 18 wildtype mice  . In maca× 10−10) –6C. Hype× 10−10) . These fng sites –6H. Finang sites . Importang sites  and S12EADAR1 and ADAR2 expression  prenatal samples only ; (2) postnatal samples only ; and (3) all samples . We defined a 1 Mb window (±) to search for SNP-editing pairs of an editing site and identified 31,324 cis-edQTLs from all samples, 20,659 cis-edQTLs from prenatal samples, and 2,066 cis-edQTLs from postnatal samples at FDR < 5% . RNA editing levels were fit to SNP genotypes, covarying for developmental period, sex, and the first five principal components of ancestry, as well as sion see . To distFDR < 5% . These eFDR < 5% . eSites FDR < 5% .CAND1 gene (GRIK2 (p.Y571C) and another in SON (p.R580G), whereby heterozygous genotypes result in higher fetal editing levels for these sites, which later equalize during postnatal development  for 23 loci across 21 traits and disorders and strong evidence of co-localization (PPH4 > 0.8) for 2 loci across 2 disorders (C16orf72 (PPH4 = 0.98)  see . We founisorders . Disease = 0.98) .25 elucidating additional spatiotemporally regulated sites across neurodevelopment, and those that are genetically regulated, allows further characterization of potentially functional mechanisms of brain development. These results provide an atlas of such sites and reveal several important aspects of RNA editing throughout development: (1) global Alu editing significantly increases throughout brain maturation, and this pattern is evolutionarily conserved in mammalian models of neurodevelopment; (2) underlying this shift, thousands of temporally regulated sites were uncovered that are spatially conserved and increase in editing levels across development, the majority in 3′ UTRs of essential neurodevelopmental genes, which are predicted to stabilize local miRNA interactions more so during postnatal development; (3) the minority of spatiotemporally regulated sites occur in protein-coding regions, and a total of 37 RNA recoding sites show significant changes in editing levels across development, including many with known and unknown functional effects; (4) hyper-edited regions amass during advanced stages of aging, and are predicted to have diverse regulatory effects; (5) thousands of sites illustrate either prenatal and/or postnatal predominate edQTLs, the majority of these sites occur in non-coding regions, although few recoding sites also exhibit significant prenatal edQTLs. Collectively, these findings establish starting lines for investigations to link RNA editing with mechanisms of brain maturation. We discuss these points in turn below.As the catalog of RNA editing sites in the brain continues to grow, the physiological relevance for most sites remains unknown. Because A-to-I editing sites with significant functional effects are known to be tightly temporally regulated,Alu editing occur during the late-fetal transition, between mid-fetal development and infancy ,12 remodel actin cytoskeleton at excitatory synapses ,49 and guide gating kinetics of inhibitory receptors .16 Analyses identified over 20 other highly conserved spatiotemporally recoding sites where the functional implications remain less clear and warrant deeper investigation. We outline five such examples here. First, the p.Q2333R site in FLNA increases ~27% through brain development. This site was recently found to be a regulator of cytoskeletal organization and cell mechanics.50 The temporal profile of this site suggests that it may increase actin crosslinking and reduce cell migration during postnatal brain development. Second, a ~15% increase in the p.R61G site in cyclin-I (CCNI) was observed in the cerebrum but not in the cerebellum. This site resides in the cyclin N-terminal domain of the protein and has been identified to be highly edited in GABAergic neurons,31 where, like its encoded gene, this site may support a role in the cell cycle by activating cyclin-dependent kinases.51 Third, a ~20% increase in the p.E1171G site in calcium-dependent secretion activator (CADPS) was observed, a site with high editing levels reported in glumatergic neurons.31 Increased editing at this site has been shown to increase expression and synaptic localization of CADPS and enhance short-term synaptic plasticity,52 which may have significant neuronal network effects throughout brain development. Fourth, divergent temporal editing profiles were observed for p.K95R in insulin-like growth factor-binding protein 7 (IGFBP7), a site that decreases ~25% in the cortex but increases ~25% in the cerebellum across development. This site is in the heparin-binding site of IGFBP7 and is also part of the recognition sequence for proteolytic cleavage. Thus, A-to-I editing may act as a regional-specific mechanism that influences heparin binding and/or proteolytic processing and its downstream effects regarding apoptosis, regulation of cell growth, and angiogenesis.53 This site is also temporally regulated during aging in porcine brain tissues.54 Fifth, recoding site p.T430S in arginine-glutamic acid dipeptide repeats (RERE) increases ~10% across development. RERE is crucial for regulating apoptosis and retinoic acid signaling during embryonic development.56 Collectively, these sites are all conserved and increase in editing levels across development, like well-known functional sites.49 Collectively, these results feature numerous entry points for functional and mechanistic interrogation of recoding sites throughout neurodevelopment.Beyond non-coding regions, 37 spatiotemporally regulated recoding sites were uncovered . Each reAlu-rich regions and within introns and 3′ UTRs that form dsRNA structures, consistent with previous reports.57 These events occur in hundreds of genes involved in transsynaptic signaling, ion channel regulator activity, and those implicated in neurodevelopmental disorders. Still, deciphering how hyper-editing influences gene function remains challenging, especially for hyper-editing within introns, which is therefore limited to premRNA. Hyper-edited transcripts can be subjected to nuclear retention or degradation59 and have been found to transiently interact with components of cytoplasmic stress granules.61 Hyper-editing has also been shown to suppress interferon induction and apoptosis, suggesting that these transcripts play a role in the stress response62 but require further study in neurodevelopment and aging. To compliment these findings, we probed additional functional consequences of hyper-editing in the developing brain. First, and in very rare cases, hyper-editing near exon-intron boundaries could cause cryptic splicing, subsequently accounting for a tiny fraction of observed isoform diversity. Second, and more generally, hyper-editing could alter the local dsRNA structure, which, in turn, will have an impact on gene expression and protein abundance. We estimated that hyper-editing increases the stability of RNA transcripts. This stabilizing effect is more pronounced for transcripts that naturally (in an unedited state) exhibit higher MFE and reduced RNA double-strandedness. MFE has been regarded as one of the most reliable predictions of RNA secondary structures,63 and RNA editing has been shown to stabilize secondary structures of RNAs in other non-CNS cell types.65 Indeed, enhanced RNA secondary structure has been linked with increases in mRNA half-life and protein abundance.66 Notably, RNA transcripts that accumulate postnatal hyper-editing events are implicated in synaptic signaling, a process that initiates and unfolds throughout postnatal development. Our findings propose a model whereby temporal hyper-editing could serve as a mechanism to ensure stability and function of select synaptic genes throughout postnatal brain maturation. Furthermore, it is plausible that hyper-editing also acts as a mechanism to create or disrupt RNA binding domains for RBPs, yet such data are currently lacking to test this hypothesis in the human brain and related cell types. Finally, it is worth noting that in non-human species, hyper-editing has been linked with social behavior,68 adaptation to temperature,70 and other physiological conditions.71Extended regions of hyper-editing also increase in frequency through development and amass in the brain during advanced stages of aging . These r31 For the minority of edQTLs in coding regions, two prenatal predominate edQTLs are identified: (1) p.Y571C in GRIK2, which may be involved in additional regulation of calcium permeability, together with the p.Q621R site in the same gene12; and (2) p.R580G in SON, which may modulate the pre-mRNA splicing function of SON.73 Such temporal edQTLs highlight unique entry points for the establishment of hiPSC neuronal model systems to further dissect the regulatory mechanisms and functional implications of these recoding sites. For the majority of edQTLs in non-coding regions, unique relationships have been identified between edQTLs, eQTLs, and miRNA expression profiles,28 whereby miRNAs can generate an eQTL signal from an edQTL locus via miRNA-mediated transcript degradation. This model has been proposed as a mechanism that alters steadystate transcript levels, linking edQTLs to eQTLs and complex traits. To this end, we also postulated that edQTLs may represent a biologically important intermediate link between genetic variation and disease phenotypes. Yet, edQTLs in the current study rarely co-localized with risk variants for neurological disorders. The most significant colocalization occurred between a variant for sleeping disorders and an editing site in the first non-coding exon of C16orf72, previously linked to cognition, learning, and sleeping difficulties.75 This editing site occurs in a non-coding regulatory region that has been shown to modulate mRNA stability and protein abundance.76 To further dissect such mechanisms and their implications for brain maturation and disease risk, it will be critical for future work to greatly increase sample sizes.Temporal-predominate edQTLs illustrate a greater effect on editing levels prenatally or postnatally . edQTLs in silico confirmation and functional predictions; however, a limitation of this work is the lack of experimental validation. Moreover, while our analyses indicate that RNA editing serves as a mechanism that mediates miRNA binding, protein function, mRNA stability, and splicing during critical developmental periods, functional validation is warranted at each of these scales. As such, we present these findings as a resource to springboard future functional investigations of RNA editing in mammalian neuronal maturation, brain development, aging, and genetic disease. Furthermore, without brain cell-type-resolved data across temporal scales, the interpretation of increased RNA editing deserves caution. Indeed, RNA editing activity is higher in neuronal compared with non-neuronal cells and mature neurons increase in frequency across brain development. Thus, our analyses controlled for the proportion of mature neurons and together suggest that this increasing pattern is likely due to higher editing activity per cell during postnatal periods, but this has yet to be studied in a systematic way. Finally, cross-species comparisons indicate higher editing rates in human and non-human primates relative to murine models. Alu elements are primate-specific repeats and most editing in mice occurs in B1 and B2 SINE elements, therefore direct comparisons are challenging.This work presents a spatiotemporally resolved atlas of RNA editing in the human brain with extensive michael.breen@mssm.edu).Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Michael S. Breen  samples of human DLPFC covering 12 distinct prenatal and postnatal developmental periods were obtain from synapse (syn21557948).n = 55) and cerebellar (n = 59) tissues sampled across prenatal and postnatal periods (4 PCWs-59 + years) were downloaded from ArrayExpress (E-MTAB-6814).33A total of 114 single-end (100 bp) samples from cerebrum  samples from the Mount Sinai Brain Bank (MSBB) covering four cortical regions, including BM10, BM22, BM36 and BM44 were downloaded from synapse (syn7416949).34A total of 24 single-end (50 bp) samples of human cerebral cortex development (CORTECON) from human embryonic stem cells (hESCs) across nine time-points  were downloaded from GEO (GSE56796).34 This dataset covers early differentiating cells (days 2–9), neural progenitor cells (day 15), assembled neuroepithelial rosettes (day 21) and more differentiated neurons (days 49–77).A total of 127 paired-end (100 bp) samples from a human induced pluripotent stem cell (hiPSC) neuronal differentiation time course were downloaded from GEO (PRJNA596331).Macaca mulatta) of prenatal and postnatal cortical development was downloaded from GEO (PRJNA448973),37 covering 96 single-end (75bp) samples, comprising 26 unique donors and four prefrontal cortical areas: MFC – medial prefrontal cortex; OFC – orbital prefrontal cortex; DFC – dorsolateral prefrontal cortex; VFC – ventrolateral prefrontal cortex.A non-human primate model (Mus musculus) of prenatal and postnatal whole brain development was downloaded from the NCBI Short Read Archive (SRP055008),38 including 18 paired-end (200 bp) cortical samples at nine time points: embryonic day 14.5 (E14.5), E16.5, postnatal day 4 (P4), P7, P17, P30, 4 months, and 21 months.A murine model , rhesus macaque (rheMac8-Mmul8) and mouse (GRCm38-mm10) in the current study. STAR produced a coordinated-sorted mapped BAM file and unmapped FASTQ file for each sample. The mapped BAM files were used to quantify selective RNA editing sites and the unmapped FASTQ files were used to quantify RNA hyper-editing sites and regions.STARnovo callers and one supervised approach:De novo caller REDItools v2.040 was applied with the following parameters: -S -s 2 -ss 5 -mrl 50 -q 10 -bq 20 -C -T 2 –os 5. All analyses considered read strandedness (-s) when appropriate.De novo caller JACUSA241 with the following parameters: -p 10 –a D, M, Y, E, -m 20. All analyses considered read strandedness when appropriate.31 This approach was applied to query known RNA editing sites cataloged through REDIportal,42 A-to-I sites cataloged across human brain cell types,31 and an extensive list of human RNA recoding sites.43A supervised approach queried nucleotide coordinates for all known RNA editing sites using samtools mpileup function, as previously described.A comprehensive RNA editing site detection pipeline was applied to all human DLPFC, cerebrum and cerebellum transcriptome samples. Short-read mapping with STAR produced a coordinate-sorted BAM file of mapped reads, including those spanning splice junctions. High-quality RNA editing sites were quantified from sorted mapped bam files using a combination of two de 91; iii) all sites mapping to common genomic variation in dbSNP(v150) and those in gnomAD with minor allele frequency greater than 0.05; iv) all sites mapping to high confidence heterozygous or homozygous genomic calls using paired WGS data (e.g. BrainVar); v) de novo called sites adjacent to read ends and splice sites; vi) de novo called sites if coverage was below ten reads, edited read coverage was below three reads and the editing ratio was below 1%; vii) de novo sites if called by only one software; viii) supervised sites if coverage was below five reads and the number of edited reads was below three reads. Following, all remaining sites were annotated using ANNOVAR78 to gene symbols using RefGene, repeat regions using RepeatMasker v4.1.1, known RNA editing sites using the most recent version of REDIportal43 and conservation metrics were gathered using phastCons from the PHAST package.79Subsequently, filtering steps were applied to retain only high-quality, high-confident bona fide RNA editing sites. The following sites were removed from all analyses: i) all multi-allelic events; ii) all sites mapping to homopolymeric regions or black listed genomic regions in the genome80 using five multiple imputations and 30 iterations.To classify common, postnatal and prenatal-specific editing sites, samples were subset from each brain region into three groups containing either all samples, prenatal samples or postnatal samples. Next, for each subset, high-quality sites were required to have detection rates in at least 70% and a minimum mean editing level of 5% across all subsetted samples. Further, samples containing more than 20% missing values were removed. All resulting RNA editing data frames (whereby rows are editing sites and columns are samples) contained no more than ~6% missing data on average. For sites classified as ‘common’, any missing values were imputed using predictive mean matching method in the mice R package,39 computed the Alu editing index (AEI) using an STAR mapped bam file as input. The AEI is computed as the ratio of edited reads (A-to-G mismatches) over the total coverage of adenosines in Alu elements and is a robust measure that retains the full Alu editing signal, including editing events residing in low-coverage regions with a low false discovery rate. The resulting metric is multiplied by 100 to compute so the index describes the percentage level of editing. For human and macaque RNA-seq samples, predetermined genomic regions were set to all SINE/Alu repeats using the Alu bed table of the UCSC genome browser defined by RepeatMasker. For mouse, these genomic regions were set to all B1-SINE and B2-SINE elements, where most RNA editing occurs in mouse. Common genetic variation was also discarded for all species using coordinates from UCSC genome browser . Notably, the AEI has is scalable and comparable across postmortem brain RNA-seq samples generated across unique laboratoaries and library preparation protocols.59The AEI method v1.092 and utilized the default Darmanis et al., signature matrix which contained a mixture of six major cell types93: mature (adult) neurons, immature  neurons, astrocytes, oligodendrocytes, oligodendrocyte precursor cells (OPCs) and microglia. NNLS, executed through the est_frac function, was applied to log2 count per-million (CPM) transformed data using the limma package in R.94 Our predictions were focused on these major cell types to reduce noise and to evaluate a distribution of cell type changes that reflect an approximate expected distribution based on prior work.To identify changes in cellular composition in bulk RNA-seq data, non-negative least squares was applied from the MIND R packagelimma R package94 was implemented and covaried for the possible influence of sex and ancestry. Given ADAR enzymes are dynamically regulated across development and RNA editing is a process that is highly abundant in neurons, which also increase in frequency through development, additional models were fit to adjusted for ADAR1, ADAR2 and estimated neuronal proportions. All significance values were adjusted for multiple testing using the Benjamini and Hochberg (BH) method to control the false discovery rate (FDR). Sites passing a multiple test corrected p-value < 0.05 were labeled significant.To identify sites with differing editing levels across cortical development, linear modeling via the 82 A genomic background was defined as all genes harboring at least one editing site and tested for significance using a one-tailed hyper-geometric distribution with a Bonferroni correction. This is a proportion test that assumes a binomial distribution and independence for probability of any gene belonging to any set. A one-sided test was applied to explicitly test for over-representation of genes harboring editing sites across hundreds of GO categories, without any a priori selection of candidate gene sets. Further, SynGO83 analysis was performed under default parameters to test for enrichment of pre- and post-synaptic genes for loci harboring A-to-I sites that either increase or decrease in editing levels across development.Genes harboring A-to-I sites with significant increases in editing levels across development were functionally annotated. GO ontology terms relevant to cellular components, molecular factors, biological processes and metabolic pathways were explored using the ToppGene Suite software.95 schizophrenia96 and intellectual disability (ID)97; (2) genome-wide association study gene sets (gene(s) nearest candidate risk variants) that implicate risk for ASD,98 EA,99 schizophrenia100 and height101 (as a negative control); (3) transcriptome-derived gene sets of RNA editing sites associated with human cortical development,26 schizophrenia,27 Fragile X Syndrome and ASD.44 To compute significance of all intersections, GeneOverlap function in R102 was leveraged which uses a Fisher’s exact test (FET) and an estimated odds ratio for all pairwise tests based on a background set of genes detected in the current study. When testing overlap across co-editing modules, all pairwise tests were adjusted for multiple testing using BH procedure to control the FDR.Genes harboring sites that increase in editing levels across development were interrogated for over-representation of neurodevelopmental and neuropsychiatric disorder-related genes. Four tiers of gene sets were collected to examine overlap with co-editing modules: (1) whole exome sequencing (WES)-derived gene sets implicated in risk for autism spectrum disorder (ASD),45 consisting of multiple steps: (1) All unmapped reads were removed by phred (<25), the presence of simple repeat structures, high N content, or the fraction of a unique nucleotide per read being too great (>60%) or too low (<10%); (2) for resulting reads, all adenosines were transformed into guanosines in both the RNA sequences and genome reference sequence; (3) RNA reads were re-aligned using BWA-aln v0.7.15, all unmapped reads were discarded and subsequently all true adenosines were recovered to identify confident A-to-G edits; (4) filtering of multi-mapped reads by selecting only the location with the largest fraction of A-to-G to all mismatches ; (5) filtering of hyper-editing clusters to obtain high-quality (Phred ≥ 30) A-to-G mismatches in which the number of A-to-G mismatches was ≥5% of the read length and >60% (80% for read lengths ≤60 bp) of the total number of mismatches; (6) filtering of clusters that were too dense (>90% of read length), A-to-G mismatching contained within the first or last 20% of reads ends and clusters >60% of an individual nucleotide. Once clusters were identified: (7) cluster boundaries were extended by the mean distance between editing sites per cluster and subsequently merged clusters with overlapping coordinates (cluster length is a commonly product of read length); (8) all resulting hyper-editing sites were annotated using ANNOVAR (described above); (9) sites mapping to common genomic variation in dbSNP(v150) and those in gnomAD (maf>0.05) were discarded; (10) sites mapping to paired high-confidence private genomic calls were discarded. This process was reiterated to search for additional hyper-editing substitution types (e.g. A-to-C).To identify hyper-edited reads in the current study, all discarded and unmapped reads following STAR alignment were converted to FASTQ format and used as input for hyper-editing analysis. A well-validated RNA hyper-editing pipeline was applied,To minimize the effect of sequencing batches and differences in library size, a normalized hyper-editing signal was computed. This metric is computed by dividing the total number of hyper-editing sites over the total number of uniquely mapped bases from the initial STAR alignment for each sample and multiplying the result by one million. Picard v2.22.3 collected the number of uniquely mapped bases for each mapped bam file. This enabled us to directly compare normalized hyper-editing levels across independent studies, developmental periods and anatomical regions.limma94 tested for prenatal and postnatal differences in the frequency of RNA hyper-editing events per gene and adjusted for the possible influence of sex and ancestry. Additional models were fit that covaried for the proportions of mature neurons, ADAR1 and ADAR2 expression and gene length. To adjust for gene length, these analyses were performed on a residualized matrix whereby the total number of edits per gene were normalized by the log gene length (geneEdits ~log(gene length)). All significance values were adjusted for multiple testing using the Benjamini and Hochberg (BH) method to control the false discovery rate (FDR). Sites passing a multiple test corrected p-value < 0.05 were labeled significant.To test for temporal changes in the frequency of hyper-editing sites at the gene level, genes were first filtered according to detection of at least one RNA hyper-editing site in at least 70 DLPFC samples (~40%). This unsupervised measure was used to remove genes with too few hyper-editing sites across all samples. All remaining missing values were treated as zero. Subsequently, the linear modeling framework of 85 to compute local alignments of 3′UTR sequences against all mature miRNAs obtained from miRbase (https://www.mirbase.org); (3) miRANDA computes the stability of the resulting RNA duplex and minimum free energy ; (4) This process was re-iterated for high confidence alignments that occur in miRNA seed regions (setting –strict parameters). A Mann-Whitney U test was used to test for significance for differences in minimum free energy.The following approach was applied to estimate differences of miRNA minimum free energy on edited versus un-edited 3′UTRs: (1) For each 3′UTR editing site, two 101bp sequences (50bp +/− the target editing site) were obtained, where the only difference between these sequences was either an unedited (A) or edited (G) site; (2) We used miRANDA86 computed the probability of intronic sites to be splice altering. This method uses a deep neural network to predicts splice junctions from an arbitrary pre-mRNA transcript sequence. All high-confidence intronic A-to-I sites near exon boundaries were included in this analysis. This approach enables prediction of RNA editing events that cause cryptic splicing and generates two outputs for each site: (1) Delta score, which is the probability of the A-to-I site being splice-altering and ranges from 0 to 1. In the primary publication of this work, a detailed characterization is provided for 0.2 , 0.5 (recommended), and 0.8 (high precision) cutoffs; (2) Delta position, which conveys information about the location where splicing changes relative to the variant position.SpliceAI87 STAR mapped bam files were provided input together with strandedness (forward) and read length (100bp). To compute the percent of intron inclusion, inclusion was measuring using the PI junction field, defined by inclusion counts divided by the sum of inclusion and skipping junction counts. We selected only introns with a unique intron annotation (U introns) that are not involved in other alternative processing events. Introns subjected to PI measurement were also required have an intron length greater than and equal to 60 and have a sum of EE + EI + IE reads be greater than and equal to 20, as previously described.87To compute the percent of intron retention over development, we applied Systematic Investigation of Retained Introns (SIRI).88 according to minimum free energy  predictions for each sequence; (3) Using the resulting Dot-Bracket notations, the degree of double-strandedness was computed for each estimated RNA secondary structure by calculating the percentage of all Watson crick ‘paired bases’ relative to all bases across the entire estimated structure; (4) Next each structure was assigned back to an individual gene and split into four tiers based on gene-level results from the differential hyper-editing analysis: Tier 3 (Adj. p < 1.0 × 10−10); Tier 2 (Adj. p 1.0 × 10−10-1.0 × 10−5); Tier 1 (1.0 × 10−5-0.05); and Tier 0 (Adj. p > 0.05); (5) Finally, a Mann-Whitney U test was used to test for significance for differences in minimum free energy and degree of double-strandedness in Tier 3 relative to the remaining tiers.The following approach was used to estimate the impact of hyper-editing on RNA stability: (1) For each hyper-edited region, two ~2100bp sequences (1000bp +/− the center of the hyper-edited region), where the only difference between these two sequences was either an unedited (A) or edited (G) site as called from the hyper-editing pipeline; (2) RNAfold computed the stability of the local RNA structure,89 using a total of 1,000 permutations. Developmental period, sex, ADAR1, ADAR2, and the first five principal components of common variant ancestry were used as covariates. This analysis was run on three partitions of the DLPFC dataset: the full sample , prenatal samples only , and postnatal samples only . Separately for the results of each analysis, false discovery rate (FDR) was calculated for all gene-variant pairs using the Benjamini-Hochberg procedure. We then classified all edQTLs with FDR ≤0.05 from at least one analysis into one of five categories defined by the temporal specificity of their edQTL effects (as previously described32): (1) Constant edQTLs (consistent effects across development) are defined as edQTLs with FDR ≤0.05 in the complete sample analysis, same direction of effect and unadjusted p ≤ 0.05 in both the prenatal and postnatal analyses; (2) Prenatal-predominant edQTLs  are defined as FDR ≤0.05 in the prenatal analysis, unadjusted p > 0.05 in the postnatal analysis; (3) Postnatal-predominant edQTLs  are defined as FDR ≤0.05 in the postnatal analysis, unadjusted p > 0.05 in the prenatal analysis; (4) Prenatal-trending edQTLs were defined as those that did not fit into earlier categories, but had higher prenatal effects (BPre > BPost); and (5) Postnatal-trending edQTLs were defined as those that did not fit into earlier categories, but had higher postnatal effects (BPost > BPre).Cis-edQTLs were identified for all high-quality, common variants within 1 Mb (±) of an editing site using the fastQTL permutation based analysis103 (ftp://share.sph.umich.edu/UKBB_SAIGE_HRC/) along with GWAS summary statics for attention deficit hyperactivity disorder (ADHD),104 schizophrenia,105 bipolar disorder,106 major depressive disorder.107 For each set of summary statistics, genome-wide significant (p < 5.0 × 10−8) loci were defined by linkage disequilibrium r2 > 0.6 start and end positions and edQTL sites overlapping those loci were considered for analysis. GWAS and edQTL summary statistics  for SNPs within each GWAS locus were used as input to coloc2,90 and posterior probabilities for five hypotheses  were estimated for each locus. Loci with posterior probability for hypothesis H4 (PPH4) between 0.3 and 0.8 were considered to have moderate co-localization while PPH4 ≥ 0.8 was considered to demonstrate strong Bayesian evidence for co-localization.GWAS summary statics were leveraged for 109 neurological and mental health-related traits and disorders from the UKBioBank1234567